Reviewerly/exHarmony huggingface dataset:
A CTR prediction model based on attention mechanism and logarithmic transformation

To address the problems of inadequate feature interaction and lack of targeting in feature combination in the clickthrough rate prediction model. We propose a click-through prediction model called SELFM. It based on attention mechanism and logarithmic transformation structure. The model first incorporates the attention mechanism in the feature embedding stage to distinguish the importance of different features and avoids the effects of invalid features. Then the field-aware factorization machine is used to learn low-order feature interactions. The logarithmic transformation structure is used to convert the power of each feature in the feature combination into the coefficients to be learned and combined with the hidden layer for higher-order nonlinear feature interactions. The final output layer is processed with the Sigmoid function to get the click-through rate prediction results. The experimental results show that the AUC and Logloss of this paper's model are better than the existing click-through rate prediction models, which effectively improves the prediction accuracy and enhances the ability of the recommendation system to process data.
The use of subject headings varied in Embase and MEDLINE: An analysis of indexing across six subject areas

Many bibliographic databases describe the content of a publication using a thesaurus. The vocabularies vary and the extent to which the databases apply them may also differ significantly. The aim of this study is to empirically explore the number of subject headings assigned to publications in two databases over time and to determine if publication characteristics are associated with the number of subject headings. Articles and reviews in MEDLINE and Embase from 1990 to 2019 assigned with one of the subject headings from six subject areas are included in this study. Each of the retrieved publications in Embase is matched with a similar publication in MEDLINE. Furthermore, multivariable linear regressions are used to explore the association of the number of subject headings in MEDLINE and Embase with six prespecified publication characteristics. The average number of assigned subject headings in MEDLINE is stable or even slightly decreasing over time. In Embase, the average number of assigned subject headings was stable until about 2000 where the average number increased dramatically during the next 3 years. Furthermore, linear regressions show that the average number of subject headings in MEDLINE and Embase is higher for publications in English, publications with longer abstract, recent publications and if it belongs to specific subject areas. However, reviews are assigned with more subject headings in Embase and fewer in MEDLINE. The implications of the results are discussed.
Role of Stocktaking Application using Barcode Scanning in Improving Stock Conformity in Hospital Pharmacy Installation

One of the most important managerial activities for hospitals is an internal control activity for drug supplies, including during the Covid-19 pandemic. A preliminary study at Graha Sehat Medika hospital (GSM hospital) showed that the inaccuracy of stocktaking results in the last six months was 40% -70%. The solution taken is the use of "SO PINTAR RS" application with barcode scanning. The purpose of this study was to determine the effectiveness of "SO PINTAR RS" application with barcode scanning in improving the accuracy of the stocktaking results at GSM hospital. The method used in this research is quasi-experimental. The data used is primary data from stocktaking activities in July-November 2020. A total of 162 drug items were used as research samples taken from the purposive sampling technique. The discrepancy between drug items in the hospital management information system (HIS) and the physical count results before and after using the application was measured in this study. The analysis of the results was carried out through nonparametric statistical tests with the Wilcoxon test. The results showed a significant difference in the results of stocktaking activities before and after using "SO PINTAR RS" application (p &lt;0.001). The difference between the calculation results of the stocktaking with the data in the HIS decreased significantly. In addition, using this application can reduce the number of staff involved and reduce the loss of uncounted medicine items. This application is still not linked to HIS, so it is necessary to manually merge the results into HIS.
Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning

Effective exploration is one of the critical factors affecting performance in deep reinforcement learning. Agents acquire data to learn the optimal policy through exploration, and if it is not guaranteed, the data quality deteriorates, which leads to performance degradation. This study investigates the effect of initial entropy, which significantly influences exploration, especially in the early learning stage. The results of this study on tasks with discrete action space show that (1) low initial entropy increases the probability of learning failure, (2) the distributions of initial entropy for various tasks are biased towards low values that inhibit exploration, and (3) the initial entropy for discrete action space varies with both the initial weight and task, making it hard to control. We then devise a simple yet powerful learning strategy to deal with these limitations, namely, entropy-aware model initialization. The proposed algorithm aims to provide a model with high initial entropy to a deep reinforcement learning algorithm for effective exploration. Our experiments showed that the devised learning strategy significantly reduces learning failures and enhances performance, stability, and learning speed.
Towards a Richer IndoWordNet with New Additions for Hindi and Gujarati Languages

