Reviewerly/exHarmony huggingface dataset:
Model encoding of binary neural networks

Though the low-bit quantization method is more efficient than the full-precision models, there is still a space to improve the efficiency of deep neural networks in both computation and memory requirements. As the last chapter introduced, the low-precision quantization networks enjoy the memory saving and efficient fixed-point computation. However, in some embedded devices, the energy consumption of quantization networks is still preventing the model deployment for real applications. To address this issue, in this chapter, we focus on a extreme quantization network, which constrains both the weight parameters and activations into discrete binary values {−1,+1}. It presents a hardware-friendly approach to improve the classification accuracy of efficient deep neural networks with binary weights and activations. Different from the standard feed-forward neural networks, the Cellular Binary Neural Network consists of multiple subnets of binary neurons, which are connected via global lateral connections. The brain-inspired lateral connections between different subnets are assumed to be sparse and grouped with respect to different source layers. Technically, the lateral connections are optimized by group sparse regularization and knowledge distillation. Experiments on the CIFAR-10 and ImageNet datasets showed that by incorporating optimized group-sparse lateral paths the Cellular Binary Neural Network can achieve higher classification accuracy than other binary deep neural networks.
An Introduction to Bayesian Inference for Finite Population Characteristics

With an increased interest in using Bayesian methods for the analysis of sample survey data, it is timely to provide an introduction. We start from first principles, progressing to relatively simple parametric models. With the latter one can see how the likelihood and prior information are combined to make inferences. Assuming that the values of Y in the finite population come from a normal distribution with known variance, a sample of size n, and a conjugate prior distribution, explicit expressions for the posterior mean and variance of the finite population mean and variance are presented and interpreted. In a similar way, explicit expressions are given for the case where the finite population is generated from a linear regression of Y on X through the origin. This is a model typically seen in establishment surveys where $$Y_i$$ and $$X_i$$ represent the survey and census values for unit i. A useful extension when there is hidden cluster structure in the data is to use a Dirichlet process rather than simple parametric models such as those described in this chapter. Multiple regression with post-stratification (MRP) is based on the use of many categorical variables and specialized hierarchical priors. MRP has seen widespread application, especially when the data are from nonprobability samples or probability samples with low response rates. Finally, there is an extensive discussion of alternative (Bayesian) inferential methods when the data are categorical. This is an attractive option when one wishes to avoid postulating parametric continuous distributions.
A fibre Bragg grating sensor-based instrumented glove for virtual rehabilitation applications

A fibre Bragg grating sensor-based instrumented glove being developed for medical applications will be demonstrated that can be used to measure finger joint angles with a resolution of 0.1°.
Generation and Explanation of Data

In the DIT model we consider the signifier of a data item as a pattern that denotes a symbol in the physical world. The signified of the symbol depends on the given environment. If the data item is intended for a human, the signified is an already formed concept in the human mind. If the data item is used by a computer, the signified is a value housed in a token where the meaning of the value is determined by the set of operations that the name of the value—the variable name—is involved in. This set of operations has been designed by a human programmer who understands the meaning of the involved tokens.
Probabilistic Term Weighting Based on Three-Way Decisions for Class Based Feature Selection

Selecting features that represent a specific class is important to achieve a high text classification performance. The core and critical part of any text feature selection method is the weighting function. Most term weighting methods only consider document level when calculating a term weight and do not consider the distribution of features among different classes. Such an approach does not accurately reflect the specificity of each individual term that can discriminate between the positive and negative documents in the document collection because of the numerous uncertainties in text documents. To address this problem, we propose an innovative and effective feature-weighing method based on three-way decisions to reduce uncertainties in selected features. The proposed model can assign a more discriminately accurate weight to terms based on their distribution in each class. The experimental results, based on the standard RCV1 dataset and R21578 and three popular performance measures, show that our model significantly outperforms six state-of-the-art baseline models and enhances the performance of text classifier models.
Spatio-Temporal Deep Feature Fusion for Human Action Recognition

