Reviewerly/exHarmony huggingface dataset:
Fuzzy Logic Controller Optimized by MABSA for DC Servo Motor on Physical Experiment

This paper represents the control system of the DC servo motor using fuzzy logic controller optimized by MABSA. The fuzzy logic controller that can use in a wide range is well known to the industry application and control system. However, there are still problem with the speed and position control of DC servo motor. Both speed and position cannot be balanced when loading and unloading materials. Therefore, the fuzzy logic controller will be designed using the Matlab toolbox and then will be optimized by the modified adaptive bats sonar algorithm (MABSA) to solve this problem. The best position of the range of the membership functions will be generated through the algorithm and then will be inserted in the membership function of the designed fuzzy logic controller. After the proposed design is fully developed, an experiment will be carried out to test the performance. The performance will be in terms of rising time, settling time and percentage of overshoot. The experiment will be using Arduino as the microcontroller and encoder as the feedback. The experiment will be compared with the cases that use the fuzzy logic controller only without the optimization of MABSA. The result shows that the proposed design gives a 19% improvement in rising time and 8% in settling time. In conclusion, the proposed design of FLC optimized by MABSA is better compared to FLC without optimization.
A Comparison of Different Bayesian Models for Leukemia Data

Maria Rafiquea, Sajid Alia* , Ismail Shaha & Bilal Ashrafba Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistanb Student Services Center, University of Gujarat, Gujarat, Pakistan
Micro Units and the First Translational Response Universal

In this chapter, we investigate the first translational response universal which posits that translators mentally segment texts into units that can be kept in memory and processed by their available cognitive resources. We investigate the observable traces of the first translational response, i.e., keystrokes, gaze patterns, and pauses of the translator’s first translation draft, which reveal characteristics that cohere, among other things, with the language pair, the properties of the text to be translated, and the “longest stretch of translation that a translator can deal with at once” (Malmkjær, Translation universals, 91, 2011). Our findings suggest that the typing pause that precedes the initial translational response depends—among other things—on the information content of the source and the non-literality of the translation, i.e., the syntactic and semantic cross-linguistic similarity. We relate our findings to the literal translation hypothesis showing that the first translational response is likely to be more literal than successive revised versions. If is also more literal when the preceding pause is short. We conclude that translators tend to proceed in the most compositional manner and produce the shortest possible stretch of translation that the context allows for.
Recommended by Google Home

Voice-enabled personal assistants like Google Home have been adopted by the masses (Hoy 2018). As speech is the main channel for communication between humans (Flanagan 1972) and is an innate human behavior (Pinker 1994), interacting with a voice interface is intuitive and easy (Cohen et al. 2004). Studies conducted under the Computers Are Social Actors (CASA) paradigm indicate that speech-output and interactivity are two main factors to elicit social reactions in users (Nass et al. 1993). Users have been shown to adopt human principles like conformity and gender stereotypes when interacting with computers (Nass and Moon 2000). As voice assistants are able to send social cues, we assumed that subjects would show social reactions towards a Google Home smart speaker. Focussing on the social norm of conformity, we measured if participants show more conformal behavior towards the voice assistant depending on their own gender and the gender of the assistants’ voice. A laboratory experiment with 62 participants was conducted. Participants interacted with a Google Home speaker that either used a male or a female voice to present them with different social dilemma situations. In addition, the voice assistant always argued for participants to choose the less likely (as determined in a pretest) of two possible choices presented. Results show a significant interaction effect on conformity between voice assistant gender and participant gender. Female participants chose significantly more options recommended by a female voice assistant meaning female participants did show conformal behavior. Regarding gender stereotypes, the voice assistant was rated differently by participants depending on the gender of its voice. A female assistant was rated as significantly warmer while a male assistant was rated to be significantly more competent in line with previous findings on gender stereotypes in human-human communication (Spence and Helmreich 1979).
A hybrid technique for intelligent bank security system based on blink gesture recognition

Abstract Enhancement of bank security system plays an important role at the present time due to increasing attempts to expose it to many infringements by criminals. This paper proposes a hybrid technique of amplitude and duration to recognize its blink gestures to increase the accuracy of the human security system’s interaction for intelligent bank security, as this hybrid technique implemented analyzing of face, facial landmark detection, and the duration of the eye aspect ratio (EAR) for blink processes. The proposed hybrid technique is evaluated by experimenting a number of blink gesture with two eyes obtained from web camera and the results shows better and more effective analysis and provide high security bank system.
Sentiment Analysis of Twitter Data Related to COVID-19

