Reviewerly/exHarmony huggingface dataset:
Dimensionality Reduction for Categorical Data

Categorical attributes are those that can take a discrete set of values, e.g., colours. This work is about compressing vectors over categorical attributes to low-dimension discrete vectors. The current hash-based methods compressing vectors over categorical attributes to low-dimension discrete vectors do not provide any guarantee on the Hamming distances between the compressed representations. Here we present FSketch to create sketches for sparse categorical data and an estimator to estimate the pairwise Hamming distances among the uncompressed data only from their sketches. We claim that these sketches can be used in the usual data mining tasks in place of the original data without compromising the quality of the task. For that, we ensure that the sketches also are categorical, sparse, and the Hamming distance estimates are reasonably precise. Both the sketch construction and the Hamming distance estimation algorithms require just a single-pass; furthermore, changes to a data point can be incorporated into its sketch in an efficient manner. The compressibility depends upon how sparse the data is and is independent of the original dimension -- making our algorithm attractive for many real-life scenarios. Our claims are backed by rigorous theoretical analysis of the properties of FSketch and supplemented by extensive comparative evaluations with related algorithms on some real-world datasets. We show that FSketch is significantly faster, and the accuracy obtained by using its sketches are among the top for the standard unsupervised tasks of RMSE, clustering and similarity search.
Handling synset overgeneration: Sense Merging in BTB-WN

The paper reports on an effort to reconsider the representation of some cases of derivational paradigm patterns in Bulgarian. The new treatment implemented within BulTreeBank-WordNet (BTB-WN), a wordnet for Bulgarian, is the grouping together of related words that have a common main meaning in the same synset while the nuances in sense are to be encoded within the synset as a modification functions over the main meaning. In this way, we can solve the following challenges: (1) to avoid the influence of English Wordnet (EWN) synset distinctions over Bulgarian that was a result from the translation of some of the synsets from Core WordNet; (2) to represent the common meaning of such derivation patterns just once and to improve the management of BTB-WN, and (3) to encode idiosyncratic usages locally to the corresponding synsets instead of introducing new semantic relations.
How Did Covid19 Increased Reliance on E-Human Resource Management: Jordanian Insurance Sector Case

Current study aimed at measuring the attention that was given to E-HRM during COVID 19 pandemic the need to move towards managing the working teams virtually. Variables of E-HRM taken into consideration included (E-Recruitment, E-Training, E-Performance Management and E-Compensation). Through adopting the quantitative methodology, an online questionnaire was answered by (365) manager and employee within insurance sector in Jordan. SPSS was used to tackle and handle the gathered primary data, results indicated that now more attention is given to E-HRM practices after it succeeded in supporting internal operations of organization during the pandemic lockdown, and many organizations are welling to keep using E-HRM practices as according to tis convenient. Among the chosen variables of E-HRM; all managed to score a high and positive relationship to independent variable except for e-compensation which scored a medium relationship to COVID 19. Study recommended the need to reconsider human resource practices in the Jordanian insurance sector by intensifying training on managing remote workers and a mechanism for solving problems that may arise in a virtual work environment. Including that, study found out that operating virtually and remotely isn't enough; there is a must to automate the organizational culture not only its operations.
Convergence Rate For Low Pass Infinite Impulse Response Digital Filter

The hybrid optimization technique is used to design a low-pass infinite impulse response (IIR) digital filter, to improve the coefficients of adaptive IIR digital filter, thus ensure stability. In this paper, the newly technique of hybrid particle swarm optimization (PSO) algorithm is proposed. This technique is a dynamic and static topology with a PSO algorithm, which called dynamic and static PSO (DS-PSO) algorithm. In the simulation, the low-pass IIR digital filter 8th order is designed. The fitness function problem is discussed based on values of the ripple of the passband, a ripple of the stopband, and a transition band. Thus, the proposed algorithm results are compared with previous studies results. From results comparison, results have shown the convergence rate with the DS-PSO algorithm outperformed the convergence rates using fuzzy gravitational search algorithm (FGSA), gravitational search algorithm (GSA), and differential evolution (DE) with mean values of 55.21%, 57.80%, and 70.40%, respectively.
Learning Stixel-based Instance Segmentation.

Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation. However, due to their sparse occurrence in the image, until now Stixels seldomly served as input for Deep Learning algorithms, restricting their utility for such approaches. In this work we present StixelPointNet, a novel method to perform fast instance segmentation directly on Stixels. By regarding the Stixel representation as unstructured data similar to point clouds, architectures like PointNet are able to learn features from Stixels. We use a bounding box detector to propose candidate instances, for which the relevant Stixels are extracted from the input image. On these Stixels, a PointNet models learns binary segmentations, which we then unify throughout the whole image in a final selection step. StixelPointNet achieves state-of-the-art performance on Stixel-level, is considerably faster than pixel-based segmentation methods, and shows that with our approach the Stixel domain can be introduced to many new 3D Deep Learning tasks.
Towards a Driver's Gaze Zone Classifier using a Single Camera Robust to Temporal and Permanent Face Occlusions.

Although exists several drivers' gaze direction classifiers to prevent traffic accidents caused by inattentive driving, making this classification while the driver's face is temporarily or permanently occluded remains exceptionally challenging. For example, drivers using masks, sunglasses, or scarves and daily light variations are non-ideal conditions that recurrently appear in an everyday driving scenario and are frequently overlooked by the existing classifiers. This paper presents a single camera framework gaze zone classifier that operates robustly even during non-uniform lighting, non-frontal face pose, and faces undergo temporal or permanent occlusions. The usage of a normalized dense aligned face pose vector, the classification result of a pre-processed right eye area pixels, and the classification result of a pre-processed left eye area pixels is the cornerstone of the feature vector used in our model. The key of this paper is double-folded: firstly, the usage of a normalized dense alignment for a robust face, landmark, and head-pose direction detection and secondly, the processing of the right and left eye images using computer vision and deep learning techniques for refining, modifying, and finally labeling eyes information. Experiments on a challenging dataset involving non-uniform lighting, non-frontal face pose, and faces with temporal or permanent occlusions show each feature's importance towards making a robust gaze zone classifier under unconstrained driving situations.
The Sentiment analysis on Social network data and its marketing Strategies: A review : Sentiment analysis

Any social media plan should include the creation of sticky content. Marketers produce viral content in the expectation that it will go viral rapidly. Customers should be encouraged to use social media marketing to create and distribute their own content, such as product reviews or comments. Influencer marketing on social media is very popular and effective. The main issue is that Influencer Marketing efforts are difficult to track and might have catastrophic ramifications. Sentiment Analysis may be used to assess Influencer Marketing efforts and assist brands in making educated decisions. The goal of the study is to determine how effective an influencer is at creating or boosting intangible assets, as well as to provide practical data for brands looking to hire the ideal influencer for their products. Through sentiment analysis, this study identifies the ideal conditions for influencer marketing. The research also outlines the opportunities and challenges it faces along the process. The nature of the research study is conceptual. The researcher analysed and drew conclusions using secondary data from reliable secondary sources and conceptual demonstration. The next consistent promoting field is social media. Currently, Facebook dominates the advanced advertising area, closely followed by Twitter. Despite the evident benefits that these platforms provide, sites, YouTube, and MySpace are less popular. To investigate the effects of various internet promotional efforts on brand awareness. The purpose of this research is to see how Social Media Sentimental Analysis affects business growth. To investigate the audience's reaction to the brand in order to develop a fresh marketing strategy. To investigate the impact of a social media campaign on the target audience. With knowledge of the public's opinion toward a product or service, one can decide whether or not to buy the product or service. By processing and analysing public sentiment received from internet reviews and social media, the polarity of the sentiment can be determined.
미중패권경쟁의 전망과 한국의 외교전략 - 아세안과 신남방정책의 협력을 중심으로

The conflict between the U.S. and China has taken a more serious turn as China raised its responsibility for the Corona 19 Pandemics, tightened sanctions on Huawei, and China’s enforcement of the Hong Kong Security Law. The U.S.-China conflict, which was reignited in 2020, has raised concerns over a new Cold War due to the rising possibility of military conflict and ideological disputes between communism and democracy, in addition to the aforementioned issues. A clearer diplomatic strategy is required for South Korea and other Indian and Pacific countries, including South Korea, which are directly affected by the U.S.-China trade dispute, strengthening protectionism, the global economic slowdown, military tensions in the Indo-Pacific region, and concerns over economic retaliation. Korea, other middle-power countries and ASEAN countries should strengthen international relations through the protection of the current liberal international order and the pursuit of universal values such as peace, human rights and democracy, and make efforts to find a solution within the current liberal international order to prevent the U.S.-China hegemonic rivalry from turning into war rather than to choose either the U.S. or China from taking sides. This study sought to determine the cause of the U.S.-China hegemony competition based on the theory of offensive realism, and predicted future U.S.-China relations by analyzing the confrontation between the U.S. and China. It considered the responses of ASEAN countries, a geopolitical battleground of the U.S.-China hegemony competition, and predicted that security cooperation with ASEAN through the New Southern Policy and the strengthening of the liberal international order could serve as a precautionary note to prevent war, the conclusion of the hegemonic competition between the U.S. and China in aggressive realism by partially offsetting the aggressiveness of the U.S.-China hegemony competition by rules and institutions.
Konflik Batin Tokoh Utama Pada Novel Demian: The Story Of Emil Sinclair's Youth Karya Herman Hesse Sebuah Kajian Psikologi Sastra Dan Implementasinya Dalam Pembelajaran Sastra di SMA

