Reviewerly/exHarmony huggingface dataset:
Learning to Solve Sequential Planning Problems Without Rewards

In this paper we present an algorithm, the Goal Agnostic Planner (GAP), which combines elements of Reinforcement Learning (RL) and Markov Decision Processes (MDPs) into an elegant, effective system for learning to solve sequential problems. The GAP algorithm does not require the design of either an explicit world model or a reward function to drive policy determination, and is capable of operating on both MDP and RL domain problems. The construction of the GAP lends itself to several analytic guarantees such as policy optimality, exponential goal achievement rates, reciprocal learning rates, measurable robustness to error, and explicit convergence conditions for abstracted states. Empirical results confirm these predictions, demonstrate effectiveness over a wide range of domains, and show that the GAP algorithm performance is an order of magnitude faster than standard reinforcement learning and produces plans of equal quality to MDPs, without requiring design of reward functions.
Performance Evaluations of Quantum Key Distribution System Architectures*

Quantum key distribution (QKD) exploits the laws of quantum physics to generate shared secret cryptographic keys and can detect eavesdroppers during the key generation process. However, previous QKD research has focused more on theory than practice. QKD is the most mature application of the quantum information field, offering the means for two parties to generate secure cryptographic keying material. Employing the laws of quantum physics, QKD can detect eavesdroppers during the key generation process, in which unauthorized observation of quantum communication induces discernible errors. However, QKD is a nascent technology where real-world systems are constructed from nonideal components and deployed in uncertain operational environments, which can adversely impact system security and performance. In this article, we study the performance impact of QKD implementation nonidealities and practical engineering limitations, evaluating three system examples using a modularized simulation framework. We also explore the QKD security–performance trade space to gain additional understanding of critical design tradeoffs associated with interactions between physical components and system-level considerations such as hardware, software, and protocols. Such evaluations provide insight and inform designers, researchers, and users when selecting among competing solutions; decision makers can also use them to guide future investments and developmental efforts. Our research team focuses on bridging the gap between QKD theory and practice. Theoretical and experimental physicists are working to advance QKD technology, but few are strongly focused on evaluating and improving the implementation of realized systems. For a general introduction to QKD, see Chip Elliot’s “Quantum Cryptography.”
Smart House Assistive Technologies for Senior Citizens

A Smart house is a special type of house that has automated services it offers to its residents to enhance their comfortability. Smart houses can implement for various purposes including luxury automation capability, entertainment, security, mitigation of disability, health, or medical reasons. In this paper, a special smart house design prototype for senior citizens is proposed. The design is proposed to have assistive technologies services easily added to or removed from the smart house. Depending on the need, a service could be added, modified, or removed with little effort. The complexity of service can be extended or limited based on the requirements of the subject senior citizen. Some of the service offerings in this house include; (a) security services, for instance in case of a fire, a fall detection, or a sharp rise in fever symptoms, an alert distress signal would be sent to the caregivers center; (b) action execution services, for example, automated preparation of bathwater, preparation of breakfast, preparation of dinner, switching lights on or off, etc.; (c) interactive services, for instance, remainders for medicine taking schedule, prompt of beverage choice, prompt to send distress signal when possible danger is detected by the system, etc. The comprehensive design would have four main modules: smart house automated services, assistive technologies services, privacy policy protocol, and security services. The design has been developed and would be implemented in the future.
A study on pre-service teachers’ experiences in creative experience activities programs development

Purpose: The purpose of this study was to analyze the pre-service teachers' experiences while developing creative experience activities programs and to suggest teaching and learning for pre-service teachers' creative activities experience program development competency.&#x0D; Methods: Qualitative data were collected from twenty creative experience activities programs results developed by 4th year pre-service teachers who took ‘Understanding of Creative Experience Activities Curriculum’ at “A” University and 63 pre-service teachers' reflective journals. For the analysis and summary of qualitative data, the key words were derived through open coding and classification processes.&#x0D; Results: The results of this study were follows. Pre-service teachers' experiences in developing creative experience activities programs included 1 in-depth understanding of the intrinsic and essential values of creative experience activities, 2 acquisition of practical knowledge for the development of creative experience activities programs, 3 experience of the value and importance of collective intelligence,4 teacher's agency experience as an agent of curriculum development, 5 change of perception regarding student evaluation of creative experience activities, 6 intrinsic motivation, achievement and growth for the development of creative experience activities programs, 7 competence as a creative experience activities programs designer, 8 teacher as researcher, and establishment of a teacher with expertise, 9 and consideration and reflection of program design and instruction.&#x0D; Conclusion: In order to improve the competency of pre-service teachers for creative experience activities programs development, it was necessary to apply project-based learning that could assist in the pre-service teacher's agency for creative experience activities program development.
A GPU-enabled mobile telemedicine training system for graphic rendering

