Reviewerly/exHarmony huggingface dataset:
Image Processing: Facilitating Retinanet for Detecting Small Objects

Abstract Detecting small objects is a challenging task in object detection due to low spatial resolution and interference by background. Specifically, one-stage detectors struggle with small objects for they generate worse candidate bounding boxes. In this paper, several modifications are made to the original Retinanet to tackle the problem. Dilated convolutional layers are added to the backbone to get fined-grained features along with semantic information. The gradient of loss function is increased near the origin to enhance the quality of candidate boxes for small objects. A novel feature fusion method is also proposed to directly guide low-level features with semantic information. Significant improvement of 5.1 mAP can be seen when evaluating on MOCOD small object dataset, which contains a large amount of small objects. Our method can be easily migrated to other backbone networks with feature pyramids for detecting small objects.
Learning to Represent Action Values as a Hypergraph on the Action Vertices

Action values are ubiquitous in reinforcement learning (RL) methods, with the sample complexity of such methods relying heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens of representation learning, good representations of both state and action can facilitate action-value estimation. While advances in deep learning have seamlessly driven progress in learning state representations, given the specificity of the notion of agency to RL, little attention has been paid to learning action representations. We conjecture that leveraging the combinatorial structure of multidimensional action spaces is a key ingredient for learning good representations of action. To test this, we set forth the action hypergraph networks framework---a class of functions for learning action representations in multidimensional discrete action spaces with a structural inductive bias. Using this framework we realise an agent class based on a combination with deep Q-networks, which we dub hypergraph Q-networks. We show the effectiveness of our approach on a myriad of domains: illustrative prediction problems under minimal confounding effects, Atari 2600 games, and discretised physical control benchmarks.
Enhanced Digital Library with Book Recommendations Based on Collaborative Filtering

This research aims to develop a digital library system by implementing a recommendation system becomes a feature of the digital library system. The main problem with the digital library system is the large number of books that users will have difficulties finding interesting books. Therefore, Machine Learning is applied using the User-based Collaborative Filtering method to provide book recommendations for users. The book recommendation system being developed not only uses book value as input but also user behavior and book borrowing data. The resulting recommendations are divided into three parts according to the input which are book value, user behavior, and book borrowing data. The system developed is web-based using the PHP programming language, with modifications to the available open-source digital library system. Experiments are carried out by testing the system to university students and satisfaction questionnaire to the students. The total result of the average precision-recall calculation has an accuracy of 79% from 35 data users which includes an assessment of 1000 types of books with different categories. It means that books recommended by the system are relevant to users. The developed models and applications are potentially used as smart library applications.
A Survey on Blockchain Based Access Control for IoT

IoT and Iot data is exponentially increasing with respect to time. Security and maintenance are the most required features while handling this. Because of the jumbled decentralization and heterogeneous condition present in IoT, the security and assurance of the data becomes a troublesome action for the planners. To regulate access to essential resources access control structures are used. Block chain has proved to be, audit-able enlisting is possible using a decentralized arrangement of many blocks joined by an open Record. Here we are trying to explore the possibility of collaborating block chain into the access control issues in IoT to enhance security.
Application of Artificial Intelligence Technology to Numerical Weather Prediction

Numerical weather prediction technology plays an increasingly important role in improving accuracy and service level of modern weather forecast. With the development of observation system and higher resolution and complexity of the numerical weather prediction model, the products of numerical weather forecast have been greatly improved in quantity and quality, and can offer rich information at high spatial-temporal frequency. However, such a large amount of prediction data are not fully explored. Artificial intelligence has achieved great success in many fields, such as pattern recognition and natural language processing, which provides an opportunity for further improving numerical weather prediction. It's also employed in initialization, numerical model and production of weather forecast service, involving observation system, data assimilation, model integration, ensemble forecast and high-performance computing methods. Both the accuracy of forecast results and computational efficiency have been improved by using error correction, parameter estimation, local surrogate model and so on. In addition, some end-to-end neural network models also show the potential of pure data-driven weather forecast. These models use spatial-temporal observation data as input and directly output the prediction results in terms of deterministic results or probabilities. Some of them perform well in short-term severe convective weather, precipitation, and long-term climate forecast. Existing works employ various artificial intelligence technology methods, mainly including large-scale calculation of neural network, feature analysis, interpretability, and customized loss function. However, there are still some challenges, the potential of artificial intelligence needs to be further explored. Some issues should be carefully considered, including weak interpretability, uncertainty analysis and the coupling with conventional numerical models, and how to use physical knowledge to guide the design of artificial intelligence model is also worth addressing. To deal with these challenges, some promising suggestions are proposed. Bayesian network and causal network will help to establish more comprehensive and profound feature engineering. Using Bayesian inference to generate distribution characteristics of current meteorological states may be an alternative to efficient and effective uncertainty quantification. The development of some standard workflow and framework will contribute to the coupling of conventional numerical model and artificial intelligence module. Successful artificial intelligence applications in weather forecast require deep cooperation between meteorological experts and computer experts who focus on artificial intelligence and high-performance computing.
Assessing Citation Bias in Scientific Literature

