Reviewerly/exHarmony huggingface dataset:
Twitter-based selection of topics that users are interested in but are not familiar with

This paper describes topic decision system to select topics that users are interested in but are not familiar with. In recent years, dialogue systems have been implemented in various devices such as smart phones and robots. Although many of them are categorized into task oriented dialogue systems, non task oriented dialogue systems are also important for longterm human-system interaction. Many dialogue systems are also question answering systems, that is, the user inputs a question and the dialogue system returns its answer. However, it is important that the dialogue system can start the conversation to users especially in non task oriented dialogue systems. Adequate topics are required to start the conversation in such cases. We therefore aim to construct a topic decision system. In this study, we define adequate topics as the topics that users are interested in but are not familiar with and propose a topic decision system using the keyword search function of Twitter.
DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine Model

Predicting user positive response (e.g., purchases and clicks) probability is a critical task in Web applications. To identify predictive features from raw data, the state-of-the-art extreme deep factorization machine model (xDeepFM) introduces a new interaction network to leverage feature interactions at the vector-wise level explicitly. However, since each hidden layer in the interaction network is a collection of feature maps, it can be viewed essentially as an ensemble of different feature maps. In this case, only using a single objective to minimize the prediction loss may lead to overfitting and generate correlated errors. In this article, an ensemble diversity enhanced extreme deep factorization machine model (DexDeepFM) is proposed, which designs the ensemble diversity measure in each hidden layer and considers both ensemble diversity and prediction accuracy in the objective function. In addition, the attention mechanism is introduced to discriminate the importance of ensemble diversity measures with different feature interaction orders. Extensive experiments on three public real-world datasets are conducted to show the effectiveness of the proposed model.
Does a Limited Defining Vocabulary Make Definitions Syntactically More Complex?

Abstract Definitions in learners’ dictionaries are usually written within a limited defining vocabulary (DV), that is, a set of lexical units specified prior to defining. Some researchers claim that this approach to vocabulary control may lead to definitions being syntactically complex, convoluted, and wordy. This paper aims to examine whether the introduction of a limited DV in OALD5 made its definitions more difficult to read for learners as compared to the definitions in OALD4 , which were written with no explicit restrictions on the definition vocabulary. The study examines a selection of construction patterns that are potentially difficult for less advanced learners, using quantitative and qualitative methods. The examination shows that the introduction of the limited DV had no effect on most parameters studied. However, it led to significant increases in the length of definitions and the number of nominal constructions with a postmodifying past participle (e.g., performance given , spice ground, phrase used ).
A new multi-party quantum private comparison based on <i>n</i>-dimensional <i>n</i>-particle GHZ state

With the help of a semi-honest third party Calvin, n parties [Formula: see text] can determine whether all of their private inputs [Formula: see text] are equal or not without leaking any information. In this paper, we present a novel protocol using special quantum unitary operations and n-dimensional n-particle GHZ states. The proposed protocol is correct. It can also resist various outside attacks and overcome the problem of information leakage.
Pattern-Aware and Noise-Resilient Embedding Models

Knowledge Graph Embeddings (KGE) have become an important area of Information Retrieval (IR), in particular as they provide one of the state-of-the-art methods for Link Prediction. Recent work in the area of KGEs has shown the importance of relational patterns, i.e., logical formulas, to improve the learning process of KGE models significantly. In separate work, the role of noise in many knowledge discovery and IR settings has been studied, including the KGE setting. So far, very few papers have investigated the KGE setting considering both relational patterns and noise. Not considering both together can lead to problems in the performance of KGE models. We investigate the effect of noise in the presence of patterns. We show that by introducing a new loss function that is both pattern-aware and noise-resilient, significant performance issues can be solved. The proposed loss function is model-independent which could be applied in combination with different models. We provide an experimental evaluation both on synthetic and real-world cases.
An Efficient Approach for Mining High-Utility Itemsets from Multiple Abstraction Levels

The goal of the high-utility itemset mining task is to discover combinations of items which that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, it ignores the categorization of items. To solve this issue, the ML-HUI Miner algorithm was presented. It combines item taxonomy with the HUIM task and is able to discover insightful itemsets, which are not found in traditional HUIM approaches. Although ML-HUI Miner is efficient in discovering itemsets from multiple abstraction levels, it is a sequential algorithm. Thus, it cannot utilize the powerful multi-core processors, which are currently available widely. This paper addresses this issue by extending the algorithm into a multi-core version, called the MCML-Miner algorithm (Multi-Core Multi-Level high-utility itemset Miner), to help reduce significantly the mining time. Each level in the taxonomy will be assigned a separate processor core to explore concurrently. Experiments on real-world datasets show that the MCML-Miner up to several folds faster than the original algorithm.
A Model for Detecting Sounds-alike Phishing Email Contents for Persons with Visual Impairments

