Reviewerly/exHarmony huggingface dataset:
Robust online video super-resolution using an efficient alternating projections scheme

Video super-resolution reconstruction (SRR) algorithms attempt to reconstruct high-resolution (HR) video sequences from low-resolution observations. Although recent progress in video SRR has significantly improved the quality of the reconstructed HR sequences, it remains challenging to design SRR algorithms that achieve good quality and robustness at a small computational complexity, being thus suitable for online applications. In this paper, we propose a new adaptive video SRR algorithm that achieves state-of-the-art performance at a very small computational cost. Using a nonlinear cost function constructed considering characteristics of typical innovation outliers in natural image sequences and an edge-preserving regularization strategy, we achieve state-of-the-art reconstructed image quality and robustness. This cost function is optimized using a specific alternating projections strategy over non-convex sets that is able to converge in a very few iterations. An accurate and very efficient approximation for the projection operations is also obtained using tools from multidimensional multirate signal processing. This solves the slow convergence issue of stochastic gradient-based methods while keeping a small computational complexity. Simulation results with both synthetic and real image sequences show that the performance of the proposed algorithm is similar or better than state-of-the-art SRR algorithms, while requiring only a small fraction of their computational cost.
DeepQuantify: deep learning and quantification system of white blood cells in light microscopy images of injured skeletal muscles

White blood cells (WBCs) are the most diverse types of cells observed in the healing process of injured skeletal muscles. In the recovery process, WBCs exhibit a dynamic cellular response and undergo multiple changes of the protein expression. The progress of healing can be analyzed by the number of WBCs or by the number of specific proteins observed in light microscopy images obtained at different time points after injury. We propose a deep learning quantification and analysis system called DeepQuantify to analyze WBCs in light microscopy images of uninjured and injured muscles of female mice. The DeepQuantify system features in segmentation using the localized iterative Otsu’s thresholding method, masking postprocessing, and classification of WBCs with a convolutional neural network (CNN) classifier to achieve a high accuracy and a low manpower cost. The proposed two-layer CNN classifier designed based on the optimization hypothesis is evaluated and compared with other CNN classifiers. The DeepQuantify system adopting these CNN classifiers is evaluated for quantifying CD68-positive macrophages and 7/4-positive neutrophils and compared with the state-of-the-art deep learning segmentation architectures. DeepQuantify achieves an accuracy of 90.64% and 89.31% for CD68-positive macrophages and 7/4-positive neutrophils, respectively. The DeepQuantify system employing the proposed two-layer CNN architecture achieves better performance than those deep segmentation architectures. The quantitative analysis of two protein dynamics during muscle recovery is also presented.
Android/iPhone Mobile Application for Quick Response Pilgrims Campaign Locator

Development and implementation of pilgrims (Hajj) Guide application to be used at Hajj season would facilitate follow-up of campaign supervisors on their campaign’s safety and location. The systems would also allow Hajj and Umrah administration to manage the crowd of the campaigns in an easy manner. In this paper, we propose Android/iPhone application that allows Hajj administration to monitor pilgrims using Quick Response Codes. Using quick response code application allows campaign supervisors and Hajj and Umrah employees to monitor pilgrims and guide them in case of getting lost. The application offers an easier and faster service than traditional approach used at Hajj season by human guidance. It is a very convenient service that would relief stresses of pilgrim’s relatives who worry about their relatives performing Hajj. The results show that the average score of our approach (QR) is 4.72 out of 5 comparing with traditional mobile applications indicating that the users are feeling enjoyable while using the new application features and the system is easy to use. Finally, the systems would assist in achieving the highest degree of pilgrims safety at Hajj season.
Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context

We explore learning web-based tasks from a human teacher through natural language explanations and a single demonstration. Our approach investigates a new direction for semantic parsing that models explaining a demonstration in a context, rather than mapping explanations to demonstrations. By leveraging the idea of inverse semantics from program synthesis to reason backwards from observed demonstrations, we ensure that all considered interpretations are consistent with executable actions in any context, thus simplifying the problem of search over logical forms. We present a dataset of explanations paired with demonstrations for web-based tasks. Our methods show better task completion rates than a supervised semantic parsing baseline (40% relative improvement on average), and are competitive with simple exploration-and-demonstration based methods, while requiring no exploration of the environment. In learning to align explanations with demonstrations, basic properties of natural language syntax emerge as learned behavior. This is an interesting example of pragmatic language acquisition without any linguistic annotation.
TMTCPT: The Tree Method based on the Taxonomic Categorization and the Phylogenetic Tree for fine-grained categorization