Our objective was to provide a further test of possible citation bias or confirmation bias in science based on differential outcomes of two articles published between 1979 and 2013. The two articles used here formed a natural experiment because the articles were from the same author and concerned similar topics except one article had more positive results for its participants. Based on Google Scholar citations, citation rates were compared using binomial tests, including normal approximation z scores, and one-sample chi-square tests. Between the two articles, the one that presented more favorable findings was cited far more often than the one with less positive results, providing empirical support for the existence of citation or confirmation bias, which may result in lower quality and less comprehensive literature reviews.
A study on vulnerabilities in Indian banking sector: with reference to blockchain technology

The vulnerabilities in today's banking system are increasing day by day, where security is the major issue. Today's banking system is totally built on centralised databases. Vulnerabilities can be reduced by rebuilding banking system with blockchain, which is a decentralised distributed ledger which will remove the centralised database architecture and decentralise the data over the blockchain. The transactions in blockchain are verified by each and every node of the chain, so that it will make the transactions faster and secure. This research paper is focused on the vulnerabilities, security and privacy issues and the impact of blockchain technology on Indian banking sector. The data was collected from both primary and secondary sources. Likert scale was employed for designing the questionnaire. Samples of 250 employees from public sector banks were collected. Various factors were identified for the analysis, which employed factor analysis, descriptive statistics, correlation, regression and hypothesis. It was found that adoption of blockchain technology reduces the vulnerabilities in the Indian banking sector.
Unsupervised Style Transfer via Dualgan for Cross-Domain Aerial Image Classification

Due to its wide applications, aerial image classification, which is also called semantic segmentation of aerial imagery, attracts increasing research interest in recent years. Until now, deep semantic segmentation network (DSSN) has been widely adopted to address aerial image classification and achieves tremendous success. However, the superior performance of DSSN highly depends on massive targeted data with labels. When DSSN is trained on data from the source domain but tested on data from the target domain, the performance of DSSN is often very limited due to the data shift between source and target domains. To alleviate the disadvantage influence of data shift, this paper proposes a domain adaptation approach via unsupervised style transfer to cope with cross-domain aerial image classification. More specifically, this paper innovatively recommends DualGAN to conduct unsupervised style transfer for mapping aerial images in the source domain to the target domain. The mapped aerial imagery with labels is adopted to train DSSN, which is further used to classify aerial imagery in the target domain. To verify the validity of the presented approach, we give two cross-domain experimental settings including: (I) variation of geographic location; (II) variation of both geographic location and imaging mode. Extensive experiments under two typical cross-domain settings show that our proposed method can obviously outperform the state-of-the-art methods.
Language and Languaging from a Phonetic Point of View

We differentiate between language-as-system, as exemplified by such constructs as “English,” or “Mandarin,'” and languaging, understood as a rich set of affiliative and coordinative behaviours that involve speech. The former is the more familiar term, and has been constructed in a specific manner that is inextricably bound to literacy, writing and normative social practices. But we argue that only the latter can inform us about what it was that happened to the human species to so differentiate us from other primates. To draw out this distinction, we lean on the contrast between emic and etic approaches, introduced by Ken Pike and rooted in the distinction between phonology and phonetics. We argue that an etic approach to speech can reveal forms of languaging that are not addressed by language-as-system. Joint speech is put forward as an important form of languaging that can be thematised for study only if the emic/etic distinction is taken seriously. Consequences for the self-understanding of phonetics as a discipline are cautiously put forward.
Development and Application of Sports Video Analysis Platform in Sports Training in the Big Data Era

