Reviewerly/exHarmony huggingface dataset:
Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)

Purpose . This paper aims to design the contract and present the profit distribution mechanism for CL platform, so as to realize the intelligent and automatic operation of the artificial intelligent- (AI-) based CL platform. Design/Methodology . A smart contract based on BT is designed for the AI-based CL platform. Profit distribution mechanism based on the Nash bargaining model for the CL platform is also put forward to coordinate different participators’ benefit relationship in CL. Findings . The AI-based CL platform and the proposed smart contract based on BT map the scenario which may be influenced by human factors and involve trust issues onto execution of codes. Practical Implications . The study will help CL practitioners in establishing effective profit mechanism and designing contracts on the platform, thus facilitating its sustainable operation. Originality/Value . The AI-based CL platform with BT smart contract can be totally free of human intervention, and hence, the problems of trust during CL platform’s operation are solved.
Going Beyond Dual Execution: MPC for Functions with Efficient Verification

The dual execution paradigm of Mohassel and Franklin (PKC'06) and Huang, Katz and Evans (IEEE '12) shows how to achieve the notion of 1-bit leakage security at roughly twice the cost of semi-honest security for the special case of two-party secure computation. To date, there are no multi-party computation (MPC) protocols that offer such a strong trade-off between security and semi-honest performance. Our main result is to address this shortcoming by designing 1-bit leakage protocols for the multi-party setting, albeit for a special class of functions. We say that function f(x, y) is efficiently verifiable by g if the running time of g is always smaller than f and $$g(x,y,z)=1$$ if and only if $$f(x,y)=z$$ . In the two-party setting, we first improve dual execution by observing that the "second execution" can be an evaluation of g instead of f, and that by definition, the evaluation of g is asymptotically more efficient. Our main MPC result is to construct a 1-bit leakage protocol for such functions from any passive protocol for f that is secure up to additive errors and any active protocol for g. An important result by Genkin et al. (STOC '14) shows how the classic protocols by Goldreich et al. (STOC '87) and Ben-Or et al. (STOC '88) naturally support this property, which allows to instantiate our compiler with two-party and multi-party protocols. A key technical result we prove is that the passive protocol for distributed garbling due to Beaver et al. (STOC '90) is in fact secure up to additive errors against malicious adversaries, thereby, yielding another powerful instantiation of our paradigm in the constant-round multi-party setting. As another concrete example of instantiating our approach, we present a novel protocol for computing perfect matching that is secure in the 1-bit leakage model and whose communication complexity is less than the honest-but-curious implementations of textbook algorithms for perfect matching.
Fast rebalanced RSA signature scheme with typical prime generation

In RSA, private exponent is usually obtained from the preselected public exponent by extended Euclid algorithm and is roughly the same bit size as a modulus number. If the private exponent is preselected as in rebalanced RSA, then the public exponent obtained by extended Euclid algorithm is roughly the same bit size as a modulus number. Hence, it is not easy to reduce both public exponent and private exponent (or CRT private exponents) in RSA key generation. In order to reduce both public exponent and CRT private exponents, the methods to search the modulus number after selecting proper public exponent and private exponent have been proposed in recent researches. However, these methods all have complicated key generation because prime generation module developed for RSA cannot be used. In this paper, we present a method to reduce both public exponent and CRT private exponents after selecting the prime numbers and propose a fast rebalanced RSA signature scheme with small public exponent. Our scheme is advantageous than other schemes in the aspect of using typical prime generation. That is, key generation is not complicated in our scheme.
Self-Improvable, Self-Improving, and Self-Improvability Adaptive Instructional System

Instructional systems that incorporate approaches and techniques that are geared to accommodate individual differences in students are termed adaptive instructional systems, and they are typically further distinguished as self-improvable, self-improving, or self-improvability. These adaptive instructional systems have long been viewed as common and effective forms of education, dating back as early as the fourth century BC. Producing computer tutors capable of achieving levels of effectiveness that rival human tutors has been an ultimate goal for learning scientists and cognitive psychologists who develop intelligent tutoring systems (ITSs). Research, development, and innovations in learning sciences occurring over the past four decades have made it possible for some of the ITS to perform nearly as well as human tutors. The effectiveness of ITS is continually improving due to ongoing theoretical research and technology advancement;, however, these consist chiefly of archiving new designs and producing version updates. Consequently, ITSs stop improving as soon as the development ceases. As human tutors are best viewed as learning resources to human learners, they in turn are constantly learning to adaptively improve their teaching from their interactions with students. This self-improvability is fostered on these self-improving educational interactions over time. Ideally, advanced ITS should be similar to human tutors and self-improve over time as well. In this chapter, we explore the potential of a particular ITS that can self-improve. Here our focus is on a more general category of learning systems, namely, an adaptive instructional system (AIS). More specifically, we introduce three concepts that allow AIS to self-improve—the self-improvable potential of AIS, self-improving process of AIS, and self-improvability used as a measure of the degree of improvement of a self-improving AIS.
A Curve Fitting Thresholding Approach for Forensic Source Identification of JPEG Compressed Images