The authors of this research paper present a mechanism for dealing with loanwords, missing words, and newly developed terms inclusion issues in WordNets.WordNet has evolved as one of the most prominent Natural Language Processing (NLP) toolkits.This mechanism can be used to improve the WordNet of any language.The authors chose to work with the Hindi and Gujarati languages in this research work to achieve a higher quality research aspect because these are the languages with major dialects.The research work used more than 5000 Hindi verse-based data corpus instead of a prose-based data corpus.As a result, nearly 14000 Hindi words were discovered that were not present in the popular Hindi IndoWordNet, accounting for 13.23 percent of the total existing word count of 105000+.Working with idioms was a distinct method for the Gujarati language.Around 3500 idioms data were used, and nearly 900 Gujarati terms were discovered that did not exist in the IndoWordNet, accounting for nearly 1.4 percent of the total of 64000+ Gujarati words in the IndoWordNet.It will also contribute almost 14000 Hindi words and around 900 Gujarati words to the IndoWordNet project.
Solving the Imbalanced and Limited Data Labeled for Automated Essay Scoring using Cost Sensitive XGBoost and Pseudo-Labeling

There are two main problems on forming the Automatic Essay Scoring Model.They are the datasets having imbalanced amount of the right and wrong answers and the minimal use of labeled data in the model training.The model forming based on these problems is divided into three main points, namely word representation, Cost-Sensitive XGBoost Classification, and adding unlabeled data with the Pseudo-Labeling Technique.The essay answer data is converted into a vector using the trained word vector fastText.Furthermore, the classification of unlabeled data was carried out using the Cost-Sensitive XGBoost Method.The data labeled by the classification model is added as training data for the new classification model form.The process is carried out iteratively.This research is about using the combination of Cost-Sensitive XGBoost Classification and Pseudo-Labeling which is expected to solve the problems.For the 0th iteration, the dataset having a ratio of the amount of "right" labeled data with the amount of "right" labeled data is close to 1, in other words a balanced dataset or a ratio that is more than 1 produces a model with better performance.Thus, the selection of training data at an early stage must pay attention to this ratio.In addition, the use of the Hybrid Method on these datasets can save labeled data 56 times compared to the AdaBoost Method.Hybrid model is able to produce F1-Measure more than 95.6%, so it can be concluded that the Hybrid Method, which combines the XGBoost and Pseudo-Labeling Cost-Sensitive Classification with Self Training, is able to overcome the problem of unbalanced datasets and data limited label.
Measuring of simple sensorimotor reaction time

Measuring of simple sensorimotor reaction time. / M. Gigineishvili, O. Ketiladze, T. Mikeladze, M. Chikhladze, T. Khachidze. / Nano Studies. – 2020. – # 20. – pp. 125-130. – geo. There is explained the essence and types of the sensorimotor response of a biological object. The method of determining the duration of a simple sensorimotor reaction developed in a physics teaching laboratory of the Technical University Study is given. The electrical scheme of the experiment is presented. Fig. 1, Ref. 3.
Querying Maximum Quasi-independent Set by Pay-and-Recycle

In the paper, we study the problem of computing a maximum quasi-independent set that admits <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> conflict edges at most but contains the set of query vertices <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> . It generalizes the task of finding a maximum independent set (shorted as MIS) for a given graph. Due to the intractable hardness of computing an exact solution, delivering a high-quality approximate solution within a time budget can be accepted. The existing algorithms for the maximum quasi-independent set are organized in two phases, namely near-MIS initialization and edge expansion (i.e., deleting edges). As both of the two phases adopt greedy strategies, error propagation degrades the quality of delivered answer sets. In contrast, we develop a novel pay-and-recycle approach interleaving the above two phases. Instead of making greedy peelings when no reduction rules can be applied, we delete an edge (i.e., edge expansion) to create opportunities for applying reduction rules. To enhance the performance, the wasted edges are detected and recycled for further expansion. Moreover, we propose an effective method to guide edge expansion based on offline samples. Extensive empirical studies show that our proposed method outperforms the state-of-the-art algorithm in finding larger quasi-independent sets.
Consistent Subgraph Matching over Large Graphs

Subgraph matching over graphs has been extensive-ly studied, due to its wide applications in knowledge bases, social networks, and among others. To catch the inconsistency and errors that commonly exist in these graphs, this paper studies consistent subgraph matching (CSM), i.e., finding the common matches in every consistent graph repair w.r.t a set of conditional graph dependencies (CGDs). We concentrate on subset, superset and symmetric difference graph repairs. We study fundamental problems for CGDs and CSM. We show that the satisfiability, im-plication, and validation problems of CGDs are coNP-complete, coNP-complete and NP-complete, respectively. We also show that the CSM problem (under any kind of repair) is NP-complete. We provide (parallel) algorithms to solve CSM, and guarantee to reduce running time when given more processors. Using real-life and synthetic graphs, we empirically verify the efficiency and effectiveness of our algorithms.
Advanced Cyber Security and Internet of Things for Digital Transformations of the Indian Healthcare Sector

