Reviewerly/exHarmony huggingface dataset:
Harnessing Data Movement in Virtual Clusters for In-Situ Execution

As a result of increasing data volume and velocity, Big Data science at exascale has shifted towards the in-situ paradigm, where large scale simulations run concurrently alongside data analytics. With in-situ, data generated from simulations can be processed while still in memory, thereby avoiding the slow storage bottleneck. However, running simulations and analytics together on shared resources will likely result in substantial contention if left unmanaged, as demonstrated in this work, leading to much reduced efficiency of simulations and analytics. Recently, virtualization technologies such as Linux containers have been widely applied to data centers and physical clusters to provide highly efficient and elastic resource provisioning for consolidated workloads including scientific simulations and data analytics. In this paper, we investigate to facilitate network traffic manipulation and reduce mutual interference on the network for in-situ applications in virtual clusters. In order to dynamically allocate the network bandwidth when it is needed, we adopt SARIMA-based techniques to analyze and predict MPI traffic issued from simulations. Although this can be an effective technique, the naïve usage of network virtualization can lead to performance degradation for bursty asynchronous transmissions within an MPI job. We analyze and resolve this performance degradation in virtual clusters.
Using Phase Instead of Optical Flow for Action Recognition

Currently, the most common motion representation for action recognition is optical flow. Optical flow is based on particle tracking which adheres to a Lagrangian perspective on dynamics. In contrast to the Lagrangian perspective, the Eulerian model of dynamics does not track, but describes local changes. For video, an Eulerian phase-based motion representation, using complex steerable filters, has been successfully employed recently for motion magnification and video frame interpolation. Inspired by these previous works, here, we proposes learning Eulerian motion representations in a deep architecture for action recognition. We learn filters in the complex domain in an end-to-end manner. We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction. We propose a phase-information extraction module, based on these complex filters, that can be used in any network architecture for extracting Eulerian representations. We experimentally analyze the added value of Eulerian motion representations, as extracted by our proposed phase extraction module, and compare with existing motion representations based on optical flow, on the UCF101 dataset.
Sub-session hijacking on the web: Root causes and prevention

Since cookies act as the only proof of a user identity, web sessions are particularly vulnerable to session hijacking attacks, where the browser run by a given user sends requests associated to the identity of another user. When n>1 cookies are used to implement a session, there might actually be n sub-sessions running at the same website, where each cookie is used to retrieve part of the state information related to the session. Sub-session hijacking breaks the ideal view of the existence of a unique user session by selectively hijacking m sub-sessions, with m<n. This may reduce the security of the session to the security of its weakest sub-session. In this paper, we take a systematic look at the root causes of sub-session hijacking attacks and we introduce sub-session linking as a possible defense mechanism. Out of two flavors of sub-session linking desirable for security, which we call intra-scope and inter-scope sub-session linking respectively, only the former is relatively smooth to implement. Luckily, we also identify programming practices to void the need for inter-scope sub-session linking. We finally present Warden, a server-side proxy which automatically enforces intra-scope sub-session linking on incoming HTTP(S) requests, and we evaluate it in terms of protection, performances, backward compatibility and ease of deployment.
Reduced‐gate convolutional long short‐term memory using predictive coding for spatiotemporal prediction

Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that uses convolutional, long short-term memory (convLSTM) modules. We introduce a novel reduced-gate convolutional LSTM(rgcLSTM) architecture that requires a significantly lower parameter budget than a comparable convLSTM. By using a single multi-function gate, our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, thereby reducing training time and memory requirements. We tested our reduced gate modules within a predictive coding architecture on the moving MNIST and KITTI datasets. We found that our reduced-gate model has a significant reduction of approximately 40 percent of the total number of training parameters and a 25 percent reduction in elapsed training time in comparison with the standard convolutional LSTM model. The performance accuracy of the new model was also improved. This makes our model more attractive for hardware implementation, especially on small devices. We also explored a space of twenty different gated architectures to get insight into how our rgcLSTM fit into that space.
Short floating-point representation for convolutional neural network inference

