Reviewerly/exHarmony huggingface dataset:
Experiment of Using Structured Techniques in Intelligence Analysis

Research into the use of structured analytical techniques has shown that analysts use specific structured techniques to achieve better analysis results than using traditional intuitive approaches. The experiment was based on solving the analytical tasks of two intelligence scenarios in a set of 32 people. To verify the null hypothesis, a chi-square test of independence was performed at a 5% significance level. In the control/intuitive and experimental/structured technique groups, responses were evaluated in four criteria, in an overall qualitative intelligence analysis, in rank corps, depending on the time of service and education. The number of correct answers was the highest in the experimental group when dealing with the intelligence analysis criteria for both scenarios. Improved analysis of the experimental group in the second scenario solution was statistically significant. The null hypothesis, using structured methodology improves qualitative intelligence analysis, is not rejected. In the experiment, other test criteria are below than the critical value. The results of the research have shown that the success of the problem solution is influenced by the correct use of the Hypothesis Testing technique. The use of structured techniques depends on the level of knowledge and the type of intelligence problem. At the same time, the conduct of research has shown the influence of analysts' cognitive biases.
Improving Face Recognition for Identity Verification by Managing Facial Directions and Eye Contact of Event Attendees

This paper proposes an identity-verification system for attendees of large-scale events using continuous face recognition improved by managing facial directions and eye contact (eyes are open or closed) of the attendees. Identity-verification systems have been required to prevent illegal resale such as ticket scalping. The problem in verifying ticket holders is how to simultaneously verify identities efficiently and prevent individuals from impersonating others at a large-scale event at which tens of thousands of people participate. We previously developed two ticket ID systems for identifying the purchaser and holder of a ticket. These systems use two face-recognition systems, i.e., one-stop face-recognition system with a single camera and non-stop face-recognition system with two cameras. The average face-recognition accuracy was respectively 90 and 91%, and the average time for identity verification from check-in to entry admission was respectively 7 and 2.8 seconds per person. One-stop systems have lower equipment cost than non-stop systems because they require fewer cameras for face recognition. Since both systems were proven effective for preventing illegal resale by verifying attendees of large concerts, they have been used at more than 110 concerts. The problem with both systems is regarding face-recognition accuracy. This can be mitigated by securing clear facial photos because face recognition fails when unclear facial photos are obtained, i.e., when event attendees have their eyes closed, are not looking directly forward, or have their faces covered with hair or items such as facemasks and mufflers. In this paper, we propose a system for securing facial photos of attendees directly facing a camera by leading them to scan their check-in codes on a code-reader placed close to the camera just before executing face recognition. The system also takes two photos of attendees with the single camera after an interval of about 0.5 seconds to obtain facial photos with their eyes open. The system achieved 93% face-recognition accuracy with an average time of 2.7 seconds per person for identity verification when they were used for verifying 8, 461 attendees of a concert of a popular music singer. The system made it possible to complete identity verification with higher accuracy than previous systems and with shorter average time than the non-stop system using a single camera, i.e., with low equipment cost. Survey results obtained from the attendees showed that 96.4% felt it provided more equity in ticket purchasing than methods without face recognition, 87.1% felt it provided added convenience in verification, and 95.4% felt it would effectively prevent illegal resale.
Novel cell clots (tissue) capture technique using tissue paper (TxT technique) for the new era of cytopathology

目的 : セルブロック (CB) 法は確定診断のみならず, その後の治療方針決定にきわめて有用である. 今回, われわれは従来法とは異なり, ティッシュペーパーを用いた CB 作製法 (TxT 法) を考案したので報告する.
Musical Preferences of the Modern Youth

The article deals with the issues of musical tastes and preferences of the modern student body of Nizhny Novgorod. The authors provide results of a sociological study “The Role of Music in the Youth’s Life” conducted on the basis of the Modern Family Research Laboratory of Minin Nizhni Novgorod State Pedagogical University in 2019 (N-300). By employing the quantitative sociological method combined with further data processing by the SPSS-21 computer programme, the researchers were able to identify the youth’s perception of modern music, determine their tastes and preferences, specify role and significance of the main agents influencing the process of musical socialisation.
The effect of Target Normalization and Momentum on Dying ReLU

