Reviewerly/exHarmony huggingface dataset:
Simple Extensible Deep Learning Model for Automatic Arabic Diacritization

Automatic diacritization is an Arabic natural language processing topic based on the sequence labeling task where the labels are the diacritics and the letters are the sequence elements. A letter can have from zero up to two diacritics. The dataset used was a subset of the preprocessed version of the Tashkeela corpus. We developed a deep learning model composed of a stack of four bidirectional long short-term memory hidden layers of the same size and an output layer at every level. The levels correspond to the groups that we classified the diacritics into (short vowels, double case-endings, Shadda, and Sukoon). Before training, the data were divided into input vectors containing letter indexes and outputs vectors containing the indexes of diacritics regarding their groups. Both input and output vectors are concatenated, then a sliding window operation with overlapping is performed to generate continuous and fixed-size data. Such data is used for both training and evaluation. Finally, we realize some tests using the standard metrics with all of their variations and compare our results with two recent state-of-the-art works. Our model achieved 3% diacritization error rate and 8.99% word error rate when including all letters. We have also generated the confusion matrix to show the performances per output and analyzed the mismatches of the first 500 lines to classify the model errors according to their linguistic nature.
Infrared transformer tracker based on Siamese network

Infrared target tracking is a fundamental task and plays an important role in military, weapon guidance and other fields because of the superiority of infrared imaging system. However, it is a challenging task due to lower resolution, smaller size and less texture information of infrared targets. Although varieties of methods demonstrate promising performance on visual target tracking, they cannot directly perform well on infrared images. To address this issue, we propose an infrared target tracker based on siamese network, named InfTrans, consisting of three components: a feature extraction backbone, an encoder-decoder transformer and a prediction head. In the InfTrans, transformer architecture is employed to exploit spatial and temporal information effectively. Firstly, considering the superior ability in capturing long-range dependencies, encoder-decoder transformer is connected after backbone to capture relationship between template and search patch along spatial and temporal dimension. Secondly, considering the parallel computing power of transformer, InfTrans receives search patch sequences containing multiple patches as input of search branch and processes multiple frames in parallel. Finally, a prediction head is employed to output bounding box sequences. InfTrans views infrared target tracking as a parallel bounding box prediction problem. Extensive experiments show that the proposed tracker achieves promising performance on public infrared dataset.
A Novel Face Forgery Detection Method Based on Edge Details and Reused-Network

In recent years, with the widespread popularity of digital media editing software and rapid development of deep learning techniques, the technology barriers for generating fake faces becomes lower and lower. As a result, forged face images have become more and more realistic, and a large number of high-quality fake face images are created and spread in our day lives. Therefore, determining the authenticity of face images is a challenging task and major focus of multimedia security research. To solve this problem, a novel face forgery detection method based on edge details and Reused-Network (RN) is proposed. Specifically, based on Sobel and Laplacian operators, we consider the differences in edge details between real and fake faces, and further learn these differences through RN to eventually extract more detailed features. The derived features are then used to train a Support Vector Machine (SVM) classifier for classification. The proposed method has a good performance on detecting fake face images, and it is experimentally verified better than some state-of-the-art works.
Efficient and Secure Design of ID-3PAKA Protocol Using ECC

In this modern era, the need for key agreement protocols equipped with high-speed Internet has become necessary in information/network security. An authenticated key agreement (AKA) protocol is a technique that negotiates a secret session key among various users in a network and is used in many scenarios like mutual authentication, data integrity, and many more. The current state-of-the-art include many AKA protocols that are either vulnerable to different attacks or incur massive communication and computational costs. This paper proposes an identity-based three-party authenticated key agreement (ID-3PAKA) protocol using elliptic curve cryptography (ECC). The proposed ID-3PAKA protocol generates a shared secret session key among three users. The security of the proposed ID-3PAKA protocol relies on the hardness assumption of the ECDLP and the CDH problems. The proposed ID-3PAKA protocol is safe against active and passive attacks, which is proved through the simulation of the OFMC and Cl-AtS back-ends of the AVISPA tool.
A Method of Image Enhancement in Shallow Water with Underwater System

