Reviewerly/exHarmony huggingface dataset:
Role of Situational Agile Distributed Model to Support Modern Software Development Teams

ADSD (Agile Distributed Software Development) is a growing trend for software development organizations to develop quality software with limited cost and time. However, it gives rise to additional situational challenges. Situational variations result in unstable agile architecture which gets highly affected. Although the present literature focuses on agile architecture but effect of situational variation on the agile architecture still needs consideration. One possible solution is development of conceptual model and incorporation of multiple situational factors. This research study aims to identify the most relevant situations and propose situational ADSD approach for the development of situation-based software architecture framework for agile distributed projects. The study focuses on agile methods, rendering identified situational variations. The approach is developed through analyzing data from literature and their associated work documents. Comprehensive survey helps to bridge the gaps from conceptual to architecture model. The experimental results are acquired through both practical and statistical analysis. The results support Conway’s law, e.g. correlation that maps architecture with the communication and coordination needs. Correlation results show that architecture has major contribution in ADSD and validate the relationship among conceptual model and architecture model. The results also suggest that stable architecture in ADSD can positively affect the product.
POS Tagger for Malayalam using Hidden Markov Model

The NLP applications uses the parts of speech tagging as the preprocessing step. For making POS tagging accurate, various techniques have been explored. But in Indian languages, not much work has been done. This paper describes Part of Speech Tagger by incorporating Hidden Markov Model is built. Supervised learning approach is implemented in which, already tagged sentences in Malayalam is used to build Hidden Markov Model.
A new perspective on online malicious comments: effects of attention and neutralization

Purpose Today, online malicious comments are serious issues. They can cause psychological distress and suicide of victims. Although prior studies have focused on the role of anonymity as a major factor in making these comments, results of these studies have been inconsistent. On the other hand, the need for attention from others can provide an alternative explanation for such malicious comments. However, this perspective has been rarely studied. Therefore, the objective of this study was to investigate effects of anonymity and need for attention on posting malicious comments online and compare these two factors, resolving dark sides of online interaction. Design/methodology/approach This study obtained 327 questionnaires of Facebook users through a survey and analyzed the research model using partial least squares (PLS) regression. Findings Results of this study revealed that the need for attention affected malicious comments through partial mediation of neutralization. On the other hand, anonymity did not significantly affect malicious comments. Research limitations/implications This study has the following academic implications. First, we empirically examined the critical influence of need for attention on making malicious comments online based on the impression management theory. Second, this study revealed that the influence of need for attention on making malicious comments was partially mediated by neutralization. Third, this study may offer an explanation for contradicting findings on the role of anonymity in the phenomenon of posting malicious comments online. Practical implications Practical implications of this study are as follows. First, SNS platforms can limit activities of persons who post malicious comments frequently. Second, this study suggests that a notice is needed to inform the seriousness and harmful consequences of malicious comments. Third, Facebook practitioners should be aware that low anonymity may not reduce malicious comments. Originality/value This study quantitatively examined the effect of need for attention on malicious comments based on the impression management theory. It provides a fact that individuals who want to attract attention from others would write malicious comments through neutralization.
Automated Scoring in Intelligent Tutoring Systems

Simulation-based assessments open the door to a radically new paradigm of learning, characterized by interaction and personalized adaptation. We discuss automated scoring (AS) in intelligent tutoring systems (ITSs) that support students’ learning through their fairly unconstrained interactions in simulation environments. We highlight the necessary interplay among theories of cognition and learning in the domain and of the domain itself, simulation and interface design, and psychometric modeling, which must all be coordinated with AS from the start and not as an afterthought. We draw on concepts from evidence-centered design to explicate the relationships and illustrate the ideas with two examples: the Hydrive ITS to help US Airforce Force trainees learn to troubleshoot aircraft hydraulics systems and the Inq-ITS system to foster middle school students’ skills in science inquiry.
An Ensemble Deep Active Learning Method for Intent Classification

Intent classification plays a primary and critical role in intelligent dialogue systems. However, faced with the lack of labeled data, the training of robust intent classification model is time-consuming and costly. Thanks to the powerful pre-trained model and active learning, it's possible to construct an integrated method to fulfill this task efficiently. Therefore, we propose an ensemble deep active learning method, which constructs intent classifier based on BERT and uses an ensemble sampling method to choose informative data for efficient training. Experimental results on both Chinese and English intent classification datasets suggest that the proposed ensemble deep active learning method can achieve state-of-the-art performance with less than half of the training data. In addition, the performance of the proposed method is stable and scalable for both datasets. In general, the proposed method shows substantial advantages in building intent classifier across different datasets.
A Framework &amp; System for Classification of Encrypted Network Traffic using Machine Learning

