Reviewerly/exHarmony huggingface dataset:
Contrast Limited Adaptive Histogram Equalization for Underwater Image Matching Optimization use SURF

Abstract Conditions of the underwater environment have its challenges in the underwater vision research process. Some things that make underwater imagery difficult is that light can be scattered by particles in the sea, besides that light can be absorbed by seawater, as well as the turbidity level of seawater, so special techniques are needed to get clear underwater imagery. In underwater environmental conditions, the images obtained are usually of very poor quality. Backlight and attenuation will occur this is due to water conditions, objects that dissolve easily in water, and other particulate matter so that there is the degradation of the underwater image. Because it is very important if the image is improved in quality to facilitate the process of describing objects. Image matching techniques to determine the key points of image pairs are needed in three-dimensional reconstruction research. Speeded Up Robust Features (SURF) is an image matching technique where the matching results are very dependent on the image quality. This study proposes the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to increase the number of matching images with SURF. The results of the experiment showed that image matching increased by an average of 76,8 %.
VSAM-Based Visual Keyword Generation for Image Caption

Image caption is to understand and describe the visual content, which is expected to be applied in automatic news reporting in future.In recent years, there has been an increasing interest in an Encoder-Decoder framework for image caption: the encoder takes the responsibility for visual semantic comprehension and the decoder is designed for sentence generation.In the Encoder-Decoder framework the translation is based on the correspondence between image feature vectors and caption vectors.Attention mechanism makes sense for a more accurate correspondence.However, the attention model works with the decoder, and the focused content changes dynamically with the generated word.It results that in many cases the salient contents are not described in the caption, or the objects described are not the salient ones.To improve the precision of image caption, to bridge the gap between image understanding and sentence generation in the Encoder-Decoder framework, and to align visual information and semantic information better, we propose a concept of visual keyword as a gang board between seeing and saying.This paper presents an image dataset derived from MSCOCO as the first collection of visual keywords: Image Visual Keyword Dataset (IVKD).Also, a Visual Semantic Attention Model(VSAM) is proposed to obtain visual keywords for generating the annotation.In VSAM, the object-level visual features are extracted by an object detector after pre-training on IVKD.Then the object features are fed in an Optimized Pointer Network(OPN) to generate visual keywords.The experiments show that the precision of visual keyword generation reaches 91.7% by the proposed model VSAM.
The Metaphor Processing and Translation Skills of Computer Technology in English Language Translation

Abstract From the study of language grammar in our country, the metaphor we can understand refers to a form of written expression that is rich in linguistic narrative skills. Going back to the history of ancient literature, we will find that the ancient people’s understanding of metaphor is not just a simple way of language description, it is a language form of human active thinking and flexible thinking. I have to say that the use of language grammar can add a lot of color to the article. In the process of English translation, many authors also like to use the grammatical form of metaphor to express the meaning of the article [1] . For the translation of English articles, the form of computer literal translation is relatively simple. However, for English writing similar to metaphorical techniques, whether computer-assisted translation skills can use metaphorical Chinese techniques for translation in time is also a point that researchers should emphasize and pay attention. This article explains some related problems of English-based computer-assisted Chinese metaphor translation from the cultural differences between Chinese and foreign cultures and customs. It is hoped that researchers on English translation can learn from or put forward different opinions.
Fine-Grained Chinese Named Entity Recognition with RoBERTa and Convolutional Attention Networks

In the field of Natural Language Processing (NLP), traditional Chinese Named Entity Recognition (NER) tasks often only involve the recognition of a few types of entities. But current real-world applications require more fine-grained types of entities for more detailed downstream NLP tasks. Since fine-grained Chinese NER is challenging for existing models, novel models need to be introduced. We propose a model with RoBERTa as word embedding, a convolutional attention layer and CRF layer for outputting the entity labels. This model has a better overall performance than baseline models on the CLUENER2020 fine-grained Chinese NER dataset.
Membership Inference Attacks: Analysis and Mitigation

