Reviewerly/exHarmony huggingface dataset:
A Study on Online Shopping Scams

Online shopping is becoming increasingly popular though there are several risks. Hence, this study investigates the language used by an international e-commerce platform customer to voice out their dissatisfaction, types of online shopping risks on the international e-commerce platform, and strategies acquired by the platform in addressing scamming issues on their site. A qualitative approach was used to analyse the data gathered from consumers’ feedback and review of the product they purchased on the international e-commerce platform. Data were analysed thematically. The study’s findings revealed that delayed delivery and receiving counterfeit products were the most frequent complaints reported. The shopping risks identified were quality of product and delivery risks. Furthermore, the findings also revealed that the international e-commerce platform used negotiation, mediation, confrontation, and litigation strategies to combat scamming issues on its platform. The study concludes with recommendations.
Differentially private partition selection

Abstract Many data analysis operations can be expressed as a GROUP BY query on an unbounded set of partitions, followed by a per-partition aggregation. To make such a query differentially private, adding noise to each aggregation is not enough: we also need to make sure that the set of partitions released is also differentially private. This problem is not new, and it was recently formally introduced as differentially private set union [14]. In this work, we continue this area of study, and focus on the common setting where each user is associated with a single partition. In this setting, we propose a simple, optimal differentially private mechanism that maximizes the number of released partitions. We discuss implementation considerations, as well as the possible extension of this approach to the setting where each user contributes to a fixed, small number of partitions.
Multiple Captions Embellished Multilingual MultiModal Neural Machine Translation

Neural machine translation based on bilingual text with limited training data suffers from lexical diversity, which lowers the rare word translation accuracy and reduces the general izability of the translation system.In this work, we utilise the multiple captions from the Multi-30K dataset to increase the lexical di versity aided with the crosslingual transfer of information among the languages in a multi lingual setup.In this multilingual and multi modal setting, the inclusion of the visual fea tures boosts the translation quality by a signif icant margin.Empirical study affirms that our proposed multimodal approach achieves sub stantial gain in terms of the automatic score and shows robustness in handling the rare word translation in the pretext of English to/from Hindi and Telugu translation tasks. Input ImageModel Outputs enhi
A Finer-Grain Analysis of the Leakage (Non) Resilience of OCB

OCB3 is one of the winners of the CAESAR competition and is among the most popular authenticated encryption schemes. In this paper, we put forward a fine-grain study of its security against side-channel attacks. We start from trivial key recoveries in settings where the mode can be attacked with standard Differential Power Analysis (DPA) against some block cipher calls in its execution (namely, initialization, processing of associated data or last incomplete block and decryption). These attacks imply that at least these parts must be strongly protected thanks to countermeasures like masking. We next show that if these block cipher calls of the mode are protected, practical attacks on the remaining block cipher calls remain possible. A first option is to mount a DPA with unknown inputs. A more efficient option is to mount a DPA that exploits horizontal relations between consecutive input whitening values. It allows trading a significantly reduced data complexity for a higher key guessing complexity and turns out to be the best attack vector in practical experiments performed against an implementation of OCB3 in an ARM Cortex-M0. Eventually, we consider an implementation where all the block cipher calls are protected. We first show that exploiting the leakage of the whitening values requires mounting a Simple Power Analysis (SPA) against linear operations. We then show that despite being more challenging than when applied to non-linear operations, such an SPA remains feasible against 8-bit implementations, leaving its generalization to larger implementations as an interesting open problem. We last describe how recovering the whitening values can lead to strong attacks against the confidentiality and integrity of OCB3. Thanks to this comprehensive analysis, we draw concrete requirements for side-channel resistant implementations of OCB3.
Comfort and Sickness While Virtually Aboard an Autonomous Telepresence Robot