Absorption and scattering are two key issues of underwater optical imaging, which lead to fogging and color deviation. To solve this problem, a shallow sea image enhancement method with underwater imaging system is proposed. Firstly, taking the 3C-HF (Color Channel Compensation-Homomorphic Filtering) algorithm aims to remove the image uneven and dimness, which equilibrate with the original image from underwater equipment. Secondly, the preliminary enhancement images perform gamma correction and contrast limited adaptive histogram equalization (CLAHE) to become the fusion image. Meanwhile, the weights of these fusion images are defined to reduce the influence of edge, color and contrast on image fusion. To avoid the artifacts in the low frequency component of the reconstructed image caused by the sharp weighted image transition, a multi-scale fusion method is adopted. This method can not only improve the contrast of underwater images and reduce the problem of scattering and blurring, but also overcome the problem of color distortion. As experiments shown, compared with the state-of-the-arts, it not only can effectively realize the sharpness and color enhancement of various underwater images in a wide range, but also improve the completion of high-level tasks of underwater images.
A Research on the Blended Teaching Mode of English Listening Course Based on “Duifene platform+ PADD class”

Taking English Listening course of Geely University as an example, this paper explores how to implement the blended teaching mode of PADD class (Presentation- Assimilation- Discussion- Dialogue) based on Duifene online teaching platform, aiming to stimulate students' learning initiative and improve the teaching quality of English listening course.
Comparative Analysis of Load Balancing Algorithms in Cloud Computing

Mondal, Anindita SarkarChattopadhyay, SamiranNowadays, cloud computing features are vastly popular with users. Due to the massive request of the resources and services generated by the customers, request management in cloud platform comes in a challenging state. Load balancing mechanism is used as the solution to this situation. In this survey article, we examine, analyze and establish the cloud system areas where and how the load balancing mechanism can be applied. To achieve this goal, we classify the load balancing mechanism based on the system it uses. We also examine the performance of widely used and classical load balancing algorithms with their mechanism. The main aim of discussing the classical load balancing algorithm is that they are the root algorithms of the load balancing paradigm. Also, it points out the reuse of existing load balancing algorithms in cloud paradigm for handling huge client’s service request. This article will help the researcher to design and draw a load balancing mechanism for the cloud computing system by understanding the system resources.
Locative Inversion, PP Topicalization, and Weak Crossover in English

The literature on locative inversion in English currently disputes whether locative inversion differs from PP topicalization in permitting a quantifier in the fronted PP to bind a pronoun in the subject. In order to resolve this dispute, this paper runs two experiments on Amazon Mechanical Turk, one an acceptability judgment task and the other a forced-choice task. Both find that PP topicalization does not differ from locative inversion: both permit variable binding. Locative inversion also does not differ from a minimally different sentence with the overt expletive there. These findings remove an argument against the null expletive analysis of English locative inversion, and they also show that weak crossover is not uniformly triggered by A-bar movement.
An Improved Lossy-Encryption System for Still-Image Data Based on the Quaternion Multiplication and Robust Chaos

In this paper, we propose an improved lossy-encryption system based on a previously vulnerable one. The original system is first analyzed for potential drawbacks, and then a new system with enhanced security level is proposed. The main strength points of our proposed system reside in the key generation which is based on robust chaos and a fast hashing scheme, and the lossy property of the scheme which contributes to make a good diffusion and confusion. Security analysis shows that the system possesses good statistical properties and it can resist the discussed attacks.
SFU-CE: Skyline Frequent-Utility Itemset Discovery Using the Cross-Entropy Method

Considering both frequency and utility, skyline frequent-utility itemsets (SFUIs) increase the amount of actionable information available for decision-making. In this paper, we propose an algorithm for mining SFUIs called skyline frequent-utility mining based on cross-entropy (SFU-CE). We first model the SFUI mining problem using CE with utility as the optimization object. We then propose critical utility pruning for unpromising itemsets with utility and its upper bound. Furthermore, we also design a random mutation strategy to make itemsets within each sample more diverse. We demonstrate the efficiency and accuracy of SFU-CE by comparing it with state-of-the-art algorithms.
Internet of things-assisted integrated framework for electronic market application

Purpose The widespread use of the internet and the rapid development of the internet of things in information technology have increased the need for network-enabled marketing. It is important to service a broad class involving logistics, buyer, seller and end-users. During various phases of the sales, purchases and marketing process, IoT will influence decision-making. Electronic commerce is a new form of trade under the development of modern information technology. Design/methodology/approach In this paper, the integrated neutrosophic framework based on the internet of things (INF-IoT) has been proposed to support marketers and companies to make a powerful marketing strategy using identified data from IoT devices. Findings The experimental results show that the proposed method has high performance and very efficient. Originality/value This approach may reduce business activity to its core components, which include, in the simplest case, a value proposal, distribution channels and customers, and explain how a network of multi-actors generates a product and services, distributes and uses the value in production. Furthermore, an efficient interface is provided by the logistic module to maintain an order list.
Research on Information Design Matching with User’s Need for Cognitive

