Reviewerly/exHarmony huggingface dataset:
Gaze estimation via self-attention augmented convolutions

Although recently deep learning methods have boosted the accuracy of appearance-based gaze estimation, there is still room for improvement in the network architectures for this particular task. Hence we propose here a novel network architecture grounded on self-attention augmented convolutions to improve the quality of the learned features during the training of a shallower residual network. The rationale is that self-attention mechanism can help outperform deeper architectures by learning dependencies between distant regions in full-face images. This mechanism can also create better and more spatially-aware feature representations derived from the face and eye images before gaze regression. We dubbed our framework ARes-gaze, which explores our Attention-augmented ResNet (ARes-14) as twin convolutional backbones. In our experiments, results showed a decrease of the average angular error by 2.38% when compared to state-of-the-art methods on the MPIIFaceGaze data set, while achieving a second-place on the EyeDiap data set. It is noteworthy that our proposed framework was the only one to reach high accuracy simultaneously on both data sets.
Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

In order to change the problem of data redundancy in a genetic algorithm, this paper proposes a computer mathematical model based on the combination of an improved genetic algorithm and mobile computing. Combined with the least square method, MATLAB software is used to solve the equations, determine the range of parameters, and solve the estimation parameter range and identification problems. The improved genetic algorithm combined with mobile computing and least square method to establish a mathematical model greatly increased the individual search space and increased the operation rate of 90% compared to the basic genetic algorithm or mobile computing. The results show that the improved genetic algorithm and mobile computing have a certain ability to identify the optimal solution and greatly improve the work efficiency.
Improving Legal Case Summarization Using Document-Specific Catchphrases

Legal case summarization is an important problem, and several domain-specific summarization algorithms have been applied for this task. These algorithms generally use domain-specific legal dictionaries to estimate the importance of sentences. However, none of the popular summarization algorithms use document-specific catchphrases, which provide a unique amalgamation of domain-specific and document-specific information. In this work, we assess the performance of two legal document summarization algorithms, when two different types of catchphrases are incorporated in the summarization process. Our experiments confirm that both the summarization algorithms show improvement across all performance metrics, with the incorporation of document-specific catchphrases.
Comparing the Difficulty of Classroom English in Primary English Teacher-Guidebooks by Levels Using Coh-Metrix

This study investigated if the difficulty of instructional classroom English in primary teacher-guidebooks of English is adequately manipulated by learner-proficiency. Corpora of classroom English were compiled from 20 guidebooks from five publishers, approved following the 2015 Revised National Curriculum of Korea. Extracted materials from grades three and four were compared with those of grades five and six to observe variations in the difficulty. Coh-Metrix, a software application that computes an extensive range of measures on cohesion and language, was used for analyses. With evidence-based assessments on (psycho)linguistic features and patterns of classroom English, we report results both congruent and incongruent with the prospect that the difficulty should increase as learners become more proficient. Overall, although partial difficulty manipulations between the two levels were noted, inconsistent results and invariances were also observed, disclosing much room for improvement of classroom English in the guidebooks. Some implications toward teacher-guidebook development, particularly in its classroom English, are suggested
Influence of Trace Elements in the Natural Environment and Public Health: A Medical Geology Perspective

The distributions and concentrations of trace elements in the environment exposed to biological materials including humans can affect life depending on the exposure and degree of concentrations. Whilst the essential trace elements impacts on human development, the potential toxic elements will have adverse health consequences on humans and animals. As observed from the study both live-threatening and life supporting trace elements all coexist in the environment. Clinical reviews on trace elements suggest that human bodies require some amounts of them at all times and their deficiencies adversely impact on human health. Mineral supplementation have always been the recommendation for people with deficiency of some trace elements as a prescription drug. In this study, X-ray fluorescence analytical technique was used to measure the trace elements in the sediment samples whereas ICP-MS was used for the soil samples. The trace elements in sediments and in soils at the study areas showed Zn, V, Mo, and Ni depletions in both areas. Among the essential trace elements Zn is the most dramatic example that impact on children health and require quick primary healthcare attention. Enrichments of Cu, Se Co and Cr were identified in the natural environments at both areas. The depleted elements are all essential elements which some are useful in the prevention of chronic diseases. Arsenic (As) was observed to be enriched in soils in Bole area and in stream sediments at Datoko- Shega; their involuntary ingestion can cause serious adverse health impact in humans. The study anticipate some of the essential trace element deficiency and the enrichment of some of the potential toxic elements to impact on Public Health and the authors fear for possible human health problem due to the bioavailability and bioaccumulation of the elevated high concentrations of PTE’s and deficiencies of some essential elements in stream sediments and soil samples.
Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