Convolutional neural networks (CNNs) are being widely used in computer vision tasks, and there have been many efforts to implement CNNs in ASIC or FPGA for power-hungry environments. Instead of the previous common representation, the fixed-point representation, this letter proposes a short floating-point representation for CNNs. The short floating-point representation is based on the normal floating-point representation, but has much less width and does not have complex cases like Not-a-Number and infinity cases. The short floating-point representation, contrary to the common belief, can produce a low-complexity computation logic because the operands of the multiplier logic can be shortened by the exponent concept of the floating-point representation. The exponent can also reduce the total length to reduce the SRAM area. The experimental results show that the short floating-point representation with 8-bit total width achieves less-than-1-percentage-point degradation without the aid of retraining in the top-5 accuracy on very deep CNNs of up to 152 layers and gives more than a 60% area reduction in the ASIC implementation.
Extraction of azimuthal asymmetries using optimal observables

Azimuthal asymmetries play an important role in scattering processes with polarized particles. This paper introduces a new procedure using event weighting to extract these asymmetries. It is shown that the resulting estimator has several advantages in terms of statistical accuracy, bias, assumptions on acceptance and luminosities compared to other estimators discussed in the literature.
A Bandit Approach to Maximum Inner Product Search

There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS). To achieve fast query time, state-of-the-art techniques require significant preprocessing, which can be a burden when the number of subsequent queries is not sufficiently large to amortize the cost. Furthermore, existing methods do not have the ability to directly control the suboptimality of their approximate results with theoretical guarantees. In this paper, we propose the first approximate algorithm for MIPS that does not require any preprocessing, and allows users to control and bound the suboptimality of the results. We cast MIPS as a Best Arm Identification problem, and introduce a new bandit setting that can fully exploit the special structure of MIPS. Our approach outperforms state-of-the-art methods on both synthetic and real-world datasets.
Forgetting in Modular Answer Set Programming

Modular programming facilitates the creation and reuse of large software, and has recently gathered considerable interest in the context of Answer Set Programming (ASP). In this setting, forgetting, or the elimination of middle variables no longer deemed relevant, is of importance as it allows one to, e.g., simplify a program, make it more declarative, or even hide some of its parts without affecting the consequences for those parts that are relevant. While forgetting in the context of ASP has been extensively studied, its known limitations make it unsuitable to be used in Modular ASP. In this paper, we present a novel class of forgetting operators and show that such operators can always be successfully applied in Modular ASP to forget all kinds of atoms – input, output and hidden – overcoming the impossibility results that exist for general ASP. Additionally, we investigate conditions under which this class of operators preserves the module theorem in Modular ASP, thus ensuring that answer sets of modules can still be composed, and how the module theorem can always be preserved if we further allow the reconfiguration of modules.
Iterated Local Search for Time-extended Multi-robot Task Allocation with Spatio-temporal and Capacity Constraints

Abstract We propose a method for task allocation to multiple physical agents that works when tasks have temporal and spatial constraints and agents have different capacities. Assuming that the problem is over-constrained, we need to find allocations that maximize the number of tasks that can be done without violating any of the constraints. The contribution of this work is the study of a new multi-robot task allocation problem and the design and the experimental evaluation of our approach, an iterated local search that is suitable for time critical applications. We created test instances on which we experimentally show that our approach outperforms a state-of-the-art approach to a related problem. Our approach improves the baseline’s score on average by 2.35% and up to 10.53%, while responding in times shorter than the baseline’s, on average, 1.6 s and up to 5.5 s shorter. Furthermore, our approach is robust to run replication and is not very sensitive to parameters tuning.
Adaptive Tiling Selection for Viewport Adaptive Streaming of 360-degree Video

360-degree video is an important component of the emerging Virtual Reality. In this paper, we propose a new adaptation method for tiling-based viewport adaptive streaming of 360-degree video. The proposed method is able to dynamically select the best tiling scheme given the network conditions and user status. Experiments show that our proposed method can improve the viewport quality by up to 2.3 dB compared to a conventional fixed tiling method.
An algorithm of image mosaic based on binary tree and eliminating distortion error

The traditional image mosaic result based on SIFT feature points extraction, to some extent, has distortion errors: the larger the input image set, the greater the spliced panoramic distortion. To achieve the goal of creating a high-quality panorama, a new and improved algorithm based on the A-KAZE feature is proposed in this paper. This includes changing the way reference image are selected and putting forward a method for selecting a reference image based on the binary tree model, which takes the input image set as the leaf node set of a binary tree and uses the bottom-up approach to construct a complete binary tree. The root node image of the binary tree is the ultimate panorama obtained by stitching. Compared with the traditional way, the novel method improves the accuracy of feature points detection and enhances the stitching quality of the panorama. Additionally, the improved method proposes an automatic image straightening model to rectify the panorama, which further improves the panoramic distortion. The experimental results show that the proposed method cannot only enhance the efficiency of image stitching processing, but also reduce the panoramic distortion errors and obtain a better quality panoramic result.
Robust and Secure Data Hiding for PDF Text Document

