Reviewerly/exHarmony huggingface dataset:
TFCrowd: a blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness

Abstract Blockchain technology has attracted considerable attention due to the boom of cryptocurrencies and decentralized applications. Among them, the emerging blockchain-based crowdsourcing is a typical paradigm, which gets rid of centralized cloud-servers and leverages smart contracts to realize task recommendation and reward distribution. However, there are still two critical issues yet to be solved urgently. First, malicious evaluation from crowdsourcing requesters will result in honest workers not getting the rewards they deserve even if they have provided valuable solutions. Second, unfair evaluation and reward distribution can lead to low enthusiasm for work. Therefore, the above problems will seriously hinder the development of blockchain-based crowdsourcing platforms. In this paper, we propose a new blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness, named TFCrowd. The core idea of TFCrowd is utilizing a smart contract of blockchain as a trusted authority to fairly evaluate contributions and allocate rewards. To this end, we devise a reputation-based evaluation mechanism to punish the requester who behaves as “false-reporting” and a Shapley value -based method to distribute rewards fairly. By using our proposed schemes, TFCrowd can prevent malicious requesters from making unfair comments and reward honest workers according to their contributions. Extensive simulations and the experiment results demonstrate that TFCrowd can protect the interests of workers and distribute rewards fairly.
Multi-Feature Fusion Target Re-Location Tracking Based on Correlation Filters

Target tracking has been a research hotspot in computer vision, and the correlation filtered target tracking algorithm has the benefits of low computational complexity and fast speed. Still, the tracking effect is not good when dealing with complicated circumstances. This paper proposes a multi-feature fusion target repositioning tracking algorithm for the target tracking problem in complex environments. First, a multi-feature weighted fusion algorithm is presented. Since each feature has different advantages in different environments, we combine HOG, CN, ULBP, and image edge features and use the weighted coefficient method to adaptively fuse each feature component. Second, to address the target occlusion problem, an occlusion judgment mechanism is introduced, and the target is re-located by fusion weighted filtering. Third, the scale pool is established, and the scale filter is trained by the classification search method. Finally, an adaptive model update strategy is proposed. We conduct comparison experiments with current mainstream algorithms on the publicly available datasets OTB-2015, VOT2018, UAV123, and TColor-128, respectively, and the experimental results show that our proposed algorithm is more robust in complex scenarios.
Method of Profanity Detection Using Word Embedding and LSTM

With the rising number of Internet users, there has been a rapid increase in cyberbullying. Among the types of cyberbullying, verbal abuse is emerging as the most serious problem, for preventing which profanity is being identified and blocked. However, users employ words cleverly to avoid blocking. With the existing profanity discrimination methods, deliberate typos and profanity using special characters can be discriminated with high accuracy. However, as they cannot grasp the meaning of the words and the flow of sentences, standard words such as “Sibaljeom (starting point, a Korean word that sounds similar to a swear word)” and “Saekkibalgalag (little toe, a Korean word that sounds similar to another swear word)” are less accurately discriminated. Therefore, in order to solve this problem, this study proposes a method of discriminating profanity using a deep learning model that can grasp the meaning and context of words after separating Hangul into the onset, nucleus, and coda.
Spatial and Temporal Distribution of Dissolved Oxygen and Suspended Sediment in Kelantan River Basin

Dissolved oxygen and suspended sediment are the most important parameters used to assess the water quality of the river.The concern of freshwater pollution problems in Malaysia since the past few decades has resulted in the importance of monitoring dissolved oxygen concentration as a barometer for river health.This study attempted to identify the spatial and temporal distribution of dissolved oxygen and suspended sediment in the Kelantan River Basin, Peninsular Malaysia.Water samples were collected during dry and wet seasons at three sampling stations along the Kelantan River in 2005, 2006, 2010, 2011, 2017, and 2018.Results showed that the dissolved oxygen in 2005 has ranged between 6.8 and 8.4 mg/L during the dry season, and there was a slight reduction of DO concentration during the wet season with a concentration between 6.3 and 7.8 mg/L.Continuous declination of DO concentrations was recorded along the Kelantan River from the year 2005 to the year 2018.The decrease of DO concentrations was linear to the increase of suspended sediment concentration.Increasing land use activities for agriculture, mining, and forest harvesting have attributed to the water pollutions in Kelantan River, Malaysia, due to the increase of suspended sediment concentration, which brings the pollutants.
Discovering group-based transnational cyber fraud actives: A polymethodological view

