Reviewerly/exHarmony huggingface dataset:
Person Re-identification on Mobile Devices Based on Deep Learning

Person re-identification, as an important supplement to face recognition, it can solve the cross-camera and cross-scene pedestrian recognition and retrieval, determining whether there is a specific pedestrian to be detected in the image or video sequence. At present, many person re-identification researches are mainly carried out through experimental verification and evaluation on large person re-identification datasets such as Market-1501, DukeMTMC-reID, MSMT17, and CUHK03. In this paper, based on the existing deep learning person re-identification research and combining with the actual application scenarios on the premise of analyzing the technical feasibility, we propose a complete process for person re-identification with mobile devices, which aims to combine pedestrian detection with person re-identification to perform real-time pedestrian detection and query. In this process, not only the features extracted by pedestrians can be reused, but also the research on person re-identification can be applied effectively, such as tracking criminals and searching for missing children.
Bitcoin Financial Forecasting

Abstract Bitcoin is one of the crypto currencies and is most unpredictable currencies. In the world of crypto currencies, the value of a coin can unpredictably upgrade or degrade. In this study, the model has been trained to predict the value of Bitcoin in USD at any given time stamp. For this prediction, three algorithms of machine learning - Linear Regression (LR), Support Vector Regression (SVR) and Neural Network Regression (NNR) have been used. The model is trained using the collected dataset. After the collection of data set, we first applied SVR algorithm, then we used LR and then NNR to calculate the error compared to the actual value. Root mean squared error (RMSE) is used as the predictive measure. Out of the three algorithms, LR was found out to be more accurate for predicting the value of bitcoin.
Fully Fuzzy Neural Network For Solving Fuzzy Differential Equations

In this work we have introduced a new method for solving fuzzy differential equations .This method "based on the" fully fuzzy "neural network" to find "the numerical solution of" the "first order fuzzy differential equations" ."The fuzzy trial solution of" the "fuzzy initial value problem is written as a sum of two parts". The first part satisfies the fuzzy condition, it contains no fuzzy adjustable parameters. The second part involves fully fuzzy feed-forward neural networks containing fuzzy adjustable parameters. "Under some conditions the proposed method provides numerical solutions with high accuracy".
Communication as a means of relationship within university libraries

SUVAC, Natalia, PROCA, Aliona. Communication as a means of relationship within university libraries. In: International Trends in Academic Library Development: the project Modernization of Academic Library Services in Moldova. Chisinau: Cartdidact, 2019, pp. 171-178. ISBN 978-9975-3354-5-4.
Uncertainty Weighted Offline Reinforcement Learning

Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration. However, existing Q-learning and actor-critic based off-policy RL algorithms fail when bootstrapping from out-of-distribution (OOD) actions or states. We hypothesize that a key missing ingredient from the existing methods is a proper treatment of uncertainty in the offline setting. We propose Uncertainty Weighted Actor-Critic (UWAC), an algorithm that models the epistemic uncertainty to detect OOD state-action pairs and down-weights their contribution in the training objectives accordingly. Implementation-wise, we adopt a practical and effective dropout-based uncertainty estimation method that introduces very little overhead over existing RL algorithms. Empirically, we observe that UWAC substantially improves model stability during training. In addition, UWAC out-performs existing offline RL methods on a variety of competitive tasks, and achieves significant performance gains over the state-of-the-art baseline on datasets with sparse demonstrations collected from human experts.
Digital technologies in education and school processes during a COVID-19 pandemic: reports analysis

However great the optimism in the context - School Education vs Pandemic COVID-19 - it's necessary to understand the challenges that will be faced in the school educational processes during and after the pandemic, so that communication is careful and subtlety in dealing with school communities, addition to institutional, regulatory and legal reform that will provides for greater investments in infrastructure in education when we talk about Information Technologies To think that although everyone has cell phones, the internet and the power necessary for remote classes are not in the domain of most young people The only way to get students and families engaged in this moment is to be calm, caring for others and understanding how the school will behave We do not have all the answers to solve the great distance between technology and the educational context, but it is already known that we can no longer go back Technologies are allies of great significance The following article brings us personal reports from teachers in different contexts and places as well our reflections about how Education is working remotely and the teacher's feelings and perceptions about it In the last 20 years, although we have available technologies, our focus was still much more in face-to-face classroom lessons and in groups called remote learning, now, in practice, on-line applications and platforms have started to be used in some moments, and teachers and students have been forced to adapt again not only for school education, but also for how they job, to their personal life, for everything
Perancangan Sistem Akuntansi Aset Tetap Pada Zangkar Adventure Equipment

