In search of the best AI customer-support bot? An AI customer-support bot is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI customer-support bot slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Screenpal
ScreenPal (formerly known as Screencast-O-Matic) is cross-platform screen capture and screen recording software originally developed in 2006. == History == The company was founded by AJ Gregory in 2006 as Screencast-O-Matic. The software includes features for screen recording, screenshot capture, video editing, image editing, and a video and image hosting service. It is available for Windows and Mac operating systems, and has mobile apps for iOS and Android. The company launched a video editor in 2015. It began offering free video and image hosting in 2019, with premium hosting options for subscribers. In 2023, it was rebranded as ScreenPal.
Sex differences in social media use
Men and women use social media in different ways and with different frequencies. In general, several researchers have found that women tend to use social network services (SNSs) more than men and primiarly to socialize. == Differences == === Predilection for usage === Many studies have found that women are more likely to use either specific SNSs such as Facebook or MySpace or SNSs in general. In 2015, 73% of online men and 80% of online women used social networking sites. The gap in gender differences has become less apparent in LinkedIn. In 2015 about 26 percent of online men and 25% of online women used the business-and employee-oriented networking site. Researchers who have examined the gender of users of multiple SNSs have found contradictory results. Hargittai's groundbreaking 2007 study examining race, gender, and other differences between undergraduate college student users of SNSs found that women were not only more likely to have used SNSes than men but that they were also more likely to have used many different services, including Facebook, MySpace, and Friendster; these differences persisted in several models and analyses. Although she only surveyed students at one institution – the University of Illinois at Chicago – Hargittai selected that institution intentionally as "an ideal location for studies of how different kinds of people use online sites and services." In contrast, data collected by the Pew Internet & American Life Project found that men were more likely to have multiple SNS profiles. Although the sample sizes of the two surveys are comparable – 1,650 Internet users in the Pew survey compared with 1,060 in Hargittai's survey – the data from the Pew survey are newer and arguably more representative of the entire adult United States population. Pinterest, Facebook, and Instagram attract more females. Picture sharing sites overall are very popular among women. Pinterest alone attracts three times as many female users than male. However, use of Pinterest by men has increased from 5% in 2012. Facebook attracts about 77% of women online. Instagram is also more likely to attract women. Men are more likely to participate in online forums like Reddit, Digg or Slashdot. One in five men claim to be a part of an online forum. === Uses === In general, women seem to use SNSs more to explicitly foster social connections. A study conducted by Pew research centers found that women were more avid users of social media. In November 2010, the gap between men and women was as high as 15%. Female participants in a multi-stage study conducted in 2007 to discover the motivations of Facebook users scored higher on scales for social connection and posting of photographs. Studies have also been conducted on the differences between females and males with regards to blogging. The Pew Research Center found that younger females are more likely to blog than males their own age, even males that are older than them. Similarly, in a study of blogs maintained in MySpace, women were found to be more likely to not only write blogs but also write about family, romantic relationships, friendships, and health in those blogs. A study of Swedish SNS users found that women were more likely to have expressions of friendship, specifically in the areas of (a) publishing photos of their friends, (b) specifically naming their best friends, and (c) writing poems to and about their friends. Women were also more likely to have expressions related to family relationships and romantic relationships. One of the key findings of this research is that those men who do have expressions of romantic relationships in their profile had expressions just as strong as the women. However, the researcher speculated that this may be in part due to a desire to publicly express heterosexual behaviors and mannerisms instead of merely expressing romantic feelings. A large-scale study of gender differences in MySpace found that both men and women tended to have a majority of