Traffic classification solutions are widely used by network operators and law enforcement agencies (LEA) for application identification. Widespread use of encryption reduces the accuracy of traditional traffic classification solutions such as DPI (Deep Packet Inspection). Machine Learning based solutions offer promise to fill the gap. However, enabling such systems to operate accurately in high speed networks remains a challenge. This paper makes multiple contributions. First, we report on the development of MLTAT, a high speed network classification platform which integrates DPI and machine learning and which supports flexible deployment of binary or multi-class classification solutions. Second, we identify a set of robust features which fulfill a dual-constraint - support 10Gbps computation rates and sufficient accuracy in the supervised machine learning models proposed for network traffic classification. Third, we develop a set of labeled data suitable for training the system and a framework for larger scale ground truth generation using co-training. Our findings indicate detection rates around 90% across 8 traffic classes, benchmarked in the system at 10Gbps rates.
Reference Datasets for Training and Evaluating RF Signal Detection and Classification Models

There are several potential uses of artificial intelligence (AI) and machine learning (ML) in next-generation shared spectrum systems. However, while recognized datasets exist in certain domains such as speech, handwriting and object recognition, there are no equivalent robust and comprehensive datasets in the wireless communications and radio frequency (RF) signals domain. Therefore, we propose creating an RF signal database focused on the signals, schemes, systems and environments found in these applications. Our intention is to create datasets that have the following characteristics: targeted around specific applications and systems relevant to the next generation of advanced communications, composed of traceable elements and carefully curated to be representative of the target application. Datasets may consist of field measurements, data collected in a controlled laboratory setting, synthetically generated waveforms or any combination of the above.
Practical Radio Frequency Learning for Future Wireless Communication Systems

In future wireless systems, intelligent capabilities are of utmost importance. To efficiently utilize resources, communication systems require knowledge of the prevalent situation in a frequency band through learning. To learn appropriately, it is critical for practitioners to select the right parameters in building learning models, use the appropriate algorithms and performance evaluation methods. In this paper, we evaluate the performance of some deep learning models compared to other machine learning methods, explore the different scenarios in which deep learning can be used for radio frequency (RF) monitoring, and evaluate performance in the various scenarios. Our work looks at the best practices and procedures for developing intelligent RF Learning. Specifically, we analysed over-the-air RF dataset collected from a USRP-based testbed to identify the number of interfering devices as a case study. From the obtained results, we discuss how Signal-to-Noise Ratio (SNR) selection for training affects the model performance as it relates to practical implementation of Deep Learning in communications systems.
BBBlockchain: Blockchain-Based Participation in Urban Development

Urban development processes often suffer from mistrust amongst different stakeholder groups. The lack of transparency within complex and long-term planning processes and the limited scope for co-creation and joint decision-making constitute a persistent problem for successful participation in urban planning. Civic technology has the potential to improve this predicament. With BBBlockchain, we propose a blockchain-based participation platform, which is able to address all layers of participation. In the development of the platform, we focus on two key aspects: How to increase transparency and how to introduce enhanced co-decision-making. To this end, we exploit the immutable nature of blockchains and effectively offer a platform that excludes monopolistic control over information. The decision-making process is governed by smart contracts implementing, for example, timestamping of planning documents, opinion polls, and the management of a participatory budget. Our architecture and prototypes show the operational capabilities of this approach in a series of use cases for urban development.
Investigative Graph Search using Graph Databases

Identification and tracking of individuals or groups perpetrating latent or emergent behaviors are significant in home-land security, cyber security, behavioral health, and consumer analytics. Graphs provide an effective formal mechanism to capture the relationships among individuals of interest as well as their behavior patterns. Graph databases, developed recently, serve as convenient data stores for such complex graphs and allow efficient retrievals via high-level libraries and the ability to implement custom queries. We introduce PINGS (Procedures for Investigative Graph Search) a graph database library of procedures for investigative search. We develop an inexact graph pattern matching technique and scoring mechanism within the database as custom procedures to identify latent behavioral patterns of individuals. It addresses, among other things, sub-graph isomorphism, an NP-hard problem, via an investigative search in graph databases. We demonstrate the capability of detecting such individuals and groups meeting query criteria using two data sets, a synthetically generated radicalization dataset and a publicly available crime dataset.
A SAR-SIFT Like Algorithm for PolSAR Image Registration

