Reviewerly/exHarmony huggingface dataset:
Understanding the information needs and information-seeking behaviours of new-generation engineering designers for effective knowledge management

Purpose This paper aims to explore the information needs and information-seeking behaviours of the new generation of engineering designers. A survey study is used to approach what their information needs are, how these needs change during an engineering design project and how their information-seeking behaviours have been influenced by the newly developed information technologies (ITs). Through an in-depth analysis of the survey results, the key functions have been identified for the next-generation management systems. Design/methodology/approach The paper first proposed four hypotheses on the information needs and information-seeking behaviours of young engineers. Then, a survey study was undertaken to understand their information usage in terms of the information needs and information-seeking behaviours during a complete engineering design process. Through analysing the survey results, several findings were obtained and on this basis, further comparisons were made to discuss and evaluate the hypotheses. Findings The paper has revealed that the engineering designers' information needs will evolve throughout the engineering design project; thus, they should be assisted at several different levels. Although they intend to search information and knowledge on know-what and know-how, what they really require is the know-why knowledge in order to help them complete design tasks. Also, the paper has shown how the newly developed ITs and web-based applications have influenced the engineers' information-seeking practices. Research limitations/implications The research subjects chosen in this study are engineering students in universities who, although not as experienced as engineers in companies, do go through a complete design process with the tasks similar to industrial scenarios. In addition, the focus of this study is to understand the information-seeking behaviours of a new generation of design engineers, so that the development of next-generation information and knowledge management systems can be well informed. In this sense, the results obtained do reveal some new knowledge about the information-seeking behaviours during a general design process. Practical implications This paper first identifies the information needs and information-seeking behaviours of the new generation of engineering designers. On this basis, the varied ways to meet these needs and behaviours are discussed and elaborated. This intends to provide the key characteristics for the development of the next-generation knowledge management system for engineering design projects. Originality/value This paper proposes a novel means of exploring the future engineers' information needs and information-seeking behaviours in a collaborative working environment. It also characterises the key features and functions for the next generation of knowledge management systems for engineering design.
IOU – Siamtrack: IOU Guided Siamese Network For Visual Object Tracking

Recently deep learning-based Siamese networks with region proposals for visual object tracking are becoming popular. These frameworks, while testing, perform extra computations on the output of a trained network to predict the bounding box (bbox). This process hinders end-to-end training of the above class of networks and hampers the precise estimation of the bbox in testing. In this paper, we propose a framework close to the Siamese class of networks, but guided by Intersection Over Union (IOU) to predict precise bbox directly in the image space rather than at the feature space. To maximise the IOU of predicted bbox with respect to ground truth, we introduce a new module and corresponding loss function in training the network. The proposed approach enables end-to-end training and testing under similar lines, circumventing the typical bottleneck of the existing Siamese trackers. When evaluated on VOT2018 and GOT-10k tracking benchmarks, the proposed approach outperformed the base approach by more than 10% in terms of average overlap and compares favourably to state-of-the-art methods.
An Approach for Detection of Walking Related Falls During Activities of Daily Living

The proposed framework at the intersection of Internet of Things (IoT), Big Data, Human Computer Interaction, Assistive Technologies and their interrelated disciplines aims to take a holistic approach towards detecting walking related falls in the constantly increasing elderly population during Activities of Daily Living (ADL). Walking is one of the common causes of falls in older adults [1] and one of the most common movements associated with ADL. Falls in elderly can have a multitude of impacts, both temporary and permanent and could even lead to death. Walking accounts for 36% of falls in elderly - making it the second-highest cause of elderly falls after transfers [1]. The main motivation of this work is addressing the global challenge of detecting a fall during walking in the context of ADL, so that necessary assistive and preventive measures can be taken to reduce the impacts of the fall. To evaluate the efficacy of the proposed approach it has been implemented on a dataset of ADL and its performance characteristics are discussed. A case study is also included which discusses the performance characteristics of different learning models to identify the learning model best suited for this approach. The results presented uphold the immense potential of this framework to address these global challenges through detection of walking related falls in elderly during ADL.
Channel Expansion Convolutional Network for Image Classification