The spaces between words and paragraphs are popular places for embedding data in data hiding techniques for text documents. Due to the low redundancy in text documents, the payload is limited to be small. As each bit of data is independently inserted into specific spaces in conventional methods, a malicious party may be able to modify the data without causing serious visible distortions. In this paper, we regard a collection of space lengths as a one-dimensional feature vector and embed watermark into its frequency components. To keep the secrecy of the embedded information, a random permutation and dither modulation are introduced in the operation. Furthermore, robustness against additive noise is enhanced by controlling the payload. In the proposed method, through experiments, we evaluated the trade-off among payload, distortion, and robustness.
Decentralized tracing protocol for fingerprinting system

In conventional studies, cryptographic techniques are used to ensure the security of transaction between a seller and buyer in a fingerprinting system. However, the tracing protocol from a pirated copy has not been studied from the security point of view though the collusion resistance is considered by employing a collusion secure fingerprinting code. In this paper, we consider the secrecy of parameters for a fingerprinting code and burdens at a trusted center, and propose a secure tracing protocol jointly executed by a seller and a delegated server. Our main idea is to delegate authority to a server so that the center is required to operate only at the initialization phase in the system. When a pirated copy is found, a seller calculates a correlation score for each user's codeword in an encrypted domain, and identifies illegal users by sending the ciphertexts of scores as queries to the server. The information leakage from the server can be managed at the restriction of response from the server to check the maliciousness of the queries.
Improved Chebyshev Polynomials-Based Authentication Scheme in Client-Server Environment

With nonstop development of communication technologies, all aspects of social life continuously change and so do network systems. When establishing connection is easy, the convenience of online-service receives many users’ attentions, for example, the patients directly access medical system to be advised by doctors at any time. Therefore, user authentication scheme is necessary when we want to provide privacy and security for working sessions. Storing a password list for verification is an old method and not secure. This list can be easily leaked, and adversary can launch an offline password-guessing attack. In addition, information exchanged between user and server needs being prevented from attacker’s decryption. It can be said that current authentication schemes are unsuitable for new security standard. We need a strong user authentication scheme using new approach to overcome existing limitations and guarantee time efficiency. In this paper, we make a design with Chebyshev polynomial to achieve our goals and resist some kinds of attacks.
Domain-Specific Chinese Word Segmentation Based on Bi-Directional Long-Short Term Memory Model

Most of the current word segmentation methods are rule-based and traditional machine learning methods. Universal word segmentation tools do not work well in the field such as metallurgy. Domain-specific Chinese word segmentation is rarely studied. In recent years, with the development of deep learning, the neural network has been proved to be effective in Chinese word segmentation. However, this promising performance relies on large-scale training data. Neural networks with conventional architectures cannot achieve the desired results in low-resource datasets due to the lack of labeled training data. This paper takes the field of metallurgy as an example and proposes a domain-specific Chinese word segmentation based on Bi-directional long-short term memory (Bi-directional LSTM) model in the metallurgical field. First, the word segmentation model is obtained by using the Bi-directional LSTM model to train the internal and external domain knowledge. Then, a series of tuning parameters are carried out and the label probability of the word is combined with the weight. Finally, the result of word segmentation is obtained by label inference layer. The experimental results show that the proposed method can create a better word segmentation effect in the field of metallurgy.
Trust: from the Philosophical to the Commercial

Abstract This is a paper about trust, with a specific focus on the ways in which trust is investigated in the business literature and the commercial sector. The lens through which the topic is approached is distinctively philosophical. We use philosophical tools to demonstrate the paucity of some of the accounts of trust that are given in the business and management literature, as well as the empirically informed literature that has flowed from them. We close with a discussion of some work on trust drawn from the commercial sector that would, as we shall demonstrate, benefit from a clearer understanding of the nature of trust. We take this to be important. Trust is a key moral and ethical component of transactional relationships. Without a clear understanding of the notion, we will be missing a central concept in our attempts to understand the commercial world that we inhabit. The paper proceeds in four parts. In the first part of the paper we introduce some reasonably standard philosophical distinctions between different kinds of trust, as well as saying a little more about our methods. In the second, we demonstrate that a reasonably widely held account of trust in the business and management literature fails to capture the nuance reflected by the philosophical literature. On the basis of this, in the third section, we suggest that various pieces of empirical work require reassessment. In the final part of the paper we explore some non-academic discussions of trust drawn from the commercial sector arguing that, there too, we require a more precise understanding of trust. In short, though, our overarching argument is simply this: if we can give a more precise analysis of trust, it follows that both our empirical research and current commercial activity can be improved.
Optimal common resource in majorization-based resource theories

