Reviewerly/exHarmony huggingface dataset:
Friendscope: Exploring In-the-Moment Experience Sharing on Camera Glasses via a Shared Camera

We introduce Friendscope, an instant, in-the-moment experience sharing system for lightweight commercial camera glasses. Friendscope explores a new concept called a shared camera. This concept allows a wearer to share control of their camera with a remote friend, making it possible for both people to capture photos/videos from the camera in the moment. Through a user study with 48 participants, we found that users felt connected to each other, describing the shared camera as a more intimate form of livestreaming. Moreover, even privacy-sensitive users were able to retain their sense of privacy and control with the shared camera. Friendscope's different shared camera configurations give wearers ultimate control over who they share the camera with and what photos/videos they share. We conclude with design implications for future experience sharing systems.
Noise-Regularized Bidirectional Gated Recurrent Unit With Self-Attention Layer for Text and Emoticon Classification

The emoji are capable of expressing emotion beyond the meaning of the text by displaying visual emotions, which makes the content more distinct. Recently, emoji and text prediction has gained more significance, since it is hard to choose the appropriate one from thousands of emoji candidates. The small-sized dataset provides a poor description of features that resulted in classification and showed overfitting and underfitting problems. Therefore, Noise Regularized Bidirectional Gated Recurrent Unit (Bi-GRU) with Self-Attention Layer (SAL) is proposed for the classification of text and emoji. The proposed Noise Regularized Bi-GRU which is an aspect-based sentiment analysis performs a series of experiments on Twitter data to predict the sentiment of a tweet. The proposed Noise Regularized BGRU with SAL method obtained an accuracy of 87.77 % better when compared to the deep learning model that obtained an accuracy of 86.27 %.
When molecules come to life – Using Augmented reality for studying protein structure in Cell Biology class

Most Undergraduate Biology courses cover an incredibly large range of topics integrating complex information at molecular, cellular, genetic and at the organismal level. Translating concepts from one level of thought to another and crossing disciplines equally and fluidly largely depends on how well students understand the structure-function relationship at the micro and macro level. A survey of the literature shows that despite having encountered these structures of biomolecules in their earlier courses, students lack an ability to visualize molecules and interactions. Most classroom curriculums use textbooks and lecture slides with straightforward two-dimensional structures that make it hard for students to visualize these macromolecules. To improve motivation and to promote student interactivity, in Cell Biology classes I have used molecular models in Augmented reality to reform content delivery by using interactive active learning techniques. This presentation will discuss ways for instructors to implement Augmented reality in their classes to help students better understand concepts of structure and function of Biomolecules.
Event-based dataset for classification and pose estimation

We present a new data processing pipeline for automatically labelled stereo vision data. The method is based on 3D positioning of 3D printed props with active LED markers. It provides both RGB frame and event-based data, automatically annotated with segmentation masks and ground truth position and orientation data.
Regional Trajectory Analysis through Multi-Person Tracking with mmWave Radar

Understanding characteristics of an environment can lead to performing more reliable multiple object tracking (MOT). This paper proposes an approach to extract entry and exit points for an environment using MOT trajectories. The observed environment is divided into a grid, in which regions are defined for. The trajectories obtained from the MOT data are organized into their respective regions. For each region an activity heatmap is formed and classified using a convolutional neural network (CNN) to determine if the region consists of either an entry or exit point. The entry and exit points classified are projected onto the MOT plane to illustrate the entry and exit points of the observed environment. The approach proposed in this paper provides a foundation in which future work can build on to enhance the MOT capability in real-time through a greater understanding of the tracking environment characteristics.
Enabling AI Adoption through Assurance

With the broader adoption of AI-enabled software systems, it is necessary to provide assurance to the layman user that the AI system will behave as intended. This interactive tutorial will provide an overview of AI assurance, introduce a new set of assurance goals for AI systems, discuss the open challenges in AI assurance, and present recommendations to overcome its drawbacks.
Northern Periphery of the Circumpontian Metallurgical Province: Metal, Alloys, Technology

The article summarizes the results of metal research and metalworking of the Eneolithic cultures of the forest zone of Eastern Europe. In total, 897 products were recorded in the regions of the North-West, Volga-Oka, Kama-Vyatka, Ural-Kazakhstan, including 21 molds. A morphological and typological study of inventory was carried out, the vast majority of which comes from settlements (901); 7 ornaments were found in the burial grounds. The chemical composition of the metal was studied by the methods of spectral and X-ray fluorescence analysis (371 items). Manufacturing technology was studied by methods of metallographic analysis (92 items). In the Forest Eneolithic, metallurgically “pure” copper was used with a slight admixture of other elements. It corresponds mainly to the chemical group MP (copper sandstones of the Urals). Archaeological and analytical data testify to the functioning of the Garino center of metallurgy and the Volosovo center of metalworking in the Volga-Kama region, which are relatively thin compared to the centers of the Circumpontian metallurgical province. Metalworking of the Eneolithic cultures of the Volga-Urals developed under the influence of the traditions of the Cis-Ural pit and Middle Volga Balanovo-Fatyanovo and Abashevo hearths. The initial impulses for the formation of metalworking in the post-Neolithic cultures of the northern forest-steppe and the south of the forest zone could come from the centers of the Khvalynsk Eneolithic culture.
Multiplayer online game for distance learning for Integrated Islamic Kindergarten students of Khairunnisa

