Reviewerly/exHarmony huggingface dataset:
Towards a Holistic Worldview: Human-Sizedness of Scientific and Non-Scientific Cognition

The article addresses the issue of the human-sizedness of various forms of cognition: the extent to which the models of reality created by various forms of cognition, «comply with» a human being, help him «incorporate into» the world and identify himself. It is the human-sizedness of various forms of cognition which becomes one of the prerequisites for building a holistic view of the reality, which, as such, could only be opposed to an «inhuman» view of the world when a cognition (mostly scientific) is focused on a subject-object scheme where a man as the subject opposes the world of objects and non-scientific forms of cognition are seen as «inferior». The author believes that human-sizedness is a foundation of all cognitive processes and this philosophical reflection as such lets us look at the process of cognition from the perspective of wholeness. It is the author’s opinion that the wholeness of mismatching and often opposing forms of cognition is ensured by means of the language in which any human experience is shaped.
Winning Ticket in Noisy Image Classification.

Modern deep neural networks (DNNs) become frail when the datasets contain noisy (incorrect) class labels. Many robust techniques have emerged via loss adjustment, robust loss function, and clean sample selection to mitigate this issue using the whole dataset. Here, we empirically observe that the dataset which contains only clean instances in original noisy datasets leads to better optima than the original dataset even with fewer data. Based on these results, we state the winning ticket hypothesis: regardless of robust methods, any DNNs reach the best performance when trained on the dataset possessing only clean samples from the original (winning ticket). We propose two simple yet effective strategies to identify winning tickets by looking at the loss landscape and latent features in DNNs. We conduct numerical experiments by collaborating the two proposed methods purifying data and existing robust methods for CIFAR-10 and CIFAR-100. The results support that our framework consistently and remarkably improves performance.
Automatic Story Generation: Challenges and Attempts

The scope of this survey paper is to explore the challenges in automatic story generation. We hope to contribute in the following ways: 1. Explore how previous research in story generation addressed those challenges. 2. Discuss future research directions and new technologies that may aid more advancements. 3. Shed light on emerging and often overlooked challenges such as creativity and discourse.
Learner-Private Online Convex Optimization.

Online convex optimization is a framework where a learner sequentially queries an external data source in order to arrive at the optimal solution of a convex function. The paradigm has gained significant popularity recently thanks to its scalability in large-scale optimization and machine learning. The repeated interactions, however, expose the learner to privacy risks from eavesdropping adversary that observe the submitted queries. In this paper, we study how to optimally obfuscate the learner's queries in first-order online convex optimization, so that their learned optimal value is provably difficult to estimate for the eavesdropping adversary. We consider two formulations of learner privacy: a Bayesian formulation in which the convex function is drawn randomly, and a minimax formulation in which the function is fixed and the adversary's probability of error is measured with respect to a minimax criterion. We show that, if the learner wants to ensure the probability of accurate prediction by the adversary be kept below $1/L$, then the overhead in query complexity is additive in $L$ in the minimax formulation, but multiplicative in $L$ in the Bayesian formulation. Compared to existing learner-private sequential learning models with binary feedback, our results apply to the significantly richer family of general convex functions with full-gradient feedback. Our proofs are largely enabled by tools from the theory of Dirichlet processes, as well as more sophisticated lines of analysis aimed at measuring the amount of information leakage under a full-gradient oracle.
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks becomes a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a uniform code library for ODAI and build a website for testing and evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images.
Research on Displacement Monitoring Method of Heavy Load Bridge Structure Based on Stereo Vision

The traditional sensor monitoring method has many problems in actual bridge health monitoring, such as low measurement accuracy and low sampling frequency. Based on this, a method for monitoring the displacement of a heavy-duty bridge structure based on stereo vision is proposed. The various physical parameters of the bridge structure are continuously acquired by installing different types of sensors, and the data-driven structure state assessment method is used to diagnose the performance and safety status of the bridge. Combined with stereo vision technology and traditional civil bridge structure monitoring methods, a set of bridge health monitoring and state evaluation system based on stereo vision was constructed to complete the structural displacement monitoring. Vibration monitoring and dynamic characteristics identification of the simply supported beams of the building simulated bridge deck and the vertical rods of the simulated bridge tower were compared with the traditional acceleration sensor method to verify that the method proposed in this paper has the advantages of structural vibration monitoring and dynamic characteristics identification. Feasibility and accuracy.
LiftPool: Bidirectional ConvNet Pooling