With the continuous evolution of research on convolutional neural networks, it is an efficient and fashionable method to introduce attention mechanism into the convolutional structure. The channel attention designed in SENet has made a great contribution to the promotion of the attention convolution model. However, our research found that SENet focuses on certain feature channels rather than objects in the channels. It will simultaneously enhance or weaken the target objects and background information in a certain channel. On the basis of the channel attention convolution network, we first perform channel sorting and group convolution on the feature map, and expand each group to β times the original feature channel during the group convolution process to construct a channel expansion convolution network (CENet), where β is an array used to represent the channel expansion coefficient. CENet captures the attention of objects in the feature channel while expanding the proportion of features in the relatively important channel. Furthermore, we improved the structure of CENet and merged it into the intra-layer multi-scale convolutional model to construct an object-level attention multi-scale convolutional neural network (OAMS-CNN). We have conducted a large number of experiments on four data sets, CIFAR-10, CIFAR-100, FGVC-Aircraft and Stanford Cars. The experimental results show that our proposed new object-level attention convolution model has achieved good image classification results.
Hardware Constructions for Lightweight Cryptographic Block Cipher QARMA With Error Detection Mechanisms

The cryptographic algorithm QARMA is a family of lightweight tweakable block ciphers targeted at applications such as memory encryption and construction of keyed hash functions. Utilizing lightweight security in hardware has the advantage of adopting the mechanisms to battery-constrained usage models including implantable and wearable medical devices. This lightweight block cipher utilizes a substitution permutation network (SPN) which is inspired by block ciphers such as PRINCE, MANTIS, and MIDORI. Moreover, it uses a three-round Even-Mansour scheme instead of the FX-construction, with its central permutation being non-involutory and keyed. In this article, we introduce error detection schemes on variations of QARMA, namely QARMA-64 and QARMA-128, which to the best of authors’ knowledge, have not been presented to date. We present our derivations for the logic-gate-based implementation, following which, we present the derivations for signature-based and interleaved signature-based schemes for the LUT-based approach. The presented, new signature-based error detection schemes, including cyclic redundancy check (CRC), are provided for the compact, involutory, and optimized S-box. Besides, recomputations through encoding the operands allow for the architectures to counter both transient and permanent faults. Also, the schemes are benchmarked on a field-programmable gate array (FPGA) hardware platform, where performance and implementation metrics show acceptable overheads and degradations. The proposed schemes are aimed to make the implementations of this lightweight tweakable block cipher more reliable.
Features of Artificial Bee Colony Based Algorithm Realization for Parametric Identification Method of the Interval Discrete Dynamic Models

This paper describes a method of parametric identification of the interval discrete dynamic models based on artificial bee colony algorithm. Realization of the proposed method is shown in the flowcharts of the main processes. The method efficiency has been proved based by computational experiments for two tasks of environmental monitoring and microbiological fermentation process modeling.
Review of Techniques for Privacy-Preserving Blockchain Systems

Privacy plays a central role in many application domains that utilize blockchain technology. It is central in Personal Data Management, Electronic Health records, or systems that interact with any public institution. However, blockchains are subject to potential privacy issues such as transaction linkability, compliance with data protection regulations, on-chain data privacy, and malicious smart contracts. To deal with these challenges, novel privacy-preserving solutions based on crypto-privacy techniques are emerging. The goal of this survey is to provide insights into the privacy-preserving techniques associated with blockchain. We analyse the existing privacy-preserving mechanisms for blockchain and propose a framework that categorizes the main techniques examined. Furthermore, we summarize some typical applications of blockchain where the privacy protection is the main requirement.
A Structured Review of Data Management Technology for Interactive Visualization and Analysis

In the last two decades, interactive visualization and analysis have become a central tool in data-driven decision making. Concurrently to the contributions in data visualization, research in data management has produced technology that directly benefits interactive analysis. Here, we contribute a systematic review of 30 years of work in this adjacent field, and highlight techniques and principles we believe to be underappreciated in visualization work. We structure our review along two axes. First, we use task taxonomies from the visualization literature to structure the space of interactions in usual systems. Second, we created a categorization of data management work that strikes a balance between specificity and generality. Concretely, we contribute a characterization of 131 research papers along these two axes. We find that five notions in data management venues fit interactive visualization systems well: materialized views, approximate query processing, user modeling and query prediction, muiti-query optimization, lineage techniques, and indexing techniques. In addition, we find a preponderance of work in materialized views and approximate query processing, most targeting a limited subset of the interaction tasks in the taxonomy we used. This suggests natural avenues of future research both in visualization and data management. Our categorization both changes how we visualization researchers design and build our systems, and highlights where future work is necessary.
A University Admission Prediction System using Stacked Ensemble Learning

