Reviewerly/exHarmony huggingface dataset:
Attribute Based Encryption for Deterministic Finite Automata from $$\mathsf{DLIN}$$

Waters [Crypto, 2012] provided the first attribute based encryption scheme ABE for Deterministic Finite Automata (DFA) from a parametrized or "q-type" assumption over bilinear maps. Obtaining a construction from static assumptions has been elusive, despite much progress in the area of ABE. In this work, we construct the first attribute based encryption scheme for DFA from static assumptions on pairings, namely, the $$\mathsf{DLIN}$$ assumption. Our scheme supports unbounded length inputs, unbounded length machines and unbounded key requests. In more detail, secret keys in our construction are associated with a DFA M of unbounded length, ciphertexts are associated with a tuple $$(\mathbf {x}, \mathsf {\mu })$$ where $$\mathbf {x}$$ is a public attribute of unbounded length and $$\mathsf {\mu }$$ is a secret message bit, and decryption recovers $$\mathsf {\mu }$$ if and only if $$M(\mathbf {x})=1$$. Our techniques are at least as interesting as our final result. We present a simple compiler that combines constructions of unbounded ABE schemes for monotone span programs (MSP) in a black box way to construct ABE for DFA. In more detail, we find a way to embed DFA computation into monotone span programs, which lets us compose existing constructions (modified suitably) of unbounded key-policy ABE ($${\mathsf {kpABE}}$$) and unbounded ciphertext-policy ABE ($${\mathsf {cpABE}}$$) for MSP in a simple and modular way to obtain key-policy ABE for DFA. Our construction uses its building blocks in a symmetric way – by swapping the use of the underlying $${\mathsf {kpABE}}$$ and $${\mathsf {cpABE}}$$, we also obtain a construction of ciphertext-policy ABE for DFA. Our work extends techniques developed recently by Agrawal, Maitra and Yamada [Crypto 2019], which show how to construct ABE that support unbounded machines and unbounded inputs by combining ABE schemes that are bounded in one co-ordinate. At the heart of our work is the observation that unbounded, multi-use ABE for MSP already achieve most of what we need to build ABE for DFA.
MiFI-Outlier: Minimal infrequent itemset-based outlier detection approach on uncertain data stream

Massive outlier detection approaches have been proposed for static datasets in the past twenty years, and they have acquired good achievements. In real life, uncertain data stream is more and more common, but most existing outlier detection approaches were not suitable for uncertain data stream environment. In addition, many outlier detection approaches have not considered the appearing frequency of each element, which resulted the detected outliers not coincide with the definition of outlier. Itemset-based outlier detection approaches provided a good solution for this problem, and they have got more attentions in these years. In this paper, a novel two-step minimal infrequent itemset-based outlier detection approach called MiFI-Outlier is proposed to effectively detect the outliers from uncertain data stream. In itemset mining phase, a matrix-based method called MiFI-UDSM is proposed to mine the minimal infrequent itemsets (MiFIs) from uncertain data stream, and then an improved approach called MiFI-UDSM* is proposed for more effectively mining these minimal infrequent itemsets using the ideas of “item cap” and “support cap”. In outlier detection phase, based on the mined MiFIs, three deviation indices including minimal infrequent itemset deviation index (MiFIDI), similarity deviation index (SDI) and transaction deviation index (TDI) are defined to measure the deviation degree of each transaction, and then the MiFI-Outlier is used to identify the outliers from uncertain data stream. Several experimental studies are conducted on public datasets and synthetic datasets, and the results show that the proposed approaches outperform in infrequent itemset mining phase and outlier detection phase.
General Linear Group Action on Tensors: A Candidate for Post-quantum Cryptography

Starting from the one-way group action framework of Brassard and Yung (Crypto'90), we revisit building cryptography based on group actions. Several previous candidates for one-way group actions no longer stand, due to progress both on classical algorithms (e.g., graph isomorphism) and quantum algorithms (e.g., discrete logarithm). We propose the general linear group action on tensors as a new candidate to build cryptography based on group actions. Recent works (Futorny–Grochow–Sergeichuk Lin. Alg. Appl., 2019) suggest that the underlying algorithmic problem, the tensor isomorphism problem, is the hardest one among several isomorphism testing problems arising from areas including coding theory, computational group theory, and multivariate cryptography. We present evidence to justify the viability of this proposal from comprehensive study of the state-of-art heuristic algorithms, theoretical algorithms, hardness results, as well as quantum algorithms. We then introduce a new notion called pseudorandom group actions to further develop group-action based cryptography. Briefly speaking, given a group G acting on a set S, we assume that it is hard to distinguish two distributions of (s, t) either uniformly chosen from $$S\times S$$, or where s is randomly chosen from S and t is the result of applying a random group action of $$g\in G$$ on s. This subsumes the classical Decisional Diffie-Hellman assumption when specialized to a particular group action. We carefully analyze various attack strategies that support instantiating this assumption by the general linear group action on tensors. Finally, we construct several cryptographic primitives such as digital signatures and pseudorandom functions. We give quantum security proofs based on the one-way group action assumption and the pseudorandom group action assumption.
Collaborative Filtering for Book Recommendation System

