Reviewerly/exHarmony huggingface dataset:
Essential C# Features

In this chapter, I describe C# features used in web application development that are not widely understood or that often cause confusion. This is not a book about C#, however, so I provide only a brief example for each feature so that you can follow the examples in the rest of the book and take advantage of these features in your projects. Table 5-1 summarizes this chapter.
Creating Form Applications

The previous chapters have focused on individual features that deal with one aspect of HTML forms, and it can sometimes be difficult to see how they fit together to perform common tasks. In this chapter, I go through the process of creating controllers, views, and Razor Pages that support an application with create, read, update, and delete (CRUD) functionality. There are no new features described in this chapter, and the objective is to demonstrate how features such as tag helpers, model binding, and model validation can be used in conjunction with Entity Framework Core.
IoT Sensors and Capacitive Sensing Applied to the Development of a 3D Interaction Device

In recent years, gestural interfaces have become quite popular. This type of interface exploits gestures, natural for most users, to create more innate and fluid interactions. This work introduces Capacitive Sensing as a low-cost, viable, and promising technique for the implementation of touchless gestural interfaces through the presentation of several projects that use this technology. In addition, it reports the experience of replicating an interface of this nature, pointing out the problems encountered as well as their solutions, advantages, and disadvantages.
Accelerating Video Analytics

MOTIVATION. The advent of inexpensive, high-quality cameras has led to a rapid increase in the volume of generated video data [19, 16]. It is now feasible to automatically analyze these video datasets at scale due to two developments over the last decade. First, researchers have designed complex, computationally-intensive deep learning (DL) models that capture the contents of a given set of video frames (e.g., objects present in a particular frame [11]) [15]. Second, the computational capabilities of hardware accelerators for evaluating these DL models have increased over the last decade (e.g., TPUs) [8]. We anticipate that automated analysis of videos will reduce the labor cost of analyzing video
Use of SPAN for Identified Network Traffic via Machine Learning

Few years back the number of wireless devices and their use in our daily life has been increased a lot. All devices cell phones, laptops, tablets, camera, TVs, home appliances have become a part of network now. As the network devices are growing and getting connected to each other the security risks are getting higher. All the companies and organizations are now establishing and implanting the public and private wireless networks. Organization have to pay heavy cost to implement and integrate all devices together on a network. As wireless networks are more vulnerable to threats and in security's a huge network all the devices should be identified whenever they enter or leave a network traffic pool the experimental work in this paper will elaborate the methods to identify the network traffic identification under encryption. This paper emphases on identification of devices based on layer 2 functionality by MAC (Media Access Code). Later on, the identification was improved using labeled or tagged traffic methods by use of SPAN (Switch port analyzer technique) technology or protocol with assistance ofVirtual Local Area Network. Many Supervised learning methods were examined during experiment and were referenced on data collected by real time traffic. The network traffic of multiple deceives gradually passes through network so incremental learning method is implemented as classification for streaming data.
Soccer Match Target Tracking System Based on Particle Swarm Optimization

With the development of science and technology and the improvement of people’s quality of life, the demand for video image processing technology is more and more extensive. Target detection and tracking in football game video is a very challenging task. The complexity of its moving background and picture makes football detection and tracking become the difficulty of image processing technology. This paper aims to study the football game target tracking system based on particle swarm optimization algorithm. Based on the analysis of the principle of image preprocessing and target tracking, combined with particle swarm optimization algorithm, a target tracking system is designed and implemented. In order to verify the effectiveness of particle swarm optimization algorithm, this paper takes a football game video on the Internet as a test sample to compare the tracking effects of particle swarm optimization algorithm and classical target tracking algorithm. The experimental results show that compared with the mean shift algorithm, the particle swarm optimization algorithm in this paper has more correct frames and better tracking effect.
Finite-key analysis for round-robin-differential-phase-shift quantum key distribution: erratum

Two errata are presented to correct two typographical errors in our paper.
An Elastic Frame Rate Up-Conversion for Sequential Omnidirectional Images

