Reviewerly/exHarmony huggingface dataset:
Privacy Vulnerabilities of Wearable Activity Monitors: Threat and Potential Defence

Nowadays, large companies including Fitbit, Garmin, and Apple provide consumers with highly accurate and real-time activity trackers. An individual can simply wear a watch or handheld IoT device to automatically detect and track any movement throughout their day. Using sensor data obtained from Arizona State&#x0027;s Kinesiology department, this study presents the privacy concerns that activity-tracker devices pose due to the extensive amount of user data they obtain. We input unidentified user sensor data from six different recorded activities to an LSTM to show how accurately the model can match the data to the individual who completed it. We show that for three out of the six activities, the model can accurately match 88-92&#x0025; of the timestep samples to the correct subject that performed them and 60-70&#x0025; for the remaining three activities studied. Additionally, we present a voting based mechanism that improves the accuracy of sensor data classification to an average of 93&#x0025;. Replacing the data of the participants with fake data can potentially enhance the privacy and anonymize the identities of those participants. One promising way to generate fake data with high quality data is to use generative adversarial networks (GANs). GANs have gained attention in the research community due to its ability to learn rich data distribution from samples and its outstanding experimental performance as a generative model. However, applying GANs by itself on sensitive data could raise a privacy concern since the density of the learned generative distribution could concentrate on the training data points. This means that GANs can easily remember training samples due to the high model complexity of deep networks. To mitigate the privacy risks, we combine ideas from the literature to implement a differentially private GAN model (HDP-GAN) that is capable of generating private synthetic streaming data before residing at its final destination in the tracker&#x0027;s company cloud. Two experiments were conducted to show that HDP-GAN can have promising results in protecting the individuals who performed the activities.
Text-to-SQL for Korean Language based on Multilingual BERT

Text-to-SQL is one of semantic parsing methods that converts natural language questions into SQL queries, and it aims to extract data from any relational database without knowledge of SQL query configuration. Although development of large amounts of datasets (WikiSQL, SPIDER) and development of pre-trained language models (BERT) contributed to the improvement of Text-to-SQL performance in English, language-specific dataset construction and model research have not been much progressed. Therefore, this study proposes a multilingual BERT-based Text-to-SQL methodology that converts the natural language question in Korean into SQL query for an English database. To this end, four strategies for translating Korean queries into English were explored, and their effectiveness was verified by applying each strategy to three text-to-SQL model structures. As a result of the experiment, it was confirmed that it showed a significant SQL generation performance even for Korean questions. The proposed methodology is meaningful in that it shows semantic inferences between database tables, column information, and questions composed of different languages are possible, and it is expected to support efficient database access by Korean users who lack proficiency in writing SQL queries.
From Leaderboard To Operations: DIVA Transition Experiences

The IARPA Deep Intermodal Video Analytics (DIVA) program has sponsored the development of systems that detect and recognize activities in security video. During the period from September 2017 to March 2021, the development and evaluation of these systems was focused on optimizing accuracy, embodied in quantified metrics, against a large but relatively static corpus of video collected and annotated by the program. This focus was aided by various software engineering decisions collaboratively reached by the program performers and Test & Evaluation (T&E) team, which established a common software framework enabling ongoing quantitative evaluation via software submissions to a leaderboard. While continuing to support the leaderboard, in March 2021 the program began efforts, still in progress, to transition capabilities developed on DIVA from the research environment to operational evaluation and deployment. As an operational system is a different use case than a research environment, it is not surprising that design decisions favoring the former will not always align with the latter. This paper discusses our work to transition DIVA systems into an operational setting, particularly identifying and resolving conflicts between the evaluation framework and operational requirements. We describe transition efforts to date, propose future work, and conclude with lessons learned from the overall transition effort.
Error Variance, Fairness, and the Curse on Minorities

Machine learning systems can make more errors for certain populations and not others, and thus create discriminations. To assess such fairness issue, errors are typically compared across populations. We argue that we also need to account for the variability of errors in practice, as the errors measured in test data may not be exactly the same in real-life data (called target data). We first introduce statistical methods for estimating random error variance in machine learning problems. The methods estimate how often errors would exceed certain magnitudes, and how often the errors of a population would exceed that of another (e.g., by more than a certain range). The methods are based on well-established sampling theory, and the recently introduced Sample-to-Sample estimation. The latter shows that small target samples yield high error variance, even if the test sample is very large. We demonstrate that, in practice, minorities are bound to bear higher variance, thus amplified error and bias. This can occur even if the test and training sets are accurate, representative, and extremely large. We call this statistical phenomenon the curse on minorities, and we show examples of its impact with basic classification and regression problems. Finally, we outline potential approaches to protect minorities from such curse, and to develop variance-aware fairness assessments.
Performance Review of Generative Adversarial Network for a Bi-directional Task

