Reviewerly/exHarmony huggingface dataset:
An identification method of mechanical properties of materials based on the full‐field measurement method based on the fringe pattern

Abstract With the development of image processing technology, optical methods based on fringe patterns, for example, the grid method, electronic speckle pattern interferometry, moiré techniques (including moiré interferometry and digital moiré), and coherent gradient sensing, have become useful techniques for measuring the full‐field deformation of materials and structures. An important application of these techniques is to offer deformation fields for extracting constitutive parameters in the inverse methods. In this paper, we proposed a novel inversion method based on fringe patterns (IMFP), which can be used to identify constitutive model parameters by comparing simulated fringe patterns obtained using the finite element method with experimentally measured fringe patterns. The feasibility and identification accuracy of IMFP were evaluated through numerical experiments, and an additional series of numerical tests were conducted to analyse the noise immunity of IMFP and its sensitivity to the number of constitutive model parameters. Finally, IMFP was applied in the identification of the mechanical parameters of selective laser melting three‐dimensional printed stainless steel.
HyFabric

In this paper, we propose HyFabric, jointing the flow and circuit scheduling to minimize flow completion times (FCT) in optical and electrical hybrid data center networks. Our extensive evaluation results show that HyFabric reduces 93% FCT and 32% energy consumption compared with Solstice[4] and ProjecToR[3]. And it delivers comparable FCT and reduces 5% ~ 14% energy consumptions compared with pFabric[1].
The Effects of Continuous Conversation and Task Complexity on Usability of an AI-Based Conversational Agent in Smart Home Environments

Conversational agents have gained increasing popularity over the last decade in a variety of personal, public, and occupational settings due to rapid advances of artificial intelligence (AI) and natural language processing (NLP). However, how users can interact with such technologies is still understudied. The objective of this study was to investigate type of conversation (presence and absence of continuous conversation) and task complexity (high vs. low) on usability metrics (i.e., task completion time, number of queries used in completing tasks, and perceived system usability) with conversational agents in smart home environments. Eighteen participants joined this study and completed required tasks. The results showed that there was a significant effect of type of conversation on task completion time and number of queries per task. Task complexity significantly extended task completion time and increased number of queries per task. The results may help with the design of more usable conversational agents.
Sequence-to-sequence modeling for graph representation learning

We propose sequence-to-sequence architectures for graph representation learning in both supervised and unsupervised regimes. Our methods use recurrent neural networks to encode and decode information from graph-structured data. Recurrent neural networks require sequences, so we choose several methods of traversing graphs using different types of substructures with various levels of granularity to generate sequences of nodes for encoding. Our unsupervised approaches leverage long short-term memory (LSTM) encoder-decoder models to embed the graph sequences into a continuous vector space. We then represent a graph by aggregating its graph sequence representations. Our supervised architecture uses an attention mechanism to collect information from the neighborhood of a sequence. The attention module enriches our model in order to focus on the subgraphs that are crucial for the purpose of a graph classification task. We demonstrate the effectiveness of our approaches by showing improvements over the existing state-of-the-art approaches on several graph classification tasks.
Identifying Implicit Polarity of Events by Using an Attention-Based Neural Network Model

Sentiment analysis has attracted extensive attention in recent years. Existing work mainly focuses on sentiment classification task in which a text or sentence usually contains sentiment word to express subjective feeling. However, little research is proposed for identifying implicit polarity of a text. Here, implicit polarity of a text means that the text does not contain sentiment words but still express a positive or negative sentiment. To address this issue, we propose an attention-based neural network model to identify implicit polarity of events. In particular, the model first learns the sentence representation by recurrent neural network with gated recurrent unit. Then multiple hops attention mechanism is used for capturing multiple aspects closely related to sentiment polarity and the event type. Experimental results on SemEval 2015 dataset show the effectiveness of the proposed model, outperforming the previous systems and strong neural baselines.
Who Activated My Voice Assistant? A Stealthy Attack on Android Phones Without Users’ Awareness

