Reviewerly/exHarmony huggingface dataset:
Behavior model construction for client side of modern web applications

Most of the behavior models with respect to Web applications focus on sequencing of events, without regard for the changes of parameters or elements and the relationship between trigger conditions of events and Web pages. As a result, these models are not sufficient to effectively represent the dynamic behavior of the Web 2.0 application. Therefore, in this paper, to appropriately describe the dynamic behavior of the client side of Web applications, we define a novel Client-side Behavior Model (CBM) for Web applications and present a user behavior trace-based modeling method to automatically generate and optimize CBMs. To verify the effectiveness of our method, we conduct a series of experiments on six Web applications according to three types of user behavior traces. The experimental results show that our modeling method can construct CBMs automatically and effectively, and the CBMs built are more precise to represent the dynamic behavior of Web applications.
Edinburgh’s Submissions to the 2020 Machine Translation Efficiency Task

We participated in all tracks of the Workshop on Neural Generation and Translation 2020 Efficiency Shared Task: single-core CPU, multicore CPU, and GPU.At the model level, we use teacher-student training with a variety of student sizes, tie embeddings and sometimes layers, use the Simpler Simple Recurrent Unit, and introduce head pruning.On GPUs, we used 16-bit floating-point tensor cores.On CPUs, we customized 8-bit quantization and multiple processes with affinity for the multicore setting.To reduce model size, we experimented with 4-bit log quantization but use floats at runtime.In the shared task, most of our submissions were Pareto optimal with respect the trade-off between time and quality.
An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

In order to solve the problem of low contrast and fuzzy detail in infrared image, we propose an infrared image enhancement method based on multi-scale and adaptive bi-interval histogram equalization with details. The method mainly consists of four parts: details enhancement, contrast stretch, edge enhancement and reconstruction of enhancement images. Firstly, the multi-scale convolution is used to enhance the details of image; Secondly, taking maximize the variance between classes and minimize the variance as fitness function and solved the threshold of the infrared image by genetic algorithm, then dividing the infrared image into two sub-intervals according to the threshold. After that, the bi-interval histogram equalization with details is applied to enhance the global contrast, at the same time, using the mean square deviation and average gray equalization to improve the brightness of the image. Finally, the enhanced image by adaptive bi-interval histogram equalization with details and the image processed by adaptive limited Laplace operator are fused by linear weighting to reconstruct the final enhancement image. The experimental results show that the proposed method can outperform state-of-the-art ones in both qualitative and quantitative comparisons.
A methodology for automatic parameter-tuning and center selection in density-peak clustering methods

The density-peak clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach. The method has the advantage of allowing non-convex clusters, and clusters of variable size and density, to be grouped together, but it also has some limitations, such as the visual location of centers and the parameter tuning. This paper describes an optimization-based methodology for automatic parameter/center selection applicable both to the DPC and to other algorithms derived from it. The objective function is an internal/external cluster validity index, and the decisions are the parameterization of the algorithm and the choice of centers. The internal validation measures lead to an automatic parameter-tuning process, and the external validation measures lead to the so-called optimal rules, which are a tool to bound the performance of a given algorithm from above on the set of parameterizations. A numerical experiment with real data was performed for the DPC and for the fuzzy weighted k-nearest neighbor (FKNN-DPC) which validates the automatic parameter-tuning methodology and demonstrates its efficiency compared to the state of the art.
Smart Contract Interactions in Coq

We present a model/executable specification of smart contract execution in Coq. Our formalization allows for inter-contract communication and generalizes existing work by allowing modelling of both depth-first execution blockchains (like Ethereum) and breadth-first execution blockchains (like Tezos). We represent smart contracts programs in Coq’s functional language Gallina, enabling easier reasoning about functional correctness of concrete contracts than other approaches. In particular we develop a Congress contract in this style. This contract – a simplified version of the infamous DAO – is interesting because of its very dynamic communication pattern with other contracts. We give a high-level partial specification of the Congress’s behavior, related to reentrancy, and prove that the Congress satisfies it for all possible smart contract execution orders.
Deduplication-Friendly Watermarking for Multimedia Data in Public Clouds

