Reviewerly/exHarmony huggingface dataset:
Optimization Model of Mathematics Instructional Mode Based on Deep Learning Algorithm

This paper proposes corresponding teaching methods and instructional modes based on predecessors’ research on mathematics instructional mode and the current state of mathematics teaching. In addition, this paper constructs a teaching evaluation model based on DL algorithm based on an in-depth study of DL-related theories in order to accurately and scientifically analyze the problems that exist in mathematics teaching. This paper constructs an instructional quality evaluation index system based on rationality and fairness, and uses the BPNN evaluation model to train and study a set of instructional quality data. Finally, the experimental results show that this system has a high level of stability, with a 96.37 percent stability rate and a 95.42 percent evaluation accuracy rate. The results of this paper’s evaluation of the mathematical instructional quality model are objective and reasonable. It can accurately assess instructional quality while also assessing problems in the teaching process based on the instructional quality scores and making reasonable recommendations for teaching improvement based on the weak links in the teaching process. It has the potential to provide a workable system for assessing instructional quality.
A Hybrid Approach Towards Machine Translation System for English–Hindi and Vice Versa

With the rapid progress in the technology and data in the public domain, the machine translation and data science have made remarkable progress. In this paper, we discuss our specific use case of developing machine translation system for English to Hindi and Hindi to English language translation. For this system, we have used the daily proceedings of the Lok Sabha as data and developed NMT-based machine translation system on the top of already available rule-based machine translation system. Developed system has been evaluated using bilingual evaluation understudy (BLEU) as well as the human evaluation metrics using comprehensibility and fluency. In machine translation (MT), there is the trend of measuring post-editing time, and thus, we have also evaluated our system by measuring post-editing time using open-source tool.
From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs

Recent years have witnessed a flurry of research activity in graph matching, which aims at finding the correspondence of nodes across two graphs and lies at the heart of many artificial intelligence applications. However, matching heterogeneous graphs with partial overlap remains a challenging problem in real-world applications. This paper proposes the first practical learning-to-match method to meet this challenge. The proposed unsupervised method adopts a novel partial optimal transport paradigm to learn a transport plan and node embeddings simultaneously. In a from-one-to-all manner, the entire learning procedure is decomposed into a series of easy-to-solve sub-procedures, each of which only handles the alignment of a single type of nodes. A mechanism for searching the transport mass is also proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art graph matching methods.
Development of a project learning program to enhance the basic vocational competency of college students

Objectives The purpose of this study is to develop a project learning program to enhance the basic vocational competency of college students.&#x0D; Methods The research methods carried out for the realization of the research purpose were literature study, case study, needs analysis, expert evaluation, and field application evaluation. The draft program was devised through literature study, case study, and needs analysis. And the completion of the program was developed through expert evaluation and field application evaluation. The needs analysis and expert evaluation were conducted for each of 6 experts in the relevant field, and the results were analyzed according to the qualitative content analysis method. The field application evaluation was applied for a total of 12 hours over 4 weeks from February 20, 2022, and the results were summarized.&#x0D; Results The program developed as a result of the research consisted of the goals, contents, methods, and evaluation of the program. And for each stage, the name of the stage, activity content, related basic vocational competency, measurement, output, preparations, and points to be noted were included. The learning phase consists of preparing, understanding the problem, devising a solution, putting it into practice, looking back, and managing change.&#x0D; Conclusions It was found that through this program, not only students' basic vocational abilities were improved, but also learners were able to proactively prepare related matters. However, it can be suggested that a hybrid type program needs to be further developed in the future for a higher quality program.&#x0D; &#x0D;
Compressing the Tree of Canonical Huffman Coding

The codebook is important for canonical Huffman coding, which needs to contain the number of leaves in each layer of the canonical Huffman tree and the corresponding symbols. Specifically, as two conventional methods in [1], [2], only the number of leaves in each level of the canonical Huffman tree is needed to store. However, we provide a new method to store the number of internal nodes in each layer and compactly encode the string of numbers according to the specific property between the internal nodes.
Very Small Entities (VSEs): Outsourcing Risk to the Supply Chain Is Placing Systems Security Engineering on a Clay Foundation, but Playing Games May Help