Transnational cyber fraud is a threat to individuals, organizations, and governments all over the world. However, there is a scarcity of studies that investigate this increasingly perilous phenomenon in the field of information systems. Through a grounded approach using actual court sentencing records and in-depth investigations by television channels and in news reports, we advance a conceptual model to unveil the causes of transnational cyber fraud from a perspective of group-based crime development. We label the characteristics, psychologies, and behaviors of those who commit such fraud (henceforth referred to as “fraudsters”); divide the fraudsters' cyber fraud development into three stages, and include personal and environmental causes for cyber fraud. Further, using data from 1,082 fraudsters, extracted manually from 176 court judgments, we find empirical support for the key contributions of the model. Then, we discuss the implications of the model for ongoing research on cybersecurity and applications.
Supporting listening comprehension by social network-based interaction in mobile assisted language learning

Listening comprehension is challenging for students because it is more than just the direct extraction of meaning from sound. The literature reflects the need to develop relevant strategies. Teachers typically try to help students structure their learning into three phases: before (pre-), during, and after (post-) listening, emphasizing different cognitive and metacognitive processes. In this paper, the role of MALL (Mobile Assisted Language Learning) is proposed as a way to support this learning process and specifically to scaffold its third phase. A study was carried out with an app that the authors have developed for listening to audio news recordings. There are two versions of this app; the first is a standalone program which the students use on an individual basis. The second links to Facebook to enable students to summarize, share and discuss what they have listened to, thereby refining and consolidating their comprehension. A research question in this study addressed the role of a social network in a MALL app in terms of motivation and learning habits. The results of the study provide pedagogical insights into the answer and the value of including social network-based interaction in a MALL app for the development of listening comprehension.
SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings

Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing the SynoExtractor pipeline, which can be used to filter similar word embeddings to retain synonyms based on specified linguistic rules. Our experiments were conducted using KSUCCA and Gigaword embeddings and trained with CBOW and SG models. We evaluated automatically extracted synonyms by comparing them with Alma’any Arabic synonym thesauri. We also arranged for a manual evaluation by two Arabic linguists. The results of experiments we conducted show that using the SynoExtractor pipeline enhances the precision of synonym extraction compared to using the cosine similarity measure alone. SynoExtractor obtained a 0.605 mean average precision (MAP) for the King Saud University Corpus of Classical Arabic with 21% improvement over the baseline and a 0.748 MAP for the Gigaword corpus with 25% improvement. SynoExtractor outperformed the Sketch Engine thesaurus for synonym extraction by 32% in terms of MAP. Our work shows promising results for synonym extraction suggesting that our method can also be used with other languages.
The environmental effects of cryptocurrencies

This chapter discusses the environmental aspects of cryptocurrency markets and as to how their rapid growth can influence the environment and through which channels this process manifests.While it is not popular discussion among investors, we find that environmental issues can be of interest by wider society, students, policy makers and stakeholders of FinTech companies.Environmentalism has a long history, and controversy around this subject is remains quite substantial, thus to not make our discussion over-complicated, in this chapter we will focus on the area where to date, we possess the most thorough data and evidence through which we can build the case, namely electricity consumption associated with the mining of cryptocurrencies.The total carbon footprint of the industry is now estimated to have surpassed that of many large industrial nations.This chapter investigates the multiple knock-on effects and consequential behaviour of this rapid growth in energy usage, such as an increase in global temperature, the growth of mining companies who have targeted third world infrastructures, and the complete shutdown of the internet as we know it.Saying that, we encourage investors not to ignore these environmentalism matters, since we also show that electricity consumption is proven to be one of the commonly used variable in valuation of mineable cryptocurrencies and identification of their fair value, which affect investments returns and should be considered in their trading strategies.
A Wireless Covert Channel Based on Dirty Constellation with Phase Drift

