Reviewerly/exHarmony huggingface dataset:
Effect of Speech Recognition on Problem Solving and Recall in Consumer Digital Health Tasks: Controlled Laboratory Experiment

Background Recent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health applications and few studies have evaluated the efficacy of conversational agents in the hands of consumers. In other consumer-facing tools, cognitive load has been observed to be an important factor affecting the use of speech recognition technologies in tasks involving problem solving and recall. Users find it more difficult to think and speak at the same time when compared to typing, pointing, and clicking. However, the effects of speech recognition on cognitive load when performing health tasks has not yet been explored. Objective The aim of this study was to evaluate the use of speech recognition for documentation in consumer digital health tasks involving problem solving and recall. Methods Fifty university staff and students were recruited to undertake four documentation tasks with a simulated conversational agent in a computer laboratory. The tasks varied in complexity determined by the amount of problem solving and recall required (simple and complex) and the input modality (speech recognition vs keyboard and mouse). Cognitive load, task completion time, error rate, and usability were measured. Results Compared to using a keyboard and mouse, speech recognition significantly increased the cognitive load for complex tasks (Z=–4.08, P&lt;.001) and simple tasks (Z=–2.24, P=.03). Complex tasks took significantly longer to complete (Z=–2.52, P=.01) and speech recognition was found to be overall less usable than a keyboard and mouse (Z=–3.30, P=.001). However, there was no effect on errors. Conclusions Use of a keyboard and mouse was preferable to speech recognition for complex tasks involving problem solving and recall. Further studies using a broader variety of consumer digital health tasks of varying complexity are needed to investigate the contexts in which use of speech recognition is most appropriate. The effects of cognitive load on task performance and its significance also need to be investigated.
Towards a Generic Approach for PoS-Tagwise Lexical Similarity of Languages

The lexical similarity measures of the languages are used to find genetic affinity among them—as the languages come closer in language tree, chances increase to have more cognates in common. In this regard, this paper describes a tool to calculate the lexical similarity between pairs of languages. We used the words present in Universal Dependency (UD) corpora to find lexical similarities of the words. Since, many of languages in the UD corpora share the same scheme of part of speech (PoS) tag-set; we got the lists of words, corresponding to standard set of PoS tags. The tool can compare words of particular PoS tags for two different languages. Hence, we can calculate lexical similarity not only for the whole language but also for the specific PoS or a subset of PoS. Further, a user can compare function-words to find genetic affinity, nouns, and proper nouns to find borrowing or the loan-words. Moreover, this tool is more flexible than using either all of the words or a list (e.g., Swadesh list).
Substitute selection for a missing tool using robot-centric conceptual knowledge of objects

When performing task in open-ended environment, a robot may not have access to conventional tools required in the task. In such case, an ideal response for the robot would be to find a suitable substitute. In this paper, we present an approach to tool substitution where robot-centric knowledge is generated in an unsupervised manner about objects from multi-modal sensory data captured by a robot. This robot-centric knowledge is subsequently exploited to identify a substitute from available objects observed in the environment.
Echo State Network Based Nonlinear Channel Equalization in Wireless Communication System

Echo state network (ESN) is a class of neuromorphic computing approach called reservoir computing consisting of a large number of randomly interconnected neurons. Only the reservoir-to-output readout mappings are variable and are modified during the process of training. ESN functions as a densely interconnected recurrent neural network and is suitable for temporal prediction tasks, but with significantly reduced training complexity. In this article, we propose an equalizer based on ESN and evaluate its performance over nonlinear dispersive channels. We also perform experiments for selection various parameters of ESN and study their effect on performance of the equalizer. Based on extensive simulations performed, proposed scheme shows notable decrease in bit-error rate (BER) compared to existing equalizer in literature.
Detection of Memory Leaks in C/C++

Memory leaks are one of the main reasons for Software Aging. Irrespective of recent countermeasures in C/C++ such as smart pointers, leak-related issues remain a troublesome issue in C/C++ code. We propose an algorithm for automatic detection of memory leaks in C/C++ programs based on solving disjoint sets of graphs which comprises of memory objects as nodes and their references as edges in order to find the memory leaks within the application. For this an object database and structure database is created and MLD algorithm is applied. Thus, it helps in keeping a record of memory leaks in the application.
Predicting Missing Information of Vulnerability Reports

