Reviewerly/exHarmony huggingface dataset:
Analysis of Division Property using MILP Method for Lightweight Blockcipher Piccolo

Division property is a generalized integral property which is a security evaluation against blockciphers. The Mixed Integer Linear Programming (MILP) method has been proposed to enable a more effective search of division property. In addition, improved MILP method proposed by Ling enables to apply Non-Bit -Permutation Linear layers. By using improved MILP method, we have advantage in division property analysis of Piccolo. In this paper, we revise and verify known evaluation results shown by Kubo and Shibayama. As the result, the validity of 8th and 12th evaluations are confirmed. Regarding 24th and 32nd evaluation, the validity concerning the analysis of Kubo and the effectiveness of MILP without SizeReduce are confirmed. Regarding 48th evaluation, we cannot find 7-round characteristic which is suggested from result of higher-order differential property shown by Shibayama. However, we found that assumption based on that 6-round 32nd order differential property can be extended is not appropriate. We confirmed this fact by computer simulations and conclude that 48th order characteristic does not hold after 7th round. For 63rd characteristic, we discover new result of 7-round division property which is one more round than known result. Therefore, we can clarify the characteristics of 63rd order which is theoretically upper-bound of Piccolo.
Hessian Regularization Semi-supervised Extreme Learning Machine for Superheat Identification in Aluminum Reduction Cell

Superheat degree identification of aluminum electrolysis cell condition has long been a challenging industrial issue due to complex and hard environment and limitation of physical devices. In addition, the degree of superheat is an influential index of the whole process, reflecting the distribution of the physical field of the electrolytic cell, the current efficiency in the electrolytic production, and the lifespan of the aluminum reduction cell. Traditional measuring method of SD is device-based which is inaccurate and with expensive cost. In this paper, we propose data-driven semi-supervised model, semi-supervised extreme learning machine (SS-ELM) for SD detection. Additionally, Hessian regularization was utilized as a regularization term to improve the model performances. The proposed HRSS-ELM was verified with industrial dataset in aluminum reduction cell. The experiment demonstrates HRSS-ELM has a maximum performance compared with expert rules and other methods, and the accuracy of test results is up to 85%.
Discussion on the Design of Computer Games System Based on Surakarta

Surakarta is two player strategy boardgame, which is one of the computer games. In computer game system, there always include four key components, which are Game show, Move generator, Evaluation functions and Search algorithm. This paper focuses on the analysis of the key technology of the Surakarta game system, and analyzes the difficulties of the computer game system by combining the designed Surakarta game system. The system won the first prize in the National Undergraduate Computer Game Competition, which verified the completeness of the system and the certain effectiveness of the evaluation algorithm.
Problem-Oriented Automatic Summarization Method with Semantic Coherence Definition and Usage of Inclusion Measures for the Search of Answers to Questions in the Internet

The article deals with the relevant problem of automatic arbitrary text summary obtaining. Several works in this field are analyzed using comparison and classification methods. The problem of obtaining a text summary by means of an answer to a random question is emphasized. The identification of semantic relations between sentences using a set of rules based on syntax and semantics of a language is described. These rules are represented in the form of regular expressions – patterns that consist of characters and metacharacters and set search rules. Taking into account semantic coherence features, an improved method of sentences similarity calculation to identify the measure of inclusion of one sentence into another one is developed. This method helps to define more precisely logical stress on the words within automatic summarization and detect contradictions. A modified automatic summarization method, oriented at a specific problem is suggested. It is concluded that the proposed method is quiet effective in the process of automatic search for answers to questions in the Internet.
Quantum remote sensing secure direct communication

Quantum remote sensing, combining quantum mechanics and remote sensing technology, uses sources in quantum level to realize remote sensing. It will improve the resolution and security of the remote sensing process benefiting from quantum properties. In this work, we focus on how to communicate securely in the process of quantum remote sensing, i.e. quantum remote sensing communication. Since both the quantum remote sensing and quantum communication use quantum states for quantum information processing, we set up a natural link between them and apply the quantum secure direct communication in remote sensing, proposing the first quantum remote sensing secure direct communication protocol.
Meanings in the Digital Age. Homo Singularity