Ramakrishnan, Gautam Saicharan, V. Chandrasekaran, K. Rathnamma, M. V. Ramana, V. VenkataCollaborative filtering is one of the most important techniques in the market nowadays. It is prevalent in almost every aspect of the internet, in e-commerce, music, books, social media, advertising, etc., as it greatly grasps the needs of the user and provides a comfortable platform for the user to find what they like without searching. This method has a few drawbacks; one of them being, it is based only on the explicit feedback given by the user in the form of a rating. The real needs of a user are also demonstrated by various implicit indicators such as views, read later lists, etc. This paper proposes and compares various techniques to include implicit feedback into the recommendation system. The paper attempts to assign explicit ratings to users depending on the implicit feedback given by users to specific books using various algorithms and thus, increasing the number of entries available in the table.
Exploration of the augmented reality model in learning

Abstract Augmented reality is a technology that integrates virtual content into real environments in the form of mobile and desktop. This technology has begun to be developed in various fields, even as a learning medium with various models. We are exploring augmented reality models that have been developed by reviewing several journals related to augmented reality. We have collected 100 journals from various data sources with augmented reality keywords. We select 41 journals and we find 5 augmented reality models, including 3D models, 3D simulations, 3D animations, video models, multimedia models. There are two types of augmented reality, using markers and without markers, for augmented reality using markers requires a marker as a trigger for the emergence of augmented reality objects into the real environment, while augmented reality without markers does not require markers to trigger the emergence of augmented reality into real environments.
On the property of blockchains: comments on an emerging literature

AbstractThe last few years have seen the emergence of a growing academic literature on the blockchain. On one side are the supporters, who see its potential to create a true, peer-to-peer (p2p) sharing economy. On the other side are the critics, who argue that the blockchain is more likely to reproduce capitalism than to disrupt it. Using the insights generated by the critical literature on the blockchain, this paper seeks to ask new questions and provide new insights about the development of this technology and how it is likely to transform the global political economy through its capacity to enforce global property rights.
A Black-Box Construction of Fully-Simulatable, Round-Optimal Oblivious Transfer from Strongly Uniform Key Agreement

We show how to construct maliciously secure oblivious transfer (M-OT) from a strengthening of key agreement (KA) which we call strongly uniform KA (SU-KA), where the latter roughly means that the messages sent by one party are computationally close to uniform, even if the other party is malicious. Our transformation is black-box, almost round preserving (adding only a constant overhead of up to two rounds), and achieves standard simulation-based security in the plain model. As we show, 2-round SU-KA can be realized from cryptographic assumptions such as low-noise LPN, high-noise LWE, Subset Sum, DDH, CDH and RSA—all with polynomial hardness—thus yielding a black-box construction of fully-simulatable, round-optimal, M-OT from the same set of assumptions (some of which were not known before).
Research on electronic medical record access control based on blockchain

For the medical industry, there are problems such as poor sharing of medical data, tampering, and leakage of private data. In view of these problems, a blockchain-based electronic medical record access control research scheme based on the role-based access control model is proposed in this article. First, the appropriate access control strategy is adopted to solve the leakage problem of the user’s medical privacy information during the access process. Then, the information entropy technology is used to quantify the medical data, so that the medical data can be effectively and maximally utilized. Using the distributed general ledger characteristics of blockchain and its inherent security attributes, data islands can be eliminated, data sharing among medical systems can be promoted, access records can be prevented from being tampered with, and medical research and precise medical treatment can be better supported. Through this research, not only can user’s medical privacy information protection be realized during the service process but also patients can manage their own medical data autonomously, which is beneficial to privacy protection under the medical data sharing.
New quantum MDS codes over finite fields

In this paper, we present three new classes of q-ary quantum MDS codes utilizing generalized Reed–Solomon codes satisfying Hermitian self-orthogonal property. Among our constructions, the minimum distance of some q-ary quantum MDS codes can be bigger than $$\frac{q}{2}+1$$. Comparing to previous known constructions, the lengths of codes in our constructions are more flexible.
Integration of a knowledge-based constraint into generative models with applications in semi-automatic segmentation of liver tumors

