Reviewerly/exHarmony huggingface dataset:
Forecasting System for Solar-Power Generation

Environmental protection is a highly concerned and thought-provoking issue, and the way of generating electricity has become a major conundrum for all mankind. Renewable or green energy is an ideal solution for environmentally friendly (eco-friendly) power generation. Among all renewable energy, solar-power generation is low cost and small footprint, which make solar-power generation available around our lives. The major uncontrollable factor in the solar-power generation is the amount of solar radiation, which completely dominates the electricity generated by solar panel. In this paper, we design prediction models for solar radiation, using data mining techniques and machine learning algorithms, and derive precision prediction models (PPM) and light prediction models (LPM). Experimental results that the PPM (LPM) with random forest regression can obtain R-squared of 0.841 (0.828) and correlation coefficient of 0.917 (0.910). Compared with highly cited researches, our models outperform them in all measurements, which demonstrates the robustness and effectiveness of the proposed models.
DEVELOPMENT OF SOCIAL NETWORKS AND SERVICES IN TEACHING STUDENTS: EXPERIENCE OF DISTANCE LEARNING IN THE CONTEXT OF THE COVID-19 PANDEMIC

The pandemic caused by the acute respiratory disease COVID-19 has forced the education system to switch to distance learning, either partially or completely. The use of social networks can bring greater interactivity into the educational process under these conditions, have a positive effect on the results of students' cognitive activity, and become an efficient means of increasing the motivation and quality of learning, organizing the collective work of students, performing joint project activities, and individualizing the student's virtual learning space. Social networks can also be used as a means of distributing educational material. The specifics of the applications and the capabilities of social networks and services in distance learning have been described in the article; a theoretical analysis of the basic concepts of the articulated topic of the article has been carried out. Learning tasks have been identified on the basis of an expert survey, which can be solved through social networks; the applications of social networks VKontakte and Facebook for organizing distance educational process have been described; and methods of combining social networks and an information and organizational distance learning environment have been presented.
Design Of Hybrid Cryptography With Vigenere Cipher And RSA Algorithm On IOT Data Security

In this paper two methods are used, namely the vigenere cipher method and the RSA method. The vigenere cipher method is an example of a symmetric algorithm, while RSA is an example of an asymmetric algorithm. The combination of these two methods is called hybrid cryptography which has the advantage in terms of speed during the encryption process. Each process, which is encryption and decryption, is carried out twice, so that security can be ensured. In the process of forming the key used the RSA method. In the encryption process using public keys that have been generated before when the key is formed. This public key is used in sending data to the recipient of a secret message where this key is used for the data encryption process. The Secret key is kept and will be used during the decryption process. There is a system architecture that describes how clients and servers communicate with each other over the internet using the TCP protocol where the client here is an IoT device and the server is a server.
An FPGA-Based Resource-Saving Hardware Accelerator for Deep Neural Network

With the development of computer vision researches, due to the state-of-theart performance on image and video processing tasks, deep neural network (DNN) has been widely applied in various applications (autonomous vehicles, weather forecasting, counter-terrorism, surveillance, traffic management, etc.).However, to achieve such performance, DNN models have become increasingly complicated and deeper, and result in heavy computational stress.Thus, it is not sufficient for the general central processing unit (CPU) processors to meet the real-time application requirements.To deal with this bottleneck, research based on hardware acceleration solution for DNN attracts great attention.Specifically, to meet various real-life applications, DNN acceleration solutions mainly focus on issue of hardware acceleration with intense memory and calculation resource.In this paper, a novel resource-saving architecture based on Field Programmable Gate Array (FPGA) is proposed.Due to the novel designed processing element (PE), the proposed architecture achieves good performance with the extremely limited calculating resource.The on-chip buffer allocation helps enhance resource-saving performance on memory.Moreover, the accelerator improves its performance by exploiting the sparsity property of the input feature map.Compared to other state-of-the-art solutions based on FPGA, our architecture achieves good performance, with quite limited resource consumption, thus fully meet the requirement of real-time applications.
Learning Sampling Strategies for Stochastic Gradient Descent using Deep Reinforcement Learning techniques