We address the problem of finding the optimal common resource for an arbitrary family of target states in quantum resource theories based on majorization, that is, theories whose conversion law between resources is determined by a majorization relationship, such as it happens with entanglement, coherence or purity. We provide a conclusive answer to this problem by appealing to the completeness property of the majorization lattice. We give a proof of this property that relies heavily on the more geometric construction provided by the Lorenz curves, which allows to explicitly obtain the corresponding infimum and supremum. Our framework includes the case of possibly non-denumerable sets of target states (i.e. targets sets described by continuous parameters). In addition, we show that a notion of approximate majorization, which has recently found application in quantum thermodynamics, is in close relation with the completeness of this lattice. Finally, we provide some examples of optimal common resources within the resource theory of quantum coherence.
Self-feedback image retrieval algorithm based on annular color moments

Content-based image retrieval (CBIR) extracts visual content features (such as color, texture, and shape) of a sample image to retrieve another similar image. Due to the existence of the semantic gap, retrieval results are often unsatisfactory. A CBIR method based on relevance feedback (RF) can reduce the semantic gap and achieve a high-retrieval accuracy by establishing a correlation between low-level image features and high-level semantics via human-computer interaction. However, the complicated human-computer interface of RF increases the burden on users; hence, some scholars have proposed the pseudo-relevance feedback (PRF) technology. To further contribute to the research, this paper proposes a self-feedback image retrieval algorithm based on annular color moments. In this approach, hashing sequences of color moments based on annular segmentation are extracted to be used as feature vectors for initial retrieval. Based on this result, improved subtractive clustering and correlation feedback techniques are used for extended queries. Thus, a self-feedback method without user participation is realized. The experimental results show that the accuracy of image retrieval can be improved, and the proposed algorithm is robust to image rotation, scaling, and translation.
The Role of Self-Attitude in the Personal and Professional Development of High School Students

Introduction. The instability of the modern world with its rapidly changing social and economic realities requires the readiness of a person for self-change. For this reason, along with vocational education, higher education has to include the programmes to form future experts’ skills of professional self-development, which success depends on many circumstances, including self-attitude. The aim of the research presented in the publication was to define the role of self-attitude in personal and professional self-development of students in the course of their professional training in higher education institution. Methodology and research methods. Empirical methods were employed in the course of the research. The complex of psycho-diagnostic techniques was used: test-questionnaire of self-attitude by V. V. Stolin and S. R. Pantileev; tests: Life-Purpose Orientations Test by D. A. Leontiev, The General Self-Efficacy Scale by M. Jerusalem, R. Schwarzer and V. Romek, Assessment of Self-Control in Communication by M. Snyder; questionnaires: Personal Orientation Inventory by E. L. Shostrom, Ability to Self-Development by V. I. Zvereva, Willingness to SelfKnowledge and Self-Development by T. A. Ratanova and N. F. Shlyahta, Determination of level of reflexivity development by A. V. Karpov, Style of Self-Regulation Behaviour of Students by V. I. Morosanova. The obtained respondents’ responses (n = 110) were processed using methods of mathematical statistics, such as correlation and cluster analysis. Results and scientific novelty. Different theoretical approaches to the problem of self-attitude highlighted in the scientific literature are considered. The affective and cognitive components of self-attitude are emphasised. Significant correlation relationships between the characteristics of self-attitude and level of motivational readiness for self-knowledge and self-development of 2 nd -4 th -year students-psychologists of N. P. Ogarev Mordovia State University have been revealed. It appears to be rather attributed by the fact that the attitude of students towards themselves in many respects is defined by the level of formation of their reflexive and regulatory qualities, understanding of the purpose and meaning of life, confidence in the professional success, which provide an incentive for self-change and self-improvement. The conducted cluster analysis allowed the authors to group the students around different levels of professional self-attitude and self-development (very high, high average and stagnant). The results of the research show that positive self-attitude is one of the important factors in self-development. The process of awareness of self-worth encourages the person to periodically reflect on career prospects and implementation of plans, providing the grounds for creative self-realisation. Practical significance. The results obtained can be used by teachers and psychologists of higher educational institutions to organise psycho-pedagogical support for personal and professional self-development of students.