Because of the influence of information presentation and individual cognitive traits, People’s cognitive decision-making behaviors are different, and the current information interface design methods are not targeted, can not well meet the information needs of different users. In this environment, it is of great practical significance to study individual’s cognitive decision-making behavior and explore its difference for optimizing interface information design and satisfying different users’ information needs in the decision-making process. In recent years, the relationship between need for cognitive (NFC), a special cognitive trait, and behavior has attracted more and more attention from scholars in other fields. Need for Cognition (NFC) describe the tendency of individuals to engage in and enjoy certain cognitive activities that require effort and are often used to predict higher-order cognition, such as attitude formation and decision-making. Taking the decision-making scenario of waiting position for rail transit as an example, this study examines the effects of heuristic cues on decision-making behavior of users with different Need for Cognition (NFC), to verify the feasibility of using heuristic clues to design interface information to help improve the decision-making efficiency of users with low Need for Cognition (NFC). The findings are as follows: (1) the users with higher need for Cognition (NFC)s take longer time to make decisions under the four kinds of information interfaces, and the correct rate of decision-making results is higher. (2) the users with low need for Cognition (NFC) have longer decision-making time and lower correct rate in the information interface without heuristic clues, and have shorter decision-making time and higher correct rate in the information interface with heuristic clues. The level of Need for Cognition (NFC) has an effect on the efficiency of users’ cognitive decision-making, and using heuristic clues to design interface information can improve the decision-making efficiency of users with low Need for Cognition (NFC).
The unreasonable effectiveness of machine learning in Moldavian versus Romanian dialect identification

Motivated by the seemingly high accuracy levels of machine learning (ML) models in Moldavian versus Romanian dialect identification and the increasing research interest on this topic, we provide a follow-up on the Moldavian versus Romanian Cross-Dialect Topic Identification (MRC) shared task of the VarDial 2019 evaluation campaign. The shared task included two subtask types: one that consisted in discriminating between the Moldavian and Romanian dialects and one that consisted in classifying documents by topic across the two dialects of Romanian. Participants achieved impressive scores, for example, the top model for Moldavian versus Romanian dialect identification obtained a macro- F 1 score of 0.895. We conduct a subjective evaluation by human annotators, showing that humans attain much lower accuracy rates compared with ML models. Hence, it remains unclear why the methods proposed by participants attain such high accuracy rates. Our goal is to understand (i) why the proposed methods work so well (by visualizing the discriminative features) and (ii) to what extent these methods can keep their high accuracy levels, for example, when we shorten the text samples to single sentences or when we use tweets at inference time. A secondary goal of our work is to propose an improved ML model using ensemble learning. Our experiments show that ML models can accurately identify the dialects, even at the sentence level and across different domains (news articles vs. tweets). We also analyze the most discriminative features of the best performing models, providing some explanations behind the decisions taken by these models. Interestingly, we learn new dialectal patterns previously unknown to us or to our human annotators. Furthermore, we conduct experiments showing that the ML performance on the MRC shared task can be improved through an ensemble based on stacking.
Identifying Correlation Patterns in Large Educational Data Sources

Data on education contains various attributes, modeling the pupils’ or students’ personal properties or educational outcomes like reading or writing performances, mathematical skills, or linguistic competences. In this paper we describe a visual analysis tool that takes into account a multitude of educational data sources, filters those into the most relevant ones, and derives attributes-of-interest which form a high-dimensional dataset. For this data we apply t-SNE as one possible projection method to visualize and uncover similarities and dissimilarities among the high-dimensional attributes in a lower-dimensional space like in a scatter plot. We also support interaction techniques and further visual variables to encode additional data attributes in the form of color codings, sizes, and shapes. We illustrate the usefulness of our approach by applying it to educational data sources focusing on pupils and students in Switzerland while also visually communicating the found insights with educational science experts. Finally, we discuss limitations and scalability issues.
Image Enhancement Using Weighted Bi-Histogram Equalization

Image enhancement is one of using in various digital signal processing areas. Advances in microcontrollers, microcomputers and computers have developed traditional algorithms in order to improve the quality of the resulting image and have implied many avenues to the design of new innovations using various techniques. This paper proposes contrast enhancement using weighted bi-histogram equalization based on distributed area ratio. Moreover, this technique must use a weighted factor which is calculated by the ratio of the histogram distribution. Likewise, an original image will be equalized by the modification of the probability density function of the gray levels. As result of the experiment, the contrast of resulting image is improved for implement and human perception – as well as also reduces the absolute mean brightness error (AMBE) than the traditional technique of image enhancement.
A Mahjong Game System Architecture Based on Empirical Knowledge