In this paper, we analyze how different path aspects affect a user's experience, mainly VR sickness and overall comfort, while immersed in an autonomously moving telepresence robot through a virtual reality headset. In particular, we focus on how the robot turns and the distance it keeps from objects, with the goal of planning suitable trajectories for an autonomously moving immersive telepresence robot in mind; rotational acceleration is known for causing the majority of VR sickness, and distance to objects modulates the optical flow. We ran a within-subjects user study (n = 36, women = 18) in which the participants watched three panoramic videos recorded in a virtual museum while aboard an autonomously moving telepresence robot taking three different paths varying in aspects such as turns, speeds, or distances to walls and objects. We found a moderate correlation between the users' sickness as measured by the SSQ and comfort on a 6-point Likert scale across all paths. However, we detected no association between sickness and the choice of the most comfortable path, showing that sickness is not the only factor affecting the comfort of the user. The subjective experience of turn speed did not correlate with either the SSQ scores or comfort, even though people often mentioned turning speed as a source of discomfort in the open-ended questions. Through exploring the open-ended answers more carefully, a possible reason is that the length and lack of predictability also play a large role in making people observe turns as uncomfortable. A larger subjective distance from walls and objects increased comfort and decreased sickness both in quantitative and qualitative data. Finally, the SSQ subscales and total weighted scores showed differences by age group and by gender.
Research on School Classroom Teaching Model Based on Clustering Algorithm and Fuzzy Control

The method of computational intelligence to monitor and evaluate the concentration of students in the teaching process can promptly and effectively adjust the learning plan and improve the learning effect. In this article, clustering algorithm and fuzzy control methods are used to construct a research model of students’ attention in class. In addition, this article uses the existing MATLAB-based image feature recognition algorithm to detect and obtain facial features and analyze the main features of facial expressions through computational techniques to realize the judgment of attention. In addition, this article optimizes the traditional AdaBoost algorithm to save computing time and improve operating efficiency and system performance stability. Finally, this article constructs the functional modules of the research model according to actual needs and designs experiments to verify the performance of the model. Experimental research results show that the model constructed in this article has a certain effect.
Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches have been adopted that are not feasible for pixel-level analysis. The convolutional neural network (CNN) model required a large dataset with detailed pixel-level information for the semantic segmentation of red blood cells in the deep learning domain. In current research work, we address these problems by proposing a multi-level deep convolutional encoder-decoder network along with two state-of-the-art healthy and Anaemic-RBC datasets. The proposed multi-level CNN model preserved pixel-level semantic information extracted in one layer and then passed to the next layer to choose relevant features. This phenomenon helps to precise pixel-level counting of healthy and anaemic-RBC elements along with morphological analysis. For experimental purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks, relevant, complete blood count (CBC), and morphology reports for performance evaluation. The proposed model results were evaluated using crossmatch analysis with ground truth mask by finding IoU, individual training, validation, testing accuracies, and global accuracies using a 05-fold training procedure. This model got training, validation, and testing accuracies as 0.9856, 0.9760, and 0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311, 0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively.
The Role Of Technology In Education

Here modern technology is defined as knowledge that is transformed into products, processes, services and organizational structures. Technology is always juxtaposed with the term science. Science is a human effort to understand the phenomena and facts of nature, and to preserve that knowledge conceptually and systematically. While technology is a human effort to use science for the benefit and welfare. Because of this relationship, the development of science is always related to technological developments, and vice versa. The development of science and technology has an exponential characteristic, which is getting faster and faster, because the results of one stage become the basis and reason for the next stage. It is not surprising that not only is its development accelerating but the role of science and technology in modern society is increasing in importance. The development of science is active in all fields, namely health, agriculture, economics, social sciences, natural sciences and so on.
Unsupervised Learning based Backlog Prioritization in Distributed Agile Software Development

Distributed Agile software development has attracted the consideration of recent industrial fields. Agile technologies rely on faster delivery of shippable product to the customer which was a limitation of earlier traditional technologies. Due to faster delivery requirements, the project should be developed well within its time frame. This may be possible only if backlogs are properly handled and resolved which includes certain new features, changes to existing features or other tasks as identified by any of the stakeholders.  At the end of each sprint an ordered list of remaining backlogs should be ready with the project managers for their review and allotment to the team members. The ordering has to be done based upon stakeholders’ priorities based business challenges, task dependencies, available skill set and budget limitations. In this way, proper and timely backlog prioritization becomes an important activity of Agile based software development. This activity becomes more critical in distributed Agile environment. In this paper, we have proposed and implemented supervised learning based prediction technique for backlog prioritization. Based on available task set (in the form of user stories) and allotted team member’s previous experience, the model predicts probable backlogs.  The list of probable backlogs (most appropriate among existing user stories), is passed to the project managers and team members. If these probable backlogs are available initially, team members pay more emphasis on those user stories, to finish them timely. Thus, the technique acts as a backlog prevention mechanism. A comparison has been made based on the total number of available backlogs after the end of each sprint. The results show that the proposed model gives better results in terms of backlog prediction and reduction in number of unattended user stories.
Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks

The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodynamic disasters are mainly determined by the changes of the index gas concentration pattern in coal mine mined-out areas collected continuously. The time series anomaly pattern detection method is mainly used to reach the state change of gas concentration pattern. The change of gas concentration follows a certain rule as time changes. A great change in the gas concentration indicates the possibility of coal spontaneous combustion and other disasters. To emphasize the features of collected maker gas and overcome the low anomaly detection accuracy caused by the inadequate learning of the normal mode, this paper adopted a method of anomaly detection for time series with difference rate sample entropy and generative adversarial networks. Because the difference rate entropy feature of abnormal data was much larger than that of normal mode, this paper improved the calculation method of the abnormal score by giving different weights to the detection points to enhance the detection rate. To verify the effectiveness of the proposed method, this paper employed simulation models of the mined-out area and adopted coal samples from Dafosi Coal Mine to carry out experiments. Preliminary testing was performed using monitoring data from a coal mine. The experiment compared the entropy results of different time series with the detection results of generative adversarial networks and automatic encoders and showed that the method proposed in this paper had relatively high detection accuracy.
Media Pembelajaran Word Wall dalam Meningkatkan Keterampilan Berbicara Bahasa Jerman

Abstract. This research is Classroom Action Research which consists of two cycles. This study aims to determine the planning, process, and results of Word Wall Learning Media in improving German speaking skills of students of class XI IBB 1 SMA Negeri 1 Polewali. This research data consists of quantitative data and qualitative data. Quantitative data was obtained through the results of the speaking skills test in cycle I and cycle II and qualitative data was obtained through observation. The data were analyzed using the percentage technique. The results showed the average score obtained by students in the first cycle reached 58.26% and the second cycle reached 84.73%. These results indicate that the use of Word Wall learning media improves the German speaking skills of class XI IBB 1 students of SMA Negeri 1 Polewali.
Utilization of Terrestrial Laser Scanning Data in Building Information Modelling (BIM) for Fire Disaster Evacuation Simulation

The development of digital building modeling technology for documentation and spatial planning is growing rapidly. Along with the development of these technologies, the urgency concerning the level of accuracy in the measurement is also increasing. One of the technologies currently being developed to address the urgency is the creation of three-dimensional objects using the Terrestrial Laser Scanner (TLS). Three-dimensional (3D) imaging sensors, such as TLS, can be used to create a Building Information Model (BIM) of the condition of buildings and other facilities in the field. However, quality control needs to be carried out to ensure that the model accurately describes conditions in the field. Therefore, this study was conducted to determine the level of accuracy of the results of the BIM made by utilizing the TLS. In this study, researchers applied a deviation analysis method. This method produced an area with a significant difference between the point cloud data and the reconstructed model. The BIM went through a virtual disaster evacuation simulation process by utilizing the A* search algorithm. The evaluation results of BIM deviation to the average point cloud were dominated by green results. This indicated that the deviation of the distance between the point cloud and the BIM data ranged from 0 to 2 mm. The results of the test of accuracy in the field using a rolling meter showed that the RMSE value of the difference between the distance of the roll meter and the BIM was 0.0139 m with an average distance difference of 0.0102 m. The results of the creation of the evacuation route showed that the maximum distance of the evacuation route in the building was 21.251 m on the ground floor, 17.290 m on the 1st floor, 17.718 m on the 2nd floor, and 17.851 m on the 3rd floor. From the results of the simulation, the disaster evacuation took 89.8 seconds with an average evacuation process of 44 seconds. The length of the path has been in line with the applicable regulations, in which according to Indonesia's Minister of Public Works and Housing Regulation No. 26/2008, the maximum length of the path in educational residential buildings is 62 m.
READER-TEXT CONNECTION: REPORTING THE ENGAGEMENT OF HIGH SCHOOL STUDENTS WITH CULTURALLY RELEVANT TEXTS