Generative Adversarial Networks (GAN) contributed many significant works in computer vision tasks in different research areas. But, to author’s knowledge, there is no research discussion about GAN’s performance in a bi-directional task. In this paper, we utilize Pix2pix network as a GAN example to test its performance in a bi-directional task, which is to transfer daylight image to night image and transfer night image back to daylight image. The experimental results review both success cases and fail cases to get several interesting observations regarding the influence of human’s perception in evaluation.
The Dod's Cybersecurity Maturity Model Certification and Resulting Tensions with U.S. Standard-Setting Policy and Established it Security Authorities

The National Institute of Standards and Technology (NIST), part of the Department of Commerce, has government-wide responsibilities for developing new information technology (IT) security standards. Federal agencies are required to apply these standards to federal networks and assets. To a greater and greater extent, the Department of Defense (DoD) has been applying these NIST-developed standards to its contractors through various procurement regulations. At some point, DoD contemplated moving beyond these NIST standards. With the help of Congress, the Secretary of Defense was authorized to develop a new cybersecurity standard and framework for its contractors under section 1648 of the National Defense Authorization Act for fiscal year 2020. This new standard became the Cybersecurity Maturity Model Certification, or CMMC. Developed by a select few trusted DoD contractors, it was done with minimal transparency and imposes severe technical requirements on domestic and foreign contractors. Federal agencies are required to consider these issues before developing a new standard and are encouraged to use voluntary consensus standards. The use of government-unique standards is strongly discouraged because they can lead to unnecessary obstacles to foreign and domestic commerce and create barriers for contractors of all sizes seeking DoD contracts. This paper suggests that the DoD cannot and should not go at setting cybersecurity standards alone for its contractors and must look to NIST, as it has historically done in the past, to help solve cybersecurity problems.
A Framework of Procurement Analytics for Fraud Coalition Prediction in Malaysia

Many research and approaches explored techniques to detect and prevent corruption in the procurement process in both Government and private sectors. Several studies have indicated methods of process and procurement selection have been enhanced through the transformation from manual methods to automation methods in line with the rapidly evolving advancement of information technology. The amount of data available is growing rapidly, and the evolving data offers an exciting opportunity to be explored and applied through data analytics. Detecting fraud coalition in procurement selection is one of the critical areas to explore as there is a lack of research in this area. Various types of data mining, business intelligence approaches, and methodologies that have been developed can build a framework to solve the problems encountered during the procurement process, which involve fraud coalition issues. Aside from utilizing categorization models, approaches like association rules, classification, and so on have been used to detect fraud coalition in procurement. This process can be identified through the use of historical procurement data to produce patterns, trends, and forecasts. Therefore, this study aims to provide a framework that identifies fraud coalition among bidders in procurement selection. The framework involved a comprehensive assessment of methods to detect abnormal and suspicious or anomalous behaviour in the procurement process through patterns generated by using Machine Learning, then provide a visualization to identify fraud in the procurement process to improve the efficiency of decision-makers in the procurement process.
Late-resizing: A Simple but Effective Sketch Extraction Strategy for Improving Generalization of Line-art Colorization

Automatic line-art colorization is a demanding research field owing to its expensive and labor-intensive workload. Learning-based approaches have lately emerged to improve the quality of colorization. To handle the lack of paired data in line art and color images, sketch extraction has been widely adopted. This study primarily focuses on the resizing process applied within the sketch extraction procedure, which is essential for normalizing input sketches of various sizes to the target size of the colorization model. We first analyze the inherent risk in a conventional resizing strategy, i.e., early-resizing, which places the resizing step before the line detection process to ensure the practicality. Although the strategy is extensively used, it involves an often overlooked risk of significantly degrading the generalization of the colorization model. Thus, we propose a late-resizing strategy in which resizing is applied after the line detection step. The proposed late-resizing strategy has three advantages: prevention of a quality degradation in the color image, augmentation for downsizing artifacts, and alleviation of look-ahead bias. In conclusion, we present both quantitative and qualitative evaluations on representative learning-based line-art colorization methods, which verify the effectiveness of the proposed method in the generalization of the colorization model.
Human Resource Recommendation Based on Recurrent Convolutional Neural Network

