Reviewerly/exHarmony huggingface dataset:
MGAD: Learning Descriptional Representation Distilled from Distributional Semantics for Unseen Entities

Entity representation plays a central role in building effective entity retrieval models. Recent works propose to learn entity representations based on entity-centric contexts, which achieve SOTA performances on many tasks. However, these methods lead to poor representations for unseen entities since they rely on a multitude of occurrences for each entity to enable accurate representations. To address this issue, we propose to learn enhanced descriptional representations for unseen entities by distilling knowledge from distributional semantics into descriptional embeddings. Specifically, we infer enhanced embeddings for unseen entities based on descriptions by aligning the descriptional embedding space to the distributional embedding space with different granularities, i.e., element-level, batch-level and space-level alignment. Experimental results on four benchmark datasets show that our approach improves the performance over all baseline methods. In particular, our approach can achieve the effectiveness of the teacher model on almost all entities, and maintain such high performance on unseen entities.
Rehearsing Posthumanism

In this paper, I explore the rehearsal process of a new opera production about mind uploading by the composer, librettist, and director Michel van der Aa, called Upload. It was produced at the Dutch National Opera in Spring and premiered at the Bregenzer Festspiele in July 2021. As an ethnographer and writer for the Opera Forward Festival, I attended two months on site in the rehearsal spaces in Amsterdam and conducted twelve interviews with the production team. Drawing on Upload as the main case study for this presentation, my key intervention lies in proposing a methodology for examining operas that explore new, digital technology not only on stage but in processes of rehearsal. By focusing on an opera on posthumanism, I seek to shed light on how nonhuman and human materialities collide and learn to intertwine – beyond the dichotomy of vision and with a focus on the sonosphere of the rehearsal space – over the course of eight weeks. Due to Upload’s extensive use of new technology in the rehearsal space, this production challenges operatic production logics. It brings both wanted and parasitic sounds to the rehearsal. The posthuman, or after-human, setting disperses agential connections between humans and machines. By eliciting new modes of collaborating with machines within the operatic framework, this piece offers ways of reconsidering the notion of the Gesamtkunstwerk in 21st-century operas. Focusing on rehearsal ethnography, I extend the concept of the operatic work beyond the score and the performance. By theorizing present rehearsal practices in their moment of emergence, I explore what it means to rehearse with an avatar in a posthuman setting. To enter this debate, I explore ways of knowing through the audible space, drawing on Steven Feld’s concept of acoustemology, as well as Jane Bennett’s notion of ‘vibrant matter’ that argues for the agency of nonhuman materialities. Moreover, I investigate the figure of the avatar, one of the epitomes of posthumanism, through taking on a science and technology studies lens. By drawing on the notion of experimental systems introduced by Hans-Jörg Rheinberger, I dissect how the avatar turns the operatic rehearsal from a production machinery with technical objects into an experimental system with unknown variables, called epistemic things. The opera deals with uploading your mind to a server, turning it into an avatar, and losing your physical body in the process. In the opera, the father turns into an avatar while his daughter remains in the world we know. Content-wise, this operatic work circles around the human in their entanglement with new media, specifically focusing on virtual realities. It sheds light on the friction arising when technology and human collide in novel ways, leading beyond binary discussions of non-human or human, virtual, or material. How does the artistic focus on a tech-driven chimera reconfigure the entire rehearsal process – even to the extent that I scribbled 'silent' as the very first fieldnote?
Philip Auslander <b>In Concert: Performing Musical Persona</b> Ann Arbor: University of Michigan Press, 2021. 294p. £36. ISBN: 978-0-472-05471-8.

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Design of Low Power Artificial Hybrid Adder using Neural Network Classifiers to Minimize Energy Delay Product for Arithmetic Application

