Reviewerly/exHarmony huggingface dataset:
LOCO-ANS: An Optimization of JPEG-LS Using an Efficient and Low-Complexity Coder Based on ANS

Near-lossless compression is a generalization of lossless compression, where the codec user is able to set the maximum absolute difference (the error tolerance) between the values of an original pixel and the decoded one. This enables higher compression ratios, while still allowing the control of the bounds of the quantization errors in the space domain. This feature makes them attractive for applications where a high degree of certainty is required. The JPEG-LS lossless and near-lossless image compression standard combines a good compression ratio with a low computational complexity, which makes it very suitable for scenarios with strong restrictions, common in embedded systems. However, our analysis shows great coding efficiency improvement potential, especially for lower entropy distributions, more common in near-lossless. In this work, we propose enhancements to the JPEG-LS standard, aimed at improving its coding efficiency at a low computational overhead, particularly for hardware implementations. The main contribution is a low complexity and efficient coder, based on Tabled Asymmetric Numeral Systems (tANS), well suited for a wide range of entropy sources and with simple hardware implementation. This coder enables further optimizations, resulting in great compression ratio improvements. When targeting photographic images, the proposed system is capable of achieving, in mean, 1.6%, 6%, and 37.6% better compression for error tolerances of 0, 1, and 10, respectively. Additional improvements are achieved increasing the context size and image tiling, obtaining 2.3% lower bpp for lossless compression. Our results also show that our proposal compares favorably against state-of-the-art codecs like JPEG-XL and WebP, particularly in near-lossless, where it achieves higher compression ratios with a faster coding speed.
A Visual Analytics Approach for Anomaly Detection from a Novel Traffic Light Data

Traffic signals are part of our critical infrastructure and protecting their integrity is a serious concern. Security flaws in traffic signal systems have been documented and effective detection of exploitation of these flaws remains a challenge. In this paper we present a visual analytics approach to look for anomalies in traffic signal data (i.e., abnormal traffic light patterns) that may indicate a compromise of the system. To our knowledge it is a first time a visual analytics approach is applied for the processing and exploration of traffic signal data. This system supports level-of-detail exploration with various visualization techniques. Data cleaning and a number of preprocessing techniques for the extraction of summary information (e.g., traffic signal cycles) of the data are also performed before the visualization and data exploration. Our system successfully reveals the errors in the input data that would be difficult to capture with simple plots alone. In addition, our system captures some abnormal signal patterns that may indicate intrusions into the system. In summary, this work offers a new and effective way to study attacks or intrusions to traffic signal control systems via the visual analysis of traffic light signal patterns.
Secure Coded Computation for Efficient Distributed Learning in Mobile IoT

Distributed computation plays an essential role in cloud and edge computing. Data such as images, audio, and text can be represented as matrices to facilitate efficient computation, especially in the domains of distributed machine learning, computer vision, and signal processing. Many coded computation algorithms have been proposed for big data applications to securely partition and distribute matrices to parallel worker devices. However, these proposals have yet to be adapted for mobile platforms beyond theoretical means. Mobile IoT networks can greatly benefit from secure distributed computing, however, commercial devices such as smartphones and tablets are much more limited in resources compared to platforms in data centers, requiring special design considerations. We investigate existing distribution schemes from an operational complexity and security viewpoint and study their performance in several mobile IoT networks, identifying performance bottlenecks in regards to communication and computation costs. From our findings, we propose new, scalable algorithms optimized to handle the unique constraints of mobile IoT. Extensive evaluations of our proposals on publicly available image classification datasets show how distributed learning can be specially optimized to enhance runtime and battery performance on mobile IoT by over 10×.
Non-Deterministic Functions as Non-Deterministic Processes

We study encodings of the λ-calculus into the π-calculus in the unexplored case of calculi with non-determinism and failures. On the sequential side, we consider λ^↯_⊕, a new non-deterministic calculus in which intersection types control resources (terms); on the concurrent side, we consider 𝗌π, a π-calculus in which non-determinism and failure rest upon a Curry-Howard correspondence between linear logic and session types. We present a typed encoding of λ^↯_⊕ into 𝗌π and establish its correctness. Our encoding precisely explains the interplay of non-deterministic and fail-prone evaluation in λ^↯_⊕ via typed processes in 𝗌π. In particular, it shows how failures in sequential evaluation (absence/excess of resources) can be neatly codified as interaction protocols.
Laplacian Fractional Revival on Graphs

