Reviewerly/exHarmony huggingface dataset:
Modeling of Dynamic Systems with Interval Parameters in the Presence of Singularities

In solving applied and research problems, there often arise situations where certain parameters are not exactly known, but there is information about their ranges.For such problems, it is necessary to obtain an interval estimate of the solution based on interval values of parameters.In practice, the dynamic systems where bifurcations and chaos occur are of interest.But the existing interval methods are not always able to cope with such problems.The main idea of the adaptive interpolation algorithm is to build an adaptive hierarchical grid based on a kdtree where each cell of adaptive hierarchical grid contains an interpolation grid.The adaptive grid should be built above the set formed by interval initial conditions and interval parameters.An adaptive rebuilding of the partition is performed for each time instant, depending on the solution.The result of the algorithm at each step is a piecewise polynomial function that interpolates the dependence of the problem solution on the parameter values with a given precision.Constant grid compaction will occur at the corresponding points if there are unstable states or dynamic chaos in the system; therefore, the minimum cell size is set.The appearance of such cells during the operation of the algorithm is a sign of the presence of unstable states or chaos in a dynamic system.The effectiveness of the proposed approach is demonstrated in representative examples.
A formal representation of appraisal categories for social data analysis

Human behavior is impacted by subjective states although currently the cyberspace becomes a replacement for real-life spaces and interactions. As social media platforms change people’s lives and impacts the way they communicate and group themselves into virtual networks of like-minded individuals, the analysis of online content offers valuable insights of processes taking place on the Internet. Social data mining revolves around subjective content analysis, which deals with the computational processing of texts conveying people’s evaluations, beliefs, attitudes and emotions. Opinion mining and sentiment analysis are the main paradigm of social media exploration and both concepts are often interchangeable. This paper investigates the use of appraisal categories to explore data gleaned for social media and describes the construction of a formal model describing the way language is used in the cyberspace to evaluate, express mood and affective states, construct personal standpoints and manage interpersonal interactions. The ontology offers a mean to investigate subjective content going beyond the traditional notions of opinion and sentiment. Pitfalls of building a formal model for appraisal categories are examined and limitations of using the model for social data exploration are discussed.
A Multidisciplinary Course and the Corresponding Laboratory Platform Development for Teaching the Fundamentals of Advanced Autonomous Vehicles

energy systems, mechatronics, robotics, and electrified and autonomous transportation.
Research on Corpus Construction of Legal English Teaching Based on Data-Driven Learning

As we all know, corpus linguistics has been widely used in the fields of language acquisition, translation studies, stylistics, foreign language teaching and forensic linguistics in lexicography, which has greatly expanded the horizon of applied linguistics, especially foreign language teaching. Many foreign and Chinese linguists and scholars have noticed this and considered that corpus-applied English teaching is an important branch of corpus linguistics. However, legal English is different from any natural language, because it is a special "artificial language", so traditional teaching method in legal English teaching is not applicable. The application of corpus in legal English teaching is helpful to the smooth implementation of legal English teaching. The teaching of legal English can use small corpus, deepen students' understanding of legal context, explore the legal spirit contained in legal language, and improve the traditional teaching methods of legal language, so that students can truly master the essence of the common law system and meet the needs of high-quality legal talents. Therefore, this paper is bound to be an exploratory exploration of corpus application in legal English teaching. Introduces the information of legal English teaching and corpus, this paper discusses the application of corpus in the teaching of theoretical basis and significance of language teaching, and discusses the construction of legal English corpus such as the construction of the corpus design, data collection and classification, etc., construction and application of corpus can enhance students' initiative and make it easy to learn legal English.
Tatamibari Is NP-Complete.

In the Nikoli pencil-and-paper game Tatamibari, a puzzle consists of an $m \times n$ grid of cells, where each cell possibly contains a clue among +, -, |. The goal is to partition the grid into disjoint rectangles, where every rectangle contains exactly one clue, rectangles containing + are square, rectangles containing - are strictly longer horizontally than vertically, rectangles containing | are strictly longer vertically than horizontally, and no four rectangles share a corner. We prove this puzzle NP-complete, establishing a Nikoli gap of 16 years. Along the way, we introduce a gadget framework for proving hardness of similar puzzles involving area coverage, and show that it applies to an existing NP-hardness proof for Spiral Galaxies. We also present a mathematical puzzle font for Tatamibari.
Application of Digital Network Resources and Multimedia Technology in Poetry Language Classroom Teaching

