Reviewerly/exHarmony huggingface dataset:
Anti-Social Behavior Detection in Urdu Language Posts of Social Media

Social media such as Facebook and Twitter has gained strong attention for sharing information, worldwide connectivity and brand marketing all over the globe. The prevalent use of social networking sites has produced exceptional amounts of data. The mining of these social media applications has its potential to excerpt illegal activities which may be helpful for individuals, business and customers. The mining of data obtained from Facebook and Twitter, can be used to predict emotions of stakeholder and its analysis can provide very valuable information regarding behavioral inclinations of the writer. The automatic sentiment analysis to detect the emotional content in textual data has been widely used in many research fields. Most of the existing sentiment analysis techniques are tailored for English Language. This paper presents Urdu based antisocial behavior detection (ASB). We aim in particular to establish antisocial behavior detection method and defining its emotional state. We are intended to introduce a sentiment analysis based behavioral model that describes emotions related to antisocial behavior. In addition to describing the negative emotional state, our model will also use the concept of behavioral tendencies and evidence to predict the possible behavior of social media activists based on input text. We will outline the design of behavior detection systems based on social media posts. The learning algorithm learn about emotions from social media posts in the Roman Urdu language to predict user's behavior regarding any specific post. The results of this study has verified that our method has outperformed state-of-the-art methods in terms of accuracy. A bilingual or multilingual ASB approach can be made in future.
The Reality of Augmented Reality in the Classroom

Science, technology, engineering, and mathematics (STEM) has school districts across the country scrambling to integrate technology into the academic day. The days of showing a PowerPoint with your lecture and calling it technology integration are gone. Today's educators, and students, are looking for interactive and student-centered immersive technology experiences. What is the reality of bringing augmented reality (AR) into the classroom? How feasible is it to introduce students to these experiences in a meaningful and academically challenging way? This chapter attempts to look at the challenges from the educators' point of view as well as its impact on the learners. In addition, the definition of AR is provided, multiple learning theories that provide the basis for using AR in the classroom are discussed, and an overview of best practices for AR integration is presented.
Assessment of M-Service Acceptance in Educational Context

The paper investigates students’ perspective regarding the role of Smartphones in an educational context. The method consists in conducting a questionnaire among 80 respondents – final-year students from four universities in Baghdad, Iraq; and data processing through Statistical Package for Social Sciences (SPSS 22.0) by applying recurrence, rate, mean score, standard deviation, autonomous example t-test and One Way ANOVA. Such a blended approach allows demonstrating that smartphones play a positive role in helping students learn and get educated. This study is significant for E-learners as it offers an innovative method of teaching; however a sound pedagogical infrastructure and training is needed.
A Proportional Algorithm for Rotary Motion Blurred Images Restoration

As for the rotation motion blur, the main reason for its poor recovery is that its blur kernel is difficult to estimate, which poses a new challenge to image restoration. In this paper, we propose a new blur kernel estimation model, which reconstructs the blurred image along the circumference concentric with the center of rotation. A series of blur kernels on the reconstructed image are easy to determine, and the blur kernel on each concentric circle is accurately described. A one-dimensional blur restoration technique such as Wiener filter can obtain the deblurred concentric circle image, and then use the inverse process of the model to refill the concentric circle image back to the Cartesian coordinate system to realize a high-definition rotating motion image restoration. The experimental results show that the proposed method can increase the Peak-Signal-Noise-Ratio (PSNR) of the rotational motion blurred image from 19.72dB to 31.42dB with less computational complexity.
Digital Simulation in the Virtual World: Its Effect in the Knowledge and Attitude of Students Towards Cybersecurity

The search for alternative delivery modes to teaching has been one of the pressing concerns of numerous educational institutions. One key innovation to improve teaching and learning is e-learning which has undergone enormous improvements. From its focus on text-based environment, it has evolved into Virtual Learning Environments (VLEs) which provide more stimulating and immersive experiences among learners and educators. An example of VLEs is the virtual world which is an emerging educational platform among universities worldwide. One very interesting topic that can be taught using the virtual world is cybersecurity. Simulating cybersecurity in the virtual world may give a realistic experience to students which can be hardly achieved by classroom teaching. To date, there are quite a number of studies focused on cybersecurity awareness and cybersecurity behavior. But none has focused looking into the effect of digital simulation in the virtual world, as a new educational platform, in the cybersecurity attitude of the students. It is in this regard that this study has been conducted by designing simulation in the virtual world lessons that teaches the five aspects of cybersecurity namely; malware, phishing, social engineering, password usage and online scam, which are the most common cybersecurity issues. The study sought to examine the effect of this digital simulation design in the cybersecurity knowledge and attitude of the students. The result of the study ascertains that students exposed under simulation in the virtual world have a greater positive change in cybersecurity knowledge and attitude than their counterparts.
Creation of quantum coherence with general measurement processes

