Reviewerly/exHarmony huggingface dataset:
CGPP-POI: A Recommendation Model Based on Privacy Protection

At present, with the popularization of intelligent equipment. Almost every smart device has a GPS. Users can use it to obtain convenient services, and third parties can use the data to provide recommendations for users and promote relevant business development. However, due to the large number of location data, there are serious data sparsity problems in the data uploaded by users. At the same time, with great value comes great danger. Once the user’s location information is obtained by the attacker, severe security issues will be caused. In recent years, a lot of researchers have studied the recommendation of point of interests (POIs) and the privacy protection of location. Yet, few of them have explored both together, which induces some drawbacks on the combination of them. This paper combines POI recommendation with a privacy protection mechanism. Besides providing user with POI recommendation service, it also protects the privacy of user’s location. We proposed a POI recommendation model with privacy protection mechanism, termed POI recommendation model for community groups based on privacy protection (CGPP-POI). This model can ensure the recommendation accuracy and reduce the leakage of user location information via taking advantages of the characteristics of location. At the same time, it deals with the problem of poor recommendation performance caused by sparse data. In addition, through the expansion of location, random and other methods are used to protect the user’s real check-in information. First, the data processed at the terminal satisfied local differential privacy. At the same time, we use the data to build a recommendation model. Then, we use a community of user in the model to improve the availability of these disturbed data, explore the relationship between users, and expand check-ins within the community. Finally, we provide the POI recommendations to users. Based on the traditional evaluation criteria, we adopted four metrics, i.e., accuracy, recall rate, coverage rate, and popularity in evaluation part, where intensive experiments conducted on real datasets Gowalla and Brightkite demonstrate that our approach outperforms the baseline methods significantly.
Persepsi (Tidak) Etis Mahasiswa Akuntansi: Ditinjau dari Pengetahuan Etika, Religiusitas dan Love of Money

The purpose of this study is to examine the influence of ethical knowledge, religiosity and love of money on the unethical perceptions of accounting students both partially and simultaneously. Respondents in this study were active students majoring in accounting at State Polytechnic of Malang. The sampling technique used purposive sampling with a sample size of 210 respondents. The data collection method uses a questionnaire containing 72 questions related to ethical knowledge, religiosity, love of money and unethical perception. The results of the study prove that the ethical knowledge has a significant and negative effect on the unethical perception of accounting students. The level of religiosity does not have a significant and positive effect on the unethical perception of accounting students. And also the love of money has a significant and positive effect on the unethical perception of accounting students. Simultaneously the ethical knowledge, religiosity, love of money have a significant effect on the unethical perception of accounting students.
Gaze behaviour: a window into quantifying task difficulty and performance using the Tower of London Task

Actively deciding where to direct our gaze is crucial to the acquisition of visual information regarding our surroundings. Previous studies have demonstrated the potential of examining gaze behaviour to establish overt indices of cognitive processes, such as attention, visuospatial planning and problem solving. The current study aimed to characterize the eye movement pattern during visuospatial planning and problem solving using the Tower of London (TOL) task. Participants (n=9) were shown a series of pictures depicting coloured balls arranged in three columns above fixation (i.e., Goalspace) and below fixation (i.e., Workspace). The task was to plan and execute the shortest movement sequence required to match the ball arrangement in the workspace to that of the goalspace. Participants completed the task across 4 difficulty levels (i.e., optimal sequence lengths 3-6). Our results demonstrated that as task difficulty increased, dwell time, saccade frequency, gaze alternations between goal- and workspace, and saccade path length increased significantly (p<0.01). Notably, non-optimal trials, where participants used more moves than necessary to solve the problem, were associated with longer fixations in areas of the display that were not relevant to the task goal during the initial planning interval (X=375 ms) compared to optimal trials (X=285 ms) (p=0.034). This suggests that fixating on irrelevant areas might interfere with information processing and problem solving. Furthermore, analysis revealed that initial gaze location had a significant influence on initial planning time. Specifically, trials with initial fixations directed to the goalspace were associated with longer initial thinking times (X=7606 ms) compared to the workspace (X=5084 ms) (p=0.021). This finding suggests that initial gaze location contributes to the efficiency of TOL performance. We conclude that gaze behaviour analyses provide useful insights into task difficulty and corresponding behavioural performance.
A Painstaking Analysis of Attacks on Hypervisors in Cloud Environment