We develop the theory of fractional revival in the quantum walk on a graph using its Laplacian matrix as the Hamiltonian. We first give a spectral characterization of Laplacian fractional revival, which leads to a polynomial time algorithm to check this phenomenon and find the earliest time when it occurs. We then apply the characterization theorem to special families of graphs. In particular, we show that no tree admits Laplacian fractional revival except for the paths on two and three vertices, and the only graphs on a prime number of vertices that admit Laplacian fractional revival are double cones. Finally, we construct, through Cartesian products and joins, several infinite families of graphs that admit Laplacian fractional revival; some of these graphs exhibit polygamous fractional revival.
Prediction of Mortality and Length of Stay with Deep Learning

Predicting the mortality and the length of stay of patients during ICU stay is important for better acute care and planning of ICU resources. The recent advancements in deep learning and with the Electronic Health Record data becoming available for researchers, there has been an increasing interest in the healthcare domain. While patient's structural data are frequently used, most of the studies in the literature do not use the clinical notes due to the complex nature. In this study, we use the clinical notes besides time-series features to improve our predictions. The results have shown that the proposed deep learning based multimodal approach outperforms on all clinical tasks over baseline models.
Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering

In recent days many people are working on twitter data as the tweets are easily available and also provide reliable data. Collecting and processing these tweets produces promising and accurate results in solving many real world problems. Common problem faced by most of the people is traffic congestion. Traffic congestion results in traffic jams, mental and physical health disturbance. So to avoid this, our paper tried to show the methodology which can bring out promising results. In this paper for processing the tweet data we have used the common approach of Term Frequency-Inverse Document Frequency (TF-IDF) and discussed the application of brainstorming optimization algorithm (BSO) to avoid traffic congestion. We have also introduced the density peak clustering (DPC) to train the brain storming optimization technique. This paper has shown the modified BSO and DPC on the tweets to bring out the results which show traffic conditions at various places. We have justified our work by conducting the experiment.
Towards the development of a medical rehabilitation system

Monitoring systems for the activities of the older adults are increasingly desired and requested by the elderly, their families and medical professionals. This paper presents a detailed analysis of depth cameras with highlights of key features, algorithms used in the development of applications using these types of cameras, limitations and problems encountered, and similar applications developed. The goal is to develop a dynamic rehabilitation system, by implementing a dynamic avatar capable of reproducing simple and complex user-defined movements, which is based on concepts similar to video games and focuses mainly on data collected from depth cameras, extraction and the processing of essential data in order to identify and track the human action performed in front of the camera.
Anxiety Barriers in Joining Digital Online Learning During Covid19 Pandemic Outbreaks

Pandemic covid19 outbreaks brings many impacts in education sectors including to conduct and maintain teaching and learning at university. This study evaluates the use of digital online learning hosted by Islamic university in Indonesia. Students’ anxiety in joining and following the distance classroom is the major factor that was analyzed. quantitative approach was used to get and investigated the data beyond the sample students of university. Throwing the students’ perception in the online survey which was conducted to portray the data on the why students felt anxiety to get the digital online learning. Research findings explain about the students owning technological items in supporting students’ online learning and students’ anxiety while attending and joining the online classroom. The discussion discloses about 1) the readiness of students’ having online devices, internet settings, and students’ literacy in using online technology. 2) Moreover, it also elaborates students’ anxiety claiming that digital online learning scared and make them less confidence, bad feeling, and lack of enthusiastic to study. the writer concludes that students presume these followings; -prefer having online classroom using smartphones android rather than laptop/computer pc, -got trouble in internet connection while having online classroom, - sacrifice much money to consume internet quota to join online learning, - scared students due to the equipment needed, - do not feel confidence in joining the lesson due to many obstacles and barriers.
Automatic Recommendation Method for Classifier Ensemble Structure Using Meta-Learning