During the Covid-19 pandemic, education in Indonesia was delivered via distance learning (PJJ). Of course, this learning is fraught with difficulties, particularly for kindergarten students who require direct teacher guidance. The IT Khairunnisa Kindergarten, which is located in Grinting Village, Bulakamba District, Brebes Regency, is no exception. As a result, innovative solutions are required to ensure that learning can take place more effectively even when it is done remotely. The innovative solution is a multiplayer online game that includes learning elements such as counting, matching, and others as a learning medium for students and teachers. This community service is carried out by socializing and assisting teachers and students in the use of learning game apps. When the socialization took place, participants were very enthusiastic about learning this application, particularly students, who carefully observed how the game was used and the features in it. The interview results revealed that the participants were interested in using the game as a medium for PJJ, and the students were interested in this game as well, because learning was like playing. This application is simple to use and interactive, making it possible to perform online KBM that feels like offline KBM.
Pengenalan Biota Akuatik Aliran Sungai Senggarang dengan E-Katalog melalui pembelajaran tatap muka sesuai protokol kesehatan Covid-19

Small islands have short rivers characteristics, small catchment areas so that the ability to absorb rainwater into the ground is also reduced, such as the flow of rivers in Senggarang, Tanjungpinang City. Research related to aquatic biodiversity in Senggarang river flow has been carried out, the availability of information about the freshwater ecosystems that exist there is an interesting thing to inform the high school students as a generation of learners who lived near Senggarang river. The introduction of the aquatic biota of Senggarang river is expected to increase the sense of care, willingness, and insight in saving the resources of aquatic biota of Senggarang river flow will be sustainable. An introduction to the aquatic biota of the Senggarang river flow with the participants of class XI State High School 6 Tanjungpinang. The activity was carried out in three stages, namely a pre-test of knowledge about aquatic biota of the Senggarang river flow through quizizz media, education of any aquatic biota in the Senggarang river through e-catalog media containing pictures and descriptions of biota identification, and post-test with the same questions as the pre-test through the quizizz media to measure the extent to which students recognition, knowledge, and understanding have increased regarding the aquatic biota of the Senggarang river. The level of recognition, knowledge, and understanding of students increased from a very low–moderate range to a moderate–high range. Teaching media in the form of an e-catalog is very effective in introducing aquatic biota from the Senggarang river flow, according to the target stakeholders who are still in high school education.
Towards Personalized Review Generation with Gated Multi-source Fusion Network

Product review generation is an important task in recommender systems, which could provide explanation and persuasiveness for the recommendation. The key challenge of review generation is a lack of semantic information that can be used, resulting in more uncertainty in the generation processing. In this paper we propose a novel encoder-decoder personalized review generation model which aims to effectively and selectively fuse multiple resources including the user/product attributes and their textual information (e.g., historical reviews, short phrases). In the encoder part, we first design an attention layer to accordingly retrieve useful information from the users/product historical reviews since not all reviews are relevant to the target review generation. Note that the attention vector is derived from the semantic features of the short phrases (e.g., review summaries, product titles) which could indicate user preference and product characteristics. In the decoder part, we design a hierarchical gated multi-source network from both intra- and inter-gate perspectives to effectively fuse the multiple context features from encoders. Experimental results on the real-world dataset show that our proposed model could achieve better review generation performance than the baselines, in terms of fluency, readability, and personalization.
Accumulating weighted segmentation in 3D face recognition

In this paper, an accumulating weighted face segmentation approach based on the rigid level of human facial areas is introduced.A mass of 3D face data is measured and analysed to define the most expression-invariant region.Different locations or regions on the human face are observed to have dissimilar invariant levels.Thus, an accumulating weight method is proposed to represent the rigid degree under expression variations.In face identification experiments, performance by employing the accumulating weight is demonstrated to be higher than methods using the expression-invariant region and the full face, respectively.This accumulating weighted face segmentation approach outperforms other state-of-the-art methods in 3D face recognition experiments.
Relating Mandelbrot and Barnes-Jarvis Power Law Noise State Space Models using Partial Fractions