Quantum measurement not only can destroy coherence but also can create it. Here, we estimate the maximum amount of coherence, one can create under a complete non-selective measurement process. For our analysis, we consider projective as well as POVM measurements. Based on our observations, we characterize the measurement processes into two categories, namely, the measurements with the ability to induce coherence and the ones without this ability. Our findings also suggest that the more POVM elements present in a measurement that acts on the quantum system, the less will be its coherence creating ability. We also introduce the notion of raw coherence in the POVMs that helps to create quantum coherence. Finally, we find a trade-off relation between the coherence creation, entanglement generation between system and apparatus, and the mixedness of the system in a general measurement setup.
The Intergration of KCF-based Object Tracking and Gesture Recognition for Computer-assisted Surgery System

In recent years, Computer-assisted Surgery (CAS) has allowed a significant increase in accuracy improvement and minimally invasive surgery in a large number of high risk surgical procedures. Nevertheless, to our knowledge, the existing CAS system still has many shortcomings in navigation system and diagnosis system. Video-optical based navigation system becomes popular because it provides a higher precision and a larger working space but weak to interference such as occlusion and reflection; in quite a few cases, doctors often choose intraoperative diagnosis, how to shorten the precious time spent in the diagnosis of surgery is an important direction. In this paper, a novel optical CAS system is proposed, which consists of two parts, one is scalpel navigation based on Kernelized Correlation Filters (KCF) multi-target tracking algorithm, and the other is a touchless hand gesture input based method to help intraoperative diagnosis. Scalpel navigation uses the fm810 camera to determine the three-dimensional position of the scalpel and display it through a computer graphics method. Unlike traditional ultrasound-based systems, our systems use optical navigation for higher accuracy and greater operating range, using KCF target tracking algorithms to overcome occlusion and other effects. By applying the KCF tracker to the RGB image to obtain the pixel coordinates, and then using the aligned depth map to calculate the 3-dimensional coordinates, the system can realize the positioning of the scalpel and display the 3d model using the OpenGL and OpenMesh libraries. Afterward, an interactive method based on contactless gesture input is proposed for intraoperative diagnosis. Unlike most systems that attach importance to gesture recognition today, we start with surgical tasks and focus on the positioning accuracy of gestures on the screen. The experimental results demonstrate our computer-assisted surgery system can meet the accurate, stable and real-time requirements of scalpel tracking and gesture input. It is hoped that this paper will inspire more research on CAS tasks.
Extending The Scrum to Introduce The Concept of Systematic Reusability

Agile Software Development (ASD) and Software Reusability are successful approaches in software engineering. ASD promises customer feedback, quick iterations, onsite customer relationship and also accommodates changes even after development. On the other hand, software reuse helps the organizations to deal with software complexity by systematic reuse, fast response to customers’ expectations and demands, reduced time to market, and increased productivity and quality. We believe that introduction of systematic reusability in ASD will improve the development process and it will be beneficial for organizations in achieving their short term and long term goals. Although few approaches have been proposed to address this issue but the scope of those studies is limited. In this work, we therefore, have proposed an approach to extend the most used agile method scrum to introduce the concept of systematic reusability. To support our idea and for the validation purpose, we have conducted control experiments. Experiment results prove overall performance of proposed approach.
Scale-Aware Feature Network for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation with image-level labels is of great significance since it alleviates the dependency on dense annotations. However, as it relies on image classification networks that are only capable of producing sparse object localization maps, its performance is far behind that of fully supervised semantic segmentation models. Inspired by the successful use of multi-scale features for an improved performance in a wide range of visual tasks, we propose a Scale-Aware Feature Network (SAFN) for generating object localization maps. The proposed SAFN uses an attention module to learn the relative weights of multi-scale features in a modified fully convolutional network with dilated convolutions. This approach leads to efficient enlargements of the receptive fields of view and produces dense object localization maps. Our approach achieves mIoUs of 62.3% and 66.5% on the PASCAL VOC 2012 test set using VGG16 based and ResNet based segmentation models, respectively, outperforming other state-of-the-art methods for the weakly supervised semantic segmentation task.
Blockchain Solutions for Security Threats in Smart Industries

