Reviewerly/exHarmony huggingface dataset:
Development and Application of Assessment Tools Based on Cognitive Diagnosis

As information technology in education penetrates the teaching process, personalized teaching has become a research hotspot in the field of education. The deep integration of cognitive diagnosis and education provides an effective measurement and assessment method for personalized teaching. Based on the theory of cognitive diagnosis and test data, this paper makes a different analysis of students’ cognitive status. The study found that the cognitive diagnosis identified each student’s attribute mastery pattern, and students who had the same score did not mean they had the same mastery pattern. This provides teachers with diversified and personalized feedback information for specific teaching.
Design and Practice of University Smart Campus Integration Platform under the Background of Big Data

The construction of intelligent campus in colleges and universities can provide more humanized services to teachers and students through rational distribution of data resources, which can better promote the progress of education. Therefore, this paper studies the design and practice of the integrated platform of smart campus in colleges and universities under the background of big data. In this paper, hadoop distributed storage and spark computing components are used, and javaweb technology is used to develop this platform. In terms of system performance, the average data response time is 53 m s, the bit error rate can reach below 0.3%, the average database size is 122.7 T B, and the number of queries can reach more than 10 000 times. This paper makes an in-depth study on the process of data sharing and exchange, and on this basis, puts forward a concrete construction scheme for the integration of intelligent campus in colleges and universities, and sums up the intelligent application and service mode of campus data. The experiment shows that this research is conducive to improving the accuracy of campus data governance, at the same time, improving the collaboration of campus management services, and meeting the long-term development needs of intelligent campus.
Mining Sentiment-Dependent Linguistic Patterns from Automotive Reviews for Product Defects

Due to the universality and rapidity of information dissemination on social media, it is of guiding significance for automobile manufacturers to improve product design and optimize quality management to timely discover the defect information of automobiles from social media. At present, the research on social media defect recognition has mined less defect information and mostly takes negative comments as product defects. To solve this problem, we put forward a comment representation model based on sentiment-dependent linguistic features, which effectively uses the domain context. In reality, the distribution of the data set is biased in some way. To avoid the major defect, we use the clustering-based under-sampling method. The experimental results show that the model can effectively identify car defects in Chinese social media, and has a high accuracy and recall rate.
Investigation of Common Issues in Biology Teachers’ Qualification Certificate Interviews and Assessment Prediction using Ensemble Learning

Evaluating professional competence has been a significant challenge for educators. Professional competence is widely evaluated, and the implications for all those seeking certification are stringent. Through research on some problems with examinees in the biology teacher qualification examination of a middle school in recent years, this paper puts forward some suggestions on the cultivation of biology teacher-students in middle schools. This work proposes an intelligent method for identifying the influential factors in predicting failure on the biology teachers’ qualification certificate exam. This work utilizes ensemble learning method to predict the examinees result. The relationship between the variables and the influential factors are identified by using correlation analysis. The statistical significance of the proposed model is assessed with the help of the proportional chance criterion. The proposed model was statistically significant according to the experimental results.
Development of VisualC++, a multimedia speech teaching system

The informationization has brought the non-negligible impact to the traditional vocal music education circle. In order to realize the optimization and reform of the vocal music profession, the digital and informationized service technology must be introduced into the vocal music teaching. In modern vocal music class, multimedia technology is the most common auxiliary teaching means, but there is no software suitable for vocal music at present. Therefore, this paper puts forward a set of VisualC++ as the core of the vocal music class development of new ideas. The implementation of the program not only increases the intuitive and interactive vocal music class, but also increases the interest of the class.
Consumer behavior in cryptocurrency adoption

The emergence of cryptocurrencies based on blockchain technology has put forward as an alternative to the way the present financial system is organized across the world. The widespread adoption of cryptocurrencies could have multiple short and long term consequences for governments, banks and society. Cryptocurrencies such as bitcoin, ethereum, and cardano have been adopted for various purposes. Some countries have even adopted bitcoin as a legal tender, thus increasing the popularity and usage of the cryptocurrencies. This chapter aims to understand various factors that are helping or hindering the adoption of cryptocurrencies through literature review. It uses the Push Pull Mooring framework to categorize various factors. It also discusses future research directions on this topic.
MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning

Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems. In many real-world scenarios, tasks often have several conflicting objectives and may require multiple agents to cooperate, which are the multi-objective multi-agent decision-making problems. However, only few works have been conducted on this intersection. Existing approaches are limited to separate fields and can only handle multi-agent decision-making with a single objective, or multi-objective decision-making with a single agent. In this paper, we propose MO-MIX to solve the multi-objective multi-agent reinforcement learning (MOMARL) problem. Our approach is based on the centralized training with decentralized execution (CTDE) framework. A weight vector representing preference over the objectives is fed into the decentralized agent network as a condition for local action-value function estimation, while a mixing network with parallel architecture is used to estimate the joint action-value function. In addition, an exploration guide approach is applied to improve the uniformity of the final non-dominated solutions. Experiments demonstrate that the proposed method can effectively solve the multi-objective multi-agent cooperative decision-making problem and generate an approximation of the Pareto set. Our approach not only significantly outperforms the baseline method in all four kinds of evaluation metrics, but also requires less computational cost.
BcBench: Exploring Throughput Processor Designs based on Blockchain Benchmarking

Benchmark suites have become the most important wheel to drive the research of GPU architecture designs. The most popular GPU benchmark suites, such as Rodinia and Parboil, cover a wide range of real-world applications, which provide a good reference for the researchers to estimate the performance behaviors of the GPU devices in production. However, the existing GPU benchmarks were initially proposed a decade ago, which unfortunately cannot reflect the unique features of the latest GPU applications accurately. Therefore, it is inappropriate to employ the stale benchmarks as the reference to evaluate the state-of-the-art GPU architectures.
An Efficient Feature-based Method for People Counting

This paper presents a novel feature-based method for detecting and counting people in smart building applications using Time-Of-Flight (TOF) cameras. A TOF camera images a scene three-dimensionally and can detect people passing through below the camera in a privacy-compliant manner. The paper shows the researched image processing chain for people counting, which uses feature extraction for the robust classification of persons and non-person objects. 34 feature candidates have been developed, tested, and statistically evaluated regarding their suitability for classification. Of these 34 candidates, the three best-suited features were selected for the final solution. Experimental results of different use cases show that 99% of 898 classifications were performed correctly. Among 600 counting events, 99% of the use cases with one or two persons walking separately were counted correctly, while less accurate counting was observed when two people were closely walking side by side. The low computational effort of this method allows efficient embedded system implementations.
Relation Modeling on Knowledge Graph for Interoperability in Recommender Systems

Network technology has changed how people consume content through various channels, which constantly generate a large amount of data. To effectively utilize this data, many researchers have focused on interoperability, or the ability to use information from multiple systems together. However, in the field of recommender systems, few studies have considered interoperability. Existing methods for guaranteeing interoperability in recommender systems have limitations in their ability to model low-order relationships for data integration. In particular, there has been no study that ensures interoperability for knowledge graph-based recommender systems, which are suitable structures for integrating heterogeneous data. Therefore, we propose an integration method for multiple systems optimized for knowledge graph-based learning. This method can extend the knowledge graph through deep learning-based relation modeling of entities and ensure interoperability for the recommendation system. Our experimental results confirm that this method improves the performance of existing recommendation algorithms.
AdvDDoS: Zero-Query Adversarial Attacks Against Commercial Speech Recognition Systems

Automatic speech recognition (ASR) has been widely and commercially employed in health care, autonomous vehicles, and finance. Yet, recent studies have shown that universal adversarial perturbations (UAPs) pose a serious threat to white-box ASR systems, when the adversary has access to the target model. Until now, the impacts of such a threat on commercial systems are still open since their models are not publicly available. To understand the security weakness in the practical black-box setting, this paper introduces the first <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">zero-query</i> UAP attacks, called AdvDDoS, with black-box access to ASR systems: we do not need to pay any query expense to estimate UAPs. Specifically, we craft targeted UAPs under a popular feature extractor and a local ASR model by reversing the robust target-category features, in which adversarial perturbations containing robust features are believed to have better transferability. Compared with vanilla UAPs, our UAPs incorporated with target-category features lead to better attacks against commercial ASR systems. We validate the efficacy of our AdvDDoS by launching attacks against a range of commercial ASR systems, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., three API services (Alibaba, Tencent, and Baidu), and three personal assistants (Apple Siri, iFlytek, and Google). Extensive experimental results demonstrate the superiority of AdvDDoS. For example, AdvDDoS achieves 83.26% word error rate (WER) and 53.25% success rates of attacks (SRoA) for the universal attack against Tencent ASR API, which outperforms the vanilla UAPs by up to 61.56% on WER and 11.6% on SRoA. The success of our attack sheds light on zero-query UAP attacks against Commercial ASR systems.
Nioh-PT: Virtual I/O Filtering for Agile Protection against Vulnerability Windows

