Reviewerly/exHarmony huggingface dataset:
Incrementally Verifiable Computation via Incremental PCPs

If I commission a long computation, how can I check that the result is correct without re-doing the computation myself? This is the question that efficient verifiable computation deals with. In this work, we address the issue of verifying the computation as it unfolds. That is, at any intermediate point in the computation, I would like to see a proof that the current state is correct. Ideally, these proofs should be short, non-interactive, and easy to verify. In addition, the proof at each step should be generated efficiently by updating the previous proof, without recomputing the entire proof from scratch. This notion, known as incrementally verifiable computation, was introduced by Valiant [TCC 08] about a decade ago. Existing solutions follow the approach of recursive proof composition and can be based on strong and non-falsifiable cryptographic assumptions (so-called “knowledge assumptions”). In this work, we present a new framework for constructing incrementally verifiable computation schemes in both the publicly verifiable and designated-verifier settings. Our designated-verifier scheme is based on somewhat homomorphic encryption (which can be based on Learning with Errors) and our publicly verifiable scheme is based on the notion of zero-testable homomorphic encryption, which can be constructed from ideal multi-linear maps [Paneth and Rothblum, TCC 17]. Our framework is anchored around the new notion of a probabilistically checkable proof (PCP) with incremental local updates. An incrementally updatable PCP proves the correctness of an ongoing computation, where after each computation step, the value of every symbol can be updated locally without reading any other symbol. This update results in a new PCP for the correctness of the next step in the computation. Our primary technical contribution is constructing such an incrementally updatable PCP. We show how to combine updatable PCPs with recently suggested (ordinary) verifiable computation to obtain our results.
Breast Cancer Classification Using Tetrolet Transform Based Energy Features and K-Nearest Neighbor Classifier

The cancer that develops in the breast tissue is referred to as breast cancer. The cancer in the breast could be sometimes symptomatic and are identified by self-examination of the breast or by a physician, whereas in certain cases there could be no symptoms at all. However, signs like a lump in the breast, change of size of the breast, dimpling and fluid discharge from the nipple are some of the symptoms which are cause of grave concern. The early diagnosis of the disease is the key to combat the deadly disease paving the path for hope of life. Mammography is a very popular technique that is used for the early diagnosis of breast cancer. In this study, a technique for breast cancer classification in digitized mammogram is put-forth employing tetrolet transform based energy features and K-Nearest Neighbor (KNN) classifier. The breast mammogram images of benign and malignant category are decomposed into sub-band coefficients using tetrolet transform and the energy features are extracted. These extracted features are given as input to the KNN classifier. Results show better classification accuracy in the breast cancer images using tetrolet transform based energy features and KNN classifier.
Multicultural Teaching in Regions (Within Sustainability Conception in Education)

This research is dedicated to the study of the sustainability conception in education adopted by multicultural processes. The results of this research are analysed from a study conducted at Samara State University of Social Sciences and Education and Ludwigsburg Pedagogical Institute. &nbsp;Multicultural teaching develops dynamically. It seems possible to develop an instrument to evaluate on an ongoing basis as to how to improve multicultural teaching further within regional policy. For this, we aim to demonstrate the importance of using Inter- and trans-disciplinary methods as a significant contributor in many countries to better understand perspectives to analyse multicultural teaching in regions. To further the ideas about the perspectives to develop multicultural teaching, we feel strongly that action-oriented and reflective and responsible methods are an important mediator to ensure multicultural teaching. Hence, in this research we speak about the integrative model, in particular, considering the relationship that exists between the educational practices to increase the role of education and the determinate needs of peoples of all nationalities and the new phenomenon of the regional policy. To do so, we use structural and functional approach. The ideas presented here attempt to visually organise the views upon which Russian intercultural experts have reached in particular considering the methods that exist to improve multicultural teaching.
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics

Value-based reinforcement-learning algorithms provide state-of-the-art results in model-free discrete-action settings, and tend to outperform actor-critic algorithms. We argue that actor-critic algorithms are limited by their need for an on-policy critic. We propose Bootstrapped Dual Policy Iteration (BDPI), a novel model-free reinforcement-learning algorithm for continuous states and discrete actions, with an actor and several off-policy critics. Off-policy critics are compatible with experience replay, ensuring high sample-efficiency, without the need for off-policy corrections. The actor, by slowly imitating the average greedy policy of the critics, leads to high-quality and state-specific exploration, which we compare to Thompson sampling. Because the actor and critics are fully decoupled, BDPI is remarkably stable, and unusually robust to its hyper-parameters. BDPI is significantly more sample-efficient than Bootstrapped DQN, PPO, and ACKTR, on discrete, continuous and pixel-based tasks. Source code: https://github.com/vub-ai-lab/bdpi . Appendix: https://arxiv.org/abs/1903.04193 .
A Visual Perspective for User Identification Based on Camera Fingerprint

User identification is to identify the online accounts’ identity, which is a critical problem in many applications. The key problem in that is to find a typical pattern from the online accounts data. Different from the previous works, we identify users based on the camera fingerprint. The underlying idea is that the accounts belonging to the same individual contain the photos taken by the same cameras. Based on that, we propose a new framework to deal with the user identification problem. Specifically, the camera feature of each image is extracted by the PRNU (Photo Response Non-Uniformity) algorithm. With the proposed hierarchical clustering approach, the camera features of different camera source can be achieved. Extensive experiments on a collected photo dataset show that the proposed framework is effective for identifying users, especially in the scenarios that users have multiple cameras and photos forwarding behavior.
The Neyman Pearson detection of microsaccades with maximum likelihood estimation of parameters

Despite the fact that the velocity threshold method is widely applied, the detection of microsaccades continues to be a challenging problem, due to gaze-tracking inaccuracy and the transient nature of microsaccades. Important parameters associated with a saccadic event, e.g., saccade duration, amplitude, and maximum velocity, are sometimes imprecisely estimated, which may lead to biases in inferring the roles of microsaccades in perception and cognition. To overcome the biases and have a better detection algorithm for microsaccades, we propose a novel statistical model for the tracked gaze positions during eye fixations. In this model, we incorporate a parametrization that has been previously applied to model saccades, which allows us to veridically capture the velocity profile of saccadic eye movements. Based on our model, we derive the Neyman Pearson Detector (NPD) for saccadic events. Implemented in conjunction with the maximum likelihood estimation method, our NPD can detect a saccadic event and estimate all parameters simultaneously. Because of its adaptive nature and its statistical optimality, our NPD method was able to better detect microsaccades in some datasets when compared with a recently proposed state-of-the-art method based on convolutional neural networks. NPD also yielded comparable performance with a recently developed Bayesian algorithm, with the added benefit of modeling a more biologically veridical velocity profile of the saccade. As opposed to these algorithms, NPD can lend itself better to online saccade detection, and thus has potential for human-computer interaction applications. Our algorithm is publicly available at https://github.com/hz-zhu/NPD-micro-saccade-detection.
Water Streak Detection with Convolutional Neural Networks for Scrubber Dryers

Avoiding gray water remainders behind wet floor cleaning machines is an essential requirement for safety of passersby and quality of cleaning results. Nevertheless, operators of scrubber dryers frequently do not pay sufficient attention to this aspect and automatic robotic cleaners cannot even sense water leakage. This paper introduces a compact, low-cost, low-energy water streak detection system for the use with existing and new cleaning machines. It comprises a Raspberry Pi with an Intel Movidius Neural Compute Stick, an illumination source, and a camera to observe the floor after cleaning. The paper evaluates six different Convolutional Neural Network (CNN) architectures on a self-recorded water streak data set which contains nearly 43000 images of 59 different floor types. The results show that up to 97% of all water events can be detected at a low false positive rate of only 2.6%. The fastest CNN Squeezenet can process images at a sufficient speed of over 30 Hz on the low-cost hardware such that real applicability in practice is provided. When using an NVidia Jetson Nano as alternative low-cost computing system, five out of the six networks can be operated faster than 30 Hz.
A hybrid approach for scheduling applications in cloud computing environment

Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list.
Improving the Visual Comfort of Virtual Reality Telepresence for Robotics