In the Internet environment, most people, organizations communicate with each other through emails. Although Email is a significant mechanism for communication, a phishing email is one of the exponential growth in cybercrime. In phishing, attackers randomly send fraud emails to victims by practicing a legitimate site's identification which encourages the user to believe in it and disclose their credentials. As increasing of phishing emails, some kinds of phishers exercise sound-alike contents within the email to target persons with visual impairments. A fundamental shortcoming of the existing research is that it does not address the difficulty of sound-alike keywords. Therefore, this paper proposes a model titled SPEDAS(Sounds-alike contents Phishing Email Detection Assist to Screen reader users). This Model detects phishing emails by verifying sound-alike contents. The result of the experiment shows that the proposed model provided 83% accuracy.
Newspaper Text Recognition Printed in Gurumukhi Script: SVM Versus MLP

With the advancement in technology, many techniques have been emerged to recognize characters printed in Gurumukhi script. Technology to recognize characters is perhaps the greatest agent of change in the modern era. While never without risk, technological progression has promised very innovative solutions to the most common challenges in recognition of text printed in any script. These kinds of techniques require a feature vector of good quality to recognize a character. Moreover, the classification of a pattern is important to classify the individual class of an image or character. In this paper, we have experimented on three most commonly used feature extraction methods that are zoning, diagonal, and centroid feature extraction method. We have analyzed the performance of all three feature extraction methods using two classifiers that are SVM (linear and RBF kernel) and MLP. Feature extraction techniques and classifiers are evaluated based on obtained recognition results. Authors have reported maximum recognition accuracy of 91.12% using diagonal features and MLP classifier.
Imitating Functional Operations for Mitigating Side-Channel Leakage

Inspired by the idiom, “Mitigation (prevention) is better than cure!”, this work presents a random yet cognitive side-channel mitigation technique that is independent of underlying architecture and/or operating system. Unlike malware and other cyber-attacks, side-channel attacks (SCAs) exploit the architectural and design vulnerabilities and obtain sensitive information through the side channels. In contrast to the existing randomization-based side-channel defenses, we introduce a cognitive perturbation-based defense, Covert-Enigma, where the introduced perturbations look legit, but lead to an incorrect observation when interpreted by the attacker. To achieve this, the perturbations are injected at appropriate time instances to introduce additional operations, thereby misleading the attacker making the extracted data futile. To further make the attack more intricate for the attacker, proposed Covert-Enigma offers two modes of operation, chosen by the user, to determine the kind of induced cognitive perturbations— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">arbitrary</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyclic</i> modes. Arbitrary mode selects a group of key bits and flips them during every execution of the victim. Cyclic mode exhibits similar behavior, except it selects a new set of bits to flip after “ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> ” cycles as chosen by the user. The cognitive perturbations are introduced in the form of a wrapper application to the victim, thus imposing no requirements on architectural level modifications nor soft updates/edits to the operating system. We report rigorous evaluation of the proposed Covert-Enigma protecting RSA cryptosystem attacked by Flush+Reload crypto SCA along with the bit(s) recovered after observing RSA under attack. Compared to traditional randomization-based defenses, proposed cognitive Covert-Enigma leads to 50% less overhead.
Content extraction of historical Malay manuscripts based on Event Ontology Framework

This article aims to explore representation of the content knowledge of historical Malay manuscripts by extracting the event features using an event ontology framework. The manuscript used during the testing is Sulalatus Salatin (Sejarah Melayu ) by Abdul Ahmad Samad and it was published at University of Malaya Digital Library database. In aligning to a domain-specific ontology, the Simple Event Model (SEM) model is adopted and an event-based ontology for historical Malay manuscripts is designed. Information extraction approach is done manually to extract events from the manuscript and mapped into Protégé editor. Competency questions were constructed and submitted to the Protégé editor using SPARQL to check the ontology capability of providing answers as well as to examine its correctness. Event-based ontology model assists in discovering and representing the content knowledge of historical Malay manuscripts and supports organisation of knowledge. All the main concepts are extracted from selected Malay manuscript and 17 concepts used to develop the event-based ontology model. The knowledge was verified by three domain experts in Malay manuscript. In the findings, the interrater reliability for Event and Actor instances is 84%, which means 16% of instances and its type are incorrect and need amendment. For Place, interrater reliability is 95% and 99% for Role. Meanwhile, the experts achieved 100% agreement for Time. In addition, the experts agreed that the concepts, properties and instances for Malay Manuscript Ontology and complied with the criteria of consistency, completeness, conciseness, expandability and ease of use. The development of the event-based model of an ontology-based system with a high level of semantic granularity reflects the various cultural riches and intellectual aspect stored in Malay manuscripts. This will enable systematic research of the knowledge embedded in the manuscripts and make it widely and easily accessible by everyone.
Secure banking transaction using Blockchain