With the advantage of faster data access than traditional disks, in-memory database systems, such as Redis and Memcached, have been widely applied in data centers and embedded systems. The performance of in-memory database greatly depends on the access speed of memory. With the requirement of high bandwidth and low energy, die-stacked memory (e.g., High Bandwidth Memory (HBM)) has been developed to extend the channel number and width. However, the capacity of die-stacked memory is limited due to the interposer challenge. Thus, hybrid memory system with traditional Dynamic Random Access Memory (DRAM) and die-stacked memory emerges. Existing works have proposed to place and manage data on hybrid memory architecture in the view of hardware. This paper considers to manage in-memory database data in hybrid memory in the view of application. We first perform a preliminary study on the hotness distribution of client requests on Redis. From the results, we observe that most requests happen on a small portion of data objects in in-memory database. Then, we propose the Application-oriented Data Migration called ADM to accelerate in-memory database on hybrid memory. We design a hotness management method and two migration policies to migrate data into or out of HBM. We take Redis under comprehensive benchmarks as a case study for the proposed method. Through the experimental results, it is verified that our proposed method can effectively gain performance improvement and reduce energy consumption compared with existing Redis database.
A multi-target tracking algorithm for objects thrown from high

As a popular research field, object tracking has been widely used in video surveillance, security system, and other fields. At the same time, people pay more and more attention to the dangers caused by a high toss in cities. To solve this problem, an improved SORT algorithm is proposed to detect and track objects thrown from a high building. First, an adaptive Gaussian mixture background model is used to detect falling objects, and a Kalman filter and correlation are initialized for each confirmed falling object. Then IOU method combined with correlation filters is used to track falling objects. In order to verify the effectiveness of the proposed algorithm, a comparative test was conducted between the proposed algorithm and the original algorithm. The results show that compared with the original SORT algorithm, the object tracking duration and accuracy are improved, with a specific practical value.
A Light-Weight Compressed Video Processing Method on Embedded Platforms for IIoT

Recently, video has become an important medium for knowledge sharing for both industrial and consumer scenarios. For industrial applications, especially Industrial IoT (IIoT), it is highly desired to transfer the video content with limited bandwidth and process the video using constrained resources, which makes compressed video processing a very challenging problem. Recently, there have been extensive works focusing on compressed video quality enhancement (VQE) tasks, many of which deploy dedicated and complex CNNs to reach amazing performances. Such advancements have enabled various applications in video-based tasks. On the other hand, since deep neural networks often require high computational resources, such complex CNNs can hardly be deployed on the embedded devices. Thus, model pruning technique and inference optimization have been appealing options for efficient deployment of VQE under resource-constrained environments. In this paper, we incorporate a novel deformable convolution method into our network architecture and propose a light-weight method for compressed video quality enhancement on an embedded platform for IIoT. The proposed system has outperformed several SOTA light-weight quality enhancement models and can achieve 15.230 FPS and 0.773 FPS/W on MFQEv2 dataset [1].
Grammatical patterns in the corpus-driven “Lexical Database of Lithuanian”

This paper describes the grammatical patterning of two parts of speech – nouns and adjectives – included in the corpus-driven “Lexical Database of Lithuanian” as a foreign language. The lexical database is a lexicographic application of the Lithuanian Pedagogic Corpus (approx. 620.000 tokens) which was used to develop headword lists and to collect word usage information in the form of corpus patterns. In this project, we adopted a partially automated inductive procedure of Corpus Pattern Analysis for 207 verbs, 386 nouns, 87 adjectives, and 41 adverbs. The detected corpus patterns reflect different meanings of the headword. Each pattern presents information on grammatical, semantic, and lexical levels. Manually selected examples illustrate all pattern components. In this paper, 673 patterns with nouns and 99 patterns with adjectives will be analysed discussing their syntactic behaviour in detail and providing some comments on lexis-grammar interface. The majority of patterns with nouns and adjectives are minimal patterns which include only the closest syntactical partners. This result is influenced by different procedures used to describe patterns with nouns, adjectives, and adverbs and patterns with verbs. Due to rich grammatical information, there are several similar patterns with one main (usually the most frequent) type and its variants. Pattern variants show that the grammatical characteristics of a specific word usage are rather individual.
Secure medical image encryption approach based on Langton's ant and jigsaw transform