Pooling is a critical operation in convolutional neural networks for increasing receptive fields and improving robustness to input variations. Most existing pooling operations downsample the feature maps, which is a lossy process. Moreover, they are not invertible: upsampling a downscaled feature map can not recover the lost information in the downsampling. By adopting the philosophy of the classical Lifting Scheme from signal processing, we propose LiftPool for bidirectional pooling layers, including LiftDownPool and LiftUpPool. LiftDownPool decomposes a feature map into various downsized sub-bands, each of which contains information with different frequencies. As the pooling function in LiftDownPool is perfectly invertible, by performing LiftDownPool backward, a corresponding up-pooling layer LiftUpPool is able to generate a refined upsampled feature map using the detail sub-bands, which is useful for image-to-image translation challenges. Experiments show the proposed methods achieve better results on image classification and semantic segmentation, using various backbones. Moreover, LiftDownPool offers better robustness to input corruptions and perturbations.
DFAC Framework: Factorizing the Value Function via Quantile Mixture for  Multi-Agent Distributional Q-Learning

In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of the other agents. To address the above issues, we integrate distributional RL and value function factorization methods by proposing a Distributional Value Function Factorization (DFAC) framework to generalize expected value function factorization methods to their DFAC variants. DFAC extends the individual utility functions from deterministic variables to random variables, and models the quantile function of the total return as a quantile mixture. To validate DFAC, we demonstrate DFAC's ability to factorize a simple two-step matrix game with stochastic rewards and perform experiments on all Super Hard tasks of StarCraft Multi-Agent Challenge, showing that DFAC is able to outperform expected value function factorization baselines.
Uncertainty in internet of things: a review

Uncertainty is a situation that has incomplete or ghost data [3,4] which may lead to severe system analysis.The uncertainty is mainly generated by the heterogeneity of devices, networks and data collected [5].Most of the data in IoT are from the sensors, which means that there is a chance of losing the data and eventually cause uncertainty in the IoT environment [6,7].Previous works [8][9][10][11][12][13][14] show that uncertainty issues can exist in different domains with different levels of uncertainty.Based on the research, there are many new and improvised approaches in handling this issue to boost the reliability of the IoT and its environment.Some approaches even work on building new frameworks to handle uncertainty.Hence, this paper is to review efficient approaches to handle uncertainty in an IoT environment based on different characteristics and parameters of multiple domains.The performance evaluation for each approach is also taken into consideration.The criteria of each paper reviewed are discussed in Appendix A.
TO THE PROBLEM OF PSYCHOLOGICAL TRAINING OF FUTURE TEACHERS

The article is devoted to the analysis of scientific publications on the problem of psychological training of future teachers, which have been published in the international database of Web of Science, for the last twenty years. It was found that until 2008 there was an underestimated level of attention of scientists to the issues of scientific research on the problem of psychological training of future teachers. The vast majority of publications are articles which explore the formation of the necessary professional competencies with a psychological component in future teachers; innovations in forms and methods of mastering psychological knowledge are covered; data on the content of educational psychology as a scientific discipline, etc. are given. However, the analyzed scientific works are limited in their nature and do not take into account the very relevant areas of research on this topic.
Agile Fitness of Software Companies in Bangladesh: An Empirical Investigation

With the mandate of light-weight working practices, iterative development, customer collaboration and incremental delivery of business values, Agile software development methods have become the de-facto standard for commercial software development, worldwide.Consequently, this research aims to empirically investigate the preparedness and the adoption of agile practices in the prominent software companies in Bangladesh.To achieve this goal, an extensive survey with 16 established software companies in Bangladesh is carried out.Results exhibit that the Scrum agile methodology is the highest practiced one.Alongside, to a great extent these software companies have the readiness to effectively adopt the Scrum methodology.However, with regard to practicing the Scrum principles, they fall short in many key aspects.
FACTORS AFFECTING THE EFFECTIVENESS OF INNOVATIVE TOOLS FOR FOOD SAFETY MANAGEMENT: A CASE STUDY OF HOTEL INDUSTRIES IN THAILAND

There are many studies of knowledge-intensive business service (KIBS) on communication technology and specialties. However, there is no study on factors affecting the efficiency of food safety management KIBS in the hotel business. The objective of this study is to consider characters that influence the capability of food safety knowledge-intensive business service in hotel business by using qualitative mixed-method from the in-depth interview of 30 hotel's stakeholders and quantitative research of 377 samples from target hotels. Data were statistically analyzed by SPSS ver.21. The result shows that factors that positively and significantly influence (alpha = 0.05) food safety management KIBS are food safety knowledge in hotels, hotel resources, hotel size, and location and development of food safety KIBS platform. This information can be used for further study of the development of knowledge-intensive business service for hotel staff.
What are the factors that influence the use of social networks as a means to serve pedagogy?

