Reviewerly/exHarmony huggingface dataset:
Abstractive Arabic Text Summarization Based on Deep Learning

Text summarization (TS) is considered one of the most difficult tasks in natural language processing (NLP). It is one of the most important challenges that stand against the modern computer system’s capabilities with all its new improvement. Many papers and research studies address this task in literature but are being carried out in extractive summarization, and few of them are being carried out in abstractive summarization, especially in the Arabic language due to its complexity. In this paper, an abstractive Arabic text summarization system is proposed, based on a sequence-to-sequence model. This model works through two components, encoder and decoder. Our aim is to develop the sequence-to-sequence model using several deep artificial neural networks to investigate which of them achieves the best performance. Different layers of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) have been used to develop the encoder and the decoder. In addition, the global attention mechanism has been used because it provides better results than the local attention mechanism. Furthermore, AraBERT preprocess has been applied in the data preprocessing stage that helps the model to understand the Arabic words and achieves state-of-the-art results. Moreover, a comparison between the skip-gram and the continuous bag of words (CBOW) word2Vec word embedding models has been made. We have built these models using the Keras library and run-on Google Colab Jupiter notebook to run seamlessly. Finally, the proposed system is evaluated through ROUGE-1, ROUGE-2, ROUGE-L, and BLEU evaluation metrics. The experimental results show that three layers of BiLSTM hidden states at the encoder achieve the best performance. In addition, our proposed system outperforms the other latest research studies. Also, the results show that abstractive summarization models that use the skip-gram word2Vec model outperform the models that use the CBOW word2Vec model.
Decision Making using the Blockchain Proof of Authority Consensus

As the modern world migrates towards an interconnected environment, be it financially, socially, or industrially, the need for distributed systems has become paramount. Emergent paradigms need decentralized solutions to better handle the single point of failure problem, one example is given by the Internet of Things (IoT), in which devices need to be connected to a central hub to function, or the secure decision making process. Blockchain comes with the solution that overcomes some of the shortcomings in existing systems and provides a secure alternative when designing decentralized systems. This paper proposes a decision making system based on the Proof of Authority consensus protocol enabled by the blockchain paradigm.
A New 4D Hyperchaotic System with Dynamics Analysis, Synchronization, and Application to Image Encryption

In this paper, a new 4D hyperchaotic nonlinear dynamical system with two positive Lyapunov exponents is presented. Exhaustive dynamic analyses of the novel hyperchaotic model using several dynamical studies are described. The dynamics of the system considered are first investigated analytically and numerically to explore phenomena and the selection of hyperchaotic behavior utilized for designing image cryptosystem. Since the proposed hyperchaotic model has rich dynamics, it displays hidden attractors. It emerges from this dynamic the existence of a single unstable equilibrium point giving rise to self-excited attractors, hysteresis phenomenon, and hyperchaotic behavior strongly recommended for securing information by its character. Furthermore, the feasibility and synchronization of the proposed system are also presented by developing, respectively, Raspberry surveys and an adaptive synchronization approach of two identical hyperchaotic systems. By employing the hyperchaotic behavior of the 4D map, an image encryption scheme is proposed as well. It is one round of a pixel-based permutation and a bit-wise diffusion phase. The secret key of the 4D map is derived from the SHA-256 value of the input image. It acts as the signature of the input image. Hence, the secret key exhibits high sensitivity to single-bit alteration in the image, which makes the cryptosystem robust against chosen/known-plaintext attacks. Performance analyses prove that the proposed cryptosystem provides the best in terms of the performance/complexity trade-off, as compared to some recently published algorithms.
A Study of Time Series Forecasting Enrollments Using Fuzzy Interval Partitioning Method