Abstract Fine-grained categorization is one of the most challenging problems in machine vision. Recently, the presented methods have been based on convolutional neural networks, increasing the accuracy of classification very significantly. Inspired by these methods, we offer a new framework for fine-grained categorization. Our tree method, named “TMTCPT”, is based on the taxonomic categorization, phylogenetic tree, and convolutional neural network classifiers. The word “taxonomic” has been derived from “taxonomical categorization” that categorizes objects and visual features and performs a prominent role in this category. It presents a hierarchical categorization that leads to multiple classification levels; the first level includes the general visual features having the lowest similarity level, whereas the other levels include visual features strikingly similar, as they follow top-bottom hierarchy. The phylogenetic tree presents the phylogenetic information of organisms. The convolutional neural network classifiers can classify the categories precisely. In this study, the researchers created a tree to increase classification accuracy and evaluated the effectiveness of the method by examining it on the challenging CUB-200-2011 dataset. The study results demonstrated that the proposed method was efficient and robust. The average classification accuracy of the proposed method was 88.34%, being higher than those of all the previous methods.
Procedurally Generated Self Overlapping Mazes in Virtual Reality

A current research topic within virtual reality is to allow the user to move by natural walking in a virtual environment that exceeds the size of the tracking area. Multiple approaches to overlapping architecture dealing with this issue already exist, but they are either custom made for a specific tracking area size or require a tracking area too large to work efficiently for personal use. This paper proposes a method to make scalable overlapping architecture by procedural generation of tile-based mazes that seamlessly teleport the user using portals. We evaluated how the tile size of the overlapping maze affects the user’s spatial awareness of their physical position. 23 participants of Aalborg University students completed the Spatial Awareness Task of finding a tile that overlapped with their starting tile in a 3 \(\times \) 3, 4 \(\times \) 4, and 5 \(\times \) 5 tile maze. Between condition 3 \(\times \) 3 and 5 \(\times \) 5, and condition 4 \(\times \) 4 and 5 \(\times \) 5, participants were more confused in the 5 \(\times \) 5 tile maze compared to 3 \(\times \) 3 (p = .0015) and 4 \(\times \) 4 (p = .0295) tile mazes.
A Comparative Study of Perspectives on Knowledge and Cognition of Ancient Chinese Education

This thesis is aimed at emphasizing that achievement becomes a disaster if human beings are not centered in the fourth industrial revolution era by studying the viewpoint of the relationship between the personality-centered knowledge theory and epistemology by Chinese ancient educators. Chinese educators explored the truth for the improvement of behavior, the completion of virtue, and the completion of personality. Confucianism combined the basic values of knowledge, life, and city councils to realize the development of personality and harmonious development of society. Hánfēizǐ became a collector of the idea of the law by accepting the ideas of advanced Confucianism, Taoism, Mòjiā, mìngjiā and advanced Fǎjiā with abundant creativity and criticism. Xunzi thought that subjective recognition ability could acquire knowledge by combining objective objects. In the age of the Fourth Industrial Revolution, if you neglect the relationship between knowledge and cognition based on personality, you and I must gather together to solve it. It is time to call for fusion and consilience everywhere, but it is time to establish a relationship with the reason through the relationship between knowledge and cognition based on personality. I hope that this paper, which claims the urgency of this issue, will be a chance to open a small discussion field.
SensorRE: Provenance support for software reverse engineers

Reverse engineering is a time-consuming process essential to software-security tasks such as malware analysis and vulnerability discovery. During the process, an engineer will follow multiple leads to determine how the software functions. The combination of time and possible explanations makes it difficult for the engineers to maintain a context of their findings within the overall task. Analytic provenance tools have demonstrated value in similarly complex fields that require open-ended exploration and hypothesis vetting. This paper introduces SensorRE, the first analytic provenance tool designed to support software reverse engineers. A semi-structured interview with experts led to the design and implementation of the system. As core components, we describe the visual interfaces and their integration within an existing software analysis tool. SensorRE automatically captures user's sensemaking actions and provides a graph and storyboard view to support further analysis. User study results demonstrate that SensorRE is easy to use and that it improved the participants' exploration process.
Selecting amongst multinomial models: An apologia for normalized maximum likelihood

The modeling of multinomial data has seen tremendous progress since Riefer and Batchelder’s (1988) seminal paper. One recurring challenge, however, concerns the availability of relative performance measures that strike an ideal balance between goodness of fit and functional flexibility. One approach to the problem of model selection is Normalized Maximum Likelihood (NML), a solution derived from the Minimum Description Length principle. In the present work we provide an R implementation of a Gibbs sampler that can be used to compute NML for models of joint multinomial data. We discuss the application of NML in different examples, compare NML with Bayes Factors, and show how it constitutes an important addition to researchers’ toolboxes.
Mitigating Cognitive Biases in Machine Learning Algorithms for Decision Making

