Reviewerly/exHarmony huggingface dataset:
Development of a Semiautomated Search Tool to Identify Grading From Pathology Reports for Tumors of the CNS and Prostate Cancers

This study demonstrates the functionality of semiautomated algorithms to classify cancer-specific grading from electronic pathology reports generated from military treatment facilities. Two Perl-based algorithms are validated to classify WHO grade for tumors of the CNS and Gleason grades for prostate cancer.Case-finding cohorts were developed using diagnostic codes and matched by unique identifiers to obtain pathology records generated in the Military Health System for active duty service members from 2013 to 2018. Perl-based algorithms were applied to classify document-based pathology reports to identify malignant CNS tumors and prostate cancer, followed by a hand-review process to determine accuracy of the algorithm classifications. Inter-rater reliability, sensitivity, specificity, positive predictive values (PPVs), and negative predictive values were computed following abstractor adjudication.The high PPV for the Perl-based algorithms to classify CNS tumors (PPV > 98%) and prostate cancer (PPV > 99%) supports this approach to classify malignancies for cancer surveillance operations, mediated by a hand-reviewed semiautomated process to increase sensitivity by capturing ungraded cancers. Early detection was pronounced where 33.6% and 50.7% of malignant records retained a CNS WHO grade of II or a Gleason score of 6, respectively. Sensitivity metrics met criteria (> 75%) for brain (79.9%, 95% CI, 73.0 to 85.7) and prostate (96.7%, 95% CI, 94.9 to 98.0) cancers.Semiautomated, document-based text classification using Perl coding successfully leveraged identification of WHO and Gleason grades to classify pathology records for CNS tumors and prostate cancer. The process is recommended for data quality initiatives to support cancer reporting functions, epidemiology, and research.
Cross Stage Interweaved Fusion Network for Salient Object Detection

Salient object detection, in the artificial intelligence field, is a fundamental and significant computer vision (CV) task which intends to recognize and segment the most remarkable objects. This paper proposes a novel SOD architecture named Cross Stage Interweaved Fusion Network (CSIFNet) which considers features both in the first stage of encoder and the second stage of decoder. In a specific, we employed the following four modules: Global Feature Interweave (GFI) module, Attentional Guidance Head (AGH) module, Residual Aggregation (RA) module, and Self Enhance (SE) module to construct the architecture of cross-stage and multi-scale feature aggregation. By adopting the designed strategy, the detailed features can be indirectly passed through to deeper layers between two main stages. Compared with nine state-of-the-art salient object detectors, CSIFNet performs well on five public datasets.
Time series forecasting for port throughput using recurrent neural network algorithm

Container throughput is a critical factor to appraise a seaport performance and predicting this measure has played a vital role in port operations. Within the scope of effective decision making, predictive techniques have been presented for forecasting port throughput. Based on observing throughput variations in seasonal patterns and business cycles, the obtained data might help port authority to make better and more accurate decisions and improve seaport productivity. By applying predictive methods to the throughput data of Singapore and Busan port, the decision-makers can assess forecasting accuracy by measuring prediction errors. The numerical tests show that the echo state network (ESN) provides a high level of accuracy for predicting container throughput. As a result, the port managers could make use of this decision support strategy to foresee short-term plans for improving facilities, establishing effective cargo loading and unloading plans, consequently ensuring port productivity and profitability.
A Hierarchical Long Short-Term Memory Encoder-Decoder Model for Abstractive Summarization

Abstractive summarization is the task of generating concise summary of a source text, which is a challenging problem in Natural Language Processing (NLP). Many recent studies have relied on encoder-decoder sequence-to-sequence deep neural networks to solve this problem. However, most of these models treat the input as a sequence of words at the same level during the encoding process. This will make the encoding inefficient, especially for long input texts. Addressing this issue, in this paper we propose a model to encode text in a hierarchical manner, which helps to encode information in a way that is consistent with the nature of the text: the text is synthesized from sentences, and each sentence is synthesized from words. Our proposed model is based on Long Short Term Memory model that we called HLSTM (Hierarchical Long Short Term Memory) and applied to the problem of abstractive summarization. We conducted extensive experiments on the two most popular corpora (Gigaword and Amazon Review) and obtain significant improvements in comparison with the baseline models.
A Case for Splitting a File for Data Placement in a Distributed Scientific Workflow