We have found that there is a certain degree of missing information through studying the current exposure of software vulnerabilities. This problem is caused by incomplete information submitted by the vulnerability report submitter. In this paper, we extract the knowledge of software vulnerability in a fine-grained way, and design a machine learning method to complete the missing information through the other information of the vulnerability itself. Our method can predict and complete the missing types and causes information of vulnerability reports.
Attention-Aware Network and Multi-Loss Joint Training Method for Vehicle Re-Identification

Vehicle ReID is a core technology in Intelligent Traffic System (ITS) and an important branch of ReID problem. However, due to the complexity of the traffic environment, the dataset commonly has serious imbalances between positive and negative sample data. To conquer these issues, a novel effective CNN is proposed in this paper. Firstly, we present an Attention-Aware Network that combines the Spatial Attention Model with the Channel Attention Model to make the network focus on high importance areas and further improve the ability to identify matching vehicles. Besides, we propose the Multi-Loss Joint Training strategy to handling the data imbalances. Then to prove the effectiveness of our proposed method. We evaluated our network on the most popular VeRi-776 dataset. Abundant experiment results have shown the effectiveness of our proposed method in vehicle re-identification compared with existing method.
Analysis of Noise Properties in Dental Images

Images used in medicine are often noisy. Noise might originate from different factors and it usually degrades image quality leading to less reliable diagnostics. There are stages and the corresponding methods of image processing for which it is extremely desired to know image characteristics to take them into account. In particular, this relates to dental images for which noise is clearly seen and its properties can differ from traditional assumptions if nonlinear operations are carried out to improve visual appearance of acquired images. In this paper, we apply several known (earlier designed) approaches to automatic (blind) estimation of noise statistical and spectral characteristics. It is shown that noise in dental images is spatially correlated and signal dependent with specific dependence. The obtained estimates of noise parameters demonstrate that signal-dependent component is prevailing and the term proportional to squared intensity is present. This can be a serious problem for many image processing techniques.
Research on CoAP Resource Directory Based on Blockchain

The security of the Internet of things(IoT) has become a very critical and sensitive issue. In the limited network based on COAP, due to the limited ability of nodes and the lack of reliable authentication and access control mechanism in the Resource Directory (RD), devices are easy to be attacked and cause data leakage. This paper proposes a method to enhance the security of COAP nodes by using blockchain. By extending the business capability of the CoAP RD, the semantics of the security attributes are added; and a security service access agent is defined to be responsible for the blockchain operation; the RD can be involved in the authentication/access control of the blockchain when the CoAP node resource access is parsed. The CoAP node can transfer the device and service data to the blockchain for secure storage. In this paper, the Californium COAP framework and Ethereum are used as the verification platform to realize the software and security service access agent software that extend the RD security semantics. The authentication / access control is carried out in combination with the blockchain, and the COAP node provides the security storage capacity in combination with the blockchain. The work of this paper shows that it is feasible to use blockchain technology to improve the security capability of COAP nodes in the IoT.
Multi-colored spanning graphs

We study a problem motivated by sparse set visualization. Given n points in the plane, each labeled with one or more primary colors, a colored spanning graph (for short, CSG) is a graph in which the vertices of each primary color induce a connected subgraph. The Min-CSG problem asks for the minimum sum of edge lengths in a colored spanning graph. We show that the problem is NP-hard for k primary colors when k≥3 and provide a (2−13+2ϱ)-approximation algorithm for k=3 that runs in polynomial time, where ϱ is the Steiner ratio. Further, we give an O(n) time algorithm in the special case that the given points are collinear and k is constant.
Superintendence Video Summarization

Today, System comprised of Surveillance cameras has become very useful and important in the every field, Mostly in the security industry. Also, Many numbers of surveillance cameras get added to the networks of surveillance or system every year as need and importance of surveillance cameras is increasing day by day. Video recorded from these surveillance cameras are large in size which require huge amount of time for monitoring and large storage space. Hence, there is a need of video summarization which has become very prominent since the last ten years because of the huge amount of available digital video content [3]. An algorithm we used for video summarization typically takes surveillance video as an input and extract a set of important frames or key-frames which is useful to represent the entire video content which are effectively more concise as compared to the original input video and convey semantic meaning. So, Our proposed solution aims at selecting keyframes from the video based on two criteria i.e. each object should appear within the scope of frame and each object should be visually presentable and must be closer to each other so that it could only show the related activities for ex. Summarization of video captured from ATM room camera should only display the part where user is interacting with the machine. So such a key frames are then used in final summarization.
Revisiting agile software development process based on latest software industry trends

