Reviewerly/exHarmony huggingface dataset:
Security Issues in Blockchain-Based Businesses

A few years back, it was arduous to identify cyber-security in the day-to-day activities of an organization. Writing extra code-lines was considered insignificant, and the term was considered more advanced than the current times. Most businesses were doubtful about changing their plans so that they could combat this danger as the notion of “it won't occur to me” was famous. Cyber-security is now no longer a “forbidden silo.” It is considered a necessity and is no longer selective. The organization must have its security planning in such a manner that it plays an important role in its business as well as the decision-forming in the significant matters of the company. In today's scenario, organizations must have a check on their defense system to ensure security in operations. They use updated solutions and have proper information on vulnerabilities and security issues that persist at the moment. This chapter throws light on security issues in blockchain-based businesses.
Interferensi Morfologi dan Sintaksis Bahasa Indonesia Terhadap Bahasa Jepang Pada Pembuatan Kalimat Pasif Bahasa Jepang oleh Pembelajar Bahasa Jepang

This study aims to explain the forms of morphological and syntactic interference of the Indonesian language in Japanese language learners' production of Japanese passive sentences. The data were then analyzed as errors because of the two interference. Research data was obtained through an open questionnaire distributed to Japanese Literature Study Program students during the 4th semester at Brawijaya University. Based on the research, the total forms of morphological interference found were 47. Morphological interference in the transfer of morphemes was the most common, namely 42 or 89%, followed by the removal of grammatical categories, as much as 4 or 9% and the most diminutive replicas of grammatical functions, as much as 1 or 2%.Meanwhile, syntactic interference is 33. The number of syntactic interference in phrase patterns is 22 or 69%, and syntactic interference in sentence patterns is 11 or 31%. Based on the data on the findings of morphological and syntactic interference in the Indonesian language, the form of errors following Parera's (1997) theory is 55. The number of 52 or 94% production errors is the highest, followed by a decrease of 2 or 4%; and the least is overproduction of 1 or 2%.
Sketching and sampling approaches for fast and accurate long read classification

Abstract Background In modern sequencing experiments, quickly and accurately identifying the sources of the reads is a crucial need. In metagenomics, where each read comes from one of potentially many members of a community, it can be important to identify the exact species the read is from. In other settings, it is important to distinguish which reads are from the targeted sample and which are from potential contaminants. In both cases, identification of the correct source of a read enables further investigation of relevant reads, while minimizing wasted work. This task is particularly challenging for long reads, which can have a substantial error rate that obscures the origins of each read. Results Existing tools for the read classification problem are often alignment or index-based, but such methods can have large time and/or space overheads. In this work, we investigate the effectiveness of several sampling and sketching-based approaches for read classification. In these approaches, a chosen sampling or sketching algorithm is used to generate a reduced representation (a “screen”) of potential source genomes for a query readset before reads are streamed in and compared against this screen. Using a query read’s similarity to the elements of the screen, the methods predict the source of the read. Such an approach requires limited pre-processing, stores and works with only a subset of the input data, and is able to perform classification with a high degree of accuracy. Conclusions The sampling and sketching approaches investigated include uniform sampling, methods based on MinHash and its weighted and order variants, a minimizer-based technique, and a novel clustering-based sketching approach. We demonstrate the effectiveness of these techniques both in identifying the source microbial genomes for reads from a metagenomic long read sequencing experiment, and in distinguishing between long reads from organisms of interest and potential contaminant reads. We then compare these approaches to existing alignment, index and sketching-based tools for read classification, and demonstrate how such a method is a viable alternative for determining the source of query reads. Finally, we present a reference implementation of these approaches at https://github.com/arun96/sketching .
Performance appraisal systems: The need to focus on employee engagement

Of the numerous functions of human resource management, performance appraisal (PA) is considered to play a critical role in the success of the organisation because its outcomes show the success of the realisation of other human resource functions. But, with the changes in a working context where winning the hearts and minds of employees is of paramount importance, there is a need for improved engagement of employees in the appraisal process. This study aims to ascertain whether the current appraisal process engages the employee fully. The study was conducted by two organisations in Dar es Salaam. The total sample size consisted of 50 subordinates. Interviews with key informants and questionnaires were used to collect data. Descriptive analysis was used to analyse the data with the use of Statistical Package for the Social Sciences version 16. Results indicate that the PA tool does not fully engage employees; the focus is more on the gap between expected performance and actual performance than on employee engagement and coaching, which are fundamental in winning the hearts and minds of employees. Keywords: Appraisal, employee engagement, performance;
Entropy Measurements for Leukocytes’ Surrounding Informativeness Evaluation for Acute Lymphoblastic Leukemia Classification