Given a machine learning model and a record, membership attacks determine whether this record was used as part of the model's training dataset. Membership inference can present a risk to private datasets if these datasets are used to train machine learning models and access to the resulting models is open to the public. To construct attack models, multiple shadow models are created that imitate the behaviour of the target model, but for which we know the training datasets and thus the ground truth about membership in these datasets. Attack models are then trained on the labeled inputs and outputs of the shadow models. There is a desideratum to conduct more analysis about this attack and accordingly to provide robust mitigation techniques that will not affect the target model's utility. In this paper, we empirically analyzed this attack from different perspectives related to the number of models and the type of training algorithms. We also proposed and evaluated different mitigation techniques against this type of attack considering different training algorithms of the target model. Our experiments show that the defence strategies mitigate the membership inference attack considerably while preserving the utility of the target model. Finally, we summarized and compared the existing mitigation techniques with our results.
Leveling Up: Using the Tracer Method to Address Training Needs for Esports Players

This paper describes a new method, the Tracer Method, that maps multiple types of decision-making activities to visual search behaviors to improve the acquisition of expertise. The method combines Critical Decision Method (CDM) with eye tracking to identify expert strategies for performance in interface-based tasks. The Tracer Method uses a network analysis of eye movement data, contingent on the critical decisions, to produce new information that neither method alone can generate. Twenty-five experienced Over-watch players were eye-tracked while playing a competitive game, followed by a CDM interview. Three types of decisions: sensemaking, uncertainty management, and coordination resulted in different eye-movement network diagrams of transitional probabilities. These different strategies can inform Esport team training. Although we demonstrate the usefulness of the Tracer Method in an Esports context, the method could apply in other human factors domains.
Detection Mechanisms of One-Pixel Attack

In recent years, a series of researches have revealed that the Deep Neural Network (DNN) is vulnerable to adversarial attack, and a number of attack methods have been proposed. Among those methods, an extremely sly type of attack named the one-pixel attack can mislead DNNs to misclassify an image via only modifying one pixel of the image, leading to severe security threats to DNN-based information systems. Currently, no method can really detect the one-pixel attack, for which the blank will be filled by this paper. This paper proposes two detection methods, including trigger detection and candidate detection. The trigger detection method analyzes the vulnerability of DNN models and gives the most suspected pixel that is modified by the one-pixel attack. The candidate detection method identifies a set of most suspected pixels using a differential evolution-based heuristic algorithm. The real-data experiments show that the trigger detection method has a detection success rate of 9.1%, and the candidate detection method achieves a detection success rate of 30.1%, which can validate the effectiveness of our methods.
What makes phishing emails hard for humans to detect?

This research investigates the email features that make a phishing email difficult to detect by humans. We use an existing data set of phishing and ham emails and expand that data set by collecting annotations of the features that make the emails phishing. Using the new, annotated data set, we perform cluster analyses to identify the categories of emails and their attributes. We then analyze the accuracy of detection in each category. Our results indicate that the similarity of the features of phishing emails to benign emails, play a critical role in the accuracy of detection. The phishing emails that are most similar to ham emails had the lowest accuracy while the phishing emails that were most dissimilar to the ham emails were detected more accurately. Regression models reveal the contribution of phishing email’s features to phishing detection accuracy. We discuss the implications of these results to theory and practice.
People Counting on Low Cost Embedded Hardware During the SARS-CoV-2 Pandemic

Detecting and tracking people is a challenging task in a persistent crowded environment as retail, airport or station, for human behaviour analysis of security purposes. Especially during the global spread of SARS-CoV-2 virus that has become part of everyday life in every country, it is important to be able to manage the flows inside and outside buildings indoors. This article introduces an approach to detect and count people when they cross a virtual line. The methods used are based on deep learning and in particular on convolutional neural networks, specifically MobileNetV3 which is used for the detection task and MOSSE filter which is used for the tracking phase. The hardware system assembled for people counting is inexpensive, as it is formed by Raspberry Pi4 and a Picamera module v2. These devices have already been installed in some supermarkets and museums in the center of Italy, precisely in the area of the Marche region.
Design of Educational Resources-oriented Fair Recommendation System Based on Consortium Blockchain

The issue of fairness and trust in matching and recommending educational resources has always been a matter of great concern. Even in large cities with abundant educational resources, the problem of school choice in compulsory education is still very prominent. In view of the fact that blockchain technology, with its advantages in decentralization, immutability and traceability, has established a new trust model for global interconnection and brought transformative solutions to the pain points of various industries. We propose an educational resources-oriented fair recommendation system based on consortium blockchain to solve the above problems. Firstly, we unite schools, educational institutions and other organizations to build an education consortium blockchain. Then, experts and teachers extensively participate in the education consortium blockchain to make recommendations through the automatic execution of smart contracts. Besides, the data of the recommendation process is written into the blockchain ledger through the consensus mechanism, which allows participants to verify safely and reliably. Finally, the system test and analysis show that the system satisfies the fairness and credibility requirements of users and provides new ideas for the application of blockchain technology in education.
Causal cognitive mapping in the entrepreneurial cognition field: A comparison of two alternative methods