Machine Learning (ML) is a field that aims to develop efficient techniques to provide intelligent decision making solutions to complex real problems. Among the different ML structures, a classifier ensemble has been successfully applied to several classification domains. A classifier ensemble is composed of a set of classifiers (specialists) organized in a parallel way, and it is able to produce a combined decision for an input pattern (instance). Although Classifier ensembles have proved to be robust in several applications, an important issue is always brought to attention is the ensemble’s structure. In other words, the correction definition of its structure, like the number and type of classifiers and the aggregation method, has an important role in its performance. Usually, an exhaustive testing and evaluation process is required to better define the ideal structure for an ensemble. Aiming to produce an interesting investigation in this field, this paper proposes two new approaches for automatic recommendation of classifier ensemble structure, using meta-learning to recommend three of these important parameters: type of classifier, number of base classifiers, and the aggregation method. The main aim is to provide a robust structure in a simple and fast way. In this analysis, five well known classification algorithms will be used as base classifiers of the ensemble: kNN (Nearest Neighbors), DT (Decision Tree), RF (Random Forest), NB (Naive Bayes) e LR (Logistic Regression). Additionally, the classifier ensembles will be evaluated using seven different strategies as aggregation functions: HV (Hard Voting), SV (Soft Voting), LR (Logistic Regression), SVM (Support Vector Machine), NB(Naive Bayes), MLP (Multilayer perceptron) e DT (Decision Tree). The empirical analysis shows that our approach can lead to robust classifier ensembles, for the majority of the analysed cases.
Recent Research on Phishing Detection Through Machine Learning Algorithm

The rapid growth of emerging technologies, smart devices, 5G communication, etc. have contributed to the accumulation of data, hence introducing the big data era. Big data imposes a variety of challenges associated with machine learning, especially in phishing detection. Therefore, this paper aims to provide an analysis and summary of current research in phishing detection through machine learning for big data. To achieve this goal, this study adopted a systematic literature review (SLR) technique and critically analyzed a total of 30 papers from various journals and conference proceedings. These papers were selected from previous studies in five different databases on content published between 2018 and January 2021. The results obtained from this study reveal a limited number of research works that comprehensively reviewed the feasibility of applying both machine learning and big data technologies in the context of phishing detection.
Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components

This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real dataset and compared it with other existing web prefetching techniques in terms of prediction accuracy. The best configuration of the proposed neural hybrid method provides an average web access prediction accuracy of 86.95%.
Teaching and Learning Mathematics with Mobile Devices

This paper investigates teachers’ and sixth grade students’ perceptions on the impact of using mobile devices in teaching and learning mathematics. A focus group was conducted with convenience sampling of ten teachers and thirty students in an elementary school in Attica. The overall findings of this study show that the use of mobile devices has a positive impact on both teaching practices and learning in mathematics. In addition, the survey showed that there are two categories of teachers: the “innovative” and the “traditional” ones. Innovative teachers adapt their style of teaching according to the benefits that mobile devices can offer, while the traditional ones do not seem to be willing to use them. Finally, the results of the study show that the use of mobile devices in teaching has positive changes in the way students learn mathematics, as they offer interactive and exciting new environments.
Generic-Group Identity-Based Encryption: A Tight Impossibility Result

Following the pioneering work of Boneh and Franklin (CRYPTO '01), the challenge of constructing an identity-based encryption scheme based on the Diffie-Hellman assumption remained unresolved for more than 15 years. Evidence supporting this lack of success was provided by Papakonstantinou, Rackoff and Vahlis (ePrint '12), who ruled out the existence of generic-group identity-based encryption schemes supporting an identity space of sufficiently large polynomial size. Nevertheless, the breakthrough result of Dottling and Garg (CRYPTO '17) settled this long-standing challenge via a non-generic construction.We prove a tight impossibility result for generic-group identity-based encryption, ruling out the existence of any non-trivial construction: We show that any scheme whose public parameters include n_pp group elements may support at most n_pp identities. This threshold is trivially met by any generic-group public-key encryption scheme whose public keys consist of a single group element (e.g., ElGamal encryption). In the context of algebraic constructions, generic realizations are often both conceptually simpler and more efficient than non-generic ones. Thus, identifying exact thresholds for the limitations of generic groups is not only of theoretical significance but may in fact have practical implications when considering concrete security parameters.
Implicit Phenomena in Short-answer Scoring Data

Short-answer scoring is the task of assessing the correctness of a short text given as response to a question that can come from a variety of educational scenarios.As only content, not form, is important, the exact wording including the explicitness of an answer should not matter.However, many state-of-the-art scoring models heavily rely on lexical information, be it word embeddings in a neural network or n-grams in an SVM.Thus, the exact wording of an answer might very well make a difference.We therefore quantify to what extent implicit language phenomena occur in short answer datasets and examine the influence they have on automatic scoring performance.We find that the level of implicitness depends on the individual question, and that some phenomena are very frequent.Resolving implicit wording to explicit formulations indeed tends to improve automatic scoring performance.
A new approach for chat sonification