For an aspiring graduate student, shortlisting the universities to apply to is a difficult problem. Since an application is extremely dynamic, students often tend to wonder if their profile matches the requirement of a certain university. Moreover, the cost of applying to a university is extremely high making it critical that students shortlist universities based on their profile. A university admission prediction system is quite useful for students to determine their chances of acceptance to a specific university. The system could make use of data related to previous applicants to various universities and their admit or reject status. Earlier models of such prediction systems suffer from several drawbacks such as not considering important parameters like GRE (Graduate Record Exam) scores or research experience. Further, the accuracy reported by earlier models is also not sufficiently high. In this paper, a stacked ensemble model that predicts the chances of admit of a student to a particular university has been proposed. The proposed model takes into consideration various factors related to the student including their research experience, industry experience etc. Further, the system proposed has been evaluated against various other machine learning algorithms including other deep learning methods. It is observed that the proposed model easily outperforms all other models and provides a very high accuracy.
Chatbot-based Interview Simulator: A Feasible Approach to Train Novice Requirements Engineers

Introduction: Although the interview is the most important and widely used requirements elicitation technique, novice engineers do not receive adequate training in Requirements Engineering (RE) courses. Objectives: Develop an AI-based interview simulator for helping novice requirements engineers in gaining interview skills. Methods: The research is based on the Design Science Methodology for Information Systems. The simulator is the outcome of six cycles; in each cycle, a proof of concept with additional features is created. Each cycle finishes with evaluation and improvement suggestions. Results: The simulator has been tested with students and results have been promising. The interview simulator understands context-free questions, retrieving the right information related to RE concepts such as goals, tasks, users, benefits, and constraints. The simulator also answers questions based on the context, makes summaries of the conversation, responds to meta-questions, and adds ambiguity and incompleteness to the conversation. Conclusions: The results have been promising. The simulator has been tested with degree and master level students. They were able to create a requirements specification using the simulator, and the feedback has been generally positive. The simulator will be tested in a real RE course in the academic year 2020-2021. Once it proves effective in the classroom, it will be opened to the RE community for free use and improvement.
Service Availability Analysis in a Virtualized System: A Markov Regenerative Model Approach

With the rapid and wide development and deployment of system virtualization, service availability analysis has become increasingly important in a virtualized system (VS) which suffers from software aging. Software rejuvenation techniques can be applied to improve service availability but its effectiveness depends on the rejuvenation policy, which defines when and where to rejuvenate, and which rejuvenation technique to be triggered. This article aims to analyze the optimal inspection time interval for maximizing application service (AS) availability under a three-level rejuvenation policy, in which rejuvenation techniques are deployed at each level, namely, AS, virtual machine (VM), and virtual machine monitor (VMM) levels. We first apply Markov regenerative process to construct an analytical model for the VS. Experiments of injecting memory leaks are conducted to measure aging-related parameters. Furthermore, numerical analysis is carried out to study the quantitative relationship between AS availability and inspection time interval, and determine the approximate optimal inspection time interval.
Multilabel Classification of Hate Speech and Abusive Words on Indonesian Twitter Social Media

Hate speech and abusive words spread widely on social media. The impact of hate speech on social media is hazardous, which can lead to discrimination, social conflict, and even genocide. Hate speech also has target types, categories, and levels. This research discusses the classification of hate speech and abusive words in the text on social media Twitter in Indonesian, English, and a mixture of both up to the types, categories, and levels. Classification of hate speech multilabel text is investigated using RFDT, BiLSTM, and BiLSTM with the pre-trained BERT model. The Classifier Chains, Label Powerset, and Binary Relevance methods are also used as data transformation, and TF-IDF is also used as feature extraction combined with the RFDT classification method. Some scenarios of the preprocessing stage are also carried out to find the best results, namely full preprocess, without stopword removal, and without stemming and without stopword removal. The problem of having Indonesian, English, and a mixture of both is solved in two ways, namely, without being translated and translated into Indonesian. The best results with an accuracy of 76.12% were obtained using the RFDT classification method with Classifier Chains, without translation, without stemming, and without stopword removal. This research also shows that the translation, stemming, and stopword removal are not effective, and the problem of dependencies between labels greatly affects the results of classification.
Weakly Supervised One-Stage Vision and Language Disease Detection Using Large Scale Pneumonia and Pneumothorax Studies

