Reviewerly/exHarmony huggingface dataset:
Specifics of the application of the socio-cultural approach in the process of developing individual’s information and legal competence

The features of the process of development of information and legal competence of a person are considered. The socio-cultural approach is substantiated and updated, the feature of which is the development of leisure self-organization of the individual in the context of a conscious choice of forms of leisure activities that ensure the development of socio-cultural activity, manifested in the initiative to master professional competencies. A review of the leading concepts on socio-cultural activities was carried out, with the emphasis on creating conditions that ensure the development of information and legal competence. With the justification of innovative approaches to self-education and professional development of the individual, emphasis is placed on gamification, which allows the learning process to be carried out not only in classrooms, but also in the process of extracurricular activities. The features of digitalization and informatization technologies are revealed, their role in the transformation of professional activities practices, which allow improving the process of developing educational programs that meet the requirements of modern society, is noted. An analysis of sources on the problems of pedagogical support for the process of personality formation led to the conclusion that the modern education system should be aimed at developing the skills of independent creative thinking, focused on achieving a high level of professionalism, and such opportunities have socio-cultural activities that have in their arsenal technological means, forms and methods of effective training and education
Image-based Jointly Sparse Maximum Margin Criterion with Pairwise Constrain for Feature Extraction

In this paper, we propose an image-based feature extraction framework. To our best knowledge, there are few methods to combine image feature extraction and pairwise constrain (PC) jointly and directly. We fill the gap between them. With the PC, we devise an objective function, taking the PC and image feature extraction consideration jointly. Besides, we introduce maximum margin criterion into the loss function, making all positive constrains and negative constrains take part in the process of feature extraction. Finally, the solution of the new framework can be deduced into two sperate individual problems. Comprehensive experiment results show our framework effective.
Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction

The cold-start problem is one of the main challenges in recommender systems and specifically in collaborative filtering methods.Such methods, albeit effective, typically can not handle new items or users that do not have any prior interaction activity in the system.In this paper, we propose a novel twostep approach to address the cold-start problem.First, we view the user-item interactions in a positive unlabeled (PU) learning setting and reconstruct the interaction matrix between users and warm items, detecting missing links and recommending warm items to existing users.Second, an inductive multi-target regressor is trained on this reconstructed interaction matrix and subsequently predicts interactions for new items that enter the system.To the best of our knowledge, this is the first time that such a two-step PU learning method is proposed to address the cold-start problem in recommender systems.To evaluate the proposed approach, we employed four benchmark datasets from movie and news recommendation domains with explicit and implicit feedback.We compared our method against three other competitor approaches that address the cold-start problem and showed that our proposed method significantly outperforms them, achieving in a case an increase of 16.9% in terms of NDCG.
TaskRec: Using Causal Graph to Debias in Task Recommendation System

In recent years, crowdsourcing has gradually become an emerging business model. Requesters from different backgrounds on the network put their requests on the crowdsourcing platform and hand them to non-professionals from different fields to complete, which not only saves working time, but also greatly reduces the economic cost. However, with the increase of crowdsourcing tasks and the expansion of platforms, the task information of crowdsourcing platforms tends to be abundant and redundant, which makes workers face the problem of information overload. It is very difficult to match the tasks they need from thousands of tasks. Therefore, task-based recommendation system emerges as The Times require, which can help workers quickly select the tasks they want to do from the massive tasks. However, there are still some problems in the current task recommendation system. For example, the existing task recommendation system tends to recommend simple and easy common tasks to workers, while the exposure probability of those complex and difficult tasks is greatly reduced, so exposure bias appears in the task recommendation system. Deviation brings great hidden dangers to crowdsourcing platforms. For example, complex tasks are hidden in the long tail for a long time and no one pays attention to them, so the task completion rate will be greatly reduced and the satisfaction of requesters will also be reduced. In this paper, an unbiased task recommendation method based on causal graph analysis is proposed to remove the bias in task recommendation system and make tasks better match.
Sentiment Analysis of Standardization using Deep Belief Network: a case of Indonesian National Standards

