Reviewerly/exHarmony huggingface dataset:
An Unsupervised Sentiment Classification Method Based on Multi-Level Fuzzy Computing and Multi-Criteria Fusion

With the rapid growth of user-generated content, unsupervised methods that do not require label training data have gradually become a research focus in the field of sentiment classification and natural language processing. But the performance of unsupervised methods is unsatisfactory. This is because the ambiguity of sentiment polarity and the fuzziness of sentiment intensity are usually ignored in existing unsupervised methods. To address these problems, we propose an unsupervised sentiment classification method based on multi-level fuzzy computing and multi-criteria fusion which involves three innovations. Firstly, we come up with a multi-level computing model to compute the sentiment intensity of reviews for partly reducing the ambiguity of sentiment polarity. Secondly, to further decrease the ambiguity of sentiment polarity, a multi-criteria fusion strategy based on sentiment category credibility and domain category representativeness is proposed. Thirdly, a fuzzy classifier is constructed to solve the fuzziness of sentiment intensity. Furthermore, a self-supervised method using pseudo-labeled training data is proposed to learn the optimum parameters of the fuzzy classifier. Experimental results in three different domain balanced datasets and one unbalanced dataset proved that our method improves 12.35% more accuracy than the competitive baselines in sentiment classification.
Cloud Computing: Issues and risks of Embracing the Cloud in a Business Environment

Over the latest couple of years, cloud computing has created from being a promising business thought to one of the rapidly creating portions of the IT business.Despite the boom of cloud and the numerous favorable circumstances, for example, financial advantage, rapid elastic resource pool, and on-demand benefit, endeavor clients are yet hesitant to send their business in the cloud and the paradigm likewise makes difficulties for the two clients and suppliers.An absence of fitting answers for such difficulties may cause dangers, which may exceed the normal advantages of utilizing the paradigm.To address the difficulties and related dangers, an orderly hazard the board practice is vital that guides clients dissect the two advantages and dangers identified with cloud-based frameworks.The point of this paper is to provide better comprehension to configuration difficulties of cloud computing and distinguish essential research heading in such manner as this is an expanding area.
Secure Modular Smart Contract Platform for Multi-Tenant 5G Applications

Trusted decentralized applications based on distributed ledger technologies provide many potential opportunities to 5G applications and verticals, as well as in fifth generation mobile network (5G) enabling technologies, systems, and services.Apart from the tamper-proof exchange of transactions, distributed ledgers can provide a software environment for the trusted execution of smart contracts.In this article, we explore the security aspects of decentralized applications and, in particular, the security of smart contracts.Distributed ledger characteristics impose distinct requirements on smart contract design, implementation, deployment, and management.We briefly present the approach to the development of secure smart contracts, and highlight key smart contract vulnerabilities and the developer tools supporting smart contract security.We developed a secure, upgradeable modular multi-contract platform.It combines percontract Smart Contract Tunnels and per-user-based access control to minimize vulnerabilities.The smart contract platform is comprised of service-agnostic, auxiliary, and service-specific smart contracts.It can be therefore easily adapted to different 5G application verticals.For illustration and evaluation, we elaborated on the proposed solution on a case of smart electric charging.During the design and development, state-ofthe-art code analysis was applied.Finally, we propose an architecture for the integration of the secure multicontract platform into a 5G architecture.The integration proposal utilizes hybrid private-public blockchain networks for possible security, scalability, performance, and transaction cost optimization.
Fast Medical Image Security Using Color Channel Encryption

Evolution of digital Health-care Information System established Medical Image Security as the new contemporary research area. Most of the researchers used either image watermarking or image encryption to address medical image security. However, very few proposals focused on both issues. This paper has implemented a Fast Medial Image Security algorithm for color images that uses both watermarking and encryption of each color channel. The proposed method starts with embedding of a smoothened key image (K) and patient information over the original image (I) to generate a watermarked image (W). Then, each color channel of the watermarked image (W) is encrypted separately to produce an encrypted image (E) using the same smoothened key image (K). This image can be transmitted over the public network and the original image (I) can be achieved using decryption algorithm followed by de-watermarking using the same key image (K) at the receiver. Qualitative and quantitative results of the proposed method show good performance when compared with the existing method with high Mean, PSNR and Entropy.
Optimization of Multimedia Computer-aided Interaction System of Vocal Music Teaching Based on Voice Recognition