A time series is a sequence of elements with numerical data in sequential order and having regular intervals. Time series are used in statistics, enrollments, signal processing, econometrics, mathematical finance and weather forecasting, etc. It helpsus to forecast and predict the time series data in different domains. There are many methods to forecast the enrollments in literature which have large applications and are presented in the field of statistics and econometrics. One of the robust methods, we used in our research is moving average. It helps to forecast and predict the data whenever the fuzziness occurs in time series data, which is not appropriate in crisp time series forecasting. To get rid of this problem, the fuzzy interval partitioning method proved to be more appropriate to generate accurate results. This research will focus to overcomethe failure of the crisp method and to show the use of a fuzzy interval partitioning method. The fuzzy interval partitioning methodis different from another interval partition schemes because it specifies the linguistic values rather than numerical values. It is also used todeal with uncertain conditions. So, fuzzy interval partitioning improves data utilization and also calculate the higher predicted accuracy rate. Besides this research, we use a quantitative method and a fuzzy moving average with the interval partitioning method. Then we compare the efficiency of moving average model and moving average with fuzzy interval partitioning method for forecasting the enrollments.
SECRET: Self-Consistent Pseudo Label Refinement for Unsupervised Domain Adaptive Person Re-identification

Unsupervised domain adaptive person re-identification aims at learning on an unlabeled target domain with only labeled data in source domain. Currently, the state-of-the-arts usually solve this problem by pseudo-label-based clustering and fine-tuning in target domain. However, the reason behind the noises of pseudo labels is not sufficiently explored, especially for the popular multi-branch models. We argue that the consistency between different feature spaces is the key to the pseudo labels’ quality. Then a SElf-Consistent pseudo label RefinEmenT method, termed as SECRET, is proposed to improve consistency by mutually refining the pseudo labels generated from different feature spaces. The proposed SECRET gradually encourages the improvement of pseudo labels’ quality during training process, which further leads to better cross-domain Re-ID performance. Extensive experiments on benchmark datasets show the superiority of our method. Specifically, our method outperforms the state-of-the-arts by 6.3% in terms of mAP on the challenging dataset MSMT17. In the purely unsupervised setting, our method also surpasses existing works by a large margin. Code is available at https://github.com/LunarShen/SECRET.
A dynamic anomaly detection method of building energy consumption based on data mining technology

Due to the equipment failure and inappropriate operation strategy, it is often difficult to achieve energy-efficient building. Anomaly detection of building energy consumption is one of the important approaches to improve building energy-saving. The great amounts of energy consumption data collected by building energy monitoring platforms (BEMS) provides potentials in using data mining technology for anomaly detection. This study proposes a dynamic anomaly detection algorithm for building energy consumption data, which realizes the dynamic detection of point anomalies and collective anomalies. The algorithm integrates unsupervised clustering algorithm with supervised algorithm to establish a semi-supervised matching mechanism, which avoids the influence of error label and improves the efficiency of anomaly detection. A particle swarm optimization (PSO) is used to optimize the unsupervised clustering algorithm. This investigation tests the effectiveness of the proposed algorithm and evaluates the performance of the energy consumption clustering algorithm by using the annual electricity consumption data of an experimental building in a university. The results show that the clustering accuracy of the algorithm can reach more than 80%, and it can effectively detect the building energy consumption data of two different forms of outliers. It can provide reliable data support for adjusting building management strategies.
Text Spotting Transformers

In this paper, we present TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild. TESTR builds upon a single encoder and dual decoders for the joint text-box control point regression and character recognition. Other than most existing literature, our method is free from Region-of-Interest operations and heuristics-driven post-processing procedures; TESTR is particularly effective when dealing with curved text-boxes where special cares are needed for the adaptation of the tra-ditional bounding-box representations. We show our canonical representation of control points suitable for text in-stances in both Bezier curve and polygon annotations. In addition, we design a bounding-box guided polygon detection (box-to-polygon) process. Experiments on curved and arbitrarily shaped datasets demonstrate state-of-the-art performances of the proposed TESTR algorithm.
SetSVM: An Approach to Set Classification in Nuclei-Based Cancer Detection

Due to the importance of nuclear structure in cancer diagnosis, several predictive models have been described for diagnosing a wide variety of cancers based on nuclear morphology. In many computer-aided diagnosis (CAD) systems, cancer detection tasks can be generally formulated as set classification problems, which can not be directly solved by classifying single instances. In this paper, we propose a novel set classification approach SetSVM to build a predictive model by considering any nuclei set as a whole without specific assumptions. SetSVM features highly discriminative power in cancer detection challenges in the sense that it not only optimizes the classifier decision boundary but also transfers discriminative information to set representation learning. During model training, these two processes are unified in the support vector machine (SVM) maximum separation margin problem. Experiment results show that SetSVM provides significant improvements compared with five commonly used approaches in cancer detection tasks utilizing 260 patients in total across three different cancer types, namely, thyroid cancer, liver cancer, and melanoma. In addition, we show that SetSVM enables visual interpretation of discriminative nuclear characteristics representing the nuclei set. These features make SetSVM a potentially practical tool in building accurate and interpretable CAD systems for cancer detection.
Cross-Line Pedestrian Counting Based on Spatially-Consistent Two-Stage Local Crowd Density Estimation and Accumulation

