Reviewerly/exHarmony huggingface dataset:
Auto-Compressing Subset Pruning for Semantic Image Segmentation

State-of-the-art semantic segmentation models are characterized by high parameter counts and slow inference times, making them unsuitable for deployment in resource-constrained environments. To address this challenge, we propose Auto-Compressing Subset Pruning, ACoSP, as a new online compression method. The core of ACoSP consists of learning a channel selection mechanism for individual channels of each convolution in the segmentation model based on an effective temperature annealing schedule. We show a crucial interplay between providing a high-capacity model at the beginning of training and the compression pressure forcing the model to compress concepts into retained channels. We apply ACoSP to SegNet and PSPNet architectures and show its success when trained on the CamVid, Cityscapes, Pascal VOC2012, and Ade20k datasets. The results are competitive with existing baselines for compression of segmentation models at low compression ratios and outperform them significantly at high compression ratios, yielding acceptable results even when removing more than $$93\%$$ of the parameters. In addition, ACoSP is conceptually simple, easy to implement, and can readily be generalized to other data modalities, tasks, and architectures. Our code is available at https://github.com/merantix/acosp .
Fostering connections: the UMass Amherst Libraries outreach series

The pandemic caused not only a shift to remote learning and programming but also a need to support students’ well-being and desire for connection. As an interdisciplinary unit on campus, the UMass Amherst Libraries found themselves in a unique position to provide such support. What began as bimonthly online game nights has since blossomed into the Libraries Outreach Series, with weekly in-person events that provide fun non-academic activities where students can relax and connect with each other and the Libraries.
In Plane and Out Of Plane Rotation Face Detection Model

Face Detection is a famous topic in computer vision. Over the most few couple of years researchers have attempted to improve the performance of face detection algorithm in plane and out of plane rotation. In this paper, we propose a quick way to deal with face detection algorithm using support vector machine (SVM) and golden ratio. For performing this new algorithm, the main prerequisite is the preparation dataset in the front facing appearances to prepare SVM for skin filtering. In the proposed algorithm first we apply color histogram equalization (If the face detection algorithm is not able to detect any face) which can address the mistake of the skin filter then apply SVM for removing non-skin color, i.e., a skin filter machine is developed using SVM and lastly apply golden ratio for detecting the face region correctly. Proposed algorithm is compared on three datasets XM2VTS, FERET, and BioID with a high discovery rate not less than 95%. The experimental result shows the proposed algorithm not only runs comparatively fast but also gives an upgrade performance.
Robot behavior debugger for non-expert users in convenience stores using behavior trees

As the problematic consequence of an aging society, with increasing labor shortage, there is a need for service robots to efficiently support the works in many places such as convenience stores. However, it is difficult to program a robot behavior and meet the needs of the shop staff (i.e. non-expert users who lack of knowledge and experience in robot programming). Hence, there is the need for a system to help non-expert users to identify and fix the issues within the robot behaviors. This paper proposes a Behavior Tree-based robot behavior creation system for non-expert users with four debugging features and a simulator. Non-expert users can use drag-and-drop composition to create the robot behavior program. Moreover, our debugger allows non-expert users to use breakpoints, log node status, monitor node execution, show robot status variables, and also can visually verify the robot behaviors via a simulator. We evaluate the effectiveness of our debugging system with 14 non-expert users by asking them to solve three tasks (i.e. creating and fixing Behavior Trees) from the given convenience store scenario. The experimental results show that more than 70% of non-expert users can utilize our debugging features to finish all the tasks. Additionally, our system usability has a high marginal level from the subjects' perspective according to the System Usability Scale (SUS).
ProxyDWRR: A Dynamic Load Balancing Approach for Heterogeneous-CPU Kubernetes Clusters

Edge computing is booming as a promising paradigm to push the service and computation resources from the cloud to the edge of network. As the de-facto standard for container orchestration, Kubernetes is more and more widely used not only in cloud computing but also in edge computing. However, Kubernetes is designed for homogenous cloud data centers, and it does not take into account heterogeneous scenarios, which is ubiquitous is the edge. This will lead to load imbalance among containers with its default rough load balancing mechanism. To deal with this problem, we firstly propose a Dynamically Weighted Random Routing (DWRR) algorithm based on the default random algorithm in Kubernetes. Besides, we design and implement ProxyDWRR, a load balancing plugin for the Kubernetes cluster with heterogeneous CPU. It is fully compatible with the existing load balancing mechanism in Kubernetes. We validated our solution based on a cloud-native microservices application. The experimental results show that ProxyDWRR can effectively balance the load between containers in clusters with heterogeneous CPU. In our experiments, DWRR can improve the CPU utilization of the containers by about 25% and the throughput of the application by about 22.6% compared to the default load balancing algorithms, which enables the cluster to evacuate bursty load more effectively.
Discussion on key problems of computer linguistics based on Chinese NLP