The paper approaches the techno-humanitarian balance of physical (accelerating) and humanitarian (controlling) technologies. It demonstrates that the absence of the human in this ba­lance makes the idea about ensuring the socio-system’s sustainable development through the establishment of techno-humanitarian balance erroneous. The required adequate proportion of “powers” between the technologies in the techno-humanitarian balance necessitates the civilization to attempt to “harness” not only the technological singularity but the humanitarian singularity, too. It is shown that the techno-humanitarian balance in the singularity mode destroys the human physical and mental nature. The human is introduced into the binary technological balance through transforming it into a triple balance and adding the semantic technologies inherent to human beings. The triple balance is characterized by the oppositions between intelligence and thinking, information and meanings. The paper explores the triple balance and its edges in the context of the ultimate singularity. It is shown that the human being, through thinking and meanings, can correlate themselves with the semantic singularity (the infinity of the semantic field) — thus becoming Homo Singularity. These conclusions are substantiated through the V.V. Nalimov’s probabilistic model of consciousess, which also mathe­matically formalizes the process of semantic decapsulation of the personality and the personal interaction with the semantic vacuum (infinity). The paper introduces the concept of finite dilatation of the cultural semantic field and the big semantic transition as the era of formation of Homo Singula­rity and beginning of their practical work with semantic infinity. The paper provides examples of such practical work in the fields of art and science. Homo Singularity not only prevents their physical and mental nature from destruction but also keeps the po­werful artificial intelligence under control by counte­ring big data with their ability to integrate the multiple into the single (whole) and make a transition from the discrete level of information to a continuous (infinite) level of meanings.
Image Zooming using Corner Matching on Grayscale and Color Images

Originally inspired by the imaging needs of UND's Open Prototype for Educational Nanosats (OPEN) satellite program. The goal of this research is to direct the choice of an image interpolation/zoom algorithm.The image is scanned and overlapping 3x3 blocks of pixels are analyzed looking for “L” patterns. Such a pattern indicates that the value of the center pixel be changed transforming the “L” pattern into a triangle pattern. We compare this approach against different types of single-frame image interpolation algorithms, such as zero-order-hold (ZOH), bilinear, bicubic, directional cubic convolution interpolation (DCCI) approach, and a neural network approach. We use the peak signal-to-noise ratio (PSNR), mean squared error (MSE), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) as the primary means of comparison. Tests cases include gray-scale and RGB images. In all test cases the proposed method resulted in a lower MSE, higher PSNR, and higher SSIM than the other zoom methods applied. The FSIM returned a higher value in every case for the ZOH method, but this was only when FSIM was calculated to the fourth decimal. Overall, this method results in a more accurate image after zooming than the other methods.
Handwritten signature verification by using a six-axis motion sensor and SVM

Signature verification, if we consider the muscle memory, is a biometric for identification technology. To access muscle memory, we use a motion sensor that consists of accelerometer and gyroscope to implement a signature verification system. The motion sensor records six motion values including three-axis accelerations and angular velocities while name signing. 14 features of signature are extracted from the sequence of accelerations and angular velocities. A support vector machine (SVM) is then applied to verify the signatures. The proposed method was applied to verify the Chinese signatures. The SVM is trained by the training data from each person. The true positive rate of the proposed method can reach to 95.66%. Fake signatures generated by tracing from true signatures can also be recognized by the proposed method.
An Architecture for Dynamic Web Services that Integrates Adaptive Object Models with Existing Frameworks

Nowadays, web services became one of the main alternatives for communication between software systems and even inside the components of the same application. In some domains, the change of requirements happens frequently, demanding flexibility from the architecture of the applications, and consequently also in the web services that they provide. In this context, the goal of this work is to provide an architecture that can be used for dynamic web services, allowing services to be changed and introduced at runtime. To fulfill these requirements this work proposes the usage of Adaptive Object Models (AOM) combined with existing web service framework, using dynamic adapters to integrate and decouple them. The framework Esfinge AOM Role Mapper received features to implement the behavioral part of the AOM model and to map the AOM rule objects with metadata to methods with code annotations in the dynamic adapters. The proposed architecture passes the adapters generated at runtime to the existing framework, which provide the web service based on its methods and code annotations. An evaluation based on a case study performed scenario-based tests to verify the architecture capability to create and change dynamic web services. Additionally, a modularity analysis verified the coupling between classes that use both frameworks. As a result, the proposed architectural solution was able to implement the dynamic web services in all the scenarios keeping the classes that handle the AOM model decoupled from the classes responsible for providing the web services.
Symmetrical Adversarial Training Network: A Novel Model for Text Generation