This paper proposes a scalable approach for counting pedestrians crossing a virtual line when the crowd is highly dynamic and possibly extremely dense. The approach mainly consists of two parts: local crowd density estimation and pedestrian counting based on accumulating local densities across the line. To obtain a fine estimation of local crowd densities, we divide the neighborhood at the line into a number of blocks. We enforce spatial consistency between local counts in the blocks and those in the enclosing regions to guarantee consistent estimation of local crowd densities. For scalability to various density levels in crowd density estimation, we propose a two-stage strategy: pre-classification of density levels and subsequent regression with overlapped operational ranges. To count pedestrians crossing the virtual line, we accumulate the crowd densities across the line according to the locally estimated velocities. Extensive experimental results demonstrate the effectiveness of the proposed approach and its scalability to crowdedness.
An iterative speckle filtering algorithm for ultrasound images based on bayesian nonlocal means filter model

In this paper, we study the problem of suppressing speckle noise in ultrasound images for better clinical diagnosis and subsequent image processes. In order to employ Bayesian nonlocal means filter (BNLMF) model in the circumstance of speckle noise to realize image restoration, we deduce the key probability density function with the help of speckle noise statistical characteristic and then present an iterative filtering algorithm. The first iteration with the noisy image itself being the input of the filtering model generates an initial estimator of the clean image which then further offers a better input of the filtering model. The constantly updated neighbor patches and probability density functions make the filtering result closer to the potential clean one. The healthy iteration process exports the favorable restored image surpassing the results obtained by some typical despeckling methods. Besides, benefit from the blockwise filtering style, pre-patch-selection operation and a small iteration number, the whole algorithm won't consume much time. Various experiments designed for processing the simulated noisy images and the real ultrasound images prove the superiority of the proposed method.
Static and Dynamic Activity Detection with Ambient Sensors in Smart Spaces

Convergence of Machine Learning, Internet of Things, and computationally powerful single-board computers has boosted research and implementation of smart spaces. Smart spaces make predictions based on historical data to enhance user experience. In this paper, we present a low-cost, low-energy smart space implementation to detect static and dynamic human activities that require simple motions. We use low-resolution (4 × 16) and non-intrusive thermal sensors to collect data. We train six machine learning algorithms, namely logistic regression, naive Bayes, support vector machine, decision tree, random forest and artificial neural network (vanilla feed-forward) on the dataset collected in our lab. Our experiments reveal a very high static activity detection rate with all algorithms, where the feed-forward neural network method gives the best accuracy of 99.96%. We also show how data collection methods and sensor placement plays an important role in the resulting accuracy of different machine learning algorithms. To detect dynamic activities in real time, we use cross-correlation and connected components of thermal images. Our smart space implementation, with its real-time properties, can be used in various domains and applications, such as conference room automation, elderly health-care, etc.
A Comparative Study of Recent Steganography Techniques for Multiple Image Formats

Steganography is the technique for exchanging concealed secret information in a way to avoid suspicion.The aim of Steganography is to transfer secrete message to another party by hiding the data in a cover object, so that the imposter who monitors the traffic should not distinguish between genuine secret message and the cover object.This paper presents the comparative study and performance analysis of different image Steganography methods using various types of cover media ((like BMP/JPEG/PNG etc.) with the discussion of their file formats.We also discuss the embedding domains along with a discussion on salient technical properties, applications, limitations, and Steganalysis.
Towards Generating Cost-Effective Test-Suite for Ethereum Smart Contract

