Reviewerly/exHarmony huggingface dataset:
Flexible quantum protocol for nearest private query

Nearest private query (NPQ) allows user to query which element in the database is the nearest to his private data without revealing any private information and this query is typically used for location query services. However, the previous NPQ protocol only involved the implementation of its functions and ignored the user’s private experience. In addition, the average number of key bits obtained by the user is only determined by the size of the database. In order to improve the flexibility and practicality of the protocol, we proposed a flexible protocol for nearest private query based on quantum oblivious key distribution (QOKD). The parameter [Formula: see text] was added to the prepared quantum state. The average number of the key bits Alice obtained can locate on any fixed value by adjusting the parameter [Formula: see text] whatever the database size was. In addition, our protocol is flexible and fault-tolerant.
Mass Media Evaluation Using Topic Modelling

Automatic evaluation of public opinion is an actual problem in many areas, including both governmental and private sectors. There is number of scientific schools and corporations which work on to solve the problem of automatic evaluation of publications in media, social networks and other internet resources, in order to solve such problems as evaluating public image of a company, product or persona, evaluating work of PR departments and agencies, analyzing the most socially significant and resonant newsmakers and issues. The problems involve area of natural language processing and understanding, which is considered to be technologically and mathematically complex, and is nowadays being solved using deep learning models, which require a large marked dataset with texts of similar domain, which is hard and expensive to obtain. Another problem of such systems is performance issues. In this work an informational system is described, which attempts to solve the outlined problems. In the paper an approach is proposed, which allows to classify the most important/positive/negative/resonant topics and publications, and to analyze their dynamic characteristics. The proposed approach is not based on manual creation of keyword dictionary, or labelling of big amounts of documents and allows to evaluate documents according to arbitrary criterion. The approach was verified on one criterion by comparing it’s results to a dictionary-based system.
A Survey on Blockchain Technologies and Its Consensus Algorithms

The evolution and development in blockchain technologies have attracted both research academia and industries. A typical blockchain stores data in a permanent and immutable way in form of blocks connecting, forming a chain of data. The whole system is made decentralized so that anyone connected to the network can verify the data, defining its P2P distributed nature. Blockchain has many components among which the core component is consensus protocol. This protocol is responsible for the security and performance of the blockchain. The consensus protocol introduced by Nakamoto in Bitcoin led the foundation stone for more innovative alternative consensus mechanisms. In this paper, we will conduct a systematic review of blockchain technology and its consensus algorithms and further analyze them based on some essential features and factors.
A parametrised functional interpretation of Heyting arithmetic

This paper presents an abstract parametrised functional interpretation of WE-HAω. It is based on families of parameters allowing for different degrees of freedom on the design of concrete interpretations. In this way, we are able to generalise previous work on unifying functional interpretations by including in the unification the more recent bounded and Herbrandized functional interpretations.
Effects of Three Common Choral Configurations on Acoustic and Perceptual Measures of a Soprano Section’s Sound

The purpose of this study was to assess the effects of three choral configurations on a soprano section’s sound. The first configuration resembled a choir section without an assigned standing position, the second configuration grouped singers by timbre, and the third used acoustic-compatibility placement. Three conductors configured a university soprano section ( N = 13) who were audio-recorded singing in each configuration and answered questions about their perceptions. Audio recordings were analyzed acoustically using long-term average spectra and perceptually through pitch analysis and listener perceptions. Results indicated that participants sang with significantly increased spectral energy in the acoustic-compatibility configuration ( p &lt; .001), and both singer and listener participants preferred intentional standing configurations over the random standing configuration. Findings from this study suggest that choral directors can use intentional configurations in conjunction with 2 ft intersinger spacing to improve singer comfort and overall sound.
Learning to Steer Swarm-vs.-swarm Engagements