Despite the growing prevalence of causal mapping procedures in the organizational strategy field and the growing body of research on entrepreneurial cognition, there have been surprisingly few investigations on how scholars might use causal cognitive mapping to elicit entrepreneurs’ cognition. Based on the complementarity between the visual graph method and the matrix-multiplication method, this article outlines an integrative method that both overcomes the methodological issues of causal cognitive mapping and expands the qualitative methods used in the field of entrepreneurial cognition. By performing an empirical comparison, this article provides step-by-step guidance to empower scholars who either choose between these two methods or seek to use these methods in a complementary manner. Particular emphasis is placed on the methodological contributions that expand the entrepreneurial cognition toolbox. The methodological limits and potential improvements of these two methods regarding causal mapping are discussed.
Freedom of speech is not freedom from ethics: the 2019 Israel Folau media controversy as a case study

This article examines how freedom of speech is framed in the media controversy surrounding the Australian rugby player Israel Folau’s April 2019 Instagram post. A content analysis and framing analysis of newspaper reportage reveals that the controversy has been largely discussed in terms of whether or not Folau’s speech was being curtailed and whether this curtailing indicates a broader, ideologically motivated censoriousness. This discussion is problematic in that it says little about the actual substance of Folau’s post. This article argues that debates surrounding freedom of speech such as the one involving Folau could and should be enriched by an engagement with ethical principles. This engagement is premised on a commitment to the free exchange of views, while acknowledging that ‘speech’ is not always inherently beneficial for democracy, nor worth defending.
Real-time Energy Management of Large-scale Data Centers: A Model Predictive Control Approach

With the development of cloud computing and 5G communication, the power load of data centers has increased rapidly. While bringing a huge burden on the environment, high electricity bills also put heavy economic pressure on cloud service providers. In order to improve the economic efficiency of enterprises, data centers need energy management. This paper proposes a real-time energy management method based on Model Predictive Control (MPC) for large-scale data centers powered by renewable energy. In this work, the energy consumption model integrates renewable energy, dynamic electricity prices, battery, TES tank, delayed execution of batch jobs, etc. In addition, Monte Carlo simulation is used to deal with the uncertainty of renewable energy output, workload and electricity prices. The proposed MPC algorithm can use predictive data and forecast error distribution to perform real-time energy management on data centers. Finally, case study demonstrates the effectiveness of the proposed MPC algorithm in real-time energy management of data centers.
MTC360: A Multi-Tiles Configuration for Viewport-Dependent 360-Degree Video Streaming

360-degree videos are ultra-high-resolution interactive with a look around nature to create an immersive experience for users. The delivery and display to achieve a high quality of experience (QoE), potentially consume excessive network bandwidth. In order to reduce bandwidth use, tile-based adaptive streaming is considered as a promising solution. However, existing heuristic solutions concentrate on fixed tiling schemes to increase the viewport bitrate. In this paper, we propose a multi-tiles configuration for the 360-degree video where the problem is to decide the tiling scheme and resolutions to stream. The proposed approach can systematically determine the size of tiles according to the great circular distance between the original and predicted viewports during each adaptation interval. A non-uniform bitrate is then allocated to the tiles belonging to the viewport and non-viewport regions to improve the QoE performance for 360-degree videos. Experimental results show that the proposed approach can effectively adapt the size and bitrate of the tiles to both user's head movements and varying network conditions. It could handle abrupt head rotations to provide improved viewport quality.
The Perception of Teachers’ Digital Competence of Preservice Pre-Primary and Primary Education Teachers. The Influence of Degree and Entrance Path

Currently, teachers' digital competence is essential for teachers. This research is a case study using a quantitative methodology about the self-perception of students in pre-primary education and primary education degrees regarding their teachers' digital competence. The results show the greatest mastery in the relational, ethical and security dimension and in the personal and professional dimension. On the contrary, the students show a lower perception of competence in the didactic and organisational dimensions. Moreover, degree and entrance path are identified as factors influencing their perception of mastery, whilst gender is ruled out as a decisive element.
Automatic Spelling Correction for ASR Corpus in Traditional Chinese Language using Seq2Seq Models