female Friends, and both men and women tended to have a majority of female "Top" Friends in the site. A later study found women to author disproportionately many (public) comments in MySpace, but an investigation into the role of emotion in public MySpace comments found that women both give and receive stronger positive emotion. It was hypothesised that women are simply more effective at using social networking sites because they are better able to harness positive emotion. A study focused on the influence of gender and personality on individuals' use of online social networking websites such as Facebook, reported that men use social networking sites with the intention of forming new relationships, whereas, women use them more for relationship maintenance. In addition to this, women are more likely to use Facebook or MySpace to compare themselves to others and also to search for information. Men, however, are more likely to look at other people's profiles with in the intention to find friends. Women were less successful at actually finding new friends, but more successful at "maintaining existing relationships, making new relationships, using for academic purposes and following specific agenda". Similarly, men also self-reported this motivation "while women reported using them more for relationship maintenance". === Personality === OCEAN personality traits are known to systematically vary between human males and females. In one study, the same women were more extraverted and agreeable, such as less neurotic while on social media than offline. Other studies associated neuroticism with female use of social media. === Privacy === Privacy has been the primary topic of many studies of SNS users, and many of these studies have found differences between male and female SNS users, although some studies have found results contradictory to those found in other studies. Some researchers have found that women are more protective of their personal information and more likely to have private profiles. Other researchers have found that women are less likely to post some types of information. Acquisti and Gross found that women in their sample were less likely to reveal their sexual orientation, personal address, or cell phone number. This is similar to Pew Internet & American Life research of children users of SNSs that found that boys and girls presented different views of privacy and behaviors, with girls being more concerned about and restrictive of information such as city, town, last name, and cell phone number that could be used to locate them. At least one group of researchers has found that women are less likely to share information that "identifies them directly – last name, cell phone number, and address or home phone number," linking that resistance to women's greater concerns about "cyberstalking", "cyberbullying", and security problems. Despite these concerns about privacy, researchers have found that women are more likely to maintain up-to-date photos of themselves. Further, Kolek and Saunders found in their sample of college student Facebook users that women were more likely to not only post a photograph of themselves in their profile but that they were more likely to have a publicly viewable Facebook account (a contradictory finding compared to many other studies), post photos, and post photo albums. Women were more likely to have: (a) a publicly viewable Facebook account, (b) more photo albums, (c) more photos, (d) a photo of themselves as their profile picture, (e) positive references to alcohol, partying, or drugs, and (f) more positive references to or about the institution or institution-related activities. In general, women were more likely to disclose information about themselves in their Facebook profile, with the primary exception of sharing their telephone number. Similarly, female respondents to Strano's study were more likely to keep their profile photo recent and choose a photo that made them appear attractive, happy, and fun-loving. Citing several examples, Strano opined that there may also be a difference in how men and women Facebook users display and interpret profile photos depicting relationships. Privacy has also been a concern for the SnapChat app, which allows you to send messages either text or photo or video which then disappear. One study has shown that security is not a major concern for the majority of users and that most do not use Snapchat to send sensitive content (although up to 25% may do so experimentally). As part of their research almost no statistically significant gender differences were found. === Cyberbullying === Past research carried out to investigate if there are any gender differences in cyber-bullying has found that boys commit more cyber verbal bullying, cyber forgery and more violence based on hidden identity or presenting themselves as other person. === Mansplaining === A 2021 article found that mansplaining could be seen more prominent online rather than offl