One of the major threats of today's digital era is detection of cyber-crime pertaining to safety and security of multimedia data. Given the huge volumes of digital data circulated online by every common man on a day-to-day basis, this threat becomes more prominent. The risk of online fraudulences is at its peak today. In relation to cyber-crime detection, source camera identification is a much-researched forensic problem, which is mapping a contentious image back to its source device. This is mainly done by exploiting the camera sensor specific artifacts left behind in an image. However, the high degrees of compression, brought about (in an image) by online networking sites, cause the statistical image properties to get destroyed along with the camera sensor specific artifacts. This inhibits the image-to-source mapping in a forensic analysis, and hugely impacts the source detection accuracy. In this work, we present a detailed analysis of traditional forensic source detection techniques and evaluate their performance in presence of JPEG compression. In this paper, we propose a curve fitting based adaptive thresholding approach for image to source mapping. The proposed approach outperforms the current state-of-the-art in terms of source detection accuracy of digital images.
Dematerialization, Archiving and Recovery of Documents: A Proposed Tool Based on a Semantic Classifier and a Semantic Search Engine

Nowadays, every organization have to manage several documents and the number and volume of these will grow more and more, causing a substantial increase in management costs and slowing down the process of recovering useful information at the desired time. Document Management Systems (DMS) are currently a very hot topic because companies can significantly improve their efficiency and productivity through their adoption and use. The aim of this article is to present a document management solution based on a semantic classifier and a semantic search engine, able to support a company in the correct management, archiving and research of documents. A Case Study approach is used through which has been analyzed potential benefits for companies, such as the reduction of problems related to the loss of paper documents and the costs related to their management, the resolution for the recovery and archiving of paper and/or thimble documents and the reduction of search time.
Characterization of opening angle correlations of a biphoton state decomposed in Bessel modes

The spontaneous parametric down-conversion of photons has been widely applied for generating entangled photon pairs. We theoretically explore the entangled down-converted state of the photon pair, also known as biphoton state, for both degenerate and nondegenerate photon pairs. In particular, the spatial structure of the biphoton state has been expressed in Bessel modes to better understand the correlation with regard to the opening angle ${\ensuremath{\vartheta}}_{k}$ of Bessel modes. In fact, the opening angles of the down-converted photon pair are not independent of each other, but rather are correlated. Furthermore, we confirm the experimentally observed conditions concerning the optimization of generating high-degree spatial entanglement by controlling the beam waist of the pump beam. We also introduce a new experimental setup for efficient measurement of the spatial entanglement of the biphoton state using geometrical optics arguments.
Fine-tuning restricted Boltzmann machines using quaternion-based flower pollination algorithm

Recent works spotlighted machine learning as the technology of the new century. One can see its fantastic capability in solving problems throughout several domains, ranging from computer vision to natural language processing tasks. Nevertheless, there is still a burden when designing a new architecture, mainly due to its parameter setting up phase, which is often performed empirically. In the pursuit of mitigating this issue, optimization techniques have risen and taken the lead, being capable of providing feasible solutions to the problem. In this work, we introduce a quaternion-based flower pollination algorithm to fine-tune restricted Boltzmann machines. The effectiveness of the proposed approach is compared with several metaheuristic techniques, from naïve versions to quaternion-based ones. The experiments were carried out in three public datasets available in the literature and demonstrated promising results when compared with other techniques.
Preliminary analysis learning media in the form of interactive multimedia based on edupark physics Carocok beach Painan Indonesia with the scientific method

Abstract The country’s South Pesisir Districts is a million charms that have a variety of tourist destinations, which are very much visited by people from various regions in West Sumatra. Carocok Beach Painan is one of the tourist icons that has an extraordinary beautiful destination. It is unfortunate if many tourist attractions found in the concept of physics are only just visited and not used as a means of learning Physics. This research was an initial stage in the development model of Plomp. The purpose of this study was to use the physics concept based on Carocok Beach Painan. The data in this study were obtained from interviews and questionnaires, that were developed based on the parameters of the learning process, learning tools, characteristics of students and region. The trend of educators was used students worksheets as well as the high interest of students to use IT based learning in the school. It was necessary to develop instructional media, in the form of interactive multimedia using Scientific Methods that can be associated with Carocok Beach Painan Edupark, which have a variety of physics concepts especially in the material-wave.
Multi-Purpose Watermarking with QR Code Applications