In this work, we propose a novel medical image encryption algorithm. It is based on the Jigsaw transform, Langton’s ant, cyclic permutation, and a novelty way to add deterministic noise. The robustness of our algorithm has been proven through several testing, such as statistical analysis (histograms and correlation distributions), visual testing, entropy testing and key space assessment, showing in each one a high-security level.
Detecting DeFi Securities Violations from Token Smart Contract Code with  Random Forest Classification

Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In the past year, DeFi has gained popularity and market capitalization. However, it has also become an epicenter of cryptocurrency-related crime, in particular, various types of securities violations. The lack of Know Your Customer requirements in DeFi has left governments unsure of how to handle the magnitude of offending in this space. This study aims to address this problem with a machine learning approach to identify DeFi projects potentially engaging in securities violations based on their tokens' smart contract code. We adapt prior work on detecting specific types of securities violations across Ethereum more broadly, building a random forest classifier based on features extracted from DeFi projects' tokens' smart contract code. The final classifier achieves a 99.1% F1-score. Such high performance is surprising for any classification problem, however, from further feature-level, we find a single feature makes this a highly detectable problem. Another contribution of our study is a new dataset, comprised of (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of valid tokens from a DeFi aggregator which conducts due diligence on the projects it lists. This paper further discusses the use of our model by prosecutors in enforcement efforts and connects its potential use to the wider legal context.
Hyper-GAN: Transferring Unconditional to Conditional GANs with  HyperNetworks

Conditional GANs have matured in recent years and are able to generate high-quality realistic images. However, the computational resources and the training data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. In this paper, we explore the transfer from high-quality pre-trained unconditional GANs to conditional GANs. To this end, we propose hypernetwork-based adaptive weight modulation. In addition, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the knowledge transfer, we propose to use a self-supervised (contrastive) loss to improve the GAN discriminator. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on several standard benchmarks.
ЗАСОБИ ІНТЕЛЕКТУАЛЬНОГО РОЗВИТКУ УЧНІВ СТАРШОЇ ШКОЛИ НА УРОКАХ МАТЕМАТИКИ

The article is devoted to the problem of appropriate use of information and communication technologies as a means of intellectual development of high school students in mathematics lessons. The positive influence of the use of information and communication technologies on the formation and development of students’ spatial imagination, logical, algorithmic and abstract thinking, increases the level of mathematical and research competence, increases the level of cognitive interests
Enhancing the Erzya-Moksha dictionary automatically with link prediction

Future workVe′rdd has relations other than translation relations to deal with.These include: "Etymological relation" for marking associations between cognates; "Compound relation" for associating compound words with the elements; "Derivation relation" for associating derived words with their source stems and derivational affixes.These could, in the future, be predicted automatically by incorporating methods such as the ones by Alnajjar et al. ( 2017), Hämäläinen -Rueter (2019) and Alnajjar (2021), test materials could be found in Keresztes (2013).Etymological relations between words of the Mordvin idioms may be readily augmented in Ve′rdd at the present by entering documentation metadata from sources such as (Keresztes 2011: 107-118), and even more extensive alignments can be found in the Mordwinisches Wörterbuch (1990Wörterbuch ( , 1992Wörterbuch ( , 1994Wörterbuch ( , 1996)), both at the lemma level and the etymology level (cf.Rueter 2020).Future work will involve the enhancement of parallel corpora development for Erzya and Moksha, both literary and vernacular in collaboration with other centers of Mordvin studies.The lexical resources we have created and described here could be used in future in research on morphological derivations, dialect areas and lexical distributions.Furthermore, this approach can be useful for other endangered languages as well.
Social Distance Monitoring Using Drone

Within the present times, the priority and danger of the COVID-19 virus still stand large. Manual looking of social distancing norms is impractical with associate in nursing oversized population moving concerning and with the short task force and resources to administer them. There is a want for a light-weight, robust, and 24*7 video-monitoring system that automates this technique. This paper proposes a comprehensive and effective resolution to perform person detection and social distancing violation detection exploitation object detection, agglomeration and Convolution Neural Network (CNN) based binary classifier. The framework uses the Scaled-YOLOv4 seeing paradigm to identify humans in video sequences. The detection formula uses a pre-trained formula that's connected to an additional trained layer using a frontal human information set. The detection model identifies peoples exploitation detected bounding box information. Exploitation the mathematician distance, the detected bounding box centroid’s pairwise distances of individuals unit determined. A threshold is used to estimate the social distance violations between people. Here, we have a tendency to tend to use Associate in nursing approximation of physical distance to pixel to line the sting price. A violation threshold is established to live whether or not the gap price breaches the minimum social distance threshold. To boot, a trailing formula is employed to sight individuals in video sequences such the one that violates/ crosses the social distance threshold is, additionally, being half-track. Experiments unit administered on whole completely different video sequences to ascertain the efficiency of the model. Findings indicate that the developed framework successfully distinguishes individuals UN agency walk too about to and breach/violate social distances.
Analysis of the Effect of Feature Reduction on Accuracy and Computational Time in Mushroom Dataset Classification