With the continuous implementation of application services in the field of artificial intelligence in the industry, the academic research on Al branch science is becoming more and more in-depth. NLP, that is, natural language processing, plays an extremely important role in the transition from the in-formation age to the intelligent age. Computer linguistics, a highly interdisciplinary emerging discipline derived from it, is also developing by leaps and bounds. Chinese natural language processing takes the conversion of natural language as the basic principle, follows the research ideas based on rules and statistics, and appropriately adds some reliable examples to complete the translation process. This paper discusses the key problems of computer linguistics based on Chinese NLP, understands the basic concepts of Chinese natural language processing through detailed analysis, and then studies the key problems and steps of Chinese natural language processing.
Hybrid Transfer Learning of Mammogram Images for Screening of Micro-Calcifications

Breast cancer is a deadly disease occurred in women due to modern lifestyles. It is a second serious disease to women's health, both physically and psychologically. Computer-aided design-based approaches support clinicians in the early diagnosis of breast cancers. It uses machine learning and deep learning algorithms to detect breast cancers in mammogram images accurately. This work proposes the hybrid ResNet and Bidirectional Long Short-Term Memories (BiLSTM) based transfer learning model for automatic micro calcifications classification in mammogram images. The hyperparameters of the proposed hybrid model are tuned by the Eurasian oystercatcher optimizer (EOO) to get superior performance in classification. The proposed model is applied in the MIAS database for classifying benign and malignant stages and compared against previously proposed models. The proposed model achieved a better result in all iterations for accuracy, precision, recall, and specificity 98.4%, 98.2%, 98.4 % and 99.4 %, respectively.
An Analytical Approach for Reconstruction of Cosmetic Surgery Images using EUCLBP and SIFT

Plastic surgery is a surgical procedure with outcomes often opposite to facial ageing. Face recognition algorithms find it difficult to anticipate such non-uniform face transformations because any changes brought on by plastic surgery procedures happen quickly. In this paper, the performance of face reconstruction is compared with that of two commonly used feature extractors, Extended Uniform Circular Local Binary Pattern (EUCLBP) and Scale Invariant Feature Transform (SIFT). An open plastic surgery facial dataset of 1800 before and after operation picture samples for 900 human face photographs has been used to test all algorithms. For each human subject, two front-facing image samples with appropriate luminance and neutral motions are gathered; the first is taken before, and the second is obtained after surgery. The proposed method is validated using parameters like accuracy, sensitivity, specificity, F-Score, G-mean and Precision. These results consistently show superior performance and high identification accuracy of 97% in combination with using both feature extractors EUCLBP+SIFT rather than using any one [EUCLBP or SIFT]alone.
Mapping Corporate Social Responsibility Research: A Bibliometric analysis

Corporate social responsibility (CSR) is a multidisciplinary concept which has gained attention with numerous exploratory, descriptive and empirical research conducted worldwide across various academic fields. This paper quantitatively analyses the existing research in order to provide scientific mapping on this subject area. The objective of the study is to summarise the existing research and identify future scope of research on the topic corporate social responsibility. For this Scopus database is selected to extract research documents, since it is a wide database that provides quality research articles on the topic. Total 1301 research documents published across 315 journals as well as 7946 sources referred by these articles were extracted following the scientific search on the Scopus database in order to conduct Bibliometric analysis over biblioshiny. Biblioshiny is a web-based software using R studio through which descriptive analysis, citation analysis, co-citation analysis, Thematic analysis, network analysis and data visualization is conducted. Thematic analysis in the study suggests various new themes have evolved across the topic making the topic interdisciplinary. Citation and co-citation analysis indicate existence of influential authors and documents with limited author collaboration in research expertise on this topic. The study identifies the emerging themes as well as prospective and new themes like environment, carbon credit, innovation, and decision making for future studies on this subject area. The results of the study provide insights for policy makers for promoting sustainable and conclusive development.
Progress in Seismology

This chapter gives an account of progress in seismology between 1889 and 1940. I argue that the difficulty of seismology is that seismic wave recordings are extremely information-rich but extremely complex, and progress in seismology during this period was the result of advances in methods for extracting information from complexly structured data. In particular, I divide the rough half-century in question into three periods. In the first period, seismological research focused on the question of whether the waves that are recorded by seismographs are correctly theoretically characterised. In the second period, the research focused on accounting for anomalies in the seismic wave recordings by finding an interpretation for each significant anomaly. In the third period, the research focus was on making inferences from interpreted seismic wave recordings to features of the earth’s interior. In particular, I draw a contrast between British and German seismology, showing that progress in British seismology was stifled by the lack of methods for properly interpreting seismic wave recordings.
Research on the generality of icon sizes based on visual attention