ABSTRACT This article addresses the inherent risk in a supply chain that comprises primarily Very Small Entities (VSE) with little to no security proficiency and limited resources and incentive to prioritize system security. In a globalized economy based on outsourcing and risk‐sharing, most engineering activities occur in the smallest companies, even for large and complex projects. The Future of Systems Engineering initiative (FuSE) appropriately has agility at the core of its Systems Security Engineering (SSE) foundation concepts, and VSEs are by their very nature agile. However, the line between agility and chaos may be thin, and engineers at VSEs must often accept a level of restraint and rigidity beyond their comfort level to achieve functional agility. The primary challenge in VSEs is adding structure without the necessary resources to enforce compliance manually. We propose that VSE focus their initial efforts on FuSE SSE Foundation Concepts that play into their nature and strengths as dynamic human social activity systems. Improvements in security proficiency and stakeholder alignment do not necessarily require much formal structure, and digital tools combined with social strategies can add structure to a resource‐constrained environment. Games can be excellent low‐cost tools to provide structure while minimizing resistance, and Agile Model‐Based Systems Engineering (AMBSE) using digital models can support automated enforcement. Here we use the card game Elevation of Privilege (EoP) as an example. Within the context of a SysML Threat Model integrated into a larger System Model, players naturally treat security requirements as traceable functional requirements. Automated model validation, re‐usable components and patterns enforce a Zero‐Trust architecture, a sufficiently formal trust model to provide evidence‐based assurance, yet achievable for small companies with limited resources.
Improved Deep Image Compression with Joint Optimization of Cross Channel Context Model And Generalized Loop Filter

Among the recent deep image compression frameworks, transform coding together with a context-adaptive entropy model is the most representative approach to achieve the best coding performance. For entropy model, 2D mask convolution is widely utilized to capture the spatial context, which omits the correlations along channel dimension. To complement to the spatial context, a cross channel context model is proposed. For transform, if given more network layers to improve its representation ability, how to allocate these network layers in forward and inverse transform is investigated. After analyzing the scheme of deep image compression connected with loop filter, we find this investigation can be regarded as a more generalized loop filter. The proposed cross channel context model and generalized loop filter (CCCMGLF) are integrated into the deep image compression framework and jointly optimized to improve the coding performance. Experimental results demonstrate that, using PSNR as distortion metric, the proposed CCCMGLF outperforms VTM-11.0 by 1.20&#x0025;, 10.82&#x0025; and 5.38&#x0025; in terms of BD-rate reductions for Y, U and V components, respectively, for the Kodak dataset. For the JVET CTC sequences, the proposed method outperforms VTM-11.0 by 1.44&#x0025; for Y but has a coding performance loss of 24.74&#x0025; and 11.91&#x0025; for U and V, respectively. Over the baseline deep compression framework, the proposed method provides 7.80&#x0025;, 12.66&#x0025; and 11.15&#x0025; performance improvement for Y, U, and V, respectively, for the Kodak dataset; 9.10&#x0025;, 12.27&#x0025;, and 12.68&#x0025; performance improvement for Y, U and V, respectively, for the JVET CTC sequences. The proposed approaches are applicable in both image compression and intra coding in video compression.
Semantic Neural Rendering-based Video Coding: Towards Ultra-Low Bitrate Video Conferencing

Providing high video quality under the lowest possible bitrate constraint is one of the critical challenges in video coding technology. Inspired by the continuous development of motion imitation [1], the model-based video coding method is derived from extracting a series of features or parameters representing the person&#x0027;s motion and reconstructing each frame by motion imitation model at the decoder. Thus, we propose a Semantic Neural Rendering-based Video Coding framework (SNRVC) to transmit video at ultra-low bitrate while maintaining high subjective quality. At the encoder, we first extract the motion parameters with specific semantic meanings from each frame and then compress the first frame and the parameters of the subsequent frames by truncating to different decimals and differential pulse code modulation coding. Finally, the decoded image and parameters are fed into the motion imitator [2] to obtain each reconstructed frame consistent with the movements of the original frame. Our SNRVC can achieve better visual quality than traditional and model-based methods [3] at the ultra-low bitrate below 0.01 bpp.
Fine-Grained Correlation Loss for Regression