With the development and progress of society, consumers are more and more inclined to choose personalized and rich products.In order to meet the needs of people's increasingly rich material and cultural products, the designed products must not only have functional practicality and beautiful appearance, but also be able to meet the internal personalized needs of consumers.The introduction of experiential teaching in computer-aided graphic design courses is mainly for students to design products with rich connotation and personalization based on mastering the technology of computer-aided graphic design and combining with practice.In experiential teaching, the starting point and end point of our teaching reform is to closely combine classroom teaching of design performance techniques with professors of design theory knowledge, while focusing on cultivating and enhancing students' practical innovation ability and personalized design level.And then achieve the purpose of training high-quality composite design talents.
A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost

Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13–14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.
Development and Validation of an Autonomous System for Measurement of Sunshine Duration

This paper presented an autonomous electronic system for sunshine duration (SD) monitoring based on the contrast method and developed to operate on a horizontal surface. The prototype uses four photoresistors arranged at 90° in a 20 mm diameter circumference separated by a shading structure used to create a shadow pattern on the detection element. Photoresistors are inserted in individual signal conditioning circuits based on the association between Wheatstone bridges and operational amplifiers to provide an analog signal to the microcontroller unit. The determination of SD occurs through the implementation of fuzzy logic with numerical calculation methods to estimate the probability (f) of solar disk obstruction and estimate SD values. The system does not require additional adjustments after installation or use of energy sources for operation due to the use of an internal battery with charge recovery by solar panels. Experimental results of the proposed system were validated with the ones provided by a government meteorology station. Statistical analysis of the results showed a confidence index (c) greater than 90%, with a precision of 94.26%. The proposed system is a feasible low-cost solution to the available commercial systems for the measurement of sunshine duration.
Knowledge-based programs as succinct policies for partially observable domains

We suggest to express policies for contingent planning by knowledge-based programs (KBPs). KBPs, introduced by Fagin et al. (1995) [32], are high-level protocols describing the actions that the agent should perform as a function of their current knowledge: branching conditions are epistemic formulas that are interpretable by the agent. The main aim of our paper is to show that KBPs can be seen as a succinct language for expressing policies in single-agent contingent planning. KBP are conceptually very close to languages used for expressing policies in the partially observable planning literature: like them, they have conditional and looping structures, with actions as atomic programs and Boolean formulas on beliefs for choosing the execution path. Now, the specificity of KBPs is that branching conditions refer to the belief state and not to the observations. Because of their structural proximity, KBPs and standard languages for representing policies have the same power of expressivity: every standard policy can be expressed as a KBP, and every KBP can be "unfolded" into a standard policy. However, KBPs are more succinct, more readable, and more explainable than standard policies. On the other hand, they require more online computation time, but we show that this is an unavoidable tradeoff. We study knowledge-based programs along four criteria: expressivity, succinctness, complexity of online execution, and complexity of verification.
Association of Game Events with Facial Animations of Computer-Controlled Virtual Characters Based on Probabilistic Human Reaction Modeling