Online Banking offers customers the accessibility of managing one’s funds anytime, anywhere. However, any online transactions will be prone to security threats. Existing system uses two way authentication factor(OTP) that are easily cracked by cyber attackers leading to customers having their account details compromised while not having the provision of attacker’s identification. Therefore in this paper we propose mobile SIM serial based verification system to secure mobile transactions on mobile devices with Blockchain based server side secure system. The subscriber identity module(sim) serial number is registered with the user’s account and in case an intruder tries to initiate a transaction from some other sim, there arises a mismatch of the login credentials then system sends location information of the intruder to the bank. The bank then alerts the registered user by mailing them the intruder’s details. Since the mobile sim serial number is unique to the sim and is not provided manually rather extracted, there is no possibility of intruder initiating the transaction from other device, thus overcoming the concerns related to OTP. The Ethereum Blockchain technology provides server side database security by verifying the digital signatures during the transaction and consensus algorithm for transaction confirmation. Blockchain based security is mathematically proved for secure Banking transaction.
Hierarchical Context-Aware Recurrent Network for Session-Based Recommendation

Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers' interactive behaviors.The user's behavior in the session not only contains the item sequence but also rich context information such as how the user navigates to the item, the operation type, and the dwell time on the item, which may impact users' next actions.How to incorporate these contextual features effectively for session-based recommendations remains a great challenge.In this paper, we divide them into two hierarchies: session-context at the macro-level and event-context at the microlevel, and then propose a Hierarchical Context-aware Recurrent Network (HiCAR) by incorporating both users' micro-interaction with the item and the two-hierarchy contexts, wherein a Session Context Learning module with the n-way hybrid strategy is adopted to model multi-feature interactions in the session-context.Moreover, an Event Context Learning module consists of TIME-LSTM with a time gate is designed to model the sequential behavior with event-context.By experimenting on two real-world datasets, we find that our HiCAR model outperforms state-of-the-art baselines on both datasets, which demonstrates its advantages in modeling users' sequential behaviors and contexts simultaneously.
Moving Target Tracking for Single UAV In Open Outdoor Environment

The task of tracking a moving target by the UAV has two major challenges: one is that the multi-angle and multi-scale changes caused by the target motion make the target detection more difficult, and the other is the target loss due to variable direction of movement. Most of the existing research is limited to simulation studies, while the remaining few studies only allow the UAV to track specific target in specific environments. In order to solve these problems, we design and validate a new system framework for UAV tracking moving target in an open outdoor environment. In this framework, we use YOLOv3 to detect the multi-angle and multi-scale moving target, locate the target based on monocular vision, and design a tracking strategy, Mass Spring Algorithm (MSA), to prevent the loss of moving target. The experimental video <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://www.bilibili.com/video/BV1SD4y197gF/ shows that the system framework we designed is effective and successful. The results of comparative experiments to MSA shows that it effectively prevents the loss of target in the tracking process.
FL-GAN: Feature Learning Generative Adversarial Network for High-Quality Face Sketch Synthesis

Face sketch synthesis refers to transform facial photos into sketches. Recent research on face sketch synthesis has achieved great success due to the development of Generative Adversarial Networks (GAN). However, these generative methods prone to neglect detailed information and thus lose some individual specific features, such as glasses and headdresses. In this paper, we propose a novel method called Feature Learning Generative Adversarial Network (FL-GAN) to synthesize detail-preserving high-quality sketches. Precisely, the proposed FL-GAN consists of one Feature Learning (FL) module and one Adversarial Learning (AL) module. The FL module aims to learn the detailed information of the image in a latent space, and guide the AL module to synthesize detail-preserving sketch. The AL Module aims to learn the structure and texture of sketch and improve the quality of synthetic sketch by adversarial learning strategy. Quantitative and qualitative comparisons with seven state-of-the-art methods such as the LLE, the MRF, the MWF, the RSLCR, the RL, the FCN and the GAN on four facial sketch datasets demonstrate the superiority of this method.
Public-Coin Statistical Zero-Knowledge Batch Verification Against Malicious Verifiers