Optimizing parameters with momentum, normalizing data values, and using rectified linear units (ReLUs) are popular choices in neural network (NN) regression. Although ReLUs are popular, they can collapse to a constant function and "die", effectively removing their contribution from the model. While some mitigations are known, the underlying reasons of ReLUs dying during optimization are currently poorly understood. In this paper, we consider the effects of target normalization and momentum on dying ReLUs. We find empirically that unit variance targets are well motivated and that ReLUs die more easily, when target variance approaches zero. To further investigate this matter, we analyze a discrete-time linear autonomous system, and show theoretically how this relates to a model with a single ReLU and how common properties can result in dying ReLU. We also analyze the gradients of a single-ReLU model to identify saddle points and regions corresponding to dying ReLU and how parameters evolve into these regions when momentum is used. Finally, we show empirically that this problem persist, and is aggravated, for deeper models including residual networks.
A LIGHTWEIGHT AUTHENTICATION TECHNIQUE FOR SECURE COMMUNICATION OF EDGE/FOG DATA-CENTERS

Edge computing has significantly enhanced the capabilities of cloud computing. Edge data-centres are used for storing data of the end-user devices. Secure communication between the legitimate edge data-centres during the load balancing process has attracted industrial and academic researchers. Recently, Puthal et al. have proposed a technique for authenticating edge datacenters to enable secure load balancing. However, the resource-constraint nature of the edge data-centres is ignored. The scheme is characterized by complex computation and memory intensive cryptographic protocol. It is also vulnerable to key escrow attack because the secret key used for encrypting and decrypting of the communicated messages is been created by the trusted cloud datacenter. Additionally, the key sharing phase of their algorithm is complex. Therefore, to address the highlighted challenges, this paper proposed a lightweight key escrow-less authentication algorithm that will ensure secure communication of resource-constrained edge data-centres during the load balancing process. The security capability of the proposed scheme has been formally evaluated using the automatic cryptographic analytical tool ProVerif. The relatively low computation and communication costs of the proposed scheme compared to the benchmark schemes proved that it is lightweight, thus suitable for resource-constrained edge datacenters.
Enabling Deletion in Append-Only Blockchains (Short Summary / Work in Progress)

Conventional blockchain implementations with append-only semantics do not support deleting or overwriting data in confirmed blocks. However, many industry-relevant use cases require the ability to delete data, especially when personally identifiable information is stored or when data growth has to be constrained. Existing attempts to reconcile these contradictions compromise on core qualities of the blockchain paradigm, as they include backdoor-like approaches such as central authorities with elevated rights or usage of specialized chameleon hash algorithms in chaining of the blocks. In this technical report, we outline a novel architecture for the blockchain ledger and consensus, which uses a tree of context chains with simultaneous validity. A context chain captures the transactions of a closed group of entities and persons, thus structuring blocks in a precisely defined way. The resulting context isolation enables consensus-steered deletion of an entire context without side effects to other contextes. This architecture opens the possibility of truncation, data rollover and separation of concerns, and can help to fulfill the GDPR regulations.
Augmented Reality Learning Media Based on Android for Sciences in Elementary Schools

This research aims to produce interactive learning media based on Augmented Reality (AR) that is appropriate for science subjects on material characteristics of the eight planets in the solar system.This research is a type of research and development (R&D) using the ADDIE model, which includes the stages of Analysis, Design, Development, Implementation, and Evaluation.Respondents in this study were elementary students of class VI (six) in Pondok Rumput Elementary Bogor City.The data collection technique is distributing questionnaires to experts and students.Data analysis was performed with simple statistics and qualitative descriptive.The results showed that the Android-based AR learning media was appropriate to be used for science learning in elementary schools.
Character Development for Class IX Students through Critical Reading Activities at MTs Nurul Hikmah and Driewanti Middle School Bekasi, West Java

Indonesian Language Learning does not merely achieve Indonesian language skills, but leads to an increase in the ability to think critically and creatively. In this regard, one way to improve the ability to think critically and creatively through reading activities. Therefore, one type of reading skills that can improve critical thinking skills is critical reading skills. The aim of community service is to foster student character through critical reading activities. This community service activity will be held in two schools in the city of Bekasi. Participants in this activity were students of class IX at MTs Nurul Hikmah and Driewanti Middle School Bekasi. In this school there are two classes of class IX class, with 42 students.
Analysis of Information Risks and Strategies for Protecting Schoolchildren from the Negative Consequences of Digitalization of Education

The article provides a brief overview of the stages of the Russian education digitalization, identifies the main problems associated with this trend, including the relevance of the risks of the digital environment in the field of information security of participants in the educational process.The list of actual risks associated with the violation of confidentiality, integrity, accessibility, authenticity, and non-repudiation of the digital environment users, as well as basic recommendations for their possible minimization are given.The relevance of these risks is confirmed by a survey of teachers conducting their activities in secondary and higher education institutions.
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks

To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN). Different from Batch Normalization (BN), which normalizes the final summation of the weighted inputs, FBN normalizes the intermediate state of the summation. We propose a novel light-weight network based on FBN, called Finet. At training time, the convolutional layer with FBN can be seen as an inverted bottleneck mechanism. FBN can be fused into convolution at inference time. After fusion, Finet uses the standard convolution with equal channel width, thus makes the inference more efficient. On ImageNet classification dataset, Finet achieves the state-of-art performance (65.706% accuracy with 43M FLOPs, and 73.786% accuracy with 303M FLOPs), Moreover, experiments show that Finet is more efficient than other state-of-art light-weight networks.
A checklist to diagnose teamwork in engineering education

Teamwork is increasingly being acknowledged as a necessary part of the engineering workplace, therefore engineering educatorsmay feel a responsibility for teaching teamwork skills to students. Engineering educators cannot improve their students’ teamworkskills without first being able to practically diagnose the students’ strengths and weaknesses. The present paper focuses ontranslating team science to a useful checklist for engineering educators to monitor their students’ teamwork skills. A qualitativedata-sorting analysis of 286 behaviors from 88 interviews resulted in the present checklist, which is broken into six components ofteamwork processes and emergent states. The checklist details effective and ineffective team-wide or team member behaviors insuch teamwork categories as communication, cognition, coordination, coaching, cooperation, and conflict. While not formallyvalidated, the checklist is empirically derived and in-line with the literature on team performance. This tool will allow educators touncover what teamwork components require further skill development in their students’ project-based learning courses.
Fuzzy cognitive mapping of Baltic Archipelago Sea food webs reveals no cliqued views of the system structure between stakeholder groups

Incorporating stakeholder views is a key element in successful environmental management, particularly if the managed system delivers cultural and provisioning ecosystem services directly to the stakeholders, or if there are conflicting views about the ecosystem functioning or its optimal management. One such system is the Archipelago Sea in the Southwestern coast of Finland. It is an area with high biodiversity, offering a range of ecosystem services, from regulating services to provisioning and cultural services. Furthermore, it is subjected to a variety of human activities ranging from eutrophication and marine transport to fishing. The management of the area is also a topic of debate, including discussions of minimum landing size of fish, seal hunting quotas, and the role of cormorants in the ecosystem. Fuzzy cognitive mapping offers a method to evaluate and quantitatively compare different actors’ views on ecosystem structure. The models can be compared quantitatively and simulated to illustrate how they respond to various pressure scenarios. This may reveal differences in the perceptions about what are the important interactions in the ecosystem, and how the system would respond to management measures, potentially explaining differing opinions about the best management strategy. In this work, 30 stakeholders, including policy makers, scientists, eNGOs, fisheries, and recreational users created fuzzy cognitive maps (FCMs) of the Archipelago Sea food web. We found that despite the debate about the management of the area, the stakeholders’ views about the food web structure were not clustered based on the stakeholder group, i.e. the different stakeholder groups did not have distinct ideas about the ecosystem structure. The FCM complexity did not show a pattern based on the stakeholder group either. While the general pattern of the FCMs indicated a shared view of the food web structure across most respondents, there was one map from the recreational group that stood out. The exact setup of the models varied. Across all maps, cod, perch, fishing, zooplankton, and herring were the variables having most links with the other variables. The simulated ecosystem responses indicated that fishing was seen as a key factor affecting food web components, while increases of salinity and oxygen levels have a positive impact on multiple ecosystem components. The value of the approach is to enable a two-way discussion about the food webs and how management of pressures may impact the components.
Estimation of Accuracy of Determination of Parameters of the Buried Archaeological Objects According to the Data of Different-Altitude Magnetic Survey on the Basis of Numerical Modeling

An algorithm for solving the inverse problem for determining the parameters of a buried archaeological object in the form of an elliptical cylinder (as a model of a typical burial or pit) is proposed. The estimation of accuracy of determination of parameters of the buried archaeological objects on the basis of the numerical solution of the inverse problem of magnetic prospecting for synthetic model is carried out. Its implementation included, firstly, the stage of modeling the magnetic field from a synthetic model of the archaeological site at three different heights for different parameters of the grid density and the size of the magnetic survey site and, secondly, the stage of solving the inverse problem from synthetic data at different heights, for different parameters of the magnetic survey and assessing the accuracy of the solution of the inverse problem. It is found that the accuracy of determining the parameters of the anomalous body is more affected by the size of the survey area than the density of the measurement network. There is also a tendency to increase the accuracy of determining parameters when using data of different heights. Approbation of the algorithm for solving the inverse problem on real data of different-altitude magnetic survey over the burial at the archaeological site Tartas-1 showed quite good results. The average error in determining the geometric parameters of the body and its occurrence is ~ 15 %.
Design of Deep Learning VLIW Processor for Image Recognition