The study of leukemia classification using deep learning techniques has been conducted by multiple research teams worldwide. Although deep convolutional neural networks achieved high quality of sick vs. healthy patient discrimination, their inherent lack of human interpretability of the decision-making process hinders the adoption of deep learning techniques in medicine. Research involving deep learning proved that distinguishing between healthy and sick patients using microscopic images of lymphocytes is possible. However, it could not provide information on the intermediate steps in the diagnosis process. As a result, despite numerous examinations, it is still unclear whether the lymphocyte is the only object in the microscopic picture containing leukemia-related information or if the leukocyte's surroundings also contain the desired information. In this work, entropy measures and machine learning models were applied to study the informativeness of both whole images and lymphocytes' surroundings alone for Leukemia classification. This work aims to provide human-interpretable features marking the probability of sickness occurrence. The research stated that the hue distribution of images with lymphocytes obfuscated alone is informative enough to facilitate 93.0% accuracy in healthy vs. sick classification. The research was conducted on the ALL-IDB2 dataset.
Review of Practical Academic Library Instruction: Learner-Centered Techniques

Abstract: Review of Oehrli, Jo Angela (2022). Practical Academic Library Instruction: Learner-Centered Techniques. Chicago: ALA Editions. 124pp.
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model

&#x0D; &#x0D; &#x0D; We provide a lowerbound on the sample complexity of distribution-free parity learning in the realizable case in the shuffle model of differential privacy. Namely, we show that the sample complexity of learning d-bit parity functions is Ω(2d/2). Our result extends a recent similar lowerbound on the sample complexity of private agnostic learning of parity functions in the shuffle model by Cheu and Ullman . We also sketch a simple shuffle model protocol demon- strating that our results are tight up to poly(d) factors.&#x0D; &#x0D; &#x0D;
The impact of Leadershift on cultural agility policy: The search for effective ways of mindfulness

To achieve an agile business culture, leaders must match styles with situations (leadershift). Such a goal can be acquired by utilizing special abilities and skills, such as Mindfulness. We tested such a framework of research to identify the role of Mindfulness as a personality trait that any leader should effectively utilize to achieve an agile organizational culture successfully. The variance model was applied by a cross-sectional survey on a sample from the Egyptian Post Authority to evaluate their success using the proposed tools in their latest era of significant changes. Findings reveal that Mindfulness is a compelling personality trait that facilitates leadershift, identified as shifting leadership styles with high flexibility to fit the situation. Such a trait helped the management of the Egyptian Post Office to achieve a thriving agile culture as a state-owned enterprise. Moreover, further implications were highlighted for practitioners and academic researchers to investigate the relationship profoundly.
The Indonesian Di- Passive and Discourse Contexts

The syntactic analysis of the formation of an Indonesian active clause into a passive one has been much done in the literature. However, the research of the Indonesian passive into its discourse contexts has been understudied. The present study seeks to fill the lacuna. The findings show that an active clause and its passive clause counterpart change in the topic-comment relation, characterized by a change of definiteness status in the NP subject of the passive clause (NP patient) which serves to maintain the semantic interdependence between the NP object of the active clause and the NP subject (of the passive clause). The prohibition of passive clauses with the first and second person agent by-phrases is dependent upon the type of verbs that the di-passive co-occur with. NP agent by-phrases is also affected by discourse contexts tied to (im)politeness. It is also shown that the definiteness status of NP patients in agentless di-passive is affected by that of the NP agent. In addition to this, the omission of the NP agent by-phrase is made to avoid attributing direct responsibility for the action to the agent itself.
Mondelez settlement underlines cyber insurance trend

Headline INT: Mondelez case underlines cyber insurance trend
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing Publishing. • Type of peer review: Double Anonymous • Conference submission management system: Easychair • Number of submissions received: 121 • Number of submissions sent for review: 121 • Number of submissions accepted: 84 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 69.4 • Average number of reviews per paper: 2 • Total number of reviewers involved: 50 • Contact person for queries: Name: Dr. Rupesh Shelke Email: rupesh.shelke@raisoni.net Affiliation: Professor and Head, Mechanical Engineering Department, G H Raisoni College of Engineering, Nagpur, Maharashtra, India
Datasets, Corpora and other Language Resources