In Ethereum, many accounts and funds have been managed by smart contracts, thereby making them easy to be targeted. Due to the persistence characteristic of blockchain, revising a deployed smart contract is almost impossible. Both realities heighten the risks of managing funds and thus increase the demand for conducting sufficient testing to Ethereum Smart Contracts (ESC). Different from the conventional software, ESC is a gas-driven program, where developers must charge gases for deploying and testing it. Therefore, it is important to provide a cost-effective yet representative test suite, where its representativeness can be typically measured by its branch coverage. In this paper, we deem the problem of ESC test generation as a Pareto minimization problem, and three objectives, minimizing (1) uncovered branch coverage, (2) time cost, and (3) gas cost are considered. Then, we propose a random based and an NSGA-II based multi-objective approach to seek cost-effective test-suites. Our empirical study on a set of smart contracts in eight of the most widely used Ethereum Decentralized Applications (DApps) verified that the proposed approaches could significantly reduce the gas cost as well as the time cost while retaining the ability to cover branches.
VLSP SHARED TASK: SENTIMENT ANALYSIS

Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns.
UNDERGRADUATE STUDENTSâ€™ PERCEPTIONS TOWARD BLENDED LEARNING THROUGH INSTAGRAM IN ENGLISH FOR BUSINESS CLASS

Addressing the needs of the millennial students brings the implemention of blended learning using social network site in the teaching and learning process. Instagram becomes one of the most popular social network sites in Indonesia. It has been a potential teaching platform to help English teachers to facilitate language learning, especially for learners at the tertiary level. This paper is intended to explore the studentsâ€™ perceptions toward English language learning and the implementation of Instagram in their English for Business class. Studentsâ€™ attitudes toward English course should be investigated to form a basis for this further study. The subjects were 116 undergraduate students who took general English for Business course at the Faculty of Engineering and Computer Science, University of Teknokrat Indonesia. They were classified into three groups. The data gained were from observation, questionnaire, and interview. The data, then, were analyzed quantitatively and qualitatively. The results indicated that most students showed high positive perceptions in using Instagram in their learning since social network Instagram influenced their motivation, engangement, and attitudes. In addition, internet connection and its speed, misunderstanding, and miscommunication toward the materials and task instructions were considered as the problems that hamper their learning. Instagram might help the students to learn English Language. Thus, it is suggested to improve the teaching of technology skills by preparing suitable digital literacy in academic content.
Efficient Traffic Video Dehazing Using Adaptive Dark Channel Prior and Spatial–Temporal Correlations

In order to restore traffic videos with different degrees of haziness in a real-time and adaptive manner, this paper presents an efficient traffic video dehazing method using adaptive dark channel prior and spatial-temporal correlations. This method uses a haziness flag to measure the degree of haziness in images based on dark channel prior. Then, it gets the adaptive initial transmission value by establishing the relationship between the image contrast and haziness flag. In addition, this method takes advantage of the spatial and temporal correlations among traffic videos to speed up the dehazing process and optimize the block structure of restored videos. Extensive experimental results show that the proposed method has superior haze removing and color balancing capabilities for the images with different degrees of haze, and it can restore the degraded videos in real time. Our method can restore the video with a resolution of 720 × 592 at about 57 frames per second, nearly four times faster than dark-channel-prior-based method and one time faster than image-contrast-enhanced method.
Gaussian Process Lstm Recurrent Neural Network Language Models for Speech Recognition

Recurrent neural network language models (RNNLMs) have shown superior performance across a range of speech recognition tasks. At the heart of all RNNLMs, the activation functions play a vital role to control the information flows and tracking longer history contexts that are useful for predicting the following words. Long short-term memory (LSTM) units are well known for such ability and thus widely used in current RNNLMs. However, the deterministic parameter estimates in LSTM RNNLMs are prone to over-fitting and poor generalization when given limited training data. Furthermore, the precise forms of activations in LSTM have been largely empirically set for all cells at a global level. In order to address these issues, this paper introduces Gaussian process (GP) LSTM RNNLMs. In addition to modeling parameter uncertainty under a Bayesian framework, it also allows the optimal forms of gates being automatically learned for individual LSTM cells. Experiments were conducted on three tasks: the Penn Treebank (PTB) corpus, Switchboard conversational telephone speech (SWBD) and the AMI meeting room data. The proposed GP-LSTM RNNLMs consistently outperform the baseline LSTM RNNLMs in terms of both perplexity and word error rate.