Reviewerly/exHarmony huggingface dataset:
A Semi-Lossless Image Compression Procedure using a Lossless Mode of JPEG

Day by day, communication through the internet is progressing; particularly video calling for which sending a massive amount of data over the internet and saving on a computer is being a big challenge. For which, there are many compression algorithms; such as block transform, vector quantization and JPEG are used to convert a big dataset in such a format so that it can be sent over the internet at a high speed and stored in a small space in computer. In this paper, a new procedure has been proposed using a lossless mode of JPEG by removing the first and last bits from the exact binary pattern of each pixel which provides a better result than the state-of-the-art techniques. In this technique, the two bits are removed after a little bit preprocessing and then the remaining binary pattern is replaced by a fixed value each. Lastly, average code word, compression ratio and PSNR are used to assess the performance of the proposed procedure with the state-of-the-art techniques. From the experimental results, it looks that the proposed procedure provides better results than the state-of-the-art techniques.
Clothing Recognition in the Wild using the Amazon Catalog

The emergence of online influencers, the explosion of video content, and the massive amount of movie collections have served as an advertising vehicle for the fashion industry. This trend has created the need for automated methods that recognize people's outfit in such image and video collections. However, existing computer vision solutions for fashion recognition require an enormous amount of labeled data for training, which is prohibitively expensive. In this work, we propose an approach to build clothing recognition models for real-world scenarios. Our approach exploits images from the Amazon Catalog as training data. By using the catalog data as an additional training source, we boost the recognition accuracy on the challenging real world images of the DeepFashion dataset achieving stateof-the-art performance. We introduce the first dataset for clothing recognition in movies. In this scenario, we find that the use of catalog data for training becomes even more crucial, as it provides an accuracy boost of 10%.
Social Functions of Public Library in the Studies of Domestic Library Scientists

The article is devoted to one of the most debatable topics of library science — the problem of social functions of library. Domestic researchers in the area of library science repeatedly addressed the question on the mission of library and considered its role in the society. The author notes that the term “social function” occupies an important place in the conceptual apparatus of library science; however, the generally accepted classification of social functions has not been developed to date. The author traces the history of formation of the ideas about the social functions of library. The article examines the main theoretical approaches and directions of research of social functions related to their classification, definition of functions of different types of libraries, search for the essential (generic) function, etc. The author emphasizes that, in the course of evolution of library science, the ideas of library scientists on the composition and content of social functions of library had been changing, and the choice of particular function as the dominant one depended on the specific historical conditions. The article notes that in the modern library science there is a tendency to expand the vision of social functions of library. The author believes that this trend will continue in the future, and this is a naturally determined process. To the traditional functions of library — information, cultural, educational — added the function of social protection. The most striking model of the modern public library, implementing this function, embodied in the concept of “third place”, according to which the library positions itself as part of the urban space, a comfortable area for self-development, informal communication and social adaptation. For people with special needs (disabled, unemployed, homeless) library often becomes a “lifeline”, giving the opportunity to find their place in society. The expansion of the range of activities of libraries through the development of additional social functions does not detract from the importance of library-information services as a leading area of work. How justified are the attempts of libraries to “fit” into the life of modern society, to attract attention, to become necessary, can be judged only in the course of time.
Real-location Reporting Based Differential Privacy Trajectory Protection for Mobile Crowdsensing

Mobile Crowdsensing (MCS) has become an effective technology for urban data sensing and acquisition. But this also brings the risk of trajectory privacy disclosure for participants. Most of the existing efforts attempt to add noise into the reported location information to achieve the trajectory privacy protection of the participating users. However, in many scenarios, the participants are required to report the real-location information (e.g., high-quality map generation, traffic flow monitoring, etc.). To address this problem, we propose a differential privacy based trajectory privacy protection scheme with real-location reporting in MCS. First, we present the definition of trajectory privacy protection based on real path reporting under differential privacy. Second, we give a differential trajectory privacy protection framework to achieve participants trajectory privacy protection under Bayesian inference attacks. Finally, we analyze and prove that differential trajectory privacy problem is an NP-Hard problem. Meanwhile, we also design an approximate algorithm to report participants road segment with the trajectory privacy guarantee. The experimental results on both the simulated data set and the real data set show that our proposed participant's trajectory privacy protection scheme has a good performance.
An Improved Method of Radar Emitter Fingerprint Recognition Based on GS-SVM

Radar emitter recognition is a subject of great importance in modern electronic warfare. To realize radar emitter fingerprint recognition, we propose a method of extracting fingerprint features containing statistic characteristics, carrier frequency and wavelet packet transform (WPT). Furthermore, we discuss the selection of kernel functions of the support vector machine classifier (SVM). The grid search algorithm (GS) is applied to optimize the SVM classifier. Finally, the real data experiment results show that the accuracy of test radar signals achieves 99.92% based on GS-SCM which performs better than the simple SVM model in the case of small samples to adapt to the battlefield environment.
Input Encoding for Sequence-to-Sequence Learning of Romanian Grapheme-to-Phoneme Conversion

