Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Task Scheduling for Time-Sharing Services of Autonomous Vehicles

Sharing of autonomous vehicles between multiple users can potentially be the ultimate solution for increasing the efficiency of the transportation system. In a time-sharing scenario, autonomous vehicles (AV) owners share their vehicles to others at their unwanted times without incurring any human efforts. However, such sharing service requires the disclosure of users' locations and route information, raising severe privacy concerns and issues. In this paper, we propose a privacy-preserving task scheduling scheme for time-sharing services of autonomous vehicles. First, we design a matching scheme that finds the feasible requesters for each available AV. Then, we propose a scheduling scheme using different approaches for assigning requesters to the AVs based on different system parameters. Both schemes work efficiently without requiring users to share their exact locations and route details while maximizing the AV owner profit and minimizing the requester cost. Specifically, our schemes enable an untrusted matching server to match owners and requesters using a set of intermediate destination (ID) locations in the travel paths. Only if the service can be given to the requester efficiently, the owner and the requester share the details of the routes. All the calculations for verification of the feasibility of the service are done on the untrusted server. We show the accuracy of our proposed scheme through extensive simulations on real data. The results confirm that our traffic-based ID selection scheme, with consideration of the traffic information and patterns in the area, outperforms the baseline scheme where the IDs are chosen randomly. Furthermore, the effectiveness of different scheduling schemes including greedy-based and first come first served are evaluated.
Size Invariant Handwritten Character Recognition using Single Layer Feedforward Backpropagation Neural Networks

Handwritten character recognition is among the most challenging research areas in pattern recognition and image processing. With everything going digital, applications of handwritten character recognition are emerging in offices, educational institutes, healthcare units and banks etc., where the documents that are handwritten are dealt more frequently. In this paper, a recognition system based on neural network that follows offline handwritten characters has been proposed for Latin digits and alphabets. Each of the characters that are extracted through query image is then resized dynamically to 60×40 pixels' size and is then passed to the neural networks for the process of recognition. Dynamic resizing enables size invariance in the proposed system and also maintains the aspect ratio of the character so that the image is not distorted during resizing. Neural networks are trained with 19,422 English alphabets' sample and 7,720 digits' sample that are written through 150 different writers in various styles of handwriting. Experimental study realized very encouraging results which are compared with the modern methods on this subject corridor.
Why information security law has been ineffective in addressing security vulnerabilities: Evidence from California data breach notifications and relevant court and government records

Why has the information security law been unsuccessful in having firms in possession of personal data take precautions against data breaches? Why are data breaches becoming more devastating notwithstanding law enforcement? This article seeks an answer from the legal system’s failure to draw a line between agency problems and externalities inherent in the information security market. Although a firm’s misaligned incentive to invest in security measures is basically an agency problem to be addressed by data breach litigation, the U.S. courts’ reluctance to grant Article III standing has reduced potential plaintiffs’ chance of winning and propensity to litigate, impairing the functionality of the private enforcement. As an exception, security vulnerabilities can have the nature of negative externalities to be addressed by the public enforcement, to the extent that those in “key holders” such as payment card processors enable intruders to easily circumvent the security measures taken by other firms in the same security chain, and that those in massive data aggregators undermine public trust in the whole data infrastructure. Government regulations, which need to be targeted at such sources of negative externalities, have actually been misaimed at a few cases arising from conventional agency problems. To test these hypotheses, this article presents an empirical study of security breach notifications filed in California during 2012–2016 and relevant court and government agency records produced until 2018.
Multi‐cue combination network for action‐based video classification

Action‐based video classification (or video‐based action recognition) is an active research area in computer vision. However, all currently utilised action‐based video classification approaches take spatial and temporal components into consideration while acoustic features ( e.g. sound and speech) are neglected. In this study, the authors propose a novel approach to combine multiple cues ( i.e. both visual and acoustic information) for action‐based video classification. Additionally, they introduce dense connections into their three‐stream network to address the gradient vanishing problem. Experimental results in the Kinetics Human Action Video data set and the Kinetics‐Sounds data set shows that their approach can effectively improve the accuracy in action‐based video classification.
A Formalized General Theory of Syntax with Bindings: Extended Version