Poetry teaching is the key and difficult point in Chinese teaching. Its concise language, rich connotation, profound implication and distinct rhythm have created the perfect artistic features of poetry. With the rapid development of multimedia technology, many teachers use multimedia more and more frequently in order to further improve the quality of poetry classroom teaching. The computer system effectively integrates video, audio and text, and its powerful interaction allows teachers to operate various learning materials freely. In this paper, the poetry language classroom teaching as a starting point, digital network resources and multimedia technology in the poetry language classroom teaching application characteristics, analysis of multimedia technology in the poetry teaching significance. Poetry teaching should pay attention to the use of modern multimedia means, so that students can grasp the content of poetry, experience the emotion contained in poetry, and understand the artistic conception of poetry in the tangible, vocal, colored and charming situation.
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models.

Although with progress in introducing auxiliary amortized inference models, learning discrete latent variable models is still challenging. In this paper, we show that the annoying difficulty of obtaining reliable stochastic gradients for the inference model and the drawback of indirectly optimizing the target log-likelihood can be gracefully addressed in a new method based on stochastic approximation (SA) theory of the Robbins-Monro type. Specifically, we propose to directly maximize the target log-likelihood and simultaneously minimize the inclusive divergence between the posterior and the inference model. The resulting learning algorithm is called joint SA (JSA). To the best of our knowledge, JSA represents the first method that couples an SA version of the EM (expectation-maximization) algorithm (SAEM) with an adaptive MCMC procedure. Experiments on several benchmark generative modeling and structured prediction tasks show that JSA consistently outperforms recent competitive algorithms, with faster convergence, better final likelihoods, and lower variance of gradient estimates.
An Efficient JAYA-Based Clustering Technique

This paper proposes a partitioned clustering technique using a relatively new evolutionary computational technique known as JAYA optimization algorithm. K-means clustering techniques happen to be widely researched partitional clustering algorithm in data mining literature. Despite its ease of implementation and widespread application, it has various shortcomings. The efficiency of k-means is heavily dependent on selection of initially cluster centroids. This apart, the time complexity of k-means is dependent on the size of the data sets to be clustered. In this work, we propose JAYA, a population-based approach which has scope to choose many candidate centroids and the initialization time and processed to evolve optimal cluster centroids and through simulation is carried out to benchmark data sets, and a comparison study is taken that JAYA-based algorithm is able to provide optimal performance, i.e., intra-cluster distance (ICD). The results are tabulated in the paper.
Internet of Things Forensics in Smart Homes: Design, Implementation, and Analysis of Smart Home Laboratory

The Internet of Things (IoT) has skyrocketed to the forefront of everyone's lives, whether they know it or not.IoT devices have been incorporated into all facets of life, from the medical industry to transportation and it has even made its way into our homes.These devices have access to vast amounts of data, especially personal data.However, due to the compact nature of these devices, insufficient effort has been made to incorporate security into their design.With that in mind, IoT devices are treasure-troves of potential evidentiary data for forensic investigators, especially when these devices are used to aid in criminal activities.Concerning IoT devices, there is a need to investigate these devices to find answers to questions such as, what data can be recovered from these devices along with their respective smartphone applications?What methods would be best suited for collecting and analyzing this data?What data do these devices share with each other?Are there any privacy concerns when using these devices?Are there any security vulnerabilities when using these devices?Finding answers to these questions will considerably reduce the amount of time investigators spend collecting evidence during a case.New IoT devices are always becoming available and the research into each cannot keep up.As such, there is a gap in the literature regarding both the privacy and security of these devices and the most efficient methods to investigate them.Therefore, in this study, we aim to address this need by first building our own IoT forensics laboratory at Purdue University.Several students (undergraduate and graduate) work under the supervision of their faculty advisor to populate this lab with relevant IoT devices to simulate that of a smart home.This setup would allow us to simulate possible real-world smart home events (i.e.IoT device compromise, IoT device as a witness) which we can then investigate to both find answers to aforementioned questions and develop efficient methods to investigate these IoT devices.In this paper we will discuss several ways in which IoT devices in a smart home can be compromised and also investigate these devices after the compromise to determine what data can be recovered, how to recover the data and where this data resides.
Modelling count data using the logratio-normal-multinomial distribution

The logratio-normal-multinomial distribution is a count data model resulting from compounding a multinomial distribution for the counts with a multivariate logratio-normal distribution for the multinomial event probabilities. However, the logratio-normal-multinomial probability mass function does not admit a closed form expression and, consequently, numerical approximation is required for parameter estimation. In this work, different estimation approaches are introduced and evaluated. We concluded that estimation based on a quasi-Monte Carlo Expectation-Maximisation algorithm provides the best overall results. Building on this, the performances of the Dirichlet-multinomial and logratio-normal-multinomial models are compared through a number of examples using simulated and real count data.
Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals.