In the rental activity, many obstacles occured for intance losing of documents, accumulating of data, doubling of related fungtions and poor internal control. The main purpose of designing a fixed assets accounting system is to know the existing procedures and overcome the weaknesses and obstacles that often occur. In this study the authors used a descriptive qualitative research type. In designing a flowchart and data flow diagrams that have gone through the analysis process of collecting company data and proposing solutions to solve problems and are expected to provide benefits in improving company performance and fulfilling elements of a good internal control system. The result of the analysis and the proposed improvements regarding the rental system are expected to minimize existing problems and can provide the information needed in making decisions and make it easier to achieve company goals.
Media Pembelajaran IPA Materi Pengelompokan Hewan Berdasarkan Jenis Makanan

The research aimed to obtain and develop product of science learning media in animals grouping based on food type by using Software Construct 2 as of to increase student's understanding and learning outcomes. The method applied in this research is Research and Development (RnD) by using ADDIE Development Method (Analysis, Design, Development, Implementation, and Evaluation). Subject of the research was the 4th graders of SD Negeri Bukuran 1, Kalijambe. Data collecting techniq used observation, questioner and test. The analyze by counting the percentage of questioner results eligibility. The result showed 1) the average percentage of material expert was about 95,95% which is included on a very worthy rate; 2) the average percentage of media expert was about 97,28% on a proper rate; 3) the average of students evaluation result was About 80,67% on an unproper rate; 4) the average of pre test result was about 45,3 while students got 79,3 on the post test. The conclusion conducted that science learning media is worth to be used since it can improve student's learning outcomes.
Experimental Research on PE Teaching Strategies Based on the Teaching Ability of Normal University Students

Strengthening the teaching demonstration based on the teaching strategy experiment can not only enrich the course construction of the theory of physical education, but also improve the scientific and effective implementation of the teaching strategy. In order to promote the ability of teaching design and application of teaching strategies, we should carry out the experiment and practice activities of teaching strategies in pre service normal students, and let every student participate in the process of classroom implementation, classroom observation, classroom display and comprehensive evaluation. It emphasizes teaching design, process analysis and teaching experience, so as to help college students improve their teaching ability and professional quality.
Design of Parallel Algorithm for Kalman Filter on SW26010 Processors

Abstract Kalman filter algorithm, an effective data processing algorithm, has been widely used in space monitoring, wireless communications, tracking systems, the financial industry, and so on. On the Sunway TaihuLight platform, we present an improved Kalman filter parallel algorithm which is according to the new architecture of the SW26010 many-core processors (260 cores) and new programming mode (master and slave heterogeneous collaboration mode). Furthermore, we propose a pipelined parallel mode for the KF algorithm based on a seven-level pipeline of the SW26010 processor. The vector optimization strategy and double buffering mechanisms are provided to improve the parallel efficiency of Kalman filter parallel algorithm on SW26010 processors. The vector optimization strategy can improve data concurrency in parallel computing. In addition, the communication time can be hidden by double buffering mechanisms of SW26010 processors. The experimental results show that the performance and scalability of the parallel Kalman filter algorithm based on SW26010 are greatly improved compared with the CPU algorithm for five different data sets, and is also improved compared to the algorithm on GPU.
Enabling Ternary Hash Tree-Based Integrity Verification for Secure Cloud Data Storage

The primary point of this venture is to give a solid and secure cloud support and furthermore increment dependability of confirmations by continuous auditing. Cloud service certifications (CSC) are a decent way to address a more prominent degree of security. Keeping in view that, cloud administrations are a piece of persistently evolving condition; question unwavering quality of such confirmations may happen in multi-year legitimacy periods. To expand dependability of accreditations, it is must to guarantee consistently solid and secure cloud administrations. CA of cloud administrations is still in its juvenile state. In this manner, we directed an exhaustive workshops, interviews writing survey with professionals to finish engineering for nonstop cloud administration inspecting. However, outsider evaluating techniques are not accessible in existing strategies. In this way, we propose a potential methods for execution that demonstrates different advantages and changes that must be figured out how to diffuse the idea of persistent cloud administration inspecting. Evaluators and suppliers who are connected together in a calculated design that are getting advantage over pertinent inward and outsider reviewing approaches. The security examination of the proposed open inspecting system demonstrates the accomplishment of wanted properties, and execution has been assessed with the itemized try set. The outcomes show that the proposed secure cloud reviewing system which is exceptionally secure and productive away, correspondence and calculation costs.
Towards Anomaly Detection in Dashcam Videos.

Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms focused on lane departures or safe following distances are finding their way into these devices. Unfortunately, the long-tailed distribution of road hazards means that these hand-crafted pipelines are inadequate for driver safety systems. We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos, which hold the promise of bridging this gap. Unfortunately, there exists almost no literature applying anomaly understanding to moving cameras, and correspondingly there is also a lack of relevant datasets. To counter this issue, we present a large and diverse dataset of truck dashcam videos, namely RetroTrucks, that includes normal and anomalous driving scenes. We apply: (i) one-class classification loss and (ii) reconstruction-based loss, for anomaly detection on RetroTrucks as well as on existing static-camera datasets. We introduce formulations for modeling object interactions in this context as priors. Our experiments indicate that our dataset is indeed more challenging than standard anomaly detection datasets, and previous anomaly detection methods do not perform well here out-of-the-box. In addition, we share insights into the behavior of these two important families of anomaly detection approaches on dashcam data.
New Challenges for the Engineering Education System and Ways of Their Solutions

Globalization and the openness of the world in case of emergencies can lead to a collapse in many areas and, as a result, to an imbalance of economic systems. In such conditions, the education system should maintain stability, be sufficiently flexible in terms of teaching methods and technologies, as well as the management of processes. In the authors’ opinion, the most effective tool to manage reactively the learning process as a complex system is a feedback. In order that teachers have a real opportunity to control the quality of this process, it is necessary to identify objective criteria for the assessment of quality in the context of the differentiation of motivational attributes. In addition, this way provides for teaching staff a possibility to evaluate the effectiveness of a particular method to influence on the motivational criteria. As a case study, different student’s groups were selected to compare their involvement in the process with traditional and online forms of learning, their performance and the impact of the learning form on the projects quality prepared by students.
Action Research on Improving the Quality of College English Courses

This paper argues on action research for improving the quality of college English courses. College English course is a compulsory basic course for undergraduates. It mainly caters for the freshman and sophomore students in all types of departments. The main purpose of the course is to improve students' English listening, speaking, reading, writing and translating comprehensions. In the practical teaching experience, the author concluded a unique teaching model, so-called an integrated teaching model, which has a significant effect on the English teaching. As a result, integrated teaching model with action research can eventually contribute to the improvement of English learning.
Navigating Procedurally Generated Overt Self-overlapping Environments in VR.

Previous implementations of self-overlapping architecture tried to hide the characteristics of their non-Euclidean environment from users. To test the outcome of showing these characteristics to users, we propose a virtual reality system with a play area of 3 m \(\times \) 3 m and procedurally generated rooms, which are connected by portals. The aim of the portals is to provide seamless transitions between rooms and render overt self-overlapping architecture for players to experience. Participants were tasked with reporting their experiences and discoveries during the playthrough. Based on this information and recordings of their view, we could determine whether they noticed any transitions. Additionally, the participants were asked specific questions regarding their experience with the overt self-overlapping environment, and how they interpreted the size of the virtual environment in relation to the physical one. The results showed that only 2 of the 20 participants who completed the full playthrough noticed any transitions, while each playthrough consisted of a minimum of 20 transitions. Therefore, we conclude that the transitions were seamless. The system did not induce significant motion sickness in participants. Most participants felt good about navigating the overt self-overlapping environment, and the general consensus was that the experience was strange, yet interesting.
Pedagogical possibilities of ethno-cultural education in the process of forming students ' communication skills

The article discusses issues related to the formation of communicative skills in students in the process of ethnocultural education, as well as the conditions for its implementation. The authors note that ethnocultural education is a process focused on the study of ethnocultural knowledge and its practical application, which has educational value, based on ethnoculture. This provision, in their opinion, is fundamental in ensuring the unity of the national and all-Russian. The practical significance lies in the development and presentation of a program aimed at the formation of communicative skills, the content of which is built taking into account the ethnoculture.
Fast weakly supervised object detection via image pixel gradient