Sentiment analysis is natural language processing technique which is used to evaluate the emotions of subjective data. Machine learning along with natural language processing is used to evaluate weighted values of sentences or phrases. The COVID-19 pandemic is one of the greatest challenges that humans have faced since World War II. With the first case of COVID-19 being reported there has been an urge of discovering efficient analytic methods to understand mass sentiment in the pandemic scenario. Along with this technique, various strategies are used such as computational etymology and information retrieval. The task of detecting text polarity becomes easier with sentiment analysis and we can classify like "negative", "neutral", "positive", or "impartial". The main objective is using or studying the methods of sentiment analysis of Twitter data and provide comparative study of mass sentiment during the chosen window, i.e., January to August. This chapter has explored Twitter as dataset. TextBlob which is a sentiment analysis library in Python was used to extract the objective of the content and also to know whether the content is positive or negative.
Bounding Perception Neural Network Uncertainty for Safe Control of Autonomous Systems

Future autonomous systems will rely on advanced sensors and deep neural networks for perceiving the environment, and then utilize the perceived information for system planning, control, adaptation, and general decision making. However, due to the inherent uncertainties from the dynamic environment and the lack of methodologies for predicting neural network behavior, the perception modules in autonomous systems often could not provide deterministic guarantees and may sometimes lead the system into unsafe states (e.g., as evident by a number of high-profile accidents with experimental autonomous vehicles). This has significantly impeded the broader application of machine learning techniques, particularly those based on deep neural networks, in safety-critical systems. In this paper, we will discuss these challenges, define open research problems, and introduce our recent work in developing formal methods for quantitatively bounding the output uncertainty of perception neural networks with respect to input perturbations, and leveraging such bounds to formally ensure the safety of system control. Unlike most existing works that only focus on either the perception module or the control module, our approach provides a holistic end-to-end framework that bounds the perception uncertainty and addresses its impact on control.
Copy Move Forgery Detection by Using Integration of SLIC and SIFT

Image falsification has become a central issue in many applications. Common techniques are used to create fake digital images (copy-moving and sewing images). Existing programs can roughly indicate suspicious, counterfeit areas especially when multiple forged regions are present. The most important contribution of this proposed work is to detect the multiple forged regions in the image. An effective method of detecting image falsification is proposed by integrating keypoint-based counterfeit detection methods with SLIC super pixel segmentation algorithms. Adaptively, the algorithm generates super-pixels with the help of Simple Linear Iterative Clustering (SLIC), extracts function points using SIFT algorithm, and matches the key- points functions to each other to locate the selected function points. SLIC image segmentation uses simple and global stopping criteria, which reduces the accuracy of image debugging and detects copy-moving image falsification. Dynamic threshold is used for the identification of suspected regions. Experimental results obtained from testing MICC-F220 dataset shows notable precision, F1-measure, and recall as comparison to existing methods. SIFT is excellent as scale-invariant and use of SLIC is overcoming the drawback of SIFT that is false positive rate.
Big Data and Deep Learning Model for FMS Score Prediction of Aerobics Athletes

In recent years, competitive aerobics has developed rapidly in my country, and the corresponding sports injury risks have gradually increased. A number of studies have shown that due to the characteristics of aerobics itself, difficult movement requirements, fast-paced music accompaniment and coherent coordinated movements, athletes will suffer sports injuries if they are not paying attention. Therefore, discovering the causes of athletes’ injuries in time and preventing them in time is crucial for improving athletes’ skill level and prolonging sports life. Through the functional movement screening (FMS) test, understanding young aerobics athletes’ insufficiency in trunk stability, joint flexibility, muscle extension, and core strength can further help athletes reduce the risk of sports injuries. Therefore, this article proposes a novel sports injury risk model based on big data technology and deep learning, which can effectively predict the risk of sports injury and can play a positive role in improving the quality of athletes’ movements and prolonging their sports life.
A Systematic Review of User-Centered Design Techniques Applied to the Design of Mobile Application User Interfaces