In recent years, with the rapid development of network technology, network recruitment has become the main channel for HR recruitment with the advantage of low cost. However, in the face of widely varying job requirements, it is very time-consuming and laborious for recruiters to find matching talents among a large number of resumes. In order to reduce the difficulty of screening and improve the efficiency of screening, this paper proposes a recurrent convolutional neural network-based model to process complex textual information of resumes and texts. That is, a neural network based on Bi-directional Long Short term Memory (BiLSTM) and Convolutional Neural Network (CNN) to extract word-level features of job requirements and resume text, and Attention mechanism is used to measure the importance of job requirements to different work experiences and the contribution of work experiences to different job requirements, which can effectively match job requirements and resume text information through the combination of the three.
Analysis of different models for Face Recognition Door Lock

The home security is necessary for every residence. There are many kinds of door locks that are present in the market like lock with keys, passcode, or pattern. After switching towards these types of modern locks the problem with security and privacy become less. By using IOT platform and deep learning technique we had introduced facial recognition door lock system. In this survey paper, we are presenting different approaches of facial recognition door lock such as using Face recognition using Raspberry Pi, Face recognition using deep learning techniques along with the other types of evaluation techniques.
An Augmented Lagrangian Artificial Bee Colony with Deterministic Variable Selection for Constrained Optimization

Nonlinear constrained optimization problems with nonlinear constraints are common in real-life application models. A viable option to handle such problems is metaheuristics that use proper penalty methods to bound solutions to the feasible space delimited by the constraints. Most penalty methods not only hinder the diversity of solutions but fail to exploit the feasible boundary of constraints from within the infeasible region. In light of this, we propose two methods to be incorporated into derivative-free algorithms for constrained optimization: a deterministic decision variable procedure based on previous works on multimodality; and a penalty method based on the augmented Lagrangian. We limit the study of the effects of our approach to the use of the Artificial Bee Colony algorithm (ABC) and several of its variants due to its simplicity and modular implementation. We validate our hypothesis by means of a numerical experiment using seven distinct nonlinear constrained optimization instances comparing the canonical ABC and some variants made for constrained optimization against their counterparts with the proposed deterministic selection and penalty method. Results suggest a positive outcome in relation to the integration of both methods to the ABC, opening up new avenues of possibilities for our proposed methods to be incorporated into other derivative-free algorithms.
Sentiment Analysis on Business Data using Machine Learning

Sentiment Analysis, also known as Opinion Mining, is the systematic identification, extraction, quantification, and study of affective states and subjective information using natural language processing and text analysis. For applications ranging from marketing to customer service, sentiment analysis is commonly used on reviews and survey answers, online and social media, and healthcare materials.The aim of this study is to determine the accuracy of the various algorithms for sentiment analysis of product reviews. For this study, 4,444 online product reviews were received from an e-commerce site.
A Research Perspective on Integrating Knowledge Management for Software Engineering

The software development organizations strive to develop quality software to survive and gain competitive edge in the unpredictable competitive environment. However, common view regarding software development is that the projects are not accomplished in the due constraints of budget and time. In addition, the software delivered is low in quality. Software developers may carry out development of similar softwares from scratch without realizing that the fast and better quality softwares are developed by storing and reprocessing the knowledge of previously developed softwares. Hence, software engineering organizations must certainly adapt knowledge management practices for enhancing performance. This study explores the different perspectives of knowledge management in software engineering in the current literature and presents the future research prospects.
Introduction

This introductory chapter provides an overview of implied licences. Copyright confers exclusive rights on copyright owners to perform certain acts in relation to their works. A person infringes copyright if she performs any of these acts in relation to a copyright work without ‘the licence of the owner of the copyright’. Alternatively, the user’s actions must be covered by one of the statutory limitations or exceptions, which address specific instances of permitted use of copyright works to achieve specific policy goals. If, however, a certain use is not authorised by either an express licence of the copyright owner or by the statutory limitations or exceptions, it does not always amount to an infringement. It may be possible for courts to imply a copyright licence to cover such use. The strength of implied licences lies in their flexibility to address a diverse set of circumstances. This may prove particularly useful in resolving issues arising from the interaction of copyright with new technologies. Armed with this flexibility and the ability to respond in a case-specific manner, implied licences can bring about a better balance among the interests of different stakeholders within the copyright system.
The Evaluation of Students and Lecturers on E-Learning Implementation

