Reviewerly/exHarmony huggingface dataset:
Elective courses “Educational Practice and” and “Industrial Practice” as the means of development of professional competences of school students

The subject of this research is the educational-methodological support and organizational-pedagogical conditions for integration of vocational education in the discipline &amp;ldquo;Horticulturist&amp;rdquo; in a public school. Research methodology is based on the fusion of theoretical and empirical approaches with application of the methods of analysis, pedagogical engineering, pedagogical experiment, testing, survey, generalization, comparison, synthesis. The relevance of this study is defined by the importance of preparing citizens (counting public school graduates) for professional self-determination and choice of profession, need for development of the corresponding pedagogical conditions, including vocational education in the disciplines &amp;ldquo;Horticulturist&amp;rdquo;. This article considers elective courses of educational and industrial practices as the effective means of formation of professional competences of school students. The conducted research demonstrated that completion of educational and industrial practices indicates an increase in the level of formedness of professional competences among students. Comparative analysis of the results of 2018 and 2020 monitoring &amp;ldquo;Employment of Graduates&amp;rdquo; illustrated 100% better preparedness of the graduates of 2020 who took elective courses. The scientific novelty relies on the empirical data and formulated conclusions that the considered innovative approach towards modification of public education curriculum through integration with the program of vocational education contribute to achievement of&amp;nbsp; not only standard educational results (personal, metasubject and subject), but also to formation of professional competences useful for selection of future profession and employment.
A Hybrid Salient Object Detection with Global Context Awareness

Salient object detection methods focus on detecting precise salient objects and acquiring clear boundaries, but many approaches fail to achieve both of them. To solve this problem, we present A Hybrid Salient Object Detection with Global Context Awareness. The proposed model consists primarily of Multi-level Feature Enhancement (MFE) and Top-layer Processing (TP). MFE module is a module that fuses high-level features, low-level features and corresponding global context information and it is used for foreground enhancement. TP module generates two kinds of features, one is the representative top-layer features, and the other is the global context information that acts on the corresponding MFE module. Comparing our method with other 12 state-of-the-art salient object detection methods with different evaluation metrics, the experimental results show that our method is superior to other methods.
Salient Attention Model and Classes Imbalance Remission for Video Anomaly Analysis with Weak Label

Recently, weakly supervised anomaly detection has got more and more attention. In several security fields, realizing what kind of anomaly happened may be beneficial for security person who have preparation to deal with. However, lots of studies use global features aggregation or topK mean, and it exists feature dilution for anomaly. An attention model is proposed to generate the segment scores, i.e. we propose a salient selection way based on attention model to efficiently detect and classify the anomaly event. With these selected highlighted features, graphs are constructed. Graph convolutional network (GCN) is powerful to learn the embedding features, anomaly event can be expressed more strongly to classify with GCN. Because normal events are common and easy to collect, there is a problem that the normal and abnormal data are imbalance. An abnormal-focal loss is adapted to reduce influence of large normal data, and augment the margin of normal and different anomaly events. The experiments on UCF-Crime show that proposed methods can achieve the best performance. The AUC score is 81.54%, and 0.46% higher than state-of-the-art method. We obtain 58.26% accuracy for classification, and the normal and anomalies are separated better.
RRT*-Based Algorithm for Trajectory Planning Considering Probabilistic Weather Forecasts

Convective weather and its inherent uncertainty constitute one of the major challenges in the air traffic management (ATM) system, entailing both safety hazards and economic losses. In the present work, we propose a stochastic algorithm for trajectory planning that ensures feasibility and safety of the path between two points while avoiding unsafe stormy regions. The uncertain zone to be flown is described by an ensemble of equally likely forecasts. We design a scenario-based optimal rapidly exploring random tree (SB-RRT*), and we able to dynamically allocate risk during its expansion so that a safety margin is not violated. The solution is a safe continuous trajectory that minimizes the distance covered. We present preliminary results assuming weather to be the only source of uncertainty. We consider an aircraft point-mass model at constant altitude and airspeed with manoeuvres being limited by a minimum turning radius.
A note on the complexity of S4.2

