Reviewerly/exHarmony huggingface dataset:
Mathematics and Cultures: A New Concept in Maintaining Cultures through the Development of Learning Devices

The game is a very close and inseparable part of the child's life.Games will be effective media in maintaining culture through learning.This study aims to develop a learning tool in the form of a Student Activity Sheet (SAS) and a learning implementation plan (LIP) that can connect between culture and mathematics through the Realistic Mathematics Education (RME) approach.Regional traditional games used in this study were Static, Long Galah, Guli and Congkak games.Static, Long Galah,, Guli, and Congkak games was selected through studies on the adaptation of Malay culture to mathematics and expert Focus Group Discussion (FGD).The development of SAS learning in this study uses a Plomp modification consisting of several phases, namely: (1) initial investigation; (2) design; (3) construction; (4) tests, evaluations, and revisions.The product trial was carried out in the Mandau district elementary school.The instrument used in this study was the LIP and SAS validation sheets used by experts, practitioners, and students to assess LIP and SAS.Data analysis in this study used descriptive statistics by comparing the validation and practical criteria of a learning tool.From the research results obtained the results of the validity test LIP Statac Games, Galah Panjang, Guli and Congkak with an average of each game; 76. 61%, 82.76, 83.39, and 84.40, with the criteria quite valid while SAS obtained the average value of each permanent namely; 77, 88.2, 81.5 and 84.1.From the questionnaire given to students after SAS was implemented, an average score of 9 was obtained with a very practical category.the results of this study indicate that this development research has produced a learning tool based on Malay folk Malay Riau game with a valid and practical.
Identification of Time-Varying Decoherence Rates for Open Quantum Systems

Parameter identification of quantum systems is a fundamental task in developing practical quantum technology. In this article, we study the identification of time-varying decoherence rates for open quantum systems. Given the measurement data of local observables, this can be formulated as an optimization problem. We expand the unknown decoherence rates into Fourier series and take the expansion coefficients as optimization variables. We then convert it into a minimax problem and apply a sequential linear programming technique to solve it. Numerical study on a two-qubit quantum system with a time-varying decoherence rate demonstrates the effectiveness of our algorithm.
Trampoline Motion Decomposition Method Based on Deep Learning Image Recognition

The automatic segmentation and classification of an unknown motion data stream based on given motion classes constitute an important research problem with applications in computer vision, animation, healthcare, and sports sciences. In this paper, the scenario of trampoline motions is considered, where an athlete performs a routine consisting of sequence of jumps that belong to predefined motion classes such as somersaults. The purpose of this study was to make theoretical discussions on the turning starting time and starting technique of trampoline somersault based on image recognition and point out that the appropriate turning starting time of trampoline somersault is the event when the spring net of the trampoline recovers and applies force to the human body, and the overturning start exists in the latter half of the take-off action. It is considered that how to obtain the ideal full reaction force of the net facing the human body is the flip starting technique. This work analyzes the key steps and events for trampoline somersaults and the application of artificial intelligence for the recognition of actions in the healthcare and sports fields. The effectiveness of the proposed study is shown through experimental results. The study can facilitate the process of recognition of trampoline somersault.
A Heuristic Grafting Strategy for Manufacturing Knowledge Graph Extending and Completion Based on Nature Language Processing: KnowTree

Applied to search, question answering, and semantic web of close-or-open domain, knowledge graph (KG) is known for its incompleteness subject to the rapid knowledge growing pace.Inspired by the agricultural grafting technology to fruit variety, this paper proposes a heuristic knowledge grafting strategy (HGS) for manufacturing knowledge graph (MKG) named KnowTree extending and completion with natural language processing (NLP) mining engineering cases document.Based on similarity analysis, firstly the grafting related definitions and mechanisms (completeness, relatedness, connectivity and reutilization) are defined.Then, focused on the four mechanisms, HGS takes a pair same engineering documents as input.KnowWords is built as a collection of KnowScion, and each scion is mined from engineering documents based on the SAO structure network, whose importance is evaluated by SAORank counting the in-out degree of centrality.On another hand, the KnowRoot system is designed based on the extended P & S ontology model to characterize the structure of abstract document into four sub-space of knowledge: know-what (problem), know-why (context), know-how (solution) and know-with (result), where a pre-trained language representation model K-BERT is used to classify the KnowScion candidates into the designed KnowRoot system with a fine-tuning classification task.In the knowledge grafting process, the connection unit is constructed based on the extracted domain knowledge triples of the K-BERT model, where the head element of a triple is from the KnowScion candidate set KnowWords satisfying the threshold value, the tail element is from the domain MKG to be extended, and a connection factor is used to evaluate the relationship of union combination.To the goal of knowledge reuse, the path based reasoning rules are designed for KnowTree reutilization.Finally, take the latest engineering case abstract (ECA) in whitegoods domain as resources, a case study is conducted to validate the proposed HGS strategy.
A Method of Deep Learning Tackles Sentiment Analysis Problem in Arabic Texts

