Reviewerly/exHarmony huggingface dataset:
Pedestrian Gender Recognition by Style Transfer of Visible-Light Image to Infrared-Light Image Based on an Attention-Guided Generative Adversarial Network

Gender recognition of pedestrians in uncontrolled outdoor environments, such as intelligent surveillance scenarios, involves various problems in terms of performance degradation. Most previous studies on gender recognition examined recognition methods involving faces, full body images, or gaits. However, the recognition performance is degraded in uncontrolled outdoor environments due to various factors, including motion and optical blur, low image resolution, occlusion, pose variation, and changes in lighting. In previous studies, a visible-light image in which image restoration was performed and infrared-light (IR) image, which is robust to the type of clothes, accessories, and lighting changes, were combined to improve recognition performance. However, a near-IR (NIR) image requires a separate NIR camera and NIR illuminator, because of which challenges are faced in providing uniform illumination to the object depending on the distance to the object. A thermal camera, which is also called far-IR (FIR), is not widely used in a surveillance camera environment because of expensive equipment. Therefore, this study proposes an attention-guided GAN for synthesizing infrared image (SI-AGAN) for style transfer of visible-light image to IR image. Gender recognition performance was improved by using only a visible-light camera without an additional IR camera by combining the synthesized IR image obtained by the proposed method with the visible-light image. In the experiments conducted using open databases—RegDB database and SYSU-MM01 database—the equal error rate (EER) of gender recognition of the proposed method in each database was 9.05 and 12.95%, which is higher than that of state-of-the-art methods.
Caliper: Measuring success in STEM learning ecosystems

Ecologized learning situated in diverse in-school, out-of-school, and after-school contexts poses daunting challenges to measuring, communicating, managing, and improving outcomes. Formal and informal educators, policymakers, funders, and other key stakeholders engaged in cross-sector collaborations defined five pillars of STEM learning ecosystem success: partnerships, measures, administrative systems, teaching and learning practices, and workforce development connections. Variation in the ecosystem success construct was mapped, existing items from an earlier ecosystem assessment were revised, four parallel survey forms with common items were devised, and an online administration system was created in early 2020. Data from over 400 stakeholders in dozens of ecosystems were fit to a probabilistic model of measurement capable of supporting metrological quality assurance standards.
Ecological communications of modern students (experience of the Moscow automotive-road state technical university)

The article presents the results of the analysis of the process of expansion and diversity of communication channels in the university environment, which is relevant from the standpoint of achieving the goals of sustainable development in the field of education. It is shown that one of the most accessible and open communication channels, in which modern students are actively involved, is the channel of environmental communications. They have many manifestations, which corresponds to the most exceptionally wide ecological space, reflecting both individual and collective interests, features of a narrow professional attitude to nature and its civilizational understanding. A significant part of the disciplines studied in higher education contributes to the formation of students' environmental competencies. The specificity of the ecological approach to education and upbringing contributes to involvement of students in activities for the nature protection. The regularities of environmental communications functioning can be traced on the example of environmental education carried out at the Moscow Automobile Road State Technical University (MADI).
Design and Implementation of a Distributed Versioning File System for Cloud Rendering

Rendering is widely used for visual effects in animations, video games, and movies. As the computational load in rendering workloads fluctuates greatly over time, it is attractive to use cloud infrastructures for cost-effective rendering. However, we analyze that cloud rendering has several technical challenges involved in the handling of rendering input data. In this article, we analyze the workload characteristics of popular rendering projects, and find out the following three observations. First, total size of rendering input files reaches tens to hundreds of gigabytes, and uploading these large data to cloud increases the startup latency of rendering significantly. Second, the consistency requirement of file systems in cloud rendering is complicated compared to that of traditional distributed file systems. Third, file accesses in rendering are highly skewed such that the top 20% files account for 60-80% of total accesses, whereas 40-70% are never used or used only once. Based on these observations, we design and implement a new file system for cloud rendering, which has the function of version control, on-demand fetch, and distributed cooperative caching for rendering data. This allows for minimizing data transmission overhead caused by the large input data of rendering and satisfying the rendering data consistency. Measurement studies under synthetic and real workloads show that the proposed file system performs better than the conventional uploading scheme and NFS by 55.4% and 29.5% on average, respectively.
A decentralized fair governance model for permissionless blockchain systems