In a scientific workflow, optimal file placement in a distributed system has been a major concern for researchers. A good file placement would ensure that required files are placed locally; hence, would require minimum data migration at run time. The problem is amplified by the fact that the storage space available is finite and it may not be possible to place all the required files at a node. When all the files cannot be placed at a node, the proposed works in literature employ different intelligent tactics that choose which files will be placed in the node. The files which are chosen are placed in their entirety, while those that are discarded are discarded in their entirety. There is a different way to place files, which is to fragment each file and place the fragments of the files in a node instead of the entire file. In this work, we investigate whether file placement in fragments is a better option than placing and discarding entire files.
Multi-Modal Visibility Improvement under Abnormal Weather Conditions using Contextual Conditional GAN

Removal of multiple weather-induced effects in a single image remains an open problem, although several methods addressing single effect removal have been proposed. We present a single method for removing multiple weather-induced effects that are fog, haze, rain streaks, and snowflakes. The proposed method is a unified model, based on context encoder and conditional generative adversarial network, with a single set of parameters. We demonstrate that our model is effective at improving visibility in weather degraded images. Though the training is done on synthetic data the model generalizes on real images during testing.
A novel security framework for densely populated Internet of Things users in pervasive service access

User density in the Internet of Things (IoT) platform changes with different applications and user demands. Security requirements are adaptable based on the services and the authentication servers attached to the resources. However, the lack of unanimous security measures increases the complexity and authentication level in a user-centric scenario. This article introduces an Ascendable Authentication Framework (AAF) for balancing security and services in a dense IoT by considering this drawback. The proposed framework adopts a high level of authentication and user services through unanimous keying and allocations. This process is defined within the framework covering the service provider and end-users. In this authentication, hyper-elliptic curve cryptography is exploited for service authentication, whereas the key distribution follows a discrete allocation process. Here the discreteness and continuity in authentication are determined using regression learning. The change in distribution function determines the authentication level and redeeming security for further service disseminations. Therefore, the regression sequence is modified without augmenting additional keys in a pervasive manner. This reduces the computation complexity, service authentication time, response latency, and failures.
Evaluation of Teaching Performance in the Context of Emergency Remote Teaching

At the international level, guaranteeing educational quality through the evaluation of teaching performance is a priority. In the Peruvian context, these processes are monitored by the National System for the Evaluation and Accreditation of Educational Quality (SINEACE) and by the National Superintendence of Higher Education (SUNEDU). In this context, the purpose of this study is to evaluate the performance of teachers in a professional primary education program at a private university in Lima-Peru in the context of emergency remote teaching. For this purpose, a sample of students of the primary education professional program were surveyed and evaluated a total of 63 subjects taught by a total of 22 teachers. Finally, it was concluded that teachers present optimal levels of performance in the mastery of the course, in the teaching and learning process, in the relationship between teacher and student, in the ethical dimension, and in the humanistic dimension.
Difference of Emotional Arousal Between Real Shooting and Cartoon Pictures in VR Environment -A Comparative Study Based on EEG Signals

This paper focuses on the emotional experience caused by VR image style. Through the design of cognitive psychology experiment, the influence of real shooting and cartoon images on emotional arousal in VR environment is compared based on EEG signals. We collected and analyzed the EEG signals of 30 subjects when watching two styles of positive, neutral and negative emotional pictures. It was found that after viewing the three groups of emotional images, there were statistical differences in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> wave activities between the two styles, and the frontal region and temporal region produced significant differences in EEG power between the different styles. At the same time, SVM was used to classify the original signal, and it was found that the EEG signals in high-frequency <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> band could be well divided into real shooting and cartoon groups, and the classification accuracy reached 93.66%. The results show that real shooting images in VR environment can trigger stronger cortical activity and bring greater emotional arousal to viewers.
The Search for Forms of Interaction between Technical Institutes, Regional Authorities and the Industries in the 1920s-1930s (by the Example of Ekaterinburg - Sverdlovsk)