The COVID-19 pandemic is not only causing impact to the health of human-being, it also gives impact to almost all aspect of people life. To limit the spread of the disease, government decided to close the schools. E-learning method has been recognized to be the best options in replacing on-site learning in a forced situation. However, most students did not intentionally plan to have e-learning method. Moreover, only few lecturers have the experience and ability to navigate e-learning system. This study aims to gain insight from both students and lecturers about the implementation of e-learning in Indonesia. A descriptive quantitative study was conducted. The survey was conducted to students and lecturers from private and state universities at Jakarta and its surrounding cities as respondents. All collected questionnaires were analyzed based on the contents and tabulated based on the similarity of the context. The finding showed that both students and lecturers somehow satisfied with e-learning implementation and method. However, they still concerned about the performance. Top reasons both of them like e-learning are flexibility, accessibility and cost effective. However, both of them realize that connection issue and lack of interaction are the major drawbacks of e-learning program.
An Approach to Sport Activities Recognition Based on an Inertial Sensor and Deep Learning

In recent years, due to changing the human lifestyle, the number of sport trainers has been increased. The conventional classifiers as Naive Bayes (NB), Decision Trees (DT) and Convolutional Neural Networks (CNNs) can be used in this domain to recognize and count sports activities of subjects and provide them qualified feedback. This paper uses literature studies and selected sport activities, namely squats, pull-ups and dips as the dataset based on three UWB sensors with additional inertial data, which contains the reduced data set consisting of 17 training sets and next for CNN training the 1444 samples describing exercises and 2024 samples with breaks, which were grouped in the ratio 70:15:15. The recognition accuracy of the NB and DT were 89.4 and 92.9 accordingly. Next, the extensive performance analysis of the CNN based on experiments for different kernel sizes, different number of filters for single and dual layer networks was carried out. Moreover, the innovative model for sport activities recognition in the form the combination of several networks forming Ensemble Neural Network (ENN) was created. The accuracy was at the level 94.81 of CNN and exceeded 95% of ENN. The proposed prototype of the measurement system and data acquisition platform for sport activities recognition was highlighted as the great potential in the privacy-training sport system.
enRoute: An Indoor Navigation Application Using Digital Signage

Navigation tools such as paper maps, vehicle navigators, and map applications have long been essential in our daily lives.For the outdoors, a GPS is used to navigate between two points.However, GPS technology does not work well indoors, and we have a weaker sense of direction when inside buildings.In particular, with the increase in the number of large-scale buildings, people more often have to navigate complex indoor spaces.To achieve indoor navigation, we need to know our current location, the intended destination, and the correct route.We can determine indoor reference points by using Bluetooth Low Energy (BLE) beacons as well as digital signage, which is widely used indoors for promotional and informational purposes.An indoor navigation system consists of digital signage, pre-installed BLE beacons, and a mobile application.When the mobile application computes the shortest path from the current location to the destination, a step-by-step navigational instruction graph is shown both on users' phones and on nearby digital signage until the user reaches the destination.We conducted an experiment and survey to understand users' attitudes toward the application.The result shows that most subjects find the system useful and easy to use and hold a positive or neutral attitude toward continuing to use it.
Multifactor Authentication Approach on Internet of Things: Children's Toys

Internet connected Children&#x0027;s toys are a type of IoT devices that the security community should pay particular attention. A cyber-predator may interact with or gather confidential data about children without being physically present if IoT toys are hacked. Authentication to verify user identity is essential for all internet-connected applications, where relying on single authentication is not considered safe, especially in children&#x0027;s applications. Children often use easy-to-guess passwords in smart applications associated with the Internet of Things (IoT) for children&#x0027;s toys. In this paper, we propose to activate multi-factor authentication on the IoTs for children&#x0027;s toys connected to the internet using companion applications. When changing the user&#x0027;s behaviour (by IP address, GPS, OS version, and browser), the child&#x0027;s identity must be verified by two-factor authentication to prevent unauthorized access to preserve the child&#x0027;s safety and privacy. This paper introduces multi-authentication mechanisms: a password and another authentication type, either mobile phone SMS, security token, digital certificate, or biometric authentication.