Blockchain systems are a new way to reduce or even eliminate the role of the middlemen in an eco-system. For example, the Bitcoin, as one of the most well-known blockchain platforms, shows that it is possible to transfer money without the need of any (intermediate) bank at all. More generally, it allows for the decentralization of roles. In this paper, we focus on permissionless blockchains, which are systems that allow participation without upfront approval on other parties, as opposed to permissioned blockchains. Permissionless blockchain systems support direct business transactions between peers, so without any intermediate and centralized entity very well. However, the organization of the governance of such systems is less obvious. We argue that, in order to arrive at a really decentralized eco-system where power is fairly distributed, the governance should be decentralized; in other words, it should not be in the hand of one controlling entity. In this paper, we analyse, in a model-based way, for three well-known blockchain systems (Bitcoin, Ethereum, Tezos) the governance processes. Based on this analysis, we draft an improved governance process for permissionless blockchains.
Deep Learning Based Classification of Turkish News Posts in Twitter

Social media platforms have become important news sources in recent years thanks to their increasing number of users and the ability to spread information much faster than traditional media. Using these platforms especially as news sources has brought the need of accessing news information easier and faster within the large amount and flow of data on these platforms. In this paper, a deep learning based approach has been proposed to classify Turkish news posts on Twitter automatically. In this regard, recurrent neural networks (RNN) and long short term memory (LSTM) which is a special type of RNN are used and the developed classifiers are evaluated in terms of different performance metrics. The results show that the achieved accuracy rates are reasonably high, despite the difficulty of classifying short tweets.
Leverage Sidechains to Reduce the Workload of Smart Contracts through Parallelization

Recently, blockchain has been evolving rapidly with new innovations, coins, and use cases every day. The platform is getting more congested due to the increase of interest and mainstream adoption, and because most of this activity is on the widely used Ethereum blockchain. This also further increases the costs of using the platform, as the gas prices become higher. Smart contracts are deployed on the Ethereum blockchain and the high usage of these smart contracts is one of the main reasons behind the congestion. We propose a new scheme using sidechains and a middleware to reduce the workload for certain smart contracts, which allows the execution of smart contract transactions in parallel through sidechains. In this way, the sidechains could be leveraged to decrease the congestion on the main blockchain and increase the rate of transactions per second. Furthermore, the sidechains could have their settings, like block time and block gas limit, adjusted to give more optimal results. We implemented a modified version of the ballot contract from the solidity documentation, and our results demonstrated that through the use of two sidechains, the transactions processed per second could be increased from 1.8× to 13.0×, depending on the sidechains settings.
On Restricting the Impact of Self-attacking Arguments in Gradual Semantics

The issue of how a semantics should deal with self-attacking arguments was always a subject of debate amongst argumentation scholars. A consensus exists for extension-based semantics because those arguments are always rejected (as soon as the semantics in question respect conflict-freeness). In case of gradual semantics, the question is more complex, since other criteria are taken into account. A way to check the impact of these arguments is to use the principles (i.e. desirable properties to be satisfied by a semantics) from the literature. Principles like Self-Contradiction and Strong Self-Contradiction prescribe how to deal with self-attacking arguments. We show that they are incompatible with the well-known Equivalence principle (which is satisfied by almost all the existing gradual semantics), as well as with some other principles (e.g. Counting). This incompatibility was not studied until now and the class of semantics satisfying Self-Contradiction is under-explored. In the present paper, we explore that class of semantics. We show links and incompatibilities between several principles. We define a semantics that satisfies (Strong) Self-Contradiction and a maximal number of compatible principles. We introduce an iterative algorithm to calculate our semantics and prove that it always converges. We also provide a characterisation of our semantics. Finally, we experimentally show that our semantics is computationally efficient.
Analisis Curah Hujan di Indonesia untuk Memetakan Daerah Potensi Banjir dan Tanah Longsor dengan Metode Cluster Fuzzy C-Means dan Singular Value Decompotition (SVD)