In every sector of the industry, virtualization has undoubtedly gained the highest rate. That has made IT distribution simpler, but also at the same time attracts several vulnerabilities. Hypervisor as the media to spread resources throughout huge users around the world, is one of the targets for an intruder to attain authority on a virtual machine. Depending on the type of functionality to be deployed on the cloud, hypervisors linked to hardware, software or firmware are used. With the endorsement of hypervisors, various instances can be fulfilled on a virtual machine beneficial for running either on the host or guest operating system. To attain control on the cloud, intruders will first attempt to acquire control of hypervisors. In this paper presents an in-depth study of various security issues, various approaches used for overcoming them parallelly with their characteristics and limitations along with this many attacks on hypervisors are addressed in depth with their defending and offending approaches
On the Relationship between Perceptual Learning and Statistical Learning: Evidence from Coherent Motion Detection

Background: What is the relationship between perceptual learning (PL) -- improvements in information pickup as a result of experience -- and statistical learning (SL) -- representing co-occurrences among environmental features? We investigated this question using signal detection theory methods in a paradigm that could allow both. Design: Pretest and posttest phases consisted of a coherent motion detection task: Participants observed random dot kinematograms (RDK) showing fields of dots moving mostly in random directions, but with one subset of dots moving coherently in a single direction. Participants adjusted a dial to indicate the direction of coherent motion on each trial. The training phase, between pretest and posttest, consisted of a motion discrimination task: On each trial, participants observed two sequential RDK displays, each with motion in a single coherent direction, and indicated whether the two displays showed motion in identical or different directions. The directional difference between intervals was adjusted during training using a staircase procedure to maintain 75% accuracy, with adjustment occurring every ten trials. For each participant, all discrimination trials were clustered around a constant direction; this basic direction was chosen from among 8 possible directions, separated by 45 deg, and randomized across participants. Feedback was provided after each response. Results: Preliminary results showed reliable transfer of PL effects from the motion discrimination task to improved sensitivity in the posttest to coherent motion, with these effects mostly confined to each participant’s training direction. Consistent SL effects were shown by criterion changes toward responding to the general direction of motion used in training. We will discuss correlational methods for assessing whether these are linked or independent processes. Conclusions: PL and SL can occur in the same learning context, with PL effects shown as sensitivity changes and SL effects as criterion changes. These two learning processes may proceed concurrently but separately.
Crossing Number Features: From Biometrics to Printed Character Matching

Nowadays, the security of both digital and hard-copy documents has become a real issue. As a solution, numerous integrity check approaches have been designed. The challenge lies in finding features which are robust to print-and-scan process. In this paper, we propose a new method of printed-and-scanned character matching based on the adaptation of biometrical features. After the binarization and the skeletonization of a character, feature points are extracted by computing crossing numbers. The feature point set can then be smoothed to make it more suitable for template matching. From various experimental results, we have shown that an accuracy of more than \(95\%\) is achieved for print-and-scan resolutions of 300 dpi and 600 dpi. We have also highlighted the feasibility of the proposed method in case of double print-and-scan operation. The comparison with a state-of-the-art method shows that the generalization of proposed matching method is possible while using different fonts.
Static analysis and family-based model checking of featured transition systems with VMC

A Featured Transition System (FTS) is a formalism for modeling variability in configurable system behavior. The behavior of all variants (products) is modeled in a single compact FTS by associating the possibility to perform an action and transition from one state to another with feature expressions that condition the execution of an action in specific variants. We present a front-end for the research tool VMC. The resulting toolchain allows a modeler to analyze an FTS for ambiguities (dead or false optional transitions and hidden deadlock states), transform an ambiguous FTS into an unambiguous one, and perform an efficient kind of family-based verification of an FTS without hidden deadlock states. We use benchmarks from the literature to demonstrate the novelties offered by the toolchain.
EFFECT OF ELECTRONIC PAYMENT ON FINANCIAL PERFORMANCE OF DEPOSIT MONEY BANKS IN NIGERIA

This study examined the effects of electronic payment on financial performance of deposit money bank in Nigeria. Data were collected from secondary sources through annual reports and statistical bulletin of Central Bank of Nigeria. Electronic banking was measured using the Automatic teller machine, internet banking and POS while financial performance was measured using the profitability of deposit money banks in Nigeria. Multiple regression technique was used. The study revealed that ATM does not contribute to profitability of the sampled banks and also is not significant to banks profitability, POS has a positive contribution to bank profitability, and is also statistically significant to bank profitability, likewise, internet banking also has a positive contribution and statistically significant to profitability of the banks. It is therefore recommended that banks that want to improve their financial performance must offer numerous products/services through POS and IBK in an effective, efficient and cost effective manner.
Document Domain Randomization for Deep Learning Document Layout Extraction