We present the formalization of a theory of syntax with bindings that has been developed and refined over the last decade to support several large formalization efforts. Terms are defined for an arbitrary number of constructors of varying numbers of inputs, quotiented to alpha-equivalence and sorted according to a binding signature. The theory contains a rich collection of properties of the standard operators on terms, including substitution, swapping and freshness—namely, there are lemmas showing how each of the operators interacts with all the others and with the syntactic constructors. The theory also features induction and recursion principles and support for semantic interpretation, all tailored for smooth interaction with the bindings and the standard operators.
A Study on Robustness of Articulatory Features for Automatic Speech Recognition of Neutral and Whispered Speech

Traditionally, automatic speech recognition (ASR) systems are trained on acoustic representations of neutral speech. As a result, their performance degrades when tested with whispered speech. In this work, we explore the robustness of articulatory features in ASR of neutral and whispered speech. We use acoustic, articulatory, and integrated acoustic and articulatory feature vectors in matched and mismatched train-test cases. The results suggest that the articulatory data is useful in ASR of both neutral and whispered speech, especially in the mismatched train-test cases. When we concatenate acoustic and articulatory feature vectors and deploy it to the mismatched train-test case where the model is trained with neutral speech and tested with whispered speech, a relative improvement in phone error rate of 27.2% is observed compared to when only acoustic features are used. This suggests that articulatory data contains information complementary to acoustic representations. A phone specific recognition error is also presented which illustrates phones where adding articulatory information gives maximum benefit.
NN-based Ordinal Regression for Assessing Fluency of ESL Speech

Automatic assessment of a language learner's speech fluency is highly desirable for language education, e.g. for English as a Second Language (ESL) learning. In this paper, we formulate the fluency assessment as a problem of Ordinal Regression with Anchored Reference Samples (ORARS), where the fluency of a speech utterance is predicted by an ordinal regression neural network (NN) trained with anchored reference samples. The ORARS is trained and tested by: picking human expert labeled samples in each mean opinion score (MOS) bucket as the anchored reference samples and pairing them with input speech samples as training couplets; training an NN-based binary classifier to determine which sample in a pair is better in fluency; predicting the rank (MOS) of a test sample based upon the posteriors of all binary comparisons between the test sample and all anchored reference samples. Experimentally, our proposed approach outperforms the traditional NN-based methods and reaches a performance of "human parity", i.e. as comparable as human experts, in its fluency assessment of collected ESL speech. To the best of our knowledge, this is the first attempt to assess speech fluency with an ordinal regression framework where a test input is paired with bucketed and anchored reference samples.
Modified Recognition Algorithms Based on the Construction of Models of Elementary Transformations

This paper deals with the issues of building a model of recognition algorithms targeted at classifying objects in conditions of high dimensionality of the feature space. A model of recognition algorithms based on the estimate calculation has been considered as the source one. The distinctive features of the approach under consideration are as follows: 1) forming subsets of correlated features; 2) selecting a set of representative features when developing recognition algorithms; 3) building models of elementary transformations in the subspace of representative features. The main advantage of the proposed algorithms is the selection of the preferred models of elementary threshold rules with the subsequent calculation of the object membership estimate and ensuring a significant reduction in the number of computational operations when recognizing unknown objects. This characteristic is very important for real-time recognition systems. To test the operability of the proposed model, experimental studies have been carried out in solving a number of problems; in particular, model problems have been generated in the space of correlated features; as well as facial recognition tasks. These experimental studies considered: a model of recognition algorithms based on the estimate calculation; a model of recognition algorithms based on the potential function; the proposed model of recognition algorithms. A comparative analysis of the above models of recognition algorithms shows that the proposed model allows increasing the recognition accuracy by an average of 7-10%. The developed recognition algorithms can be used in medical and technical diagnostic, geological forecasting, biometric identification and other areas, in which solving problems of classification of objects specified in the high dimension feature space.
Cloud base height estimates from sky imagery and a network of pyranometers