Watermarking plays an important role in the applications of copyright protection and data authentication. In this paper, we apply the commonly used discrete cosine transform (DCT), and embed two watermarks into color images. For the first watermark we choose the QR code, and for the second one we employ the binary random sequence. With the classification of watermarking, robust watermarking is suitable for copyright protection, while fragile watermarking aims at authentication purposes. We embed the two watermarks into the low and mid frequency bands, respectively. When intentional attacks are applied, the first watermark can be extracted, while no attack is applied, both watermarks can survive. Simulations have demonstrated the capabilities of multi-purpose watermarking with our method.
False Positive Detection in Sender Domain Authentication by DMARC Report Analysis

The number of spoofed emails is increasing rapidly and become a serious problem, especially in business and e-commerce. Sender domain authentication is an effective countermeasure for spoofed e-mail. Although SPF, DKIM, and DMARC are famous sender domain authentication methods, these methods erroneously determine legitimate e-mails as malicious e-mails, such as forwarded messages. On the other hand, DMARC has a reporting function, which e-mail senders can receive DMARC reports that include SPF and DKIM authentication results, and the sender's domains, and so on. Generally, spam e-mails countermeasures are combined with three approaches: TCP/SMTP session monitoring, sender domain authentication, and contents filtering. Since sender domain authentication is usually processed before contents filtering, the occurrence of many false positives in sender domain authentication is a serious problem. In this paper, we propose a method to detect legitimate IP addresses by adapting X-means clustering to DMARC reports data in order to detect false positive deliveries in sender domain authentications. We apply actual DMARC reports data received from 28th September to 5th October 2019 to our approach. As a result, our method classified 254 to 480 IP addresses per day as legitimate addresses. As an evaluation, we confirmed that 2.8% to 11.1% of e-mails from legitimate IP addresses detected by our method were failed the combination of SPF or DKIM verification, and 36.9% to 62.7% of them were failed to DMARC authentication. From these results, we confirmed the proposed method can detect false positive deliveries caused by conventional sender domain authentication with high accuracy.
Enhancing Data Security in Cloud Computing Using Threshold Cryptography Technique

Modern technological advances have given rise to the popularity and success of cloud in organizations and industries because it provides data storage facility and computing services at minimal cost. Data security is one of the important requirements for outsourced data in a cloud storage environment. It also brings new security vulnerabilities for assuring the data confidentiality, data integrity and data access control. Data security of a cloud system depends heavily on the cloud service provider, data owner and cloud users. So, there needs to be some methodology to monitor security vulnerabilities and providing data security in cloud environment. Now a day's providing data security in cloud storage is one of the most critical tasks. For the past few years many works have been done to assure data security but they are failed in providing data security such as information leakage, secret-key management, cloud-user authentication, breach of data confidentiality, because of collusion attack and lots of computational activities (because of large number of secret keys).In this paper, we used threshold cryptography technique for providing data security in cloud storage system. Proposed approach addresses the data security issues in cloud system more efficiently. In this Threshold cryptography technique data owner create users in groups based on place or location, project, and department, data owner allocates single secret key to each group of users for decryption of data and every users in the group shares parts or pieces of the secret key. Proposed methodology also uses data access control list to control the data access. Proposed approach provides data security more efficiently, it also increases the performance of the system and also it minimizes the number of secret keys.
Design of a Cloud Storage Security Encryption Algorithm for Power Bidding System

To solve the problem of poor security and performance caused by traditional encryption algorithm in the cloud data storage of power bidding system, we proposes a hybrid encryption method based on symmetric encryption and asymmetric encryption. In this method, firstly, the plaintext upload file is divided into several blocks according to the proportion, then the large file block is encrypted by symmetrical encryption algorithm AES to ensure the encryption performance, and then the small file block and AES key are encrypted by asymmetric encryption algorithm ECC to ensure the file encryption strength and the security of key transmission. Finally, the ciphertext file is generated and stored in the cloud storage environment to prevent sensitive files Pieces from being stolen and destroyed. The experimental results show that the hybrid encryption method can improve the anti-attack ability of cloud storage files, ensure the security of file storage, and have high efficiency of file upload and download.
Differential attacks on reduced‐round SNOW 3G and SNOW 3G <sup>⊕</sup>