Abstract Icon size is one of the key factors affecting the efficiency of information search. The advent of the era of intelligent interaction has made it difficult for icon design to meet the requirements of noncontact, large information volume, and high precision proposed by natural interaction technology in the future. At the same time, with the continuous improvement of display technology, the display resolution has been increased from 720 P to 8 K. Different sizes of display carriers use different resolutions. In order for icons to have efficient recognition at different display resolutions, it is necessary to obtain the best proportional relationship between icon size and display resolution. This paper summarizes the existing relevant research, calculates the ratio of recommended icon size and display resolution as the research variables, and comprehensively evaluates the recommended optimal ratio range of 1:641–1:334 through eye movement, EEG and behavioral response experiments and entropy‐weight TOPSIS method, providing a reference for icon design in various forms of interactive interfaces in the future.
A Study on the Effective Cooperative Class of Korean language and Theater to Improve Self-Expression in Lower Grades of Elementary School

The purpose of this study is to propose efficient co-teaching for school teachers and theater instructors in the Korean language curriculum to improve self-expression among lower grade levels of elementary school. To this end, after identifying the basic concepts and components of self-expression and investigating self-expression from the perspective of theater, this study investigated self-expression education for the lower grades of elementary school. As a result of conducting a survey on theater teaching and learning methods on 10 theater instructors, it was identified that both Korean listening·speaking education and theater education are closely related to self-expression. Therefore, this study conducted a survey and in-depth interview on 6 elementary school teachers to investigate how theater education is conducted in Korean curriculum to improve self-expression. As a result, it was identified that applying the theater teaching and learning method to Korean language curriculum is effective in improving self-expression of students but it is difficult for teachers to apply the theater teaching and learning method alone in the educational field. Under the recognition of this situation, this study presented co-teaching by school teachers and theater instructors as a practically possible way of effective self-expression education.
Learning SHACL shapes from knowledge graphs

Knowledge Graphs (KGs) have proliferated on the Web since the introduction of knowledge panels to Google search in 2012. KGs are large data-first graph databases with weak inference rules and weakly-constraining data schemes. SHACL, the Shapes Constraint Language, is a W3C recommendation for expressing constraints on graph data as shapes. SHACL shapes serve to validate a KG, to underpin manual KG editing tasks, and to offer insight into KG structure. Often in practice, large KGs have no available shape constraints and so cannot obtain these benefits for ongoing maintenance and extension. We introduce Inverse Open Path (IOP) rules, a predicate logic formalism which presents specific shapes in the form of paths over connected entities that are present in a KG. IOP rules express simple shape patterns that can be augmented with minimum cardinality constraints and also used as a building block for more complex shapes, such as trees and other rule patterns. We define formal quality measures for IOP rules and propose a novel method to learn high-quality rules from KGs. We show how to build high-quality tree shapes from the IOP rules. Our learning method, SHACLearner, is adapted from a state-of-the-art embedding-based open path rule learner (Oprl). We evaluate SHACLearner on some real-world massive KGs, including YAGO2s (4M facts), DBpedia 3.8 (11M facts), and Wikidata (8M facts). The experiments show that our SHACLearner can effectively learn informative and intuitive shapes from massive KGs. The shapes are diverse in structural features such as depth and width, and also in quality measures that indicate confidence and generality.
An Anomalous Behavior Detection Method for IoT Devices Based on Power Waveform Shapes

In recent years, with the wide spread of the Internet of Things (IoT) devices, security issues for hardware devices have been increasing, where detecting their anomalous behaviors becomes quite important. One of the effective methods for detecting anomalous behaviors of IoT devices is to utilize operation duration time and consumed energy extracted from their power waveforms. However, the existing methods do not consider the shape of time-series data and cannot distinguish between power waveforms with similar duration time and consumed energy but different shapes. In this paper, we propose a method for detecting anomalous behaviors based on the shape of time-series data by incorporating a shape-based distance (SBD) measure. The proposed method firstly obtains the entire power waveform of the target IoT device and extract several application power waveforms. After that, we give the invariances to them and we can effectively obtain the SBD between every two application power waveforms. Based on the SBD values, the local outlier factor (LOF) method can finally distinguish between normal application behaviors and anomalous application behaviors. Experimental results demonstrate that the proposed method successfully detects the anomalous application behaviors, while the existing method fails to detect them.
A New Decoding Method for Double Error Correcting Cross Parity Codes