UAVs are becoming increasingly commonplace, and with their growing popularity, the question of how to counter a swarm of UAVs operated by bad actors becomes more critical. In this paper, we explore the possibility of using a team of fixed-wing UAVs to counter an adversarial swarm of fixed-wing UAVs. To learn to coordinate counter-swarm tactics, we propose Situation-Dependent Option-action Evaluation (SDOE), a distributed and scalable actor-critic RL architecture. Our approach enables each UAV to evaluate options over a set of scripted tactics as well as the option to maneuver freely, allowing for emergent team behavior. A key to the scalability of our approach is a novel, distributed neural network architecture that enables agents to share situational awareness and select tactics in a pairwise fashion, allowing agents to choose who to coordinate with, when, and how regardless of the size of the swarm. We test agents trained with our approach in simulated engagements of up to 16-vs.-16 UAVs, and find that, even as the size of the engagement increases, the agents trained using SDOE against a greedy, non-coordinating tactic win engagements against a team of greedy agents more reliably than another team of greedy agents.
An Efficient Login Authentication System against Multiple Attacks in Mobile Devices

Access management of IoT devices is extremely important, and a secure login authentication scheme can effectively protect users’ privacy. However, traditional authentication schemes are threatened by shoulder-surfing attacks, and biometric-based schemes, such as fingerprint recognition and face recognition, that are commonly used today can also be cracked. Researchers have proposed some schemes for current attacks, but they are limited by usability. For example, the login authentication process requires additional device support. This method solves the problem of attacks, but it is unusable, which limits its application. At present, most authentication schemes for the Internet of Things and mobile platforms either focus on security, thus ignoring availability, or have excellent convenience but insufficient security. This is a symmetry problem worth exploring. Therefore, users need a new type of login authentication scheme that can balance security and usability to protect users’ private data or maintain device security. In this paper, we propose a login authentication scheme named PinWheel, which combines a textual password, a graphical password, and biometrics to prevent both shoulder-surfing attacks and smudge attacks and solves the current schemes’ lack of usability. We implemented PinWheel and evaluated it from the perspective of security and usability. The experiments required 262 days, and 573 subjects participated in our investigation. The evaluation results show that PinWheel can at least effectively resist both mainstream attacks and is superior to most existing schemes in terms of usability.
Improved Cross-Lingual Document Similarity Measurement

We present an efficient and effective system to identify similar documents in the target language for a given document in the source language. For our work, we used source and target documents from the Sinhala and English languages. However, the system can be extended to any other languages for which suitable embeddings exist. We have improved both accuracy and speed compared with the current state-of-the-art. We have compiled a corpus of possible target documents in each of the two languages of interest. For a source document, we compute the distance between it and each of the documents in the corpus using their sentence embeddings. We used nearest neighbor retrieval to speed up the matching by restricting the set of target documents searched for a given source document. We used a scoring function and matching algorithm to properly pair the identified sentences. To improve accuracy, we used number matching and named entity matching.
Approbation of software agent Evaluator in a nonspecific environment for extension of its purpose

The main goal of DeLC (Distributed eLearning Center) system is to provide students and teachers with an interactive, proactive and personalized e-Learning environment. The scope of the project includes many different developments, covering aspects of learning, teaching and getting statistical information of educational process. One of these developments refers to extending the standard architecture of this portal with agent-oriented environment, adding reactivity, pro-activity and some additional aspects of personalization. The agents in the agent-oriented extension (referred to as Agent Village) are working for last eight years and gathered many data about their work and students' behavior, generating different ideas for improvements. This paper presents approbation of the Evaluator agent, put in very different conditions than usually, proving that it's capable to extend its work in different areas than those previously intended for.
Exploiting Spatial Relation for Reducing Distortion in Style Transfer

The power of convolutional neural networks in arbitrary style transfer has been amply demonstrated; however, existing stylization methods tend to generate spatially inconsistent results with noticeable artifacts. One solution to this problem involves the application of a segmentation mask or affinity-based image matting to preserve spatial information related to image content. The main idea of this work is to model spatial relation between content image pixels and thus to maintain this relationship in stylization for reducing artifacts. The proposed network architecture is called spatial relation-augmented VGG (SRVGG), in which long-range spatial dependency is modeled by a spatial relation module. Based on this spatial information extracted from SRVGG, we design a novel relation loss which can minimize the difference of spatial dependency between content images and stylizations. We evaluate the proposed framework on both optimization-based and feedforward-based style transfer methods. The effectiveness of SRVGG in stylization is demonstrated by generating stylized images of high quality and spatial consistency without the need for segmentation masks or affinity-based image matting. The quantitative evaluation also suggests that the proposed framework achieve better performance compared with other methods.
Scalable Log Auditing on Private Blockchains via Lightweight Log-Fork Prevention