Mobile Applications have become greatly important in current days, as they allow the execution of certain tasks in an efficient way. Furthermore, nowadays every person has a mobile phone which includes a huge variety of mobile applications for diverse purposes. In contrast, User-Centered Design is a framework which is used to improve or guarantee high usability and user experience in any kind of application, including mobile applications. In this paper, we present a systematic review of User-Centered Design Techniques Applied to the Design of Mobile Application User Interfaces. We performed the systematic review followed the proposed guidelines by Kitchenham and Charters, which includes 3 phases, which are the followings: Planning, Conduction and Review and analysis. 3 Research questions were developed in order to get reliable information related to User-Centered Design techniques applied to mobile applications. It is important to mention that we made an exhaustive review of each paper to determine in which phase of UCD should be classified each technique found according to the presented context. Finally, 221 studies were identified, where only 35 were finally selected for the review.
Software Reuse Management for better efficiency and turnaround time

In this ever growing, competitive market with Technological advancements Reuse management plays an important role in reducing the time to market and optimizing the R&D to sales ratio. It’s now clear that to achieve better software, more quickly and at lower cost, we need a design process that is based on systematic software reuse In any R&D engineering center like ours, the reuse management strives to launch competitive, focus on innovative products with high profiting business models in the shortest lead time possible. In this paper, a novel approach is proposed that embarks the journey of reuse component identification wrt,•Common across customer projects,•Common across product lines•Common across Business unit levelThis method is derived from both AutoSAR and Non AutoSAR project architecture. This method has been piloted in our Engineering Center and has proven to be a very method for computing (evaluating) and thereby improving the reuse. This in turn minimizes effort and cost in product development and adapting market strategy in its business models. Keywords-Feature, Reuse component identification, Reuse Index calculation, Market estimation, Value addition
Preference-Oriented Password-Based Authentication

Cyberspace security is largely authenticated by password-based schemes. Its usage is increasing day by day, and efficiency considerations are inevitable along with the effectiveness. Consideration of preference of users enhances the credential safety as well as the efficiency of an authentication mechanism. This paper presents a novel preference-based password authentication scheme (PPA). The scheme uses a recognition-based graphical design and offers greater resistance to shoulder surfing attacks. The trial involved the simulation of the basic PPA scheme, which provided users to choose their preferred method to register and log in. This freedom of choosing their authentication process conforms to the usability principle and can enhance the security of the process. The results obtained in the user trial indicate that the PPA is efficient and secure.
Transactions and contracts based on reaction systems

Smart contracts are currently en vogue, thanks to the infrastructure provided by the blockchain technology. However, their effective use requires that the textual (legalese) specification of the contract be accompanied by a precise computational definition of the actions leading to its satisfaction or breach, as well as of their admissible sequences. Insofar as contracts can be viewed as prescribing transactional exchanges of well-specified resources among well-specified actors, contract execution can be modelled as following some protocol in a closed world. This suggests a modelling of such executions as interactive processes in reaction systems, where the entities in the background set represent possible allocations of resources to actors and reactions describe changes in such allocations. We use this type of reaction systems and interactive processes as a basis for the modelling of transactions and contracts and explore properties of such processes, highlighting their peculiarities with respect to the original notion of interactive processes in reaction systems. We also discuss several constructions for composition and decomposition of processes, which guarantee equivalence of effects: two interactive processes are equivalent if, starting from the same initial set of allocations, they produce the same final set of allocations.
Beneath the radar: Exploring the economics of business fraud via underground markets

Underground marketplaces have emerged as a common channel for criminals to offer their products and services. A portion of these product comprises the illegal trading of consumer products such as vouchers, coupons, and loyalty program accounts that are later used to commit business fraud. Despite its well-known existence, the impact of this type of business fraud has not been analyzed in depth before. By leveraging longitudinal data from 8 major underground markets from 2011-2017, we identify, classify and quantify different types of business fraud to then analyze the characteristics of the companies who suffered from them. Moreover, we investigate factors that influence the impact of business fraud on these companies. Our models show that cybercriminals prefer selling products of well-established companies, while smaller companies appear to suffer higher revenue losses. Stolen accounts are the most transacted items, while pirated software together with loyalty programs create the heaviest revenue losses. The estimated criminal revenues are relatively low, at under 600 000 in total for the whole period; but the total estimated revenue losses are up to 7.5 millions.
Efficient Weakly-Supervised Object Detection With Pseudo Annotations

