Reviewerly/exHarmony huggingface dataset:
TopicNet: Semantic Graph-Guided Topic Discovery

Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic hierarchy. However, it is unclear how to incorporate prior beliefs such as knowledge graph to guide the learning of the topic hierarchy. To address this issue, we introduce TopicNet as a deep hierarchical topic model that can inject prior structural knowledge as an inductive bias to influence learning. TopicNet represents each topic as a Gaussian-distributed embedding vector, projects the topics of all layers into a shared embedding space, and explores both the symmetric and asymmetric similarities between Gaussian embedding vectors to incorporate prior semantic hierarchies. With an auto-encoding variational inference network, the model parameters are optimized by minimizing the evidence lower bound and a regularization term via stochastic gradient descent. Experiments on widely used benchmarks show that TopicNet outperforms related deep topic models on discovering deeper interpretable topics and mining better document~representations.
Clustering via Ant Colonies: Parameter Analysis and Improvement of the Algorithm

An ant colony optimization approach for partitioning a set of objects is proposed. In order to minimize the intra-variance, or within sum-of-squares, of the partitioned classes, we construct ant-like solutions by a constructive approach that selects objects to be put in a class with a probability that depends on the distance between the object and the centroid of the class (visibility) and the pheromone trail; the latter depends on the class memberships that have been defined along the iterations. The procedure is improved with the application of K-means algorithm in some iterations of the ant colony method. We performed a simulation study in order to evaluate the method with a Monte Carlo experiment that controls some sensitive parameters of the clustering problem. After some tuning of the parameters, the method has also been applied to some benchmark real-data sets. Encouraging results were obtained in nearly all cases.
FEATURES OF THE ORGANIZATION OF BLENDED LEARNING WITH THE USE OF DIGITAL TECHNOLOGIES

The domestic experience of the introduction of distance and blended learning in secondary schools of Ukraine is presented. In particular, combinations of various forms and systems of education are established. The experience of using digital technologies is reflected (mainly within the framework of distance learning organization). Criteria for selecting digital technologies for the implementation of blended learning in general secondary education: forms of presentation of educational material and multimedia, the structure of information presentation, interaction with educational content, variability of educational content.
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique that can provide efficient querying mechanism over large and incomplete databases. Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection. However, their geometry is restrictive and leads to non-smooth strict boundaries, which further results in ambiguous answer entities. Furthermore, previous works propose transformation tricks to handle unions which results in non-closure and, thus, cannot be chained in a stream. In this paper, we propose a Probabilistic Entity Representation Model (PERM) to encode entities as a Multivariate Gaussian density with mean and covariance parameters to capture its semantic position and smooth decision boundary, respectively. Additionally, we also define the closed logical operations of projection, intersection, and union that can be aggregated using an end-to-end objective function. On the logical query reasoning problem, we demonstrate that the proposed PERM significantly outperforms the state-of-the-art methods on various public benchmark KG datasets on standard evaluation metrics. We also evaluate PERM's competence on a COVID-19 drug-repurposing case study and show that our proposed work is able to recommend drugs with substantially better F1 than current methods. Finally, we demonstrate the working of our PERM's query answering process through a low-dimensional visualization of the Gaussian representations.
Deep Learning-Based Object Tracking via Compressed Domain Residual Frames

The extensive rise of high-definition CCTV camera footage has stimulated both the data compression and the data analysis research fields. The increased awareness of citizens to the vulnerability of their private information, creates a third challenge for the video surveillance community that also has to encompass privacy protection. In this paper, we aim to tackle those needs by proposing a deep learning-based object tracking solution via compressed domain residual frames. The goal is to be able to provide a public and privacy-friendly image representation for data analysis. In this work, we explore a scenario where the tracking is achieved directly on a restricted part of the information extracted from the compressed domain. We utilize exclusively the residual frames already generated by the video compression codec to train and test our network. This very compact representation also acts as an information filter, which limits the amount of private information leakage in a video stream. We manage to show that using residual frames for deep learning-based object tracking can be just as effective as using classical decoded frames. More precisely, the use of residual frames is particularly beneficial in simple video surveillance scenarios with non-overlapping and continuous traffic.
Sharpness and Brightness Quality Assessment of Face Images for Recognition