Voice Assistant (VAs) are increasingly popular for human-computer interaction (HCI) smartphones. To help users automatically conduct various tasks, these tools usually come with high privileges and are able to access sensitive system resources. A comprised VA is a stepping stone for attackers to hack into users’ phones. Prior work has experimentally demonstrated that VAs can be a promising attack point for HCI tools. However, the state-of-the-art approaches require ad-hoc mechanisms to activate VAs that are non-trivial to trigger in practice and are usually limited to specific mobile platforms. To mitigate the limitations faced by the state-of-the-art, we propose a novel attack approach, namely Vaspy, which crafts the users’ “activation voice” by silently listening to users’ phone calls. Once the activation voice is formed, Vaspy can select a suitable occasion to launch an attack. Vaspy embodies a machine learning model that learns suitable attacking times to prevent the attack from being noticed by the user. We implement a proof-of-concept spyware and test it on a range of popular Android phones. The experimental results demonstrate that this approach can silently craft the activation voice of the users and launch attacks. In the wrong hands, a technique like Vaspy can enable automated attacks to HCI tools. By raising awareness, we urge the community and manufacturers to revisit the risks of VAs and subsequently revise the activation logic to be resilient to the style of attacks proposed in this work.
Offline Signature Verification Using Textural Descriptors

Offline signature verification has been the most commonly employed modality for authentication of an individual and, it enjoys global acceptance in legal, banking and official documents. Verifying the authenticity of a signature (genuine or forged) remains a challenging problem from the perspective of computerized solutions. This paper presents a signature verification technique that exploits the textural information of a signature image to discriminate between genuine and forged signatures. Signature images are characterized using two textural descriptors, the local ternary patterns (LTP) and the oriented basic image features (oBIFs). Signature images are projected in the feature space and the distances between pairs of genuine and forged signatures are used to train SVM classifiers (a separate SVM for each of the two descriptors). When presented with a questioned signature, the decision on its authenticity is made by combining the decisions of the two classifiers. The technique is evaluated on Dutch and Chinese signature images of the ICDAR 2011 benchmark dataset and high accuracies are reported.
New construction of binary and nonbinary quantum stabilizer codes based on symmetric matrices

In this paper, we propose two construction methods for binary and nonbinary quantum stabilizer codes based on symmetric matrices. In the first construction, we use the identity and symmetric matrices to generate parity-check matrices that satisfy the symplectic inner product (SIP) for the construction of quantum stabilizer codes. In the second construction, we modify the first construction to generate parity-check matrices based on the Calderbank–Shor–Stean structure for the construction of quantum stabilizer codes. The binary and nonbinary quantum stabilizer codes whose parameters achieve equality of the quantum singleton bound are investigated with the code lengths ranging from 4 to 12.
The Theory behind Controllable Expressive Speech Synthesis: A Cross-Disciplinary Approach

As part of the Human-Computer Interaction field, Expressive speech synthesis is a very rich domain as it requires knowledge in areas such as machine learning, signal processing, sociology, and psychology. In this chapter, we will focus mostly on the technical side. From the recording of expressive speech to its modeling, the reader will have an overview of the main paradigms used in this field, through some of the most prominent systems and methods. We explain how speech can be represented and encoded with audio features. We present a history of the main methods of Text-to-Speech synthesis: concatenative, parametric and statistical parametric speech synthesis. Finally, we focus on the last one, with the last techniques modeling Text-to-Speech synthesis as a sequence-to-sequence problem. This enables the use of Deep Learning blocks such as Convolutional and Recurrent Neural Networks as well as Attention Mechanism. The last part of the chapter intends to assemble the different aspects of the theory and summarize the concepts.
Russian education faults

Despite the efforts made, various and progressive innovations in the education system remain a number of national problems, referred to as the “faults” of Russian education, which do not allow to achieve the strategic goals. The purpose of the study: to analyze the evolution of the reform of the domestic education system for a long historical period, analyzes the content of legal acts relating to Russian education, in particular the Law of the Russian Federation “On education in the Russian Federation”. The formation of education goals and the problems currently facing the Ministry of science and higher education of the Russian Federation, the Ministry of education of the Russian Federation and the independent Federal authority – Rosobrnadzor are analyzed. Two concepts of basic models of education, European and Anglo-American, are considered. It is noted that the Russian education in the present period of time, as well as other public institutions, in many respects are at the crossroads between the two models of management systems. Materials and method: comparative analysis of approaches to education management, as well as methods of scenario modeling and structural and functional analysis were chosen as research methods. The results of the comparative analysis allow us to state that the Russian education now, are at the crossroads of the choice of management system, the choice of which is hidden in the “rivalry” of the two concepts of the main models of education: European or Anglo-American. The European model assumes orientation of education on formation of the personality, the statement about need of orientation of formation of the personality in the mode of pragmatism is the basis of the Anglo-American model. The analysis allows us to conclude that the choice of management system largely depends on the socio-economic environment and the processes taking place in the country.
DESIGN AND APPLICATION OF BOUNDING VOLUME HIERARCHY COLLISION DETECTION ALGORITHM BASED ON VIRTUAL SPHERE