Weakly-supervised object detection (WSOD) has attracted lots of attention in recent years. However, there is still a big gap between WSOD and generic object detection. The main barriers to the efficiency of WSOD are the ineffective data augmentations and inaccurate bounding box predictions. Given only image-level annotations, it is hard for WSOD to effectively utilize variant data augmentations and accurately regress the bounding boxes. Although a fully-supervised object detector can be trained using annotations generated from the weakly-supervised object detector, the performance is still severely limited due to the low quality of mined pseudo annotations. This paper proposes an efficient WSOD method with pseudo annotations (EWPA) to make better use of imperfect annotations. With the assistance of pseudo annotations, EWPA can effectively regress more accurate bounding boxes while the traditional WSOD can only locate the salient parts of an object. Furthermore, pseudo annotations can help design more complex data augmentations, driving the network to learn more discriminative feature representations. Extensive experiments are conducted on PASCAL VOC 2007 and 2012 datasets and validate the effectiveness of EWPA.
Unnecessary Input Heuristics and PayJoin Transactions

Over the years, several privacy attacks targeted at UTXO-based cryptocurrencies such as Bitcoin have been proposed. This has led to an arms race between increasingly sophisticated analysis approaches and a continuous stream of proposals that seek to counter such attacks against users’ privacy. Recently, PayJoin was presented as a new technique for mitigating one of the most prominent heuristics, namely common input ownership. This heuristic assumes that the inputs of a transaction, and thus the associated addresses, belong to the same entity. However, a problem with PayJoin is that implementations can accidentally reveal such transactions if the corresponding inputs from involved parties are not chosen carefully. Specifically, if a transaction is formed in a way such that it contains seemingly unnecessary inputs, it can be identified through so-called unnecessary input heuristic (UIH). What is not yet clear is the impact of naive coin selection algorithms within PayJoin implementations that may flag such transactions as PayJoin. This paper investigates the resemblance of PayJoin transactions to ordinary payment transactions by examining the significance of the unnecessary input heuristic in transactions with more than one input and exactly two outputs which is the common template of recent PayJoin transactions.
A Robust Face Recognition model using Deep Transfer Metric Learning built on AlexNet Convolutional Neural Network

Exertion of Transfer Learning for Convolutional Neural Networks (CNNs) describes a dynamic solution for face recognition instead of building and training a neutral network from scratch. Transfer Learning gains and stores the knowledge while solving one problem, and utilizes it for a different related problem. So, choosing a suitable CNN for Deep Face Recognition among the state of art CNN is a challenging task. Deep face recognition requires a large density of datasets with standard subjects. Subjects with parameters like pose, illumination and expression are major constraints for frontal facial images. Ignoring the parameters in training as well as testing phase will result to significant decrease in recognition rate; also show indelible impact on accuracy. Training the network with improper illumination facial data base can lead to biased classifier in deep CNN. In this paper, we propose AlexNet-CNN architecture-based transfer learning framework to enlarge the facial feature space on the constrained face dataset. The proposed framework is self-possessed with input layer, convolutional layers, activation layers, pooling layers, fully connected layer connected to a classifier layer and runs on standard CUHK (China University of Hong Kong) and ORL (Olivetti Research Laboratory) database in a standard frontal pose faces, under ordinary illumination state and with unbiassed expression ensuing to excellent accuracy in facial recognizing tasks.
WHEN WORLDS COLLIDE – A COMPARATIVE ANALYSIS OF ISSUES IMPEDING ADOPTION OF AGILE FOR HARDWARE

Abstract The objective of this paper is to explore challenges identified for implementation of scrum for hardware development intersect with agile principles found in the literature. A series of comparative analyses are done at the textual level, through logical intersections, and through thematic analysis. It is shown that there are five underlying themes found across two sets of scrum challenges (constraints of physicality and the 13 principles). These five themes include: flexiblity, chunkability, scalability, endurability, and teamability. These five themes further are found related to the defining principles of the agile manifesto. Using this understanding, future efforts will include empirical case study work to determine the impact that these have on application of scrum methods and tools. Additionally, guidelines should be developed to help hardware product engineers in applying scrum.
Hierarchical Authorization of Convolutional Neural Networks for Multi-User

Convolutional neural networks (CNNs) are widely used in many aspects and achieve excellent results. Due to the authorization from different users, we need to consider the right management caused by multi-user. This paper proposes a novel concept of hierarchical authorization of CNNs, and it can help owners control the output results according to accesses. To realize this idea, we refer to differential privacy and use the Laplace mechanism to perturb the output of the model to vary degrees. In experiments, we run on CIFAR-10 and MNIST datasets using ResNet and VGG, and the results show our method has significant grading effects.