Reviewerly/exHarmony huggingface dataset:
An Industrial Investigation into Effort Estimation Predictors for Mobile App Development in Agile Processes

Effort estimation is essential for all development teams in order to ensure the user's story for the software project is delivered on time, boxed released. The characteristics of mobile apps make app development different from other traditional software. There are many challenges in developing a mobile app that could lead to inaccurate estimation of user stories. Therefore, the aim of this paper is to provide a comprehensive investigation into the factors that affect the accuracy of effort estimation. The effort estimation factors have been collected from previous studies and validated with 20 practitioners across 18 organisations in the development field of mobile applications.
Iterative and Adaptive Sampling with Spatial Attention for Black-Box Model Explanations

Deep neural networks have achieved great success in many real-world applications, yet it remains unclear and difficult to explain their decision-making process to an enduser. In this paper, we address the explainable AI problem for deep neural networks with our proposed framework, named IASSA, which generates an importance map indicating how salient each pixel is for the models prediction with an iterative and adaptive sampling module. We employ an affinity matrix calculated on multi-level deep learning features to explore long-range pixel-to-pixel correlation, which can shift the saliency values guided by our long-range and parameter-free spatial attention module. Extensive experiments on the MS-COCO dataset show that the proposed approach matches or exceeds the performance of state-of-the-art black-box explanation methods. Our source code is available at https://github.com/vbhavank/IASSA-Saliency.
How Different Genders Use Profanity on Twitter?

Social media, is often the go-to place where people discuss their opinions and share their feelings. As some platforms provide more anonymity than others, users have taken advantage of that privilege, by sitting behind the screen, the use of profanity has been able to create a toxic environment. Although not all profanities are used to offend people, it is undeniable that the anonymity has allowed social media users to express themselves more freely, increasing the likelihood of swearing. In this study, the use of profanity by different gender classes is compiled, and the findings showed that different genders often employ swear words from different hate categories, e.g. males tend to use more terms from the "disability" hate group. Classification models have been developed to predict the gender of tweet authors, and results showed that profanity could be used to uncover the gender of anonymous users. This shows the possibility that profiling of cyberbullies can be done from the aspect of gender based on profanity usage.
Information technology for monitoring and modeling the atmospheric pollution by harmful emissions from vehicles

The tasks of monitoring and modeling harmful emissions in the atmosphere from motor vehilces in the cities are considered in this paper. It was developed an information technology that include methods, tools and algorithms for data obtaining, processing and visualizing. A mathematical model of harmful emissions in the atmosphere represented in kind of interval discrete dynamic model. For model identification, the algorithm of artificial bee colony used. Results of information technology using, were showed on example of atmosphere pollution by motor vehicles in Ternopil city.
Block Interval Adjustment Toward Fair Proof-of-Work Blockchains

We propose a finer measure of fairness than that found in the literature. We then propose a method of adjusting the block interval that maintains fairness and high transaction throughput. The existing fairness measure is so rough that its judgement is considered fair as long as a node can start mining by the time another node mines a block. However, the time left for mining differs between nodes; this can result in unfairness. Therefore, we define the proposed fairness as the mining success rate of miners depends only on computational power. This is a finer measure of fairness than existing one. We propose a method for adjusting the block interval to maintain a certain level of fairness. Most Proof of Work (PoW) blockchains fix the block intervals and do not consider fairness. In contrast, our proposed technique adjusts the block interval based on the fork incidence rate. Our method maintains fairness even if the network conditions change. Simulations confirm that our technique maintains fairness even when the Internet performance varies.
Design of Myoelectric Control Command of Electric Wheelchair as Personal Mobility for Disabled Person

Paralysis is a condition that causes the sufferer to be unable to move the leg muscles or hand muscles. However, a paralyzed subject can still use the electrical activity of other intact muscles as a control command for the electric wheelchair. This system includes instrumentation and processing of electromyography (EMG) signal, and interpretation for controlling the wheelchair to move forward, backward, stop, turn left, and turn right. The EMG signal of the Flexor Carpi Radialis muscles was measured and amplified by a designed instrumentation amplifier. Then, the signal was filtered to eliminate noise and maintain the EMG frequency range of 20500 Hz. After filtering, the signal was processed more to produce a linear envelope and hold the maximum value of the envelope of the EMG signal. Interpretation of command from subject was determined by thresholing method to the envelope of the EMG signal. The results of wheelchair navigation with basic movements had an average success rate of 100% of 8 subjects. The test results past the determined track have an average success rate of 95.83% for 3 subjects. It is expected that in the future, the subjects can use the wheelchair with subject's intention speed.
The Effect of Data Theft on a Firm’s Short-Term and Long-Term Market Value