Solving finite-sum minimization problems could be done by the use of a gradient descent algorithm. The algorithm evaluates the gradient with respect to the current state of the parameters and updates the parameters in the direction of the steepest descent. For certain problems, evaluating the full gradient at every iteration is computationally heavy and the gradient of the objective function is thus approximated by evaluating the gradient with respect to one of the summands given in the finitesum. This method is referred to as stochastic gradient descent, where the gradient approximator summand is chosen with respect to a sampling strategy. The purpose of this thesis is to learn such a sampling strategy by the use of deep reinforcement learning namely, deep Q-learning. Reinforcement learning is a field within machine learning where software, an agent, interacts with an environment in order to learn how to act optimally over time. The agent performs actions which affect the state of the environment, and observes a real-valued feedback, a reward, based on the action. In order to not explicitly store the expected outcome of each (state, action)-pair in memory, the focus is turned towards deep learning in order to approximate the expected outcome. The finite sum objective investigated were least squares, where the Lipschitz constants of the summands were of different order of magnitude. The results showed that the sampling strategy generated by the deep Q-network performed similarly to stochastic gradient descent using uniform and Lipschitz sampling. One experiment showed that the deep Q-learning agent could converge faster towards the analytical solution of the least squares objective, when trained for a fewer iterations per episode. The work could be extended further by constructing objective functions where uniform and Lipschitz sampling perform differently. (Less)
A Metric Learning Approach to Graph Edit Costs for Regression

Graph edit distance (GED) is a widely used dissimilarity measure between graphs. It is a natural metric for comparing graphs and respects the nature of the underlying space, and provides interpretability for operations on graphs. As a key ingredient of the GED, the choice of edit cost functions has a dramatic effect on the GED and therefore the classification or regression performances. In this paper, in the spirit of metric learning, we propose a strategy to optimize edit costs according to a particular prediction task, which avoids the use of predefined costs. An alternate iterative procedure is proposed to preserve the distances in both the underlying spaces, where the update on edit costs obtained by solving a constrained linear problem and a re-computation of the optimal edit paths according to the newly computed costs are performed alternately. Experiments show that regression using the optimized costs yields better performances compared to random or expert costs.
Social Networking Services as Informal Contexts for Foreign Language Learning

This article focuses on online informal learning contexts, namely social networks to which learners may resort for their linguistic needs. The distinguishing feature of such platforms is that content is neither provided by an educational institution, nor is it necessarily presented in a formal fashion. While research has extensively focused on the connection between technology and autonomy, the social aspect behind 2.0 tools has been partly neglected. Therefore, we argue that learner training, i.e. preparing language learners for autonomous language study through technology, should be partly rethought and include such skills as communicating in didactic interactions and evaluating information.
An Analysis of Politeness Strategies Found in Pesbukers Variety Show

– This research was a pragmatic approach, which was focused on politeness strategies found in Pesbukers Variety show. The objectives of this research were to analyzed types of politeness strategies, to find out the most dominant type of politeness strategies, and to know factors influenced the choice of politeness strategies found in Pesbukers variety show. The theory of politeness strategies by Brown and Levinson (1987) was used to analyze the politeness strategies. The research method was descriptive qualitative research with content analysis, because the data were collected from document. The researcher chooses episode 10 broadcasted on 15 th May 2019 with duration 1 hour 12 minutes. There were some steps in collected the data, such as: familiarizing and organizing, coding and reducing, interpreting and representing. The result showed that there were37politeness strategies found in Pesbukers variety show, they were: bald on-record 6 (16%), positive politeness 26 (70%), negative politeness 4 (11%), and off-record 1 (3%).The most dominant type of politeness strategies found in Pesbukers variety show was positive politeness. Positive politeness was done by speaker and hearer to made enjoyable situation, build good relation, good interaction. The factor influenced choice of politeness strategies found in Pesbukers was circumstances. Circumstances deal with sociological variables include the relative power2 (5%) and social distance35 (95%). Relative power (P) used a greater degree of politeness with others who have higher power or authority. Social distance (D) saw as the composite of psychological factors such as status and age. In conclusion, with this research we must be careful when used a language to communicate with other so that the conversation become run well.
Analysis of the use of a mobile simulation unit using the principles of a managed educational network

Methods: This article shares 5 years of data from a mobile simulation unit (MSU) analysed using the principles of a national managed educational network (MEN).An action research approach was used to identify how the MSU was changing the delivery of simulation based education as part of a national MEN for clinical skills education.One of the key deliverables of the Scottish Clinical Skills Strategy was to support the training needs of the National Health Service workforce, particularly in remote and rural Scotland.The MSU was proposed as part of the solution to the 'inequity of provision of clinical skills education using simulation'.Between 2014 and 2018, data were collected by the Clinical Skills Managed Educational Network (CSMEN) team on the performance of the MSU.Venues visited, job titles of participants and courses attended (a mix of local and national) were recorded by the venue hosts and collated by CSMEN staff.Results: Evidence of the impact of the MSU using the seven MEN principles to analyse the data (functioning with explicit management arrangements, embedding systems of accountability, agreed expected outcomes, using an evidence base, having a multiprofessional and multidisciplinary focus, engaging in a system of dissemination, generating better value for money and engaging in research and development) are shared. Conclusion:Few examples of mobile simulation facilities exist in the UK, and none have the longevity of the CSMEN MSU.The results of the analysis supported the business case for an upgraded new MSU and identified the need to explore in more depth the variation in impact across the venues visited.However, some localities did not use the MSU during 2014-2018 and there are still some unanswered questions around its impact in some venues, which require further exploration.
Generating multi-type sequences of temporal events to improve fraud detection in game advertising.

