Reviewerly/exHarmony huggingface dataset:
ISEkFT

Public-key encryption with keyword search (PEKS) is a well-known technique which allows searching on encrypted data using the public key system. However, this technique suffers from the keyword guessing attack (KGA). To address this problem, a modified version of PEKS called public key encryption with fuzzy keyword search (PEFKS) has been introduced where each keyword is associated with an exact search trapdoor (EST) and a fuzzy search trapdoor (FST) which is provided to the cloud server. PEFKS prevents KGA in such a way that two or maximum three keywords share the same FST. Hence, even if the cloud server knows the FST it cannot link it to the corresponding keyword. But, with a probability of 1/3 the malicious cloud server can still guess the keyword corresponding to FST. Therefore, in this article, the authors present an approach which can improve the security of the PEFKS technique by reducing the probability of guessing the keyword to 1/k where k is the number of keywords that share the same FST, thus enhancing the overall reliability. In addition, the authors have used an identity-based encryption (IBE) as an underlying technique to construct the searchable encryption scheme and proved its security in the standard model.
A novel model based on deep learning for Pedestrian detection and Trajectory prediction

Pedestrian trajectory prediction is faced with the difficulty of data set acquisition, and the traditional models considers a single pedestrian in isolation and ignores the influence of the target pedestrian's neighborhood. This paper presents a pedestrian prediction model that integrates pedestrian detection, multi-target tracking and circular neighborhood adding. Our method uses two different LSTMs to capture the interaction information between the target person and neighboring pedestrians. For the pedestrian interaction information, the circular neighborhood is used instead of the traditional rectangular neighborhood in the social scale. We evaluate the performance of four prediction models on three common datasets. The results reflect that the proposed method can solve the problem of manually labeling datasets, and the circular neighborhood can improve the accuracy of trajectory prediction.
Artefact Determination by GMM-Based Continuous Detection of Emotional Changes in Synthetic Speech

The paper is focused on a description of a system for automatic detection of speech artefacts based on the Gaussian mixture model (GMM) classifier. The system enables to detect one or more artefacts in synthetic speech produced by a text-to-speech system. Our speech artefact detection uses continual GMM classification of emotional states in 2-D affective space of valence and arousal within the whole sentence and calculates the final change in the evaluated emotions. The detected shift to negative emotions indicates presence of an artefact in the analysed sentence. The basic experiments confirm functionality of the developed system producing results with sufficient correctness of artefact detection. These results are comparable to those attained by a standard listening test method. Additional investigations show relatively great influence of the number of mixtures, the number of used emotional classes, and types of speech features on the evaluated emotional shift.
Single Image Dehazing from Repeated Averaging Filters

In this paper, we present a method of Repeated Averaging Filters for estimating the atmospheric light from a single hazy image, which further contributes to better radiance recovery. Existing methods of dehazing are suffering from the problem of halo artifacts in the final output image after dehazing. For this purpose, an averaged channel is obtained from a single image by the repeated averaging filters via integral images which provides a faster and efficient way for removing halo artifacts. The proposed method of dehazing reveals competitive results in regards to qualitative, quantitative and computational analysis and out-performs many previous states of the art techniques.
Blind Image Sharpness Assessment And Enhancement via Deep Auxiliary Learning

In this paper, we propose an unified deep auxiliary learning network to train the blind image sharpness assessment (BISA) metric and enhancer simultaneously. Instead of using the BISA as a parameter tuner like existing works, the proposed method aims to exploit the complementary information between two tasks and boost both of their performance. On the one hand, the enhancement subnetwork tries to separate a blurry image into the clear version and disparity map, which provide additional mask effect and blurry degree information for accurate BISA. On the other hand, the BISA subnetwork help determine the enhancement degree by feeding sharpness-aware features to the enhancer, which is helpful for avoiding under-/over-enhancing. Experimental results on three publicly available databases show that the proposed method outperforms many state-of-the-art algorithms in both the BISA and sharpness enhancement tasks.
Anticipatory Computing for Human Behavioral Change Intervention: A Systematic Review

With the rapid development of computer and communication technology, anticipatory computing has been identified as one of the most important factors affecting human behavioral change. The future of anticipatory computing will not be bright if it fails to provide useful help to human life and work. Anticipatory computing applied to behavioral change intervention (BCI) is full of challenges and is a research topic of increasing interest and importance. This paper provides an overview of the concept of anticipatory computing, BCI, as well as anticipatory computing for the BCI and offers a multistage literature analysis. Also, a systematic analytical framework articulated from the existing literature is presented to reveal the progress and details of anticipatory computing for the BCI. This framework is divided into four dimensions: 1) sensing and context inferring; 2) context prediction; 3) behavioral guidance and intervention, and; 4) application. Based on our literature analysis, 11 elements of anticipatory computing for BCI are identified and discussed in terms of principles, enablers, and activities. Afterward, contributions and possible future directions for research are summarized at the end of this paper.
Hierarchical Active Learning for Model Personalization in the Presence of Label Scarcity