Abstract This chapter provides an overview of what is available in ELG in terms of datasets, corpora and other language resources (LRs) and how this has been achieved. We look at the procedures and steps that have been followed to complete the full resource ingestion cycle, which goes from repository and LR identification to metadata description and ingestion. We explain the approaches, priorities and methodology. The chapter also outlines the repositories that have been integrated into ELG, discussing the different procedures followed (metadata conversion, extraction, and completion, as well as harvesting) and the reasons behind these choices. Furthermore, the ELG catalogue content is described, with details on key elements and features as well as accomplishments. The last two sections are devoted to the crucial legal issues behind such a complex platform and its data management plan, respectively.
A Quality of Service Aware VM Placement for User Applications in Cloud Data Center

The placement of virtual machines in cloud data centers faces the challenge of multiple optimization goals. For example, the benefits of cloud providers are to reduce the cost of cloud resources and energy consumption, while the requirement of cloud users are to increase the efficiency and service quality of applications. Providing a solution that reduces the cost of cloud infrastructure to an optimal level and also satisfies users in terms of service quality, is always one of the challenges of virtual machines placement. Service quality criteria are broad and some of them are related to the quality of experience that has been considered in this paper. Many current placement techniques do not take into account the workload fluctuations, response times, and user preferences. In this paper, a cloud user application, which will be in the form of virtual machines, is placed in the data center based on the service quality parameters. By changing the service quality parameters at any time, replacement for that application is performed with the least overhead. Our simulation results show that we have been able to improve the quality of service by 37% compared to previous work for 30 applications that lead to the placement of 90 virtual machines in a data center. Our simulation results for 30 applications show that previous approaches that are not sensitive to service quality, experience a 60% reduction in service quality when the application load is increased by 50%.
A Vision-Based System for Abnormal Behavior Detection and Recognition of Bus Passengers

The passenger's safety issue is an emerging problem for autonomous driving, especially for the public transport vehicles. In the unmanned operating environment, it is necessary to be aware of improper and dangerous activities onboard the vehicle. In this paper, we propose a vision system for abnormal passenger behavior detection and recognition in bus application scenarios. Different from the current human activity recognition approaches, the images are acquired from an overhead camera to provide a large viewing area with less occlusion. Hence, it is a more challenging task for feature extraction and classification. An action recognition network for top-view images is proposed with the consideration of spatial and temporal information. For detection and classification of dangerous passenger behavior, a new image dataset, BUS-HAR, is created. Experiments carried out with the real scene images have demonstrated the feasibility of our method compared to the existing approaches. The codes and image dataset are made available publicly at https://github.com/richardkuo1999/passenger-action-recognition.
Motion Embedding for On-road Motion Object Detection for Intelligent Vehicle Systems

Accurate motion object detection (MOD) using in-vehicle cameras in driving vehicles is a challenging task. Several deep learning based motion segmentation approaches have been reported based on the interpretable optical flow feature. However, the interpretable optical flow feature has not been explored by object-level MOD approaches. In this paper, we propose a motion embedding pipeline (MEP) architecture that utilizes interpretable optical flow and deep learning to solve object-level MOD problems. The MEP is a three-stage pipeline that consists of an object detector, a novel feature extraction algorithm to capture relative motion between objects and the background, as well as a motion predictor for representation learning with stacked autoencoder to determine motions. A new dataset, Singapore motion object detection (SG-MOD) dataset is constructed in this work with much larger variations in urban environments. Experimental results show that the proposed MEP outperforms other pipeline-based architecture and deep learning based approaches on the SG-MOD and KITTI-MOD datasets in most metrics.
Influencing Factors of Mobile Commerce Personalization with Immersive Technology: A Systematic Literature Review

Globally, the number of transactions made through mobile devices continued to increase and reach high numbers. Besides, the rapid development of technology also offers a new technological innovation called immersive technology. This technology attracted a lot of attention from various parties to participate in developing and implementing this innovative technology that has high value and potential. Immersive technology can provide the newest digital experience by removing the boundaries between the real and virtual worlds. The appropriate and effective implementation is needed in the development of new technology. So, the factors influencing users to adopt immersive technology in various research fields need to be identified and explored. In this study, 31 studies about the use of immersive technology in multiple areas were selected to assist this research. Those selected study primarily discusses the factors that influence the use of certain immersive technologies in a particular context. The factors studied are all factors that have been proven to affect the use of immersive technology, both positively and negatively. The results of this study are expected to recognize what factors can influence the adoption of immersive technology in mobile commerce. These factors are expected to be a recommendation for developing mobile commerce that applies immersive technology and can provide information for managers to help them create the right strategy.
Implementation of Maggot Cage Temperature and Humidity Control Using ESP8266 Based On the Internet of Things