Torus interconnect
A torus interconnect is a switch-less network topology for connecting processing nodes in a parallel computer system. == Introduction == In geometry, a torus is created by revolving a circle about an axis coplanar to the circle. While this is a general definition in geometry, the topological properties of this type of shape describes the network topology in its essence. === Geometry illustration === In the representations below, the first is a one dimension torus, a simple circle. The second is a two dimension torus, in the shape of a 'doughnut'. The animation illustrates how a two dimension torus is generated from a rectangle by connecting its two pairs of opposite edges. At one dimension, a torus topology is equivalent to a ring interconnect network, in the shape of a circle. At two dimensions, it becomes equivalent to a two dimension mesh, but with extra connection at the edge nodes. === Torus network topology === A torus interconnect is a switch-less topology that can be seen as a mesh interconnect with nodes arranged in a rectilinear array of N = 2, 3, or more dimensions, with processors connected to their nearest neighbors, and corresponding processors on opposite edges of the array connected.[1] In this lattice, each node has 2N connections. This topology is named for the lattice formed in this way, which is topologically homogeneous to an N-dimensional torus. == Visualization == The first 3 dimensions of torus network topology are easier to visualize and are described below: 1D Torus: one dimension, n nodes are connected in closed loop with each node connected to its two nearest neighbors. Communication can take place in two directions, +x and −x. A 1D Torus is the same as ring interconnection. 2D Torus: two dimensions with degree of four, the nodes are imagined laid out in a two-dimensional rectangular lattice of n rows and n columns, with each node connected to its four nearest neighbors, and corresponding nodes on opposite edges connected. Communication can take place in four directions, +x, −x, +y, and −y. The total nodes of a 2D Torus is n2. 3D Torus: three dimensions, the nodes are imagined in a three-dimensional lattice in the shape of a rectangular prism, with each node connected with its six neighbors, with corresponding nodes on opposing faces of the array connected. Each edge consists of n nodes. communication can take place in six directions, +x, −x, +y, −y, +z, −z. Each edge of a 3D Torus consist of n nodes. The total nodes of 3D Torus is n3. ND Torus: N dimensions, each node of an N dimension torus has 2N neighbors, Communication can take place in 2N directions. Each edge consists of n nodes. Total nodes of this torus is nN. The main motivation of having higher dimension of torus is to achieve higher bandwidth, lower latency, and higher scalability. Higher-dimensional arrays are difficult to visualize. The above ruleset shows that each higher dimension adds another pair of nearest neighbor connections to each node. == Performance == A number of supercomputers on the TOP500 list use three-dimensional torus networks, e.g. IBM's Blue Gene/L and Blue Gene/P, and the Cray XT3. IBM's Blue Gene/Q uses a five-dimensional torus network. Fujitsu's K computer and the PRIMEHPC FX10 use a proprietary three-dimensional torus 3D mesh interconnect called Tofu. === 3D Torus performance simulation === Sandeep Palur and Dr. Ioan Raicu from Illinois Institute of Technology conducted experiments to simulate 3D torus performance. Their experiments ran on a computer with 250GB RAM, 48 cores and x86_64 architecture. The simulator they used was ROSS (Rensselaer’s Optimistic Simulation System). They mainly focused on three aspects: Varying network size Varying number of servers Varying message size They concluded that throughput decreases with the increase of servers and network size. Otherwise, throughput increases with the increase of message size. === 6D Torus product performance === Fujitsu Limited developed a 6D torus computer model called "Tofu". In their model, a 6D torus can achieve 100 GB/s off-chip bandwidth, 12 times higher scalability than a 3D torus, and high fault tolerance. The model is used in the K computer and Fugaku. === Cost === While long wrap-around links may be the easiest way to visualize the connection topology, in practice, restrictions on cable lengths often make long wrap-around links impractical. Instead, directly connected nodes—including nodes that the above visualization places on opposite edges of a grid, connected by a long wrap-around link—are physically placed nearly adjacent to each other in a folded torus network. Every link in the folded torus network is very short—almost as short as the nearest-neighbor links in a simple grid interconnect—and therefore low-latency.