Accurate delineation of liver tumors in medical images is a vital step in diagnosis, treatment planning, and monitoring. In this paper, we utilize a generative model for segmentation of abnormal liver regions. After preprocessing of an input image, the ROI of the tumor is determined, and the boundary of the abnormal region in a single slice is specified. Then, the remaining slices are processed by a generative model that is enhanced by the integration of a constraint. We search for the boundary of the tumor by a probabilistic approach and obtain the solution using the Bayesian inference. The Kullback-Leibler divergence is used to measure the consistency of the results to the model's constraint. We evaluated the proposed method using synthetic and clinical data. In the public dataset, we achieved a Dice measure of 0.84 ± 0.06, which outperforms state-of-the-art hepatic tumor segmentation algorithms. Concerning all available clinical images, the Dice index of the proposed method is 0.90 ± 0.03.
How and when AR technology affects product attitude

Purpose Augmented reality (AR) has emerged as a novel communication tool, which adds virtual computer-generated information to a viewer’s real life. The purpose of this paper is to investigate the effect of AR technology on consumers’ experience. Design/methodology/approach Four experiments were performed to test the effect of AR-based product display on the consumer’s attitude and the underlying mechanism of the effect. Findings The results have demonstrated that self-referencing reality simulation mediates the effect of AR technology on consumers’ product attitude, and the effect is attenuated when the consumers hold quality beliefs rather than taste beliefs or when the product is a luxury brand. Practical implications The current research suggests that managers should develop and adopt AR technology when presenting their products in front of consumers, which can improve consumers’ product attitude. Additionally, emphasizing the taste of target products rather than quality will strengthen the positive effect of AR technology on product attitude. Furthermore, managers of luxury brands should present their products in a traditional way, which induce an exclusive perception rather than using AR technology. Originality/value This paper investigates the underlying mechanism of how AR technology improves consumers’ product attitude and shopping experience, to fill the gap in consumers’ unique experience with AR technology. Furthermore, the moderated effect of consumer beliefs and product difference has also been examined in this study, which attributes to the research of product differentiation.
CaaSSET: A Framework for Model-Driven Development of Context as a Service

Developing a stand-alone context-aware application is costly and time-consuming due to the diversity and complexity of the requirements. Context-as-a-Service (CaaS) seeks to overcome the issues by following the idea of Software-as-a-Service (SaaS). The CaaS delivery model attempts to separate context service consumers from context service providers. However, CaaS shifts the complexities of context service provisioning and management from consumers toward providers. Considering the merits of Model-Driven Development (MDD) in coping with the complexities of problems in different domains, we leverage MDD in the CaaS domain and propose the CaaSSET framework to ease the development of context services. A prerequisite for realizing this aim is to specify the CaaS domain as a meta-model. Our CaaSSET framework consists of a CaaS reference model, a context service meta-model, a graphical modeling tool, a code generation tool, and a context service management tool. Using CaaSSET, the context services are modeled in a graphical environment and transformed into executable context web services (semi-) automatically. To evaluate the applicability of the CaaSSET framework, we have modeled and developed a tourism context service as a case study. We have also compared the framework with the related work based on 44 evaluation criteria, which we have extracted from 14 context service requirements.
An empirical catalog of code smells for the presentation layer of Android apps

Abstract Software developers, including those of the Android mobile platform, constantly seek to improve their applications’ maintainability and evolvability. Code smells are commonly used for this purpose, as they indicate symptoms of design problems. However, although the literature presents a variety of code smells, such as God Class and Long Method, characteristics that are specific to the underlying technologies are not taken into account. The presentation layer of an Android app, for example, implements specific architectural decisions from the Android platform itself (such as the use of Activities, Fragments, and Listeners) as well as deal with and integrate different types of resources (such as layouts and images). Through a three-step study involving 246 Android developers, we investigated code smells that developers perceive for this part of Android apps. We devised 20 specific code smells and collected the developers’ perceptions of their frequency and importance. We also implemented a tool that identifies the proposed code smells and studied their prevalence in 619 open-source Android apps. Our findings suggest that: 1) developers perceive smells specific to the presentation layer of Android apps; 2) developers consider these smells to be of high importance and frequency; and 3) the proposed smells occur in real-world Android apps. Our domain-specific smells can be leveraged by developers, researchers, and tool developers for searching potentially problematic pieces of code.
Conceptual framework for smart university

Abstract Determine the potential value of the technology that an organization has, for example, the university, is an issue that is not easy to answer. The only way to look to the future, since there is no data, is to have a very good theory or model. The application of maturity models creates useful benefits. Its application covers a diversity of problem areas related to the development, use and impact of information technology. The human-computer interaction is part of a process of creating technology. Generating a model of intelligent maturity can be seen as an evolutionary approach for a traditional university to progress to several levels of maturity of a smart university.
Pipe-Torch: Pipeline-Based Distributed Deep Learning in a GPU Cluster with Heterogeneous Networking