Causal parameters may not be point identified in the presence of unobserved confounding. However, information about non-identified parameters, in the form of bounds, may still be recovered from the observed data in some cases. We develop a new general method for obtaining bounds on causal parameters using rules of probability and restrictions on counterfactuals implied by causal graphical models. We additionally provide inequality constraints on functionals of the observed data law implied by such causal models. Our approach is motivated by the observation that logical relations between identified and non-identified counterfactual events often yield information about non-identified events. We show that this approach is powerful enough to recover known sharp bounds and tight inequality constraints, and to derive novel bounds and constraints.
A Survey about the Internet of Things (IoT): What does IoT Mean to Senior-level Industrial Design Students?

The concept of the Internet of Things (IoT) is not new.The first "traceable" practical application of the IoT technology was a vending machine, which reports the condition of the beverages inside, developed by Carnegie Mellon University in 1982 [1].It was a simple system with simple sensors, compared to today's extremely sophisticated IoT applications.Since its first conception, IoT came a long way in consumer products and industrial applications.Numerous research projects have been conducted; and, countless research papers have been published.IoT gained momentum in recent years and became one of the hottest topics in the Industrial Design (ID) discipline.IoT transformed the way once acceptable design methodologies into obsolete.New design disciplines started to emerge to solve complex information architecture problems.The consumer market is experiencing a growth of products that work by networking "things" with sensors.House appliances with sophisticated sensors help owners by carrying out house chores.Autonomous homes control indoor climate while the owners are not present.Smart devices even feed pets and play with them while their owners are at work or out of town.As the demand for skilled designers is increasing in the industry, it is not a surprise that the IoTrelated courses started to emerge in the ID curriculums.The interconnectivity of products is becoming a standard in the product development process rather than an option; consequently, companies look for designers who can conceptualize such products.This paper asks the vital question, perhaps as a self-criticizing way: "Are we ready to address the demand when potential employers ask for a new breed of designers who are capable of designing interconnected products?"The authors of this paper investigated this question by conducting mixed methodology research with ID students on all levels.Their focus was to understand whether the ID students were aware of the technology, its implications, and future impacts on the ID discipline.
Randomized kernel multi-view discriminant analysis

In many artificial intelligence and computer vision systems, the same object can be observed at distinct viewpoints or by diverse sensors, which raises the challenges for recognizing objects from different, even heterogeneous views. Multi-view discriminant analysis (MvDA) is an effective multi-view subspace learning method, which finds a discriminant common subspace by jointly learning multiple view-specific linear projections for object recognition from multiple views, in a non-pairwise way. In this paper, we propose the kernel version of multi-view discriminant analysis, called kernel multi-view discriminant analysis (KMvDA). To overcome the well-known computational bottleneck of kernel methods, we also study the performance of using random Fourier features (RFF) to approximate Gaussian kernels in KMvDA, for large scale learning. Theoretical analysis on stability of this approximation is developed. We also conduct experiments on several popular multi-view datasets to illustrate the effectiveness of our proposed strategy.
On human-AI collaboration in artistic performance

Live artistic performance, like music, dance or acting, provides an excellent domain to observe and analyze the mechanisms of human-human collaboration. In this note, we use this domain to study hu ...
Research on Prediction of Time Between Failures for Onboard Subsystem of Train Control System

The prediction of Time Between Failures (TBF) is one of the key issues of the fault prediction. There is little effective method to predict the TBF of onboard subsystem for train control system. In this paper, a combined prediction model of TBF with high accuracy is proposed. Firstly, single prediction method including Echo State Network (ESN), Back Propagation(BP) neural network, Support Vector Machine (SVM) is used to predict the time between failures of onboard subsystem. In order to enhance the performance of the prediction, a combined prediction model is established on the basis of Seasonal-Trend decomposition procedure based on Loess (STL). Finally, simulation is employed with the accuracy of 96.49%. This study is helpful for the implementation of Preventive Maintenance (PM) tasks.
FPGA Implementation of PICO Cipher