A distinct innovation in the development of frame rate up-conversion for an omnidirectional video is presented in this paper.The innovation allows an omnidirectional video to decide the number of omnidirectional frames that the main algorithm can create based on the characteristic of the motion of objects in the omnidirectional video itself.This elastic omnidirectional frame production is achievable since the main algorithm works based on an optical flow method with a self-improvement mechanism.The optical flow concept has proven to be adaptable greatly with the character of such a video.The experiment results, in which ten omnidirectional videos were involved, show that the algorithm's exertion and adaptability named the elastic frame rate up-conversion (E-FRUC) are incredible.This achievement is confirmed by both the excellent visual quality and the high score of the PSNR (33 to 59 dB) of the reconstructed omnidirectional frame (RF).The E-FRUC can generate at least one RF from two consecutive omnidirectional images, although these images are synthetically designed with an ideal motion.The more complex the motion objects condition inside the omnidirectional video is, the more RFs the E-FRUC can create.By using the E-FRUC, the continuity of motion of moving objects can be preserved.Therefore it can be useful to maintain the frame rate of incomplete omnidirectional video frames when such video is played back.Such a condition usually happens when the omnidirectional video is transmitted through error-prone telecommunication networks.
Mobile Game Usability: Design and Research

Mobile games represent almost half of the total gaming market, with around 30% of the world’s population playing mobile games. This constantly evolving platform has a highly diverse user base that requires special attention from game developers. There are a wide variety of constraints to consider, such as small screen sizes and touch input limitations. This chapter will walk readers through these and many other important considerations for designing and testing mobile games.
Research on Location and Capacity Planning of Data Centers Under New Energy Access

The data centers with high power consumption and stable power characteristics can be integrated into the smart grid with new energy as a potential load to reduce the phenomenon of wind and light abandonment of new energy. Considering the constraints such as voltage and branch power that cannot exceed the limit, this paper proposed a strategy to find the best location and capacity of the data center, established an optimization model based on discrete particle swarm algorithm, and analyzed the impact of different configuration schemes of the data center on the overall benefits as well as the total loss of the system. Simulation results show that the proposed method can better solve the location and capacity planning of multiple data centers, reduce the total loss of power grid while considering economic benefits, and effectively promote the consumption of new energy.
Steganalysis of neural networks based on parameter statistical bias

Many pretrained deep learning models have been released to help engineers and researchers develop deep learning-based systems or conduct research with minimall effort. Previous work has shown that at secret message can be embedded in neural network parameters without compromising the accuracy of the model. Malicious developers can, therefore, hide malware or other baneful information in pretrained models, causing harm to society. Hence, reliable detection of these vicious pretrained models is urgently needed. We analyze existing approaches for hiding messages and find that they will ineluctably cause biases in the parameter statistics. Therefore, we propose steganalysis methods for steganography on neural network parameters that extract statistics from benign and malicious models and build classifiers based on the extracted statistics. To the best of our knowledge, this is the first study on neural network steganalysis. The experimental results reveal that our proposed algorithm can effectively detect a model with an embedded message. Notably, our detection methods are still valid in cases where the payload of the stego model is low.
FPGA-based design and implementation of the location attention mechanism in neural networks

The location attention mechanism has been widely applied in deep neural networks. However, as the mechanism entails heavy computing workload, significant memories consumed for weights storage, and shows poor parallelism in some calculations, it is hard to achieve high efficiency deployment. In this paper, the field-programmable gate array (FPGA) is employed to implement the location attention mechanism in hardware, and a novel fusion approach is proposed to connect the convolutional layer with the fully connected layer, which not only improves the parallelism of both the algorithm and the hardware pipeline, but also reduces the computation cost for such operations as multiplication and addition. Meanwhile, the shared computing architecture is used to reduce the demand for hardware resources. Parallel computing arrays are utilized to time-multiplex a single computing array, which can speed up the pipeline parallel computing of the attention mechanism. Experimental results show that for the location attention mechanism, the FPGA’s inference speed is 0.010 ms, which is around a quarter of the speed achieved by running it with GPU, and its power consumption is 1.73 W, which is about 2.89% of the power consumed by running it with CPU. Compared with other FPGA implementation methods of attention mechanism, it has less hardware resource consumption and less inference time. When applied to speech recognition tasks, the trained attention model is symmetrically quantized and deployed on the FPGA. The result shows that the word error rate is only 0.79% higher than that before quantization, which proves the effectiveness and correctness of the hardware circuit.
Interview: With Laura Taylor, Senior UX Designer, Unity Technologies

The public view of the industry romanticizes game design, art, sound, and animation. The current processes for building and shipping a game are complex and time consuming. Many current game editors and tools were developed for use by engineers and are difficult for artists to learn to use in their daily workflow. They often require understanding of programming or scripting languages and lack streamlined processes to make simple edits. Even visual scripting options have usability problems around complexity, debugging, or performance and often have to be fixed or re-written by an engineer. While many artists’ tools outside of the industry have made significant strides in usability to be approachable to artists and non-artists alike, the same cannot be said about making game development tools more approachable to the non-engineer.
Efficient reversible data hiding scheme based on prediction-error expansion and optimal parameters dynamic selection

