Reviewerly/exHarmony huggingface dataset:
Sec-ALG: An Open-source Application Layer Gateway for Secure Access to Private Networks

Middleboxes such as Network Address Translators (NATs), proxy servers or Application Layer Gateways (ALGs) provide remote access to end-hosts in the private address space. The middleboxes offer proprietary solutions and encrypted traffic poses a challenge when middleboxes employ packet payload inspection techniques for connection establishment. Session key sharing and decryption followed by re-encryption of the traffic, for correctly routing to the private host, increases the connection latency and also poses a higher threat in case of traffic interception by a malicious third-party. In this paper, we present a novel open-source ALG, called Sec-ALG, for providing secure end-to-end communication to the web servers situated in the private address space. Sec-ALG relies on the technique of light Deep Packet Inspection (DPI) for protocol detection and session establishment using a novel parser-lexer generator called YaLe. The proposed approach offers increased security by maintaining end-to-end encryption for an HTTPS connection. Our experimental analysis demonstrates that Sec-ALG reduces the HTTPS connection latency in comparison to the NGINX reverse proxy using a 24-core host machine. Moreover, Sec-ALG handles requests at a three-fold increased rate than NGINX proxy when tested with 100 concurrent connections. The ALG can be used either as a standalone solution or a component of the Realm Gateway, that is a generic interworking solution between public and private networks. The presented work is part of an extensive ongoing research at Aalto University focusing on embedding policy based trust into the network.
Temporal Calibrated Regularization for Robust Noisy Label Learning

Deep neural networks (DNNs) exhibit great success on many tasks with the help of large-scale well annotated datasets. However, labeling large-scale data can be very costly and error-prone so that it is difficult to guarantee the annotation quality (i.e., having noisy labels). Training on these noisy labeled datasets may adversely deteriorate their generalization performance. Existing methods either rely on complex training stage division or bring too much computation for marginal performance improvement. In this paper, we propose a Temporal Calibrated Regularization (TCR), in which we utilize the original labels and the predictions in the previous epoch together to make DNN inherit the simple pattern it has learned with little overhead. We conduct extensive experiments on various neural network architectures and datasets, and find that it consistently enhances the robustness of DNNs to label noise.
Awareness without Neural Networks: Achieving Self-Aware AI via Evolutionary and Adversarial Processes

A key difficulty in achieving self-aware artificial intelligence (AI) is the achievement of epistemological knowledge, i.e. a machine that “knows what it knows” and “knows what it does not know” with respect to some model of itself or its surroundings. Given a nonlinear dynamical system with known algebraic structure expressible as differential equations, with sensors able to create a time-series of measurements of sufficient variables to create a suitable partial state vector, then novel forms of evolutionary machine learning and adversarial processes are sufficient to create a form of AI that is “aware” of its knowledge set regarding this system, and can use a form of differential Game Theory and adversarial processes to “think” about its knowledge set to address ambiguities and achieve objectives, including moving beyond its original training data. This may itself constitute a form of “self-awareness”. Results from successful use of these techniques in medical and engineering problems are outlined. This AI architecture does not involve neural networks or their derivative architectures, but instead is inspired by evolutionary ecosystems. Implications for self-aware operating systems are discussed.
ACADEMIC INTEGRITY OF STUDENTS: PEDAGOGICAL CONDITIONS OF ITS CONTROL

In the article, the problem of academic integrity of students of higher education institutions is considered and the main pedagogical conditions for reduction of manifestations of academic insanity are offered on the basis of three interrelated psychological and pedagogical components: motivational, normative and cultural value.
A Qualitative Case Study on the Process of Objective Formation in Art Education Partnership Focused on corporate-linked gifted art education program