Hypervisor vulnerabilities cause severe security issues in multi-tenant cloud environments because hypervisors guarantee isolation among virtual machines (VMs). Unfortunately, hypervisor vulnerabilities are continuously reported, and device emulation in hypervisors is one of the hotbeds because of its complexity. Although applying patches to fix the vulnerabilities is a common way to protect hypervisors, it takes time to develop the patches because the internal knowledge on hypervisors is mandatory. The hypervisors are exposed to the threat of the vulnerabilities exploitation until the patches are released.
The EVIL Machine: Encode, Visualize and Interpret the Leakage

Unsupervised side-channel attacks allow extracting secret keys manipulated by cryptographic primitives through leakages of their physical implementations. As opposed to supervised attacks, they do not require a preliminary profiling of the target, constituting a broader threat since they imply weaker assumptions on the adversary model. Their downside is their requirement for some a priori knowledge on the leakage model of the device. On one hand, stochastic attacks such as the Linear Regression Analysis (LRA) allow for a flexible a priori, but are mostly limited to a univariate treatment of the traces. On the other hand, model-based attacks require an explicit formulation of the leakage model but have recently been extended to multidimensional versions allowing to benefit from the potential of Deep Learning (DL) techniques. The EVIL Machine Attack (EMA), introduced in this paper, aims at taking the best of both worlds. Inspired by generative adversarial networks, its architecture is able to recover a representation of the leakage model, which is then turned into a key distinguisher allowing flexible a priori. In addition, state-of-the-art DL techniques require 256 network trainings to conduct the attack. EMA requires only one, scaling down the time complexity of such attacks by a considerable factor. Simulations and real experiments show that EMA is applicable in cases where the adversary has very low knowledge on the leakage model, while significantly reducing the required number of traces compared to a classical LRA. Eventually, a generalization of EMA, able to deal with masked implementation is introduced.
RMC-PVC: A Multi-Client Reusable Verifiable Computation Protocol

The verification of computations performed by an untrusted server is a cornerstone for delegated computations, especially in multi-clients setting where inputs are provided by different parties. Assuming a common secret between clients, a garbled circuit offers the attractive property to ensure the correctness of a result computed by the untrusted server while keeping the input and the function private. Yet, this verification can be guaranteed only once.
Block-Sorting Compression

This chapter describes a data compression technique devised by Mike Burrows and David Wheeler in 1994 at DEC Systems Research Center. This technique is known as the Burrows–Wheeler Transform (or BWT) and offers a revolutionary alternative to dictionary-based and statistical compressors. It is the algorithmic core of a new class of data compressors (such as bzip2), as well as of new powerful compressed indexes (such as the FM-index). The chapter describes the algorithmic details of the BWT and of two other simple compressors, Move-to-Front and Run-Length Encoding, whose combination constitutes the design core of bzip-based compressors. This description is accompanied by the theoretical analysis of the impact of BWT on data compression, in terms of the k-th order empirical entropy of the input data, and by a sketch of the main algorithmic issues that underlie the design of the first provably compressed suffix array to date, namely the FM-index. Given the technicalities involved in the description of the BWT and the FM-index, this chapter offers several running examples and illustrative figures which should ease their understanding.
Random Sampling

This chapter attacks an easy-to-state problem, which is to select a subsequence of items uniformly at random from a given input sequence. This problem is the backbone of many randomized algorithms, and admits solutions that are algorithmically challenging to design and analyze. In particular, this chapter deals with the two cases where the size of the input sequence is either known or unknown to the algorithm, and also addresses the cases where the sequence length is smaller or larger than the internal memory of the computer. This will allowus to introduce another model of computation, the streaming model.
Statistical Coding

This chapter deals with a classic topic in data compression and information theory, namely the design of compressors based on the statistics of the symbols present in the text to be compressed. This topic is addressed by means of an algorithmic approach that gives much attention to the time efficiency and algorithmic properties of the discussed statistical coders, while also evaluating their space performance in terms of the empirical entropy of the input text. The chapter deals in detail with the classic Huffman coding and arithmetic coding, and also discusses their engineered versionsc known as canonical Huffman coding and range coding. Its final part is dedicated to describing and commenting on the prediction by partial matching (PPM) coder, whose algorithmic structure is at the core of some of the best statistical coders to date.
Integer Coding