The binary adder is a primary computational block in many arithmetic processors and digital signal processing applications.Artificial Neural Network (ANN)s validate a group of neuron particles to configure a feed-forward neural network, a perceptron that executes functionally accomplished basic logic gate operations and provides a re-programmable, re-configurable, extensible computing system and FPGA board to form ANNs and make analytical findings.Different methodologies are analyzed, such as ANNs, which are one of the most encouraging subsequent innovative designs, and researchers are exploring and enhancing different tradeoff characteristics such as delay, dynamic power dissipation, and area.With the constraint of purposeful computational time, the use of the intended style of software implementation provides the advantages of easy programming and low cost.Hardware implementation can be used to control the limits of software perception in neural networks.The proposed neural network hybrid adder's major goal is to design a low-energy-delay device with a small footprint.In this paper, first consider the design of basic logic gates using neural networks followed by 1-bit hybrid full adder circuits, which are the primary components in computing.The hybrid adder designs are simulated and synthesized using Xilinx Vivado for the XC7Z020clg400-1 configurable device and implemented on the FPGA ZYBOZ7 board.The implementation findings reveal that, in comparison to Proposed Full Adder and single layer perceptron hybrid adder, the proposed multi-layer perceptron hybrid adder design achieved substantial refinement, with reductions of (60%, 60%) and (30.8%, 28.2%) in dynamic power dissipation and EDP, respectively.
Monitoring Earthquakes at the Speed of Light

New research uses gravity and a machine learning model to instantaneously estimate the magnitude and location of large earthquakes.
Data Stealing Attack on Medical Images: Is It Safe to Export Networks from Data Lakes?

In privacy-preserving machine learning, it is common that the owner of the learned model does not have any physical access to the data. Instead, only a secured remote access to a data lake is granted to the model owner without any ability to retrieve the data from the data lake. Yet, the model owner may want to export the trained model periodically from the remote repository and a question arises whether this may cause is a risk of data leakage. In this paper, we introduce the concept of data stealing attack during the export of neural networks. It consists in hiding some information in the exported network that allows the reconstruction outside the data lake of images initially stored in that data lake. More precisely, we show that it is possible to train a network that can perform lossy image compression and at the same time solve some utility tasks such as image segmentation. The attack then proceeds by exporting the compression decoder network together with some image codes that leads to the image reconstruction outside the data lake. We explore the feasibility of such attacks on databases of CT and MR images, showing that it is possible to obtain perceptually meaningful reconstructions of the target dataset, and that the stolen dataset can be used in turns to solve a broad range of tasks. Comprehensive experiments and analyses show that data stealing attacks should be considered as a threat for sensitive imaging data sources.
Feedback Delay of Sports Intelligent Learning System Based on Model Predictive Control

In this paper, a trajectory tracking controller based on linear time-varying model prediction is developed, and the model predictive control theory based on the six degrees of freedom dynamic model and tire model is applied. Combined with the soft constraint of turning angle and the control algorithm to ensure the stability, the trajectory tracking is realized. The terminal node is a wearable device. The terminal node is equipped with a pressure sensor and attitude sensor network to collect human data, and then the terminal node sends the data to the ZigBee network. The data in the sensor network will be received by the gateway, and the data will be processed and displayed on the PC software after being received by the gateway. Finally, the data is saved to the remote server for archiving. The application of intelligent learning systems in sports makes up for many shortcomings of traditional sports. The teaching of the course consists of seven types of intelligent courses, which not only conform to the spirit of high-quality education but also combine the shortcomings of traditional physical education learning and combines several intelligent theories and the need for time development. The purpose is to develop a new sports model suitable for students’ all-round development. Under the guidance of the theory of intelligent learning system, this paper establishes a new and diverse movement model, including various educational models, educational contents, educational methods, student-based learning model, and multidimensional evaluation. By comparing and analyzing the results of the two groups, the multiple intelligences physical education is more suitable for the development of modern students’ various intelligences. According to the physical education learning questionnaire, students also learn more methods and contents and accept diversified teaching evaluation, which expands the development direction of students.
Development and performance evaluation of object and traffic light recognition model by way of deep learning

Deep &lt;span&gt;learning models have shown incredible achievement in the field of autonomous driving, covering different aspects ranging from recognizing traffic signs and traffic lighs, vehicle detection, license plate detection, pedestrian detection. Most of the algorithms perrform better when the traffic lights are bigger in size, but the performance degrades in case of small-sized traffic lights. In this paper, the main emphasis is on evaluating two most promising deep learning architectures: single shot detector (SSD) and faster region convolutinal network (Faster R-CNN) on “la route automatisée (LaRA) traffic light dataset” which contains small traffic lights as objects. The strengths and weaknesses are evaluated based on different parameters. The performance is compared in terms of mean average Precision (mAP@0.50) and average recall. The impact of data augmentation on the two architectures is also analyzed. ResNet50 V1 as feature extractor for Faster R-CNN achieved 96% mAP (mean average precision) which performed better than Original ResNet50 V1 Faster R-CNN pipeline. Also, different parameters such as batch size, learning rate and optimizer are tuned for detecting and classifying small traffic lights into different categories. &lt;/span&gt;
Enhancing Event-Level Sentiment Analysis with Structured Arguments

Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, time and location) that have potential effects on the sentiment are not well studied. In this paper, we redefine the task as structured event-level SA and propose an End-to-End Event-level Sentiment Analysis (E3SA) approach to solve this issue. Specifically, we explicitly extract and model the event structure information for enhancing event-level SA. Extensive experiments demonstrate the great advantages of our proposed approach over the state-of-the-art methods. Noting the lack of the dataset, we also release a large-scale real-world dataset with event arguments and sentiment labelling for promoting more researches.
The Pros and Cons of Seeking Endowment Gifts

The Major Gifts ReportVolume 24, Issue 7 p. 2-2 Food for Thought The Pros and Cons of Seeking Endowment Gifts Kim Pawlak, Kim PawlakSearch for more papers by this author Kim Pawlak, Kim PawlakSearch for more papers by this author First published: 30 May 2022 https://doi.org/10.1002/mgr.31940Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume24, Issue7July 2022Pages 2-2 RelatedInformation
Characterizing ML training performance at the tactical edge

The commercial industry has been working to develop ever-larger and more capable machine learning (ML) models (such as recent models from OpenAI, Microsoft and Google with more than ten billion parameters) for everything from general language processing to computer vision that have larger and larger computational, memory and other resource requirements. These powerful models generally need hardware accelerators to accommodate their workloads. GPU systems have been a popular choice among users and deep learning model designers either for their ability to run the inherently parallel deep learning workloads efficiently or for their large memory resource that would fit large deep learning models. We profile and analyze different deep learning workloads using various GPUs and configurations, emphasizing how deep learning architectures have diverse compute requirements. We analyze three popular deep learning workloads: Ultralytics’s You-Only-Look-Once model (Yolov5) on COCO dataset, Bidirectional Encoder Representations from Transformers (BERT), and Deep Learning Recommendation Model (DLRM) on the Criteo Kaggle Display Advertising Challenge Dataset. This work aims to shed light on the performance bottlenecks when using GPU systems as accelerators for training recent deep learning models.
An Adaptively Secure NIPE Scheme based on DCR Assumption

Non-zero inner product encryption provides fine-grained access control to private data, but the existing non-zero inner product encryption schemes are mainly constructed based on the problem of bilinear groups and lattices without homomorphism. To meet the needs of users to control private data and cloud servers to directly process ciphertexts in a cloud computing environment, this paper designs a non-zero inner product encryption scheme based on the DCR assumption. Specifically, the access control policy is embedded in the ciphertext by a vector y, and the user attribute vector x is embedded in the secret key. If the inner product of the policy vector y of the encryptor and the attribute vector x of the decryptor is not zero, the decryptor can decrypt correctly. This scheme has additive homomorphism in the plaintext-ciphertext space, and it can be proved to be additive homomorphic and adaptively secure.
Understanding literature references in academic full text (ULITE)

The goal of the Understanding Literature References in Academic Full Text (ULITE) workshop is to engage communities interested in the broad topic of literature reference understanding and automatic processing of scientific full text publications. The workshop has a focus on working with open infrastructures/tools and offering the extracted information as open data for reuse. Our view is to expose people from one community to the work of the respective other community and to foster fruitful interaction across communities.
Block Chain and its Applications in the Field of Business

Many people view blockchain to be a game-changing core technology. Despite the fact that many scholars have recognized the importance of blockchain, blockchain research is still in its infancy. As a result, this research examines current academic research on blockchain, particularly in the fields of business and economics. We investigate the most referenced publications, most prolific countries, and most prevalent keywords based on a comprehensive evaluation of the literature retrieved from the Web of Science service. We also perform a clustering analysis and discover five research themes: "economic advantage," "blockchain technology," "initial coin offerings," "fintech revolution," and "sharing economy." This study also includes recommendations for future research topics and practical applications.
A Tale of Two Cyclicities: Counting in Finite Cyclic Groups and Finite Fields

We study several combinatorial identities that arise naturally from the structure of finite cyclic groups and finite extensions of finite fields. We are particularly interested in interconnections between the identities.
FORMATION OF ENGINEERING THINKING OF PRE-SCHOOL CHILDREN AS A GUIDELINE OF DIDACTIC TRAINING OF FUTURE MASTERS OF PRE-SCHOOL EDUCATION