Cybersecurity is not just about fortification of data. It has wide implications such as maintaining safety, privacy, integrity, and trust of the patients in the healthcare sector. This study methodically reviews the need for cybersecurity amid digital transformation with the help of emerging technologies and focuses on the application and incorporation of blockchain and the internet of things (IoT) to ensure cybersecurity in the well-being of the business. It was found in the study that worldwide, advanced technology has been used in managing the flow of data and information, India should focus on maintaining the same IT-enabled infrastructure to reduce causalities in the nation and on the other hand improve administration, privacy, and security in the hospital sector. Depending on the network system, resource allocation, and mobile devices, there is a need to prioritize the resources and efforts in the era of digitalization.
End-Effector Collision Avoidance System for Four SCARA Robots Using Buffered Voronoi Cell

Collaboration of multiple robots for performing complicated tasks with uncertainty, such as manipulation of soft materials like clothes, has been a highly concerned research topic. Meanwhile, collision avoidance for the robots with overlap of workspace is crucial for their safe collaboration as well. In this paper, a multi-robot platform consists of four SCARA robots is introduced first, with the aim of manipulating and spreading soft materials in a small area, where end-effectors of robots may collide with each other. Subsequently, in this paper end-effector collision avoidance system for robots is proposed with the application and adaptation of Buffered Voronoi Cell, a method for collision avoidance of multi-agent systems. Finally, through the experiments in simulator developed by the authors for SCARA robots, it is verified that the proposed end-effector collision avoidance system is effective.
Feedback control and quantum error correction assisted quantum multi-parameter estimation

Quantum metrology provides a fundamental limit on the precision of multi-parameter estimation, called the Heisenberg limit, which has been achieved in noiseless quantum systems. However, for systems subject to noises, it is hard to achieve this limit since noises are inclined to destroy quantum coherence and entanglement. In this paper, a combined control scheme with feedback and quantum error correction (QEC) is proposed to achieve the Heisenberg limit in the presence of spontaneous emission, where the feedback control is used to protect a stabilizer code space containing an optimal probe state and an additional control is applied to eliminate the measurement incompatibility among three parameters. Although an ancilla system is necessary for the preparation of the optimal probe state, our scheme does not require the ancilla system to be noiseless. In addition, the control scheme in this paper has a low-dimensional code space. For the three components of a magnetic field, it can achieve the highest estimation precision with only a 2-dimensional code space, while at least a 4-dimensional code space is required in the common optimal error correction protocols.
Underwater image enhancement method based on entropy weight fusion

Abstract Due to the absorption and scattering of underwater light, underwater images often have some phenomena such as colorcast, low contrast, and blurred details. Therefore, an underwater image enhancement method based on entropy weight fusion is proposed. First, white balance processing can effectively correct the blue (green) color appearance of the image. Then the white‐balanced images are converted from RGB space to LAB space, and L channel is processed with improved adaptive gamma correction, and then converted back to RGB space. CLAHE and bilateral filtering are performed in RGB space. The RGB space is converted to HSV space, the V channel is processed by single‐scale retinex algorithm combined with guided filtering, stretching the R channel with s‐cosine curve,and then converted back to RGB space. Finally, the three results are fused by entropy weight to obtain the final enhanced image. Experimental results show that the proposed algorithm can improve the contrast and clarity of underwater images, and effectively remove colorcast.
Efficient and Oblivious Query Processing for Range and kNN Queries (Extended Abstract)

Oblivious RAMs (ORAMs) are proposed to completely hide access patterns. However, most ORAM constructions are expensive and not suitable to deploy in a database for supporting query processing over large data. In this work, we design a practical oblivious query processing framework to enable efficient query processing over a cloud database. In particular, we focus on processing multiple range and kNN queries asynchronously and concurrently with high throughput. The key idea is to integrate indices into ORAM which leverages a suite of optimization techniques (e.g., oblivious batch processing and caching). Our construction shows an order of magnitude speedup in comparison with other baselines over large datasets.
Multi-Keyword Searchable Encryption for E-Health System With Multiple Data Writers and Readers