This chapter addresses a basic integer encoding problem whose impact on the total memory footprint and speed performance of the underlying application is too easily underestimated or neglected. The problem consists of squeezing the space (in bits) required to store an increasing sequence of integers, and then supporting efficient query operations such as decompressing the sequence from the beginning or from some other position, checking whether an integer occurs in the sequence, or finding the smallest integer larger than the queried one. This problem occurs in several common applications, such as in the storage of the posting lists of search engines, or of the adjacency lists of trees and graphs, or of the encoding of sequences of offsets (pointers). The integer coders here discussed, analyzed, and illustrated with many running examples are Elias’ γ- and δ-codes, Rice’s code, PForDelta code, variable-byte code, (s, c)-dense codes, interpolative code, and, finally, the very elegant and powerful Elias–Fano code.
Searching Strings by Substring

This chapter deals with the design of data structures and algorithms for the substring search problem, which occurs mainly in computational biology and textual database applications to date. Most of the chapter is devoted to describing the two main data-structure champions in this context, the suffix array and the suffix tree. Several pseudocodes and illustrative examples enrich this discussion, which is accompanied by the evaluation of time, space, and I/O complexities incurred by their construction and by the execution of some powerful query operations. In particular, the chapter deals with the efficient/optimal construction of large suffix arrays in external memory, hence describing the DC3 algorithm and the I/O-efficient scan-based algorithm proposed by Gonnet, Baeza-Yates, and Snider, and the efficient direct construction of suffix trees, via McCreight’s algorithm, or via suffix arrays and LCP arrays. It will also detail the elegant construction of this latter array in internal memory, which is fundamental for several text-mining applications, some of which are described at the end of the chapter.
Dictionary-Based Compressors

This chapter describes another family of compressors that do not derive statistics about the text to be compressed; rather, they derive a dictionary of strings and substitute their occurrences in the input text via proper references to that dictionary. The choice of the dictionary is of course crucial in determining how well the file is compressed, and it is sometimes ineffective to transmit the full dictionary along with each compressed file. Starting in 1977, Abraham Lempel and Jacob Ziv introduced a family of compressors that successfully addressed these problems by designing two algorithms, named LZ77 and LZ78, that process the input text to incrementally construct a proper dictionary that is, thus, not transmitted. The Lempel–Ziv compressors are very popular because of their gzip instantiation, and constitute the base of more sophisticated compressors in use today, such as 7zip, LZMA, Brotli, and LZ4. The chapter discusses LZ77, LZ78, and an interesting variant, known as LZW, and at the end presents some theoretical considerations about the effectiveness of those compressors in terms of the empirical entropy of the compressed text.
Anxiety, Online Fora and This Study

In this chapter we introduce the topic and aims of the book and define key terms such as anxiety, corpus linguistics and discourse. We provide the motivation for writing the book and outline other studies which have examined language in healthcare contexts, in particular focusing on studies which have looked at healthcare forums and/or mental health issues, as well as studies which have used corpus linguistics techniques for corpus-assisted discourse analysis. We then outline the research questions which drive the analysis in the book. We introduce the corpus that we worked with and discuss ethical issues in dealing with online data, as well as issues relating to data processing. We also provide a description of the tools and techniques that we used to carry out our analysis. We then reflect on our own position in relationship to the topic we are researching. Finally, we provide an outline of the remaining chapters of the book.
Compressed Data Structures

This chapter deals with the design of compressed data structures, an algorithmic field born just 30 years ago which now offers plenty of compressed solutions for most, if not all, classic data structures such as arrays, trees, and graphs. This last chapter aims at providing just an idea about these novel approaches to data structure design, by discussing the ones that we consider the most significant and fruitful, from an educational point of view. A side effect of this discussion will be the introduction of the paradigm called “pointerless programming,” which waives the explicit use of pointers (and thus integer offsets of four–eight bytes to index arbitrary items, such as strings, nodes, or edges) and instead uses compressed data structures built upon proper binary arrays that efficiently subsume the pointers, and support efficiently/optimally some interesting query operations over them.
SocioPedia: Visualizing Social Knowledge over Time

In this paper, we introduce SocioPedia, which is a real-time automatic system for efficiently visualizing and analyzing the variations, characteristics, and evolutions of social knowledge following the change of time. SocioPedia has been developed to provide a full knowledge graph life cycle and combined the temporal information into each processed knowledge. To benefit different classes of users, SocioPedia provides a user-friendly and intuitive environment with different visualization types including static knowledge visualization, timeline knowledge visualization, timeline characteristic visualization, and dynamic timeline visualization.