Reviewerly/exHarmony huggingface dataset:
Generalizability: Machine Learning and Humans-in-the-Loop

Automated decision tools, which increasingly rely on machine learning (ML), are used in decision systems that permeate our lives. Examples range from high-stakes decision systems for offering credit, university admissions and employment, to decision systems serving advertising. Here, we consider data-driven tools that attempt to predict likely behavior of individuals. The debate about ML-based decision-making has spawned an important multi-disciplinary literature, which has focused primarily on fairness, accountability, and transparency. We have been struck, however, by the lack of attention to generalizability in the scholarly and policy discourse about whether and how to incorporate automated decision tools into decision systems.This chapter explores the relationship between generalizability and the division of labor between humans and machines in decision systems. An automated decision tool is generalizable to the extent that it produces outputs that are as correct as the outputs it produced on the data used to create it. The generalizability of a ML model depends on the training process, data availability, and the underlying predictability of the outcome that it models. Ultimately, whether a tool’s generalizability is adequate for a particular decision system depends on how it is deployed, usually in conjunction with human adjudicators. Taking generalizability explicitly into account highlights important aspects of decision system design, as well as important normative trade-offs, that might otherwise be missed.
3D Human Motion Prediction Based on Graph Convolution Network and Transformer

Extracting, recognizing and predicting human actions from image information plays an essential part in the fields of human intention understanding, behavior emergency avoidance and automatic driving. In recent years, with deep learing method developing rapidly, the methods of behavior detection and intention understanding for human actions are also glowing with new vitality. In this paper, based on spatial-temporal synchronous graph convolution network and multi-head self-attention mechanism, a new method of human skeleton action recognition and prediction is proposed. By extracting the spatial features of short-term time series at the same time, we can predict the long-term time series actions, and also we have achieved satisfactory experimental results. Our experiment is based on Human3.6M dataset for training and testing. At the end of the paper, we put forward the limitations of the current research and some future research directions.
The behavior of gradual types: a user study

There are several different gradual typing semantics, reflecting different trade-offs between performance and type soundness guarantees. Notably absent, however, are any data on which of these semantics developers actually prefer. We begin to rectify this shortcoming by surveying professional developers, computer science students, and Mechanical Turk workers on their preferences between three gradual typing semantics. These semantics reflect important points in the design space, corresponding to the behaviors of Typed Racket, TypeScript, and Reticulated Python. Our most important finding is that our respondents prefer a runtime semantics that fully enforces statically declared types.
A data-aware dictionary-learning based technique for the acceleration of deep convolutional networks

The deployment of high performing deep learning models on platforms of limited resources is currently an active area of research. Among the main directions followed so far, pre-trained neural networks are accelerated and compressed by appropriately modifying their structure and / or parameters. Capitalizing on a recently proposed codebook of a special structure that can be utilized in the frame of the so-called weight sharing methods, this paper describes a "data-driven" technique for designing such a codebook. The performance of the technique, in terms of the observed representation error and classification accuracy versus the achieved acceleration ratio, is demonstrated by considering the VGG16 and the ResNet18 models, pre-trained on the ILSVRC2012 dataset.
Posterior Cramér-Rao Bounds for Tracking Intermittently Visible Targets in Clutter

In this paper, the posterior Cramér-Rao bound (PCRB) is determined for a scenario in which a single target is only intermittently visible to a multi-sensor system, with visibility governed by a discrete time Markovian switching system. The scenario also allows for missed detections and false alarms. Two PCRB methodologies are developed. The first approach adjusts the probability of detection to account for the a-priori probability that a target is visible, resulting in a time-dependent "information reduction factor" that degrades the measurement contribution accordingly. The second approach determines a conditional PCRB by sampling potential visibility/non-visibility sequences, and then calculates an unconditional bound as a weighted average. The resulting PCRBs are compared to the performance of an integrated expected likelihood particle filter (IELPF) and an integrated probabilistic data association filter (IPDAF),for scenarios with one or two sensors, and a range of clutter densities. It is shown that there is good agreement between the best performing filter and the PCRBs in the one sensor scenarios. However, when a second high-rate sensor is added to the system, the filter performance is similar to the bound only when the clutter density is low, with the PCRBs shown to be optimistic when the clutter density is high.
Copyright Issues in a Digital Library Environment