Telepresence technologies enable users to exhibit a presence in a remote location, through the use of sensors, networks and robotics. State-of-the-art telepresence research swaps conventional desktop monitors for Virtual Reality (VR) headsets, in order to increase the user’s immersion in the remote environment, though often at the cost of increased nausea and oculomotor discomfort. We describe a novel method for telepresence via VR, aimed at improving comfort, by accounting for discrepancies between robot and user head pose. This is achieved through a “decoupled” image projection technique, whereby the user is able to look across captured imagery rendered to a virtual display plane. Evaluated against conventional projection techniques, in a controlled study involving 19 participants, decoupled image projection significantly reduced mean perceived nausea and oculomotor discomfort while also improving immersiveness and the perceived sensation of presence.
Automated Subject Indexing of Domain Specific Collections Using Word Embeddings and General Purpose Thesauri

In the era of enormous information production human capabilities have reached their limits. The need for automatic information processing which would not be incommensurate to human sophistication seems to be more than imperative. Information scientists have focused on the development of techniques and processes that would assist human contribution while improve, or at least guarantee, information quality. Automatic indexing techniques may lay on various approaches offering different results in information retrieval. In this paper we introduce an automated methodology for subject analysis, including both the determination of the aboutness of the documents and the translation of the related concepts to the terms of a knowledge organization system. Focusing on a corpus consisting of articles related to the Digital Library Evaluation domain, topic modeling algorithms are utilized for the aboutness of the documents, while the context of the words in topics, as captured by Word Embeddings, are used for the assignment of the extracted topics to the concepts of the EuroVoc thesaurus.
Psychological Readiness For Professional Activity Of Future Psychologists

The article deals with the process of readiness formation of future psychologists to professional activity. Professional activity of consulting psychologist and preparation for it are considered from the position of humanistic and existential-humanistic approaches. The authors describe the possibilities of the phenomenological method for the study of the image of professional role and representation in the consciousness of value-semantic and motivational-volitional aspects of the activity. Based on the conceptual model of preparation for professional activity a technological algorithm of specialized training for professional role performance was developed. In addition, peculiarities of readiness formation among students with different level of training were studied. The study showed that a special procedural and professional form of study makes it possible to integrate the axiological and motivational-volitional aspects of psychological readiness, which is expressed by awareness and acceptance of themselves in the professional role of a consulting psychologist. The need to highlight psychological readiness in the context of training for professional activity is determined by its key factor in the process of becoming a professional.
Opposition Theory Enabled Intelligent Whale Optimization Algorithm

Performance of any optimization technique is dependent on the perfect combination of operators; these operators are used for analyzing the exploration and exploiting capability. In the absentia of this combination, an algorithm inclined toward premature convergence is trapped in local optima and is unable to find the best solution. In this paper, a new variant of whale optimization algorithm (WOA) is introduced which combines the concept of opposition-based learning, crossover operator, and sinusoidal function to accelerate the convergence rate and to avoid the trapping in local optima. This will be called as an opposition theory enabled intelligent whale optimization algorithm (OIWOA). To test the effectiveness of OIWOA, it is evaluated on an array of 23 classical benchmark functions. The performance measures show that the proposed variant possesses a better capability in solving optimization problems.
Identity in the digital age

Consumers frequently use digital and social media to express their identities. In this chapter, the authors begin to delve into the many implications that the digital world has on consumer identity creation and maintenance, expression, and subsequent behaviors. In a broad sense, they believe that expressing identity online is a multi-step process with several considerations and ramifications of each decision. These steps include the antecedents to, and consequences of, online identity expression, the online platform chosen to express identity, and possible audiences to digital identity expression. Critically, they believe that the considerations and consequences of online identity expression differ from offline identity expression as it has traditionally been studied. Throughout this chapter, the authors reflect on the different processes, questions and consequences a consumer may go through when expressing identities online. They also pose a number of future research questions within this domain.
Metric for Automatic Machine Translation Evaluation based on Pre-trained Sentence Embeddings

This study describes a segment-level metric for automatic machine translation evaluation (MTE).Although various MTE metrics have been proposed, most MTE metrics, including the current de facto standard BLEU, can handle only limited information for segment-level MTE.Therefore, we propose an MTE metric using pre-trained sentence embeddings in order to evaluate MT translation considering global information.In our proposed method, we obtain sentence embeddings of MT translation and reference translation using a sentence encoder pre-trained on a large corpus.Then, we estimate the translation quality by a regression model based on sentence embeddings of MT translation and reference translation as input.Our metric achieved state-ofthe-art performance in segment-level metrics tasks for all to-English language pairs on the WMT dataset with human evaluation score.
The development of ECO AR learning media based on augmented reality technology on the topic of ecosystem