In order to further improve the scientific guidance level of sports training, this paper realizes the development and application research of a new sports training video analysis platform. The core part of the experiment is target tracking. When target tracking, multiple trackers are used to realize comprehensive data collection, and then human-computer interaction is realized through visualization mode. Experimental data shows that the system based on big data technology can accurately describe the sports training process, can effectively improve the accuracy and recall rate of video key frame extraction, and can be used in sports guidance training. The experimental results show that by using big data technology research methods and traditional methods to select 50, 100, 150, and 200 frames of data for video platform analysis and comparison, it is found that the extraction rate and recall rate under the big data technology method are higher than those of the traditional method. The sports training sports video analysis system designed based on big data technology has strong target tracking ability, and the economic cost is relatively optimistic.
SOLAR ACTIVITY FORECASTING METHODS

An overview of the currently used methods for forecasting solar activity is presented. The applicability of various forecasting methods is evaluated, their advantages and disadvantages are described. Practical recommendations are given on the use of the combined method as the most effective for making a forecast of solar activity.
Opinion Mining on Usability Testing Data

Ensuring customer happiness is a must for companies. To achieve this, they should know about how much the products of them are liked. Methods like surveys can be used for that purpose. Instead of surveys, companies may benefit from another source: Usability testing data. Within the scope of this project, opinion mining and sentiment analysis is applied on Casper customers' usability testing data. Before applying opinion mining, product aspects are automatically extracted from the data. And to increase success rate of the process, CEP (Complex Event Processing) is applied. Highest success rate obtained in this research is eighty two percent without the contribution of CEP module.
Classifying Judgements using Transfer Learning

We describe our method for classifying short texts into the APPRAISAL framework, work we conducted as part of the ALTA 2020 shared task. We tackled this problem using transfer learning. Our team, “orangutanV2” placed equal first in the shared task, with a mean F1-score of 0.1026 on the private data set.
Self-Testing of a Single Quantum Device Under Computational Assumptions.

Self-testing is a method to characterise an arbitrary quantum system based only on its classical input-output correlations, and plays an important role in device-independent quantum information processing as well as quantum complexity theory. Prior works on self-testing require the assumption that the system’s state is shared among multiple parties that only perform local measurements and cannot communicate. Here, we replace the setting of multiple non-communicating parties, which is difficult to enforce in practice, by a single computationally bounded party. Specifically, we construct a protocol that allows a classical verifier to robustly certify that a single computationally bounded quantum device must have prepared a Bell pair and performed single-qubit measurements on it, up to a change of basis applied to both the device’s state and measurements. This means that under computational assumptions, the verifier is able to certify the presence of entanglement, a property usually closely associated with two separated subsystems, inside a single quantum device. To achieve this, we build on techniques first introduced by Brakerski et al. (2018) and Mahadev (2018) which allow a classical verifier to constrain the actions of a quantum device assuming the device does not break post-quantum cryptography.
A Methodical Procedure for Home Autonetics System Using Internet of Things with Multi-functional Sensors

In today’s internet era, people are in need to do all house hold activity wherever they are. They wish to control certain activities even in their work environment. They don’t want to go to the particular environment for a minor work. Moreover, with the use of mobile and its applications we can easily achieve this. This project focuses on the smart home application to make human work easy and efficient. This project is used to reduce the necessity of user work for productive of goods and then service automation. In this scope the project is developed, for automation for step further on mechanization. This type of automation provides good insights to the dare models of software and hardware. By the project deployment of automation, it becomes possible to expand a dissimilar diversity of engineering concepts that includes computer programming, printed circuit board models, Wi-Fi, TCP and IP protocols, Web Server logic design.
Towards Rebuilding the Design Model of Programs by Static Analysis and Abstraction

Abstract It is difficult to understand a complex software program if its detailed design is not available. A model-rebuilding approach is presented to recover the design on an abstract level. In this approach, by static analysis of the program, a design is constructed and described in an abstract program which includes all behaviour that is observable at a set of selected program points. The approach provides an effective way for the understanding of complex programs.
Effect of Organizational Silence on Employees’ Productivity

Productivity and development of any organization depends highly on the right application of human resources in the organization. One of the factors affecting productivity is organizational silence which has not been considered today. Since silence can has unfavorable consequences, identifying the factors and their relationship with other phenomena is important. Hence, this research tries to investigate the effect of organizational silence on human productivity. Statistical population is 25 personnel in Arak Tax Organization. Findings showed that organizational silence has a direct and negative effect on human productivity.
Spoken Arabic Digits Recognition System Using Convolutional Neural Network