To store large volumes of cloud data, cloud storage providers (CSPs) use deduplication, by which if data from multiple owners are identical, only one unique copy will be stored. Deduplication can achieve significant storage saving, benefiting both CSPs and data owners. However, for ownership protection, data owners may choose to transform their outsourced multimedia data to “protected formats” (e.g., by watermarking) which disturbs deduplication since identical data may be transformed differently by different data owners. In this work, we initiate research of resolving the fundamental conflict between deduplication and watermarking. We propose DEW, the first secure Deduplication-friEndly Watermarking scheme which neither requires any interaction among data owners beforehand nor requires any trusted third party. Our key idea is to introduce novel protocols which can ensure that identical data possessed by different data owners are watermarked to the same “protected format”. Security analysis and experimental evaluation justify security and practicality of DEW.
Solar Power Plant Site Selection: A Systematic Literature Review on MCDM Techniques Used

Site Selection is a crucial step in installing Solar Power Plant (SPP) as it is determined by a set of quantitative and qualitative factors, which are vague in nature. In this review, various suggestions for site location of Photovoltaic Power System (PVPS) are studied. The solar power plants are mainly installed in remote regions where solar radiation is high. But these regions are far from the generation site and will face problems in transmission and distribution. Thus, various criteria/factors have to be considered in the site selection. Factors like solar radiation, location, climate, orography, environment, public acceptance have to be taken into account for optimal site selection. Every form of electricity generation has its strengths and weakness. The main objective in the site selection process is finding the optimum site satisfying the desired conditions given by the selection criteria. This review suggests how to define and classify particular criteria considered for Solar Photo Voltaic (PV) farm siting. The best criteria among all the other evaluations are obtained from the multi-criteria evaluation which gives scope for various applications. The aim of this paper is to define how the ideal locations for solar PV are selected using various Multi-Criteria Decision Making (MCDM) techniques. A large scale PV-project should generate at least 5 MW power. In site suitability, India ranks third next to China and Spain.
Personalization as an approach to education

The article is devoted to the notion and role of personalization in teaching English as a tool of day-to-day and professional communication, considering modern methodology which heavily relies on Internet technologies. The authors analyze the development of the ideas, which have served as the basis for personalized education, and come up with a detailed inventory of popular models of personalized education both in Russia and abroad to check their adherence to basic principles of personalization. The methods used in the article comprise synchronic and diachronic forms of comparative analysis. The authors’ personal experience of teaching English in a personalized way at Skyeng School via its online platform has become an important extra source of empirical material for the article. The results of the undertaken study can be of practical value for administrators of educational organizations, specialists in teaching methodology, school teachers who would like to implement various methods of personalized education or devise a personalized technique on their own
Deep Learning for Spectrum Anomaly Detection in Cognitive mmWave Radios

Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection.
Fast Hybrid Karatsuba Multiplier for Type II Pentanomials

We continue the study of Mastrovito form of Karatsuba (MK) multipliers under the shifted polynomial basis (SPB). An MK multiplier utilizes Karatsuba algorithm and Mastrovito approach to optimize polynomial multiplication and modular reduction, which lead to a better space and time tradeoff for all trinomials. Based on this work, we make two types of contributions: 1) We derive a new modular reduction formulation for constructing Mastrovito matrix associated with special types of pentanomials, i.e., Types I, II, and Type C.1 pentanomial. Through related formulations, we demonstrate that Type I pentanomial is less efficient than Type II because of a more complicated modular reduction under the same SPB; conversely, Type C.1 pentanomial is as good as Type II under generalized polynomial basis (GPB). 2) We introduce a new MK multiplier for Type II pentanomial. It is shown that our proposal is only one $T_{X}$ slower than the fastest quadratic multipliers for Type II pentanomial, but its space complexity is roughly 3/4 of those schemes. To the best of our knowledge, it is the first time for hybrid multiplier to achieve such a time delay bound.
Constacyclic and Linear Complementary Dual Codes Over Fq + uFq