Cognitive biases are an ingrained part of the human decision-making process. Nearly all machine learning algorithms that mimic human decision-making use human judgments as training data, which propagates these biases. In this paper, we conduct an empirical study in which 150 applicants are rated for suitability for three separate job openings. We develop an algorithm that learns from human judgments and consequently develops biases based on these human-generated inputs. Next, we explore and apply techniques to mitigate these algorithmic biases, using a combination of pre-processing, in-processing, and post-processing algorithms. The results from our study show that biases can be mitigated using these approaches but involve a tradeoff between complexity and effectiveness.
Legitimacy of Experimental Use of Patented Research Tools by Chinese Universities

This article takes the reality in China as its basis and examines the meaning of “production or business purpose” in Article 11 of the Chinese Patent Law to explain the difficulties faced by Chinese universities in claiming non-infringement. Looking at the range of experimental applications, the particularity of patented method tools, and the exception to infringement for experimental use, the appropriateness of experimentally using patented research tools is discussed. Finally, the rationale for and feasibility of the experimental use exception are demonstrated by explaining and analyzing the functions and missions of Chinese universities and the regulations regarding experimental use.
Supervised Machine Learning Approach for Identification of Malicious URLs

Malignant URL is a normal and certifiable risk to cyber security. A malicious URL has a grouping of unconstrained substance as phishing and spam so as to dispatch assaults. Guiltless clients visit such sites to advance towards getting to be setbacks of different sorts of tricks, including fiscal misfortune and burglary of private data (identity, credit-cards, etc.). It is fundamental to recognize and catch up on such perils in an ideal manner. In this paper, we had applied two procedures for distinguishing malignant URLs with two extraordinary datasets and verified at execution of Naïve Bayes and support vector machines (SVM) as per two datasets.
STRUCTURAL ANALYSIS OF SEMANTIC RELATIONS REGARDING INTEGRATION AND ASSOCIATION OF SEMANTIC NETWORK IN VOCBENCH AS AN AGRICULTURAL ONTOLOGY

The purpose of this article is to analyze semantic relations based on graph-independent structural analysis in VocBench. The mix-method of deductive and inductive approach is adapted in operating the research methodology, especially for data collection. The research data are structural domains of semantic relations in ontologies. The data resource is the authoritative agricultural ontology, VocBench, that has been originated by Food and Agricultural organization (FAO), United Nation. VocBench includes around 40000 concepts. The sample size is around 1500 concepts. Sampling technique used is the stratified random sampling. The data analysis results are employed in the SPSS and Excel software using descriptive and proportional analysis. The research results reveal that the taxonomic relations cover a wide area in VocBench. Moreover, the overloading was not seen in the usage of non-taxonomic relations. The high frequency in the usage of the semantic relations’ output might be implied the possibility of the width (i.e., exhaustivity) in semantic network in VocBench.
Extracting Personal Information from Conversations

In my doctoral research, I focus on inferring personal information from conversational texts. Such unstructured input does not provide sufficient explicit assertions, hence, personal facts have to be inferred from latent cues or speech style. In my work I investigate what salient attributes can be extracted from dialogues and which tools may be applicable for that.
Overcoming Laboratory Anxiety through Technology-Integrated Laboratory Activities

This study aimed to determine the effect of technology-integrated activities on the students' laboratory anxiety. The adapted instrument, Laboratory Anxiety Questionnaires (LAQ) was administered as a pre-test to 46 BS Food Technology students who enlisted in Organic Chemistry Laboratory at the Mariano Marcos State University, City of Batac, Philippines. The researchers conducted action research in a search for ways to achieve 21st-century pedagogy to gear students in gaining 21st-century skills for them to become future proof and to thrive in a transforming world. Mobile applications and video clips were used in the instruction. After technology integration and performing the laboratory activities in two weeks, LAQ was applied as a post-test. The study was designed as a pre-test-postest without control group action research. Frequency and percentages were computed to determine the specific laboratory anxieties that were overcome after the intervention. Results showed that the anxiety in handling chemicals, laboratory materials and equipment, laboratory accidents and committing mistakes during the experiment were overcome in high percentages. The integration of technology such as mobile applications and videos reduces the students' laboratory anxiety.
Optimal Multi-view Correction of Local Affine Frames