Imperfect-information game has always been the field that artificial intelligence computer game researchers want to crack. As a typical imperfect-information game, mahjong has also received extensive attention from researchers. This paper mainly takes mahjong as the research carrier, and proposes a mahjong game system architecture based on empirical knowledge, which mainly including professional mahjong game terms analysis, the design of mahjong computer game system and core game strategies. In the strategy, a combination of empirical algorithm and improved search tree algorithm is designed to formulate the player's game behavior in different periods. In the search tree algorithm, an evaluation function is constructed and a method to judge if the hand is in winning state is proposed. Finally, the experimental results show that the system architecture proposed in this article has a high level of game for the rules of popular mahjong, and it also has positive reference significance for other rules of mahjong game and imperfect-information items.
Evaluation model and application of college students' physical fitness based on clustering extraction algorithm

College students are an important talent resource of the country, and their physical quality is the foundation of all qualities, which not only directly affects their own healthy growth, but also affects their ability to serve the society in the future. Therefore, the cluster extraction algorithm is introduced, and five cluster features, such as body mass index, vital capacity, endurance quality, flexibility strength quality and speed dexterity quality, are used to carry out fuzzy cluster analysis on college students' physical quality. The extracted correlation dimension is taken as the mixed pool feature of big data clustering, and the big data is clustered according to the extracted chaotic correlation dimension feature. Examples show that the results have important reference value for helping the all-round development of students' quality, optimizing education and teaching plans and perfecting talent training programs in colleges and universities.
Research on a New Type of Chaotic Image Encryption Algorithm Combining DNA Operation and S-box

Given the fixed DNA coding and operation rules of DNA coding, the single S-box operation function cannot meet the more complex and secure encryption requirements. Therefore, a new type of complex symmetric chaotic image encryption combining DNA up and down cyclic shift operation and S-box operation is the proposed algorithm. In this way, we propose a new chaotic map. The encryption system combines the sensitivity and unpredictability of the chaotic system to the initial value to generate random sequences and S-boxes It replaces the sequence matrix with S-box to further increase the randomness. Finally, the replaced matrix and the original image are DNA-encoded and combined with the proposed new calculation to complete the encryption operation. The algorithm proposed in this paper is more secure and robust through simulation experiments and performance index analysis.
Q-Spark: QoS Aware Micro-batch Stream Processing System Using Spark

Unlike the event-driven stream processing systems, the micro-batch stream processing systems collect input data for a certain period of time before processing. This is because they focus on improving the throughput of the entire system rather than reducing the latency of each data. However, ingesting a continuous stream of data and its real-time analysis is also necessary in micro-batch stream processing systems where reducing the latency is more important than improving the throughput. This paper presents Q-Spark, a QoS (Quality of Service) aware micro-batch stream processing system that is implemented on Apache Spark. The main idea of Q - Spa rk design is to set a deadline time for each query and dynamically adjust the batch size so as not to exceed it. Since Q - Spa r k executes a micro-batch by buffering as much as possible until the deadline set for each query is exceeded, it guarantees the QoS requirement of each query while maintaining the throughput as much as the original Spark batching mechanism. Experimental results show that the tail latency of Q - Spa rk is always bound to the deadline compared to the original Spark where data is buffered using triggers for a certain period. As a result, Q - Spa r k reduces the tail latency per query by up to 75%, while maintaining the throughput stably compared to the original Spark without the concept of a deadline.
Microcontroller Based Intelligent Chinese-Speech Keywords Detector by Transferring the Mid-level Features of Deep Speech

Speech Keywords Detection (SKD) can be described as a task of finding keywords in audio streams, while only a few samples of the keyword are available. SKD has immense applicability in terms of making the temporal signature of audio, to implement the word clouds, voice-command-based appliances, and intelligent voice-based agents. Existing SKD applications suffer from high power requirement constraints, false-positive results due to low training data, and missing keywords in languages like Chinese. Besides, SKD is an enormously imbalanced classification delinquent. In this work, we proposed to implement a lower power consumer and low memory occupying solution, which is built on microcontroller-based applications. We addressed the false-positive and data imbalancing problem by employing a novel combination of objective functions metric loss and Prototypical loss with the mid-level feature engineering of Deep Speech. The extensive experiments on real-time microcontroller showed that our technique is state of the art and provide comparable results with 11% improvement in F1 score.