Computer-controlled virtual characters are essential parts of most virtual environments and especially computer games. Interaction between these virtual agents and human players has a direct impact on the believability of and immersion in the application. The facial animations of these characters are a key part of these interactions. The player expects the elements of the virtual world to act in a similar manner to the real world. For example, in a board game, if the human player wins, he/she would expect the computer-controlled character to be sad. However, the reactions, more specifically, the facial expressions of virtual characters in most games are not linked with the game events. Instead, they have pre-programmed or random behaviors without any understanding of what is really happening in the game. In this paper, we propose a virtual character facial expression probabilistic decision model that will determine when various facial animations should be played. The model was developed by studying the facial expressions of human players while playing a computer videogame that was also developed as part of this research. The model is represented in the form of trees with 15 extracted game events as roots and 10 associated animations of facial expressions with their corresponding probability of occurrence. Results indicated that only 1 out of 15 game events had a probability of producing an unexpected facial expression. It was found that the “win, lose, tie” game events have more dominant associations with the facial expressions than the rest of game events, followed by “surprise” game events that occurred rarely, and finally, the “damage dealing” events.
Stable and Supported Semantics in Continuous Vector Spaces

We introduce a novel approach for the computation of stable and supported models of normal logic programs in continuous vector spaces by a gradient-based search method. Specifically, the application of the immediate consequence operator of a program reduct can be computed in a vector space. To do this, Herbrand interpretations of a propositional program are embedded as 0-1 vectors in $\mathbb{R}^N$ and program reducts are represented as matrices in $\mathbb{R}^{N \times N}$. Using these representations we prove that the underlying semantics of a normal logic program is captured through matrix multiplication and a differentiable operation. As supported and stable models of a normal logic program can now be seen as fixed points in a continuous space, non-monotonic deduction can be performed using an optimisation process such as Newton's method. We report the results of several experiments using synthetically generated programs that demonstrate the feasibility of the approach and highlight how different parameter values can affect the behaviour of the system.
Document-level Event-based Extraction Using Generative Template-filling Transformers.

We revisit the classic information extraction problem of document-leveltemplate filling. We argue that sentence-level approaches are ill-suited to thetask and introduce a generative transformer-based encoder-decoder frameworkthat is designed to model context at the document level: it can make extractiondecisions across sentence boundaries; is \emph{implicitly} aware of noun phrasecoreference structure, and has the capacity to respect cross-role dependenciesin the template structure. We evaluate our approach on the MUC-4 dataset, andshow that our model performs substantially better than prior work. We also showthat our modeling choices contribute to model performance, e.g., by implicitlycapturing linguistic knowledge such as recognizing coreferent entity mentions.Our code for the evaluation script and models will be open-sourced athttps://github.com/xinyadu/doc_event_entity for reproduction purposes.
Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints

Deep Learning (DL) algorithms are being applied to network intrusion detection, as they can outperform other methods in terms of computational efficiency and accuracy. However, these algorithms have recently been found to be vulnerable to adversarial examples – inputs that are crafted with the intent of causing a Deep Neural Network (DNN) to misclassify with high confidence. Although a significant amount of work has been done to find robust defence techniques against adversarial examples, they still pose a potential risk. The majority of the proposed attack and defence strategies are tailored to the computer vision domain, in which adversarial examples were first found. In this paper, we consider this issue in the Network Intrusion Detection System (NIDS) domain and extend existing adversarial example crafting algorithms to account for the domain-specific constraints in the feature space. We propose to incorporate information about the difficulty of feature manipulation directly in the optimization function. Additionally, we define a novel measure for attack cost and include it in the assessment of the robustness of DL algorithms. We validate our approach on two benchmark datasets and demonstrate successful attacks against state-of-the-art DL network intrusion detection algorithms.
The association of entrepreneurial education and support with entrepreneurial intention at Northern Boarder University: the case of students from College of Business Administration

This study aims at empirically examining the association of entrepreneurial education and support with entrepreneurial intention among students in College of Business Administration at the Northern Boarder University for the academic year 2019-2020.Using data from a self-administered survey with a final sample of 266 students, the simple regression result indicates to a significantly positive association of the entrepreneurial education and support with entrepreneurial intention among College of Business Administration's students at Northern Boarder University.The results of this study should be useful to policy makers in Saudi Arabia at the country, ministry of education, the College of Business Administration, Northern Boarder University and elsewhere, as the KSA is aiming at achieving the ambitious KSA 2030 Vision.
WHY WE FEAR GENETIC INFORMANTS: USING GENETIC GENEALOGY TO CATCH SERIAL KILLERS.