Hardware random number generator
In computing, a hardware random number generator (HRNG), true random number generator (TRNG), non-deterministic random bit generator (NRBG), or physical random number generator is a device that generates random numbers from a physical process capable of producing entropy, unlike a pseudorandom number generator (PRNG) that utilizes a deterministic algorithm and non-physical nondeterministic random bit generators that do not include hardware dedicated to generation of entropy. Many natural phenomena generate low-level, statistically random "noise" signals, including thermal and shot noise, jitter and metastability of electronic circuits, Brownian motion, and atmospheric noise. Researchers also used the photoelectric effect, involving a beam splitter, other quantum phenomena, and even nuclear decay (due to practical considerations the latter, as well as the atmospheric noise, is not viable except for fairly restricted applications or online distribution services). While "classical" (non-quantum) phenomena are not truly random, an unpredictable physical system is usually acceptable as a source of randomness, so the qualifiers "true" and "physical" are used interchangeably. A hardware random number generator is expected to output near-perfect random numbers ("full entropy"). A physical process usually does not have this property, and a practical TRNG typically includes a few blocks: a noise source that implements the physical process producing the entropy. Usually this process is analog, so a digitizer is used to convert the output of the analog source into a binary representation; a conditioner (randomness extractor) that improves the quality of the random bits; health tests. TRNGs are mostly used in cryptographical algorithms that get completely broken if the random numbers have low entropy, so the testing functionality is usually included. Hardware random number generators generally produce only a limited number of random bits per second. In order to increase the available output data rate, they are often used to generate the "seed" for a faster PRNG. PRNG also helps with the noise source "anonymization" (whitening out the noise source identifying characteristics) and entropy extraction. With a proper PRNG algorithm selected (cryptographically secure pseudorandom number generator, CSPRNG), the combination can satisfy the requirements of Federal Information Processing Standards and Common Criteria standards. == Uses == Hardware random number generators can be used in any application that needs randomness. However, in many scientific applications additional cost and complexity of a TRNG (when compared with pseudo random number generators) provide no meaningful benefits. TRNGs have additional drawbacks for data science and statistical applications: impossibility to re-run a series of numbers unless they are stored, reliance on an analog physical entity can obscure the failure of the source. The TRNGs therefore are primarily used in the applications where their unpredictability and the impossibility to re-run the sequence of numbers are crucial to the success of the implementation: in cryptography and gambling machines. === Cryptography === The major use for hardware random number generators is in the field of data encryption, for example to create random cryptographic keys and nonces needed to encrypt and sign data. In addition to randomness, there are at least two additional requirements imposed by the cryptographic applications: forward secrecy guarantees that the knowledge of the past output and internal state of the device should not enable the attacker to predict future data; backward secrecy protects the "opposite direction": knowledge of the output and internal state in the future should not divulge the preceding data. A typical way to fulfill these requirements is to use a TRNG to seed a cryptographically secure pseudorandom number generator. == History == Physical devices were used to generate random numbers for thousands of years, primarily for gambling. Dice in particular have been known for more than 5000 years (found on locations in modern Iraq and Iran), and flipping a coin (thus producing a random bit) dates at least to the times of ancient Rome. The first documented use of a physical random number generator for scientific purposes was by Francis Galton (1890). He devised a way to sample a probability distribution using a common gambling die. In addition to the top digit, Galton also looked at the face of a die closest to him, thus creating 64 = 24 outcomes (about 4.6 bits of randomness). Kendall and Babington-Smith (1938) used a fast-rotating 10-sector disk that was illuminated by periodic bursts of light. The sampling was done by a human who wrote the number under the light beam onto a pad. The device was utilized to produce a 100,000-digit random number table (at the time such tables were used for statistical experiments, like PRNG nowadays). On 29 April 1947, the RAND Corporation began generating random digits with an "electronic roulette wheel", consisting of a random frequency pulse source of about 100,000 pulses per second gated once per second with a constant frequency pulse and fed into a five-bit binary counter. Douglas Aircraft built the equipment, implementing Cecil Hasting's suggestion (RAND P-113) for a noise source (most likely the well known behavior of the 6D4 miniature gas thyratron tube, when placed in a magnetic field). Twenty of the 32 possible counter values were mapped onto the 10 decimal digits and the other 12 counter values were discarded. The results of a long run from the RAND machine, filtered and tested, were converted into a table, which originally existed only as a deck of punched cards, but was later published in 1955 as a book, 50 