In recent years, the attention of weakly supervised object detection has greatly increased. This is due to the advantage that weakly supervised object detection only needs image-level annotation information during the training process, further reducing the cost of detection. Most of the existing weakly-supervised object detection algorithms use region proposal generation network to generate region proposal to determine the object category and location, and then generate pseudo-labels and input it into the fully-supervised network to predict the object category and location. This leads to a large amount of low-level information being ignored and falling into a local optimum due to the influence of noise. To solve the above problems, this paper proposes a weakly supervised detection model similar to the SSD framework, which uses the image pixel gradient map (IPG) to locate potential objects in the object image, and uses object instance mining for each potential object in order to improve the positioning accuracy of the model. Use the mining result as a pseudo ground-truth to fine-tune in the model to obtain the final detection result. Experiments on the PASACL VOC 2007 and 2012 datasets prove that the accuracy of this model is higher than that of the existing weakly supervised object detection model, and the detection speed of the model without any object proposal is significantly improved.
“This small gift is dedicated to...” Dedicatory inscriptions of Tambov bibliophiles on the civil press books of the 18th century

The tradition of book-making is closely connected with the formation and development of private library collections in Russia in the 18th century, which began its development with representatives of the noble class as the main book-holders who had the opportunity to purchase fairly expensive publications. Subsequently, the transfer of books began to spread among the middle strata, who viewed book-making primarily as a kind of mark for summing up a certain stage in their life – training, service, etc. Often on such gifts, short inscriptions (inscripts) were applied, containing personal information of a different nature. This work is dedicated to the inscriptions on the civil press books of the 18th century from the collections of Tambov second-hand booksellers. We prove that a careful reading of the dedicatory inscriptions allows us to trace the motives of do-nation, the nature of the relationship between second-hand booksellers and the history of their collections. Thanks to the conducted work, we create new and supplement the existing meetings of book owners. We show the connections of individuals with institutions revealed through donation inscriptions. We trace the individual trajectories of individual editions, which can be identified through the presence of the records of several donors who consecutively donated books. Consideration of the scripts allows us to present the book as a translator of the personality of its owner and a reflection of historical events.
STUDENTS’ ATTITUDE TOWARD HERITAGE LANGUAGE

This research investigates the language attitude of university students in South Jakarta toward the heritage language and language maintenance by employing a questionnaire and an interview. The study also tries to find out students’ efforts in maintaining heritage language and difficulties dealing with heritage language maintenance. Based on the research study most of the students have positive attitude that heritage language maintenance is very important. The challenges with heritage language maintenance are caused by many factors such as the environment, parent’s policy, lack of exposure to learn and to practice the heritage language in speaking, listening, and reading. By taking the case study approach, the present study found that students whose parent from mixed culture tend to neglect heritage language. They prefer to use Indonesian language or other foreign languages instead of heritage language
Recurrent Exploration Networks for Recommender Systems

Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems. However, they usually focus solely on item relevance and fail to effectively explore diverse items for users, therefore harming the system performance in the long run. To address this problem, we propose a new type of recurrent neural networks, dubbed recurrent exploration networks (REN), to jointly perform representation learning and effective exploration in the latent space. REN tries to balance relevance and exploration while taking into account the uncertainty in the representations. Our theoretical analysis shows that REN can preserve the rate-optimal sublinear regret (Chu et al., 2011) even when there exists uncertainty in the learned representations. Our empirical study demonstrates that REN can achieve satisfactory long-term rewards on both synthetic and real-world recommendation datasets, outperforming state-of-the-art models.
Investigation of Optimum Tilt Angle for Solar PV Installation in Southern India

In the recent years solar energy source contribution vital among all renewable energy to meet out the energy demand. At the same time lot of factors affect the solar PV system efficiency such as purity of materials, climate condition, solar shading and the orientation, inclination etc. Usually PV panel is installed fixed horizontally. This paper considers the effect of orientation and inclination on the performance of the PV system in different climate regions. The analysis focuses on determining the optimum tilt angle of solar PV installation on residential buildings of the southern state of India, Tamilnadu, for six different climate zones. The investigations involved tilting the PV module at different slope angles of 10°, 15°, 20°, 30° and 40° using Photovoltaic Geographical Information System (PVGIS) simulation tool. This simulation results predicted the best tilt angle for solar PV installation in six different regions of Tamilnadu for extracting maximum energy yields from a PV panel. Analysis is carried out in terms of the In-plane irradiation, Annual energy production, Angle of incidence losses, Performance ratio, Temperature and low irradiation losses.Keywords- Solar PV system,PVGIS,Tilt angle,Performance ratio