Reviewerly/exHarmony huggingface dataset:
COMPOSER: Compositional Learning of Group Activity in Videos

Group Activity Recognition (GAR) detects the activity performed by a group of actors in a short video clip. The task requires the compositional understanding of scene entities and relational reasoning between them. We approach GAR by modeling the video as a series of tokens that represent the multi-scale semantic concepts in the video. We propose COMPOSER, a Multiscale Transformer based architecture that performs attention-based reasoning over tokens at each scale and learns group activity compositionally. In addition, we only use the keypoint modality which reduces scene biases and improves the generalization ability of the model. We improve the multi-scale representations in COMPOSER by clustering the intermediate scale representations, while maintaining consistent cluster assignments between scales. Finally, we use techniques such as auxiliary prediction and novel data augmentations (e.g., Actor Dropout) to aid model training. We demonstrate the model's strength and interpretability on the challenging Volleyball dataset. COMPOSER achieves a new state-of-the-art 94.5% accuracy with the keypoint-only modality. COMPOSER outperforms the latest GAR methods that rely on RGB signals, and performs favorably compared against methods that exploit multiple modalities. Our code will be available.
Optimizing frequency allocation for fixed-frequency superconducting  quantum processors

Fixed-frequency superconducting quantum processors are one of the most mature quantum computing architectures with high-coherence qubits and low-complexity controls. However, high-fidelity multi-qubit gates pose tight requirements on individual qubit frequencies in these processors and their fabrication suffers from the large dispersion in the fabrication of Josephson junctions. It is inefficient to make a large number of processors because degeneracy in frequencies can degrade the processors' quality. In this article, we propose an optimization scheme based on mixed-integer programming to maximize the fabrication yield of quantum processors. We study traditional qubit and qutrit (three-level) architectures with cross-resonance interaction processors. We compare these architectures to a differential AC-Stark shift based on entanglement gates and show that our approach greatly improves the fabrication yield and also increases the scalability of these devices. Our approach is general and can be adapted to problems where one must avoid specific frequency collisions.
Context vs Target Word: Quantifying Biases in Lexical Semantic Datasets

State-of-the-art contextualized models such as BERT use tasks such as WiC and WSD to evaluate their word-in-context representations. This inherently assumes that performance in these tasks reflect how well a model represents the coupled word and context semantics. This study investigates this assumption by presenting the first quantitative analysis (using probing baselines) on the context-word interaction being tested in major contextual lexical semantic tasks. Specifically, based on the probing baseline performance, we propose measures to calculate the degree of context or word biases in a dataset, and plot existing datasets on a continuum. The analysis shows most existing datasets fall into the extreme ends of the continuum (i.e. they are either heavily context-biased or target-word-biased) while only AM$^2$iCo and Sense Retrieval challenge a model to represent both the context and target words. Our case study on WiC reveals that human subjects do not share models' strong context biases in the dataset (humans found semantic judgments much more difficult when the target word is missing) and models are learning spurious correlations from context alone. This study demonstrates that models are usually not being tested for word-in-context representations as such in these tasks and results are therefore open to misinterpretation. We recommend our framework as sanity check for context and target word biases of future task design and application in lexical semantics.
Considering Constraint Monotonicity and Foundedness in Answer Set Programming

Should the properties of constraint monotonicity and foundedness be mandatory requirements that every answer set and world view semantics must satisfy? This question is challenging and has incurred a debate in answer set programming (ASP). In this paper we address the question by introducing natural logic programs whose expected answer sets and world views violate these properties and thus may be viewed as counter-examples to these requirements. Specifically we use instances of the generalized strategic companies problem for ASP benchmark competitions as concrete examples to demonstrate that the requirements of constraint monotonicity and foundedness may exclude expected answer sets for some simple disjunctive programs and world views for some epistemic specifications. In conclusion these properties should not be mandatory conditions for an answer set and world view semantics in general.
Characterizing Retweet Bots: The Case of Black Market Accounts

Malicious Twitter bots are detrimental to public discourse on social media. Past studies have looked at spammers, fake followers, and astroturfing bots, but retweet bots, which artificially inflate content, are not well understood. In this study, we characterize retweet bots that have been uncovered by purchasing retweets from the black market. We detect whether they are fake or genuine accounts involved in inauthentic activities and what they do in order to appear legitimate. We also analyze their differences from human-controlled accounts. From our findings on the nature and life-cycle of retweet bots, we also point out several inconsistencies between the retweet bots used in this work and bots studied in prior works. Our findings challenge some of the fundamental assumptions related to bots and in particular how to detect them.
WECHSEL: Effective initialization of subword embeddings for  cross-lingual transfer of monolingual language models