Face image quality has an important effect on recognition performance. Recognition-oriented face image quality assessment is particularly necessary for the screening or application of face images with various qualities. In this work, sharpness and brightness were mainly assessed by a classification model. We selected very high-quality images of each subject and established nine kinds of quality labels that are related to recognition performance by utilizing a combination of face recognition algorithms, the human vision system, and a traditional brightness calculation method. Experiments were conducted on a custom dataset and the CMU multi-PIE face database for training and testing and on Labeled Faces in the Wild for cross-validation. The experimental results show that the proposed method can effectively reduce the false nonmatch rate by removing the low-quality face images identified by the classification model and vice versa. This method is even effective for face recognition algorithms that are not involved in label creation and whose training data are nonhomologous to the training set of our quality assessment model. The results show that the proposed method can distinguish images of different qualities with reasonable accuracy and is consistent with subjective human evaluation. The quality labels established in this paper are closely related to the recognition performance and exhibit good generalization to other recognition algorithms. Our method can be used to reject low-quality images to improve the recognition rate and screen high-quality images for subsequent processing.
Intelligent Learning Environment and Future Development Tendency

In the recent years, intelligent learning environment replace the traditional learning environment gradually. Compare with the traditional learning environment, intelligent learning environment consider more about the learning experience of students, improve the communication between students and intelligent learning environment. One of the most paramount factors is intelligent tools in intelligent learning environment. These intelligent tools contain 5G communication technology, Mobile library, artificial intelligent and virtual reality. In this paper, we will mention the impact of these tools on intelligent learning environment, and how these tools influent students learning methods as well as teaching approach during the class. Besides, we still analyze the merits and disadvantages about intelligent tools. What is more, what kind of development will the intelligent tools going to happen in the future? We should deliberate it.
Visual relation of interest detection based on part detection

Visual relation detection (VRD) aims to describe images with relation triplets like &lt;subject, predicate, object&lt;, paying attention to the interaction between every two instances. To detect the visual relations that express the main content of a given image, visual relation of interest detection (VROID) is proposed as an extension of the traditional VRD task. The existing methods related to the general VRD task are mostly based on instance-level features and the methods that adopt detailed information only use part-level attention or human body parts. None of the existing methods take advantage of general semantic parts. Therefore, on the basis of the IPNet for VROID, we further propose an interest propagation form part (IPFP) method which propagates interest along “part-instance-pair-triplet” to detect visual relations of interest. The IPFP method consists of four modules, Panoptic Object-Part Detection (POPD) module, Part Interest Prediction (PartIP) module, Instance Interest Prediction (InstIP) module, Pair Interest Prediction (PairIP) module, and Predicate Interest Prediction (PredIP) module. The POPD module extracts instances with instance features and instance parts with part features; the PartIP module predicts interest for every single part; the InstIP module predicts interest for every single instance; the PairIP module predicts interest for each pair of instances; and the PredIP module predicts possible predicates for each instance pairs. The interest scores of visual relations are the product of pair interest scores and predicate possibilities for pairs. We evaluate the performance of the IPFP method and the effectiveness of important components using the ViROI dataset for VROID.
Prototyping for policy

There is great potential in combining a design approach with a legal approach, particularly for tackling complex policy challenges. A combined legal and design problem-solving approach goes beyond refining legal communications and services through human-centered design. That is useful, as the application of design to the legal system. But there is another level of work, in which a legal approach - of systematic, critical, and detailed exploration of possible solutions - is combined with a design approach - of creative, empathetic, and experimental work. This can lead to civic innovation work, particularly in the form of prototyping and testing new policies, that is both large-scale and human-centered. This chapter presents literature and examples that detail this developing practice of prototyping for policy, in which human-centered design is adapted and combined with legal analysis to create new policy development.
Denoise Auto-Encoder Based Speckle Reduction for RISAT-1 SAR Imagery

Synthetic aperture radar (SAR) images can pass through cloud cover, dry particles, and haze except for heavy rainfall. Therefore, they are available in all climates, all the time. However, the SAR images are corrupted by speckle noise generated by coherent processing of SAR signal. In this paper, the denoise auto-encoder model is proposed to reduce the speckle noise in SAR images, and the performance of the auto-encoder model is compared with different spatial-domain adaptive filters viz. Lee, Frost, Enhanced Lee, Enhanced Frost. The performance of the proposed denoising encoder is assessed using visual analysis, and quantitative evaluation using metrics, viz. equivalent number of looks (ENL), speckle suppression index (SSI) and speckle suppression and mean preservation index (SMPI). The evaluation of the denoise auto-encoder reveals that its performance is better than spatial- domain adaptive filters.
Disease Diagnostic System Using Deep Learning