Aiming for overcoming the constraints in graphic rendering of mobile devices used in telemedicine training for 3D images, this paper presents a GPU-enabled mobile telemedicine training system for graphic rendering. Two improvements are made to seamlessly display interactive 3D images of human organs, bones and blood vessels with realtime rendering. Firstly, instead of multiple stages of instruction translation between OpenGL ES and OpenGL, a bespoke GPU driver is added in Virtual Android to invoke GPU resources directly. Secondly, a Video Process Unit (VPU) is added to the hardware layer replacing CPU to code rendered results in H.264 format, reducing CPU load significantly. Test results suggest that the system can deliver consistent performance even in mobile devices of weak capability and a single server can support up to 24 concurrent virtual Android operating systems, each of which connects to 5 clients. The framework proposed by this paper is not only suitable for telemedicine training, but also for other application areas such as Virtual Reality and Augmented Reality in mobile environment.
LIFELONG LEARNING CHALLENGES AND OPPORTUNITIES FOR TRADITIONAL UNIVERSITIES

Through increasing differentiation of skills and globalization, it has become necessary to continually update knowledge and competences to ensure an individual’s life development and self-fulfilment. To ensure that the Republic of Moldova maintains its high standard of living and social cohesion, it is necessary to further enhance the citizens’ high level of qualification and to draw less educated people into the modern society. Responding to the rapid economic and social changes, lifelong learning aims to actively deal with the dynamics of changing the environment and to take advantage of new opportunities in the social, cultural and employment contexts. The development of the informational society and the widespread diffusion of information technologies give rise to new opportunities for learning and establish new challenges and practices regarding the way how teaching and learning should be organized and carried out.
Research on the application of hyperledger fabric in treasury centralized payment

Although the electronic level of treasury centralized payment management has made great progress, there is still great room for progress in data security, information sharing, tamper proof, traceability and so on. This paper fully considers the technical characteristics of blockchain and proposes a treasury centralized payment system based on Hyperledger Fabric. All participants, such as the financial department, the budget units, the budget departments, the agent banks, the clearing banks and the regulators, join the system as nodes of blockchain. Several channels are designed according with the business of treasury centralized payment to realize the goal of data supervision and data privacy. The system is expected to promote the management level of treasury centralized payment.
Analysis of XSS attack based on ensemble learning

In this paper, we collected real XSS attack logs from web servers for the problem of XSS attack defense in real environment. Relevant features were selected and a low memory consumption XSS matching filtering model was constructed using the Random Forest algorithm. By comparing with traditional machine learning and other integrated algorithms, it is found that Random Forest is faster to train and consumes less memory during training while ensuring accuracy. The experimental results show that the model has better classification results and is more suitable for deployment on browser clients.
Grammar and Genre

Chapter 9 of Discourse Syntax (Grammar and Genre) deals with patterns of English grammar as tied to the situational settings of a register or the conventions of a genre. It notes that a feature of grammar, such as the passive voice in academic discourse, may either be pervasive in some as compared to other registers, or that it can be typical, like a hashtag in tweets. The chapter also refers to the modality of the discourse situation and distinguishes between conceptually oral and written language. For a written genre, it discusses the scientific abstract and the various grammatical forms of expressing agentivity that typically occur in it. It also examines the way in which scientific genres show a condensation of information through syntactic complexity, particularly within the noun phrase, and how to study that type of complexity based on a sample of attestations retrieved from an electronic corpus. The chapter also discusses digital genres and the use of hashtags in discourse on social media platforms as well as the “because X” construction as a characteristic case of linguistic innovation within that medium.
On the Information Content of Predictions in Word Analogy Tests

An approach is proposed to quantify, in bits of information, the actual relevance of analogies in analogy tests. The main component of this approach is a softaccuracy estimator that also yields entropy estimates with compensated biases. Experimental results obtained with pre-trained GloVe 300-D vectors and two public analogy test sets show that proximity hints are much more relevant than analogies in analogy tests, from an information content perspective. Accordingly, a simple word embedding model is used to predict that analogies carry about one bit of information, which is experimentally corroborated.
NFGen