This work presents TxChecker, a secure logging system over a private blockchain with two salient features: 1) TxChecker prevents log forking attacks without trusting any external party other than the blockchain, 2) TxChecker achieves a low cost on log auditors that is proportional to the data being audited. TxChecker employs a novel scheme to map the misbehavior of forking logs to the double-spending transactions, which are invalidated by the underlying blockchain. In the TxChecker protocol, clients and the server in the domain infrastructure both attest to a history of concurrent operations and send blockchain transactions. A prototype of TxChecker is implemented in HyperLedger Fabric. Evaluation shows that TxChecker reduces the costs of log auditors significantly compared to replication-based log schemes.
SS-AOE: Subspace Based Classification Framework for Avoiding Over-Confidence Errors

Misclassification with high confidence can cause great harm, especially in high-risk tasks. This is because when faced with unfamiliar samples which outside the distribution of known samples, classifiers are prone to give overly high prediction confidence. In this paper, we propose a classification framework with effective confidence estimation based on a reduced dimensional space (subspace). Intuitively, our method is designed to give low confidence to "unfamiliar" or out-of-domain test samples, so to "know what we do not know". The effectiveness of this method is supported by a real-world Stroke diagnosis data set. We use multiple metrics for evaluation, including Brier score, calibration curve, and expected calibration error (ECE).
Improving Arabic dependency parsers by using dependency relations

Arabic dependency parsers perform poorly compared to parsers of other languages. There is little research on improving the performance of Arabic parsers. However, recent research has shown slight improvements in the performance of dependency parsers by utilizing the lexical level of a dependency treebank. To our knowledge, no previous study has studied the effect of utilizing the syntactic level. In this study, we empirically investigated the impact of varying the set of dependency relations on the performance of Arabic dependency parsers. The results were compared to those of previous studies, and showed that having an appropriate set of dependency relations could improve the performance of an Arabic dependency parser.
Transformer-Based Unified Neural Network for Quality Estimation and Transformer-Based Re-decoding Model for Machine Translation

In this paper, we describe our submitted system for CCMT 2020 sentence-level quality estimation subtasks and machine translation subtasks. We propose two models: (i) a Transformer-based unified neural network for the quality estimation of machine translation, which consists of a Transformer bottleneck layer and a bidirectional long short-term memory network that are jointly optimized and fine-tuned for quality estimation, and (ii) a Transformer-based re-decoding model for machine translation, which takes the generated machine translation outputs as the approximate contextual environment of the target language and synchronously re-decodes each token in the machine translation outputs. Experimental results on the development set show that the proposed approaches outperform the baseline systems.
What Affects the Word Order of Target Language in Simultaneous Interpretation

A simultaneous interpretation has to be conducted to shorten delay and reduce working memory load. The interpreters adopt a strategy that generates a target language in a word order which is similar to the source language. To make it clear how the interpreters decide the word order in practice, we conduct a statistical study based on the comparison of the word order between translation and simultaneous interpretation. The analysis reveals that in 10.5% of all cases, interpreters maintain the word order of source language, instead of adopting inversion as the translation, and that syntactic factors, including dependencies of chunk pairs, length of post-modifier, affect the word order decision more than non-syntactic factors.
Fault Tolerance in Multiagent Systems

A decentralized multiagent systems (MAS) is comprised of autonomous agents who interact with each other via asynchronous messaging. A protocol specifies a MAS by specifying the constraints on messaging between agents. Agents enact protocols by applying their own internal decision making. Various kinds of faults may occur when enacting a protocol. For example, messages may be lost, duplicates may be delivered, and agents may crash during the processing of a message. Our contribution in this paper is demonstrating how information protocols support rich fault tolerance mechanisms, and in a manner that is unanticipated by alternative approaches for engineering decentralized MAS.
Identification and Formal Privacy Guarantees