We present document domain randomization (DDR), the first successful transfer of CNNs trained only on graphically rendered pseudo-paper pages to real-world document segmentation. DDR renders pseudo-document pages by modeling randomized textual and non-textual contents of interest, with user-defined layout and font styles to support joint learning of fine-grained classes. We demonstrate competitive results using our DDR approach to extract nine document classes from the benchmark CS-150 and papers published in two domains, namely annual meetings of Association for Computational Linguistics (ACL) and IEEE Visualization (VIS). We compare DDR to conditions of style mismatch, fewer or more noisy samples that are more easily obtained in the real world. We show that high-fidelity semantic information is not necessary to label semantic classes but style mismatch between train and test can lower model accuracy. Using smaller training samples had a slightly detrimental effect. Finally, network models still achieved high test accuracy when correct labels are diluted towards confusing labels; this behavior hold across several classes.
FirstCry: A Hybrid Store Strategy Creating Big Wonders!

On May 17, 2010, Supam Maheshwari and Amitava Saha founded BrainBees Solution with a seed capital of US$39.01 million, which led to the launch of the e-commerce site FirstCry.com. An online e-commerce website that offers all kinds of baby and kids-focused products from diaper pins to strollers. The global and Indian baby care market has seen huge growth in the last few years. It is estimated that by the year 2020, the projected growth of this market will be over 17%. The rising birth rate and disposable income of young parents have also favoured the growth of baby care products. In a country like India, the mortar and brick stores held the major share of the baby care products market. Supam Maheshwari was in the midst of a major decision, but he was faced with some questions. Would he be able to design a unique hybrid model? Would this model help them compete with mortar and brick stores? Would FirstCry be able to build an ecosystem to provide solutions to parents? What strategy should FirstCry adapt for long-term success?
CORRELATION BETWEEN STUDENTS' INTEREST AND THEIR ACADEMIC ACHIEVEMENT

This study focuses on students’ interest in English, and how it influences students’ achievement. The aim of this study is to figure out whether there is a correlation between students’ interest and their academic achievement in learning English. The number of the sample in this research are 87 students at the second grade of SMA Negeri 1 Tomohon. This study used quantitative research design which is non-experimental research. The data collected with questionnaire to obtain the information about students’ interest and students’ achievement in learning English. The questionnaire consists of 31 items divided into positive and negative statements. The items were tested with SPPS to determent the validity and the reliability of each items. The data was analysed by using the formula of Pearson Product Moment Correlation. It was found that the correlation score was 0.424. The significance value was 0.217 showed in the r table. The score of correlation 0.424 was higher than the significance value 0.217, means that the alternative hypothesis was accepted. Based on the result, it can be concluded that there was a positive correlation between students’ interest and their academic achievement in learning English. The researcher suggests that the case interest is very important particularly in affecting students’ achievement, therefore it must be improved.
Changes in Viewing Behaviour in Healthy Aging and Amnestic Mild Cognitive Impairment

Memory-related viewing behavior is diminished in older adults, and lacking in amnesic cases who have reduced integrity of the medial temporal lobe (MTL), including the hippocampus, a region involved in memory. Given the link between viewing behavior and memory, the present study investigates perturbations in eye movements as a consequence of declining MTL integrity with normal and abnormal aging. Younger adults, healthy older adults, and older adults with amnestic mild cognitive impairment (aMCI) participated in a scene viewing task in which they freely viewed images presented on the screen as their eye movements were monitored. Amnestic MCI is typically associated with volume declines in the MTL, specifically the hippocampus, and is associated with subjective and objective cognitive impairment. Therefore, this group may provide key understanding of how eye movements are perturbed due to abnormal aging. We analyzed eye movement metrics to quantify the manner and extent of scene exploration. A multivariate analysis, partial least squares (PLS), was used to test if the three groups differed on the eye movement metrics. PLS results revealed that the pattern of viewing for aMCI adults was associated with more gaze fixations, more saccades, more regions sampled, yet their effective area of exploration was limited (i.e., lower root mean square deviation), saccade amplitude was lower, and viewing entropy was higher. The opposite pattern was expressed by younger adults; older adults did not express either pattern. These results demonstrate that, even in the absence of an overt memory task, patterns of eye movements recorded during free viewing can be used to differentiate groups with varying integrity of memory-related brain regions. Such findings suggest that eye movements may reveal disturbances in the moment-to-moment formation of representations in memory.
TROUBLESHOOTING AND TACKLING THE COMMON PROBLEMS IN VAT PHOTOPOLYMERIZATION AND FDM 3D PRINTING