Recently, large pretrained language models (LMs) have gained popularity. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceedingly expensive to train these models in other languages. To alleviate this problem, we introduce a method -- called WECHSEL -- to transfer English models to new languages. We exchange the tokenizer of the English model with a tokenizer in the target language and initialize token embeddings such that they are close to semantically similar English tokens by utilizing multilingual static word embeddings covering English and the target language. We use WECHSEL to transfer GPT-2 and RoBERTa models to 4 other languages (French, German, Chinese and Swahili). WECHSEL improves over a previously proposed method for cross-lingual parameter transfer and outperforms models of comparable size trained from scratch in the target language with up to 64x less training effort. Our method makes training large language models for new languages more accessible and less damaging to the environment. We make our code and models publicly available.
Quantum effective action for the bosonic Josephson junction

We investigate a bosonic Josephson junction by using the path integral formalism with relative phase and population imbalance as dynamical variables. We derive an effective only-phase action performing functional integration over the population imbalance. We then analyze the quantum effective only-phase action, which formally contains all the quantum corrections. To the second order in the derivative expansion and to the lowest order in $\hbar$, we obtain the quantum correction to the Josephson frequency of oscillation. Finally, the same quantum correction is found adopting an alternative approach. Our predictions are a useful theoretical tool for experiments with atomic or superconducting Josephson junctions.
Lessons Learned?

Mexico faced two public-health crises in 11 years: the (A)H1N1 crisis in 2009 and the current COVID-19 one. While different in magnitude and impact, each one compromised higher education institutions' (HEIs) academic continuity and international programs. In this chapter, we analyze, from an organizational perspective, whether or not, and to what extent the response to the 2009 crisis helped inform Mexican HEIs' response to the current one. Through a document analysis, interviews with 20 Senior International Officers (SIOs) and higher education experts, and results from 18 institutions to a COVID-19 survey conducted by a multinational consortium, we found that institutions in Mexico adapted quickly to both crises and showed resilience toward the many challenges they faced. We also found mixed results about generating and using organizational learnings from the previous crisis to face the current one. We end with recommendations for HEIs in Mexico and elsewhere to capitalize on their present experience with the COVID-19 pandemic to prepare for imminent future crises.
Understanding Docker Volumes

In the previous chapters, you learned about Docker and its associated terminologies and took a deeper look into how you can build Docker images using the Dockerfile. In this chapter, you look at data persistency strategies for Docker containers and learn why you need special strategies for data persistence.
Unilateral Termination and Adjustment of the Consumer Contract on Digital Content

Unilateral termination and adjustment of the business to consumer contract in the perimeter of digital content continuously raises new questions for legal practitioners.The study approaches the intricacies inherent to the consumer's right to seek the judicial termination of the modified contract, in the case of the unilateral adjustment of the contract for the supply of the digital content by the seller.According to Article 19, para. 2 of Directive (EU) 2019/770, the termination of the contract remains open to the consumer, who is entitled to terminate the contract in the circumstances when the unilateral alteration of the contractual terms negatively impacts the consumer's access to or use of the digital content, unless such negative impact remains to have only minor repercussions for him/her.Our analysis insists on the fact that in the case of a minor lack of conformity, the consumer's request for the termination of the contract is legally unacceptable.On the other hand, in cases in which the impact of the unilateral modification significantly affects the consumer's rights, the consumer is entitled to terminate the contract in a non-onerous manner within 30 days of the time when the digital content has been modified.We argue that the consumer's anticipatory renunciation of the right to termination of contract represents unfair terms, as are the contractual clauses which are imposing the consumer's unilateral adjustment relating to the supply of digital content.
Fundamental evaluation of workload induced by using the system to provide independent views to both of eyes by critical flicker-fusion frequency

In previous work, we developed a system, “Virtual Chameleon”, that provide independent field of views to both eyes of a human user, and we conducted fundamental experiments to investigate human performance and conditions of user’s vision. From oral reports, the use of Virtual Chameleon posed binocular rivalry to visions of the users. In addition to it, the users of Virtual Chameleons under binocular rivalry often reported fatigue and malaise. In this study, to investigate workload with the use of Virtual Chameleons, we developed an experimental version of Virtual Chameleon which is equipped with an omnidirectional mobile device and can be controlled by touchpad devices, and we conducted a basic experiment using critical flicker fusion frequency as evaluation criteria. Six healthy volunteers (21-22 years old) with normal or corrected-to-normal vision participated in the study. The results showed the user of Virtual Chameleon increased workload.
Study of University Students’ Learning Motivation for Improving the Educational Process Efficiency (in Riga and Smolensk Samples)