The objectives of this study are: (1) to describe the structure of Demian's novel: The Strory of Emil Sinclair's Youth by Herman Hesse, (2) describe the form of inner conflict experienced by the characters in the novel Demian by Herman Hesse, (3) describe the relevance of the inner conflict of characters in the Demian novel by Herman Hesse. This type of research is descriptive qualitative. Data in the form of words, phrases, sentences, paragraphs that contain information about novel elements developer and the inner conflict of the character of the novel Demian by Herman Hesse. The data source in this study is the novel Demian: The Story of Emil Sinclair's Youth by Herman Hesse published by Mata Aksara in 2017, 326 pages thick. The data collection technique used is literature study. The data analysis technique used is the semiotic reading method, which is heuristic and hermeneutic reading. Data validation techniques with triangulation of data sources. The results of this study are: (1) the structure of Demian's novel shows unanimity and wholeness shown by the theme of the search for identity, progression, characterization depicted through three dimensions (sociological, psychological and physiological) and has a background (home, school, hostel, taverns, parks, and battlefields), the time setting (when Sinclair was ten years old), and the social setting (showing strong ideologies), (2) the manifestations of inner conflict in this novel consist of conflicts approaching, moving closer -close, stay away, (3) this study can be of relevance as literary teaching material in high school in KD learning. 3.9 class XII and in accordance with the learning criteria which include three aspects, namely language, psychology, and cultural background.
Refining the Semantics of Epistemic Specifications.

Answer set programming (ASP) is an efficient problem-solving approach, which has been strongly supported both scientifically and technologically by several solvers, ongoing active research, and implementations in many different fields. However, although researchers acknowledged long ago the necessity of epistemic operators in the language of ASP for better introspective reasoning, this research venue did not attract much attention until recently. Moreover, the existing epistemic extensions of ASP in the literature are not widely approved either, due to the fact that some propose unintended results even for some simple acyclic epistemic programs, new unexpected results may possibly be found, and more importantly, researchers have different reasonings for some critical programs. To that end, Cabalar et al. have recently identified some structural properties of epistemic programs to formally support a possible semantics proposal of such programs and standardise their results. Nonetheless, the soundness of these properties is still under debate, and they are not widely accepted either by the ASP community. Thus, it seems that there is still time to really understand the paradigm, have a mature formalism, and determine the principles providing formal justification of their understandable models. In this paper, we mainly focus on the existing semantics approaches, the criteria that a satisfactory semantics is supposed to satisfy, and the ways to improve them. We also extend some well-known propositions of here-and-there logic (HT) into epistemic HT so as to reveal the real behaviour of programs. Finally, we propose a slightly novel semantics for epistemic ASP, which can be considered as a reflexive extension of Cabalar et al.'s recent formalism called autoepistemic ASP.
Reviewing the scientific review article

The scientific review article plays an important role in curating information to guide what is worthy of being included in the accepted knowledge base of a disciplinary field. This chapter examines how such knowledge bases are constructed in science and the types of review articles that have guided their development. Until the end of the twentieth century, review articles could be written by an experienced scientist or group in the discipline. However, the rapidly increasing volume of papers being published recently requires the development and implementation of information technology tools to aid in the curation of research and the preparation of reliable reviews.
BULGARIA – MY ANCIENT, YET YOUNG HOMELAND

Every teacher knows that raising a pupil is a constant exchange of thoughts and feelings between the teacher and the children. Somewhere in the process of this exchange it happens that the teacher feels the spark of the national feeling in the pupils’ souls, so he must support it with the relevant knowledge in order to ignite and intensify the feeling. The education of a worthy citizen of his country is one of the leading tasks of the preschool education. Its essence consists of the creation and implementation of a well-thought-out system of pedagogical actions, aimed at the formation of a civic consciousness and feelings in the child. The main goals in my work with the children in the group „Flame“ of kindergarten „Brezichka“ in Vratsa was to get them acquainted with the concept of the Motherland in a suitable format, to instill in them a sense of pride for their country, a desire to actively participate in public life and last, but not least – in the preservation of the Bulgarian language.
In-Cabin vehicle synthetic data to test Deep Learning based human pose estimation models.