Maintaining students’ engagement with reading texts that reflect their background knowledge has positive effect on their comprehension. Culturally relevant materials provide more equitable opportunities to students, in the sense that more of them can reap the benefits of being able to connect to the text (Christ &amp; Sharma, 2018). This article aims to identify the characteristics of texts, particularly those categorized as culturally relevant, which make them appropriate for a specific group of students. Three data gathering methods were used: ratings of texts, reflective journals, and discussion points during Literature Circles. Results indicate that the students participating in the study consider that similar experiences, relatability of the story in terms of feelings and motivation conveyed, actions of the characters, and possibility of experiencing the same event are the main factors that made reading texts culturally relevant to them. These text characteristics can guide classroom teachers, materials developers, and curriculum designers in choosing materials and preparing lessons for specific groups of students, as these underscore the importance of text relatability in building and keeping the students’ reading interest and motivation.
What Makes us Curious? Analysis of a Corpus of Open-Domain Questions

Every day people ask short questions through smart devices or online forums to seek answers to all kinds of queries. With the increasing number of questions collected it becomes difficult to provide answers to each of them, which is one of the reasons behind the growing interest in automated question answering. Some questions are similar to existing ones that have already been answered, while others could be answered by an external knowledge source such as Wikipedia. An important question is what can be revealed by analysing a large set of questions. In 2017, “We the Curious” science centre in Bristol started a project to capture the curiosity of Bristolians: the project collected more than 10,000 questions on various topics. As no rules were given during collection, the questions are truly open-domain, and ranged across a variety of topics. One important aim for the science centre was to understand what concerns its visitors had beyond science, particularly on societal and cultural issues. We addressed this question by developing an Artificial Intelligence tool that can be used to perform various processing tasks: detection of equivalence between questions; detection of topic and type; and answering of the question. As we focused on the creation of a “generalist” tool, we trained it with labelled data from different datasets. We called the resulting model QBERT. This paper describes what information we extracted from the automated analysis of the WTC corpus of open-domain questions.
Mineralization Characterization of Psammitic Rocks in Efon-Alaaye and Environs using Remote Sensing and Field Studies

This paper analyses remotely sensed data over a part of the Psammitic Rocks in Efon-Alaaye and Environs to elucidate its geology and structural framework as well as delineate the potential zones of mineralization. The study area belongs to the Precambrian Basement Complex terrain of Southwestern Nigeria and lies within the eastern unit of the Ilesha schist belt. The lithologic units comprise schistose quartzites, quartz schists, quartz-mica-schists, and granitic gneisses. Landsat 8 satellite imagery and Shuttle Radar Topography Mission imagery (SRTM), both of path 190 and row 055, were used for the purpose of this study. A total of 138 lineaments of total length of 147.948 Km, were automatically extracted from both data. Rose diagram showed that the lineaments are bi-directional, with major lineaments trending NE-SW, E-W and the subsidiary ones in the NW-SE direction. These lineaments are mostly mapped on the schistose quartzites. Also, N-S trending faults were mapped from the Digital Elevation Model map generated from the SRTM data which was confirmed to be reverse fault based on field studies. The mineral alteration map generated revealed signatures of gold, iron oxides and silica minerals, while the mineralization potential map was done with reference to established geological settings of the area in conjunction with the structural elements such as folds, faults, joints and fractures mapped and measured on the field. These structures are typical of gold mineralization and associated metallic minerals in the Ilesha schist belt. This study also confirmed that mineralization in the study area is structurally controlled and type of mineralization is epigenetic in nature as a result of favorable ground preparation.
Review on quantum advantages of sampling problems

Exploiting the coherence and entanglement of quantum many-qubit states, quantum computing can significantly surpass classical algorithms, making it possible to factor large numbers, solve linear equations, simulate many-body quantum systems, etc., in a reasonable time. With the rapid development of quantum computing hardware, many attention has been drawn to explore how quantum computers could go beyond the limit of classical computation. Owing to the need of a universal fault-tolerant quantum computer for many existing quantum algorithms, such as Shor’s factoring algorithm, and considering the limit of near-term quantum devices with small qubit numbers and short coherence times, many recent works focused on the exploration of demonstrating quantum advantages using noisy intermediate-scaled quantum devices and shallow circuits, and hence some sampling problems have been proposed as the candidates for quantum advantage demonstration. This review summarizes quantum advantage problems that are realizable on current quantum hardware. We focus on two notable problems—random circuit simulation and boson sampling—and consider recent theoretical and experimental progresses. After the respective demonstrations of these two types of quantum advantages on superconducting and optical quantum platforms, we expect current and near-term quantum devices could be employed for demonstrating quantum advantages in general problems.