The rise in ubiquitous computing connecting resource-constrained devices had led to the emergence of a sub-domain in cryptography called lightweight cryptography. The algorithms in this domain target moderate security while satisfying the constraints set for a desired application. PICO is one of the lightweight SPN ciphers suitable for low-area and low-power applications. In this paper, two hardware architectures ideal for low-area footprint and low power are proposed. This comprises a serial architecture and a reduced datapath architecture which are designed specifically for reduced area and low-power applications while choosing trade-offs from latency and performance. The results are evaluated for Xilinx FPGA platforms like Spartan 3, Virtex 4 and Virtex 6. The synthesis and implementation of the proposed architectures are performed using Verilog HDL in Xilinx ISE Design Suite 14.6. The area footprint is estimated in terms of the slices, flip flops and LUTs. The power consumption is estimated using XPower Analyzer for a clock frequency of 10 MHz. The simulations for the proposed designs are verified using ISim Simulator. Further, the area in terms of slices occupied is compared with the existing ciphers for Spartan 3 and Virtex 4 families.
Privacy-Preserving Face Redaction Using Crowdsourcing

Redaction of private information from images is the kind of tedious, yet context-independent, task for which crowdsourcing is especially well suited. Despite tremendous progress, machine learning is not keeping pace with the needs of sensitive applications in which inadvertent disclosure could have real-world consequences. Human workers can detect faces that machines cannot; however, an open call to crowds would entail disclosure. We present IntoFocus, a method for engaging crowd workers to redact faces from images without disclosing the facial identities of people depicted. The method works iteratively, starting with a heavily filtered form of the image, and gradually reducing the strength of the filter, with a different set of workers reviewing the image at each step. IntoFocus exploits the gap between the filter level at which a face becomes unidentifiable and the level at which it becomes undetectable. To calibrate the algorithm, we performed a perceptual study of detection and identification of faces in images filtered with the median filter. We present the system design, the results of the perception study, and the results of a summative evaluation of the system
Educação 4.0: um Estudo de Caso com Atividades de Computação Desplugada na Amazônia Brasileira

This case study aimed to cultivate 21st Century competences and skills related to Education 4.0, such as problem-solving, collaboration and autonomy. To this end, unplugged computing activities were performed with 25 students from four rural schools located in Tapajós-Arapiúns (Brazilian Amazon). As part of the methodology, a maze based on Google's Blockly Games tool was produced, inspired by hopscotch, a popular joke among children. After this case study, a qualitative analysis was performed, and it indicates that Unplugged Computing activities instigate students' skills and competences, as autonomy, communication and teamwork. Besides, this methodology proves to be useful for disseminating Education 4.0 concepts, due to its ease of application and low cost, especially in distant places with few resources.
A Context-Based Video Compression: A Quantum-Inspired Vector Quantization Approach

This paper proposes a modified video compression model that optimizes vector quantization codebook by using the adapted Quantum Genetic Algorithm (QGA) that uses the quantum features, superposition, and entanglement to build optimal codebook for vector quantization. A context-based initial codebook is created by using a background subtraction algorithm; then, the QGA is adapted to get the optimal codebook. This optimal feature vector is then utilized as an activation function inside the neural network’s hidden layer to remove redundancy. Furthermore, approximation wavelet coefficients were lossless compressed with Differential Pulse Code Modulation (DPCM); whereas details coefficients are lossy compressed using Learning Vector Quantization (LVQ) neural networks. Finally, Run Length Encoding is engaged to encode the quantized coefficients to achieve a high compression ratio. As individuals in the QGA are actually the superposition of multiple individuals, it is less likely that good individuals will be lost. Experiments have proven the system’s ability to achieve a higher compression ratio with acceptable efficiency measured by PSNR.
Implementasi Metode Naive Bayes Classifier (NBC) Pada Komentar Warga Sekolah Mengenai Pelaksanaan Pembelajaran Jarak Jauh (PJJ)

Many credit card issuing companies experience problems related to bill payments by their customers or also known as bad credit payments that are not according to the agreement so that they are detrimental to the company itself. In this case, there is still a pile of unclassified credit cardholder customer data and problem-solving patterns are found. The C4.5 algorithm is used to predict whether a customer is a credit default payment or not. This study uses a data set that has determining criteria, namely the amount of credit, status, age, and payment status for 1-3 months. From the results of research using 4199 customer data results in an evaluation that the C4.5 algorithm is applied accurately to predict whether or not customer credit card payments are bad with an accuracy level of 70.93%.