One of the climate changes impact is extreme rainfall. Extreme rainfall can cause some hydrometeorological disasters, especially floods and landslides. In 2016, BNPB recorded that there were 2,342 disaster events in Indonesia, 92% of which were disasters dominated by floods, landslides, and cyclones. There were 766 floods, 612 landslides, and 74 combinations of both. In this case, clustering is one of the important method to identify groups of areas with similar characteristics of high or low rainfall. Another analysis that can be applied is the identification of the extreme rainfall and the determination of dominant rainfall pattern using Singular Value Decomposition (SVD) for each region in Indonesia. The data was taken from Indonesia’s rainfall data in January 1998 - March 2017, obtained from the Tropical Rainfall Measuring Mission (TRMM) meteorological satellite, with 12,834 observation units. The highest average rainfall in Indonesia in December is 290,26 mm/month. Rainy season is predicted from November to April because they have the highest average rainfall. The SVD analysis formed four dominant rainfall patterns in Indonesia with a variance of 25.59%, where Papua and West Papua are the regions with the highest rainfall. The area around the Indian Ocean is the area with the most extreme rainfall events compared to other regions. Using Fuzzy C-means, three clusters can be formed, with criteria for areas of high, medium, and low rainfall. Based on the results of the analysis, the area has the potential for flooding and landslides due to high rainfall, so that preventive and mitigation efforts against the risk of flooding and landslides can be treated better.
An Intelligent Evaluation Algorithm of Practical Innovation Ability for Students

The existing evaluation method indicators are not specific and the index weights are highly subjective. This paper selects the evaluation indicators to estimate the practical innovation ability of graduate students and undergraduates, and proposes an evaluation algorithm on the basis of deep belief network (DBN), and an improved algorithm based on practical innovation ability model of graduate students. Since the evaluation indicators and data distribution of undergraduate students are very similar to those of graduate students, the improved algorithm adopts the parameter based transfer learning method. The weight of the same characteristics of undergraduates and graduate students is directly multiplied by the difference factor as the initial weight of the undergraduate fine-tuning. The weight of disparate characteristics for undergraduates and graduates needs to be fine-tuned and re-trained. Experiment results show the improved algorithm has wider application ranges and higher accuracy rate, overcomes the problem of strong subjectivity about index weights, and it is beneficial to promote reform of talent training and the overall improvement of talent training quality. The comprehensive evaluation algorithms on the basis of fuzzy mathematics, the evaluation algorithms of probabilistic neural networks, the general deep learning evaluation algorithms, and the presented algorithm are compared to verify the effectiveness of the proposed evaluation algorithm.
Partial success in closing the gap between human and machine vision

A few years ago, the first CNN surpassed human performance on ImageNet. However, it soon became clear that machines lack robustness on more challenging test cases, a major obstacle towards deploying machines the wild and towards obtaining better computational models of human visual perception. Here we ask: Are we making progress in closing the gap between human and machine vision? To answer this question, we tested human observers on a broad range of out-of-distribution (OOD) datasets, adding the missing human baseline by recording 85,120 psychophysical trials across 90 participants. We then investigated a range of promising machine learning developments that crucially deviate from standard supervised CNNs along three axes: objective function (self-supervised, adversarially trained, CLIP language-image training), architecture (e.g. vision transformers), and dataset size (ranging from 1M to 1B). Our findings are threefold. (1.) The longstanding robustness gap between humans and CNNs is closing, with the best models now matching or exceeding human performance on most OOD datasets. (2.) There is still a substantial image-level consistency gap, meaning that humans make different errors than models. In contrast, most models systematically agree in their categorisation errors, even substantially different ones like contrastive self-supervised vs. standard supervised models. (3.) In many cases, human-to-model consistency improves when training dataset size is increased by one to three orders of magnitude. Our results give reason for cautious optimism: While there is still much room for improvement, the behavioural difference between human and machine vision is narrowing. In order to measure future progress, 17 OOD datasets with image-level human behavioural data are provided as a benchmark here: this https URL
An Efficient Method for Automatic Antipatterns Detection of REST Web Services

REST Web Services is a lightweight, maintainable, and scalable service accelerating client application development. The antipatterns of these services are inadequate and counter-productive design solutions. They have caused many qualitative problems in the maintenance and evolution of REST web services. This paper proposes an automated approach toward antipattern detection of the REST web services using Genetic Programming (GP). Three sets of generic, REST-specific and code-level metrics are considered. Twelve types of antipatterns are examined. The results are compared with the manual rule-based approach. The statistical analysis indicates that the proposed method has an average precision and recall scores of 98% (95% CI, 92.8% to 100%) and 82% (95% CI, 79.3% to 84.7%) and effectively detects REST antipatterns.
Geology of the central Sudan