본 연구에서는 사업 목표와 성과의 공유, 새로운 가치 창출로서 파트너십의 개념을 고찰하고, 시스템적 측면에서 파트너십의특징과 의미를 논의하였다. 이를 토대로 기업과 미술영재 교육기관이 어떤 의사소통과 상호작용을 통해 미술교육 파트너십을구축해 나가는가를 살펴보기 위한 질적 사례연구 진행하였다. 연구의 결과로 미술교육 파트너십은 고정된 하나의 목표를중심으로 선형적으로 이루어지는 것이 아니라, ‘설득하면서 목표를 인식하다’, ‘조정하면서 공동의 목표를 만들다’, ‘학생 경험으로부터 목표에 의미부여 하다’ 단계로 공동의 목표를 만들어가는 과정 그 자체임을 알 수 있었다. 본 연구의 시사점은다음과 같다. 첫째, 사전에 설정된 명확한 목표보다 개방적인 태도로 함께 목표를 만들기 위해 인식을 공유하는 노력과 과정이 성공적인 파트너십의 핵심이 된다. 둘째, 참여자들이 각각의 위치에서 점진적으로 주체성을 가질 수 있는 조건을 만들어가는 것이 중요함을 알 수 있다.In this study, the concept of partnership was discussed by sharing objective and outcomes and creating new values, and the characteristics and meaning of partnership were discussed in terms of system. Based on this, a qualitative case study was conducted to examine how companies and gifted art education institutes establish art education partnership through which communication and interaction. As a result of the study, art education partnership does not take place linearly around a fixed objective. It was found that it is the process of creating a common objective in the steps of recognizing the objective while persuading , making a common objective while making adjustments , and giving meaning to the objective from student experience . Therefore, as a research implication, first, efforts and processes to share awareness to create objective together with an open attitude, rather than a clear set objective, are the key to successful partnerships. Second, it can be seen that it is important to create conditions for participants to gradually become subjective at each location.
A mixed approach of statistical weighting method and unsupervised method to improve Uyghur sentiment classification

Considering the scarcity of Uyghur sentiment resources, in this paper proposed a new combined unsupervised sentiment classification method for Uyghur text without any labeled corpora. In the first part, a Uyghur sentiment dictionary, UYSentiDict, was adopted to classify the sentences. For the sentiment vocabulary matching, both the matching of the original word and the stem were considered, and the influence of sentence patterns, negation words, and degree adverbs were further considered as well. Based on different thresholds, the sentences with higher sentiment values were selected from the lexicon-based classification results as a pseudo-labeled dataset. In the second part, different sentiment characteristics were learned from the pseudo-labeled dataset by the machine learning classifier, and the remaining categorical data were further classified. It can be concluded that the method proposed in this paper has good classification efficiency in Uyghur sentiment corpora in four different fields, and some results were performed better than the classification results of machine learning classifier. Moreover, this method is not restricted by the field of data and does not need to be marked in advance with good training corpus, and can solve the resource shortage problem in the field of Uyghur sentiment classification effectively.
Visualization of Methods Changeability Based on VCS Data

Software engineers have a wide variety of tools and techniques that can help them improve the quality of their code, but still, a lot of bugs remain undetected. In this paper we build on the idea that if a particular fragment of code is changed too often, it could be caused by some technical or architectural issues, therefore, this fragment requires additional attention from developers. Most teams nowadays use version control systems to track changes in their code and organize cooperation between developers. We propose to use data from version control systems to track the number of changes for each method in a project for a selected time period and display this information within the IDE's code editor. The paper describes such a tool called Topias built as a plugin for IntelliJ IDEA. Its source code is available at https://github.com/JetBrains-Research/topias. A demonstration video can be found at https://www.youtube.com/watch?v=xsqc4gCTxfA.
Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning

Heterogeneous domain adaptation adapts a machine learning model, here classification model, from a source domain to a target domain to leverage data from both domains. Thereby, supervised heterogeneous domain adaptation expects labeled data from the target domain, while unsupervised heterogeneous domain adaptation does not. In this article, we study the inclusion of active learning to bridge unsupervised and supervised domain adaptation. The active learning approach iteratively queries the most useful instances from the target domain, which are then labeled and used to improve the classification model. Using active learning, the selection of training instances can focus on areas where ambiguity in the source domain resolves in the target domain. Hence, we achieve the same performance with fewer labels. Experiments on real activity recognition data confirm our claims.
Transfer Learning based Task-oriented Dialogue Policy for Multiple Domains using Hierarchical Reinforcement Learning

Development of Virtual Agents (VAs) for Goal/Task-oriented conversations capable of handling complex tasks pertaining to multiple domains and its various intents is quite an onerous task. Lack of high quality, domain specific conversational data required to train policies is one of the biggest challenges for the success of any dialogue system. In this paper, we present a multi-domain, multi-intent based task-oriented dialogue system by successfully combining Hierarchical Deep Reinforcement Learning and Transfer Learning paradigms. The notion is to exploit or take advantage of the resemblance between domains as various domains share considerable amount of overlapping data or slots. Thus, Options framework along with Transfer Learning is employed to curate VAs with better and faster learning performance. Our proposed approach reduced the data requirement to train multi-domain VAs by atleast 20% for distant domains and almost 38% for close domains. It also significantly curtailed the learning time and aided faster learning for transfer learning based policies.
INFLUENCE ON EMOTIONAL SPHERE OF FUTURE TEACHER’S PERSONALITY IN THE PROCESS OF IT’S GENERAL PEDAGOGICAL TEACHER TRAINING ( PRACTICAL ASPECT)