Modern telecommunications systems require the use of various transmission techniques, which are either open or hidden. The open transmission system uses various security techniques against its unauthorized reception, and cryptographic solutions ensure the highest security. In the case of hidden transmissions, steganographic techniques are used, which are based on the so-called covert channels. In this case, the transparency and stealth of the transmission ensure its security against being picked up by an unauthorized user. These covert channels can be implemented in multimedia content, network protocols, or physical layer transmissions. This paper focuses on wireless covert channels. We present a novel method of steganographic transmission which is based on phase drift in phase-shift keying or quadrature amplitude modulation (QAM) and is included in the so-called dirty constellation techniques. The proposed approach is based on the drift correction modulation method, which was previously used in the watermarking of audio-signals. The developed solution is characterized by a variable bit rate, which can be adapted to the used modulation type and transmission conditions occurring in radio channels. In the paper, we present the method of generating and receiving hidden information, simulation research, and practical implementation of the proposed solution using the software-defined radio platform for selected QAM.
Toward a Knowledge-based Personalised Recommender System for Mobile App Development

Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world&amp;#39;s software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users&amp;rsquo; query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user&amp;rsquo;s query with the minimum mismatches.
Analisis dan Perancangan Sistem Informasi Akuntansi Pengelolaan Tabungan Siswa pada SD Ar-Raudah Bandarlampung

Application of information technology in an organization is very important to facilitate and accelerate the management of activities that occur. This is the basis for the development of accounting information systems for managing student savings at SD Ar-Raudah Bandarlampung. Management of student savings is still done manually so that calculation errors often occur, filling in data when reporting savings and less responsive when searching for data. In addition, administrative staff also needs a long time to produce savings reports per student and overall savings report. The purpose of developing student savings information system is to facilitate administration staff in making reports and to minimize errors in recording student savings.
Rank/select queries over mutable bitmaps

The problem of answering rank/select queries over a bitmap is of utmost importance for many succinct data structures. When the bitmap does not change, many solutions exist in the theoretical and practical side. In this work we consider the case where one is allowed to modify the bitmap via a flip (i) operation that toggles its ith bit. By adapting and properly extending some results concerning prefix-sum data structures, we present a practical solution to the problem, tailored for modern CPU instruction sets. Compared to the state-of-the-art, our solution improves runtime with no space degradation. Moreover, it does not incur in a significant runtime penalty when compared to the fastest immutable indexes, while providing even lower space overhead.
Robust object detection under harsh autonomous‐driving environments

In the autonomous driving environment, object instances in an image can be affected by various factors such as camera, driving state, weather, and system component. However, the deep learning-based vision systems are vulnerable to perturbation, which contains noise. Thus, robust object detection under harsh autonomous-driving environments is a more difficult than the generic situation. In this paper, it is found that not only the accuracy, but also the speed of the non-maximum suppression-based detector can be degraded under harsh environments. Therefore, object detection is handled under a harsh situation with adversarial mechanisms such as adversarial training and adversarial defence. Adversarial defence modules are designed to improve robustness in feature extraction level and define perturbations under a harsh environment for training object detectors to improve the robustness of the model's decision boundary. The proposed adversarial defence and training mechanisms improve the object detector in both accuracy and speed. The proposed method shows a 43.7% mean average precision for the COCO2015 dataset in generic object detection and 39.0% mean average precision for the BDD100K dataset in a driving environment. Furthermore, it achieves a real-time capability of 23 frames per second.
Distributed geometric quantum computation based on the optimized-control-technique in a cavity-atom system via exchanging virtual photons

We propose a scheme for quantum geometric computation on a fiber-cavity-fiber system, in which two atoms are located in two single-mode cavities, respectively, connected with each other by optical fiber. This scheme not only has the feature of virtual excitation of photons in the cavity quantum electrodynamics (CQED) that can reduce the effect of decay effectively but also has the advantage of geometric phase to withstand noises due to its built-in noise-resilience feature and robust merit. Specifically, our proposal combined with optimized-control-technology (OCT) can reduce gate operation error by adjusting the time-dependent amplitude and phase of the resonant field which further enhances the robustness of the quantum operation. The robustness against decoherence is demonstrated numerically and the scheme may be applied in the remote quantum information processing tasks and quantum computation.
A Survey on Opinion Reason Mining and Interpreting Sentiment Variations