In recent years, scientific journals emphasize the problem of quality training of future teachers in higher education in general and future masters of preschool education in particular. The results of the study of domestic and international experience convincingly prove that the professional training of future masters of preschool education should be based on teaching modern preschool children belonging to the generation of Alpha children, which are characterized by clip thinking, inability to concentrate, need to visualize information, value other information using digital technologies, etc. The article is devoted to the analysis of the theoretical foundations of didactic training of future masters of pre-school education on the formation of engineering thinking of pre-schoolers. The purpose of the article is a thorough theoretical and methodological analysis of the relevance of the process of didactic training of future educators of pre-school education in the master’s program as a guide to the formation of engineering thinking of preschoolers. The following theoretical research methods were used in the work: analysis, synthesis, comparison, generalization, theoretical methods related to the study of scientific literature, periodicals. The article offers substantiation of the problem of professional training of future masters of preschool education, relevance of engineering thinking of preschool children, highlights teaching methods and didactic tools aimed at training future masters of pre-school education in the educational process of higher education to form engineering thinking of pre-school children. The publication summarizes the goals and principles of application of the proposed methods and technologies, and offers a number of recommendations that, in our opinion, will improve the educational process of higher education institutions and the quality of training of future educators by introducing innovative forms and methods of teaching preschool children.
Research on Practical Curriculum Reform in Physical Education Colleges Based on Innovative Theory

The rapid development of the knowledge economy society is the driving force behind the reform of college courses, and the cultivation of innovative talents has become the main direction of college curriculum reform. From the perspective of improving quality education, the thesis conducts in-depth theoretical research on the goals, structural system, teacher training, resource integration evaluation content and innovation of college physical education, aiming to provide useful references for college physical education.
Travelogue: A Travel Application using MERN and Augmented Reality

Abstract: Now a days web application plays a vital role from ordering food from a restaurant, ordering any electronic stuff or anything from web apps, watching movies, book a seat for a movie theatre, for study, download pictures from the internet, etc. and MERN stack is one of the most popular JavaScript stacks used for easier and faster deployment of full-stack web application comprising backend, frontend and database components. Travel enthusiasts have always desired for traveling fascinating places and archiving their memorable experiences. Currently, there are a wide range of travel based web applications. This paper aims at exploring features that could be integrated with travel applications for offering customizable user experience. Further, the paper discusses the prevailing drawbacks of the existing travel applications and looks forward for embracing modern features to make travel applications accessible. Currently there’s no simple standalone platform where people can share their travel experiences, receive suggestions for places to travel and recommend travel places to their dear ones. So we came up with an idea to build a full stack web application where folks can share their journey through images and location. That way it won’t be a tedious process anymore to surf the web and head on to travel. Keywords: Augmented Reality, Chatbot, MERN Stack, Web Application
Predicting classifier performance using distributional separation measures

In real world applications, machine learning classifiers are only as good as their performance on real world data. In practice, this data comes without truth labels — that is why you need a classifier. In this paper we consider the scenario where we have a trained classifier, and a set of unlabeled test data that we wish to run through it. If the unlabeled data is not within the distribution of the classifier’s training data, we should not expect the classifier to work well on the test set. We explore the use of the Henze-Penrose divergence, a measure of separation between two multivariate distributions, as a way to predict performance of a classifier on a dataset and to detect distributional shift, and we find that by computing Henze-Penrose scores between the training and test sets first in the input space, and then in the feature space of the classifier, we can get an indication that the test data is out of distribution and that classification accuracy will be unreliable.
Panic Button Mobile Application Usability Study

As in all areas of life, the adaptation of disaster and emergency management with technology is inevitable. In particular, the increase in the use of smart phones has led to the development of emergency applications for mobile phones. The usability of applications, which are vitally important to be used easily in emergency situations, should be at a high level. In this study, the “Panik Butonu” application, which was developed for use on smart phones in emergency situations, was subjected to heuristic evaluation and the evaluation results were shared. By applying the System Usability Scale with the help of heuristic evaluation, significant and fast feedback was received and this feedback was provided to increase the usability of the “Panik Butonu” application for emergency situations. It is aimed to prevent errors and to provide an environment where users can communicate quickly by recommending the help and documentation information of the “Panik Butonu” application.