In this paper a new and simple method for 2-bit error correction for cross parity codes is proposed. All single and double bit errors, concerning data bits, are corrected. For checkbit errors up to a weight of 2, this method ensures that the data bits are free of errors. In a cross parity code the data bits are conceptually arranged in a rectangular array. The check sums are formed over bits along columns, rows and diagonals. In addition, the parity of all data bits is determined. If a 1-bit error occurs in the data bits, the erroneous bit is located at the intersection of three straight lines: a row, a column and a diagonal. For 2-bit data errors on distinct lines, the erroneous bits are located in the same way. For 2-bit data errors, that share a line, two straight lines per erroneous bit can be identified by the check bits. In a first step the bits at the four intersection points are inverted. Thereby the two errors are corrected and two new errors are generated. In case of odd side length, the two generated errors are located at three different straight lines each and can be easily corrected in a second step.
Exploiting an Intermediate Latent Space between Photo and Sketch for Face Photo-Sketch Recognition

The photo-sketch matching problem is challenging because the modality gap between a photo and a sketch is very large. This work features a novel approach to the use of an intermediate latent space between the two modalities that circumvents the problem of modality gap for face photo-sketch recognition. To set up a stable homogenous latent space between a photo and a sketch that is effective for matching, we utilize a bidirectional (photo → sketch and sketch → photo) collaborative synthesis network and equip the latent space with rich representation power. To provide rich representation power, we employ StyleGAN architectures, such as StyleGAN and StyleGAN2. The proposed latent space equipped with rich representation power enables us to conduct accurate matching because we can effectively align the distributions of the two modalities in the latent space. In addition, to resolve the problem of insufficient paired photo/sketch samples for training, we introduce a three-step training scheme. Extensive evaluation on a public composite face sketch database confirms superior performance of the proposed approach compared to existing state-of-the-art methods. The proposed methodology can be employed in matching other modality pairs.
Designing AR/VR Learning Experiences for K-12 and Higher Education

Join us for an insightful panel presentation with Canadian teacher-scholars, instructional designers, and multimedia developers. Each panellist will share a case study and research findings focussed on integrating AR/VR technologies in service to the teaching and learning mission. We highlight the need for designing immersive learning experiences and environments to meet specific educational goals and learning objectives. Practical recommendations will offer tangible curricular resources and innovative instructional strategies. We aim to inspire and provoke discussion around immersive education challenges and possibilities, including accessibility, safety, assessment, presence, social interaction, and shifting learning into the global classroom. Guests will gain a deeper understanding of how AR/VR technologies are impacting K-12 and higher education.
The use of VR technology in counselling and therapy

The aim of this paper is to examine the use of virtual reality technology in psychological counselling and therapy. Enter the precise search for virtual reality and psychotherapy through CNKI's advanced search topic with 171 results. Read carefully and thoroughly research and analyse the findings of others, VR technology is a leading-edge human-computer interactive user interface that allows users to fully immerse themselves in a natural environment created by a computer, resulting in an increasingly sophisticated realistic simulation effect. VR technology was once known for its use in the prevention of childhood phobias and PTSD, but in recent years it has been expanded to include applications for autism, intellectual disability and brain damage recovery. Although there are still some shortcomings in virtual reality technology, its use in mental illness has accelerated significantly with the development of science and technology. Virtual reality technology can be combined with counselling therapy to improve the patient's psychiatric symptoms, improve the coordination and motivation of the patient's limbs; allowing the patient to achieve more psychological satisfaction and pleasure in life, thus improving the quality of life.
Dense pedestrian detection based on improved YOLO-v5

Aiming at the problems of low model detection speed and low detection accuracy caused by pedestrian occlusion and changing pedestrian poses in dense pedestrian detection, a dense pedestrian detection algorithm with Hierarchical Vision Transformer Prediction Head (STPH)-YOLOv5 is proposed. Based on YOLOv5, the original YOLOv5 prediction head is replaced with a Hierarchical Vision Transformer Prediction Head (STPH) to explore the prediction potential with selfattention mechanism. In addition, a coordinate attention mechanism is integrated to accurately capture the location and orientation information of pedestrians in the image. In order to avoid the loss of feature information caused by the deepening of the network, the fused multi-scale feature network (BiFPN) is used in the neck part of the improved algorithm to replace the PANet in the original YOLOv5, which makes the prediction network more sensitive to targets of different sizes and improves the overall model. The detection ability reduces the missed detection rate and false detection rate of the model.