This paper addresses two state space models for generating approximate power law noise and shows that these models are related through partial fractions in frequency domain.
Proposal of an Automated Tool for the Application of Sentiment Analysis Techniques in the Context of Marketing

Currently, the opinions and comments made by customers on e-commerce portals regarding different products and services have great potential for identifying customer perceptions and preferences.Based on the above, there is a growing need for companies to have automated tools based on sentiment analysis through polarity analysis, which allow the examination of customer opinions to obtain quantitative indicators from qualitative information that enable decisionmaking in the context of marketing.In this article, we propose the construction of an automated tool for conducting opinion mining studies, which can be used in a transparent way to the algorithmic process by the marketing units of companies for decision making.The functionality of the proposed tool was verified through a case study, in which the opinions obtained from electronic commerce website concerning one of the bestselling technological products were investigated.
Library Services Utilization and Teacher Education Students ‘academic Performance in Selected State Universities in Region III, Philippines

This study primarily determined the utilization of various library services by teacher education students and their academic performance. The study covered the libraries of Central Luzon State University and Tarlac State University during the second semester of academic year 2018–2019. A survey questionnaire was utilized in gathering the needed data. Statistical analysis employed the use of the following: frequency counts, percentages, mean, standard deviation, Pearson r, and multiple linear regression. The majority of the teacher education students were found to have a very good grade point average from the previous semester. The father’s highest educational attainment showed a strong positive correlation with academic performance. On the contrary, the mother’s occupation and monthly family income had weak to moderate negative correlations. The perceived usefulness of using the library into the academic works by teacher education students revealed a moderate positive correlation. Library satisfaction was found to have a moderate positive correlation with students’ academic performance. Father’s educational attainment, utilization, and satisfaction with library services were found to be positive predictors of students’ academic performance. However, the mother’s occupation and monthly family income seemed not variable predictors of students’ academic performance in the context of library utilization.
DAMPAK COVID-19 TERHADAP KINERJA DAN MANAJEMEN SUMBER DAYA DOSEN

DAMPAK COVID-19 TERHADAP KINERJA DAN MANAJEMEN SUMBER DAYA DOSEN&#x0D; &#x0D; Sutangsa dan Lysda Reviyanti&#x0D; sutangsapls@gmail.com dan lysdarevi@gmail.com&#x0D; Universitas Subang dan Universitas Halim Sanusi PUI&#x0D; &#x0D; &#x0D; ABSTRAK&#x0D; &#x0D; The impact of the pandemic has disrupted the learning process for higher education in Indonesia. The purpose of this study was to determine the impact of COVID-19 on lecturer performance and resource management. This research was conducted at the University of Subang on Jl. R.A. Kartini KM. 3 villages of Nyimplung, Pasirkareumbi, Kec. Subang, Subang Regency, West Java 41285. This study used a qualitative research method with a case study approach. During the COVID-19 pandemic at the University of Subang, the lecturers' performance was less effective. It can be seen that lecturers teach not face-to-face but online. Lecturers give online lectures using Zoom, Google Classroom and Whatsapp Group. The use of Zoom and Google Meet media when delivering material to students is not without obstacles. Often, when online lectures are taking place, the internet network can be cut off or the signal is weak, and even the internet quota runs out, whether it happens to lecturers or students. Zoom technology which is free also has a time limit, and Google Meet also has a limit in the number of users. The biggest challenge when giving lectures online for approximately three months is how to know that the material we convey is sufficiently accepted and understood by students. Human Resource Management (MSDM) in the COVID-19 era at the University of Subang, namely (1) HR management, which must present a professional lecturer profession where the final output of the campus is able to produce graduates who are competent in their respective fields. (2) If one of the higher education tridharma instruments is neglected by the lecturer, for example lecturers and higher education institutions merely carry out educational and teaching functions on a regular basis and ignore other functions, then the lecturers and tertiary institutions will not have the passion as a professional education provider and in turn, it will have an impact on the low work culture of lecturers and universities. (3) Human Resources Management at the University of Subang requires lecturers to have the ability, work motivation, work discipline, work ethics, work culture, work ethic, and high work morale. (4) Human Resource Management at the University of Subang, both at the top to the lowest level of leadership and staffing as academic support, must be able to work to present professional lecturers by serving all out, taking inventory of lecturers' needs, innovating for solutions, maintaining relationships.&#x0D; &#x0D; Kata Kunci : Covid-19, Kinerja Dosen, MSDM&#x0D;
Improved Real-Time Three-Dimensional Stereo Matching with Local Consistency