In mobile health (mHealth) and human activity recognition (HAR), collecting labeled data often comes at a significantly higher cost or level of user burden than collecting unlabeled data. This motivates the idea of attempting to optimize the collection of labeled data to minimize cost or burden. In this paper, we develop active learning methods that are tailored to the mHealth and HAR domains to address the problems of labeled data scarcity and the cost of labeled data collection. Specifically, we leverage between-user similarity to propose a novel hierarchical active learning framework that personalizes models for each user while sharing the labeled data collection burden across a group, thereby reducing the labeling effort required by any individual user. We evaluate our framework on a publicly available human activity recognition dataset. Our hierarchical active learning framework on average achieves between a 20% and 70% reduction in labeling effort when compared to standard active learning methods.
Semantic Contextual Personalization of Virtual Stores

Virtual stores and showrooms gain increasing attention in e-commerce, marketing and merchandising to investigate customers' behavior, preferences and the usefulness of shopping and exhibition spaces. Although virtual stores may be designed using numerous available 3D modeling tools and game engines, efficient methods and tools enabling development and personalization of virtual stores are still lacking. In this paper, we propose a novel approach to the development of personalizable contextual virtual stores that can be generated and configured on-demand, using interfaces based on semantic web technologies. A virtual store model is created as a combination of three elements: an exposition model, a collection of product models, and a virtual store configuration. The first element visually reflects an existing or imaginary 3D store layout. The second element contains 3D models of all products that can be presented in the exposition. The third element is an ontology, which connects the two previous elements using domain-specific knowledge and reasoning. Based on a virtual store model, a personalized virtual store is generated in response to a specific user's request.
Association Likelihoods for Directional Estimation

In multitarget tracking, using the association of tracks to measurements that maximizes the association likelihood is a well-established strategy in Euclidean spaces. We explain how this strategy can be adopted for circular domains. Formulae are provided for the association likelihood for three density representations used by important filters for periodic domains-von Mises densities, density approximations based on trigonometric polynomials, and particle-based representations. The presented closed-form formulae allow for efficiently determining the most likely association. In the evaluation, the approaches based on particles and trigonometric polynomials outperform an approach based on a Kalman filter that was adapted to the periodic domain.
Measuring the impact of data center failures on a cloud‐based emergency medical call system

Summary Emergency call services are expected to be highly available in order to minimize the loss of urgent calls and, as a consequence, minimize loss of life due to lack of timely medical response. This service availability depends heavily on the cloud data center on which it is hosted. However, availability information alone cannot provide sufficient understanding of how failures impact the service and users' perception. In this paper, we evaluate the impact of failures on an emergency call system, considering service‐level metrics such as the number of affected calls per failure and the time an emergency service takes until it recovers from a failure. We analyze a real data set from an emergency call center for a large Brazilian city. From stochastic models that represent a cloud data center, we evaluate different data center architectures to observe the impact of failures on the emergency call service. Results show that changing data center's architecture in order to improve availability from two to three nines cannot decrease the average number of affected calls per failure. On the other hand, it can decrease the probability to affect a considerable number of calls at the same time.
Breaking the binding: Attacks on the Merkle approach to prove liabilities and its applications

Proofs of liabilities are used for applications, function like banks or Bitcoin exchanges, to prove the sums of money in their dataset that they should owe. The Maxwell protocol, a cryptographic proof of liabilities scheme which relies on a data structure well known as the summation Merkle tree, utilizes a Merkle approach to prove liabilities in the decentralized setting, i.e., clients independently verify they are in the dataset without any trusted auditor’s involvement. In this paper, we go into the Maxwell protocol and the summation Merkle tree. We formalize the Maxwell protocol and show it is not secure. We present an attack with which the proved liabilities are less than the actual values when adopting the Maxwell protocol. This attack can have significant consequences: A Bitcoin exchange controlling a total of n client accounts can present valid liabilities proofs including only one account balance. We suggest two improvements to deal with this problem, and we present a formal proof for the improvement that is closest in spirit to the Maxwell protocol. Moreover, we show the DAM scheme, a micropayment scheme of Zerocash which adopts the Maxwell protocol as a tool to disincentivize double/multiple spending, is vulnerable to a multi-spending attack. We show the Provisions scheme, which adopts the Maxwell protocol to extend its privacy-preserving proof of liabilities, is also infected by a similar attack.
ALGator—An automatic algorithm evaluation system