Regression learning is classic and fundamental for medical image analysis. It provides the continuous mapping for many critical applications, like the attribute estimation, object detection, segmentation and non-rigid registration. However, previous studies mainly took the case-wise criteria, like the mean square errors, as the optimization objectives. They ignored the very important population-wise correlation criterion, which is exactly the final evaluation metric in many tasks. In this work, we propose to revisit the classic regression tasks with novel investigations on directly optimizing the fine-grained correlation losses. We mainly explore two complementary correlation indexes as learnable losses: Pearson linear correlation (PLC) and Spearman rank correlation (SRC). The contributions of this paper are two folds. First, for the PLC on global level, we propose a strategy to make it robust against the outliers and regularize the key distribution factors. These efforts significantly stabilize the learning and magnify the efficacy of PLC. Second, for the SRC on local level, we propose a coarse-to-fine scheme to ease the learning of the exact ranking order among samples. Specifically, we convert the learning for the ranking of samples into the learning of similarity relationships among samples. We extensively validate our method on two typical ultrasound image regression tasks, including the image quality assessment and bio-metric measurement. Experiments prove that, with the fine-grained guidance in directly optimizing the correlation, the regression performances are significantly improved. Our proposed correlation losses are general and can be extended to more important applications.
Infinite hidden Markov models for multiple multivariate time series with missing data

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time-resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state-specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals.
Cyber Security Awareness, Knowledge and Behavior in the Banking Sector in Jordan

Considering the increment in the size, variety, complexity of the data, it became critical and difficult for organizations to handle their information’s assets on-premise. Therefore, by utilizing the advancements of technology, digitalization, digitization, it became easier for banks to turn their data and information to be online and on the cloud. Despite all the powerful features that the cloud provides, it became challenging for organizations to tackle the threats in information security, moreover the cyber security regulations rise, so it became necessary for organizations to pay more attention to the cyber security practices by measuring the awareness and knowledge of their end-users and to see how these two dimensions affect their behaviors. This paper investigates the effect of cyber security awareness and knowledge on the cyber security behavior among the employees in the banking sector in Jordan.
SVM-D: Efficient SHVC Video Multicast through D2D Offloading over 5G Cellular Networks

Device-to-Device (D2D) offloading is an emerging technique in 5G where cache-enabled mobile devices receive data from a base station and distribute to the users within its D2D coverage. Storage capacity and battery energy limitation impose additional constraints in implementing such techniques. In this paper, we propose a novel D2D offloading scheme (SVM-D) for multicast transmissions of Scalable High Efficiency Video Coding (SHVC) videos in 5G multimedia mobile wireless networks. Instead of selecting a single cluster head, SVM-D selects multiple users for caching and forwarding scalable video content to the cluster members. A hybrid approach has been adopted for selecting a subset of users as the potential D2D transmitters (cluster heads) depending on their device category, channel quality, and available battery energy. The objective is to reduce the resource utilization at BSs and to increase the cluster lifetime while providing sufficient quality of experience (QoE) to the users. The local servers (i.e., mobile edge computing servers) perform an on-demand SHVC encoding of multimedia content and distribute it to the users via base stations. We evaluate the performance of SVM-D in terms of its overall resource utilization, average battery lifetime, and average QoE of the cluster users. The comparison is made with conventional unicast and multicast schemes in 5G network with and without D2D offloading scheme (where a single cluster head stores and forwards the data). Simulation results show that SVM-D out performs other popular existing schemes from the literature.
Counting with Prediction: Rank and Select Queries with Adjusted Anchoring

Rank and select queries are the fundamental building blocks of the compressed data structures. On a given bit string of length <tex>$n$</tex>, counting the number of set bits up to a certain position is named as the rank, and finding the position of the <tex>$k$</tex>th set bit is the select query. We present a new data structure and the procedures on it to support rank/select operations. The proposed scheme introduces (<tex>$\frac{\log 2m}{d}+\frac{\log n}{s\cdot d}$</tex>) overhead bits per each bit over the <tex>$n$</tex>-bits long input bit string, where <tex>$d$</tex> is the inner-block size in bits, <tex>$s$</tex> is the number of inner-blocks in a super-block, and <tex>$m$</tex> is a properly chosen constant modulus value. When compared to the previous two-level hierarchical data structures that generate <tex>$(\frac{\log(s\cdot d)}{d}+\frac{\log n}{s\cdot d})$</tex> overhead bits per bit, the new approach reduces the space consumption significantly with proper selection of the parameters. With the new data structure, the rank queries are usually (&#x2248; 90&#x0025; of the time) executed in <tex>$O(t_{d})$</tex> time, where <tex>$O(t_{d})$</tex> is the time required to compute a rank in an inner-block of length <tex>$d$</tex>-bits, which is assumed to be constant via the wide-register instructions in modern processors. Seldom, it may require to investigate more than one block, where on average this is observed to be around two blocks, empirically. We provide probabilistic analyses on how to choose the appropriate parameters and present several trade-offs to guarantee constant-time rank. We also investigate using the same data structure to support the select queries as well. Experimental evaluation of the introduced scheme revealed that the proposed data structure consumes nearly 30&#x0025;-50&#x0025; less space than its alternatives by introducing less than 5&#x0025; overhead, while the speed is either better or very competitive when compared with the current state-of the art implementations both in terms of rank and select.
Spans Detection of Toxic Phrases in Arabic Tweets