Text generation has always been the core issue in the field of natural language processing. Over the past decades, Generative Adversarial network (GAN) has proven its great potential in generating realistic synthetic data, performing competitively in various domains like computer vision. However, the characteristics of text discretization limit the application of GANs in natural language processing. In this paper, we proposed a novel Symmetrical Adversarial Training Network (SATN) which employed symmetrical text comparison mechanism for the purpose of generating more realistic and coherent text samples. In the SATN, a Deep Attention Similarity Model (DASM) was designed to extract fine-grained original-synthetic sentence feature match loss for improving the performance of generative network. With DASM, the SATN can identify the difference between sentences in word level and pay attention to relevant meaningful words. Meanwhile, we utilize the DASM loss to compensate for the defect of the objective function in adversarial training. Our experiments demonstrated significant improvement in evaluation.
PRAANGGAPAN DALAM PAMFLET SOSIALISASI PELESTARIAN LINGKUNGAN DI KABUPATEN WAKATOBI

The presuppositions in the environmental conservation pamphlet in Wakatobi District are quite diverse. This study aims to explain the use of presupposition in the socialization pamphlet of environmental conservation in Wakatobi District. This research is a qualitative descriptive reseacrh that attempts to study presupposition phenomena with pragmatic approach. Type and data of this research is writing data sourced from pamphlet exixting in Wangi-wangi, Wakatobi District. Data were collected using observation method with recording and record technique. Data that has been obtained, classified and analyzed descriptively with qualitative approach. The result showed that, the use of presupposition in the environmental conservation pamphlet in Wakatobi District consisted of four types, namely existential presuppositions, factive presuppositions, structural presuppositions, and conterfactual presuppositions. The use of these presupposes there are differences in each pamphlet publisher, both goverment, non-govermental organizations, and pamphlet published collectively by goverment and non-govermental organizations. This difference is due to shared knowledge, participants, and the context of the situation.
A Heuristic Pruning Technique for Dialectical Trees on Argumentation-Based Query-Answering Systems

Arguments in argumentation-based query-answering systems can be associated with a set of evidence required for their construction. This evidence might have to be retrieved from external sources such as databases or the web, and each attempt of retrieving a piece of evidence comes with an associated cost. Moreover, a piece of evidence may be available at one moment but not at others, and this is not known beforehand. As a result, the set of active arguments (whose entire set of evidence is available) that can be used by the argumentation machinery of the system may vary from one scenario to another. In this work we propose a heuristic pruning technique for building dialectical trees in argumentation-based query-answering systems, with the aim of minimizing the cost of retrieving the pieces of evidence associated with the arguments that need to be accounted for in the reasoning process.
iSNoW

We developed a highly-visible head-mounted novelty wearable to be used in social settings. We tested our Interactive Social Novelty Wearable (iSNoW) prototype in a partner-based user study to see if perceptions of the experience would change if the information displayed on the wearable was contextually relevant. Thematic analyses revealed important considerations for the design of future devices, regarding distraction and pressure to understand the rules of the game. Participants wearing contextually relevant information were more likely to recommend the device to their friends. We highlight future opportunities for exploration in this relatively untouched space.
Sign Based Derivative Filtering for Stochastic Gradient Descent

We study the performance of stochastic gradient descent (SGD) in deep neural network (DNN) models. We show that during a single training epoch the signs of the partial derivatives of the loss with respect to a single parameter are distributed almost uniformly over the minibatches. We propose an optimization routine, where we maintain a moving average history of the sign of each derivative. This history is used to classify new derivatives as “exploratory” if they disagree with the sign of the history. Conversely, we classify the new derivatives as “exploiting” if they agree with the sign of the history. Each derivative is weighed according to our classification, providing control over exploration and exploitation. The proposed approach leads to training a model with higher accuracy as we demonstrate through a series of experiments.
Correlation Distribution Analysis of a Two-Round Key-Alternating Block Cipher

Abstract In this paper we study two-round key-alternating block ciphers with round function f ( x )= x (2 t +1)2 s , where t, s are positive integers. An algorithm to compute the distribution weight in respect to input and output masks is described. Also, in the case t = 1 the correlation distributions depending on input and output masks are completely determined for arbitrary pairs of masks.
Probabilistic Classification Vector Machines for Multiclass Classification