Action Recognition plays a vital role in many secure applications. The objective of this paper is to identify actions more accurately. This paper focuses on the two stream network in which keyframe extraction method is utilized before extracting spatial features. The temporal features are extracted using Attentive Correlated Temporal Feature (ACTF) which uses Long Short Term Memory (LSTM) for deep features. The spatial and temporal features are fused and classified using multi Support Vector Machine (multiSVM) classifier. Experiments are done on HMDB51 and UCF101 datasets. The results of the proposed method are compared with recent methods in terms of accuracy. The proposed method is proved to work better than other methods by achieving an accuracy of 96% for HMDB51 dataset and 98% for UCF101 dataset.
Credible Text Summarization in Social Media

In the past decade, social media have become increasingly popular for information consumption, due to its easy access, fast dissemination, and low-cost production. However, compared to this explosion of information content, social media presents two major problems. The first concerns the potential propagation of misinformation, often in the form of fake news, which can have significant negative societal effects. The second concerns the extreme difficulty that users may have in finding information of interest in a context characterized by information overload. In this paper, both of these problems are considered and analyzed together. In fact, the main goal of this work is to study the problem of generating automatic summaries of texts (around specific topics of interest) that are at the same time credible. This task has been very little studied in the literature, so this contribution can be considered one of the first works about credible text summarization.
Building Resources for Georgian Treebanking-Based NLP

We describe past and present work surrounding the development of treebank related NLP resources for Georgian. In particular, we provide an overview of efforts made in the development of a morphologically and syntactically annotated treebank for this non-configurational language, as well as its application in the development of a syntactic parser. Building on this, we also report ongoing work in utilizing manual and automatic alignment solutions for the creation of a Georgian/German parallel treebank. The end goal is the development of resources and tools for improved computational processing and linguistic analysis of the Georgian language.
A Multi-Feature Fusion Network for Pathological Identification of Tumor Cells

A novel multi-feature fusion neural network (MFNet) is proposed to address the lack of applicability of existing medical aid diagnostic methods for cross-site and cross-tissue cytopathic lesion screening.MFNet consists of a data-sharing layer, a detailed feature transfer branch, a microscopic identification branch, and an auxiliary loss function.The data-sharing layer converts data images into a feature matrix and extracts detailed elements such as cell morphology, contour, and texture.The microscopic recognition branch obtains multilevel elements by convolving the input elements in stages and fusing them, so that the network can focus on high-level semantic elements such as minor differences of cytopathy.The detail feature transfer branch transfers detail elements across layers and achieves cross-layer fusion with semantic elements.The auxiliary loss function enables the network feature classification capability to be enhanced.MFNet is experimentally compared with AlexNet, VGG-16, ResNet-50, and other models on the tumor cell datasets, and the results show that the proposed method can effectively improve the recognition accuracy.
Are Bitcoins the New Payment Tools Suitable for Extinguishing a Pecuniary Obligation?

This chapter aims to examine the issue of cryptocurrencies and to develop in particular a reflection on the possibility of counting bitcoins, which represent the best known and most widespread of the existing cryptocurrencies, among the payment instruments suitable to extinguish a pecuniary obligation, recognizing in them the function of means of payment typical of currency. The reflection moves from the crucial question of whether bitcoins are currency, reconstructing first of all the meaning of currency and its function. The research aims to conduct a cross-sectional analysis of the bitcoin system through an argumentation that highlights its potentialities and limitations.
Application of Big Data Information Processing Technology in the Improvement of Intercultural Sensitivity in English Teaching

Language is the carrier of culture, learning a language is bound to master the related culture. Intercultural sensitivity is the core competence of the emotional part of cross-cultural communicative competence. The current era of big data has brought great changes to people's lives and work. While bringing opportunities to school teaching, it also brings challenges. Based on the analysis of the connotation of big data and the reasons for its application in English teaching, combined with the shortcomings of traditional teaching, this article puts forward some effective measures to improve the intercultural sensitivity in English teaching by using big data, so as to improve the application level of big data technology in English teaching.
Federated Learning Method Based on Knowledge Distillation and Deep Gradient Compression

Federated learning is a new type of multi-agency collaborative training model paradigm, which is widely used in many fields, among which communication overhead is a key issue. In order to reduce the amount of data transmitted in the communication process, we propose a federated learning algorithm based on knowledge distillation and deep gradient compression (Fed-KDDGC-SGD). First, we use local data on the client to train the teacher network, and then use the soft labels generated by the teacher network to train the student network and upload the gradient to the central server during the training process. In order to further reduce the communication bandwidth occupied by sending the gradient, the deep gradient compression algorithm is used to compress the gradient vector, and only the gradient value of the top R% of the absolute value is sent. The experimental results show that the improved federated learning algorithm effectively reduces the communication overhead and has certain practical significance.
Quantum-Secure Aggregate One-time Signatures with Detecting Functionality