Social networks are a key element in young people's and teenagers' leisure time. But their influence goes beyond the field of entertainment further reaching educational and training environments. These tools can support and improve student learning, but it is clear that they can also be an inconvenience or a potential danger for the youth. The purpose of this research is to describe the profiles of teachers who use social networks as resources for their teaching, both inside and outside the classroom. It also seeks to know the characteristics that influence their incorporation as learning elements. For this reason, a secondary exploitation of the PISA 2018 database is carried out. It contains the data of 21,621 teachers of the secondary education level from the 19 regions of Spain. Multilevel analysis is used as a method of data analysis. The results point out differences in use according to the Autonomous Communities, showing the importance of the age of the teacher and the number of years of experience, both for their use in the classroom and to cover their training needs. Quality teaching requires a commitment to technological resources; therefore, decisive to know these elements in order to design teachers and educational institutions.
Cross-Gradient Aggregation for Decentralized Learning from Non-IID data

Decentralized learning enables a group of collaborative agents to learn models using a distributed dataset without the need for a central parameter server. Recently, decentralized learning algorithms have demonstrated state-of-the-art results on benchmark data sets, comparable with centralized algorithms. However, the key assumption to achieve competitive performance is that the data is independently and identically distributed (IID) among the agents which, in real-life applications, is often not applicable. Inspired by ideas from continual learning, we propose Cross-Gradient Aggregation (CGA), a novel decentralized learning algorithm where (i) each agent aggregates cross-gradient information, i.e., derivatives of its model with respect to its neighbors' datasets, and (ii) updates its model using a projected gradient based on quadratic programming (QP). We theoretically analyze the convergence characteristics of CGA and demonstrate its efficiency on non-IID data distributions sampled from the MNIST and CIFAR-10 datasets. Our empirical comparisons show superior learning performance of CGA over existing state-of-the-art decentralized learning algorithms, as well as maintaining the improved performance under information compression to reduce peer-to-peer communication overhead. The code is available here on GitHub.
Pelatihan Penggunaan Sistem Inventory Data Barang Pada Gudang CV Tanaya

CV. Tanaya is one of the companies engaged in the field of suppliers, this CV buys raw materials in several Agents. The availability of goods is very influential in production planning, therefore inventory data is needed. Goods inventory data processing in the CV. Tanaya is still manual, namely for recording data using a stock card. This raises the motivation to design and conduct training in the use of information systems about inventory data items, especially in the warehouse CV. Tanaya The method used consists of two stages, the first is observation and interviews with the CV. The second stage is to carry out a community education approach, in this case training the use of the system. This aims to make it easier for companies to manage data so that it is faster, accuracy in data processing and improve accuracy in managing the entry and exit of goods stock in the warehouse of CV. Tanaya
THE CONE OF EXPERIENCE IN TEACHING MATHEMATICS SYNCHRONOUSLY AND ASYNCHRONOUSLY

Teaching online is a new challenge for every single teacher. Mathematics in particular remains the school subject that requires special teaching tools. This article describes Edgar Dale’s «Cone of experience» and Bruner’s learning approaches for synchronous and asynchronous teaching in Mathematics. It also describes the most important tools that can be used for online teaching in a combination of both formats, asynchronous and synchronous. These teaching methods are described not only in terms of digital tools, but also in terms of Jerome Bruner’s theories on information processing.
Simulation on RFID Interactive Tabletop of Working Conditions in Industry 4.0

In a changing world, in permanent international competition, Industry 4.0 must be able to adapt, to transform itself, especially from an organizational point of view. In case of reorganization or major changes, having the means for the design, training and appropriation of future working conditions becomes an asset. For this purpose, a simulator on interactive tabletop with RFID sensors is proposed. This tabletop is associated with tangible objects equipped with RFID tags. This simulator allows Industry 4.0 stakeholders to project themselves into future work situations. These situations can be simulated collectively, around the interactive tabletop, in relation to scenarios that can be replayed. Principles of implementation in Industry 4.0 are described. Different research perspectives are also highlighted.
Optimized Extraction of Records from the Web Using Signal Processing and Machine Learning

In this paper, we present an optimization of our previous record extraction approach from web pages. The proposed optimization improves the upper bound from O(nlogn) to O(n) while maintaining the same qualitative results as before (i.e., no loss in efficacy). We have achieved the following results: a 47% improvement in runtime efficiency when compared to our previous work and 95% f-score (same as our previous work).
Is E-voting Systems based on Blockchain Technology Efficient in Nigeria General Elections?