Due to the absence of a library for non-linear function evaluation, so-called general-purpose secure multi-party computation (MPC) are not as "general'' as MPC programmers expect. Prior arts either naively reuse plaintext methods, resulting in suboptimal performance and even incorrect results, or handcraft ad hoc approximations for specific functions or platforms. We propose a general technique, NFGen1, that utilizes pre-computed discrete piecewise polynomials to accurately approximate generic functions using fixed-point numbers. We implement it using a performance-prediction-based code generator to support different platforms. Conducting extensive evaluations of 23 non-linear functions against six MPC protocols on two platforms, we demonstrate significant performance, accuracy, and generality improvements over existing methods.
Three Efficient All-Erasure Decoding Methods for Blaum–Roth Codes

Blaum–Roth Codes are binary maximum distance separable (MDS) array codes over the binary quotient ring F2[x]/(Mp(x)), where Mp(x)=1+x+⋯+xp−1, and p is a prime number. Two existing all-erasure decoding methods for Blaum–Roth codes are the syndrome-based decoding method and the interpolation-based decoding method. In this paper, we propose a modified syndrome-based decoding method and a modified interpolation-based decoding method that have lower decoding complexity than the syndrome-based decoding method and the interpolation-based decoding method, respectively. Moreover, we present a fast decoding method for Blaum–Roth codes based on the LU decomposition of the Vandermonde matrix that has a lower decoding complexity than the two modified decoding methods for most of the parameters.
Universal algorithms for quantum data learning

Abstract Operating quantum sensors and quantum computers would make data in the form of quantum states available for purely quantum processing, opening new avenues for studying physical processes and certifying quantum technologies. In this Perspective, we review a line of works dealing with measurements that reveal structural properties of quantum datasets given in the form of product states. These algorithms are universal, meaning that their performances do not depend on the reference frame in which the dataset is provided. Requiring the universality property implies a characterization of optimal measurements via group representation theory.
Deep learning-based defect detection on livestock operations

The recent growth of computer vision system research has contributed to the advancement of livestock operations including meat processing. However, the defect detection of livestock products has not been actively studied due to the scarcity of dataset despite its significance. In the real world, the ratio of the defects in the livestock dataset is extremely low, compared to the normal products, resulting in insufficient model training. To address this problem, this study propose a deep learning-based anomaly detection method for an extremely unbalanced dataset. Adopting an anomaly detection framework from prior research, we suggest an adversarial autoencoder, which includes loss inversion to optimize reconstruction error for training skewed data, and a linear perturbation method called the Fast Gradient Sign Method (FGSM) to generate noises as anomaly cases. The model was then evaluated on the real-world dataset acquired from a livestock factory to detect defective chicken carcass (e.g., broken leg, injured skin, bent wing). The experiment results show that the proposed model outperforms existing models used for anomaly detection problems (i.e. DevNet and DeepSAD) and loss inversion and FGSM successfully improve the model performance when detecting livestock defects, even if the dataset contains extremely few defects.
Non-Iterative Optimization of Pseudo-Labeling Thresholds for Training Object Detection Models from Multiple Datasets

We propose a non-iterative method to optimize pseudo-labeling thresholds for learning object detection from a collection of low-cost datasets, each of which is annotated for only a subset of all the object classes. A popular approach to this problem is first to train teacher models and then to use their confident predictions as pseudo ground-truth labels when training a student model. To obtain the best result, however, thresholds for prediction confidence must be adjusted. This process typically involves iterative search and repeated training of student models and is time-consuming. Therefore, we develop a method to optimize the thresholds without iterative optimization by maximizing the $F_\beta$-score on a validation dataset, which measures the quality of pseudo labels and can be measured without training a student model. We experimentally demonstrate that our proposed method achieves an mAP comparable to that of grid search on the COCO and VOC datasets.
An analysis on Japanese qualification training curriculum for the first grade regular teacher