Suppose that a problem $$\varPi $$ has a statistical zero-knowledge ( $$\mathsf {SZK}$$ ) proof with communication complexity m. The question of batch verification for $$\mathsf {SZK}$$ asks whether one can prove that k instances $$x_1,\dots ,x_k$$ all belong to $$\varPi $$ with a statistical zero-knowledge proof whose communication complexity is better than $$k \cdot m$$ (which is the complexity of the trivial solution of executing the original protocol independently on each input). In a recent work, Kaslasi et al. (TCC, 2020) constructed such a batch verification protocol for any problem having a non-interactive $$\mathsf {SZK}$$ ( $$\mathsf {NISZK}$$ ) proof-system. Two drawbacks of their result are that their protocol is private-coin and is only zero-knowledge with respect to the honest verifier. In this work, we eliminate these two drawbacks by constructing a public-coin malicious-verifier $$\mathsf {SZK}$$ protocol for batch verification of $$\mathsf {NISZK}$$ . Similarly to the aforementioned prior work, the communication complexity of our protocol is $$\big (k+{\mathsf {poly}}(m) \big ) \cdot \mathrm {polylog}(k,m)$$ .
s-Elusive codes in Hamming graphs

A code is a subset of the vertex set of a Hamming graph. The set of s-neighbours of a code is the set of all vertices at Hamming distance s from their nearest codeword. A code C is s-elusive if there exists a distinct code $$C'$$ that is equivalent to C under the full automorphism group of the Hamming graph such that C and $$C'$$ have the same set of s-neighbours. We show that the minimum distance of an s-elusive code is at most $$2s+2$$ , and that an s-elusive code with minimum distance at least $$2s+1$$ gives rise to a q-ary t-design with certain parameters. This leads to the construction of: an infinite family of 1-elusive and completely transitive codes, an infinite family of 2-elusive codes, and a single example of a 3-elusive code. Answers to several open questions on elusive codes are also provided.
The Nonlinear Impact of Political Institutional Quality on Financial Inclusion

This study investigates the nonlinear relationship between political institutions and financial inclusion in a panel of 74 developing countries using annual data from 2007 to 2016. This study uses the financial inclusion index and two components of financial inclusion, namely access and availability to financial services. The estimated model using the generalized method of moments (GMM) system (SYS-GMM) revealed that democracy has a significant U-shaped impact on the financial inclusion index, and access to and availability of financial services. However, a similar effect on the index of financial inclusion is not robust to the removal of outliers. Overall, the findings confirm that a better quality of political institutions, i.e., when it exceeds an inflection/threshold point, would lead to a higher degree of financial inclusion—as captured by the access and availability of financial services, for example the number of deposit accounts, automated teller machines (ATM), and bank branches. Our finding of the U-shaped impact on overall financial inclusion indicators are robust to outliers. The implication is that countries with a better quality of political institutions are predicted to be associated with high levels of financial inclusion. Whereas, countries with a low quality of (democratic) political institutions hinder the delivery of financial inclusion.
Differentially Private Histograms in the Shuffle Model from Fake Users

There has been much recent work in the shuffle model of differential privacy, particularly for approximate d-bin histograms. While these protocols achieve low error, the number of messages sent by each user—the message complexity—has so far scaled with d or the privacy parameters. The message complexity is an informative predictor of a shuffle protocol’s resource consumption. We present a protocol whose message complexity is two when there are sufficiently many users. The protocol essentially pairs each row in the dataset with a fake row and performs a simple randomization on all rows. We show that the error introduced by the protocol is small, using rigorous analysis as well as experiments on real-world data. We also prove that corrupt users have a relatively low impact on our protocol’s estimates.
Hybrid dual attack on LWE with arbitrary secrets

Abstract In this paper, we study the hybrid dual attack over learning with errors (LWE) problems for any secret distribution. Prior to our work, hybrid attacks are only considered for sparse and/or small secrets. A new and interesting result from our analysis shows that for most cryptographic use cases a hybrid dual attack outperforms a standalone dual attack, regardless of the secret distribution. We formulate our results into a framework of predicting the performance of the hybrid dual attacks. We also present a few tricks that further improve our attack. To illustrate the effectiveness of our result, we re-evaluate the security of all LWE related proposals in round 3 of NIST’s post-quantum cryptography process, and improve the state-of-the-art cryptanalysis results by 2-15 bits, under the BKZ-core-SVP model.