The technique requires the epipolar geometry to be pre-estimated between each image pair. It exploits the constraints which the camera movement implies, in order to apply a closed-form correction to the parameters of the input affinities. Also, it is shown that the rotations and scales obtained by partially affine-covariant detectors, e.g., AKAZE or SIFT, can be completed to be full affine frames by the proposed algorithm. It is validated both in synthetic experiments and on publicly available real-world datasets that the method always improves the output of the evaluated affine-covariant feature detectors. As a by-product, these detectors are compared and the ones obtaining the most accurate affine frames are reported. For demonstrating the applicability, we show that the proposed technique as a pre-processing step improves the accuracy of pose estimation for a camera rig, surface normal and homography estimation.
Reachability and Differential Based Heuristics for Solving Markov Decision Processes

The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states. In this paper, we present new heuristics to speed up the solution convergence of MDPs. First, we quantify the level of reachability of every state using the Mean First Passage Time (MFPT) and show that such reachability characterization very well assesses the importance of states which is used for effective state prioritization. Then, we introduce the notion of backup differentials as an extension to the prioritized sweeping mechanism, in order to evaluate the impacts of states at an even finer scale. Finally, we extend the state prioritization to the temporal process, where only partial sweeping can be performed during certain intermediate value iteration stages. To validate our design, we have performed numerical evaluations by comparing the proposed new heuristics with corresponding classic baseline mechanisms. The evaluation results showed that our reachability based framework and its differential variants have outperformed the state-of-the-art solutions in terms of both practical runtime and number of iterations.
Principles of correlation and regression analysis

In statistics, the correlation analysis quantifies the strength of the association between two quantitative variables. In the present article, we discuss how to establish a relationship or an association between two quantitative variables. While the correlation provides a quantitative way of measuring the degree or strength of a relation between two variables, regression analysis describes this relationship. Correlation and agreement are also distinguished, and pitfalls of correlation are discussed.
Low code app development

The average cost of a software development project ranges from $434,000 for a small company to $2,322,000 for a large company[1]. In addition to high costs, 31.1% of projects are cancelled before c...
Algebraic classifications for fragments of first-order logic and beyond.

Complexity and decidability of logical systems is a major research area currently involving a huge range of different systems from fragments of first-order logic to modal logic, description logics, et cetera. Due to the sheer number of different frameworks investigated, a systematic approach could be beneficial. We introduce a research program based on an algebraic approach to systematic complexity classifications of fragments of first-order logic and beyond. Our base system GRA, or general relation algebra, is equiexpressive with FO. The system GRA resembles cylindric algebra and Codd's relational algebra, but employs a finite signature with seven different operators. We provide a comprehensive classification of the decidability and complexity of the systems obtained by limiting the allowed sets of operators. Furthermore, we also investigate algebras with sets of operators not included in the list of the seven basic ones and use such operator sets to give algebraic characterizations to some of the best known decidable first-order fragments. To lift the related studies beyond FO, we also define a notion of a generalized relation operator. These operators can be seen to slightly generalize the notion of a generalized quantifier due to Mostowski and Lindstrom.
Between Mutation and Glitch

The following paper considers the immanent principles of digital media evolution. The features of the evolutional route of digital objects are conditioned by glitches, errors and bugs, which appear in media functioning, what in its turn gives birth to the new forms, structures and configurations of digital reality. The glitches are considered not only as a kind of digital mutations, but also as a sign of activity of media. Decisions elaborated from the programs’ failures enlarge the resolution capacity of new technologies. The paper provides an analysis of certain errors and glitches, which engineers, programmers, game designers faced with during their work with digital environment. The theoretical framework includes Henri Bergson’s theory of creative evolution, Gram Harman’s object-oriented philosophy, media philosophical approach to the problem of the activity of object proposed by Valery Savchuk and the theory of self-organisation and autopoiesis of the social systems worked out by Niklas Luhmann. The analysis of digital objects activity demonstrates two levels of functioning. The first one, fictional level, reveals mainly (but not only) in the computer games and concerns the content of media, when we gain a habit to interact with digital objects/characters as if they are real. The second level, operational, realises in the digital environment in general and concerns the form of media. On this level, glitches and bugs have crucial meaning, because they might evoke the changes in the digital world organisation, starting from the local decisions for the certain program (e.g. the elaboration of the new ways in solving locomotional tasks in simulations, which might be later use in other projects) and ending with the replacement of practices and representations typical for the human of digital era. The paper shows that it is possible to talk about digital evolution not in terms of the history of technological inventions, but in terms of the changes in digital objects caused by the inner logic of media, independently from the human will and expectations.