Digit recognition has a vital use in multiple human-machine interaction applications. It is used in telephone-based services, such as dialing systems, airline reservation systems, different bank transactions, and price extraction. This research aims to develop a new Convolution Neural Network (CNN) based spoken digits recognition system for the Arabic digits. The developed system used a classification approach to perform the recognition task. First, the Mel frequency cepstral coefficients of the spoken digits were conducted and reduced in the convolution phase. Then in the classification phase, the most appropriate digit label for the testing utterances is produced. The proposed approach has shown a remarkable performance when compared to similar systems. The recognition system achieved a 99% correct digit recognition compared to 98% using Recurrent Neural Networks based digit recognition system.
Tight Hardness Results for Training Depth-2 ReLU Networks

We prove several hardness results for training depth-2 neural networks with the ReLU activation function; these networks are simply weighted sums (that may include negative coefficients) of ReLUs. Our goal is to output a depth-2 neural network that minimizes the square loss with respect to a given training set. We prove that this problem is NP-hard already for a network with a single ReLU. We also prove NP-hardness for outputting a weighted sum of $k$ ReLUs minimizing the squared error (for $k>1$) even in the realizable setting (i.e., when the labels are consistent with an unknown depth-2 ReLU network). We are also able to obtain lower bounds on the running time in terms of the desired additive error $\epsilon$. To obtain our lower bounds, we use the Gap Exponential Time Hypothesis (Gap-ETH) as well as a new hypothesis regarding the hardness of approximating the well known Densest $\kappa$-Subgraph problem in subexponential time (these hypotheses are used separately in proving different lower bounds). For example, we prove that under reasonable hardness assumptions, any proper learning algorithm for finding the best fitting ReLU must run in time exponential in $1/\epsilon^2$. Together with a previous work regarding improperly learning a ReLU (Goel et al., COLT'17), this implies the first separation between proper and improper algorithms for learning a ReLU. We also study the problem of properly learning a depth-2 network of ReLUs with bounded weights giving new (worst-case) upper bounds on the running time needed to learn such networks both in the realizable and agnostic settings. Our upper bounds on the running time essentially matches our lower bounds in terms of the dependency on $\epsilon$.
Interpreting Linear Beta Coefficients Alongside Feature Importances in Machine Learning

Machine-learning regression models lack the interpretability of their conventional linear counterparts. Tree- and forest-based models offer feature importances, a vector of probabilities indicating the impact of each predictive variable on a model’s results. This brief note describes how to interpret the beta coefficients of the corresponding linear model so that they may be compared directly to feature importances in machine learning.
Fuzzy Approach to Evaluate Performance of Teaching Staff in Technical Institutions

Higher Education plays a primary role in creating and developing resources within the country and also improves the existence of the people to whom it serves. To construct a highly effective and reliable decision-making system rule-based system has higher significance. Consequently, assessment of teaching faculty helps to characterize well-organized plans to improve teaching quality and learning process. The proposed research paper focuses on Fuzzy based Inference System (FIS) to assess the concert of teaching staff including student feedback reports. Appropriate associative rules and suitable fuzzy inference mechanisms discussed in this research paper. Proposed research work also focuses on the comparative study of classical analysis and fuzzy analysis.
MENINGKATKAN KEMAMPUAN BERBICARA BAHASA INGGRIS DASAR MELALUI KEGIATAN ENGLISH CAMP

This study aims to improve the English speaking skills of STKIP MB students majoring in elementary school teacher education who take English courses, especially speaking skills at the basic level through English camp activities. English camp activities are outdoor activities carried out as practical speaking activities outside the classroom. . This activity is carried out after the lecture enters the midterm exam where students have first learned the basic theory for seven meetings at the beginning of the lecture. This research is a classroom action research conducted in two cycles with each cycle consisting of two meetings of English camp activities. The results of this study show their speaking ability at the basic level with the topics that have been determined increases significantly each meeting and at the end of each cycle taken from the speaking ability data with categories of assessment of topic accuracy, use of vocabulary, pronunciation and fluency in speaking. . The conclusion in the study shows that English camp activities can improve basic level speaking skills for PGSD STKIP MB students with success indicators of 80% of students getting an oral speaking exam score of 75 or more