Sentiment Analysis (SA) is a field of Natural Language Processing (NLP) whose goal is to extract the emotion, sentiment or more general opinion expressed in a human-written text. Opinions and emotions play a central role in human life. Therefore, there are many academic researches in this field for processing many languages like English However, there is scarce in its implementation with addressing Arabic Sentiment Analysis (ASA). It is a challenging field where Arabic language has a rich morphological structure and there are many other defies more than in other languages. For that, the proposed model tackles ASA by using a Deep Learning approach. In this work, one of word embedding methods, such as a first hidden layer for features extracting from the input dataset and Long Short-Term Memory (LSTM) as a deep neural network, has been used for training. The model combined with Softmax layer is applied to turn numeric outputs from LSTM layer into probabilities to classify the outputs to positive or negative. There are two datasets that are used for training the model separately with each one. The first one is ASTD dataset as a dialectal Arabic type about different tweets from internet, the results with this dataset is compared with another academic work that used the same one. The results from this work outperforms through accuracy about 14.95% and F-score about 15.14% more than what performed in the previous work. The second one is HTL dataset as a modern standard Arabic type about opinions of reviewers on different hotels from several countries. This dataset is bigger in size than the first one to show the size effect on the results of this model. So, the accuracy increased about 11% and F-score about 10.8% more than what performed with the first dataset.
Reservoir Computing Assisted Ultrafast User Localization in Beam Steering Optical Wireless System

A novel approach to achieving ultrafast real-time user localization in optically controlled beam-steering wireless communications (OWC) is proposed and demonstrated in this letter. Ultrafast wavelength encoded user localization at 50 MHz rate is achieved based on photonic time stretch concept and a passive beam scanner using a 45° tilted fibre grating (TFG). Not only the angular separation but also the linear distance from the access point can be determined simultaneously. More importantly, challenging instantaneous optical wavelength identification within nanosecond range is successfully achieved through chirped microwave frequency encoding and reservoir computing (RC) based frequency identification. A proof-of-concept experiment was carried out to verify the proposed approach. The results show that RC is more efficient in frequency chirp identification than the conventional short-time Fourier transform approach.
Method of Information Technology Enhanced Japanese Vocabulary Learning and Evaluation

Japanese vocabulary proficiency is directly related to Japanese language proficiency, and the learning and evaluation of Japanese vocabulary is vital in Japanese learning. To evaluate the learners’ Japanese vocabulary proficiency, in this study, we propose an effective method for evaluating Japanese vocabulary and implement it into a system. To prove the evaluation ability of the proposed method and implemented system, we conducted an experiment involving 80 students from the Japanese Group of the department of applied foreign languages of a senior high school to investigate the effectiveness of the method and system. The results of this study confirmed that (1) the reliability and validity of the proposed Japanese vocabulary evaluation method and system are favorable; and (2) the correlation coefficient between the experimental results and the students’ academic performance is higher than 0.80. Theoretically, the multidimensional Japanese vocabulary quotient model proposed herein can be the basis for Japanese vocabulary detection. Practically, the low-cost and rapid Japanese vocabulary evaluation system developed is applicable to educational evaluation, whereas the technical evaluation method and system for Japanese vocabulary can improve the efficiency and effectiveness in evaluating the Japanese vocabulary of learners.
A Unified Pretraining Framework for Passage Ranking and Expansion