The goal of Automatic Speech Recognition (ASR) service is to translate spoken language into text. There exist many factors that will degrade the performance of the ASR system, such as environmental noise, human pronunciation, etc. This research focuses on automatic spelling correction for traditional Chinese corpus generated by ASR systems. We show that a Sequence to Sequence (Seq2Seq) neural network model with attention mechanism could be improved by adding pointer network with auxiliary phoneme features of the input word sequence.
Improving the similarity search between images of agricultural products: An approach based on fuzzy rough theory

Many methods have been presented in recent years for identifying the quality of agricultural products using machine vision that due to the huge amount of redundant information and noisy data of images of products, the retrieval accuracy and speed of such methods were not much acceptable. All of them try to provide approaches to extract efficient features and determine optimal methods to measure similarity between images. One of the basic problems of these methods is determination of desirable features of the user as well as using an appropriate similarity measure. This study tries to recognize the importance of each feature according to user’s opinion in every feedback stage through using weighted feature vector, rough theory and fuzzy logic for identifying important features and finding a higher accuracy in retrieval result. The proposed method is compared with fuzzy color histogram, combined approach and fuzzy neighborhood entropy characterized by color location. The simulation results indicate that the proposed method has higher applicability in image marketing compared to the existing methods.
Secure Off-chain Payment in Consortium Blockchain System

Off-chain payment channel is a significant technology to improve transaction performance of blockchain by enabling two blockchain nodes to conduct micro-payments at high frequency without committing all of the transactions on the blockchain, ensuring the “instant finality” of transactions, reducing transaction costs and improving the scalability of the blockchain. Among different types of blockchains, the transaction performance of consortium blockchain is better than that of public blockchain, thus the application of state channel technology in consortium blockchain will further improve the transaction throughput. However, as far as we know, there seems to be seldom existing work in this aspect. A critical challenge is that the existing work applies the state channel technology only in the public blockchain system, and there are many differences between the consortium and public blockchain system in terms of architecture, application scenarios and privacy, etc., leading to certain technical thresholds for migration of the state channel technology. In this paper, we address this challenge by designing CBOP, an off-chain payment service that can interact with the consortium blockchain infrastructure of the Hyperledger Fabric. CBOP can carry out on-chain or off-chain transactions according to the demand of both parties, and provide strong security to protect against malicious attacks. It implements these benefits through two novel designs, dynamic partitioning algorithm for transactions and trusted security guarantee for off-chain payments. Experimental results show that CBOP can provide 300 % more transaction throughput than original design.
Idempotents of generalized quaternion group algebras and their applications

Let Fq be a finite field with order q, Q4n the generalized quaternion group with 4n elements, and gcd(4n,q)=1. Firstly, central (right) irreducible idempotents of Fq[Q4n] are determined in two cases: q≡1(mod4) and q≡3(mod4). Secondly, precise descriptions and enumerations of linear complementary dual (LCD) codes and self-orthogonal codes in the generalized quaternion group algebras Fq[Q4n] are obtained. Some numerical examples are also presented to illustrate our applications.
MAUSPAD: Mouse-Based Authentication Using Segmentation-Based, Progress-Adjusted DTW

Biometric user authentication is at the core of multifactor authentication, and mouse-based biometric authentication comes at no additional cost for most computer systems. This paper describes a mouse-based user authentication scheme, called MAUSPAD, which uses a novel progress-adjusted dynamic time warping (PADTW) algorithm, along with a segmentation algorithm, to accurately and meaningfully measure the differences between observed data and reference data. By introducing a new concept, which we call progress, into standard DTW, the new PADTW can have better control of the warping and mapping process and hence is more suitable for comparing time-stamped spatial sequences such as mouse cursor movements. Furthermore, in order to preserve the important but transient details in the cursor movement (which may be critical in identifying a specific user), we apply a segmentation algorithm to divide each reference cursor movement into multiple smaller segments, and measure the differences between cursor movements at the segment level. Evaluation results on two mouse-behavior datasets show that MAUSPAD yields the best overall performance among tested schemes, and demonstrate the effectiveness of PADTW over DTW, and segmentation over non-segmentation. The processing techniques developed herein can be extended to applications that rely on sequence comparison, and where relevant sequence information spans multiple semantic domains.