Free trade era requires increasing the competitiveness of local products in the global market, through standardization. The standardization policy is including how to plan, formulate, establish, implement, enforce, maintain, and supervise National Standard, e.g Indonesian National Standard called SNI. SNI is useful in order to create competitiveness and consumer protection. The consistency of standardization shows through standardization activity, that requires time and high resources. The number of SNI and the breadth of products distribution cannot be monitor simultaneously in the same years, also another obstacle in standardization activities. Therefore the aim of this study is to find a classification of standardization activity, which to becomes an important part of evaluation policy. The development of media plays a role in policy making, information and opinions from the media can change standardization's policy strategies. The contribution of this research is using text mining from standardization publication in media, to find useful knowledge. It's useful to build an input alternative, in the form of media sentiment analysis in standardization activity, that has never been done before. It gives an agile method for dealing with rapid changes in the standardization process. This study uses a deep belief network (DBN) method for the classification of media sentiment. Besides using DBN, this study also compares DBN with other classification methods, namely Naive Bayes (NB) and Support Vector Machine (SVM). These research results show the accuracy of the classification model with DBN reaches 77%, NB reaches 74% and SVM reaches 77%. Moreover, the results show that the most negative sentiment is 19% and the most positive sentiment is 29.20%. Both of the sentiments are the member of class about implementation and the mandatory regulation of SNI, and those aspects becoming media concentration. Standardization situation is expected to be captured as the output of this study so that it can contribute to improving the standardization policy in Indonesia.
PCFBCD: An Innovative Approach to Accelerating Collaborative Filtering

Recommendation system is important for internet applications such as Netflix or Tiktok. Collaborative Filtering algorithm is a graph computing algorithm commonly used in recommendation systems. In this paper, we propose an innovative approach called PCFBCD, which stands for Parallel Collaborative Filtering using Block Coordinate Descent to accelerate Collaborative Filtering. First we introduce two new algorithms, Multiple Computation and Permutation (MCP) and Normal Parallel Processing (NPP) that both use BCD method to optimize CF algorithm for higher level parallelism. Based on it we propose a hardware architecture that fully utilize the parallelism. Then we simulate PCFBCD architecture using a general-purpose architecture simulator. Experimental results show that our new approaches achieve 3. 10x to 3. 58x speedup compared to traditional method.
Subsequence Covers of Words

We introduce subsequence covers (s-covers, in short), a new type of covers of a word. A word C is an s-cover of a word S if the occurrences of C in S as subsequences cover all the positions in S. The s-covers seem to be computationally much harder than standard covers of words (cf. Apostolico et al., Inf. Process. Lett. 1991), but, on the other hand, much easier than the related shuffle powers (Warmuth and Haussler, J. Comput. Syst. Sci. 1984). We give a linear-time algorithm for testing if a candidate word C is an s-cover of a word S over a polynomially-bounded integer alphabet. We also give an algorithm for finding a shortest s-cover of a word S, which in the case of a constant-sized alphabet, also runs in linear time. Furthermore, we complement our algorithmic results with a lower and an upper bound on the length of a longest word without non-trivial s-covers, which are both exponential in the size of the alphabet.
DEVELOPMENT OF BEHAVIOUR OF SPECIAL NEED CHILDREN WITH HEARING IMPAIRED IN THE CLASSROOM

In this paper I have described the symptoms, causes and remedies of the problem behaviour of the children in the class of Std. 5 to 8 i.e. 10 to 12 years of age. There are many types of problem behaviour but based on my experience in this paper as a teacher I have tried to know the reasons behind the problem behaviour in the children in my class and try to find a solution to it. It is necessary to know the different aspects and causes of it, so the symptoms and causes are also included along with the solution
Application of GPU Accelerated Deep Learning Neural Networks for COVID-19 Recognition from X-Ray Scans

In this paper we are presenting the results of creation and training of a stand-alone expert system aimed at detection and diagnosis of COVID infection, that is based on automatic readings of X-ray imaging X Ray, which determines whether the patient has COVID pneumonia. The system is realized with deep learning neural networks and is accelerated with GPU utilized, instead of CPU.
Efficient Verifiable Unbounded-Size Database From Authenticated Matrix Commitment

Verifiable database with update (VDB) enables the client to store a large dataset in the outsourced database, and then efficiently query and update the data with a new value. It is attractive for the merits of checking the validity of the queried data and detecting the malicious actions of tampering with the outsourced database concurrently. However, the database in the context of VDB is merely suitable to store a fixed-size dataset. Hence, VDB is inapplicable to the unbounded-size database that provides the capability to store and manage the arbitrary-size datasets in the incremental manners. To circumvent the weaknesses, we research on the verifiable unbounded-size database with update (VUSDB). The VUSDB is sufficient for multiple clients to store their own arbitrary-size datasets in the database that has already contained some datasets. In order to design a VUSDB scheme, we first put forward a primitive called authenticated matrix commitment and give a scheme. This primitive is qualified to commit to a collection of ordered data represented in the form of matrix, and assure the ownership of the opened data. Then we utilize the authenticated matrix commitment scheme to construct a VUSDB scheme. The performance evaluation shows that the proposed schemes are efficient and practical.
Interoperability in Blockchain based Healthcare