This chapter examines copyright issues in a digital library environment. The author observes that the advent of digital technology has greatly accelerated the dissemination and distribution of information which greatly accelerated the dissemination and distribution of information with great speed and accuracy never seen before. The challenges in creating digital libraries include copyright management, development of appropriate technologies, issues related to storage, architectural and technical challenges, collection development challenges and the challenges of user engagement. The chapter recommends ways of managing orphan works and the roles of digital libraries in open access to knowledge. The author concludes that current developments point to the dominant role of information and communication technology in libraries for improved access to information.
CAPTCHA Programming

This chapter presents the most advanced algorithms which are used to successfully design a CAPTCHA test. In the first part, the most important techniques which are employed for designing a text CAPTCHA are described. In fact, although it is the most attacked type of CAPTCHA, it is still used in many web sites. Then, the attention will be moved toward the design of the image-based CAPTCHA, for which different techniques of image transformation are usually employed. The third part will be dedicated to algorithms and methods for designing other types of CAPTCHA, such as text-based reCAPTCHA, NoCAPTCHA reCAPTCHA, and game-based CAPTCHA. Finally, the last part of the chapter provides a practical mini-guide on how to design a simple text and image-based CAPTCHA in JavaScript and PHP.
SCADA Systems Cyber Security for Critical Infrastructures

Past cyber-attacks on Supervisory Control and Data Acquisition (SCADA) Systems for Critical infrastructures have left these systems compromised and caused financial and economic problems. Deliberate attacks have resulted in denial of services and physical injury to the public in certain cases. This study explores the past attacks on SCADA Systems by examining nine case studies across multiple utility sectors including transport, energy and water and sewage sector. These case studies will be further analysed according to the cyber-terrorist decision-making theories including strategic, organisational and psychological theories based on McCormick (2000). Next, this study will look into cyber-terrorist capabilities in conducting attacks according to Nelson's (1999) approach that includes simple-unstructured, advance-structured and complex-coordinated capabilities. The results of this study will form the basis of a guideline that organisations can use so that they are better prepared in identifying potential future cybersecurity attacks on their SCADA systems.
Smart Mobile Learning Activities

The rapid technical development with new media forms and increased mobility is integrated into our everyday lives, as well as in more digitalized higher education and at distance. A new research area has been developed on mobile learning (m-learning) about the integration of self-directed and motivated opportunities. This chapter describes smart mobile learning activities with an emphasis on the importance of the connection by using resource-enriched and technology-embedded mobile devices for student-centered learning that allow students' to learn self-directed and motivated to obtain learning materials at anywhere and anytime, what learning benefits are being observed among students and teachers, and how different issues are being addressed. Methodically, it illustrates different frameworks for mobile learning and theoretically is the analysis of excerpts based on dialogical theories. The findings display that the space for m-learning offers many opportunities, as well as challenges, to design smart “mobile pedagogy” with a focus on student-centered learning.
Benefits of Effective Utilization of Mobile Technologies and Inquiry-Based Teaching Methods in University of Ilorin, Nigeria

This article examines the benefits of effective utilization of mobile technologies and inquiry-based teaching methods in Nigerian universities. Specifically, the study explores the techniques that guide undergraduate students to learn with digital support from mobile devices and wireless communication during their classroom activities and outside the classroom with use of PDF and WAP technology. This research uses mobile phones because of its easy accessibility to students and lecturers. The researchers are interested in finding out the benefit of effective use of mobile technology in teaching and learning, identifying types of mobile application used by students and examining the time spent on effective learning by students and benefits of receiving classroom material through pdf and WAP technology. Four research questions were raised, and 80 undergraduate students participated in the study. The research design was a descriptive survey, and frequency count, simple percentage and mean score were used for the research analysis. It was revealed that students always use their mobile phones for academic purposes and academic performances of students have been improved through the use of mobile phones to solve classroom questions. The time spent on using mobile phones for learning process was also unveiled and recommendations were made based on the researchers' findings.
Breeding unicorns: Developing trustworthy and scalable randomness beacons

Randomness beacons are services that periodically emit a random number, allowing users to base decisions on the same random value without trusting anyone: ideally, the randomness beacon does not only produce unpredictable values, but is also of low computational complexity for the users, bias-resistant and publicly verifiable. Such randomness beacons can serve as an important primitive for smart contracts in a variety of contexts. This paper first presents a structured security analysis, based on which we then design, implement, and evaluate a trustworthy and efficient randomness beacon. Our approach does not require users to register or run any computationally intensive operations. We then compare different implementation and deployment options on distributed ledgers, and report on an Ethereum smart contract-based lottery using our beacon.
AI-Based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox&amp;nbsp;Scandal in 2017 on Doctissimo&amp;nbsp;Forum