The stream cipher SNOW 3G is the core of the 3G Partnership Project (3GPP) for implementing a confidentiality algorithm and data integrity algorithm. In this study, the authors analyse the initialisation stage based on the chosen IV differential attacks on the reduced-round SNOW 3G and SNOW . Firstly, they show a distinguisher for 12-round SNOW 3G and 255 distinguishers for 13-round SNOW , respectively. Secondly, they use the input differences and the output differences of the S-box to recover the input of S-box, which can recover full keys in real-time for 12-round SNOW . The data complexity is 36 and the time complexity is small. Finally, they use the impossible differences of the S-box as a filter to extend the initialisation rounds of the attack to 16-round SNOW . The data complexity is 28 and the time complexity is . So far, the authors’ attack results are the best in terms of chosen IV differential attacks. At the same time, their attack results are superior to multiset collision attacks in terms of data complexity, and their attack method can recover full keys, while multiset collision attacks can only partially recover the internal states in 15-round SNOW .
Projection-based Relevance Model for Table Retrieval

We propose a novel relevance model for table-retrieval using table columns ("projections") as pseudo-relevance feedback. To demonstrate the merits of our proposed approach, we re-rank tables that were initially retrieved from a Wikipedia corpus by state-of-the-art table retrieval methods. Overall, our proposed projection-based relevance model (PTRM) results in significant performance gains.
GAMMA: A Graph and Multi-view Memory Attention Mechanism for Top-N Heterogeneous Recommendation

Exploiting heterogeneous information networks (HIN) to top-N recommendation has been shown to alleviate the data sparsity problem present in recommendation systems. This requires careful effort in extracting relevant knowledge from HIN. However, existing models in this setting have the following shortcomings. Mainly, they are not end-to-end, which puts the burden on the system to first learn similarity or commuting matrix offline using some manually selected meta-paths before we train for the top-N recommendation objective. Further, they do not attentively extract user-specific information from HIN, which is essential for personalization. To address these challenges, we propose an end-to-end neural network model – GAMMA (Graph and Multi-view Memory Attention mechanism). We aim to replace the offline meta-path based similarity or commuting matrix computation with a graph attention mechanism. Besides, with different semantics of items in HIN, we propose a multi-view memory attention mechanism to learn more profound user-specific item views. Experiments on three real-world datasets demonstrate the effectiveness of our model for top-N recommendation setting.
Characteristic Traits of Software Testers

Although there has been extensive research on software testing technicalities - such as testing tools and practices - little research have been conducted within human factors of software testing. In collaboration with Itera, a consultancy company, we begin to fill this research gap. Our current qualitative data-set consists of observation notes, interview transcripts, and conversation logs. Our findings suggest that creativity, being structured, having the ability to see the whole picture, having good interpersonal skills, and eagerness-to-learn are desired traits for a software tester.
Topological delocalization in the completely disordered two-dimensional quantum walk

We investigate numerically and theoretically the effect of spatial disorder on two-dimensional split-step discrete-time quantum walks with two internal "coin" states. Spatial disorder can lead to Anderson localization, inhibiting the spread of quantum walks, putting them at a disadvantage against their diffusively spreading classical counterparts. We find that spatial disorder of the most general type, i.e., position-dependent Haar random coin operators, does not lead to Anderson localization but to a diffusive spread instead. This is a delocalization, which happens because disorder places the quantum walk to a critical point between different anomalous Floquet-Anderson insulating topological phases. We base this explanation on the relationship of this general quantum walk to a simpler case more studied in the literature and for which disorder-induced delocalization of a topological origin has been observed. We review topological delocalization for the simpler quantum walk, using time evolution of the wave functions and level spacing statistics. We apply scattering theory to two-dimensional quantum walks and thus calculate the topological invariants of disordered quantum walks, substantiating the topological interpretation of the delocalization and finding signatures of the delocalization in the finite-size scaling of transmission. We show criticality of the Haar random quantum walk by calculating the critical exponent $\eta$ in three different ways and find $\eta$ $\approx$ 0.52 as in the integer quantum Hall effect. Our results showcase how theoretical ideas and numerical tools from solid-state physics can help us understand spatially random quantum walks.
An Improved Genetic Bat algorithm for Unconstrained Global Optimization Problems

Metaheuristic search algorithms have been in use for quite a while to optimally solve complex searching problems with ease. Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. Genetic algorithm (GA) is successfully applied in several engineering fields for the past four decades but it still has a problem of slow convergence due to its reliability on the initial state of its operators. Therefore, to ensure that GA converges to a global solution, this paper proposed a two-stage improved Genetic Bat algorithm (GBa) in which the GA finds the optimal solution first and then Bat starts from where the GA has converged. This multi-stage optimization ensures that optimal solution is always reached through fine balance in between exploration and exploitation behavior of Genetic algorithm.