The prevalence of the internet as firm’s primary channel of operations and marketing in recent decades has made information security management a critical issue for firms. Yet, previous research on the information security issue have mostly focused on the impact of information security events on firms’ short-term value. Their impact on firms’ long-term value is rarely analyzed. Corporate managers have also largely dismissed the negative long-term impact of information security breaches by considering them as accidental and arguing that their frequency in recent years has instigated a sense of numbness in customers to their regards. Consequently, managers have paid little attention to information security investments. To assess the importance of information security investment, this study examines short-term and long-term stock market assessment of data breaches events at publicly traded companies. The findings of this study offer new insights for firms in assessing information security investment.
Computational modelling of an auditory lexical decision experiment using jTRACE and TISK

We present a series of computational simulations of the auditory lexical decision task using the jTRACE and TISK models of spoken word recognition. Simulation 1 replicates high accuracy in word recognition and similar performance of these models using the small, default dictionary. Simulation 2 expands the set of words and phonemes, leading to issues in representing certain phonemes in jTRACE. Simulation 3 expands the lexicon of competitors and we find that TISK struggles to select the target word as the winner. Finally, Simulation 4 shows that the decision criteria employed leads to many false positives when pseudowords are presented to the model. None of the model estimates of the time cycle when the winner should be selected predicted participant response latency in the auditory lexical decision task. We discuss these findings and offer suggestions as to what a contemporary model of spoken word recognition should be able to do.
Occlusion Management Techniques for Everyday Glanceable AR Interfaces

To maintain safety and awareness of the real world while using head-worn AR glasses, it is essential for the system to manage occlusions involving virtual content that blocks the user’s view of the real world. We study this issue in the context of Glanceable AR interfaces, which involve presenting virtual information that the user can quickly access as a secondary task while performing other tasks in the real or virtual worlds. We propose eight different techniques to resolve these occlusions. The techniques differ in their content prioritization, automation level, and adaptation mechanism for resolving occlusion. We designed an experiment to understand the user experience with the techniques in a scenario that required both awareness of the real world and information access with the digital content. We measured task performance and user preference. The results show that techniques that prioritize real world viewing and those that automatically resolve occlusions result in better task performance. These techniques are also preferred by users, particularly when translucency is used to resolve occlusions. Despite the ease of information access, techniques that prioritize viewing of the virtual content were seen as less desirable by participants.
ALEEDSA: Augmented Reality for Interactive Machine Learning

In this work, we present ALEEDSA: the first system for performing interactive machine learning with augmented reality. The system is characterized by the following three distinctive features: First, immersion is used for visualizing machine learning models in terms of their outcomes. The outcomes can then be compared against domain knowledge (e.g., via counterfactual explanations) so that users can better understand the behavior of machine learning models. Second, interactivity with augmented reality along the complete machine learning pipeline fosters rapid modeling. Third, collaboration enables a multi-user setting, wherein machine learning engineers and domain experts can jointly discuss the behavior of machine learning models. The effectiveness of our proof-of-concept is demonstrated in an experimental study involving both students and business professionals. Altogether, ALEEDSA provides a more straightforward utilization of machine learning in organizational and educational practice.
TwoStreamVAN: Improving Motion Modeling in Video Generation

Video generation is an inherently challenging task, as it requires modeling realistic temporal dynamics as well as spatial content. Existing methods entangle the two intrinsically different tasks of motion and content creation in a single generator network, but this approach struggles to simultaneously generate plausible motion and content. To improve motion modeling in video generation task, we propose a two-stream model that disentangles motion generation from content generation, called a Two-Stream Variational Adversarial Network (TwoStreamVAN). Given an action label and a noise vector, our model is able to create clear and consistent motion, and thus yields photorealistic videos. The key idea is to progressively generate and fuse multi-scale motion with its corresponding spatial content. Our model significantly outperforms existing methods on the standard Weizmann Human Action, MUG Facial Expression and VoxCeleb datasets, as well as our new dataset of diverse human actions with challenging and complex motion. Our code is available at https://github.com/sunxm2357/TwoStreamVAN/.
Can We Give Seated Users in Virtual Reality the Sensation of Standing or Even Walking? Do We Want To?