In this paper, we investigate and develop different techniques and deep learning models to detect the spans of characters within an Arabic content that drive a model to classify it as being toxic. Incorporating a model capable of providing such a detailed output into an automated toxicity detection system would significantly reduce the amount of time required to investigate measures taken by automated systems against contents classified as toxic. The dataset used in this study contains 1800 tweets and was originally used for sentiment analysis, however it was re-annotated on the character level to match the requirements of this work. The proposed approach has achieved 0.8289 on the modified F1-score metric and is based on both word2vec word embeddings and BERT-base pooled embeddings. To our knowledge, this is the first effort aiming at approaching this task in Arabic contents.
Results of geological and geophysical study of the deep structure of Angara and Trans-Baikal mantle plumes and their connection with mineral deposits.

The purpose of the article is to consider the issues of geological and geophysical interpretation of the data of balloon and satellite magnetic measurements along the regional profile crossing the territory of the Angara-Baikal region. In order to conduct scientific research along the regional Trans-Siberian geological and geophysical profile A-B the author used various geological and geophysical materials including the magnetic field digital data measured by the MAGSAT artificial Earth satellite and a balloon; data on the values of the electrical resistivity in the mantle of this region; geothermal data; seismic data on the location of earthquake hypocenters in the area of the profile under investigation. The research methods involved multilevel measurements of satellite and balloon magnetic fields, which significantly expanded the possibilities of geological and geophysical interpretation of the data obtained. The conducted study revealed that the geological and geophysical interpretation of multilevel aeromagnetic data allows for a reasonably accurate determination of the location coordinates and lithospheric penetration depth of tectonic faults associated with the Angara and Trans-Baikal mantle plumes, which are of significant interest in terms of exploration of coal and uranium deposits. The spatial and depth characteristics of tectonic faults obtained from balloon and satellite data are confirmed by a set of analyzed independent geophysical data: magnetotelluric sounding, geothermy, seismology and other geophysical methods. In conclusion it should be noted that the author has demonstrated the application possibility of satellite and balloon magnetic surveys for the study of the deep structure of the Angara and Trans-Baikal mantle plumes. In addition, it was found out that according to balloon and satellite magnetic data, large deep tectonic faults in the lithosphere (Barguzinsky, Ikatsky, Tukolamsky, Tungirsky) can be identified, which also allow marking various subhorizontal boundaries of lithospheric layers in the location area of the Angara and Trans-Baikal mantle plumes using special points of the magnetically active zones of these faults. The practical significance of the conducted research is in the identification of the spatial relationship between the location of the Angara mantle plume and coal deposits of the Irkutsk basin, as well as uranium deposits in the zone of the Trans-Baikal mantle plume.
Project Management Effort Estimation Using Agile Manager game platform

Effort estimation research studied the effort spent in direct software project development. While the effort involved in indirect software project development increased, management is considered as an indirect effort, but it influenced the overall effort since many foundations prefer buying Software rather than building it. The goal of this research paper is to study the effect of manager personality and task nature on management effort. To achieve our aims, we relied on a dataset that consists of detailed data, which forms distinct decision situations, methods and outcomes from 1,144 participants from distinct backgrounds. The dataset was gathered from an interactive game called the Agile Manager Game platform (AMG). The variables utilized in our dataset were manager personality (age, education and gender) and task difficulty. We applied a statistical analysis to our dataset using IBM SPSS which is flexible and relies on machine learning techniques, and we utilized different classifiers of different types. These classifiers are Naïve bayes, IBK, Decision tree J48 and AdaBoostM1. We applied also k-means clustering approach to our dataset. These classification and clustering techniques were applied using the well-known toll which is called WEKA. The results showed that task nature and manager education are considered significant determinants of management effort. However, manager personality age and gender do not affect management effort.