A good professional education is a prerequisite for successful functioning of the state system of labour resources management. For improving the educational process efficiency, a comprehensive study of motivation for learning is necessary. The article presents the results of the Russian-Latvian research project on the learning motivation of university students. The aim is to analyse and compare the learn-ing motivation of students of the first and last courses of the universities of Riga and Smolensk, and to examine the correlation between motivation and the psychological atmosphere in the student group. The data were obtained by the survey in which 230 students of the EKA University of Applied Sciences (Riga, Latvia) and Smolensk State University (Russia) took part. The leading groups of learning motives are identified, and the correlations between them and certain aspects of the psychological atmosphere in the students’ group are found out. The ways of increasing students’ motivation are proposed.
Role of Media in Covering Stock Market and its Impact on Investors Behavioral Finance

In the last one decade, the investor’s education and movement in stock market has increased significantly because of the growth of media in various forms-print, electronic, social and unconventional. All the registered firms use media as a strategy to promote and influence stakeholder’s decision. Digital media too have forced business organization to adapt social media to engage shareholders in disseminating information and generating interest about their stocks and creating interest on the investors buying process. The objective of the study is accomplished by collecting primary data through survey method from 270 respondents. We used frequency analysis, ANOVA and factor analysis to analyze the role of media on investor buying decision. The factors identified are attention and awareness, company management and decision making, financial investment decision and investment goal. The significance of these factors varies through different demographic factors such as agender, income, age, education and occupation. The findings will certainly contribute to the stock companies by analyzing the usage of media in influencing investors.
Designing User Interfaces with SwiftUI

Every app needs a user interface. The basic idea behind SwiftUI is to create a user interface using building blocks known as "views." A view displays a single item on the user interface such as text, an image, or a button as shown in Figure 2-1.
On the Characteristics of Chinese Wushu Kung Fu Fan Teaching Method

Chinese Wushu Kung Fu fan; Teaching methodology
Press review

We present ViewDF: a flexible and declarative framework for incremental maintenance of materialized views (i.e., results of continuous queries) over streaming data. The main component of the proposed framework is the View Delta Function (ViewDF), which declaratively specifies how to update a materialized view when a new batch of data arrives. We describe and experimentally evaluate a prototype system based on this idea, which allows users to write ViewDFs directly and automatically translates common classes of streaming queries into ViewDFs. Our approach generalizes existing work on incremental view maintenance and enables new optimizations for views that are common in stream analytics, including those with pattern matching and sliding windows.
Grid-Based Exploration Methods Using Parent and Child Robots with Different Functions

This paper presents simulation results for grid-based exploration methods using parent and child robots with different functions. Child robot explores sub-map that parent robot can’t explore or the one that need to be explored precisely. Parent robot explores the other map and also carries the child robot to the sub-map. Parent-child system allows application with different functions or types of robots about exploration. Furthermore, we propose a Grass-fire algorithm based Coverage Path Planning algorithm using distance value of the map also used in path planning, and validate the effectiveness of this algorithm.
Open set recognition through Monte Carlo dropout-based uncertainty

Open set recognition has received much attention in recent years. In this paper, we present a novel open set recognition method that is able to obtain improved recognition by applying Monte Carlo dropout to capture uncertainty in order to yield high quality predicted probabilities. Experimental results on six benchmark datasets show that our method gives better open set recognition performance than other state-of-the-art methods, with at least 6.4%, 3.9%, 2.9% and 1.0% performance increase in AUROC on the challenging object datasets CIFAR-10, CIFAR+10, CIFAR+50 and TinyImageNet respectively. We also perform an analysis on the benefits of combining predictive uncertainty with an EVT-based open set recognition model which indicates that Monte Carlo dropout-based uncertainty allows to obtain high quality predicted probabilities and to learn more accurate open set recognition scores. This, in turn, helps to reduce the overlap between known and unknown classes, thus making them more separable.
Yo-yo machines

opinion Share on Yo-yo machines: self-build peripheral awareness communication devices Author: Interaction Research Studio View Profile Authors Info & Claims InteractionsVolume 28Issue 6November - December 2021 pp 22–25https://doi.org/10.1145/3490446Online:10 November 2021Publication History 0citation436DownloadsMetricsTotal Citations0Total Downloads436Last 12 Months436Last 6 weeks14 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Making action recognition robust to viewpoint changes based on extended 2D skeleton coordinates