The importance of 3D printing as a builder of the digital future is growing all the time. When it comes to the digital future, one clear goal is that, for example, spare parts are no longer necessarily shipped from factories around the world, but are 3D printed using a digital model of the spare part purchased from the manufacturer. Using 3D printers for replacement printing or home use is relatively simple. However, with 3D printers, you come across a wide variety of situations that require fault detection, troubleshooting, and general adjustment.&#x0D; &#x0D; This article highlights the most common problems with 3D printers. The technologies chosen for this study are Vat Photopolymerization and FDM (Fused Deposition Modeling) printing technologies because they are very common techniques for spare parts printing and home use. This study was conducted in concrete way, experimenting and producing problems intentionally, then solving them with solutions and techniques, which are common and gained through experience.&#x0D; &#x0D; As a result, this article is written as a guide to the most common problems with these chosen technologies. Solutions to the problems are showcased in simple and effective steps. After all, this study showed well the diversity of problem situations in 3D printing as well as the need for concrete expertise in troubleshooting solutions. The problems faced and tackled in this study were the most common ones but there can exist other problems too.
Alsat-2B/Sentinel-2 Imagery Classification Using the Hybrid Pigeon Inspired Optimization Algorithm

Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.
PENGARUH PENERAPAN STRATEGI PQ4R MENGGUNAKAN MEDIA FLIP BOOK TERHADAP HASIL BELAJAR IPS SISWA KELAS V SD DI ERA PANDEMI

This research aims to apply the PQ4R strategy with social studies flip book media to 5th grade students of SDN Geluran 1 in online learning. This is done in order to improve student learning outcomes during online learning that is less interactive due to the Covid-19 pandemic. Data were obtained from class 5 SDN Geluran 1, Taman District, Sidoarjo Regency. This research method uses pretest-posttest control group design. The results of this study are as follows: (1) Learning activities with the application of PQ4R strategy through flip book media, there are two activities, namely teacher activities, the results of which are 86.25% can be categorized as very good and for student activities, 84% are obtained in the very good category. (2) Student responses to the implementation of the PQ4R strategy through flip book media on social studies learning outcomes in class V, there is a percentage of 73% which can be categorized as good. (3) The application of the PQ4R strategy through flip book media on social studies learning outcomes in grade 5 in a similar sample, namely the experimental group there is thitung &gt; ttabel or 33,609 &gt; 1,708. While the learning outcomes with different types of data, namely the experimental group and the control group obtained thitung of 2,262 and ttabel of 2,000, then thitung &gt; ttabel. In other words, Ha is accepted and Ho is rejected. Thus, it can be concluded that the application of PQ4R using flip book media has a very good effect on learning outcomes for social studies subjects for grade 5 students at SDN Geluran 1, Taman Subdistrict, Sidoarjo Regency.&#x0D; Keyword : Flip Book Media, PQ4R Strategy, Learning Outcomes, and Online Learning
Privacy-Preserving Analytics, Processing and Data Management

Abstract Typically, data cannot be shared among competing organizations due to confidentiality or regulatory restrictions. We present several technological alternatives to solve the problem: secure multi-party computation (MPC), trusted execution environments (TEE) and multi-key fully homomorphic encryption (MKFHE). We compare these privacy-enhancing technologies from deployment and performance point of view and explain how we selected technology and machine learning methods. We introduce a demonstrator built in the DataBio project for securely combining private and public data for planning of fisheries. The secure machine learning of best catch locations is a web solution utilizing Intel® Software Guard Extensions (Intel® SGX)-based TEE and built with the Sharemind HI (Hardware Isolation) development tools. Knowing where to go fishing is a competitive advantage that a fishery is not interested to share with competitors. Therefore, joint intelligence from public and private sector data while protecting secrets of each contributing organization is an important enabler. Finally, we discuss the wider business impact of secure machine learning in situations where data confidentiality is a concern.