본 연구는 2018･2020년도 1급 정교사(일본어) 자격연수의 운영 사례 분석과 참가교사들의 만족도 조사를 통해 추후 효율적인 자격연수 운영을 위한 개선방향을 제안하고자 한다. 교육부 고시 자격연수 표준교육과정은 교사의 전문성 강화와 미래 교육환경의 변화에 대응할 수 있는 역량 중심에 초점을 두었다. 하지만 필수･권장교과 개설과 영역 및 역량군 분류의 애매모호함이 개선되었음에도 불구하고, 주제내용 및 예시가 역량 분류나 취지에 맞지 않는 사례나 강사들의 수업 역량의 미흡함 등의 개선점을 살펴볼 수 있었다. 이에 앞으로의 개선 방향으로 자격연수 표준교육과정의 재정비와 비중 조절, 필수 및 권장 교과의 우수 콘 텐츠 개발과 활용 및 다양한 형태의 수업 도입, 자격연수의 각 시･도교육별진행을 제안한다. Through the case analysis and survey, this study examines a case of Japanese qualification training curriculum for the first grade regular teacher in 2018 and 2020 in order to suggest the improvement points and implications for the operation of the first grade regular teacher qualification training curriculum. The standard curriculum for the teacher qualification training has focused on teachers’ professional competence to adapt to the rapid change in future educational environments. However, despite the improvements on the establishment of required/recommended subjects and the classification of teacher competences, the current study finds the issues in the operation of training, including mismatch between the contents or themes and classification or purpose of subjects as well as inefficiency in lecturers’ teaching ability. As a result, this study suggests the reorganization of training curriculum, the development of content and diverse formats of courses, and the regional operation of training as ways for more efficient operation. 本稿は2018･2020年度1級日本語教師の資格研修の事例の分析と参加者のアンケートを通し、効果的な資格研修の運営のための改善方案を提示する。教育部の資格研修のための標準教育課程は、教師の専門性の強化と, 未来の教育環境の変化に対応できる力量に焦点を当てている。しかし、必修･勧奨教科の解説、領域および力量群の分類の曖昧さが改善されたにも関わらず、教科目の内容および例が力量分類やその目的に適切ではない、または講師の授業力量の不十分さなどの改善点が見られた。そこで, 本稿はこれからの改善方案として、資格研修の標準教育課程の再整備および比重の調整、必修･勧奨教科のコンテンツ開発と多様な授業形態の導入、資格研修の各市･都による運営を提案する。
Temporal disambiguation of relative temporal expressions in clinical texts

Temporal expression recognition and normalization (TERN) is the foundation for all higher-level temporal reasoning tasks in natural language processing, such as timeline extraction, so it must be performed well to limit error propagation. Achieving new heights in state-of-the-art performance for TERN in clinical texts requires knowledge of where current systems struggle. In this work, we summarize the results of a detailed error analysis for three top performing state-of-the-art TERN systems that participated in the 2012 i2b2 Clinical Temporal Relation Challenge, and compare our own home-grown system Chrono to identify specific areas in need of improvement. Performance metrics and an error analysis reveal that all systems have reduced performance in normalization of relative temporal expressions, specifically in disambiguating temporal types and in the identification of the correct anchor time. To address the issue of temporal disambiguation we developed and integrated a module into Chrono that utilizes temporally fine-tuned contextual word embeddings to disambiguate relative temporal expressions. Chrono now achieves state-of-the-art performance for temporal disambiguation of relative temporal expressions in clinical text, and is the only TERN system to output dual annotations into both TimeML and SCATE schemes.
Adapting conversational strategies in information-giving human-agent interaction

In this work, we focus on human-agent interaction where the role of the socially interactive agent is to optimize the amount of information to give to a user. In particular, we developed a dialog manager able to adapt the agent's conversational strategies to the preferences of the user it is interacting with to maximize the user's engagement during the interaction. For this purpose, we train an agent in interaction with a user using the reinforcement learning approach. The engagement of the user is measured using their non-verbal behaviors and turn-taking status. This measured engagement is used in the reward function, which balances the task of the agent (giving information) and its social goal (maintaining the user highly engaged). Agent's dialog acts may have different impact on the user's engagement depending on several factors, such as their personality, interest in the discussion topic, and attitude toward the agent. A subjective study was conducted with 120 participants to measure how third-party observers can perceive the adaptation of our dialog model. The results show that adapting the agent's conversational strategies has an influence on the participants' perception.
A Feature Extraction Algorithm Based on Optimal Neighborhood Size

Abstract The feature extraction of a point cloud fragment model is the basis of fragment splicing, which provides the technical support for research on the segmentation, splicing, and restoration of fragment surfaces. High-quality feature extraction, however, is a complicated process due to the diversity of the surface information of a fragment model. For this subject, a high-efficient point cloud feature extraction method was proposed to address a new method for extracting feature lines. First, the projection distance feature of the point cloud model was calculated to identify the potential feature points. Furthermore, the local information of the possible feature points was used to construct the adaptive neighborhoods for identifying the feature points based on neighborhoods of the model. The clustering fusion of the feature points was proposed according to the discrimination threshold values of the feature points. Finally, the Laplace operator was utilized to refine and connect the feature points to form smooth feature lines. The experimental results showed that the proposed method was automatic, highly efficient, and with good adaptability that could effectively extract the detailed features and construct the complete feature lines. Moreover, results showed that the provided framework could extract the features of simple structure models and be feasible to a certain extent for fragment models with abundant features.