The process of studying pedagogical disciplines in this direction almost wasn‟t researched. But the disciplines of the pedagogical cycle next to literature, history, culture hide in themselves an extraordinary educational potential, which under certain conditions could be aimed at successfully forming the future teacher‟s personality. This determines the relevance of the topic we have chosen. The educational process provides a consequence when its semantic component is evaluated not only through “the eyes of the curriculum”, but also through “the eyes of students”, their interests, the problems concerning them, needs, and value orientations. With this approach, it ceases to be a purely cognitive process, becoming personally significant. However, a successful consequence must be preceded, the purpose is set - the teacher's content focus of and actions are not only on the instrumental sphere of personality, but also necessarily on motivational value. Such a purpose becomes the compass that directs its action to the result (those positive changes in the structure of the personality that are a productive consequence).
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate

In time-to-event prediction problems, a standard approach to estimating an interpretable model is to use Cox proportional hazards, where features are selected based on lasso regularization or stepwise regression. However, these Cox-based models do not learn how different features relate. As an alternative, we present an interpretable neural network approach to jointly learn a survival model to predict time-to-event outcomes while simultaneously learning how features relate in terms of a topic model. In particular, we model each subject as a distribution over "topics", which are learned from clinical features as to help predict a time-to-event outcome. From a technical standpoint, we extend existing neural topic modeling approaches to also minimize a survival analysis loss function. We study the effectiveness of this approach on seven healthcare datasets on predicting time until death as well as hospital ICU length of stay, where we find that neural survival-supervised topic models achieves competitive accuracy with existing approaches while yielding interpretable clinical "topics" that explain feature relationships.
Topic Modeling-based Web Architecture Redesign

Data-driven web architecture redesign optimizes the overall efficiency of the website in a holistic way. This research employed the topic modeling method to redesign the information architecture based on the user journey's systematic analysis. The findings provide a practical methodology for information architecture redesign.
Reoriented Short-Cuts (RSC): An Adjustment Method for Locally Optimal Path Short-Cutting in High DoF Configuration Spaces

This paper presents Reoriented Short-Cuts (RSC): A modification of the traditional Short-Cut technique, allowing almost sure, single homotopy class, asymptotic convergence in high degree of freedom (DoF) problems. An additional Informed Gaussian Sampling (IGS) technique is also introduced for convergence comparison. Traditionally, Short-Cut methods are used as a final technique to further optimize an initially found path. Typical Short-Cut methods fail as a single DoF may converge faster than the remaining, creating a zero-volume region between path segments and objects, halting further improvements. Previous attempts to solve this separate DoFs individually, drastically increasing collision checking computation. RSC and IGS control the shifting of the vertex to be Short-Cut, moving vertex positions by reorienting the line segments, removing the zero-volume convergence region. These methods are compared to similar strategies in a variety of problems including random worlds, and robot manipulation, to show the convergence across both translation and rotation oriented problems.
Detriments to Cultural Sensitivity in HCI Design Processes: Insights from Practitioners’ Experiences in India

Culture influences human preferences and behaviour. Many HCI products are being designed and developed in cross cultural contexts. Culture’s influence needs to be accommodated in the HCI design process. There is a need to understand how the current design processes for HCI products are accommodating for culture’s influence. Discussion with HCI designers is one possible method to do this. This paper reports findings of interviews with 30 HCI designers from India. The one on one semi structured qualitative interviews were conducted to understand whether the current design processes under practice are sensitive to culture’s influence on design and what are the detriments to accommodate cultural influences in the design process. The key findings suggest that the inclusion of cultural concerns in the design process are directly influenced by the project leadership’s awareness and the client’s awareness of the impact of culture in acceptance of HCI products. In projects where the client had not specifically asked for cultural sensitivity of the HCI product, design processes did not include the cultural concerns in the development of the product.
Identification and Management of Frauds in Edge Computing Systems

Edge computing enables edge devices to perform intricate tasks competently. However, identifying fraud while performing these tasks is a foremost necessity. Phishing is a type of fraudulency that is sent in by the hackers via email, URL's, blogs redirecting to a new page and so on, where the attacker imitates as a genuine person. The main prospect is to collect data relating to a person's credentials to confidential data and use in form of blackmail for huge ransom money. Foremost attackers sent in Phishing mails where URLs are redirected via HTTP protocol in edge computing systems that is undetectable leading to huge loss for the corporate world, now anti-phishing algorithms, Machine Learning Algorithms, anti-phishing frameworks, anti-phishing simulators are built in order to detect faults in many domains to scale down activities of Phishing. Cyber activity in form of phishing has become one of the day-to-day prompting attacks to the corporate society, in the world that leads to many sinful activities an anti-phishing is used in order to distinguish these faults and scale down the amount of damage that is being performed. This paper deliberates on the types of phishing and anti-phishing methods with a common algorithm for suspicious phishing host, and an anti-phishing pseudo code for captcha images that can be used for identifying fraudulency and manage the same in edge computing devices.
Collective Affinity Aware Computation Mapping