As a new research hotspot in the realm of information security, reversible data hiding (RDH) technology has garnered a lot of attention and has been widely used in recent years. An RDH scheme with a high embedding capacity (EC) based on prediction-error expansion is proposed. Specifically, we first divide the host image into two regions of the same size. Then the local complexity value of each pixel is calculated. Furthermore, we employ five parameters, i.e., the local complexity threshold, the embedding bins on the left and right of the prediction error histogram (PEH) and their number to calculate the distortion of the image. The parameters corresponding to the minimal distortion are the optimal parameters. Finally, we embed secret data into the optimal embedding bins on PEH and obtain the best embedding performance. The experimental results reveal that our scheme has a higher EC and better performance than some other state-of-the-art methods.
Application of the Finite State Machine Method in the Desktop-Based “Heroes Of Dawn” RPG Turn-Based Game

FSM (Finite State Machine) is a method of implementing artificial intelligence that is applied to make a decision on NPC (Non Player Character). The application of FSM that is often encountered is to form an NPC with intelligence, so that the NPC can respond to the player's character so that the NPC seems to be able to think. Games have various types (genres) and are increasingly varied in line with the development of hardware and software technology. Writing will focus on games with the Role Playing Game genre or often called RPG. Games in general use Artifical Intelligence in their systems to make the game more interesting to play. Artifical Intelligence is usually applied to NPC (Non Player Character) / Enemy in the game or opponents who must be defeated, one of the applications of Artifical Intelligence in the game to be used in this research is the Finite State Machine (FSM) method.&#x0D;
Threaded Gröbner bases : a Macaulay2 package

The complexity of Gr\"{o}bner computations has inspired many improvements to Buchberger's algorithm over the years. Looking for further insights into the algorithm's performance, we offer a threaded implementation of classical Buchberger's algorithm in {\it Macaulay2}. The output of the main function of the package includes information about {\it lineages} of non-zero remainders that are added to the basis during the computation. This information can be used for further algorithm improvements and optimization.
Sentiment analysis of online discussion of LIS professionals using R

Purpose The main focus of the present study was threefold. Firstly, extraction of the data from the Library and Information Science (LIS) Links; secondly, pre-process the data to remove noises on maximum parameters; and lastly, to know the polarity of the discussion posted on LIS Links. Design/methodology/approach The advancement in the internet and Web 2.0 technologies have facilitated the academic community with many online platforms like Q&amp;A sites, mailing lists, discussion forums, etc. for disseminating or seeking information. LIS Links is one such discussion forum commonly used by Indian LIS professionals. The analysis of these discussions can help in knowing the experience of LIS professionals in different aspects. The present study is based on a lexicon-based approach, which works on the bag of word model. The open source environment R and its different packages were used for the analysis. Findings The analysis shows that the majority of the posts on LIS Links were discussed with positive sentiments. There were only very few words of negative sentiments on the discussion forum of LIS Links. Originality/value The above review of the literature shows that there have been many studies in different domains. The user responses on different platforms were explored. Also, some studies have conducted sentiment analysis in LIS. However, this was limited to content posted by the libraries or the reviews of books. No research was found in the published literature on the analysis of the opinion of LIS professional.
An Implementation of Mobile Shareable Content Object Reference Model for Online Assessment Using Virtual Learning Environment

At present, mobile applications play a vital role in the learning environment. Kids play, learn, and develop their knowledge through the mobile applications. This paper proposed the implementation of a mobile application that would be used to access the online assessment for students. The students can access the assessment and develop their technical skills. This paper is mainly focused on two sections. In the first section, the steps involved in designing a mobile application for online assessment of students mainly focused on MSCORM using a virtual learning environment. The second section is used to evaluate the mobile application based on the mobile shareable object reference model for online assessment to improve the technical skills of the student. This paper implements M-SCORM for online assessment using virtual learning environments.
Preface to Special Issue for DCC 2020