Black Soldier Fly (BSF) is a fly that can produce a maggot or larvae that are useful for human life, like a decomposer waste in the form of composting, animal feed, animal oil production, source of chitin, and for the economic incomes of the society. This study aims to develop a device that can be used to control the maggot cage temperature and humidity using the ESP8266 microcontroller based on the Internet of Things (IoT). The benefit of this study is the utilization of nozzle-based water spraying that can be used to maintain the maggot cage temperature and humidity to improve the quality of maggot cultivation results. In this study, the sensor used to read the temperature and humidity on the maggot cage is DHT11, then used a water spraying method to handle the temperature and humidity controlled by using the ESP8266 and online based on the Blynk IoT platform. This study result shows that the device built in this study can maintain the maggot cage temperature between 28 to 30oC and humidity over 60%.
A Lightweight Online Interactive Assessment Platform for SQL Teaching

Practical SQL exercises are important for database course learning and teaching. However, lots of existing database online SQL test platform requires complex environmental construction and can not work across platform. Backend database software running requires high level hardware configuration for large number of concurrencies from students during online test or examination. Light-weight, flexible and open-sourced database SQL exercise and assessment platform with pedagogical features for teaching is rare. This paper thus makes use of the recent frontend database technology to develop an open-sourced SQL assessment platform which is based on JavaScript engine and Node.js server for teaching and scoring of SQL programming skills. With this platform, the student can view the query results immediately after SQL execution and teachers can also collect exercisers’ information at the backend server. Browser fingerprint technique is also used to detect the plagiarism and cheating behaviour.
Understanding the Impact of Bots on Developers Sentiment and Project Progress

Software developers advance the project process by contributing and discussing on the code platform. Software bot acts as an assistant to help developers deal with repetitive tasks. In this paper, we explore whether the adoption of bots has an impact on developer sentiment and projects progress. We collected issues, pull requests and comments from GitHub popular projects. And we found that human users had significantly reduced positive sentiment in bot-created issues and PRs. The average merging time of the bot-created PRs is significantly shorter than human-created ones. The average solving time of bot-commented issues is significantly longer than human-created ones.
An Attack Detection Method Based on Spatiotemporal Correlation for Autonomous Vehicles Sensors

Environment perception technology is significant in the intelligent transportation system (ITS), which is the premise of the safety and stability of the unmanned vehicle system. The environmental perception system is mainly composed of lidar, camera, inertial measurement unit (IMU), global positioning system (GPS). Studies have shown that these sensors are vulnerable to external attacks, resulting in the distortion of the information perceived by the unmanned vehicle, and ultimately resulting in wrong driving decisions, which bring serious safety threats to life and property. In this paper, we utilize correlation of sensors in the space domain to establish distance models between multi-sensor and the distance models of a single sensor in time domain respectively. With the established distance models and distance distributions, we can accurately identify anomalous or attacked sensors with a certain degree of confidence. The hierarchical detection method of spatial correlation and temporal correlation is adopted, which makes that this method not only takes into account the detection efficiency, but also ensures the detection accuracy. Our experimental results quantitatively show that the method realizes real-time attack detection, and prove the effectiveness and robustness of the proposed method based on the open source KITTI datasets.
Digital Forensic Analysis on Caller ID Spoofing Attack

Misuse of caller ID spoofing combined with social engineering has the potential as a means to commit other crimes, such as fraud, theft, leaking sensitive information, spreading hoaxes, etc. The appropriate forensic technique must be carried out to support the verification and collection of evidence related to these crimes. In this research, a digital forensic analysis was carried out on the BlueStacks emulator, Redmi 5A smartphone, and SIM card which is a device belonging to the victim and attacker to carry out caller ID spoofing attacks. The forensic analysis uses the NIST SP 800-101 R1 guide and forensic tools FTK imager, Oxygen Forensic Detective, and Paraben’s E3. This research aims to determine the artifacts resulting from caller ID spoofing attacks to assist in mapping and finding digital evidence. The result of this research is a list of digital evidence findings in the form of a history of outgoing calls, incoming calls, caller ID from the source of the call, caller ID from the destination of the call, the time the call started, the time the call ended, the duration of the call, IMSI, ICCID, ADN, and TMSI.