Pretrained language models have recently advanced a wide range of natural language processing tasks. Nowadays, the application of pretrained language models to IR tasks has also achieved impressive results. Typical methods either directly apply a pretrained model to improve the re-ranking stage, or use it to conduct passage expansion and term weighting for first-stage retrieval. We observe that the passage ranking and passage expansion tasks share certain inherent relations, and can benefit from each other. Therefore, in this paper, we propose a general pretraining framework to enhance both tasks with Unified Encoder-Decoder networks (UED). The overall ranking framework consists of two parts in a cascade manner: (1) passage expansion with a pretraining-based query generation method; (2) re-ranking of passage candidates from a traditional retrieval method with a pretrained transformer encoder. Both the two parts are based on the same pretrained UED model, where we jointly train the passage ranking and query generation tasks for further improving the full ranking pipeline. An extensive set of experiments have been conducted on two large-scale passage retrieval datasets to demonstrate the state-of-the-art results of the proposed framework in both the first-stage retrieval and the final re-ranking. In addition, we successfully deploy the framework to our online production system, which can stably serve industrial applications with a request volume of up to 100 QPS in less than 300ms.
Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders

Learning prerequisite chains is an essential task for efficiently acquiring knowledge in both known and unknown domains.For example, one may be an expert in the natural language processing (NLP) domain but want to determine the best order to learn new concepts in an unfamiliar Computer Vision domain (CV).Both domains share some common concepts, such as machine learning basics and deep learning models.In this paper, we propose unsupervised cross-domain concept prerequisite chain learning using an optimized variational graph autoencoder.Our model learns to transfer concept prerequisite relations from an information-rich domain (source domain) to an information-poor domain (target domain), substantially surpassing other baseline models.Also, we expand an existing dataset by introducing two new domains--CV and Bioinformatics (BIO).The annotated data and resources, as well as the code, will be made publicly available.
Using Online Planning and Acting to Recover from Cyberattacks on Software-defined Networks

We describe ACR-SDN, a system to monitor, diagnose, and quickly respond to attacks or failures that may occur in software-defined networks (SDNs). An integral part of ACR-SDN is its use of RAE+UPOM, an automated acting and planning engine that uses hierarchical refinement. To advise ACR-SDN on how to recover a target system from faults and attacks, RAE+UPOM uses attack recovery procedures written as hierarchical operational models. Our experimental results show that the use of refinement planning in ACR-SDN is successful in recovering SDNs from attacks with respect to five performance metrics: estimated time for recovery, efficiency, retry ratio, success ratio, and costEffectiveness.
An Improved Vulnerability Exploitation Prediction Model with Novel Cost Function and Custom Trained Word Vector Embedding

Successful cyber-attacks are caused by the exploitation of some vulnerabilities in the software and/or hardware that exist in systems deployed in premises or the cloud. Although hundreds of vulnerabilities are discovered every year, only a small fraction of them actually become exploited, thereby there exists a severe class imbalance between the number of exploited and non-exploited vulnerabilities. The open source national vulnerability database, the largest repository to index and maintain all known vulnerabilities, assigns a unique identifier to each vulnerability. Each registered vulnerability also gets a severity score based on the impact it might inflict upon if compromised. Recent research works showed that the cvss score is not the only factor to select a vulnerability for exploitation, and other attributes in the national vulnerability database can be effectively utilized as predictive feature to predict the most exploitable vulnerabilities. Since cybersecurity management is highly resource savvy, organizations such as cloud systems will benefit when the most likely exploitable vulnerabilities that exist in their system software or hardware can be predicted with as much accuracy and reliability as possible, to best utilize the available resources to fix those first. Various existing research works have developed vulnerability exploitation prediction models by addressing the existing class imbalance based on algorithmic and artificial data resampling techniques but still suffer greatly from the overfitting problem to the major class rendering them practically unreliable. In this research, we have designed a novel cost function feature to address the existing class imbalance. We also have utilized the available large text corpus in the extracted dataset to develop a custom-trained word vector that can better capture the context of the local text data for utilization as an embedded layer in neural networks. Our developed vulnerability exploitation prediction models powered by a novel cost function and custom-trained word vector have achieved very high overall performance metrics for accuracy, precision, recall, F1-Score and AUC score with values of 0.92, 0.89, 0.98, 0.94 and 0.97, respectively, thereby outperforming any existing models while successfully overcoming the existing overfitting problem for class imbalance.
Addressing the trade off between smells and quality when refactoring class diagrams.