An aggregate signature (ASIG) scheme allows any user to compress multiple signatures into a short signature called an aggregate signature. While a conventional ASIG scheme cannot detect any invalid messages from an aggregate signature, an ASIG scheme with detecting functionality (D-ASIG) has an additional property which can identify invalid messages from aggregate signatures. Hence, D-ASIG is useful to reduce the total amount of signature-sizes on a channel. On the other hand, development of quantum computers has been advanced recently. However, all existing D-ASIG schemes are insecure against attacks using quantum algorithms, which we call quantum attacks. In this paper, we propose a D-ASIG scheme with quantum-security which means security in a quantum setting. Hence, we first introduce quantum-security notions of ASIGs and D-ASIGs because there is no research on such security notions for (D-)ASIGs. Second, we propose a lattice-based aggregate one-time signature scheme with detecting functionality, and prove that this scheme satisfies our quantum-security in the quantum random oracle model and the certified key model. Hence, this scheme is the first quantum-secure D-ASIG.
FicBit: An improved Fractal Image Compression using Bio-Inspired Techniques

With the surge of modern communication technologies, image compression has attracted significant interest in the research community. Fractal image compression (FIC) is regarded as one of the prominent compression methods, due to the high compression ratio and short decompressing time. However, one major drawback of this approach is its high computational cost, which results in a long compression time. In this work, we propose FICBIT, an improved FIC technique leveraging numerical properties of fractional encoding and binary conversion system. We augment the fractional-to-quotient number-based image compression technique by performing channel splitting and optimal sub-imaging. We further optimize the compression by leveraging efficient meta-heuristic algorithms, specifically genetic algorithm (GA) and particle swarm optimization (PSO). We introduce a novel generalized fitness function for both the algorithms and experiment with state-of-the-art image processing data-set for validating the proposed strategy.
SR3: Sentence Ranking, Reasoning, and Replication for Scenario-Based Essay Question Answering

Scenario-based question answering is a trending research topic in artificial intelligence. While existing efforts are focused on multiple-choice questions, in this article, we consider scenario-based essay question answering (SEQA). This task requires generating a paragraph-long answer from both the scenario description that contextualizes the question and from external knowledge. To solve the task, our approach sentence ranking, reasoning, and replication (SR3) selects top-ranked sentences from the scenario and external knowledge to feed a novel sequence-to-sequence model, which enhances the synergy between sentences by reasoning over a sentence graph and then generates an answer with a new sentence-level copying mechanism. SR3 significantly outperforms a variety of strong baseline methods on GeoSEQA, a dataset containing 4003 scenario-based essay questions collected from China’s high-school geography exams.
Pattern-Based Resolution of Integration Mismatches in Enterprise Applications

Modern applications integrate various heterogeneous software services, typically based on Enterprise Integration Patterns (EIPs). At the same time, such applications can include hundreds of interacting components, being these services or EIPs. This makes it complex to manually check whether the typed messages sent by a component to another are such that the latter can understand and suitably process them. We propose a design-time methodology for automatically identifying type mismatches in the messages exchanged among services and EIPs in a multi-service application. Our methodology also recommends how to refactor the architecture of a multi-service application to resolve the type mismatches therein. We assess the practical applicability of our methodology by presenting a proof-of-concept implementation, which we used to run a case study based on an existing, third-party application.
Research on Intimacy between Teachers and Students in English Classrooms in the Context of Artificial Intelligence

This article mainly describes the development process of artificial intelligence (AI) and the full range of opportunities that exist in the process of empowering English education.In the field of education, educational objectives, educational content, educational process and educational mode will be reformed and reshaped.The intimacy between teachers and students will inevitably change. Teachers must change their concepts, learn to teach collaboratively with artificial intelligence, learn for life, and be charming teachers in order to construct a new type of teacher-student relationship.
Distribution of the Minimum Distance of Random Linear Codes