This article discusses linear complementary dual (LCD) codes over ℜ = Fq+uFq(u2=1) where q is a power of an odd prime p. Authors come up with a new Gray map from ℜn to F2nq and define a new class of codes obtained as the gray image of constacyclic codes over .ℜ Further, we extend the study over Euclidean and Hermitian LCD codes and establish a relation between reversible cyclic codes and Euclidean LCD cyclic codes over ℜ. Finally, an application of LCD codes in multisecret sharing scheme is given.
Application of the technical - pedagogical resource 3D holographic LED-fan display in the classroom

Abstract The currently secondary education needs the contribution of new techno-pedagogical resources, where the resource, teaching and learning skills are integrated to motivate students in their learning. For this reason, the field of holographics is presented as a dynamic alternative to the growing use of image projectors. However, these resources are not available to teachers and educational institutions because of their high costs. This research seeks to show how led-based technologies can be applied in secondary education, so allow the classroom attention of students and improve their interest in learning through the use of the 3D Holographic LED-Fan Display. In this way, the objective is to improve the student’s motivation for learning using current resources according to the realities of the current context of education, which may not have availability of mobile devices due to their high costs, thus they have not been considered for the present study. The research approach is a mixed methodology combining qualitative and quantitative information through an educational study case applied to the Kléber Franco Cruz School with the intervention of pre-professional practices carried out by the Technical University of Machala in Ecuador, through the realization of an integrative knowledge project with students with different profiles according to their curriculum, in order to face the different challenges of teaching.
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?

In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing. But quite a few works focus on joint tagging and parsing models to avoid error propagation. In contrast, recent studies suggest that POS tagging becomes much less important or even useless for neural parsing, especially when using character-based word representations. Yet there are not enough investigations focusing on this issue, both empirically and linguistically. To answer this, we design and compare three typical multi-task learning framework, i.e., Share-Loose, Share-Tight, and Stack, for joint tagging and parsing based on the state-of-the-art biaffine parser. Considering that it is much cheaper to annotate POS tags than parse trees, we also investigate the utilization of large-scale heterogeneous POS tag data. We conduct experiments on both English and Chinese datasets, and the results clearly show that POS tagging (both homogeneous and heterogeneous) can still significantly improve parsing performance when using the Stack joint framework. We conduct detailed analysis and gain more insights from the linguistic aspect.
Communication Cost of Quantum Processes

A common scenario in distributed computing involves a client who asks a server to perform a computation on a remote computer. An important problem is to determine the minimum amount of communication needed to specify the desired computation. Here we extend this problem to the quantum domain, analyzing the total amount of (classical and quantum) communication needed by a server in order to accurately execute a quantum process chosen by a client from a parametric family of quantum processes. We derive a general lower bound on the communication cost, establishing a relation with the precision limits of quantum metrology: if a v-dimensional family of processes can be estimated with mean squared error n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-β</sup> by using n parallel queries, then the communication cost for n parallel executions of a process in the family is at least (β v/2 - ε) logn qubits at the leading order in n, for every ε > 0. For a class of quantum processes satisfying the standard quantum limit (β = 1), we show that the bound can be attained by transmitting an approximate classical description of the desired process. For quantum processes satisfying the Heisenberg limit (β = 2), our bound shows that the communication cost is at least twice as the cost of communicating standard quantum limited processes with the same number of parameters.
Security Assessment of Blockchain in Chinese Classified Protection of Cybersecurity

Classified protection is one of primary security policies of information system in many countries.With the increasing popularity of blockchain in various fields of applications, it is extremely necessary to promote classified protection for blockchain's risk assessment in order to push forward the sustainable development of blockchain.Taking the Level 3 in Chinese classified protection 2.0 as an example, this paper proposes the common evaluation rules on blockchain to ensure that blockchain can meet the needs of countries to build it as critical infrastructure.Both assessment requirements and enforcement proposals are presented and analyzed from the standpoint of blockchain's core technologies, e.g., peer-to-peer network, distributed ledger, contract's scripting system, and consensus mechanism.Moreover, the assessment results on three main platforms, Bitcoin, Ethereum, and Hyperledger, are summarized and analyzed in compliance with the control points specified in the level 3. Our investigation indicates that the current blockchain is able to satisfy the requirements of evaluation items in many aspects, such as software fault tolerance, resource control, backup and recovery, but further improvements are still needed for some aspects, including security audit, access control, identification and authentication, data integrity, etc., in order to satisfy the requirements of important fields on national security, economic development and human life.
Quantum linear network coding for entanglement distribution in restricted architectures