S4.2 is the modal logic of directed partial pre-orders and/or the modal logic of reflexive and transitive relational frames with a final cluster. It holds a distinguished position in philosophical logic, where it has been advocated as the ‘correct’ logic of knowledge; it has also found interesting applications in the temporal logic of relativistic spacetime and the metamathematics of forcing in set theory. The satisfiability problem for S4.2 is PSPACE-complete: this is a result established in an AiML 2004 paper of Shapirovsky [(2004). On PSPACE-decidability in transitive modal logic. In R. A. Schmidt, I. Pratt-Hartmann, M. Reynolds, & H. Wansing (Eds.), Advances in modal logic 5 (pp. 269–287). King's College Publications] where the complexity classification of S4.2 emerges as a consequence of a very general method for constructing PSPACE decision procedures for transitive modal logics. We provide here a ‘classical’ proof in the standard Halpern-Moses style of Halpern and Moses [(1992). A guide to completeness and complexity for modal logics of knowledge and belief. Artificial Intelligence, 54(2), 319–379]. With little extra work, it is shown that the PSPACE-completeness result extends to S4.2n, the multimodal version of S4.2. We prove that the PSPACE-completeness characterisation of monomodal S4.2 persists even if we restrict ourselves to fragments with bounded modal depth, but the problem is NP-complete when it is restricted to formulae with modal depth at most one. The complexity of S4.2 satisfiability for the fragment of the language with a finite number of propositional variables (but unbounded modal depth) remains PSPACE-hard. For a finite language and bounded modal depth, S4.2-satisfiability can be checked in linear time.
State Action Separable Reinforcement Learning

Reinforcement Learning (RL) based methods have seen their paramount successes in solving serial decision-making and control problems in recent years. For conventional RL formulations, Markov Decision Process (MDP) and state-action-value function are the basis for the problem modeling and policy evaluation. However, several challenging issues still remain. Among most cited issues, the inefficiency in utilizing training data is an important factor that causes difficulties in accurately approximating the state-action-value function. We observe that although actions directly define the agents' behaviors, for many problems the next state after a state transition matters more than the action taken, in determining the return of such a state transition. In this regard, we propose a new learning paradigm, State Action Separable Reinforcement Learning (sasRL), wherein the action space is decoupled from the value function learning process for higher learning efficiency. Then, a light-weight transition model is learned to assist the agent to determine the action that triggers the associated state transition. Our convergence analysis reveals that under certain conditions, the convergence time of sasRL is O(T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/k</sup> ), where T is the convergence time for updating the value function in the MDP-based formulation and k is a weighting factor. Experiments on several gaming scenarios show that sasRL outperforms state-of-the-art MDP-based RL algorithms by up to 75%.
A New Method to Protect Video using the Watermark Embedded by SWT Transform and Genetic Algorithm

This research, design a watermarking system for embedding and extracting the watermark in video files.This research presented a new method that is efficient and powerful to embed and extract the watermark, which relied on the conversion of SWT (Stationary Wavelet Transform) on each data frame of the video file that required for by, then this method has been improved by using a genetic algorithm for embedding the watermark.Watermark was efficient and robust against various attacks which was found by measuring the efficiency of the method by calculating the values of the PSNR (Peak Signal-to-Noise Ratio) and the NC (Normalized correlation Coefficient).
Phishing Attacks in Mobile Platforms

On facing this fast-moving world every bit of the world is leading by technology. Mobile phones are the major part of our day to day life. Mobile platforms are said to be the hardware and software environment for laptops, tablets, smartphones and also for other portable devices. Mobile operating systems and browsers lack secure application identity indicators, so the user cannot always identify whether a link takes to the expected application. Phishing is a most popular attack and it gets targeted mostly at financial organizations. It is a type of online theft where the sensitive information is obtained by redirecting the victim to malicious websites. This chapter discusses about Phishing attacks in Mobile Platforms.
Healthcare Analytics: An Advent to Mitigate the Risks and Impacts of a Pandemic

Healthcare analytics is a broad term for a specific facet of analytics which sweeps in a wide sash of the healthcare industry, providing macro- and micro-level insights on patient data, risk scoring for chronic diseases, hospital management, optimizing costs, expediting diagnosis and so on. An efficacious way of implementing healthcare analytics on this sector is to combine predictive analysis, data visualization tools and business suites to get valuable information that can both deliver actionable insights to support decisions, as well as reduce variations to optimize utilization. In order to device a more potent approach to prepare for the next pandemic, the clinical data has to be improved which would provide more granulated information to augment treatment effectiveness and success rate. Confirmatory data analysis combined with predictive modeling can be used to furnish unexpected gaps in terms of getting the required clinical data, which is vital for the success of this approach. The healthcare analytics domain has got the potential to monitor the health of the masses to identify disease trends and can provided enhanced health strategies based on demographics, geography and socio-economics. Hence, healthcare analytics is indeed an advent to counter the imminent threats of outbreaks such as epidemics and pandemics provided it gets the necessary course of action to fulfill its grail.
Remittances, Finance and Industrialisation in Africa