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q\geq 2$ </tex-math></inline-formula> be a prime power. In this paper, we study the distribution of the minimum distance (in the Hamming metric) of a random linear code of dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}^{n}$ </tex-math></inline-formula> . We provide quantitative estimates showing that the distribution function of the minimum distance is close ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">superpolynomially</i> in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> ) to the cumulative distribution function of the minimum of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(q^{k}-1)/(q-1)$ </tex-math></inline-formula> independent binomial random variables with parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {1}{q}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . The latter, in turn, converges to a Gumbel distribution at integer points when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {k}{n}$ </tex-math></inline-formula> converges to a fixed number in (0, 1). Our result confirms in a strong sense that apart from identification of the weights of proportional codewords, the probabilistic dependencies introduced by the linear structure of the random code, produce a negligible effect on the minimum code weight. As a corollary of the main result, we obtain an improvement of the Gilbert–Varshamov bound for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2&lt; q&lt; 49$ </tex-math></inline-formula> .
Corpus-based bilingual terminology extraction in the power engineering domain

Abstract This paper presents the resources and tools used to extract and evaluate bilingual, English-Serbian terminology in the power engineering domain. The resources consist of existing general and domain lexica, and a domain parallel corpus; tools include term extractors for both languages and a tool for aligning the segments belonging to corpus sentences. The system was tested by varying a match function that establishes the presence of an extracted term in an aligned segment (a chunk), ranging from very loose to strict. The evaluation of results showed that the precision of English term extraction was 92%, Serbian term extraction 86%, while the precision of bilingual pair extraction was 72% based on the strictest match function. The result of extraction was 2,684 correct bilingual pairs that enhanced the terminology database and can further be used to support the search of the power engineering aligned collection stored in a digital library.
Path planning of mobile robot based on genetic-ant colony algorithm

Abstract Mobile robots are widely used in daily life, and ant colony algorithm is often adopted for its path planning. However, the classic ant colony algorithm has poor search ability and search speed due to the monotony of individuals within the population. In order to solve the problems of local optimal solution, slow convergence speed and low search efficiency of classic ant colony algorithm, a genetic-ant colony algorithm is proposed in this paper. The algorithm parameters are optimized by simulation experiment to determine the best parameter combination. The method is to give a certain range of random pheromone values to the primary ant individuals to enrich the population diversity. In each cycle, m ants are sent for foraging. After each foraging cycle, aiming at the growth of individual pheromone value, the fitness function of genetic algorithm is established to evaluate individual fitness value, and the parental individuals carry out genetic operations such as selection, crossover and variation to produce offspring individuals with the same number and higher pheromone level, the offspring individuals carry out the next cycle of foraging and keep cycling. The population diversity is maintained at a high level, while the increase in the level of individual pheromone speeds up the convergence and operation of the algorithm. The simulation results show that the running time of the improved algorithm is significantly shorter than that of the traditional ant colony algorithm in the 40 * 40 grid map, and the running time of the algorithm is reduced by 21.34%; in the complex grid environment, the frequency of obtaining the optimal path of the algorithm is significantly higher than that of ordinary ant colonies, and the frequency is increased; and the number of iterations when the optimal path is reached for the first time is significantly reduced,–which has verified the effectiveness, stability and superiority of genetic-ant colony algorithm.
TABULARITY AND POST-COMPLETENESS IN TENSE LOGIC

Abstract A new characterization of tabularity in tense logic is established, namely, a tense logic L is tabular if and only if $\mathsf {tab}_n^T\in L$ for some $n\geq 1$ . Two characterization theorems for the Post-completeness in tabular tense logics are given. Furthermore, a characterization of the Post-completeness in the lattice of all tense logics is established. Post numbers of some tense logics are shown.
Content Based Image Retrieval Using HDMR Constant Term Based Clustering

The studies related with the content-based image retrieval (CBIR) has increased because of both necessity for efficient image retrieval and the limitations in large-scale systems. Efficient image retrieval refers to finding accurate image from the database with high speed. This paper presents a new efficient image retrieval method using High Dimensional Model Representation (HDMR). The method has two main steps, clustering and retrieval. In clustering part, we use k-means method on HDMR constant term while in the subsequent part, we retrieve the most similar images to a given query image from a relevant cluster. We experiment the efficiency and effectiveness of the new algorithm on Columbia Object Image Library (COIL-100) and get conspicuous results. These results are tabulated in the paper.