AbstractAbstractIn today's software development process, we have reached a tipping point in time where being agile is equally important to having, the ability to release quality assured software at our discretion. This has been made possible by following the agile process that we have known since it came into existence in the early 2000s. Agile process provides us a way to streamline our product management, engineering and testing teams to work closely and efficiently in iterative cycles. By following agile processes, we are today able to achieve rapid software development as per our product requirements. Nowadays having competitive advantage is deemed essential for business, as is having the ability to release software products at will. Inf order to achieve this goal agile software development process has improved over the last 2 decades. In this paper we aim to highlight how this agile process function in the present-day software industry.Subject Classification: (2010): Primary 68N30Keywords: Agile Software Development ProcessSoftware DevelopmentSoftware Testing
A Skip-Connected Multi-column Network for Isolated Handwritten Bangla Character and Digit Recognition

Finding local invariant patterns in handwritten characters and/or digits for optical character recognition is a difficult task. Variations in writing styles from one person to another make this task challenging. We have proposed a non-explicit feature extraction method using a multi-scale multi-column skip convolutional neural network in this work. Local and global features extracted from different layers of the proposed architecture are combined to derive the final feature descriptor encoding a character or digit image. Our method is evaluated on four publicly available datasets of isolated handwritten Bangla characters and digits. Exhaustive comparative analysis against contemporary methods establish the efficacy of our proposed approach. The implementation of our present work can be found at: https://github.com/DVLP-CMATERJU/Skip-Connected-Multi-column-Network .
CS1 and CS2 Curriculum Recommendations: Learning from the Past to Try not to Rediscover the Wheel Again

Initial programming curricular units are of great importance to computer courses. There has been very important work with curriculum recommendations, notably those from Association for Computing Machinery (ACM) and later ACM in conjunction with the Institute of Electrical and Electronics Engineers (IEEE): so far almost twenty curriculum recommendations have been published.Computing is a constantly evolving area, as is society and the way new generations learn. Why are so many recommendations needed? And what are the developments in these recommendations?This article lists initial course units that are suggested or used as examples in each of the curriculum recommendation reports, both initial and generic, and those that address a specific area, namely Computer Engineering (CE), Computer Science (CS), Information Systems (IS), Information Technology (IT), and Software Engineering (SE).This study is of great importance for those who have the responsibility to design and redesign curricula as it points out a number of different paths, namely in relation to the already mentioned distinction by areas but also the distinction that is made by university size, previous knowledge of the students, and also duration of studies, among other variables. Knowing history makes it possible to understand the present and even make better choices for the future.
The Essence and Content of the Concept «Career Cruidance Environment» Formation Conditions

Global changes taking place in all spheres of life have had an impact on the sphere of education. The school should prepare a graduate who is able to successfully socialize and be in demand and competitive. That is why the problem of professional self-determination and choice of the future profession for students is especially acute. According to the authors, the training of professional personnel of the XXI century should be carried out in educational organizations with preprofessional training, which is the basis for obtaining high-quality professional education in the very near future. The authors of the article describe the environment of professional self-determination on the basis of early vocational guidance and pre-professional training of schoolchildren, which includes the following blocks: resources, activities, results. The material of the authors can be useful for creating an environment for professional self-determination, early career guidance and pre-professional training of students in educational organizations that will ensure the emergence of a graduate of a “new type” — a young man with the ability to consciously choose a professional path in unity with professional mobility.
Analyzing and Detecting Adversarial Spam on a Large-scale Online APP Review System

The online review system provides a platform for consumers to express their opinions and make decisions. However, the spam review has become a widespread problem recently. Existing detection systems are mostly designed for fraudsters who write fake reviews to promote their products or mislead consumers in E-commerce (e.g., Amazon, Yelp, Alibaba). In this paper, we focus on spams in the large-scale online app review platform. Through an in-depth analysis of 5 million reviews from the Tencent App store, we find almost half of the reviews are spams. Even worse, most of the reviews are deliberately designed by fraud reviewers for misleading classifiers. Specially, we conclude three popular patterns of generating adversarial spams by attackers.
Performance Analysis of E-Mail Spam Classification using different Machine Learning Techniques