The design of algorithms for solving various kinds of computer-related problems can generally be divided into two parts: the theoretical evolution of the algorithms and the practical assessment of their behavior. While the aim of the first part is to derive algorithms and to prove their theoretical characteristics, the second part struggles to discover the usefulness of the presented solutions when it comes to solving existing problems on real-world computers. Due to the necessary simplifications used in the models of the theoretical analysis, the results of both parts of the process are not always consistent. From a theoretical point of view, it is very important to develop good algorithms, theoretically prove their efficiency and thus sometimes show the theoretical boundaries of a particular problem. However, if these algorithms do not perform well in practice, all the theoretical work is not much more than “art for art's sake.” Therefore, an unbiased and holistic algorithm evaluation is a very important element of the algorithm-design process. To create a fair and reproducible environment in which the algorithm's implementations can be compared is a big challenge; therefore, researchers normally use more or less reasonable simplifications, which leads to results that are not comparable and therefore sometimes misleading. To overcome this problem we introduce a system called ALGator that was developed to facilitate and automate the algorithm-evaluation process. ALGator creates an environment in which the time complexity and other project-specific indicators can be measured and provides tools for the automatic and semi-automatic analyses of the results of the algorithms' execution. In this chapter we present the implementation and the functionalities of the ALGator system and give several practical examples of its use in the evaluation of real algorithms.
Bi-adapting kernel learning for unsupervised domain adaptation

Unsupervised domain adaptation aims to use labeled instances from a source domain to train a good learning model, which can classify unlabeled instances from a target domain as accurate as possible. The biggest challenge is that datasets from the source and target domains have different distributions, thus the general classification model trained on the source domain can not perform well on the target domain data. The classic methods solve this problem mainly by narrowing the distance between the source and target domains. Those methods, however, is not optimal since the nonlinear feature space may not match the kernel-based learning machine. In this paper, we design a new method called bi-adapt kernel learning (BAKL) to learn a domain-invariant kernel by transferring the source and target domains to each other simultaneously. Specifically, we derive the new source and target domain kernel matrix according to the Mercer’s theorem. The domain-invariant kernel machines are then constructed by minimizing the approximation error between the newly generated kernel matrices and the ground truth source domain kernel matrices. Experiments on benchmark tasks of text and object recognition demonstrate that it significantly improves classification accuracy compared to the state-of-art methods.
Portrait of a Miner in a Landscape

Mining is one of the core elements of the proof-of-work based cryptocurrency economy. In this paper we investigate the generic landscape and hierarchy of miners on the example of Ethereum and Zcash, two blockchains that are among the top 5 in terms of USD value of created coins. Both chains used ASIC resistant proofs-of-work which favors GPU mining in order to keep mining decentralized. This however has changed with recent introduction of ASIC miners for these chains. This transition allows us to develop methods that might detect hidden ASIC mining in a chain (if it exists), and to study how the introduction of ASICs effects the decentralization of mining power. Finally, we describe how an attacker might use public blockchain information to invalidate the privacy of miners, deducing the mining hardware of individual miners and their mining rewards.
Virtual Reality Travel Training Simulator for People with Intellectual Disabilities

Being able to travel in public transportation autonomously is one daily task that doesn't imply any special effort or learning process for most of the human population. But, for people with intellectual disabilities, it requires an extensive learning process where many possible situations should be considered before the person can perform successfully this task. Currently these people are taught by specialists following supervised learning methodologies in real environments. But, through the use of immersive virtual reality and interaction within a virtual world, they could be trained in a safe environment, increasing their self-confidence in a saver and quicker way. This paper describes the creation of a customized simulator that prepares subjects to use the bus as a means of transportation in a medium-size city. The design of the virtual reality environment and the interaction mechanics is thought to be highly customizable for each subject with a very realistic environment, thanks to the use of photogrammetric digitalization technologies. The environment has been developed in Unreal engine and uses the head mounted display Oculus rift, as well as Oculus touch for interaction with the different assets.
Review of Text Mining Techniques for Software Bug Localization