This paper evaluates the use of sequence-to-sequence learning models for the Romanian grapheme-to-phoneme conversion. The strategies explore the use of different input feature encoding: one-hot letter encoding, additional embedding layer and grapheme embeddings learned from a large corpus of Romanian text. Additional lexical information, such as syllabification and lexical stress is also taken into consideration for augmenting the orthographic form of the word and providing more accurate phonetic transcriptions.The sequence-to-sequence models are also compared to a baseline decision tree algorithm in terms of both phone- and word-level accuracy. The best results are achieved by the model which uses grapheme embeddings and all additional linguistic information. Its accuracy is 97.90% at word-level, and 99.62% at phone-level. However, only minor differences exist between the tested systems.
BakingTimer

Cookies were originally introduced as a way to provide state awareness to websites, and are now one of the backbones of the current web. However, their use is not limited to store the login information or to save the current state of user browsing. In several cases, third-party cookies are deliberately used for web tracking, user analytics, and for online advertisement, with the subsequent privacy loss for the end users.
Adaptive user-oriented fuzzy-based service broker for cloud services

This paper presents an adaptive fuzzy-based cloud service brokering algorithm (AFBSB). The proposed algorithm employs an adaptive fuzzy-based engine to select the most appropriate data center for user cloud service requests considering user preferences in terms of cost and performance. The algorithm is implemented using an open-source cloud computing simulation tool. The algorithm results are tested against the results of other existing techniques within two types of cloud environments. First, a performance-constrained environment in which performance improvement is the main objective for cloud users. Second, a preference-aware environment, in which cloud users have different cost and performance preferences. Simulation results show that the proposed algorithm can achieve significant performance improvement in performance-constrained environments. As for the preference-aware environment, they show that AFBSB can operate in user-oriented manner that guarantees performance/cost improvement, as compared to other algorithms.
Density Peak-based Pre-clustering Support Vector Machine for Multi-class Imbalanced Classification

Imbalanced classification using a support vector machine (SVM) is a normal but crucial problem in machine learning. Compared with binary classification, multiclass classification is much more complicated. Most existing studies on imbalanced classification using SVM focus on binary imbalanced classification; while only few of them look into imbalanced classification with multiple classes. Pre-clustering is a useful technique to prepare proper data from an imbalanced dataset for a classifier. It can be used to extract the feature of a dataset first and improve classification performance. Density peak based on Euclidean distance proves its effectiveness and generality in clustering. Motivated by this and the fact that the number of clusters is known in multi-class classification using a one-vs-rest strategy, we combine density peak clustering and SVM to propose a new pre-clustering method to perform effective imbalanced classification with multiple classes. Specifically, we transform a multi-class classification problem into several binary classification tasks. The results on 5 public datasets in terms of F-measure, G-mean and Area Under Curve (AUC) show its superiority over the original SVM and SVM with other methods including random under-sampling, Synthetic Minority Oversampling Technique, pre-clustering using K-Means and EasyEnsemble methods using either a one-vs-rest or one-vs-one strategy.
Automated Nuclei Detection on Pleural Effusion Cytopathology Images using YOLOv3

Nuclei detection is a critical step in cytopathology for pleural cancer diagnosis. Because nuclei provide quantitative information about malignancy of cancer. It is known that Convolutional Neural Networks (CNNs) provide considerable performance on object detection. We proposed convolutional object detector, YOLOv3, to detect nuclei on pleural effusion (PE) cytopathology images. The experiments were conducted on 80 PE cytopathology images containing 11157 nuclei. The proposed method achieved precision of %94.10, recall of %98.98, and F-measure of %96.48. The most important contribution of the YOLOv3 is that it provides 10x speed up versus the some state-of-the-art published methods, which is very important for real time Computer-Aided Diagnosis (CAD) applications in digital cytopathology.
DA&amp;FD–Deadline-Aware and Flow Duration-Based Rate Control for Mixed Flows in DCNs

Data center has become an important facility for hosting various applications. For data center networks, deadline missing rate and average flow completion time are two main metrics for the performance of applications. In this paper, we find deadline-aware methods can only reduce the percentage of flows missing deadline, while flowsize-aware and information-cumulative methods can only optimize the average flow completion time. However, traffic in data center is the mixture of various flows and focusing on the single goal is not enough. We advocate to incorporate deadline and flow duration time into flow rate control. Then we design DA&FD (Deadline-Aware and Flow Duration) based rate control mechanism and analyze its performance in theory. At last, we evaluate DA&FD under different topologies, real world traffic and load scenarios, both by simulation and in real testbed. Our results show that DA&FD performs close to D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TCP and about 15%, 25%, 30%, 35% better than Ameon, L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DCT, Karuna, DCTCP on deadline missing rate. For average FCT, the performance of DA&FD is similar to L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DCT and compared with Ameon, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TCP, Karuna, DCTCP, DA&FD can reduce average FCT by 10%, 15%, 20%, 25%.
Calibration method of Risley-prism imaging System