The result of collision detection is closely related to the further deformation or cutting action of soft tissue. In order to further improve the efficiency and stability of collision detection, in this paper, a collision detection algorithm of bounding volume hierarchy based on virtual sphere was proposed. The proposed algorithm was validated and the results show that the detection efficiency of the bounding volume hierarchy algorithm based on virtual sphere is higher than that of the serial hybrid bounding volume hierarchy algorithm and the parallel hybrid bounding volume hierarchy algorithm. Different collision detection algorithms were tested and the results show that the collision detection algorithm based on virtual sphere has high detection efficiency and good stability. As the number of triangular patches increased, the advantage was more and more obvious. Finally, the proposed algorithm was applied to two large and medium-sized virtual scenes to implement the collision detection between the vastus lateralis muscle, thigh and surgical instrument. Based on the virtual sphere, the collision detection algorithm of bounding volume hierarchy can implement efficient and stable collision detection in a virtual surgery system. Meanwhile, the algorithm can be combined with other acceleration algorithms (such as the multithread acceleration algorithm) to further improve detection efficiency.
Two Feistel rounds in image cryptography acting at the nucleotide level exploiting dna and rna property

In recent years, a variety of chaos-based image encryption algorithm have been proposed. The majority of image encryption systems, employ the confusion-diffusion architecture and operate at the pixel level. In this paper, a new color image encryption algorithm at the nucleotide-level applied, will be proposed. The first block will be altered by confusion with a boot vector generated from the clear image. After that, two Feistel rounds will be imposed on each 12 nucleotides clear block. The first round is performed by chaotic displacement of the nucleotides, and, the second uses a confusion matrix created in preconceived designs from chaotic maps used. Ultimately, a diffusion will be implemented to maximize the avalanche impact and keep the system out of differential attacks. On output, a transition to the nucleotides complementary will be established, and a confusion with the chaos vector will be developed. Next, the RNA application can be used to synthesize amino acids, and as a result, the isolation of the exon genes from introns. Simulations made on a large database with images in a variety of sizes and formats, show that our strategy can be prevented from being attacked by any known attack.
Entanglement properties of a tunable non-Gaussian quantum state by virtue of multi-photon conditional measurement

In this work, we present a theoretical scheme for generating a new kind of tunable non-Gaussian entangled state, by means of beam splitters and multi-photon conditional measurement with two-mode squeezed vacuum state (TMSVS) inputs. The output state is proven to be two-variable Hermite polynomial excited squeezed vacuum states, whose success probability of detection relates to the Jacobi polynomials. We then mainly study the entanglement properties of the output state for the symmetric case quantified by the von Neumann entropy and Einstein–Podolsky–Rosen correlation. It is shown that the entropy of entanglement is valid for verifying the improvement of entanglement within a certain range of squeezing parameter and transmissivity, and the resulting state case may possess a stronger correlation than the initial TMSVS by increasing the transmissivity or photon number of detection. In addition, the violation of the Clauser–Horne–Shimony–Holt (CHSH) inequality for the output state is also evaluated in terms of Wigner representation in phase space. It is found that the negative region of Wigner function distribution becomes more visible with the increase of the photon number of detection, and the degree of the violation of the CHSH inequality significantly increases as the photon number of detection for the symmetrical case increases. The resulting state with highly nonclassical characteristics will provide a useful application in the fields of quantum information processing.
Ten Years of "Impact" Columns&amp;mdash;The Good, the Bad, and the Ugly

Ten years ago, we started the "Impact" column in IEEE Software. The goal when we started was to "build better quantitative insight into how software impacts various businesses. How the product uses the software and how the company built it are equally important."<sup>1</sup> This article is the 45th instance of the column. We have had 69 authors, mostly industry experts writing about the impact of software on their business and, in some cases, on its end users. Many of the authors had neither taken the time nor been given the opportunity to publish about their work before, so it gave them a forum to write about real systems: their size, their development methods, their economic and end-user impacts, and, perhaps most compelling of all, their ubiquity. The columns have been cited more than 400 times and have given our readers insights into some beautiful software solutions, in terms of both technology (e.g., the Mars rover<sup>2</sup>) and in terms of its impact on people (e.g., supporting farmers in Africa<sup>3</sup>). Our colleague, Zeljko Obrenovic, created a complete overview of the "Impact" column articles, which is available at https://www.obren359.com/collection.html?id=ieeesw/impact.
Data Storage in Cloud Using Key-Policy Attribute-Based Temporary Keyword Search Scheme (KP-ABTKS)