Fraudulent activities related to online advertising can potentially harm the trust advertisers put in advertising networks and sour the gaming experience for users. Pay-Per-Click/Install (PPC/I) advertising is one of the main revenue models in game monetization. Widespread use of the PPC/I model has led to a rise in click/install fraud events in games. The majority of traffic in ad networks is non-fraudulent, which imposes difficulties on machine learning based fraud detection systems to deal with highly skewed labels. From the ad network standpoint, user activities are multi-type sequences of temporal events consisting of event types and corresponding time intervals. Time Long Short-Term Memory (Time-LSTM) network cells have been proved effective in modeling intrinsic hidden patterns with non-uniform time intervals. In this study, we propose using a variant of Time-LSTM cells in combination with a modified version of Sequence Generative Adversarial Generative (SeqGAN)to generate artificial sequences to mimic the fraudulent user patterns in ad traffic. We also propose using a Critic network instead of Monte-Carlo (MC) roll-out in training SeqGAN to reduce computational costs. The GAN-generated sequences can be used to enhance the classification ability of event-based fraud detection classifiers. Our extensive experiments based on synthetic data have shown the trained generator has the capability to generate sequences with desired properties measured by multiple criteria.
No-substitution k-means Clustering with Adversarial Order

We investigate $k$-means clustering in the online no-substitution setting when the input arrives in \emph{arbitrary} order. In this setting, points arrive one after another, and the algorithm is required to instantly decide whether to take the current point as a center before observing the next point. Decisions are irrevocable. The goal is to minimize both the number of centers and the $k$-means cost. Previous works in this setting assume that the input's order is random, or that the input's aspect ratio is bounded. It is known that if the order is arbitrary and there is no assumption on the input, then any algorithm must take all points as centers. Moreover, assuming a bounded aspect ratio is too restrictive -- it does not include natural input generated from mixture models. We introduce a new complexity measure that quantifies the difficulty of clustering a dataset arriving in arbitrary order. We design a new random algorithm and prove that if applied on data with complexity $d$, the algorithm takes $O(d\log(n) k\log(k))$ centers and is an $O(k^3)$-approximation. We also prove that if the data is sampled from a ``natural distribution, such as a mixture of $k$ Gaussians, then the new complexity measure is equal to $O(k^2\log(n))$. This implies that for data generated from those distributions, our new algorithm takes only $\text{poly}(k\log(n))$ centers and is a $\text{poly}(k)$-approximation. In terms of negative results, we prove that the number of centers needed to achieve an $\alpha$-approximation is at least $\Omega\left(\frac{d}{k\log(n\alpha)}\right)$.
Improved Extended Progressive Visual Cryptography Scheme Using Pixel Harmonization

Security of information is of much concern in the modern internet era. Secret sharing schemes provide mechanism of encrypting secret information to prevent illicit usage. Visual cryptography is a secret sharing technique that facilitates encryption of a secret image. Visual cryptography allows us to effectively and efficiently share secrets among a number of trusted parties by hiding secrets within images. These images are encoded into multiple shares as per the rules indicated in basis matrices and later decoded by stacking required number of shares. Progressive visual cryptography has a specialty of recovering secret image as soon as more than one shares received gradually. Existing progressive visual cryptography schemes have severe limitations like data disclose on shares and higher pixel expansion. Improved progressive visual cryptography scheme deals with these limitations. Improved extended progressive visual cryptography scheme solves the issue of management of noise like meaningless shares by creating meaningful shares without any pixel expansion efficiently.
A Scalable Approach for Data Mining – AHUIM

Utility itemset mining, which finds the item sets based on utility factors, has established itself as an essential form of data mining. The utility is defined in terms of quantity and some interest factor. Various methods have been developed so far by the researchers to mine these itemsets but most of them are not scalable. In the present times, a scalable approach is required that can fulfill the budding needs of data mining. A Spark based novel technique has been recommended in this research paper for mining the data in a distributed way, called as Absolute High Utility Itemset Mining (AHUIM). The technique is suitable for small as well as large datasets. The performance of the technique is being measured for various parameters such as speed, scalability, and accuracy etc.
A rule based approach for aspect extraction in hindi reviews