Technological advancements in the field of Blockchain and Cryptocurrency have continued to improve the transparency, global connectivity, and trust in today's internet. As a result, adoption of Blockchain Based Application for plethora of domains continue to grow. Interoperability is the capability of electronic health records (EHRs) and other healthcare information management systems to communicate with each other and to exchange information seamlessly. An effective health information exchange (HIE) delivers data about the patient | regardless of where care or services have been delivered | to the clinician and the care team quickly and securely. Creating a single source of truth is integral to semantic and structural interoperability. Instead of having sections of patient records scattered across several facilities and systems, all available patient information is gathered in a central hub so that care teams can easily access and view all of the same information. By making this information centralized and accessible, providers can budget their available time accordingly. Instead of spending hours mining information from disjointed, complicated, and varying technologies and locations, they can focus on more meaningful patient care interactions, conversations, and treatment plans. Despite the interest in using blockchain for healthcare interoperability, however, little information is available in the concrete architectural styles and patterns for applying blockchain to healthcare apps. With these capabilities we built an web application which performs as an single destination portfolio for every participants of the healthcare sector where the participant like patients can perform activities such as booking an appointment, requesting for medicine and acknowledging feedbacks and also participants like pharmacist and hospital can approve the patient requests accordingly, All this operations was conducted successfully by following traditional approaches like peer-to-peer network.
LCFIL: A Loss Compensation Mechanism for Latest Data in Federated Incremental Learning

Federated Learning(FL) has become a research hot spot in recent years because of its advantages in privacy protection. With FL, user data no longer needs to be transferred to a data center, but only trained on edge devices. Instead, the parameters of the trained model are sent to the server. FL can also use the computing power of each device to protect data from being compromised. On the other hand, in the mobile edge computing scenario, there is a large amount of data, smart phones, wearable devices and other IOT devices can not collect all the data at the beginning, the device has to waste resources and time until all the data is collected to train its local model. Furthermore, in some scenarios, the model needs to be deployed in a short period of time. Thus, incremental learning (IL) is needed, which allows continuous training with continuously collected data. In this paper, we first design a new birth-epoch computing scheme and a new batch-oriented aggregation method. Then, around these, we reformulated the Federated Incremental Learning (FIL) framework which combines FL and IL in the IoT scenario. In addition, we propose an algorithm called Latest-Compensation Federated Incremental Learning (LCFIL) that emphasizes new arrivals by adjusting the loss of each batch of data. A subtle loss function is designed to improve the convergence speed and learning performance. Extensive experiments have shown that LCFIL is effective in different data or model or data distribution Settings. Index Terms-Federated Learning, Internet of Things, Incremental Learning
Efficient Scalar Multiplication on Koblitz Curves with Pre-computation

Scalar multiplication is the basic operation in elliptic curve cryptography. The double-base number system (DBNS) is an effective tool for speeding up scalar multiplication on elliptic curves. This paper proposes a novel decomposition algorithm for scalar n based on the specific double bases $$(\overline{\tau },\tau )$$ instead of the ordinary window $$\tau $$ -NAF. On $$\mu _{4}$$ -Koblitz curves, we evaluate the cost of our scalar multiplication method and compare it to related work. We also consider scalar multiplication using LD coordinates. Experiment results show that $$\mu _{4}$$ -Koblitz curves perform well.
Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments

In both industrial and service domains, a central benefit of the use of robots is their ability to quickly and reliably execute repetitive tasks. However, even relatively simple peg-in-hole tasks are typically subject to stochastic variations, requiring search motions to find relevant features such as holes. While search improves robustness, it comes at the cost of increased runtime: More exhaustive search will maximize the probability of successfully executing a given task, but will significantly delay any downstream tasks. This trade-off is typically resolved by human experts according to simple heuristics, which are rarely optimal. This paper introduces an automatic, data-driven and heuristic-free approach to optimize robot search strategies. By training a neural model of the search strategy on a large set of simulated stochastic environments, conditioning it on few real-world examples and inverting the model, we can infer search strategies which adapt to the time-variant characteristics of the underlying probability distributions, while requiring very few real-world measurements. We evaluate our approach on two different industrial robots in the context of spiral and probe search for THT electronics assembly.
Integration of Reconfigurable RF Manifolds with Software Defined Systems