Tracking social media sentiment on a desired target is certainly an important query for many decision-makers in fields like services, politics, entertainment, manufacturing, etc. As a result, there has been a lot of focus on Sentiment Analysis. Moreover, some studies took one step ahead by analyzing subjective texts further to understand possible motives behind extracted sentiments. Few other studies took several steps ahead by attempting to automatically interpret sentiment variations. Learning reasons from sentiment variations is indeed valuable, to either take necessary actions in a timely manner or learn lessons from archived data. However, machines are still immature to carry out the full Sentiment Variations’ Reasoning task perfectly due to various technical hurdles. This paper attempts to explore main approaches to Opinion Reason Mining, with focus on Interpreting Sentiment Variations. Our objectives are investigating various methods for solving the Sentiment Variations’ Reasoning problem and identifying some empirical research gaps. To identify these gaps, a real-life Twitter dataset is analyzed, and key hypothesis for interpreting public sentiment variations are examined.
NISQ Algorithm for Hamiltonian simulation via truncated Taylor series

Simulating the dynamics of many-body quantum systems is believed to be one of the first fields that quantum computers can show a quantum advantage over classical computers. Noisy intermediate-scale quantum (NISQ) algorithms aim at effectively using the currently available quantum hardware. For quantum simulation, various types of NISQ algorithms have been proposed with individual advantages as well as challenges. In this work, we propose a new algorithm, truncated Taylor quantum simulator (TQS), that shares the advantages of existing algorithms and alleviates some of the shortcomings. Our algorithm does not have any classical-quantum feedback loop and bypasses the barren plateau problem by construction. The classical part in our hybrid quantum-classical algorithm corresponds to a quadratically constrained quadratic program (QCQP) with a single quadratic equality constraint, which admits a semidefinite relaxation. The QCQP based classical optimization was recently introduced as the classical step in quantum assisted eigensolver (QAE), a NISQ algorithm for the Hamiltonian ground state problem. Thus, our work provides a conceptual unification between the NISQ algorithms for the Hamiltonian ground state problem and the Hamiltonian simulation. We recover differential equation-based NISQ algorithms for Hamiltonian simulation such as quantum assisted simulator (QAS) and variational quantum simulator (VQS) as particular cases of our algorithm. We test our algorithm on some toy examples on current cloud quantum computers. We also provide a systematic approach to improve the accuracy of our algorithm.
Inclusive Education Importance and Problems for Students Social Integration

The article relevance. Modern concepts of education development, recognition of the uniqueness and self-worth of human individuality have led to the search for ways to socialize individuals with disabilities, to the development of new pedagogical strategies aimed at developing ideas of independent life for this category of the population. The research purpose is to study the features and identify ways to improve the quality of inclusive education. Research methods: as a research method, we used a questionnaire survey as a method of collecting primary information, which allows us to identify various aspects related to the problems of formation and perception of inclusive education by participants in the educational process. Research results: the article describes the peculiarity of the current stage of inclusive education development, analyzes the social mechanisms for improving the quality of inclusive education.
Unsupervised Outlier Detection via Transformation Invariant Autoencoder

Autoencoder based methods are the majority of deep unsupervised outlier detection methods.However, these methods perform not well on complex image datasets and suffer from the noise introduced by outliers, especially when the outlier ratio is high.In this paper, we propose a framework named Transformation Invariant AutoEncoder (TIAE), which can achieve stable and high performance on unsupervised outlier detection.First, instead of using a conventional autoencoder, we propose a transformation invariant autoencoder to do better representation learning for complex image datasets.Next, to mitigate the negative effect of noise introduced by outliers and stabilize the network training, we select the most confident inliers likely examples in each epoch as the training set by incorporating adaptive self-paced learning in our TIAE framework.Extensive evaluations show that TIAE significantly advances unsupervised outlier detection performance by up to 10% AUROC against other autoencoder based methods on five image datasets.