Most of the business and general communication is done through email because of its cost effectiveness. This efficiency leads email exposed to various attacks including spamming. Nowadays spam email is the foremost concern for email users. These spams are used for sending fake proposals, advertisements, and harmful contents in the form of executable file to attack user systems or the link to the malicious websites resulting in the unessential consumption of network bandwidth. This paper elucidates the different Machine Learning Techniques such as J48 classifier, Adaboost, K-Nearest Neighbor, Naive Bayes, Artificial Neural Network, Support Vector Machine, and Random Forests algorithm for filtering spam emails using different email dataset. However, here the comparison of different spam email classification technique is presented and summarizes the overall scenario regarding accuracy rate of different existing approaches.
A blockchain-based decentralized booking system

Abstract Blockchain technology has rapidly emerged as a decentralized trusted network to replace the traditional centralized intermediator. Especially, the smart contracts that are based on blockchain allow users to define the agreed behaviour among them, the execution of which will be enforced by the smart contracts. Based on this, we propose a decentralized booking system that uses the blockchain as the intermediator between hoteliers and travellers. The system enjoys the trustworthiness of blockchain, improves efficiency and reduces the cost of the traditional booking agencies. The design of the system has been formally modelled using the CSP# language and verified using the model checker Process Analysis Toolkit. We have implemented a prototype decentralized booking system based on the Ethereum ecosystem.
Ensembled sparse‐input hierarchical networks for high‐dimensional datasets

Abstract In high‐dimensional datasets where the number of covariates far exceeds the number of observations, the most popular prediction methods make strong modeling assumptions. Unfortunately, these methods struggle to scale up in model complexity as the number of observations grows. To this end, we consider using neural networks because they span a wide range of model capacities, from sparse linear models to deep neural networks. Because neural networks are notoriously tedious to tune and train, our aim is to develop a convenient procedure that employs a minimal number of hyperparameters. Our method, Ensemble by Averaging Sparse‐Input hiERarchical networks (EASIER‐net), employs only two L 1 ‐penalty parameters, one that controls the input sparsity and another for the number of hidden layers and nodes. EASIER‐net selects the true support with high probability when there is sufficient evidence; otherwise, it performs variable selection with uncertainty quantification, where strongly correlated covariates are selected at similar rates. On a large collection of gene expression datasets, EASIER‐net achieved higher classification accuracy and selected fewer genes than existing methods. We found that EASIER‐net adaptively selected the model complexity: it fit deep networks when there was sufficient information to learn nonlinearities and interactions and fit sparse logistic models for smaller datasets with less information.
Detection of Account Cloning in Online Social Networks

This research proposes a framework to detect account cloning in online social networks. The main concept is to analyze user profiles, friend and follower networks, and user posting behaviors. The framework consists of 3 parts: Twitter Crawler, Attribute Extractor, and Cloning Detector. Twitter was used as a case study. Experimental results suggested that it was not easy to completely clone profiles and friend/follower networks of the victims. And even the attackers managed to do so or somehow fool other users, posting behaviors and writing styles could help distinguish between fake and authentic (i.e. done by the victims) posts. The average accuracy of classifying whether the posts were fake or authentic was 80%. Decision tree was found to yield the best classification performance.
A SILK Adaptive Steganographic Scheme Based on Minimizing Distortion in Pitch Domain

SILK, as an efficient speech codec, has been widely used in real-time interactive communication and can be a good carrier for hiding information. This paper proposes a secure steganographic scheme in SILK pitch domain based on speech signal correlation and statistical distribution of pitch lags. Considering that even minor modification of pitch lags will reduce time correlation of speech signal and the statistical characteristics will be disturbed, this paper elaborately designs a distortion function to measure the loss of signal correlation and statistical features introduced by embedding operation. Using syndrome-trellis codes, this paper realizes a SILK adaptive steganography algorithm based on a minimum distortion framework in pitch domain. Experiments show that the proposed scheme can maintain good voice quality and resist the detection of existing steganalysis schemes.