VR is often experienced in a seated position, similar to 3D video games, that almost exclusively are. The reasons for this are diverse and can go beyond just being more comfortable [18]. Interestingly, even when users are physically seated, often designers try to simulate standing and moving experiences, using head-bobbing and other ways to make users believe they are walking. Does this actually work? Can we get seated users to believe they are standing, walking, or even running? Moreover, is trying to provide the illusion of being standing or moving while being seated even relevant and helpful? Does it improve user experience or performance? Or might it be more effective just to be ”honest” and ensure that the virtual locomotion metaphor matches the users’ physical posture? In this extended abstract, we aim to explore these little-researched questions and analyze different situations and scenarios, hoping to help motivate future research and discussion on this topic.
Overview of Information Security Standards in the Field of Special Protected Industry 4.0 Areas &amp; Industrial Security

Nowadays, not many areas exist where no IT device is available, moreover, these tools are mostly interconnected via internet. In many cases, private life and work are not separated, but they are integrated into each other. Industry 4.0 represents a new step in the value chain, pushing production also towards the utilization of cyber-physical systems. It is necessary that the control of these IT eco-systems is established. The regulation is very important in case of protected / controlled areas both in terms of technology and human behavior. Regulation forms are trying to keep up with this challenge. It is clear that there are existing uniform methodologies (e.g. COBIT, ITIL) and globally established procedures as well (e.g. ISO27000 and NIST800). In the course of the research of controlled areas, it is necessary to investigate the currently available standards from the aspect of information security. The study demonstrates how those organizations and regulations are shaped which define today’s IT security recommendations and norms. Moreover, it describes those standards on a personal approach, the parallel application of which can accomplish the complex security of controlled areas.
Evolutionary Neural Architecture Search (NAS) Using Chromosome Non-Disjunction for Korean Grammaticality Tasks

In this paper, we apply the neural architecture search (NAS) method to Korean grammaticality judgment tasks. Since the word order of a language is the final result of complex syntactic operations, a successful neural architecture search in linguistic data suggests that NAS can automate language model designing. Although NAS application to language has been suggested in the literature, we add a novel dataset that contains Korean-specific linguistic operations, which adds great complexity in the patterns. The result of the experiment suggests that NAS provides an architecture for the language. Interestingly, NAS has suggested an unprecedented structure that would not be designed manually. Research on the final topology of the architecture is the topic of our future research.
Robust Object Detection in Aerial Imagery Based on Multi-Scale Detector and Soft Densely Connected

Object detection in aerial images is vital for autonomous guidance, navigation and control, and situational awareness. However, there are still many challenges facing researchers in this filed, including the target scales, the perspectives in taking pictures, and the highly complex background. The present paper introduces a robust object detector which is optimized for handling with multi-scale objects and the overhead capturing perspective object instances in aerial images. Firstly, in the feature extraction stage, an effective multi-scale detector (MSD) is designed to search for objects with different scales in feature maps. After that, when detecting a small target from a cluttered background, both the shallow and deep layer features are densely connected by the deconvolution after tackling the issues of low dimensionality in deep layers and inadequate representation of small objects. In the experiments part, we analyze the impacts of the above mentioned components on the model and make a comparison between the method at issue and other state-of-the-art approaches on two publicly-available datasets captured by satellites and high-altitude UAVs. The results show that the proposed method, which is applicable to a wider range of aerial images, is more effective and robust.
Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions

Cognitive Behavioral Therapy (CBT) is a goal-oriented psychotherapy for mental health concerns implemented in a conversational setting. The quality of a CBT session is typically assessed by trained human raters who manually assign pre-defined session-level behavioral codes. In this paper, we develop an end-to-end pipeline that converts speech audio to diarized and transcribed text and extracts linguistic features to code the CBT sessions automatically. We investigate both word-level and utterance-level features and propose feature fusion strategies to combine them. The utterance level features include dialog act tags as well as behavioral codes drawn from another well-known talk psychotherapy called Motivational Interviewing (MI). We propose a novel method to augment the word-based features with the utterance level tags for subsequent CBT code estimation. Experiments show that our new fusion strategy outperforms all the studied features, both when used individually and when fused by direct concatenation. We also find that incorporating a sentence segmentation module can further improve the overall system given the preponderance of multi-utterance conversational turns in CBT sessions.
Mind the Balance: Revealing the Vulnerabilities in Low Entropy Masking Schemes