In the 1920–1930s the Ural industries and regional authorities were in need of qualified economists and engineers, which resulted in establishing in Ekaterinburg (Sverdlovsk) its own university, technical institutes, and the Ural Branch of the USSR Academy of Science. The article reveals the reasons of failures in technical institutes’ development and the mistakes in training of economic specialists. The educational organizations and scholarly institutions faced the lack of funding from national and local authorities, whose intention was to make the process cheaper and faster, which resulted in disrupting operation of technical institutes and decreasing the quality of the students’ training. Such attitude on the part of the authorities became the crucial factor for the crises of higher education in the city. Poor communication with the local industry, ideologically based restrictions in teaching of economic disciplines, as well as separate reporting lines of technical institutes, higher education establishments and scholarly institutions also negatively affected the situation. The article proves that the best solution for the city could have been establishing a single research and education center at the State University, which was the original intention of its foundation in 1920. The lack of funding and inconsistent policy of the national and local authorities have led to abandoning the initial program of the university development. Only in 2019, they returned to the program at a new level, when they started to establish a research and education cluster at the Ural Federal University.
Efficient Pyramid-Structured Networks Using Channel Separating Gate

Convolutional neural networks (CNNs) have been brought noticeable improvement on numerous image processing applications. In particular, spatial pyramid structures have been widely used, but the number of required parameters increases exponentially as the network deepens. Therefore, we propose a channel separating gate (CSG) that can determine the number of channels in convolutional layers according to the spatial level. For a given spatial level, the CSG separates the channels of feature maps for use at the smaller level. Consequently, the standard non- pooling model can be altered to an efficient pyramid structure by performing the proposed method repeatedly. For single image super-resolution (SR) and deblurring, we demonstrate that our method improves the baseline network in terms of peak signal- to-noise ratio (PSNR) and floating-point operations (FLOPs).
TEMoD: Target-Enabled Model-Based De-Drifting of the EOG Signal Baseline using a Battery Model of the Eye

The electrooculography (EOG) signal baseline is subject to drifting, and several different techniques to mitigate this drift have been proposed in the literature. Some of these techniques, however, disrupt the overall ocular pose-induced DC characteristics of the EOG signal and may also require the data to be zero-centred, which means that the average point of gaze (POG) has to lie at the primary gaze position. In this work, we propose an alternative baseline drift mitigation technique which may be used to de-drift EOG data collected through protocols where the subject gazes at known targets. Specifically, it uses the target gaze angles (GAs) in a battery model of the eye to estimate the ocular pose-induced component, which is then used for baseline drift estimation. This method retains the overall signal morphology and may be applied to non-zero-centred data. The performance of the proposed baseline drift mitigation technique is compared to that of five other techniques which are commonly used in the literature, with results showing the general superior performance of the proposed technique.
A Time-Bound Continuous Authentication Protocol for Mesh Networking

This paper proposes a novel lightweight authentication protocol for fast and efficient continuous authentication of constrained Internet of Things (IoT) mesh network devices. An initial static authentication takes place at the beginning of a session, during which the secret is securely shared between two parties. Once the session is established, the continuous authentication scheme generates time-bound tokens using the shared secret, a time-varying component, and a random value. These are used to verify the identity of the connected device in fixed time intervals. These time-bound tokens, which only remain valid for a predetermined time frame and ensure the continuity aspect of the mechanism, can be linked back to the original secret at the server for verification. Therefore, lightweight continuous authentication is achieved using only a few low complexity cryptographic operations, like hash and MAC, without the need to perform costly cryptographic operations. In case of failure, the node is temporarily blocked for an exponential function period of past occurrences of failure. Performance evaluation over a mesh routing protocol shows that our proposal fulfills lightweight and low bandwidth constraining requirements while satisfying the security requirements of an authentication scheme.
FORMATION OF CREATIVE THINKING OF STUDENTS ON THE BASIS OF SOCIAL AND PEDAGOGICAL DESIGN

The article is devoted to the problem of researching the creative abilities of junior schoolchildren, the specificity of the formation of which has long been central to the problems of pedagogical science, and today does not lose its relevance. This problem with the emergence of education as a specific form of human interaction, the purpose of which is to transfer the experience of previous generations. Thinking, as one of the mental processes, manifests itself in different forms. Creative thinking is defined as the ability to find solutions and analyze a problem in non-standard circumstances. Creative thinking of junior schoolchildren is an intense thought process that promotes the activation of the cognitive activity of junior schoolchildren, the manifestation of independence, the development of the ability to come to the necessary conclusions by oneself in the process of active creative search. Speaking about the current state of the national primary school, it should be noted that the pivotal place in it is still occupied by the cognitive activity of students at the level of reproductive perception and reproduction of educational material, and not creative activity based on the productive transformation of the learned knowledge and skills. The article proves the thesis that the implementation of socio-pedagogical design, as a means of forming the creative thinking of younger schoolchildren, should be based on the integration of design, research and play activities carried out in a general education school under the guidance of a teacher.
Designing Touchless Hand Gestures Interactions on Desktop Virtual Reality (dVR) in classroom: Understanding Issues Through Thematic Analysis