Software Bug Localization (SBL) is a task of locating the buggy source code. There are various ways of doing SBL and one of them is static SBL which utilizes the power of Text Mining (TM) in association with software repositories. Most of static SBL models are based on Information Retrieval (IR) methodology in which bug report works as a query and source code as database. In this paper we review state of the art SBL models which uses text mining techniques as their back bone in conjunction with other techniques. Essential features are extracted and summarized with the help of tabular representation. Aim of doing this study is to find the gaps in previous SBL models for proposing a novel SBL model in future.
An Appropriate Computing System and Its System Parameters Selection Based on Bottleneck Prediction of Applications

Recent computing systems have different characteristics as they consist of various processors such as a scalar processor, an accelerator, and a vector processor. As calculations patterns best suited for individual systems are different and diverged, it is difficult to determine in advance which computing system is appropriate when an application is given. Furthermore, due to the increase in the complexity of computing systems such as the many-core and new memory technologies, it is necessary to adjust many system parameters and find appropriate system parameters for each application to deliver the high performance of a computing system. One of the ways to find a computing system and its system parameter combinations suitable for an HPC application is a bruteforce approach of trial and error executions of an application on various computing systems and various system parameter combinations, which is no longer a realistic way due to its high cost of time and efforts. This paper proposes a method to carefully select a candidate of a computing system and system parameter combinations appropriate for executions of an HPC application by considering both characteristics of the application and computing systems. Thus, the proposed method can narrow down a large search space of computing systems and their system parameter combinations suitable for executing the HPC application.
Divisible formal weight enumerators and extremal polynomials not satisfying the Riemann hypothesis

In this paper, first we formulate the notion of divisible formal weight enumerators and propose an algorithm for the efficient search of the formal weight enumerators divisible by two. The main tools are the binomial moments. It leads to the discovery of several new families of formal weight enumerators. Then, as a result, we find examples of extremal formal weight enumerators which do not satisfy the Riemann hypothesis.
CNN-SIFT Consecutive Searching and Matching for Wine Label Retrieval

Wine label retrieval is key to automatic wine brand search through the web or mobile phone in our daily life. In comparison with the general image retrieval tasks, it is a rather challenging problem with a huge number of unbalanced wine brand images. In this paper, we propose a CNN-SIFT Consecutive Searching and Matching (CSCSM) framework for wine label retrieval. In particular, a CNN is trained to recognize the main-brand (manufacturer) for narrowing the searching range, while the SIFT descriptor is improved by adopting the RANSAC and TF-IDF mechanisms to match the final sub-brand (item attribute under the manufacture). The experiments are conducted on a dataset containing approximately 548k images of wine labels with 17, 328 main-brands and 260, 579 sub-brands. It is demonstrated by the experimental results that our proposed CSCSM method can solve the wine label retrieval problem effectively and efficiently and outperform the competitive methods.
Automatic Weak Learners Selection for Pattern Recognition and its application in Soccer Goal Recognition

In this paper, we propose an automatic weak learners selection approach to perform advanced weak learners. In pattern recognition, the weak learners play a critical role in order to explain distinguishable features. Our approach relies on analyzing the weak learners by means of probability density functions (PDFs). Since the PDFs are appropriate statistical tools for computer vision applications that can explain objects very well, we propose to use them as the discriminant factor. In our work, we compute a new measurement for the common surface under the PDFs curves. According to our automated measure, we decide about the weak learners, whether they are eligible or not for recognition purposes? And also how much they are appropriate for distinguishing the target objects within the images. For evaluating our approach, we recognize the soccer goals in soccer videos by means of selected weak learners. The experimental results on real-world videos show the success of our approach and its superiority in comparison to the approaches that use all weak learners.
GDPR Impacts and Opportunities for Computer-Aided Diagnosis Guidelines and Legal Perspectives

The General Data Protection Regulation (GDPR) is lengthy and it is essential to resume the impacts of it in specific use-cases to diminish the gap between system developers and regulators. Computer-aided diagnosis is one of such use-cases with increased importance on clinical screening programs. The regulation has distinct mentions that affect automated-decision solutions and healthcare records. This work identifies the fundamental legal issues, challenges and opportunities for this scenario and propose architectural guidelines to tackle them. The result is purely theoretical, however it is based on known architectures such as signaling networks, already applied in the telecommunication sector.
Traps, Pitfalls and Misconceptions of Machine Learning applied to Scientific Disciplines

In the last decade, Machine Learning has experienced a dramatic increase in performance on a wide variety of tasks, including computer vision, speech recognition, text parsing, and language translation, just to name a few. This has corresponded to an understandable hype especially for the remarkable results achieved in some cases. Therefore, practitioners of Scientific Disciplines have become interested in utilizing new Machine Learning techniques, and have sometimes started doing so with mixed success.