Cloud base height (CBH) is an important parameter for physics-based high resolution solar radiation modeling. In sky imager-based forecasts, a ceilometer or stereographic setup is needed to derive the CBH; otherwise erroneous CBHs lead to incorrect physical cloud velocity and incorrect projection of cloud shadows, causing solar power forecast errors due to incorrect shadow positions and timing of shadowing events. In this paper, two methods to estimate cloud base height from a single sky imager and distributed ground solar irradiance measurements are proposed. The first method (Time Series Correlation, denoted as “TSC”) is based upon the correlation between ground-observed global horizontal irradiance (GHI) time series and a modeled GHI time series generated from a sequence of sky images geo-rectified to a candidate set of CBH. The estimated CBH is taken as the candidate that produces the highest correlation coefficient. The second method (Geometric Cloud Shadow Edge, denoted as “GCSE”) integrates a numerical ramp detection method for ground-observed GHI time series with solar and cloud geometry applied to cloud edges in a sky image. CBH are benchmarked against a collocated ceilometer and stereographically estimated CBH from two sky imagers for 15 min median-filtered CBHs. Over 30 days covering all seasons, the TSC method performs similarly to the GCSE method with nRMSD of 18.9% versus 20.8%. A key limitation of both proposed methods is the requirement of sufficient variation in GHI to enable reliable correlation and ramp detection. The advantage of the two proposed methods is that they can be applied when measurements from only a single sky imager and pyranometers are available.
New constructions of entanglement-assisted quantum MDS codes from negacyclic codes

When constructing quantum codes under the entanglement-assisted (EA) stabilizer formalism, one can ignore the limitation of dual-containing condition. This allows us to construct EA quantum error-correcting codes (QECCs) from any classical linear codes. The main contribution of this manuscript is to make a general statement for determining the optimal number of pre-shared qubits instead of presenting only specific cases. Let [Formula: see text] and [Formula: see text], where [Formula: see text] is an odd prime power, [Formula: see text] and [Formula: see text]. By deeply investigating the decomposition of the defining set of negacyclic codes, we generalize the number of pre-shared entanglement pairs of Construction (1) in Lu et al. [Quantom Inf. Process. 17 (2018) 69] from [Formula: see text] to arbitrary even numbers less than or equal to [Formula: see text]. Consequently, a series of EA quantum maximum distance separable (EAQMDS) codes can be produced. The absolute majority of them are new and the minimum distance can be up to [Formula: see text]. Moreover, this method can be applied to construct many other families of EAQECCs with good parameters, especially large minimum distance.
How do empirical software engineering researchers assess the credibility of practitioner-generated blog posts?

Background: Blog posts offer potential benefits for research, but also present challenges. The use of blog posts in SE research is contentious for some members of the community. Also, there are no guidelines for evaluating the credibility of blog posts.
Towards the Prediction of the Quality of Experience from Facial Expression and Gaze Direction

In this paper we investigate on the potentials to implicitly estimate the Quality of Experience (QoE) of a user of video streaming services by acquiring a video of her face and monitoring her facial expression and gaze direction. To this, we conducted a crowdsourcing test in which participants were asked to watch and rate the quality when watching 20 videos subject to different impairments, while their face was recorded with their PC's webcam. The following features were then considered: the Action Units (AU) that represent the facial expression, and the position of the eyes' pupil. These features were then used, together with the respective QoE values provided by the participants, to train three machine learning classifiers, namely, Support Vector Machine with quadratic kernel, RUSBoost trees and bagged trees. We considered two prediction models: only the AU features are considered or together with the position of the eyes' pupils. The RUSBoost trees achieved the best results in terms of accuracy, sensitivity and area under the curve scores. In particular, when all the features were considered, the achieved accuracy is of 44.7%, 59.4% and 75.3% when using the 5-level, 3level and 2-level quality scales, respectively. Whereas these results are not satisfactory yet, these represent a promising basis.
An AEAD Variant of the Grain Stream Cipher

A new Grain stream cipher, denoted Grain-128AEAD is presented, with support for authenticated encryption with associated data. The cipher takes a 128-bit key and a 96-bit IV and produces a pseudo random sequence that is used for encryption and authentication of messages. The design is based on Grain-128a but introduces a few changes in order to increase the security and protect against recent cryptanalysis results. The MAC is 64 bits, as specified by the NIST requirements in their lightweight security standardization process.
Emojinating: Evolving Emoji Blends

Graphic designers visually represent concepts in several of their daily tasks, such as in icon design. Computational systems can be of help in such tasks by stimulating creativity. However, current computational approaches to concept visual representation lack in effectiveness in promoting the exploration of the space of possible solutions. In this paper, we present an evolutionary approach that combines a standard Evolutionary Algorithm with a method inspired by Estimation of Distribution Algorithms to evolve emoji blends to represent user-introduced concepts. The quality of the developed approach is assessed using two separate user-studies. In comparison to previous approaches, our evolutionary system is able to better explore the search space, obtaining solutions of higher quality in terms of concept representativeness.
Quality Assessment for Adaptive Virtual Reality Video Streaming: A Probabilistic Approach on the User’s Gaze