The probabilistic classification vector machine (PCVM) is an effective sparse learning approach for binary classification. This paper presents an extension of the PCVM to the multiclass case, which aims to provide reliable probabilistic outputs by taking advantage of the inherent probabilistic nature of the PCVM. The performance of the proposed multiclass PCVM is evaluated on several benchmark datasets. In particular, the probabilistic outputs are used to assess the confidence in the prediction. The experimental results demonstrate that multiclass PCVMs can make more confident correct predictions than probabilistic multiclass support vector machines and multiclass relevance vector machines.
Where to Look for What You See Is What You Sign? User Confusion in Transaction Security

The What You See Is What You Sign (WYSIWYS) scheme is a popular transaction verification method in online banking which is designed to prevent fraud even if the transfer-issuing device is compromised. To evaluate its practical effectiveness, we asked 100 online banking customers to pay two invoices by credit transfer. The second transfer was attacked by secretly replacing the beneficiary’s account number and displaying the fraudulent transaction details on the confirmation page that asks a customer for a one-time password as generated by their second factor device. The attacked authentication method was the same the participants also use in private with their principal bank. Our attack is highly effective and causes many participants to use the fraudulent details displayed onscreen for verification instead of the original invoice. On top of that, a majority did not verify their transactions at all. Participants with a technical background and experience with certain as well as multiple transaction authentication methods were seen to be less likely to fall victim to the attack.
A Formal Model for Checking Cryptographic API Usage in JavaScript

Modern JavaScript implementations include APIs offering strong cryptography, but it is easy for non-expert developers to misuse them and introduce potentially critical security bugs. In this paper, we formalize a mechanism to rule out such bugs through runtime enforcement of cryptographic API specifications. In particular, we construct a dynamic variant of Security Annotations, which represent security properties of values via type-like information. We formalize Security Annotations within an existing JavaScript semantics and mechanize it to obtain a reference interpreter for JavaScript with embedded Security Annotations. We provide a specification for a fragment of the W3C WebCrypto standard and demonstrate how this specification can reveal security vulnerabilities in JavaScript code with the help of a case study. We define a notion of safety with respect to Security Annotations and extend this to security guarantees for individual programs.
Path Planning Considering Motion Uncertainty for Exploration Robots in Natural Terrain

This paper proposes a new path planning architecture with consideration of motion uncertainty for wheeled robots in rough terrain with multiple types of terrain. The proposed scheme uses particles to describe the uncertainty propagation between different terrain types and to expand RRT (Rapidly-exploring Random Tree) based on the uncertainty of each node in order to prevent the expansion of position uncertainty. The effectiveness of the proposed method is evaluated on the simulation which hypothesizes Mars enviroment. The simulation results show that the proposed method can prevent the expansion of position uncertainty substantially, which can decrease the times of path following or re-planning.
Blockchain Technology Is Changing the Innovation Aspect in the Digital Economy

In the digital era, banks are moving from their traditional methods of innovation to high-tech technologies. They have been working hard to provide a secure platform to their customers. With their continuous efforts and hard work, the concept of Blockchain technology came into notice. Blockchain technology is popularly known for Bitcoins. Nearly every third person or organization either knows about blockchain technology or uses it. Blockchain technology can address most of the issues related to Digital Transactions, Double Spending, and Currency Reproduction. The chapter will discuss the basic concept of Blockchain, its history, how its networks and technology work, and how its primary technologies evolved. By the end of the chapter, the technical design and expected outcomes will be clearly understood. The chapter also tries to review other research done in this domain.
Action Recognition Based on Divide-and-Conquer

Recently, deep convolutional neural networks have made great breakthroughs in the field of action recognition. Since sequential video frames have a lot of redundant information, compared with dense sampling, sparse sampling network can also achieve good results. Due to sparse sampling’s limitation of access to information, this paper mainly discusses how to further improve the learning ability of the model based on sparse sampling. We proposed a model based on divide-and-conquer, which use a threshold α to determine whether action data require sparse sampling or dense local sampling for learning. Finally, our approach obtains the state-the-of-art performance on the datasets of HMDB51 (72.4%) and UCF101 (95.3%).
Structuring of Interactive Style Guides

In order to promote consistency and usability in the development of interactive systems, style guides have repeatedly been recommended as supporting tools. In practice, however, their consistent implementation and use rarely take place. While earlier research emphasized the relevance of style guides in human-centered design, the usability of style guides themselves has been considered to a lesser degree. An important aspect is the structuring of style guides to ensure that information and design elements are easily accessible to users. Based on results of a card sorting study, design principles for structuring interactive style guides are presented and discussed.