The use of vehicle in-cabin monitoring has been increasing to fulfil the specifications of European safety regulations. These regulations present several requirements for detecting driver distraction, and more complex requirements are soon to be expected in higher automation levels. Today's restraint systems provide optimal protection in standard frontal seat positions and deviations to this might cause severe airbag-induced injuries. This makes in-cabin monitoring critical to improve safety and mitigate dangerous situations in case of a crash, and especially in high levels of autonomous driving. Defining the best sensor positioning inside the vehicle's cabin is a challenge due to its constraints and limitations. The main aim of this work was to verify if simulated 3D human models integrated into a 3D modelled vehicle interior environment can be used to run Deep Learning based human pose estimation models. To perform such task, we utilized the software MakeHuman combined with Blender, to build the virtual environment and create photorealistic scenes containing selected front occupants' postures use cases and then feed into Openpose and Mask R-CNN models. The results showed that using a 2D HPE (Human Pose Estimation) network pre-trained on real data, can detect successfully photorealistic synthetic data of humans under complex scenarios. It is also shown that complex and rare postures can cause failure on 2D HPE detections, as shown in the literature review. This work helps to define the most suitable camera positions which, in combination with specific camera lenses, can deliver quality images for a robust pose detection.
Improving Students’ English Skill by Using The Papets (Paper Puppets) Learning Media

Fun learning can make students interested in participating in a lesson. The teacher as the main key in the classroom must have various ways to attract students' interest in learning. One way that teachers can do to attract students' interest in learning is with use teaching media. Media or teaching tools are tools that can be used to stimulate students in learning various kinds of material, especially English. The use of learning media can also help teachers in transferring knowledge to students in a fun way. One of the learning media that teachers can do is the use of the Papets (Paper Puppets) media. This study aims to see how far the paper puppets learning media can improve students' understanding in learning English. The research was conducted in one of the elementary schools in Bengkulu province. The method used in this research is qualitative using a descriptive approach. The data were obtained through observation, interviews and direct field practice in schools. Then the data is analyzed by going through several stages namely; data reduction, data display and conclusion. The results showed that students were very enthusiastic about learning English by using the papets media. In addition, interesting and funny pictures also make students excited so that students' knowledge of English increases very well.
Implementation of Project-Based Learning in Information Technology in an Electronic Learning Environment

This article examines project-based learning in an e-learning environment. An analysis has been made of the educational projects and the functional capabilities of the specialized software, suitable for their implementation in remote access learning. The paper proposes examples that justify the thesis of increasing learner motivation and knowledge effectiveness.
A Faster Algorithm for Finding Closest Pairs in Hamming Metric.

We study the Closest Pair Problem in Hamming metric, which asks to find the pair with the smallest Hamming distance in a collection of binary vectors. We give a new randomized algorithm for the problem on uniformly random input outperforming previous approaches whenever the dimension of input points is small compared to the dataset size. For moderate to large dimensions, our algorithm matches the time complexity of the previously best-known locality sensitive hashing based algorithms. Technically our algorithm follows similar design principles as Dubiner (IEEE Trans. Inf. Theory 2010) and May-Ozerov (Eurocrypt 2015). Besides improving the time complexity in the aforementioned areas, we significantly simplify the analysis of these previous works. We give a modular analysis, which allows us to investigate the performance of the algorithm also on non-uniform input distributions. Furthermore, we give a proof of concept implementation of our algorithm which performs well in comparison to a quadratic search baseline. This is the first step towards answering an open question raised by May and Ozerov regarding the practicability of algorithms following these design principles.
Research on Distribution Strategy of Region of Interest in Panoramic Video Based on Improved Deepsort

The difficulty of data transmission of panoramic video has always limited the wide application of panoramic video. In order to solve this problem, a panoramic distribution algorithm based on the region of interest is proposed. Since the user's focus is often on the person appearing in the video, we consider the area around the person as a potential area of interest. The acquisition of the region of interest is based on a deep learning method. YOLOv4 is used to perform target detection on the panoramic video, and the detected pedestrian target is input into the multi-target tracking module based on the Deepsort algorithm. We use the center of the bounding box output by the Deepsort algorithm as the center of the region of interest and distribute the region of interest in the panoramic video. This greatly reduces the amount of data transmitted by panoramic video, and basically does not affect the user's viewing experience. In order to reduce the switching of target id, this paper improves the appearance extraction model in Deepsort, and proposes an appearance extraction network that focuses on both local and global information, so that the appearance extraction model can extract more representative appearance features of pedestrians. The validity of the model is verified on multiple pedestrian re-identification data sets.