Because training a deep neural network (DNN) takes arduous amounts of time and computation, often researchers expedite the training process via distributed parallel training on GPUs. On one hand, this lower computing-to-communication ratio makes traditional data parallelism difficult to scale, and traditional model parallelism leads to low GPU utilization. Both make it difficult to obtain a higher speedup. On the other hand, multi-GPU systems exhibit complex connectivity among GPUs. Overall, workload schedulers must consider hardware topology and workload communication requirements to allocate GPU resources for optimal execution time and improved utilization in GPU clusters with heterogeneous networking. Thus, in this paper, we introduce Pipe-torch, an improved pipeline-hybrid parallelism method (using both data and model parallelism) in a heterogeneous network environment. Ultimately, the framework's model partition algorithm aims to expedite pipeline-hybrid parallelism training between heterogeneous network-connected GPUs. Experiments with four different DNN models show that Pipetorch averages 1.4x speedup compared to data parallelism.
Comparison of Deep Autoencoder Architectures for Real-time Acoustic Based Anomaly Detection in Assets

This paper compares several different Autoencoder architectures for unsupervised anomaly detection in acoustic signals. The goal of unsupervised anomaly detection in sound is to detect anomalies without having prior knowledge regarding potential anomalies. Use of autoencoders (AE) to learn a normal model is a state-of-the-art technique for unsupervised anomaly detection. However, the main focus is almost always to increase the difference between the reconstruction error of normal and anomalous data, without taking into account network architecture and speed. This is not a problem when enough computational power is available. However, if the aim is to bring this system to the edge, meaning implementing it on the sensor or close to the sensor, speed and amount of parameters of the network become more important. In this paper we will do a comparative study between different AE architectures. For this comparison both the detection accuracy and the computational complexity will be taken into account. Based on this information it can be decided which AE is most suited to be implemented on hardware for real-time applications.
An Empirical Study Assessing Source Code Readability in Comprehension

Software developers spend a significant amount of time reading source code. If code is not written with readability in mind, it impacts the time required to maintain it. In order to alleviate the time taken to read and understand code, it is important to consider how readable the code is. The general consensus is that source code should be written to minimize the time it takes for others to read and understand it. In this paper, we conduct a controlled experiment to assess two code readability rules: nesting and looping. We test 32 Java methods in four categories: ones that follow/do not follow the readability rule and that are correct/incorrect. The study was conducted online with 275 participants. The results indicate that minimizing nesting decreases the time a developer spends reading and understanding source code, increases confidence about the developer's understanding of the code, and also suggests that it improves their ability to find bugs. The results also show that avoiding the do-while statement had no significant impact on level of understanding, time spent reading and understanding, confidence in understanding, or ease of finding bugs. It was also found that the better knowledge of English a participant had, the more their readability and comprehension confidence ratings were affected by the minimize nesting rule. We discuss the implications of these findings for code readability and comprehension.
Inorganic geochemistry and petroleum source evaluation of organic black shale in the Mamfe Basin (West Africa)

The shales of the Mamfe basin, West Africa, were studied using inorganic geochemical analysis to investigate the origin of the organic matter, environment of its deposition, paleoproductivity and organic matter preservation, organic matter maturity and petroleum potential. Shale samples from sites 1-7 have V/Ni ratios of: 2.9, 2.36, 3.14, 2.94, 3.04, 1.76 and 2.23 respectively. The V/Ni values of samples from sites 1,2,4, and 7 indicate a mixed terrestrial and marine organic matter origin, while shales from the other three sites have values close to those that represent mixed marine-terrestrial organic matter. Ni/Co, V/Cr and U/Th ratios of the black shales are <3, <2, and <0.75 respectively, indicating organic matter deposition in an oxic environment. The Ba/Al ratios of the studied shales average 64 while the P/Ti ratio averages 1.84 indicating a moderate to high paleoproductivity. The moderate to high paleoproductivity and rapid sedimentation influence the preservation of organic matter in the Mamfe shales. The shales have K/Rb ratios, ranging between 425 and 640, enrichment of LREE over HREE, MREE over HREE, positive Gd, Eu and negative Ce anomalies. These results indicate that the organic matter found in the shales is not yet at the level of maturity to produce petroleum, but may became good petroleum source rocks in the near future.
Intelligent tutoring chatbot for solving mathematical problems in High-school

Nowadays, information technology is applied for teaching and learning in high-school. It is an essential trend for the modern education, especially building intelligent systems to support the learning of mathematics. In the math subjects of Vietnamese high-school, problems about surveying properties of a function are popular in high-school exams. There are some current tools can solve them automatically. However, they only can display the graph of a function; they cannot solve problems about determining the value of a parameter to satisfy some conditions about this function's properties. In this paper, we will present a method to design an intelligent chatbot for solving problems in this knowledge domain. It plays as an instructor to give some tips and tutor the learner how to solve problems automatically. This program can communicate with the learner through the question-answering process by chatbot. This tutoring of our system simulates the tutoring method of the teacher to the students in practice.