Detecting clinically relevant objects in medical images is a challenge despite large datasets due to the lack of detailed labels. To address the label issue, we utilize the scene-level labels with a detection architecture that incorporates natural language information. We present a challenging new set of radiologist paired bounding box and natural language annotations on the publicly available MIMIC-CXR dataset especially focussed on pneumonia and pneumothorax. Along with the dataset, we present a joint vision language weakly supervised transformer layer-selected one-stage dual head detection architecture (LITERATI) alongside strong baseline comparisons with class activation mapping (CAM), gradient CAM, and relevant implementations on the NIH ChestXray-14 and MIMIC-CXR dataset. Borrowing from advances in vision language architectures, the LITERATI method demonstrates joint image and referring expression (objects localized in the image using natural language) input for detection that scales in a purely weakly supervised fashion. The architectural modifications address three obstacles – implementing a supervised vision and language detection method in a weakly supervised fashion, incorporating clinical referring expression natural language information, and generating high fidelity detections with map probabilities. Nevertheless, the challenging clinical nature of the radiologist annotations including subtle references, multi-instance specifications, and relatively verbose underlying medical reports, ensures the vision language detection task at scale remains stimulating for future investigation.
The COLLECTORS ranking scale for ‘at‐scene’ digital device triage

Abstract As digital evidence now features prominently in many criminal investigations, such large volumes of requests for the forensic examination of devices has led to well publicized backlogs and delays. In an effort to cope, triage policies are frequently implemented in order to reduce the number of digital devices which are seized unnecessarily. Often first responders are tasked with performing triage at scene in order to decide whether any identified devices should be seized and submitted for forensic examination. In some cases, this is done with the assistance of software which allows device content to be “previewed”; however, in some cases, a first responder will triage devices using their judgment and experience alone, absent of knowledge of the devices content, referred to as “decision‐based device triage” (DBDT). This work provides a discussion of the challenges first responders face when carrying out DBDT at scene. In response, the COLLECTORS ranking scale is proposed to help first responders carry out DBDT and to formalize this process in an effort to support quality control of this practice. The COLLECTORS ranking scale consists of 10 categories which first responders should rank a given device against. Each devices cumulative score should be queried against the defined “ seizure thresholds ” which offer support to first responders in assessing when to seize a device. To offer clarify, an example use‐case involving the COLLECTORS ranking scale is included, highlighting its application when faced with multiple digital devices at scene.
Efficient Runtime Assertion Checking for Properties over Mathematical Numbers

Runtime assertion checking is the discipline of detecting at runtime violations of program properties written as formal code annotations. These properties often include numerical properties, which may rely on either (bounded) machine representations or (unbounded) mathematical numbers. The verification of the former is easier to implement and more efficient at runtime, while the latter are more expressive and often more adequate for writing specifications. This short paper explains how the runtime assertion checker E-ACSL reconciles both approaches by presenting a type system that allows the tool to generate efficient machine-number based code when it is safe to do so, while generating arbitrary-precision code when it is necessary. This type system and the code generator not only handle integers but also rational arithmetics. As far as we know, it is the first runtime verification tool that supports the verification of properties over rational numbers.
A Deep CNN Technique for Detection of Breast Cancer Using Histopathology Images

Analysis of Histopathology images is an essential technique used for the detection process of breast cancer at an early stage. To enhance efficiency of BC i.e. Breast Cancer detection using histopathology images and also to reduce the burden from doctors, we design a deep learning methodology to diagnose cancer using medical images. Here in this paper, we use deep learning technology Convolutional Neural Network (CNN) for the recognition process. Features are extracted by using the CNN model called DenseNet-201. The classification task has two classes: Malignant and Benign. The dataset we used for classification process is BreakHis (Breast cancer Histopathological dataset) highest classification accuracy obtained is 95.58%, precision and recall are 0.90 and 0.99 respectively and F1-score obtained is 0.89. Experimental results and comparison of other related work explain quite reliable performance and the efficiency of proposed work.
A New Approach to Hiding Data in the Images Using Steganography Techniques Based on AES and RC5 Algorithm Cryptosystem

In the new era of modern science and technology is developing day by day, data confidentiality is risky, all over the world and it increases rapidly. In this paper, a new approach to hiding the data using steganography techniques is proposed based on AES and RC5 algorithm cryptosystem. Steganography is the beauty of hiding secret data behind the digital images, videos, audios and text to cover the secret communication. A cryptosystem is the process which given our method more perfection. The visual quality of the cover image nice, no one can think about it how confidential data are transmitted using this method. This proposed method and algorithm capacity is highly flexible than other published algorithm. The AES and RC5 algorithm had no complexity and it looks like very well to hide the confidential data.