Sudan can be divided geologically into a number of clearly defined units. The central part of the country that enclosed by the two Niles, contains elements of almost all the units known elsewhere. In the relationship of geology to man’s settlement the younger, unconsolidated units of river alluvium and superficial soils dominate, but man has made use of a variety of stones for implements, most of which are to be found in or close to the area under discussion. The alluvium along the Nile is of importance to the inhabitants for, away from the Gezira, it is the main area available for cultivation and irrigation and is the site of some of the earliest settlement. In the central Gezira region the few hundreds of metres of silts and clays are called the Gezira Formation. The formation is important as an aquifer and has been extensively drilled for water in the Gezira plain and elsewhere.
Semantic Similarity Comparison of Word Representation Methods in the Field of Health

Natural Language Processing has become an important issue with the rapid increase in textual data in the health sector recently. Especially with the effect of COVID-19, easy and fast analysis of health data is important for research. Traditional text representations such as BoW (bag of words), TF-IDF (term frequency-inverse document frequency), and modern word representation methods such as FastText and BERT are used to represent words. The BERT models are provided high performance recently. The BERT models are divided into pre-trained and fine-tuned BERT models. In order to get good results in the field of health, BioBERT models are obtained by fine-tuning the basic BERT models with datasets containing biomedical articles. In this study, semantic similarities in datasets are evaluated by the Pearson correlation method by using BoW, TF-IDF, FastText, BERT, and BioBERT models. As a result of the evaluations, it was observed that BioBERT models gave higher values compared to other models and methods used.
3D Video Quality Evaluation Based on SSIM Model Improvement

In order to provide improved multimedia services to the end users, developing objective models efficiently predicting 3 Dimensional (3D) video Quality of Experience (QoE) can currently be considered as one of the most significant research areas. Nevertheless, there is currently no model standardized and widely utilized by the researchers due to its efficient and reliable assessment of the 3D video quality. Therefore, highly exploited 2 Dimensional (2D) video quality assessment models such as Structural SIMilarity Index (SSIM) are preferred for the 3D video quality evaluation. However, providing efficiency and reliability for the 3D video quality assessment using the 2D video quality assessment models can only be ensured if they include 3D video related features effecting Human Visual System (HVS). Under the light of these information, the SSIM model is improved for the 3D video quality assessment using perceptually significant feature, contrast and motion characteristics having impact on the HVS in this study. The results obtained by utilizing the improved SSIM model clearly present that the model is quite competent to provide enhanced multimedia services to the end users.
An Improved artificial sheep algorithm based on a novel hybrid strategy

Artificial sheep algorithm (ASA) is a novel swarm intelligence algorithm. Although ASA performs a good optimization ability, it cannot completely avoid local optimum, premature and imbalance between exploration and exploitation. To solve those problems proposed above, a hybrid improvement strategy is employed on ASA. The hybrid improvement strategy includes two parts, reconstruction strategy and competition strategy which both can not only enhance the diversity of IASA, but also improve the global search ability. Moreover, 13 benchmark functions are run on ASA and the improved ASA named IASA respectively. Test results show that IASA have significantly performed better performance than ASA.
Formalizing Statistical Beliefs in Hypothesis Testing Using Program Logic

We propose a new approach to formally describing the requirement for statistical inference and checking whether the statistical method is appropriately used in a program. Specifically, we define belief Hoare logic (BHL) for formalizing and reasoning about the statistical beliefs acquired via hypothesis testing. This logic is equipped with axiom schemas for hypothesis tests and rules for multiple tests that can be instantiated to a variety of concrete tests. To the best of our knowledge, this is the first attempt to introduce a program logic with epistemic modal operators that can specify the preconditions for hypothesis tests to be applied appropriately.
Deep-learning-based deflectometry for simultaneous multi-surface measurement of freeform refractive optics

Due to the highly general surface geometry of freeform optics, the measurement of freeform optical surfaces is still a challenging and rewarding issue. Here, we propose a simultaneous multi-surface measurement method based on deep learning for freeform refractive optics, in which the surfaces are reconstructed based on the transmitted wavefront measured with computer-aided deflectometry. By adopting the deep learning approaches in geometrical error calibration and wavefront reconstruction, both the efficiency and robustness is significantly improved, and the surface measurement accuracy in the order of nanometers can be achieved. The proposed method provides an effective, robust and accurate way for testing freeform refractive optics with multiple surfaces and a large slope range