Now a days, it is open for information holder to re-appropriate their information to the cloud. Brief catchphrase seek on secret data in an exceedingly cloud setting is the principle focal point of this investigation. Here, the data in cloud storage isn`t trustworthy. So, it is compulsory to source information with encryption and used quality-based keyword search plans.In ABKS, The inquiry tokens might be utilized to extricate the ciphertexts that are created whenever and include the comparing catchphrase. The catchphrase mystery property and is secure against specifically picked keyword assault (SPKA) below the balance of Decisional Bilinear Diffie-Hellman (DBDH) presumption. This may lead information leakage and complexity, so we have to give more preservation. In this paper, we propose the KP-ABKTS plans, where the information proprietor creates an accessible cipher text identified with a catchphrase and the season of encoding as indicated by an expected access control strategy, and re-appropriates it to the cloud. This schemes enables in multiusers scenario for avoiding complexity when compared with PEKS.
Randomly Weighted CKF for Multisensor Integrated Systems

The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.
Towards a standards-compliant pure-software trusted execution environment for resource-constrained embedded devices

Trusted execution environments (TEEs) are becoming a requirement to protect a wide range of platforms, from cloud servers to embedded sensors. In this paper, we take a first step towards providing a pure-software Trusted Execution Environment (TEE) for resource-constrained embedded devices that lack basic hardware-based security features, such as Memory Protection Units (MPUs). Our key instrument in providing the features of TEEs is a formally-verified software-based memory isolation technique called the Security MicroVisor (SμV), that serves as a programmable trusted layer, which provides isolation by policing and regulating the execution of assembly-level instructions. This paper contributes the first architecture of a pure software TEE that supports all of the features that have been standardized by the GlobalPlatform Organization. Our implementation and evaluation results demonstrate the feasibility of implementing a standards-compliant software-based TEE for low-end embedded devices without hardware support or modification that has proven security against all networked attacks.
Solar irradiance forecasting using fuzzy logic and multilinear regression approach: A case study of Punjab, India

&lt;p class="MDPI17abstract"&gt;The accurate forecasting of solar irradiance depends on various uncertain parameters like time of day, temperature, wind speed, humidity, and atmospheric pressure. All these play an important role in calculating PV power output. In this paper, a novel approach for forecasting of solar irradiance using flexible and accurate fuzzy logic and robust multi-linear regression approach has been proposed considering the above mentioned five variables. Based on the simultaneous consideration of those five variables, the solar irradiance is forecasted using the proposed methodology at a particular location in India, and the results are compared with the real time measured value of solar irradiance at that location on the days for which solar irradiance are forecasted. The proposed method is validated by comparing the results with real time data. The error analysis of the fuzzy logic based proposed system shows the root mean square error of 10.011 and mean absolute percentage error of 1.703%, while compared with real time data measured by instruments pyranometer, anemometer etc. The same results are found better while compared with the results obtained using multilinear regression approach.&lt;/p&gt;
Automated Planning Encodings for the Manipulation of Articulated Objects in 3D with Gravity

The manipulation of articulated objects plays an important role in real-world robot tasks, both in home and industrial environments. A lot of attention has been devoted to the development of ad hoc approaches and algorithms for generating the sequence of movements the robot has to perform in order to manipulate the object. Such approaches can hardly generalise on different settings, and are usually focused on 2D manipulations. In this paper we introduce a set of PDDL+ formulations for performing automated manipulation of articulated objects in a three-dimensional workspace by a dual-arm robot. Presented formulations differ in terms of how gravity is modelled, considering different trade-offs between modelling accuracy and planning performance, and between human-readability and parsability by planners. Our experimental analysis compares the formulations on a range of domain-independent planners, that aim at generating plans for allowing a dual-arm robot to manipulate articulated objects of different sizes. Validation is performed in simulation on a Baxter robot.
Human Motion Denoising Using Attention-Based Bidirectional Recurrent Neural Network

In this paper, we propose a novel method of denoising human motion using a bidirectional recurrent neural network (BRNN) with an attention mechanism. The corrupted motion that is captured from a single 3D depth sensor camera is automatically fixed in the well-established smooth motion manifold. Incorporating an attention mechanism into BRNN achieves better optimization results and higher accuracy because a higher weight value is selectively given to the more important input pose at a specific frame for encoding the input motion when compared to other deep learning frameworks. The results show that our approach efficiently handles various types of motion and noise. We also experiment with different features to find the best feature and believe that our method will be sufficiently desirable to be used in motion capture applications as a post-processing step after capturing human motion.