This paper presents a new approach of chat sonification based on a deep neural network for music generation. The advantage of chat sonification is that the feeling of something artificial in the generated music is less present than for other artificial intelligence approaches. The new method of sonification introduced in the paper is based on the polyphonic model theory, similarly to the idea used in the MusicXML Creator Platform, the novelty being that it uses sequence-to-sequence neural networks for an improved pitch generation. The duration generation algorithm remains the same as it was implemented in the MusicXML Creator. The results are evaluated by multiple participants
Using Adaptive Flashcards for Automotive Maintenance Training in the Wild

The U.S. military is interested in modernizing its training and educational technology resources to support tailored, learner-centric experiences more consistent with the digital-age. The goal of this research was to explore the benefits of adaptive training in the context of a USMC course. To this end, we describe the development of an adaptive flashcard system to help Marines study during an automotive maintenance course. Specifically, the system incorporated techniques, such as adaptive sequencing and the use of mastery criteria to drop flashcards from study. These techniques have been effective in the context of short-term laboratory studies but not for longer-term course performance. Therefore, we conducted an initial pilot study to compare course performance outcomes between cohorts before and after the addition of the adaptive training system in the course. Overall, there was a 50% reduction in exam learning objective failures in the adaptive training cohort compared to the cohort without adaptive training. Furthermore, we found that greater usage of the system was related to higher course GPA. Taken together, these results demonstrate a promising use-case for an adaptive training solution implemented successfully “in the wild,” and future research plans for this system are discussed.
The use of an online grammar checker in English writing learning

The purpose of the study is to examine online grammar checkers and suggest when they could be used in English writing classes. The study was conducted in the second semester of 2019 at D University in Chungcheong-do, with a total of 35 first-year students participating in the study. For data collection, pre and post grammar tests, questionnaires, and learning journals were collected and analyzed. The results of this study are as follows. First, based on the results of the English grammar test, the online grammar checker was found to be effective in English writing class. Second, students judged whether accepting or not rather than accepting feedback provided by online grammar checker. Third, among the feedback provided by the online grammar checker, the order of (in)definite article, preposition, punctuation, verb number, and noun number were found. The several implications and limitations of this study are discussed.
Search trajectories illuminated

This article summarizes our recent journal paper entitled "Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics", where we propose a graph-based, data-driven modeling tool (STNs) to visualize and analyze the dynamics of any type of metaheuristic (evolutionary, swarm-based or single-point).
Designing Limitless Path in Virtual Reality Environment

Walking in a Virtual Environment is a bounded task. It is challenging for a subject to navigate a large virtual environment designed in a limited physical space. External hardware support may be required to achieve such an act in a concise physical area without compromising navigation and virtual scene rendering quality. This paper proposes an algorithmic approach to let a subject navigate a limitless virtual environment within a limited physical space with no additional external hardware support apart from the regular Head-Mounted-Device (HMD) itself. As part of our work, we developed a Virtual Art Gallery as a use-case to validate our algorithm. We conducted a simple user-study to gather feedback from the participants to evaluate the ease of locomotion of the application. The results showed that our algorithm could generate limitless paths of our use-case under predefined conditions and can be extended to other use-cases.
Online Learning of Riemannian Hidden Markov Models in Homogeneous Hadamard Spaces

Hidden Markov models with observations in a Euclidean space play an important role in signal and image processing. Previous work extending to models where observations lie in Riemannian manifolds based on the Baum-Welch algorithm suffered from high memory usage and slow speed. Here we present an algorithm that is online, more accurate, and offers dramatic improvements in speed and efficiency.
CSECU-DSG at SemEval-2021 Task 1: Fusion of Transformer Models for Lexical Complexity Prediction

Lexical complexity prediction (LCP) conveys the anticipation of the complexity level of a token or a set of tokens in a sentence.It plays a vital role in the improvement of various NLP tasks including lexical simplification, translations, and text generation.However, multiplemeaning of a word in multiple circumstances, grammatical complex structure, and the mutual dependency of words in a sentence make it difficult to estimate the lexical complexity.To address these challenges, SemEval-2021 Task 1 introduced a shared task focusing on LCP and this paper presents our participation in this task.We proposed a transformerbased approach with sentence pair regression.We employed two fine-tuned transformer models including BERT and RoBERTa to train our model and fuse their predicted score to the complexity estimation.Experimental results demonstrate that our proposed method achieved competitive performance compared to the participants' systems.