The paper assesses how remittances directly and indirectly affect industrialisation using a panel of 49 African countries for the period 1980-2014. The indirect impact is assessed through financial development channels. The empirical evidence is based on three interactive and non-interactive simultaneity-robust estimation techniques, namely: (i) Instrumental Fixed Effects (FE) to control for the unobserved heterogeneity; (ii) Generalised Method of Moments (GMM) to control for persistence in industrialisation and (iii) Instrumental Quantile Regressions (QR) to account for initial levels of industrialisation. The non-interactive specification elucidates direct effects of remittances on industrialisation whereas interactive specifications explain indirect impacts. The findings broadly show that for certain initial levels of industrialisation, remittances can drive industrialisation through the financial development mechanism. Policy implications are discussed.
Private Information Retrieval from Coded Storage in the Presence of Omniscient and Limited-Knowledge Byzantine Adversaries

This paper investigates an adversarial model in the scenario of private information retrieval (PIR) from n coded storage servers, called Byzantine adversary. The Byzantine adversary is defined as the one altering b server responses and erasing u server responses to a user's query. In this paper, two types of Byzantine adversaries are considered; 1) the classic omniscient type that has the full knowledge on n servers as considered in existing literature, and 2) the reasonable limited-knowledge type that has information on only b+u servers, i.e., servers under the adversary's control. For these two types, this paper reveals that the resistance of a PIR scheme, i.e., the condition of b and u to correctly obtain the desired message, can be expressed in terms of a code parameter called the coset distance of linear codes employed in the scheme. For the omniscient type, the derived condition expressed by the coset distance is tighter and more precise than the estimation of the resistance by the minimum Hamming weight of the codes considered in existing researches. Furthermore, this paper also clarifies that if the adversary is limited-knowledge, the resistance of a PIR scheme could exceed that for the case of the omniscient type. Namely, PIR schemes can increase their resistance to Byzantine adversaries by allowing the limitation on adversary's knowledge.
Distributed Training of Deep Neural Network for Segmentation-Free Telugu Word Recognition

This paper presents a distributed modeling approach of deep neural network (DNN) classifier for segmentation-free Telugu word recognition. The DNN is a hybrid neural network model. It involves the convolutional neural network (CNN) and recurrent neural network (RNN) layers. Moreover, the connectionist temporal classification (CTC) layer gives the output. The presence of CNN layers requires advanced computer systems consisting of graphics processing units (GPUs) for speedy training of a DNN model. The distributed modeling approach involves the distribution of load across a TPU. As a comparative study, we also train the same model on CPU and GPU systems. Our method aims to handle large batch sizes and reduce modeling time, which is precious to researchers. The word error rate (WER) of the test set is 5%, which is promising.
Human Pose-Estimation and low-cost Interpolation for Text to Indian Sign Language

Throughout India, the deaf community comprising millions of people uses Indian Sign Language. However, India houses only about 300 certified interpreters causing a lack of trained teachers and teaching materials in the country. This creates a language barrier with no means for translation without an interpreter. Previous approaches for creating a translation medium between spoken languages and sign languages are heavily dependent on Motion Capture technologies which are extremely expensive and not ideal for use by non-professionals even though highly accurate. Thus, computer vision approaches can be adopted, however, such approaches are highly dependent on huge labeled datasets. Datasets for sign language are hard to acquire and even more so for Indian Sign Language which has received little to no predominant attention in the past years. Based on that, this paper aims at generating 2D pose data of Indian signers from existing single-camera RGB videos. However, the videos are expected to be of varying quality, made using differently positioned cameras and consists of multiple signers against unified single-signer datasets. Unlike other applications, such as gait analysis, activity recognition, for sign-language face and hand key points play a major role. This paper represents the sub-project which is planned to be implemented as the preliminary stage to an actual ISL translation medium. In this paper, video is initially stabilized to reduce jitters and correct the perspective errors in videos. Then, 2D human-pose key points from the video are extracted and saved. Further, this saved data is analyzed and solved for missing key points which arise due to occlusions inherent in signs. For this, a low-cost trajectory interpolation method is used to fill the missing joint points. Overall, this vision-based motion capture approach gives a fairly non-distorted 2D pose model that is further corrected by using the information from neighboring frames simultaneously reducing time and computational cost. The resulting dataset serves as the base for further phases and research work.
Advancing interactivity: graphic designers’ practice-based contribution to developing the Web