Fast growth of technology and the tremendous growth of population has made millions of people to be active participants on social networking forums. The experiences shared by the participants on different websites is highly useful not only to customers to make decisions but also helps companies to maintain sustainability in businesses. Sentiment analysis is an automated process to analyze the public opinion behind certain topics. Identifying targets of user’s opinion from text is referred to as aspect extraction task, which is the most crucial and important part of Sentiment Analysis. The proposed system is a rule-based approach to extract aspect terms from reviews. A sequence of patterns is created based on the dependency relations between target and its nearby words. The system of rules works on a benchmark of dataset for Hindi shared by Akhtar et al., 2016. The evaluated results show that the proposed approach has significant improvement in extracting aspects over the baseline approach reported on the same dataset.
Decoding of graphically encoded numerical digits using deep learning and edge detection techniques

The encoding of a message is the creation of the message. The decoding of a message is how people can comprehend, and decipher the message. It is a procedure of understanding and interpretation of coded data into a comprehensible form. In this paper, a self-created explicitly defined function for encoding numerical digits into graphical representation is proposed. The proposed system integrates deep learning methods to get the probabilities of digit occurrence and Edge detection techniques for decoding the graphically encoded numerical digits to numerical digits as text. The proposed system’s major objective is to take in an Image with digits encoded in graphical format and give the decoded stream of digits corresponding to the graph. This system also employs relevant pre-processing techniques to convert RGB to text and image to Canny image. Techniques such as Multi-Label Classification of images and Segmentation are used for getting the probability of occurrence. The dataset is created, on our own, that consists of 1000 images. The dataset has the training data and testing data in the proportion of 9 : 1. The proposed system was trained on 900 images and the testing was performed on 100 images which were ordered in 10 classes. The model has created a precision of 89% for probability prediction.
Weakly Supervised Label Smoothing

We study Label Smoothing (LS), a widely used regularization technique, in the context of neural learning to rank (L2R) models. LS combines the ground-truth labels with a uniform distribution, encouraging the model to be less confident in its predictions. We analyze the relationship between the non-relevant documents—specifically how they are sampled—and the effectiveness of LS, discussing how LS can be capturing “hidden similarity knowledge” between the relevant and non-relevant document classes. We further analyze LS by testing if a curriculum-learning approach, i.e., starting with LS and after a number of iterations using only ground-truth labels, is beneficial. Inspired by our investigation of LS in the context of neural L2R models, we propose a novel technique called Weakly Supervised Label Smoothing (WSLS) that takes advantage of the retrieval scores of the negative sampled documents as a weak supervision signal in the process of modifying the ground-truth labels. WSLS is simple to implement, requiring no modification to the neural ranker architecture. Our experiments across three retrieval tasks—passage retrieval, similar question retrieval and conversation response ranking—show that WSLS for pointwise BERT-based rankers leads to consistent effectiveness gains. The source code is available at https://github.com/Guzpenha/transformer_rankers/tree/wsls .
Design and Implementation of SPOC Online Courses for Real Estate Investment and Financing

Real estate investment and financing is a very practical cross-course, which has very high requirements for students' preliminary knowledge.Since the real estate development and management major belongs to the first-level discipline of management science and engineering, students generally lack the knowledge of finance.How to allow students to systematically master the theoretical knowledge of real estate investment and financing within the limited class hours, and apply it to practice, has become an important issue for class teachers.By introducing the SPOC teaching model and carefully designing online and offline teaching content, it can not only improve the classroom teaching effect, but also cultivate students' autonomous learning ability.This article intends to systematically study the design and implementation of SPOC online courses in real estate investment and financing based on the new development trend of the real estate industry.
Designing English Teaching Materials Containing Popular Culture

There are two main objectives in conducting this research, namely: 1) describing how popular culture can be designed into English teaching materials; 2) explaining how to implement English teaching material containing popular culture in the classroom. This study uses qualitative methods using interviews and text or document analysis techniques as research instruments. The informants interviewed were postgraduate students of the English Language Education Department, University of Muhammadiyah Malang, Indonesia. They took the Material Development course. And the documents analyzed were learning devices such as syllabus and teaching plan that had been designed. The research data is in the form of interviews and texts related to popular culture such as pictures, music, food and drinks, sports and books. The results of the study show that popular culture such as images of food and drinks, music, sports and entertainment can be developed into English teaching materials. Informants implemented English teaching material that contained popular culture in the classroom through peer teaching programs.