This work defines the concept of collective affinity. It is claimed that collective affinity has more potential than single core-centric affinity, for data locality optimization in manycores. The reason is that collective affinity captures the potential benefits of transferring computations originally assigned to one core to other cores. Next, building upon the collective affinity concept and a cache content estimation strategy, it presents a computation-to-core mapping strategy, specifically tuned for exploiting near data computing by reducing distance-to-data.
A Block Access Control in Wireless Blockchain Networks

Blockchain, a distributed ledger technology, has attracted many attentions to enable a decentralized and safe wireless networks for various applications. Considering the high density of nodes and the massive service requests in next-generation wireless network will result in a surge of blockchain forking, this paper proposes a Block Access Control (BAC) approach to address forking problem and transmit block effectively while improving transaction throughput and saving computational power. Then, using a Markov chain model, we analyse the performance of a wireless blockchain network by involving the effect of BAC approach. The results show that the BAC approach can help the network to achieve a high transaction throughput while addressing forking problem.
The role of corpora for robots’ acquisition of linguistic structure through observation and natural language instructions

When aiming at automatic linguistic structure learning, the developed algorithms highly depend on the data they can be trained on. We present several multimodal datasets employed for grounded language learning in artificial agents. Based on evidence on the close tying of motor action and language, we developed the Linguistic, Kinematic and Gaze information in task descriptions Corpus (LKG-Corpus) as a resource to (i) investigate fundamental questions concerning the relation between sensorimotor processes and linguistic structure, and to (ii) develop computational models for grounded language learning in robots. For embodied structure learning, we emphasize the importance of data sets which can be automatically interpreted by the robot and do not need prior knowledge about linguistic structure or actions.
Reinforced Sample Re-weighting for Pedestrian Attribute Recognition

Pedestrian Attribute Recognition aims to recognise person attributes including age, gender, clothing and accessories in a given image. It is challenging due to the high variance in sample quality and attribute content. Many network structures have been proposed to better capture the fine-grained details in an image. However, how to learn from each training sample based on their importance to the model still remains to be addressed. In this paper, we propose Reinforced Sample Re-weighting (RSR), a novel approach to re-weight samples in a batch during back-propagation through reinforcement learning. RSR agents are proposed to assign sample weights based on both the sample itself and the recognition model status. The agent learns in an on-policy manner, where it learns together with the attribute recognition model and no additional training is required. The proposed approach achieves state-of-the-art performance against other existing methods on three large scale pedestrian attribute datasets PETA, PA-100K and RAP, which demonstrates the effectiveness of our method.
“Eating Right” and User Experience with an Employee Wellness Program

This project investigates how employees experience eating-related health communications and technologies in an Employee Wellness Program (EWP) at a large midwestern university. This poster presents preliminary results from a UX-informed survey of employees in order to argue that users are predominantly dissatisfied with EWP diet-related programming, and that the EWP would be well served by implementing a more user-engaged design process in order to better meet the wants and needs of its users.
Transformation of the Properties of the Educational Text as an Object of State Regulation

The paper analyzes the dynamics of the properties of the educational text depending on the state poli-cy in education. During the weakening of State con-trol several types of educational material is being implemented. “Educational texts of the anomie peri-od” do not take into account the characteristics of the addressee; they lack topics that are traditional in the national education system. The only task of these texts is to inculcate the values of the author. During the same period, educational text, aimed at solving specific problems, may appear. So, until 1933, textbook journals were used in the educational process, prompted by the state task of eliminating illiteracy. In the same period, the first editions of conceptual textbooks, that have contributed to the following ones, appear. With the strengthening of state control over the educational sector, a new type of a textbook is being formed – “educational text as a standard of response”. The study provides exam-ples of textbooks that were actively used in the So-viet period and retained their relevance in the educa-tional process of modern Russia. Their viability is determined by the communication program and the fundamental nature of the content. In the modern period, when education is positioned by the state as a service, there is a demand for an “educational text that forms competencies”, the communication pro-gram of which should ensure its customization.