Lossless data compression has been widely studied in computer science. One of the most widely used lossless data compressions is Lempel-Ziv (LZ) 77 parsing, which achieves a high compression ratio. Bidirectional (a.k.a. macro) parsing is a lossless data compression technique that computes a sequence of phrases copied from another substring (target phrase) on either the left or the right position in an input string. Navarro et al. recently showed that a large gap may exist between the size of the smallest bidirectional parse of a given string and that of LZ77 parsing. In addition, finding the smallest bidirectional parse of a given text is NP-complete. Several variants of bidirectional parsing algorithm have been proposed thus far, but no prior work for bidirectional parsing algorithm has achieved compression that is at least as good as LZ77 parsing for any string. In this paper, we present the first practical bidirectional parsing algorithm named LZ77 parsing with right reference (LZRR), in which the number of LZRR phrases is theoretically guaranteed to be not larger than the number of LZ77 phrases. Experimental results using benchmark strings show that the number of LZRR phrases is approximately six percent smaller than that of LZ77 phrases.
Developing Applications in Snowflake

This chapter is all about providing you with the high-level knowledge to understand the different ways Snowflake allows you to work with data in the platform to build custom applications.
Medical Term Translation Techniques Analysis on Five Feet Apart Novel

The medical term is one of the most fascinating yet essential to analyze in translation study. This research aims to scrutinize medical terms translation in the Five Feet Apart novel. The researcher inquiries the types of medical terms found and the translation techniques applied to translate them from English to Indonesian. This research is qualitative research. Molina and Albir's (2002) translation techniques theory is used to analyze the data in the form of words and clauses. The data collecting technique is documentation. The researcher managed to collect 424 data, 57 data in the form of phrases and 367 data in the form of words. Based on the analysis, the results show that the most dominant technique used is the established equivalent technique that has 172 data or 40.56%. The order is followed by pure borrowing technique (17.68%), naturalized borrowing technique (14.86%), amplification (12.03%), calque techniques (8.48%), transposition (4.95%), generalization (0.24%) and reduction (0.24%). The most dominant medical term is human body parts terms (33.50%). The list was followed by the medical abbreviation (24.30%), medical tool (12.5%), procedures, treatments, and actions (12.02%), diseases, signs, symptoms, and injuries (8.96%), medication (4.95%) and healthcare occupation (1.65%).
KAJIAN MORPHOMETRI PADA SATUAN WILAYAH PENGELOLAAN DAS PRIORITAS DI KOTA PADANG (STUDI KASUS PADA DAS ARAU, DAS KURANJI, DAN DAS AIR DINGIN)

In line with the sustainable development process, it is necessary to regulate activities with the main priority to re-create the ecological balance of the environment. The three rivers which were expected to receive rainfall, such as Batang Kuranji, Batang Arau and Batang Air Dingin, were unable to cope with the flooding. The hydrological conditions of the 3 watersheds are currently experiencing changes in hydrological characteristics which are marked by increased flood potential. This threat, apart from natural factors such as climate and extreme rainfall, can also occur due to human factors such as cultivating floodplain areas, land allocations in flood plains that are not in accordance with land functions. Changes in land use in an area can affect the function of the water system. This is in accordance with the opinion of Arsyad (2010), which states that any treatment given to a plot of land can affect the water system in that place and places downstream. From the above threat parameters, flooding is also strongly influenced by natural phenomena in the form of morphometry consisting of watershed area, watershed shape, river network, flow density, flow pattern, land use and river steepness gradient. For this reason, research on watershed morphometry needs to be carried out so that the causes of flooding in the watershed can be identified. Floods that occurred from 2016 to 2021, have harmed the community. By using Paimin's theory for morphometry, the ARC GIS program to determine the extent of flooding, as well as Thiessen's theory to determine rainfall and a rational theory to analyze discharge, it was obtained into 3, namely the Arau watershed, Kuranji watershed and Air Dingin watershed prone to flooding with the highest score due to the use of land.
Digital Ecosystems in the Socio-anthropological Dimension

The characteristic of the new social structure known as the concept of Society 5.0 in the context of total digitalisation of all spheres of social life is given.The consonance of the Society model 5.0. to the ideas of transhumanism in the views on the formation of the posthuman as a person of the coming digital era, preserving his biological essence and anthropological attributes is revealed.It is shown that the latest digital technologies are designed according to the principle of organisation of natural biological systems, in particular, computer neural networks, evolutionary computing, genetic programming, digital ecosystems simulate neural connections in the biological brain, the biosphere principle of natural selection, chromosomal mutations in living cells, the structure of natural objects, etc.The features of adaptive management as a way of self-organisation of digital ecosystems are formulated: modular structure, compatibility of products and technologies, multi-vector communications, actions of all system participants according to unified protocols.The social and personal risks of digitalisation are highlighted.The prerequisites of the potential problem of the imbalance of artificial and human intelligence from the position of the current ethical norms are outlined.The need for socio-humanitarian diagnostics and ontological examination of the prospects of human compatibility with the new digital reality is substantiated.