Abstract: Early disease detection cannot be neglected in the healthcare domain and especially in the diseases where a person's life is at stake. According to the WHO, if the diseases are predicted on time, then the death rates could reduce. The paper's goal is to find out how to detect Breast Cancer, Skin Cancer, Lung Cancer, and Brain Tumor at the early stages with the help of Deep Learning techniques. The authors of different papers have used different techniques and Algorithms like Adaptive Median Filters, Gaussian Filters, CNN algorithms, etc. Keywords: Breast Cancer, Skin Cancer, Brain Tumor, Lung Cancer, Deep Learning, CNN, SVM, Random Forest
APPLICATION OF LANDSAT-8 OPERATIONAL LAND IMAGER AND SHUTTLE RADAR TOPOGRAPHY MISSION-DIGITAL ELEVATION MODEL IN THE STUDY OF IKARA AND ITS ENVIRONS, NORTHWESTERN NIGERIA

Images of the earth’s surface captured by radiometric detectors on air-borne (satellite) sensors were employed in geologic reconnaissance survey. They were used to study rock units, pattern and trends of geologic structures on the surface in 2D. Multispectral resolution Landsat-8 Operational Land Imager (Landsat-8 OLI), have nine spectral band with spatial resolution of 30 m (bands 1 to 7, and 9) and 15 m for band 8. The aim of this research is to discriminate lithological units/complexes and delineate fractures (lineaments), hence, only bands 2 to 7 were used for this study. This technique was used to discriminate existing lithological units and identify major and minor extensive geologic structures within the study area. These selected bands were selected, grouped and combined (Band Combination (BC)) and processed for interpretation using applicable Software Packages. Hence, for each procedure and maximum clarity in the output of results, three bands were combined for each analytical process. Bands 4,3,2; 6,5,4; 7,5,3 were combined for Natural Colour Composite (NCC), False Colour Composite (FCC) and Principle Component Analysis (PCA) respectively. The results revealed two distinct lithological Complexes due to their similarities in appearance and differences in mineralogical compositions. SRTM-DEM was used to highlight the topography showing the lowest relief at the northeast and the highest relief at the south east. More so, elongated ridges of intermediate-high reliefs were exposed at the southern part of the study area trending NW-SE. A lineament map and Rose plot shows dominant trend of fractures to be NNW-SSE, NW-SE, N-S,
Value-Semantic Measurement of Commitment: an Empirical Study

The article considers the value-semantic dimension of commitment using the case of students’ involvement in classroom activities. Commitment is a state that appears if the person and the environment match each other. Commitment has several levels: as a current state, as motivation and values, and as an integral characteristic of human life. Meanings and values are responsible for the relations between the person and the environment, which are expressed as emotions, e.g., life satisfaction, psychological well-being, etc. The research objective was to determine the nature of the relationship between the current state of students' involvement in educational and professional activities and their motives, life orientations, life satisfaction, and psychological well-being. The Utrecht Work Engagement Scale (UWES-17) was used to diagnose commitment as a current state (W. Schaufeli, A. Bakker). The value-semantic aspects of commitment were measured using the Academic Motivation Scale (T. O. Gordeeva, O. A. Sychev, E. N. Osin), the Test of Life-Purpose Orientations (D. А. Leontiev), the Questionnaire of Life Orientations (E. Yu. Korzhova), the Scale of Psychological Well-Being (C. Ryff, in N. N. Lepeshinsky’s modification), and Life Satisfaction Index (A. O. Neugarten et al., adapted by N. V. Panina). The data were analyzed separately for secondand third-year students of Amur State University with high, moderate, and low commitment. The comparative analysis was based on ANOVA. Commitment as a current state appeared to correlate more with academic motivation, life-purpose orientations, and life satisfaction than with life subject-object orientations and psychological well-being. The results obtained can help psychological counselors to organize events aimed at increasing students’ commitment.
Danzer's problem, effective constructions of dense forests and digital sequences

A 1965 problem due to Ludwig Danzer asks whether there exists a set in Euclidean space with finite density intersecting any convex body of volume 1. A recent approach to this problem is concerned with the construction of dense forests and is obtained by a suitable weakening of the volume constraint. A dense forest is a discrete point set of finite density getting uniformly close to long enough line segments. The distribution of points in a dense forest is then quantified in terms of a visibility function. Another way to weaken the assumptions in Danzer's problem is by relaxing the density constraint. In this respect, a new concept is introduced in this paper, namely that of an optical forest. An optical forest in R d $\mathbb {R}^{d}$ is a point set with optimal visibility but not necessarily with finite density. In the literature, the best constructions of Danzer sets and dense forests are not deterministic. The goal of this paper is to provide deterministic constructions of dense and optical forests which yield the best known results in any dimension d ⩾ 2 $d \geqslant 2$ in terms of visibility and density bounds, respectively. Namely, there are three main results in this work: (1) the construction of a dense forest with the best known visibility bound which, furthermore, enjoys the property of being deterministic; (2) the deterministic construction of an optical forest with a density failing to be finite only up to a logarithm and (3) the construction of a planar Peres-type forest (that is, a dense forest obtained from a construction due to Peres) with the best known visibility bound. This is achieved by constructing a deterministic digital sequence satisfying strong dispersion properties.
Predicting Software Defect Severity Level using Sentence Embedding and Ensemble Learning