One of the most common problems of election in Nigeria is inefficient data management.All subsequent elections were blighted by inefficient data management that resulted in violence in the country and distrust among political parties.These flaws prompted the government at different times to modify the nation's electoral systems ranging from party systems to electoral management body reformation and electronics verification technologies.In this paper, we investigated the opinions of 71 Nigeria citizens about the Nigeria General Elections (NGEs) processes and data management in these processes.We found that the majority of the participants rated the existing voting system in Nigeria to be of low effectiveness and reliability.The majority of the participants believe that an e-voting system based on Blockchain technology has the capability to prevent alterations in the voting processes.
PENERAPAN METODE CLUSTERING DENGAN ALGORITMA K-MEANS PADA PENGELOMPOKAN DATA PENGHASILAN ORANG TUA SISWA

In Indonesia there are still many students who cannot continue their education because of the high cost of education. One of the reasons for students not continuing their education is the social and economic factors that affect their parents. Therefore, the school does not want students to drop out of school because the parents are unable to pay the full tuition fee each semester of the increase to the right students and are not wrongly targeted so that students can complete their education without being burdened. unpaid tuition fees. In determining usually there are problems because of the large number of student data which causes problems in grouping student data, it is likely that errors will occur when determining students who really have to delay payment.&#x0D; For this research, a system was built in order to be able to apply the K-Means method, in assisting schools in determining the classification of student data that requires a delay in payment every time the semester increases with the right target. So that this process can help schools in shortening the process of processing a lot of student data. While the criteria used in the system are 5, namely father's job, father's income, mother's job, mother's income and number of children. The system is designed using CodeIgniter, PHP, JavaScript and MYSQL as the database.&#x0D; In the process of application testing results that have used the K-Means method for the process of grouping student data that requires late payment, it can produce an accuracy rate of 85%. For this testing process, it is carried out by comparing old student data with the results of the K-Means calculation, so that this system is expected to help the school in classifying student data accurately.
Virtual Link Aggregation Network Performance Using MikroTik Bonding

Quality of Service in a network is a big thing that must be resolved and dealt with as best as possible. The limitation of the maximum transfer rate in network devices creates an obstacle in the process of transferring data packets. To maximize the transfer rate in network devices, you can use Virtual Link Aggregation which can offer bandwidth optimization and failover in the network. Link aggregation is a solution in combining several physical links into one logical link. The method used in this research is to consider the allocation of bandwidth, load balancing and failover in the link aggregation. From the results of the link aggregation test using two (2) interface bonding, the results of the bandwidth averages when there is a UPD data packet transfer to 0 bps / 184.9 Mbps, which was previously around 0 bps / 91.6 Mbps. While the result of the bandwidth averages when the TCP data packet transfer occurs is 0 bps / 105.5 Mbps, which was previously around 0 bps / 93.8 Mbps. Link Aggregation using a Mikrotik Router is a solution to produce a larger Throughput Bandwidth by combining two (2) Ethernet Physical Links into one logical link.
Reinforcement Learning with External Knowledge by using Logical Neural Networks

Conventional deep reinforcement learning methods are sample-inefficient and usually require a large number of training trials before convergence. Since such methods operate on an unconstrained action set, they can lead to useless actions. A recent neuro-symbolic framework called the Logical Neural Networks (LNNs) can simultaneously provide key-properties of both neural networks and symbolic logic. The LNNs functions as an end-to-end differentiable network that minimizes a novel contradiction loss to learn interpretable rules. In this paper, we utilize LNNs to define an inference graph using basic logical operations, such as AND and NOT, for faster convergence in reinforcement learning. Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide. Our results empirically demonstrate that our method converges faster compared to a model-free reinforcement learning method that doesn't have such logical constraints.