E-health is a cloud-based system to store and share medical data with the stakeholders. From a security perspective, the stored data are in encrypted form that could further be searched by the stakeholders through searchable encryption (SE). Practically, an e-health system with support of multiple stakeholders (that may work as either data owner [writer] or user [reader]) along with the provision of multi-keyword search is desirable. However, the existing SE schemes either support multi-keyword search in multi-reader setting or offer multi-writer, multi-reader mechanism along with single-keyword search only. This chapter proposes a multi-keyword SE for an e-health system in multi-writer multi-reader setting. With this scheme, any registered writer could share data with any registered reader with optimal storage-computational overhead on writer. The proposed scheme offers conjunctive search with optimal search complexity at server. It also ensures security to medical records and privacy of keywords. The theoretical and empirical analysis demonstrates the effectiveness of the proposed work.
Improvement of a hamming code and logistic-map based pixel-level active forgery detection scheme

In this paper, we present an improvement of a previously proposed watermarking scheme. We proved in a previous work that the scheme suffers from security flaws and we exploited those flaws to attack the scheme resulting in the revelation of the secret keys used in the embedding process. With possession of the keys watermarked images could be manipulated without being detected by the extraction scheme. In this paper, we propose an improvement of the scheme to cover the security flaws in the scheme. This work falls into the context of improving the design of security systems taking into consideration the different cryptanalysis techniques.
Quality Enhancement of Compressed 360-Degree Videos Using Viewport-based Deep Neural Networks

360-degree video provides omnidirectional views by a bounding sphere, thus also called omnidirectional video. For omnidirectional video, people can only see specific content in the viewport through head movement, i.e., only a small portion of the 360-degree content is exposed at a given time. Therefore, the viewport quality is of particular importance for 360-degree videos. In this article, we propose a quality enhancement of compressed 360-degree videos using viewport-based deep neural networks, named V-DNN. V-DNN is mainly composed of two modules: viewport prediction network (VPN) and viewport quality enhancement network (VQEN). VPN based on spherical convolution and 2D convolution generates potential viewports for omnidirectional video. VQEN takes the current viewport and its reference viewports as the input and enhances residual for the current viewport based on bidirectional offset prediction and Spatio-temporal deformable convolutions. Compared with HM16.16 baseline at QP = 37 under the Low Delay P (LDP) configuration, experimental results show that V-DNN achieves an average 0.605 dB and 0.0139 gains in viewport-based ΔPSNR and ΔMS-SSIM, respectively, and is 0.379 dB (59.63%) and 0.0073 (110.61%) higher than the multi-frame quality enhancement (MFQE-2.0) scheme at QP = 37, respectively. Moreover, V-DNN consistently outperforms MFQE-1.0, MFQE-2.0, and HM16.16 baseline at the other QPs in terms of ΔPSNR, ΔWS-PSNR, and ΔMS-SSIM.
How a machine can understand the command intent

With recent technological advances, commanders request the support of artificial intelligence (AI)-enabled systems during mission planning. Future AI systems may test a wide range of courses of action (COAs) and use a simulator to test each COA’s effectiveness in a war game. The COA’s effectiveness is however dependent on the commanders’ intent. The question arises to what degree a machine can understand the commanders’ intent? Currently, the intent has to be programmed manually, costing valuable time. Therefore, we tested whether a tool can understand a freely written intent so that a commander can work with an AI system with minimal effort. The work consisted of letting a tool understand the language and grammar of the commander to find relevant information in the intent; creating a (visual) representation of the intent to the commander (back brief); and creating an intent-based computable measure of effectiveness. We proposed a novel quantitative evaluation metric for understanding the commanders’ intent and tested the results qualitatively with platoon commanders of the 11th Airmobile Brigade. They were positively surprised with the level of understanding and appreciated the validation feedback. The computable measure of effectiveness is the first step toward bridging the gap between the command intent and machine learning for military mission planning.
On Montague’s “The Proper Treatment of Quantification in Ordinary English”

In a series of papers published between 1970 and 1973, the late Richard Montague developed a mathematical theory of semantics and its interface with syntax. The theory, later to be called Montague Grammar (or Montague Semantics), was not only intended as an alternative to generative grammar but also designed so broadly as to cover both natural language and systems of formal logic. An outline of the theory was contained in the short and somewhat hermetic essay ‘Universal Grammar’ (1970). However, it was the slightly more accessible descriptive application in Montague’s last publication that was to define the vantage ground of semantic research for at least the next decade: ‘The Proper Treatment of Quantification in Ordinary English’ (1973), aka ‘PTQ’. PTQ was presented both as an illustration of the power of Montague’s general theory and as a contribution to the semantic description of English, thereby extending the range of phenomena addressed in his earlier papers. Avoiding technicalities, this survey offers a glimpse of Montague’s general theoretical contributions and the more specific descriptive aspects of PTQ from a modern perspective. It also addresses some later developments in formal semantics that were arguably provoked by inadequacies in the theoretical apparatus and descriptive details of PTQ.