In recent years, with the rapid growth of computing power and data transmission capacity of portable devices, research on action recognition has attracted much attention. Cross-view action recognition is one of the important branches. Cross-view action recognition has always been a difficult problem for conventional traditional action recognition based on 2D data, also limits its practical application ability. To solve this problem, we propose a method to extend the 2D data into 3D, and then perform action recognition based on 3D data.Through a series of experiments, we find that the proposed method has robustness in the action recognition with camera viewpoint change．
Self-Adaptive based Medical Image Encryption using Multiple Chaotic Systems

With the advent of medical imaging tools and telemedicine technology, the transmission of patient data over the Internet has reduced distance barriers in healthcare delivery.To ensure patient privacy, data is encrypted while being transmitted over an insecure network.In order to overcome the problem of traditional techniques, a self-adaptive image encryption techniques based on multiple maps has been developed.Initially, plain image dependent multiple secret keys are generated for multiple maps.Input image is permuted by N umber of rounds using Arnold cat map.Value of N determined by the secret key.During diffusion process, a random image is generated with the help of 2D Logistic-Sine-Coupling Map (2DLSCM) with another secret key.Finally encrypted image obtained by performing bitwise XOR operation between permuted image and random image.Advantage of proposed multiple chaotic image encryption that incorporates two chaotic maps, Arnold's Cat Map and 2D Logistic Sine Coupling Map (2DLSCM), to improve the randomness and security of encrypted images.It also analyses the performance and security of the scheme and compares it to other known chaotic image encryption schemes.
Experimental verification of pedagogical conditions for formation of values and semantics experience among future foreign philology specialists in the process of professional training

The current socio-cultural situation actualizes special attention to values, value orientations and other axiological phenomena that comprise axiological knowledge. The rapidly changing world, and therefore the change of axiological guidelines, is a fundamental issue for higher education. This argues the need for special attention to axiological problems in the context of the organization of higher education, in particular the training of future specialists in foreign philology. It has become more relevant because the Standard of Higher Education of Ukraine for the first (bachelor's) level, field of knowledge 03 Humanities, specialty 035 "Philology" does not actually provide any value component of professional training for future professionals.&#x0D; The research hypothesis is based on the thesis that special value-oriented pedagogical conditions in the process of professional training are a prerequisite for the acquisition of values and semantics experience of future specialists in foreign philology, which creates important prerequisites for the formation of axiological competencies. The proposed study is aimed at determining the level of formation of values and semantics experience among future specialists in foreign philology at the initial and final stages of the experiment. The paper shows the methods that allow to achieve this goal and have practically confirmed the formulated research hypothesis.
Managing Security and Privacy

How would you feel if your laptop or PC were stolen, with your files and data on board, including online and business logins and passwords, financial details, and personal photos? Worse, how would you feel if you didn’t have a password on your PC, or if that password were easily guessable? Now imagine how your employer would feel if it were your company laptop or tablet that were stolen, complete with confidential customer details and information on current and future projects?
Does a reduction in subjective value due to delaying extra reward promote donation?

報酬獲得までに遅延が生じることにより報酬の主観的価値が低下する現象を遅延割引という。本研究では，追加報酬を受け取るまでの遅延時間（1日／1ヶ月／6ヶ月）を操作した2つのオンライン実験により，受け取りまでの遅延時間が長いほど追加報酬に対する主観的価値が低下し，寄付をする人が多くなるという仮説を検討した。参加報酬が謝金であった実験1には100名の大学生が，お菓子であった実験2には147名の大学生が参加した。参加者は遅延割引率，社会的価値志向性，主観的社会経済地位，お金に対する信念を測定する尺度に回答した後，500円の追加報酬に当選したことを知らされ，追加報酬について「受け取る」「拒否する」「寄付をする」から選択を求められた。遅延時間および個人差変数を投入したロジスティック回帰分析を行った結果，両実験で遅延時間の効果および個人の遅延割引率の効果は見られず仮説は支持されなかった。コースクレジットが参加報酬であった実験（Kurita, Igarashi, ＆ Kiyokawa, 2020）では個人の遅延割引率が高いほど寄付が選択されていた。参加報酬により参加者の追加報酬についての判断が異なる可能性があり，その点を考慮した検討が必要である。
Dexmo Software Product Line and Product Composition Method

With the spread of service-based software, software product lines where software components are services or even microservices have started to emerge. In this paper, the Dexmo software product line approach developed in this way at Univera is explained. Dexmo software product line is different from the classical software product lines. A novel marketing engineering process and a control tower application are added on top of domain and application engineering. Moreover, Dexmo software product line approach offers significant changes in both domain and application engineering compared to the classics. The software product composition method, one of the most important of these, is also explained in detail.