Existing stereo matching algorithms are unable to meet the real-time and high-performance dual requirements in practical applications. To address this problem, a novel stereo network is proposed, which utilizes the prior of local disparity consistency to improve the performance of real-time disparity estimation. Based on the initial disparity estimation by the light-weight pyramid matching network, novel spatial consistency refinement (SCR) module and time consistency refinement (TCR) module are designed for further disparity refinement. SCR module propagates the neighborhood high-confidence predictions of sparse sampling to unreliable regions for disparity refinement. A single-layer Dynamic Local Filter (DLF) is designed to realize the content-adaptive propagation, which effectively improves the disparity quality without significantly increasing the burden of computation and memory. For real-time disparity estimation of consecutive frames, novel TCR module is further proposed to refine the disparity estimation based on the time local consistency of disparity. The proposed method is evaluated on the Scene Flow and KITTI 2015 datasets with comprehensive experiments. Experimental results demonstrated that our method can achieve high-accuracy disparity estimation and real-time running speed of over 40 FPS, which significantly outperforms the compared networks with similar runtimes.
An Intelligent Collaborative System for Robot Dynamics

In this article, we propose an intelligent collaborative system for robotic navigation and control (CNaC) governed by the Euler–Lagrange equation. First, a state reconstruction based on neural networks navigation (SR-NNN) law is designed to estimate the current position of the robot for intelligent CNaC. The SR-NNN makes full use of partial truth information and the mighty local fitting ability of neural networks. In the absence of landmark, SR-NNN still exhibits navigation performance with high precision. The maximum root-mean-squared error (RMSE) of DR is 0.096 and the maximum RMSE of SR-NNN is 0.053, which has been improved by 55%. In addition, the motion model obtained by SR-NNN online training can avoid the error introduced by the predetermined motion model and overcome the interference of the external environment. The intelligent CNaC still can achieve satisfactory control performance based on the estimated position given by the SR-NNN rather than the ground truth which is formed by postprocessing. The intelligent CNaC has been demonstrated by simulation tracking sample and real experiments, which verifies the effectiveness of the intelligent CNaC.
Multiple Parallel Activity Detection and Recognition to Avoid COVID-19 Spread-Out

COVID-19 has been affecting people around the globe. It is affecting almost every country currently, according to the World Health Organization (WHO). This virus is transmitted to another person if an asymptomatic person makes close contact with the everyday person. There is no cure for this virus, and the only solution is social distancing and avoids the people doing these activities. In this paper, we proposed a system for detecting and recognizing the activities that violate social distancing. These activities involve handshakes and hugging. We implement a system that is capable of detecting and identifying multiple parallel activities. Temporal features are extracted for similar activities in 16 frames. We use the two models for this purpose: Faster RCNN for the detection and ResNet-50 to recognize the activities. First, Faster RCNN detects the group of people and that region of interest ROI saved and passes to the ResNet-50 to recognize the activities. We also generated our dataset on the local environment in multiple parallel activities. This system achieves the accuracy of 95.03% for the detection of testing dataset and recognition of multiple parallel activities 92.88% accuracy accomplished. The system used different public datasets and generated some local datasets for handshake and hugging activities.
Analysing and designing visualizations – Diagrammatics (1984) revisited

This paper reviews how the authors’ current framework – the DNA of visualization – has evolved from the work laid out in Diagrammatics (Richards, 1984). The goal of this line of work was, and is, to enable the analysis and specification of an extensive range of different types of visual representations of information, such as statistical charts, maps, family trees, Venn diagrams, flow charts, texts using indenting, technical drawings and scientific illustrations. Inspired by an analogy with language, fundamental possibilities of graphic organization were identified in 1984. This work has been further developed into the current DNA of visualization framework. We identify the main concepts within the current framework and point to their roots to the 1984 work.
Comparison of Training Effectiveness of Neural Networks in Whole and in Blocks

Despite the fact that artificial neural networks are used as a research and forecasting tool in various fields of knowledge, the choice of the most important properties of neural networks remains mainly empirical. Based on the study of sufficiently informative examples, the paper compares the effectiveness of two training methods, when one step of training of an artificial neural network is performed for the entire training set as a whole, and when for one step of training this set is divided into separate blocks. As a result of the conducted numerical experiments, recommendations were obtained for the initial choice of parameters such as the size of blocks and the learning rate, as well as for changing these parameters during the training of a neural network.
Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs

In this work, our aim is to provide a structured answer in natural language to a complex information need. Particularly, we envision using generative models from the perspective of data-to-text generation. We propose the use of a content selection and planning pipeline which aims at structuring the answer by generating intermediate plans. The experimental evaluation is performed using the TREC Complex Answer Retrieval (CAR) dataset. We evaluate both the generated answer and its corresponding structure and show the effectiveness of planning-based models in comparison to a text-to-text model.