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Basic Audio Compression Techniques

In this chapter, compression of audio information is reviewed, with special consideration paid to speech compression. To begin with, we recall some of the issues covered in Chap. 6 on digital audio in multimedia. Here, this is combined with techniques that exploit the temporal redundancy present in audio signals. We extend the Pulse Code Modulation (PCM) scheme to DPCM, prepending the word “Differential,” as briefly introduced in Chap. 6 but fleshed out here. Specifically, in this chapter, we look at ADPCM, Vocoders, and more general Speech Compression: LPC, CELP, MBE, and MELP. Adaptive DPCM is ADPCM. In speech coding, a number of standards have evolved and we set these out here, including some of their fundamental strategies. We then go on to study coders (encoding/decoding algorithms) specifically aimed at speech compression. The properties of Vocoders are examined, including the notion of phase insensitivity, channels, and formants. Next, LPC (Linear Predictive Coding) vocoders are discussed, followed by CELP (Code Excited Linear Prediction), a more complex family of coders. Hybrid Excitation Vocoders are another large class of speech coders, for that MBE (Multi-Band Excitation) and MELP (Multiband Excitation Linear Predictive) vocoders are introduced. We round the discussion off by having a look at two open source speech codecs: Speex and Opus.
A Psychological Perspective on Mobile Learning

The mobile internet offers 24/7 access to a wide range of social, commercial, and educational opportunities. If the educational opportunities are to be maximized, then a full understanding of user skills, motivations, preferences, and learning profiles is required. The authors propose that this detailed understanding is best informed by adopting a psychological perspective on the mobile learning process. Therefore, this chapter considers the insights that can be derived from viewing mobile learning through a psychological lens: in particular the psychological challenges that mobile learning presents and the importance of considering individual differences in learner skills and behaviors. They also consider the insights that psychology offers in addressing these challenges, especially the importance of media literacy skills, the presentation of learning resources in a way that considers the limited cognitive resources of the learner, and the importance of context and contextualized behavior.
Programming methods

This chapter focuses on Ascend AI processor programming. Firstly, the general programming ideas of deep learning applications and the principles of the deep learning Inference Engine are introduced. Then, for the programming on Ascend AI processor, three steps are proposed: model generation phase, application compilation phase, and application deployment phase. The rest of the chapter focuses on the development methods for the customized operator and the customized application. For customized operator development, there are two cases: AI CPU operator development and AI core operator development, depending on where the operator runs. For customized application development, the motivation and typical procedure are explained.
Numerical modeling approach of wastewater treatment processes

The work contains theoretical and practical approaches to various methods of numerical analysis and mathematical modeling, allowing to form the most holistic and comprehensive approach to solving problems associated with filtration. The topic of this work is extremely important since at the moment there is significant damage to the environment due to a lack of approach to filtration issues. Within the framework of this model, factors such as the variability of the filtration rate, the rate of change in the concentration of the suspension, as well as the formation of a sedimentary layer on the surface. All related calculations were performed on a computer. The calculations used to make it possible to determine those key parameters and variables that have the greatest impact on the filter performance, as well as its deterioration. The proposed methods have passed a full range of checks and are sufficiently effective for their full implementation.
Single-target density for tracking indistinguishable objects

The concept of indistinguishability in multi-target tracking leads to correlations in their statistical description even without explicit interactions between the objects. These correlations can be described in terms of a wave function – the square-root of the multi-target probability density function (pdf) – which is necessarily either symmetric or anti-symmetric under the exchange of two target indices. [1] This symmetry dichotomy, well-known in quantum many particle physics as bosonic and fermionic behavior, leads to specific properties of the multi-target pdf. While anti-symmetry results in a repulsive behavior in terms of the single object states, symmetry leads to clustering of single objects into the same state. This different behavior can be exploited to describe macroscopic objects which either tend to avoid each other or to form groups.In this paper, we develop an approach for tracking multiple non-interacting indistinguishable targets in the presence of false alarms. The goal is to avoid the treatment of the high-dimensional multi-target pdf by approximating it in terms of the square of so-called Slater determinants and permanents build from single target pdfs. From the intensity (first order statistical moment) of the multi-target pdf, we derive approximations for single target pdfs which show the specific fermionic and bosonic behavior. These "corrected" single target pdfs can serve as input into standard data association and filtering algorithms. Exemplary implementations in a JPDAF framework demonstrate the mitigation of track coalescence.
Steganography Using LSB Substitution and Pixel Value Differencing