Consumer genetics has exploded, driven by the second-most popular hobby in the United States: genealogy. This hobby has been co-opted by law enforcement to solve cold cases, by linking crime-scene DNA with the DNA of a suspect's relative, which is contained in a direct-to-consumer (DTC) genetic database. The relative’s genetic data acts as a silent witness, or genetic informant, wordlessly guiding law enforcement to a handful of potential suspects. At least thirty murderers and rapists have been arrested in this way, a process which I describe in careful detail in this article. Legal scholars have sounded many alarms, and have called for immediate bans on this methodology, which is referred to as long- range familial searching (or LRFS). The opponents’ concerns are many, but generally boil down to fears that LRFS will invade the privacy and autonomy of presumptively innocent individuals. These concerns, I argue, are considerably overblown. Indeed, many aspects of the methodology implicate nothing new, legally or ethically, and might even better protect privacy while exonerating the innocent. Law enforcement’s use of LRFS to solve cold cases is a bogeyman. The real threat to genetic privacy comes from shoddy consumer consent procedures, poor data security standards, and user agreements that permit rampant secondary uses of data. So why do so many legal scholars fear a world where law enforcement uses this methodology? I surmise that our fear of so-called genetic informants stems from the sticky and long-standing traps of genetic essentialism and genetic determinism, where we incorrectly attribute intentional action to our genes and fear a world where humans are controlled by our biology. Rather than banning the use of genetic genealogy to catch serial killers and rapists, I call for improved direct-to-consumer consent processes, and more transparent privacy and security measures. This will better protect genetic privacy in line with consumer expectations, while still permitting the use of LRFS to deliver justice to victims and punish those who commit society's most heinous acts.
Digital Contact tracing in the COVID-19 Pandemic: A tool far from reality

Digital contact tracing applications are being developed by governments across the world, to track and trace contacts. With little evidence, citizens are being forced and made to believe that it is an important step in pandemic control. We discuss briefly if contact tracing will be successful in the control of the Corona virus pandemic or is it just a tool governments are using to cover their helplessness.
Feeding the troll detection algorithm

The authenticity of public debate is challenged by the emergence of networks of non-genuine users (such as political bots and trolls) employed and maintained by governments to influence public opinion. To tackle this issue, researchers have developed algorithms to automatically detect non-genuine users, but it is not clear how to identify relevant content, what features to use and how often to retrain classifiers. Users of online discussion boards who informally flag other users by calling them out as paid trolls provide potential labels of perceived propaganda in real time. Against this background, we test the performance of supervised machine learning models (regularized regression and random forests) to predict discussion board comments perceived as propaganda by users of a major Romanian online newspaper. Results show that precision and recall are relatively high and stable, and re-training the model on new labels does not improve prediction diagnostics. Overall, metadata (particularly a low comment rating) are more predictive of perceived propaganda than textual features. The method can be extended to monitor suspicious activity in other online environments, but the results should not be interpreted as detecting actual propaganda.
Underwater No-Reference Image Quality Assessment for Display Module of ROV

The optical images collected by remotely operated vehicles (ROV) contain a lot of information about underwater (such as distributions of underwater creatures and minerals), which plays an important role in ocean exploration. However, due to the absorption and scattering characteristics of the water medium, some of the images suffer from serious color distortion. These distorted color images usually need to be enhanced so that we can analyze them further. However, at present, no image enhancement algorithm performs well in any scene. Therefore, in order to monitor image quality in the display module of ROV, a no-reference image quality predictor (NIPQ) is proposed in this paper. A unique property that differentiates the proposed NIPQ metric from existing works is the consideration of the viewing behavior of the human visual system and imaging characteristics of the underwater image in different water types. The experimental results based on the underwater optical image quality database (UOQ) show that the proposed metric can provide an accurate prediction for the quality of the enhanced image.