rows of 50 digits on each page (A Million Random Digits with 100,000 Normal Deviates). The RAND table was a significant breakthrough in delivering random numbers because such a large and carefully prepared table had never before been available. It has been a useful source for simulations, modeling, and for deriving the arbitrary constants in cryptographic algorithms to demonstrate that the constants had not been selected maliciously ("nothing up my sleeve numbers"). Since the early 1950s, research into TRNGs has been highly active, with thousands of research works published and about 2000 patents granted by 2017. == Physical phenomena with random properties == Multiple different TRNG designs were proposed over time with a large variety of noise sources and digitization techniques ("harvesting"). However, practical considerations (size, power, cost, performance, robustness) dictate the following desirable traits: use of a commonly available inexpensive silicon process; exclusive use of digital design techniques. This allows an easier system-on-chip integration and enables the use of FPGAs; compact and low-power design. This discourages use of analog components (e.g., amplifiers); mathematical justification of the entropy collection mechanisms. Stipčević & Koç in 2014 classified the physical phenomena used to implement TRNG into four groups: electrical noise; free-running oscillators; chaos; quantum effects. === Electrical noise-based RNG === Noise-based RNGs generally follow the same outline: the source of a noise generator is fed into a comparator. If the voltage is above threshold, the comparator output is 1, otherwise 0. The random bit value is latched using a flip-flop. Sources of noise vary and include: Johnson–Nyquist noise ("thermal noise"); Zener noise; avalanche breakdown. The drawbacks of using noise sources for an RNG design are: noise levels are hard to control, they vary with environmental changes and device-to-device; calibration processes needed to ensure a guaranteed amount of entropy are time-consuming; noise levels are typically low, thus the design requires power-hungry amplifiers. The sensitivity of amplifier inputs enables manipulation by an attacker; circuitry located nearby generates a lot of non-random noise thus lowering the entropy; a proof of randomness is near-impossible as multiple interacting physical processes are involved. === Chaos-based RNG === The idea of chaos-based noise stems from the use of a complex system that is hard to characterize by observing its behavior over time. For example, lasers can be put into (undesirable in other applications) chaos mode with chaotically fluctuating power, with power detected using a photodiode and sampled by a comparator. The design can be quite small, as all photonics elements can be integrated on-chip. Stipčević & Koç characterize this technique as "most objectionable", mostly due to the fact that chaotic behavior is usually controlled by a differential equation and no new randomness is introduced, thus there is a possibility of the chaos-based TRNG producing a limited subset of possible output strings. === Free-running oscillators-based RNG === The TRNGs based on a free-running oscilla
Comparison of video editing software
This is a comparison of non-linear video editing software applications. See also a more complete list of video editing software. == General information == This table gives basic general information about the different editors: === Active === === Discontinued / Inactive === ==== Definition ==== professional: used for full length Hollywood movies; professional (small): mainly used for paid commercials, short films or podcasts/YouTube channels; prosumer: Mainly targeting private use, anything that can do more than just trimming a film; basic: trimming a film; == System requirements == This table lists the operating systems that different editors can run on without emulation, as well as other system requirements. Note that minimum system requirements are listed; some features (like High Definition support) may be unavailable with these specifications. "Unix" includes the similar Linux, BSD and Unix-like operating systems. == High definition/High resolution import == The table below indicates the ability of each program to import various High Definition video or High resolution video formats for editing. == Feature set == == Output options == Please note that recording to Blu-ray does not imply 1080@50p/60p . Most only support up to 1080i 25/30 frames per second recording. Also not all formats can be output.
Sumazi
Sumazi is a social media and social intelligence platform for enterprises, brands, and celebrities. Its technology performs social data analysis across social networking services including Facebook, Twitter and LinkedIn, to identify key people in his/her network who are experts, influencers or are located in a specific area for marketing, advertising or sales campaigns. The technology company was founded in 2011 by former Sun Microsystems employee Sumaya Kazi. The company was headquartered in San Francisco, California. The company was out of business by 2017. == Reception == Sumazi was one of 25 startups selected out of more than 1,200 to compete at TechCrunch Disrupt Startup Battlefield, where it won the Omidyar Network award for the startup "Most Likely to Change the World." Sumazi, which was based out of San Francisco, California, had been profiled in The New York Times as well as USA Today, which commented the advantages of the startup's location in the Silicon Valley. American Express OPEN Forum also featured Sumazi as a "Startup of the Week". Sumazi has additionally been mentioned in articles by Mashable, The Wall Street Journal, Current Editorials, Harvard Business Review, Smashing Magazine, and TechCrunch.