In order to adapt the application demands of high resolution images recognition and efficient processing of localization in aviation and aerospace fields, and to solve the problem of insufficient parallelism in existing researches, an extensible multiprocessor cluster deep learning processor architecture based on VLIW is designed by optimizing the computation of each layer of deep convolutional neural network model. Parallel processing of feature maps and neurons, instruction level parallelism based on very long instruction word (VLIW), data level parallelism of multiprocessor clusters and pipeline technologies are adopted in the design. The test results based on FPGA prototype system show that the processor can effectively complete the image classification and object detection applications. The peak performance of processor is up to 128 GOP/s when it operates at 200 MHz. For selecting benchmarks, the processor speed is about 12X faster than CPU and 7X faster than GPU at least. Comparing with the results of the software framework, the average error of the test accuracy of the processor is less than 1%.
Enhancing Communication Using 8 × 8 Extended Playfair Cipher and Steganography

&lt;div&gt;In this world of communication, the security of the&lt;/div&gt;&lt;div&gt;messages to be transferred plays a very important role. Strategies like Encryption/Decryption, Digital Signatures, Steganography etc., have been developed to ensure the security, privacy and integrity of these messages. Playfair cipher is one of the well known polyalphabetic ciphers used for the encryption and decryption. However in account of few drawbacks inherent in existing 5*5 playfair cipher, recently improved version of Playfair cipher i.e., Extended 8*8 Playfair cipher has been developed. In this work, extended playfair cipher with Least Significant bit steganography are applied to hide the presence of any such messages. The main objective of this work is to build a secure mechanism of sending and receiving messages. The results show that combination of both extended 8 * 8 playfair cipher and stegnography increases the security of messages in contrast to the existing traditional Playfair ciphers. &lt;/div&gt;
The Cultural, Economic and Technical Milieu of Social Media Misconduct Dismissals in Australia and South Africa

The intersection between social media activity and employment is an emerging global issue. This article examines the cultural, economic and technical milieu that has generated contested social media misconduct dismissals in Australia and South Africa. Through an analysis of 42 Australian and 97 South African decisions, it is argued that the ubiquitous, enduring and open nature of social media affects employment quite differently depending on country specific factors. In Australia, the absence of entrenched political rights has meant that employee social media use is not subject to reasonable expectations of privacy. However, there is also tolerance for a certain level of larrikin behaviour. In South Africa, the existence of enshrined rights manifests differently in the context of social media dismissal. Within a culturally diverse population with deeply fractured race relations, the decisions reveal a White minority still perpetuating dominance over a historically disadvantaged Black workforce.
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes

We consider a scenario in which an autonomous vehicle equipped with a downward facing camera operates in a 3D environment and is tasked with searching for an unknown number of stationary targets on the 2D floor of the environment. The key challenge is to minimize the search time while ensuring a high detection accuracy. We model the sensing field using a multi-fidelity Gaussian process that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i) addresses the coverage-accuracy trade-off: sampling at locations farther from the floor provides wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection. We rigorously analyze the algorithm and establish formal guarantees on the target detection accuracy and the expected detection time. We illustrate the algorithm using a simulated multi-target search scenario.
The Effects of High Intensity Interval Exercise and Moderate Intensity Continuous Exercise on Emotional Response, Dopamine and Lactate Concentration in Obesity

The purpose of this study was to investigate the difference of emotional response, dopamine and lactate concentrations in moderate-intensity continuous exercise(MICE) and high-intensity interval exercise(HIIE)) according to with or without obesity. The subjects of this study were 30 adult males in their 20s and were divided into obesity group (OG, n=15) and non-obesity group (NOG, n=15). Treadmill exercise was performed with MICE (warm up for 5 minutes at 50% VT, exercise for time to consume 150㎉ at 90% VT, cool down for 5 minutes a t 50% VT) and HIIE ( repeated 5 t imes for 2 minutes at 115% VT, active recovery was repeated 4 times for 2 minutes at 85% VT). The results of the statistical analysis are as follows. MICE showed no difference in feeling scale between OG and NOF, and in HIIE, the feeling scale of OG was significantly negative compared to the NOG. In addition, the OG was more negative than the NOG in the PACES after exercise. Dopamine expression showed that both groups increased significantly after 10 minutes of exercise compared to before exercise in MICE and HIIE. At the concentration of lactate, the OG was significantly higher at the end of exercise than the NOG at HIIE.