This chapter describes four techniques that use both the principles of LSB substitution and pixel value differencing (PVD). These techniques use 1×3, 2×2, 2×3, and 3×3 size pixel blocks. In a block, LSB substitution is applied on one of the pixels, which is designated as the central pixel. Using the new value of this central pixel, pixel differences are computed with all its neighboring pixels. Based on these pixel value differences, embedding capacity is decided, and embedding is performed by addition and subtraction operations. The experimental results reveal that when the block size increases, the tolerance to RS analysis and pixel difference histogram (PDH) analysis also increases.
Problems and Improvement Measures in Detection of Coal Mine Safety Monitoring System under the Background of Internet Technology

With the rapid development of big data technology and the gradual improvement of security management theory, data mining technology has attracted the attention of many scholars and enterprises in the field of security management decision-making. Faced with the increasing safety needs of employees and the high attention of public opinions, coal mining enterprises have to improve the safety management level to adapt to the rapid development of society. Based on the data mining theories and methods, such as from the background of Internet technology, to the current Chinese coal mine safety management efficiency assessment and management of coal mine safety, efficiency and so on several aspects to solve the coal mine enterprises in the implementation of the efficiency of the existing problems in the process of safety management, to improve coal mine safety management level of the targeted development path and related strategies. Effectively reduce the coal mine safety monitoring system multiple independent systems integration complexity and difficulty, has realized the centralized maintenance, centralized management, data sharing and convenient construction goal, improve the system operation is stable, reliable and safe, for management personnel at all levels according to the working condition of scientific and effective scheduling management information in real time and decision-making to provide technical support, and big data platform for the construction of the coal mine for the future to lay the foundation.
Tracking Confirmation

Confirmation is a graded notion: evidence can confirm a hypothesis to a greater or lesser degree. There has been debate about how to measure degree of confirmation. Starting from the observation that we would like evidence to be a discriminating indicator of truth, we conduct computer simulations to determine how well the various known measures of confirmation predict the extent to which a given piece of evidence fulfills that role, given a hypothesis of interest. The outcomes show that some measures are markedly better indicators of truth than others.
Knowledge Representation Technologies Using Semantic Web

The World Wide Web (WWW) is global information medium, where users can read and write using computers over internet. Web is one of the services available on internet. The Web was created in 1989 by Sir Tim Berners-Lee. Since then a great refinement has done in the web usage and development of its applications. Semantic Web Technologies enable machines to interpret data published in a machine-interpretable form on the web. Semantic web is not a separate web it is an extension to the current web with additional semantics. Semantic technologies play a crucial role to provide data understandable to machines. To achieve machine understandable, we should add semantics to existing websites. With additional semantics, we can achieve next level web where knowledge repositories are available for better understanding of web data. This facilitates better search, accurate filtering and intelligent retrieval of data. This paper discusses about the Semantic Web and languages involved in describing documents in machine understandable format.
Understanding a Revolutionary and Flawed Grand Experiment in Blockchain

In spring 2016, the Distributed Autonomous Organization (The DAO) was created on Ethereum. As with Bitcoin, Ethereum uses a P2P network, where distributed ledgers are implemented as daisy-chained blocks of data. Ethereum's native cryptocurrency, Ethers are spent to execute pieces of code called smart contracts. Investors paid their Ethers for the DAO to operate and received the opportunity to vote on and become investors in venture projects proposed by Ethereum-based startups. Transactions and settlements between investors and startups are executed autonomously. The DAO experiment failed shortly after inception as an anonymous hacker stole over $50M USD worth of Ethers out of the $168M invested. The Ethereum community voted to return (or fork) the state of the network to one prior to the hack, returning Ethers back to investors and shuttering the DAO. However, this action arguably represented as a bailout—ironically, Bitcoin was conceived as a reaction against the 2008 bailout of US banks—and violated the ledger immutability and “code is law” ethos of the blockchain community.