The reliance of empirical economic research on highly sensitive individual datasets and the increasing availability of public individual-level data that comes. e.g., from social networks, public government records and directories creates privacy risks as adversaries may potentially de-identify anonymized records in sensitive research datasets. To deal with such risks, the computer science research proposed differential privacy (DP) -- a formal criterion for the evaluation of non-disclosure guarantees for released statistics and the related methodology to ensure such guarantees. While previous work on DP focused on DP guarantees for specific data statistics, its impact on identification of parameters of interest determined from the population distribution has not been studied. This paper bridges this gap.In this paper we find that there is a broad class of population parameters that are not identified. Moreover, those parameters are not even partially identified, i.e. one cannot construct a set that would contain their population values. Population parameters of interest can be only characterized as elements random sets which requires the application of the toolkit of the random set theory to analyze their population properties. Identification becomes possible if the target parameter can be deterministically mapped within the random set. In that case, a full exploration of the support of the distribution of the random set of the weak limits of differentially private estimators can allow the data curator to select a sequence of instances of differentially private estimators that is guaranteed to converge to the target parameter in probability. We provide a decision-theoretic approach to this selection.Our results indicate that expansion of formal privacy guarantees to socio-economic datasets requires further work on integrating data analysis with results and concepts from the random set theory as well as techniques for partial identification and inference.
Multilingual and Multimodal Access

Though semantic data models have become mainstream and gained popularity, they largely handle only textual data, and that only in English. Language-independent preservation of semantics and context related to a domain is required to attain equal status for all languages. Through this we can overcome national barriers which come from variations in languages. This will also facilitate cross-lingual access of information. This chapter discusses the motivation of multilingualism and multimodality and presents and exemplifies the ways through which they can be achieved in semantically enriched knowledge. Different use cases to access and manage this setup are also presented.
Cyber-Attacks, Cryptocurrencies and Cyber Security

This chapter provides comprehensive evidence on the effects of cyber-attacks (cyber-crime, cyber espionage, cyber warfare and hacktivism) and cyber security on three types of cryptocurrencies (Bitcoin, Ethereum and Litecoin). Our methodology is based on time series regressions using cyber-attack data; for the analysis, we consider realised returns, realised volatilities, trading volumes and risk-adjusted returns. Stronger cyber security is found to increase realised and risk-adjusted returns, to reduce risk and to increase trading volumes as investors build up their confidence to trade in a safer digital environment. Ethereum, which is a smart contract, benefits the most from cyber security. Bitcoin and Litecoin investors can be characterised as risk-loving and overreact in their attempt to exploit arbitrage opportunities in the event of cyber-attacks targeting cryptocurrency exchanges and industry sector; this type of behaviour is attenuated (intensified) by stronger cyber security in the case of the former (latter) type of attacks. On the other hand, Bitcoin investors in particular become risk-averse when cyber-attacks hit the US government sector. Ethereum is relatively more immune to cyber-attacks regardless of their targets and types in comparison to Bitcoin and Litecoin. However, hacktivism increases risk for all three cryptocurrencies considered. Our results suggest that policy makers and regulators need to be better informed about the impact of cyber-attacks on cryptocurrencies and that more appropriate strategies should be designed and put in place to enhance cyber security.
Saliency prediction based on object recognition and gaze analysis

Abstract Predicting the human visual attention in an image is called saliency prediction and is an active research area in the field of neuroscience and computer vision. Early works on saliency prediction was performed by using low‐level features. In recent years, convolutional neural networks have been adapted for saliency prediction and achieved the state‐of‐the‐art performance. However, the eye‐gaze depends on the personality of each viewer and conventional methods did not take into account such individual properties of the viewer. Therefore, this paper proposes a novel saliency prediction method considering the influence of eye‐gaze. Assuming that personality can be expressed as the degree of attention to an object, our proposed method considers the personality by learning which objects are likely to be perceived by each viewer and weighting the universal saliency map with the generated mask based on the object detection results. The experimental results show that the proposed universal saliency map achieves higher accuracy than conventional methods on the public dataset, and the proposed weighted saliency map can reflect the variation of the eye‐gaze influences among viewers.