Low Entropy Masking Schemes (LEMS) have attracted wide attention due to their implementations simplicity and relatively good performance in protecting cryptographic implementations against Side-Channel-Attacks (SCAs). To achieve desired security, it is necessary (but not sufficient) to find proper low entropy mask sets to protect all sensitive secret-dependant intermediate variables. However, one crucial problem concerning this intuitive idea is that what `proper' mask sets should be. To formally capture such crucial qualification, we introduce the notion of balancedness to characterize this natural attribute of mask sets themselves. Considering that this notion is limited to characterize first-order security, we generalize it to d-dimension balancedness to accommodate d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> -order security, then we exhibit lower and upper bounds on d-dimension balancedness for any d. With the help of these essential definitions, we prove that no balanced low entropy mask set really exists, which implies that LEMS implementations always have vulnerabilities in theory due to the unbalancedness of underlying mask sets. In order to further demonstrate the practical implications of balancedness, we show 4 different kinds of attacks on three state-of-the-art LEMS implementations. Specifically, the distribution attack proposed in this paper is a general first-order attack on LEMS. The results demonstrate that unbalanced mask sets actually do lead to serious vulnerabilities.
Optimized Radial Basis Neural Network for Classification of Breast Cancer Images

Breast cancer is a curable disease if diagnosed at an early stage. The chances of having breast cancer are the lowest in married women after the breast-feeding phase because the cancer is formed from the blocked milk ducts.Nowadays, cancer is considered the leading cause of death globally. Breast cancer is the most common cancer among females. It is possible to develop breast cancer while breast-feeding a baby, but it is rare. Mammography is one of the most effective methods used in hospitals and clinics for early detection of breast cancer. Various researchers are used in artificial intelligence- based mammogram techniques. This process of mammography will reduce the death rate of the patients affected by breast cancer. This process is improved by the image analysing, detection, screening, diagnosing, and other performance measures.The radial basis neural network will be used for classification purposes. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for the training process. The cuckoo search algorithm will be used for this purpose.Thus, the proposed optimum RBNN is determined to classify breast cancer images. In this, the three sets of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a since the proposed system is most efficient than most recent related literature.Thus, it concluded with the efficient classification process of RBNN using a cuckoo search algorithm for breast cancer images. The mammogram images are taken into recent research because breast cancer is a major issue for women. This process is carried to classify the various features for three sets of properties. The optimized classifier improves performance and provides a better result. In this proposed research work, the input image is filtered using a wiener filter, and the classifier extracts the feature based on the breast image.
Application of deep learning and BP neural network sorting algorithm in financial news network communication

The reasonable ranking of web pages is an important step in the realization of search engine technology, and plays a key role in improving the information quality of high retrieval and presentation web pages. In this paper, the authors analyze the application of deep learning and BP neural network sorting algorithm in financial news network communication. According to the historical data of users in the process of searching and browsing, we can extract the potential connection between the information of the page itself and the user’s behavior habits, so as to mine the user’s potential preference page. Finally, we use a variety of technologies to mix recommendation and sorting. By observing the effect of multiple training, it is found that the convergence speed of the network model is fast, the training time is short and the training effect is good. We need to fully understand the characteristics of financial news communication in the new media era, and then fully grasp people’s financial news reading habits on this basis, and then put forward the innovative mode of financial news communication in the new media era on the basis of these two points.
Architectural Considerations for Blockchain Based Systems for Financial Transactions

Systems supporting financial dealings at an enterprise scale must be able to handle large volumes of transactions in a secure manner while maintaining high level of operational availability. These systems must also have the flexibility to interface with many third-party applications with ease. In this paper we explore these architectural considerations for systems supporting financial transactions using the blockchain technology. We identify the strengths of this technology in achieving the qualities associated with these architectural considerations and suggest architectural tactics that can be used to overcome its weaknesses. A reference architecture is presented along with a prototype that implements this architecture using Iroha, a commercially available financial ledger for blockchain systems.