The synthetic aperture radar scale-invariant feature transform (SAR-SIFT) algorithm has been applied to synthetic aperture radar (SAR) image registration and has good performance. However, compared with single polarization radar images, polarimetric synthetic aperture radar (PolSAR) images have more polarization combinations, which can obtain more polarization information. Therefore, based on the scattering mechanism of PolSAR, this paper proposes a registration algorithm suitable for PolSAR images. The algorithm improves the SAR-SIFT algorithm and uses the polarization information of the PolSAR images to introduce a new gradient definition and construct new scale spaces. The algorithm proposed in this paper is tested on multi-time and multi-angle PolSAR images. The experimental results show that the proposed method can make feature points more widely distributed and obtain more correct matching point pairs, which effectively improves the registration performance of PolSAR images.
XIEv

Researchers and practitioners in the fields of testing, security assessment and web development seeking to evaluate a given web application often have to rely on the existence of a model of the respective system, which is then used as input to task-specific tools. Such models may include information on HTTP endpoints and their parameters, available user actions/event listeners and required assets. Unfortunately, this data is often unavailable in practice, as only rigorous development practices or manual analysis guarantee their existence and correctness. Crawlers based on static analysis have traditionally been used to extract required information from existing sites. Regrettably, these tools can not accurately account for the dynamic behavior introduced by JavaScript and other technologies that are prevalent on modern sites. While methods based on dynamic analysis exist, they are not fully capable of identifying event listeners and their effects. This work presents XIEv, an approach for dynamic analysis of web applications that produces an execution trace usable for the extraction of navigation graphs, identification of bugs at runtime and enumeration of resources requested by each page. It offers improved recognition and selection of event listeners as well as a greater range of observed effects compared to existing approaches.
Sequential Semi-Orthogonal Multi-Level NMF with Negative Residual Reduction for Network Embedding

Network embedding is intended to produce low-dimensional vector representations of nodes in a network to preserve and extract the latent network structure, which has higher robustness to noise, outliers, and redundant data. Although a recently proposed multi-level nonnegative matrix factorization (NMF)-based approach has exhibited superior performance on network analysis, it is adversely affected by performance degradation because of discarded negative residual and redundant base selection throughout sequential multiple factorization processes. To alleviate this shortcoming, this paper presents a proposal of a sequential semi-orthogonal NMF with negative residual reduction for the network embedding (SSO-NRR-NMF). The proposed approach reduces the negative residuals to be discarded, and avoids redundant bases with a semi-orthogonal constraint. Numerical evaluations conducted using several real-world datasets demonstrate the effectiveness of the proposed SSO-NRR-NMF.
Exploration of the best performance method of emotions classification for arabic tweets

&lt;p&gt;&lt;span&gt;Arab users of social media have significantly increased, thus increasing the opportunities for extracting knowledge from various areas of life such as trade, education, psychological health services, etc. The active Arab presence on Twitter motivates many researchers to classify and analysis Arabic tweets from numerous aspects. This study aimed to explore the best performance scenarios in the classification of emotions conveyed through Arabic tweets. Hence, various experiments were conducted to investigate the effects of feature extraction techniques and the N-gram model on the performance of three supervised machine learning algorithms, which are Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR). The general method of the experiments was based on five steps; data collection, preprocessing, feature extraction, emotion classification, and evaluation of results. To implement these experiments, a real-world Twitter dataset was gathered. The best result achieved by the SVM classifier when using a bag of words (BoW) weighting schema (with unigrams and bigrams or with unigrams, bigrams, and trigrams) exceeded the best performance results of other algorithms.&lt;/span&gt;&lt;/p&gt;
MedType: Improving Medical Entity Linking with Semantic Type Prediction.

Medical entity linking is the task of identifying and standardizing medical concepts referred to in an unstructured text. Most of the existing methods adopt a three-step approach of (1) detecting mentions, (2) generating a list of candidate concepts, and finally (3) picking the best concept among them. In this paper, we probe into alleviating the problem of overgeneration of candidate concepts in the candidate generation module, the most under-studied component of medical entity linking. For this, we present MedType, a fully modular system that prunes out irrelevant candidate concepts based on the predicted semantic type of an entity mention. We incorporate MedType into five off-the-shelf toolkits for medical entity linking and demonstrate that it consistently improves entity linking performance across several benchmark datasets. To address the dearth of annotated training data for medical entity linking, we present WikiMed and PubMedDS, two large-scale medical entity linking datasets, and demonstrate that pre-training MedType on these datasets further improves entity linking performance. We make our source code and datasets publicly available for medical entity linking research.
Enhancing business processes with trustworthiness using blockchain