Continued advances in software defined systems (SDS) have greatly enabled agility to RF spectrum search and analysis. Recognizing the criticality for dynamic spectrum access across 5G and 6G networks, correlating complex RF waveforms have leveraged digital wideband arrays and open architectures for data and information additives. This paper examines how state-of-the-art SDSs will need to interface with novel RF manifold technologies resulting in architectures that demonstrate the combined sensing functionality (communications, radar, etc.) operating under cooperative and non-cooperative conditions. These hybrid-SDS concepts are likely to extend the use of machine learning applied to complex waveform analysis/generation
Image Segmentation Algorithm Based on Attention Mechanism and Jump Connection

With the development of deep learning, convolutional neural networks have become the mainstream of computer vision algorithms. In recent years, the biggest problem of applying convolutional neural networks to image segmentation is that they cannot achieve accurate segmentation of the last layer, also cause resolution loss when extracting features, and cannot meet the demand of different pixels requiring different context dependencies. To address these issues, we add an attention mechanism and a jump feature fusion method to deeplabv3+ so that features are extracted without severe feature loss and a broader range of contextual information can be encoded into local features. The feature map is further enriched by adding a module combining bilinear upsampling and deconvolution in the process of feature restoration. Compared to previous algorithms, the results of this algorithm are superior. A performance of 85.73% is achieved on PASCAL VOC2012 using the proposed model.
Table of Contents Recognition using Deep Learning

As far as modern technology is concerned, Optical Character Recognition (OCR) technology is becoming more and more stable. Electronic files are easier to store than paper files, do not take up too much space, and are less likely to suffer from data loss. However, the Document’s Table of Contents (TOC) provides the overall structure of the document, such as chapters and appendices. Therefore, TOC recognition can analyze OCR documents more efficiently. In this study, we developed a novel deep learning method based on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Connectionist Temporal Classification (CTC) to identify TOCs. Finally, the experimental results show that our proposed method outperforms other methods.
Time Crystals in Optics

Optical techniques could help take “time crystals”—examples of temporal order and symmetry-breaking analogous to solid-state crystals in space-out of the lab and into real-world use.
Parallelizing VOCA to Reduce the Latency

VOCA (Voice Operated Character Animation) is an end-to-end system that generates 3D facial animations. In this work, we parallelized VOCA to reduce its latency. In this research, we made three design decisions : data parallelism over task parallelism for simplicity, multiprocessing over multithreading for high performance , and shared memory over message copying to reduce communication overhead. We evaluated the performance of our proposed method with a various dataset on different Google Cloud virtual machine types. With our parallelization method, we showed a performance close to the theoretical speed-up with an audio of 1 hour length where the latency is reduced from 3453 sec to 587 src using 6 processors. Our parallelization method works well especially when the input text and speech length are long which occurs frequently in the real-world settings.
Bounding Generalization Error Through Bias and Capacity

We derive generalization bounds on learning algorithms through algorithm capacity and a vector representation of inductive bias. Leveraging the algorithmic search framework, a formalism for casting machine learning as a type of search, we present a unified interpretation of the upper bounds of generalization error in terms of a vector representation of bias and the mutual information between the hypothesis and the dataset.
A Vision Enhancement Network for Image Quality Assessment

With the development and update of electronic equipment, image quality assessment has become one of the hot topics. Recently, digital image processing and convolutional neural networks (CNN) have made significant progress. However, the models based on human vision characteristics and neural feedback have poor performance in previous studies. Inspired by this, we propose a CNN-based network, vision enhancement network (VE-Net). It can filter images adaptively according to the key regions. Key regions are extracted with the incentive support method from deep information learned by CNN. The adaptive filter uses Laplacian filter and Gaussian filter. Laplacian filter adopts a linear lifting algorithm, aiming to attach the image texture to the original image. Squared earth mover’s distance (EMD) loss is selected to predict the image aesthetic score distribution. VE-Net is evaluated on AVA dataset for the regression task and the classification task. Experiments show the superiority of VE-Net.