Bug tracking is one of the prominent activities during the maintenance phase of software development. The severity of the bug acts as a key indicator of its criticality and impact towards planning evolution and maintenance of various types of software products. This indicator measures how negatively the bug may affect the system functionality. This helps in determining how quickly the development teams need to address the bug for successful execution of the software system. Due to a large number of bugs reported every day, the developers find it really difficult to assign the severity level to bugs accurately. Assigning incorrect severity level results in delaying the bug resolution process. Thus automated systems were developed which will assign a severity level using various machine learning techniques. In this work, five different types of sentence embedding techniques have been applied on bugs description to convert the description comments to an n-dimensional vector. These computed vectors are used as an input of the software defect severity level prediction models and ensemble techniques like Bagging, Random Forest classifier, Extra Trees classifier, AdaBoost and Gradient Boosting have been used to train these models. We have also considered different variants of the Synthetic Minority Oversampling Technique (SMOTE) to handle the class imbalance problem as the considered datasets are not evenly distributed. The experimental results on six projects highlight that the usage of sentence embedding, ensemble techniques, and different variants of SMOTE techniques helps in improving the predictive ability of defect severity level prediction models.
Text documents screen watermarking by changing background brightness in the interline spacing

One of the most common ways documents leak is taking a picture of document displayed on the screen. For investigation of such cases data leakage prevention technologies including screen watermarking are used. The article gives short review on the problem of screen shooting watermarking and the existing research results. A novel approach for watermarking text images displayed on the screen is proposed. The watermark is embedded as slight changes in luminance into the interline spacing of marked text. The watermark is designed to be invisible for human eye but still able to be detected by digital camera. An algorithm for extraction of watermark from the screen photo is presented. The extraction algorithm doesn’t need the original image of document for successful extraction. The experimental results show that the approach is robust against screen-cam attacks, that means that the watermark stays persistent after the process of taking a photo of document displayed on the screen. A criterion for watermark message extraction accuracy without knowledge about the original message is proposed. The criterion represents the probability that the watermark was extracted correctly.
Investigation of Heavy Metal Traces during Drilling Operations in the Bibi Hakimeh Oil Field, Iran

Drilling operations release considerable amounts of drilling fluids that contain pollutants such as heavy metals and hydrocarbons. By means of the ICP-OES device, the concentration of metals in the drilling mud deposited in the accumulation pools (around the drill rigs), the drilling mud used in the well (initial, input, and output mud), the soil around the drilling rig, and the control sample can be determined. Comparison of metal accumulation volume with current standards (upper crust and the region’s soil) indicates that the initial drilling mud contains As, Cd, Mo, Cu, and Ag metals. Also, the outflow and associated cuttings are contaminated with Sb, showing a geological formation origin for this metal. In case of Pb, Ba, Cr, and Mn, the level of contamination is in a medium range wherein the source of the contamination is recycled mud and drilling rocks in the previous phases. Contamination of other metals is evaluated in the low range. Geo-accumulation index (Igeo), Enrichment Coefficient (EF), and Contamination Factor (CF) are used to determine the level of pollution. Results show that As, Pb, Ba, Mn, Mo, Cd, Co, Cu, and Zn have caused a lot of pollution in the area and their amounts should be controlled.
Life Is Not Black and White - Combining Semi-supervised Learning With Fuzzy Labels.

The required amount of labeled data is one of the biggest issues in deep learning. Semi-Supervised Learning can potentially solve this issue by using additional unlabeled data. However, many datasets suffer from variability in the annotations. The aggregated labels from these annotation are not consistent between different annotators and thus are considered fuzzy. These fuzzy labels are often not considered by Semi-Supervised Learning. This leads either to an inferior performance or to higher initial annotation costs in the complete machine learning development cycle. We envision the incorporation of fuzzy labels into Semi-Supervised Learning and give a proof-of-concept of the potential lower costs and higher consistency in the complete development cycle. As part of our concept, we discuss current limitations, futures research opportunities and potential broad impacts.