The Education Performance and Delivery Unit (PADU) in Malaysia encourages educators to provide world-class education by incorporating virtual reality (VR) into classroom to accommodate the needs of 21st-century learning[1] and desktop virtual reality (dVR) remains relevant to be used. Nonetheless, an enjoyable and cost-effective medium of interaction should be considered. Because of intuitiveness and naturalness of touchless hand gestures interaction, it is suitable to be used as a means of interacting on dVR. However, designing precise touchless hand gestures interactions on dVR for classroom learning is a challenging task. Thus, it is vital to gain a better understanding of learners' issues that arise while using such interactions. Through use of ethnography in gathering data using observation and informal interview during in-person experiment as well as thematic analysis (TA) in analyzing qualitative data helps authors to get a clearer idea of issues patterns when learners use touchless hand gestures on dVR. The findings reveal a pattern of issues in which a lack of visual cues affected the precision of interaction and control. Skill and barrier are themes derived from issues associated with eight interaction design codes, such as control, complexity, out of range, layout, affirmation, sensitivity, confidence level, and peripheral. Hopefully, these findings will help experts and educators better understand the issues and factors to consider when designing touchless hand gestures interactions for VR in the future.
Investigation of Transient Electronics Dissolution Process Based on Artificial Intelligence

The processing of image saliency is widely used in image retrieval, image compression, image video and other fields. In the field of national defense, it involves the self-destruct function of electronic products and the transient dissolution of substrates. In order to keep this process confidential, the researchers used image saliency processing to monitor whether the substrate was significant in the transient dissolution process, find out the defects, and further improve it. In the image saliency processing, according to the difference of the contrast area, it is divided into local and global algorithms. This article first improves the AC algorithm used for local comparison in the distance calculation method, weighting coefficient and multi-scale, and then compares the RC algorithm used for the global comparison Use image pre-segmentation algorithm improvement and significant center position optimization for analysis and improvement. And through the final experimental results, it is proved that the improved algorithm has a better effect in the processing of image saliency, and it also has obvious advantages in precision rate and recall rate.
Energy-saving optimization control of air compressor based on particle swarm optimization

Energy-saving optimization control of the air compressor based on particle swarm optimization is studied in this paper. From the perspective of quantum mechanics, through the study of particle convergence behavior, based on the particle swarm algorithm, a new algorithm model-quantum behavior particle swarm algorithm has been proposed. Compared with the other latest methods, this paper proposes the novel parameter updating function to adjust the drawbacks of the traditional PSO. In addition, on the basis of the established subsystems, the associations between the general various "sub-problems" are comprehensively considered to form the overall model of the large system. With this comprehensive model, the optimization control of the air compressor is constructed. The proposed model is simulated on real testing, and compares it with the other latest models.
Practical Attestation for Edge Devices Running Compute Heavy Machine Learning Applications

Machine Learning (EdgeML) algorithms on edge devices facilitate safety-critical applications like building security management and smart city interventions. However, their wired/wireless connections with the Internet make such platforms vulnerable to attacks compromising the embedded software. We find that in the prior works, the issue of regular runtime integrity assessment of the deployed software with negligible EdgeML performance degradation is still unresolved. In this paper, we present PracAttest, a practical runtime attestation framework for embedded devices running compute-heavy EdgeML applications. Unlike the conventional remote attestation schemes that check the entire software in each attestation event, PracAttest segments the software and randomizes the integrity check of these segments over short random attestation intervals. The segmentation coupled with the randomization leads to a novel performance-vs-security trade-off that can be tuned per the EdgeML application’s performance requirements. Additionally, we implement three realistic EdgeML benchmarks for pollution measurement, traffic intersection control, and face identification, using state-of-the-art neural network and computer vision algorithms. We specify and verify security properties for these benchmarks and evaluate the efficacy of PracAttest in attesting the verified software. PracAttest provides 50x-80x speedup over the state-of-the-art baseline in terms of mean attestation time, with negligible impact on application performance. We believe that the novel performance-vs-security trade-off facilitated by PracAttest will expedite the adoption of runtime attestation on edge platforms.