Classification is a technique to mapping the class of a certain data from its attribute or feature values. One of things that affects the classification result is the correlation of its features to the class classification results. Research conducted to determine the effect of the reduction in features that are least correlated or have a distant relationship with the classification result class (dependent variable). Because features that do not have much correlation, have no effect on the classification results. From the research, the accuracy of the reduction of each feature per test scenario has a range between 83% -88% higher than the initial accuracy without feature selection at 82% accuracy. Meanwhile, the computation time obtained does not have a significant difference in changing compared to without feature reduction, in the range of 2.3-2.7. For the data used is the Mushroom dataset obtained from the UCI Machine Learning Repository
Jordan: Second Review Under the Extended Arrangement Under the Extended Fund Facility, Request for Augmentation of Access, and Modification of Performance Criteria-Press Release; Staff Report; Staff Statement; and Statement by the Executive Director for Jordan

©International Monetary Fund.Not for Redistribution made very strong progress on a large number of critical structural reforms.Moreover, Jordan's vaccination program, one of the first in the world to cover refugees, has recently accelerated."In the near term, the priority remains to manage the fallout from the pandemic.Thus, the revised fiscal targets for 2021 appropriately aim to accommodate higher spending on critical health, social protection, and job-supporting schemes.The authorities remain committed to implementing a gradual, growth-friendly, and equitable fiscal consolidation as the recovery becomes entrenched, in order to bolster public debt sustainability and ensure inclusive growth.To this end, they have advanced key reforms to close tax loopholes, broaden the tax base, and strengthen tax administration capacity.Continued high-quality reforms to enhance the efficiency and transparency of public finances will also be important."Monetary policy has been appropriately accommodative since the onset of the pandemic while supporting the peg.Moving forward, monetary policy needs to remain flexible and data driven, balancing the need to entrench the recovery and maintain monetary and financial stability.While the financial sector remains sound, continued vigilance is warranted given that it will likely take time for the full effects of the pandemic to be reflected in banks' asset quality."Continued progress on structural reforms remains essential to ensure a durable and inclusive recovery.Reforms in the electricity sector, where the authorities are working to address the high electricity costs for businesses, remains crucial for fostering job-rich growth and enhancing competitiveness.Other reforms should also focus on improving the business environment, reducing unemployment, particularly among women and youth, and strengthening governance.In this context, the recent review of the most significant power purchase agreement was an important step."The pandemic has significantly increased Jordan's external financing needs, underscoring the criticality of continued donor support, including to help shoulder the disproportionate burden Jordan has borne in hosting refugees.The authorities have demonstrated strong reform momentum and a commitment to fiscal transparency.These, together with continued implementation of reforms, as well as stepped up financial assistance from development partners, will help Jordan achieve the objectives of its program and build a stronger, more resilient, and inclusive economy."©International Monetary Fund.
MusCare: A Mobile Design for Muscle Monitoring

Muscles not only help people move, but they also help the body work. However, muscular disorders have become common nowadays, such as Muscular Dystrophy (MD) and Muscle Atrophy (MA), which are genetic and lifestyle diseases causing the loss of muscle mass. The research question we have is whether Internet of Things (loT) and mobile engineering techniques can help monitor muscle conditions, such as overuse of muscles, muscle recovery from surgery, etc. We propose MusCare, a microcontroller-based system with EMG sensors and an Android mobile app, to help monitor muscle status and visualize the results for the users. The current results show that the system can provide real-time monitoring results on a mobile device. In addition, a neural network algorithm was able to be implemented on a mobile phone to detect anomalies from the EMG data. Currently, the prototype system uses a MyoWare EMG sensor to keep track of the targeted muscle group, but the design of the system can be extended to work with vibrators to provide an advanced improvement in the interaction.