Revolutionary changes are brought in all aspects of life, which made life easier through smart solutions. Smart solutions have been adopted by industries in making their processes more robust and efficient. The improvements in the sector entail more technical challenges for the industry. One of the major technical issues that need to be addressed in the industry is security and privacy. In this paper considering the constrained environment, we try to review the blockchain solutions that can be integrated within an industrial environment, as a defence against security challenges that ensures privacy, availability, and confidentiality.
Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction

Relation ties, defined as the correlation and mutual exclusion between different relations, are critical for distant supervised relation extraction. Previous studies usually obtain this property by greedily learning the local connections between relations. However, they are essentially limited because of failing to capture the global topology structure of relation ties and may easily fall into a locally optimal solution. To address this issue, we propose a novel force-directed graph to comprehensively learn relation ties. Specifically, we first construct a graph according to the global co-occurrence of all relations. Then, we borrow the idea of Coulomb’s law from physics and introduce the concept of attractive force and repulsive force into this graph to learn correlation and mutual exclusion between relations. Finally, the obtained relation representations are applied as an inter-dependent relation classifier. Extensive experimental results demonstrate that our method is capable of modeling global correlation and mutual exclusion between relations, and outperforms the state-of-the-art baselines. In addition, the proposed force-directed graph can be used as a module to augment existing relation extraction systems and improve their performance.
Analyzing CMMI RSKM with small software industries at level-1

Risk is inherently associated with each and every entity of this universe and software is not an exception to this fact. Risk can be anything that adversely affects the progress (during development) and success of software after deployment. It can occur due to incorrect assessment of user-requirements, fallacious estimates of overall development cost, improper project schedule, or quality specifications that are unattainable within the software-specific constraints. Though risks are involved in every phase of software development life cycle (SDLC) and it has been universally admitted that testing phase consumes significant part of SDLC. Testing phase is the most vulnerable one and poses a great threat to the acceptance as well as to the post deployment success of software. This paper presents the possible consequences of including risk management activities at the very first level of CMMI exclusively for small software industries (SSI) like start-ups. Based on these consequences a process improvement model for SSI is also proposed in this paper.
A Framework for Fraud Detection in Government Supported National Healthcare Programs

Fraud, waste and abuse have created prominent cost overruns in health care sector for the last few decades. There is a critical need of fraud detection system to control and monitor health insurance claims specially in developing countries. In many developing countries, governments have initiated various programs in which states take the responsibility to bear cost of medical services consumed by under privileged class. The paper proposed a framework for detecting fraudulent insurance claims by creating time series, traces of every transaction to identify invalid procedures and claims. The proposed framework is evaluated for different types of anomalies like age based anomaly, gender based, service providing pattern based and service availing pattern-based anomalies, on local hospital transactional data. The detection and monitoring of fraud is necessary to increase the quality and efficiency of healthcare.
A Self-Evolving Design of Blockchain-based Open Source Community

Blockchain technology allows an open, trustless and democratic organization structure, where software engineers in the world can contribute towards open source and community-owned software products, and can receive compensations for their contributions. The idea of designing Blockchain-based Open-source Community (BOC) is rapidly growing however, lack of the future envision of Global Software Engineering (GSE) based open coordination. This limit the self-evolvability and coordination within the existing BOCs. This paper, presents the existing BOCs and demonstrates how their design limits the open coordination. Then, this paper presents a self-evolving design of BOC which based on the third generation modular architecture of the Blockchain and open intercloud architecture. Such a design will allow the dynamic infrastructure and open coordination within the community. To demonstrate presented community design, paper discusses one of the most commonly used case study in GSE, from organization structure maintenance to the technical level, in context of the solutions provided by presented design. Researchers can employ the presented modular selfe-volving BOC design towards addressing the research challenges associated with BOC and engineers can use it as reference when implement or maintain a BOC. Researchers can employ the presented modular self-evolving BOC design towards addressing the research challenges associated with BOC and engineers can employ it when implement or maintain a BOC to allow open-coordination.
Application of DIC Technology to Shear Crack Measurement of Concrete Beam