Accurately estimating the users’ perception of adaptive tile-based Virtual Reality (VR) video streaming is fundamental and is often used to determine subsequent streaming decisions. This is due to the fact that adaptive tile-based VR video streaming solutions aim to optimize the bandwidth usage by only streaming the areas (tiles) within the field of view of the user (viewport) at the highest quality, while keeping the remainder of the 360° environment at low quality levels. Thus, understanding the quality perceived by the user in real-time determines the success of the service. Current quality assessment approaches tend to map the quality of the viewport’s center (center tile) to the overall perceived quality. However, while such models enable to measure how long the user spend on each quality layer, they ignore the fact that the user’s eyes also move within the viewport. In this paper, we present a novel video quality assessment metric for adaptive tile-based VR video streaming which takes into account the distribution of eye movement within the viewport, as well as, the tiling scheme. Both elements are combined by means of a probability distribution fit from real users data. The model was evaluated in a well-known VR database and using an emulation environment, benchmarking it against state-of-the-art solutions as well as objective full reference metrics.
Links as a Service (LaaS): Guaranteed Tenant Isolation in the Shared Cloud

The most demanding tenants of shared clouds require complete isolation from their neighbors, in order to guarantee that their application performance is not affected by other tenants. Unfortunately, while shared clouds can offer an option, whereby tenants obtain dedicated servers, they do not offer any network provisioning service, which would shield these tenants from network interference. In this paper, we introduce links as a service (LaaS), a new abstraction for cloud service that provides isolation of network links. Each tenant gets an exclusive set of links forming a virtual fat-tree, and is guaranteed to receive the exact same bandwidth and delay as if it were alone in the shared cloud. Consequently, each tenant can use the forwarding method that best fits its application. Under simple assumptions, using bipartite graph properties and pigeonhole-based analysis, we derive theoretical conditions for enabling the LaaS without capacity over-provisioning in fat-trees. New tenants are only admitted in the network, when they can be allocated hosts and links that maintain these conditions. We also provide new results on the numbers of tenants and hosts that can fit while guaranteeing network isolation. The LaaS is implementable with common network gear, tested to scale to large networks, and provides full tenant isolation at the cost of a limited reduction in the cloud utilization.
Personal recommender system based on user interest community in social network model

Collaborative filtering is an effective method to help users find their interested items or services in e-commerce, such as Tmall, Amazon. The development of recommendation algorithms has been focused on how to provide accurate recommendation results. One of the big challenges on recommendation system is to make the best of outdated information sources. In order to solve this problem, an efficient time weighted collaborative filtering algorithm is proposed in this paper. In our presented recommendation algorithm, changes of interest over time are fully mined. Firstly, combining with rounding–forgetting function, a time weighted score matrix is constructed. The newfound matrix reflects many users' interests. Then, the users and items with higher correlation are clustered into the same community according to differential equations. Stable same state values of users mean they own similar interests and then they are assigned into the same community. Finally, the real-time prediction results are obtained by dynamic similarity measurements. Effectiveness of our proposed algorithm is proven by extensive experimental evaluations which are based on different datasets. Diverse comparing results with several better methods are given to test the efficiency of our algorithm.
Speech Processing in Healthcare: Can We Integrate?

This chapter focuses on the way speech recognition, processing, and synthesis help in the healthcare. The chapter begins with the basic idea of speech recognition in the domain, and it particularly focuses on a complete healthcare project so as to obtain a clear understanding of the value of speech processing. The chapter also provides detailed information about how speech synthesis affects healthcare and its business model.
Scatter-and-Gather Revisited

Recent works have shown that there exist microarchitectural timing channels in contemporary GPUs, which make table-based cryptographic algorithms like AES vulnerable to side channel timing attacks. Also, table-based cryptographic algorithms have been known to be vulnerable to prime-and-probe attacks due to their key-dependent footprint in the data cache. Such analysis casts serious concerns on the feasibility of accelerating table-based cryptographic algorithms on GPUs. In this paper, we revisit the scatter-and-gather (SG) approach and make a case for using this approach to implement table-based cryptographic algorithms on GPUs to achieve both high performance and strong resistance to side channel attacks. Our results show that our SG-based AES achieves both high performance and strong resistance against all the known side channel attacks on these different generations of NVIDIA GPUs. We also reveal unexpected findings on a new timing channel in the L1 data cache (D-cache) on NVIDIA Maxwell and Pascal GPUs.