This article examines how graphic designers’ innovations in web design revealed the potential and capabilities of interactivity as a communication experience tool. During the early World Wide Web (1995–2008), graphic designers’ practice-based contribution to interactivity enhanced how people experienced this new technological domain. Few designers contributed to the scholarly discussion of interactivity. The main academic fields that did—Human–Computer Interaction, Interaction Design and Communication Theory—disregarded graphic design's contribution, slowing knowledge production around the nature and potential of interactivity. The article compares the three fields’ main ideas about interactivity to an analysis of 47 websites from 2008. It argues that although the idea of interactivity emerged as a transdisciplinary phenomenon, graphic designers’ practical implementation of interactivity in web design through the development of qualities of intuitiveness, enablement, responsiveness, connectivity, intentionality and experience extended its conceptualization beyond technical standards, functionality and theorization.
The Design of Access Control by using Data Dependency To Reduce the Inference of Sensitive Data

From the back-end data system point of view, the primary personal information protection mechanism is to block the direct accessing of sensitive data. The possibility that sensitive data may be indirectly inferenced by public information, have not been addressed. In United States, there are cases and discussions about "Mosaic theory". And responsibilities of data holders were legally stated. But no known researches were invested to create a responsible mechanism. This research explores the functional dependencies, and compute risky column sets based on them. We can then process users' queries and initiate protection operation if risky data are involved.
Research on Dialogue Generation Algorithm Based on Explicit Weighted Context

At present, generating responses based on neural network learning has become a hot spot, and has gradually entered a new stage of pre-training language model. The intelligence and transferability of the dialogue system in the open domain is becoming more and more obvious, but there are still many problems, such as single reply, logic contradiction, general security answer. So as to solve the weakness in reply, we starting from the context, this paper uses the method of Point Mutual Information (PMI)to calculate the relevant weight between the context and the current dialogue to explicitly weight the context and give full play to the effective information of the current dialogue. Further inputing into the dialogue generation of the pre-training language model for fine-tuning. In the experimental evaluation, we make a comprehensive analysis from three aspects: automatic evaluation, objective index calculation and manual evaluation. The results show that our method of explicit weighted context coding will enrich the coding information further generating more diverse and meaningful responses, can be significantly improved compared with the baseline model.
Self-calibrated Attention Residual Network for Image Super-Resolution

Deep Convolutional Neural Networks (DCNNs) have achieved remarkable performance in single image super-resolution (SISR). However, most SR methods restore high resolution (HR) images from single-scale region in the low resolution (LR) input, which limits the ability of method to infer multi-scales of details for high resolution (HR) output. In this paper a novel basic building block called self-Calibrated residual block (SARB) is proposed to solve this problem. SARB consists of carefully designed multi-scale paths, which can capture rich structure information from different scale. In addition, self-Calibrated residual block is introduced to adaptively learn informatively context to make network generate more discriminative representations. These blocks are composed of self-calibrated attention residual network (SARN) for image super-resolution. Experiments results on five benchmark datasets demonstrate that the proposed SARN achieves comparable results compared with the previous most of the state-of-the-art methods.
Exploration and Practice of "Two Courses in One" Teaching of Computer Foundation and Programming in Independent Colleges

Promoting the teaching of "Two Courses in One" of computer foundation and programming is helpful to the effective promotion of computer science and technology in our country. This paper mainly introduces the teaching advantages of "Two Courses in One" in computer foundation and program design, discusses the implementation elements of "Two Courses in One" teaching scheme, and proposes a three-stage "Two Courses in One" teaching design scheme of reorganizing course content, optimizing teaching scheme and arousing deep integration. The integration of online teaching, situational teaching and cooperative teaching promotes the implementation of the "Two Courses in One" reorganization teaching plan, aiming at optimizing the teaching environment and deepening the educational purpose.
Optimized Predictive Coding Of 5d Light Fields

With the emergence of Light Field (LF) technology, the number of dimensions representing light has once again increased. 4D light fields captured with additional temporal information per ray or as assemblies of rays include the 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> dimension, namely time and thus produce 5D light fields. This is very crucial when we have moving objects in the scene. In the recent years, research has paved way to several ideas on efficient 4D light field compression. However, techniques for compression and storage for higher dimensions is still an open challenge. In this paper we have introduced a low-complexity predictive coding of 5D light fields by automatic generation of per frame customized coding structure exploiting both spatial and temporal neighbors. Evaluations with HEVC codec shows an increase of more than 1.4 dB gain in quality.