Reinforced concrete (RC) beam is one of the most important structural components widely found in civil engineering. When a crack occurs on the surface of a beam, it probably means that the load-carrying capacity of the beam is reduced. Therefore, crack strain and crack width research on the beam is a major subject in the field of civil engineering. In this paper, the shear crack of one concrete beam was investigated using a digital image correlation (DIC) technology. The result demonstrates that the detailed information of crack width, crack position, and crack propagation direction can be properly captured by DIC method. Thus it is strongly recommended that the DIC method be used in the future research of shear crack of the concrete beam.
Dynamic Route Optimization using A* Algorithm with Heuristic Technique for a Grocery Store

A grocery store is a place where people can buy food items. As time goes by, grocery stores become bigger with increasing number of variety of items they offer and people tend to use more time and energy when looking for items in their grocery list. This study aims to develop a system that maps the shortest path needed by buyers when they go to groceries, to give the user the freedom to alter the list and to recalculate the best path to take to save time using A* (A-star) algorithm. The system was tested using a pseudo grocery store that imitated the layout of a real grocery store using two experimental set-ups, for unaltered and altered grocery lists, with ten trials for each set-up. The study was successful because the shortest map to follow was generated by the system. When the user altered the grocery list, the system recalculated the shortest path and generated a new map. Initial results showed the system greatly reduced the distance covered to improvement for the unaltered list and 89.71% improvement for the altered list.
Towards Geo-Context Aware IoT Data Distribution

In the Internet of Things, the relevance of data often depends on the geographic context of data producers and consumers. Today's data distribution services, however, mostly focus on data content and not on geo-context, which would benefit many scenarios greatly. In this paper, we propose to use the geo-context information associated with devices to control data distribution. We define what geo-context dimensions exist and compare our definition with concepts from related work. By example, we discuss how geo-contexts enable new scenarios and evaluate how they also help to reduce unnecessary data distributions.
Accelerating temporal action proposal generation via high performance computing

Temporal action proposal generation aims to output the starting and ending times of each potential action for long videos and often suffers from high computation cost. To address the issue, we propose a new temporal convolution network called Multipath Temporal ConvNet (MTCN). In our work, one novel high performance ring parallel architecture based is further introduced into temporal action proposal generation in order to respond to the requirements of large memory occupation and a large number of videos. Remarkably, the total data transmission is reduced by adding a connection between multiple-computing load in the newly developed architecture. Compared to the traditional Parameter Server architecture, our parallel architecture has higher efficiency on temporal action detection tasks with multiple GPUs. We conduct experiments on ActivityNet-1.3 and THUMOS14, where our method outperforms-other state-of-art temporal action detection methods with high recall and high temporal precision. In addition, a time metric is further proposed here to evaluate the speed performancein the distributed training process.
Turbo‐coded secure and reliable quantum teleportation

Quantum teleportation allows an unknown arbitrary quantum state to be transmitted between two separate locations. To achieve this, the system requires both classical and quantum channels, for communicating two classical bits and an entangled quantum bit from the transmitter to the receiver. It is commonly assumed that both channels are error-free, however, under realistic conditions, this is unlikely to be the case. This study proposed and investigated a secure and reliable quantum teleportation scheme when both classical and quantum channels exhibit errors. It was found that the security and reliability of the teleportation could be improved when powerful turbo codes are employed.
The Prolog Debugger and Declarative Programming

Logic programming is a declarative programming paradigm. Programming language Prolog makes logic programming possible, at least to a substantial extent. However the Prolog debugger works solely in terms of the operational semantics. So it is incompatible with declarative programming. This report discusses this issue and tries to find how the debugger may be used from the declarative point of view. The results are rather not encouraging. Also, the box model of Byrd, used by the debugger, is explained in terms of SLD-resolution.
Kannada Dialect Classification using Artificial Neural Networks

In this paper, Automatic Dialect Classification (ADC) system is proposed for dialects of Kannada language (the Dravidian language spoken in Southern Karnataka). ADC system is proposed by extracting spectral Mel Frequency Cepstral Coefficients (MFCCs), and log filter bank features along with Linear predictive coefficients. In addition, prosodic pitch and energy features are extracted to capture dialect specific cues. A Kannada dialect speech corpus consisting of five prominent dialects of Kannada language is used for designing the ADC system. An attempt is made by using Artificial Neural Networks (ANNs) technique for classification of Kannada dialects. As, recently, ANNs and its variants are gaining more popularity in the area of speech processing application. Hyperparameter tuning of ANN has resulted with an increase in performance.