Blockchain technologies are intended to help enhance the trustworthiness of information, by improving transparency, traceability, and immutability of business logic and information, hence with the potential to be applicable to business process reengineering (BPR). However, an ad hoc approach to adopting blockchain technologies during BPR may lead to not better, but worse, than the current business processes, and with disappointments. In this paper, we present Fides - a framework for systematically utilizing blockchain towards enhancing business processes with trustworthiness. Fides takes a goal-oriented approach, in which trust-related concerns are explicitly represented as (soft)goals to be achieved, problems for achieving the goals are diagnosed, and then alternatives are explored in terms of business processes for eliminating or alleviating the problems, while at the same time achieving the goals. Finally, a selection is made among the alternatives that best utilize blockchain. To illustrate, and also see both strengths and weaknesses of Fides, a retail chain for a food supply chain is used throughout the paper, and is implemented using Ethereum and Hyperledger Fabric. Feedback from companies and students indicates that Fides leverages the level of confidence in the quality of the reengineered business processes, in utilizing blockchain.
QoS-Aware Multicast for Scalable Video Streaming in Software-Defined Networks

Scalable Video Coding (SVC) is a promising coding technique that enables flexible video transmission to support heterogeneous devices. However, the current best-effort delivery model characterized by the Internet cannot fully exploit the scalability feature of SVC because the network nodes are transparent for multimedia streaming applications. Software-defined networking (SDN) has emerged as an innovative network paradigm that decouples the control and forwarding planes while routing. This architecture with the OpenFlow protocol enables network operators to obtain per-flow QoS control in a more scalable, flexible, and finely granular manner compared to the conventional network architecture. Inspired by these facts, this paper designs a comprehensive QoS-aware multicast scheme for scalable video streaming over SDN networks. First, we present the designed multimedia streaming multicast system architecture and formulate the QoS-aware multicast routing problem as a non-linear programming model. We then propose a fundamental tree construction algorithm that decomposes the video streaming requests into subrequests and serves them in a bottom-up manner. Furthermore, we design a layer switching strategy based on the video distortion model to mitigate network-induced video quality distortion. Finally, the experimental results are presented to validate the performance of our methods in terms of scalability, network utility, video playout quality, and playback interruption ratio.
Comparison of Two Meta –Heuristic Algorithms for Path Planning in Robotics

Path planning for robots is essential to seek the most feasible path due to power requirement, environmental factors and other limitations. The Grey wolf optimization (GWO) is a metaheuristic algorithm that is widely used for optimization of different parameters in the discrete search space to solve the various problems. Here in this paper, we have used improved version of Grey Wolf Optimization known as Variable Weight Grey Wolf Optimization (VM-GWO) for path planning of robots. The VM-GWO was implemented using two different 3 dimensional maps in order to obtain the better path planning. The results of VM-GWO were compared with the conventional GWO in terms of speed and shortest distance covered. The result demonstrates the better performance of VM-GWO in comparison to GWO.
Wearable Accelerometer Optimal Positions for Human Motion Recognition

An intelligent human activity recognition system is influenced to some extent by sensor placement. In this paper, the number, and placement positions, of wearable accelerometers have been investigated to determine their influence on a human activity recognition system. Given 17 possible human sensor placements, we developed a multi-stage and multi-swarm discrete particle swarm optimization algorithm to explore the optimal sensor combination for various required sensor amounts. Relevant experimentation involved 10 different human daily activities, achieving an average prediction accuracy for a 4-sensor optimal combination of 95.12% via support vector machine classifier. The number and corresponding placement of sensors required for activity recognition have also been provided in this paper.
Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

Data generators are applications that produce synthetic datasets, which are useful for testing data analytics applications, such as machine learning algorithms and information visualization techniques. Each data generator application has a different approach to generate data. Consequently, each one has functionality gaps that make it unsuitable for some tasks (e.g., lack of ways to create outliers and non-random noise). This paper presents a data generator application that aims to fill relevant gaps scattered across other applications, providing a flexible tool to assist researchers in exhaustively testing their techniques in more diverse ways. The proposed system allows users to define and compose known statistical distributions to produce the desired outcome, visualizing the behavior of the data in real-time to analyze if it has the characteristics needed for efficient testing. This paper presents in detail the tool functionalities and how to create datasets, as well as a usage scenario to illustrate the process of data creation.