Models are core artifacts of modern software engineering processes, and they are subject to evolution throughout their life cycle due to maintenance and to comply with new requirements as any other software artifact.Smells in modeling are indicators that something may be wrong within the model design.Removing the smells using refactoring usually has a positive effect on the general quality of the model.However, it could have a negative impact in some cases since it could destroy the quality wanted by stakeholders.PARMOREL is a framework that, using reinforcement learning, can automatically refactor models to comply with user preferences.The work presented in this paper extends PARMOREL to support smells detection and selective refactoring based on quality characteristics to assure only the refactoring with a positive impact is applied.We evaluated the approach on a large available public dataset to show that PARMOREL can decide which smells should be refactored to maintain and, even improve, the quality characteristics selected by the user.
Scene Graph Embeddings Using Relative Similarity Supervision

Scene graphs are a powerful structured representation of the underlying content of images, and embeddings derived from them have been shown to be useful in multiple downstream tasks. In this work, we employ a graph convolutional network to exploit structure in scene graphs and produce image embeddings useful for semantic image retrieval. Different from classification-centric supervision traditionally available for learning image representations, we address the task of learning from relative similarity labels in a ranking context. Rooted within the contrastive learning paradigm, we propose a novel loss function that operates on pairs of similar and dissimilar images and imposes relative ordering between them in embedding space. We demonstrate that this Ranking loss, coupled with an intuitive triple sampling strategy, leads to robust representations that outperform well-known contrastive losses on the retrieval task. In addition, we provide qualitative evidence of how retrieved results that utilize structured scene information capture the global context of the scene, different from visual similarity search.
A Novel Approach for Digital Evidence Management Using Blockchain

In the proceedings of the court of law, it is of vital importance that the digital evidences are produced untampered during the entire Chain of Custody (Coc) process. Recent advancements in computer technology have enabled hackers to compromise with the integrity of the evidences. In order to secure these evidences from such external agents we have proposed a Blockchain model. Unlike the traditional security systems, Blockchain Technology uses cryptographic hashing in which each block has a cryptographic hash of its previous block i.e., A growing list of records that are called Blocks. These blocks are secure and tamperproof. The idea of this work is to handle the digital evidence from the time it is retrieved to the point it is presented as an evidence in the court of law. It ensures safeguarding of the evidence during the entire Chain of Custody process, which is fundamental to the idea of digital forensics. The aim of Digital Forensics is to maintain evidences in their most original form while performing a structured investigation by the collection, identification and validation of the digital information for the purpose of reconstructing events from the past. The proposed system ensures integrity, traceability, authenticity and security of the evidences.
An Efficient Chosen-Plaintext Attack on an Image Fusion Encryption Algorithm Based on DNA Operation and Hyperchaos

This paper proposes a more efficient attack method on an image fusion encryption algorithm based on DNA operation and hyperchaos. Although several references have reported some methods to crack the image encryption algorithm, they are not the most efficient. The proposed chosen-plaintext attack method can break the encryption scheme with (4×N/M+1) or (M/(4×N)+1) chosen-plaintext images, which is much less than the number of chosen-plaintext images used in the previous cracking algorithms, where M and N represent the height and width of the target ciphertext image, respectively. The effectiveness of the proposed chosen-plaintext attack is supported by theoretical analysis, and verified by experimental results.
ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences

Atomic clauses are fundamental text units for understanding complex sentences.Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering.Previous work mainly relies on rulebased methods dependent on parsing.We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task.Our neural model learns to Accept, Break, Copy or Drop elements of a graph that combines word adjacency and grammatical dependencies.The full processing pipeline includes modules for graph construction, graph editing, and sentence generation from the output graph.We introduce DeSSE, a new dataset designed to train and evaluate complex sentence decomposition, and MinWiki, a subset of MinWikiSplit.ABCD achieves comparable performance as two parsing baselines on MinWiki.On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline.Results include a detailed error analysis.
Emotion Recognition on Edge Devices: Training and Deployment

Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature extractors, and simple hardware-friendly classifiers based on linear separators can achieve competitive performance while allowing real-time inference and fast training. Various solutions including batch and Online Sequential Learning are analyzed. Additionally, our experiments show that latency and performance can be further improved via dimensionality reduction and pre-training, respectively. The resulting system is implemented on two types of edge device, namely an edge accelerator and two smartphones.