Augmented Reality Technology has been widely applied in various fields, one of which is in the development of Ecology Augmented Reality (ECO AR) learning media that applies augmented reality technology. The used model in development is 4D. This research has reached the first 3 stages, namely Define, Design, and Develop. The results of the assessment of material experts, media experts, augmented reality experts, and visual spatial experts obtained ECO AR media quality scores of 89% in the very good category. Score results of student responses when using the ECO AR media obtained 85% in the very good category. Based on the assessment of the experts and limited trials on students, it was concluded that the media was suitable for use in learning.
Structure Learning for Headline Generation

Headline generation is an important problem in natural language processing, which aims to describe a document by a compact and informative headline. Some recent successes on this task have been achieved by advanced graph-based neural models, which marry the representational power of deep neural networks with the structural modeling ability of the relational sentence graphs. The advantages of graph-based neural models over traditional Seq2Seq models lie in that they can encode long-distance relationship between sentences beyond the surface linear structure. However, since documents are typically weakly-structured data, modern graph-based neural models usually rely on manually designed rules or some heuristics to construct the sentence graph a prior. This may largely limit the power and increase the cost of the graph-based methods. In this paper, therefore, we propose to incorporate structure learning into the graph-based neural models for headline generation. That is, we want to automatically learn the sentence graph using a data-driven way, so that we can unveil the document structure flexibly without prior heuristics or rules. To achieve this goal, we employ a deep &amp; wide network to encode rich relational information between sentences for the sentence graph learning. For the deep component, we leverage neural matching models, either representation-focused or interaction-focused model, to learn semantic similarity between sentences. For the wide component, we encode a variety of discourse relations between sentences. A Graph Convolutional Network (GCN) is then applied over the sentence graph to generate high-level relational representations for headline generation. The whole model could be optimized end-to-end so that the structure and representation could be learned jointly. Empirical studies show that our model can significantly outperform the state-of-the-art headline generation models.
INNOVATIVE MODEL TO FORM A TEACHER’S SUBJECTIVE PROFESSIONAL POSITION

The paper introduces the author’s definitions of the notions “teacher’s subjective professional position” and “formation of teacher’s subjective professional position”. The researcher develops and theoretically justifies a model to form a teacher’s subjective position in professional activity, which includes three interconnected blocks: diagnostic, operational and resultative ones. Pedagogical principles and conditions to implement the model are identified. Criteria to assess the level of a teacher’s subjectivity in professional activity are ascertained.
Modeling Requirements of Multiple Single Products to Feature Model

This work investigates how the requirements of multiple single products can be modeled into a feature model as part of domain engineering process in software product line engineering (SPLE) methodology. It adopts an extractive strategy in devising a two-step process for creating the feature model. The first step is by identifying the business process and list of features across all products into a product roadmap. The second step is identifying the commonality and variability of features based on the product roadmap into a feature model. The proposed approach may help a software company that produces software products for certain domain to migrate from traditional single product development lifecycle to software product line development lifecycle.
Does the Government Effectively in Optimizing Open Data? Analysis "Jago Data" in Yogyakarta Municipality

Open Government Data (OGD) is a database produced by the government that can be freely accessed, used or modified, therefore, this is a support to increase transparency and public participation. This article aims to analyze the effectively optimizing Open Government Data on the Jogja Go Open Data “JAGO DATA” in Yogyakarta Municipality. Furthermore, the methodology of this article uses qualitative research with in-depth interviews and literature, moreover, the variables of this research are focused on legal guidelines and frameworks (crosstab query is 47.5%), the ability of web portal system (crosstab query is 27.5%), and human resource in government and its attitude (crosstab query is 25%), therefore, its data findings were analyzed with Nvivo 12 plus software. The result of this article shows that the Yogyakarta city government successfully implemented JAGO DATA to support transparency, accountability, and encourage public participation, moreover, it is has implemented directives regarding Open Data regulation and its innovation designed with a collaborative among regional-level work units (SKPD). Therefore, the JAGO DATA portal is rated as user-friendly with the model used at the information stage and its maturity of a web portal is still in the pre-adoption level.