In this paper we propose a technique for distributing entanglement in architectures in which interactions between pairs of qubits are constrained to a fixed network<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math>. This allows for two-qubit operations to be performed between qubits which are remote from each other in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math>, through gate teleportation. We demonstrate how adapting<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">quantum linear network coding</mml:mtext></mml:mrow></mml:math>to this problem of entanglement distribution in a network of qubits can be used to solve the problem of distributing Bell states and GHZ states in parallel, when bottlenecks in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math>would otherwise force such entangled states to be distributed sequentially. In particular, we show that by reduction to classical network coding protocols for the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-pairs problem or multiple multicast problem in a fixed network<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math>, one can distribute entanglement between the transmitters and receivers with a Clifford circuit whose quantum depth is some (typically small and easily computed) constant, which does not depend on the size of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math>, however remote the transmitters and receivers are, or the number of transmitters and receivers. These results also generalise straightforwardly to qudits of any prime dimension. We demonstrate our results using a specialised formalism, distinct from and more efficient than the stabiliser formalism, which is likely to be helpful to reason about and prototype such quantum linear network coding circuits.
Broadcast-Optimal Two Round MPC with an Honest Majority

This paper closes the question of the possibility of two-round MPC protocols achieving different security guarantees with and without the availability of broadcast in any given round. Cohen et al. [CGZ20] study this question in the dishonest majority setting; we complete the picture by studying the honest majority setting. In the honest majority setting, given broadcast in both rounds, it is known that the strongest guarantee—guaranteed output delivery—is achievable [GLS15]. We show that, given broadcast in the first round only, guaranteed output delivery is still achievable. Given broadcast in the second round only, we give a new construction that achieves identifiable abort, and we show that fairness—and thus guaranteed output delivery—are not achievable in this setting. Finally, if only peer-to-peer channels are available, we show that the weakest guarantee—selective abort—is the only one achievable for corruption thresholds $$t> 1$$ and for $$t=1$$ and $$n=3$$ . On the other hand, it is already known that selective abort can be achieved in these cases. In the remaining cases, i.e., $$t= 1$$ and $$n\ge 4$$ , it is known [IKP10, IKKP15] that guaranteed output delivery (and thus all weaker guarantees) are possible.
Local Foreground Removal Disocclusion Filling Method for View Synthesis

View synthesis is an effective method to generate the contents of multiple views based on a limited number of reference views, which can be used in 2D to 3D conversion, free viewpoint video and multiview video rendering.Depth-image-based rendering (DIBR) is a practical technique to generate virtual view by using a 2D reference view and its depth image.However, a critical problem in DIBR process is that disocclusions might be produced in the synthesized image because the background occluded by the foreground objects in the reference view may be exposed in the virtual view.In this paper, a local foreground removal method is proposed for disocclusion filling.Morphology-based depth image preprocessing is performed before DIBR, aiming to correct the depth value of the ghosts and remove ghost artifacts.In the synthesized virtual image, pixels on the disocclusion edge are identified and classified.Then they are positioned in the reference image by inverse 3D warping.Local foreground regions that occlude the corresponding background are removed from both the reference image and its depth image based on the disocclusion edge pixels.Removed region is filled with surrounding background contents, and depth information is used in this process to prevent foreground penetration.The predicted background contents are warped to the disocclusion region, thereby achieving the hole filling.Experimental results show that the proposed method performs better than the other methods in disocclusion filling, and improves the subjective and objective quality of the synthesized view.In the evaluation results of PSNR, SSIM, FSIMc and VSI, our method improves by 0.32-2.43dB,0.0036-0.0155,0.0041-0.0198and 0.0012-0.0057respectively compared with competitive methods.