In this paper, a Risley-prism imaging system is proposed, which incorporates a camera and two coaxial Risley prisms to capture various scenes, namely images captured by equivalent cameras. An equivalent camera calibration method is established to obtain the extrinsic parameters of the equivalent cameras from rectified images captured by the system. To improve calibration accuracy, a geometric method is applied to rectify the degraded images over the whole scanning area. Such opto-electronmechanical configuration makes possible an agile and compact imaging system suitable for boresight adjustment. Experiments that apply imaging system located in different positions are conducted to validate the proposed method, and results are compared to verify its efficiency and accuracy.
Automatic Screening Of Children With Speech Sound Disorders Using Paralinguistic Features

Subjective screening of children with speech disorders is costly, time consuming and infeasible due to the limited availability of Speech and Language Pathologists (SLPs). Therefore, there is an increasing interest in automatic speech analysis of children with speech disorders as it can offer a practical alternative to human assessment. Paralinguistic features are a set of low-level descriptors commonly used in speech emotion recognition. However, they have not yet been examined with childhood speech sound disorders such as, apraxia-of-speech and phonological and articulation disorders. In this paper, we investigated the effectiveness of paralinguistic features in discriminating between typically developing children and those who suffer from different types of speech sound disorders. Two types of standard paralinguistic features were explored, the Geneva Minimalistic Acoustic Parameter Set (GeMAPS) and its extended version, (eGeMAPS) feature sets. We applied feature selection to find the most discriminant set of features and employed binary classification using a support vector machine (SVM) to discriminate between the two groups. The method was tested on a recently-released public speech corpus collected from typically developing children and children with various types of speech sound disorders. The system achieved segment-level and subject-level unweighted average recall (UAR) of around 78% and 87% respectively.
GPU Acceleration of Range Queries over Large Data Sets

Data management systems commonly use bitmap indices to increase the efficiency of querying scientific data. Bitmaps are usually highly compressible and can be queried directly using fast hardware-supported bitwise logical operations. The processing of bitmap queries is inherently parallel in structure, which suggests they could benefit from concurrent computer systems. In particular, bitmap-range queries offer a highly parallel computational problem, and the hardware features of graphics processing units (GPUs) offer an alluring platform for accelerating their execution. In this paper, we present three GPU algorithms and one CPU based algorithm for the parallel execution of bitmap-range queries. We show that in 95% of our tests, using real and synthetic data, the GPU algorithms greatly outperform the parallel CPU algorithm. For these tests, the GPU algorithms provide up to 87.7× speedup and an average speedup of 30.22× over the parallel CPU algorithm. In addition to enhancing performance, augmenting traditional bitmap query systems with GPUs to offload bitmap query processing allows the CPU to process other requests.
Adapting ROS Logs to Facilitate Transparency and Accountability in Service Robotics

Understanding why a particular robot behavior was triggered is a cornerstone for having human-acceptable social robots. Thus, every robot action should be explained in order to audit, recognize, and control expected and unexpected behaviors using usual sources of information provided by robot software components. Enabling intelligibility goes through a correct translation of system logs. This paper presents an initial approach for arranging low-level knowledge extracted from logs available in the de facto standard for service robotics ROS in order to be useful for developers and regulators.
Testing the Use of a Collocation Retrieval Tool Without Prior Training by Learners of Spanish

Abstract This experimental study tests the usability of a collocation retrieval tool that combines corpus and dictionary features in order to help learners of Spanish as a Foreign Language to improve their production of collocations. More specifically, the study tests the extent to which such a tool is effective without prior training, and evaluates which of its features might be improved. Two experiments were conducted. The first experiment involved Japanese-L1 learners of Spanish. One subgroup completed a gap-fill test both with and without the collocation tool, while another subgroup used electronic bilingual dictionaries instead of the collocation tool. In a second experiment, we chose a more heterogeneous group of participants with less experience in using lexicographic tools. Results show that learners do not need previous training to benefit from the collocation tool, but also that they do not check corpus examples as often as they might.
Proofs that yield nothing but their validity and a methodology of cryptographic protocol design

research-article Share on Proofs that yield nothing but their validity and a methodology of cryptographic protocol design Authors: Oded Goldreich Technion TechnionView Profile , Silvio Micali MIT MITView Profile , Avi Wigderson Hebrew University Hebrew UniversityView Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 285–306https://doi.org/10.1145/3335741.3335754Published:04 October 2019Publication History 0citation215DownloadsMetricsTotal Citations0Total Downloads215Last 12 Months13Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Active contour image segmentation model with de‐hazing constraints

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which makes the objects and their features difficult to be identified by computer vision systems. In the past decades, image de-hazing is used to remove the influence of weather factors and improve image visualisation in hazy scenes by providing easy image post-processing towards human assistance systems benefit. In this study, the authors present a variational segmentation model equipped with de-hazing constraint terms in a new coupled dehazing-segmentation model. The proposed hybrid formulation not only recovers/restores the fog/haze degradation but at the same time segments image degraded object/objects by solving in this way the difficulties of simultaneously performed dehazing and segmentation pre/post-processing. This combination takes into account the image structure boundaries and the image quality, leading in this way to a robust dehazing segmentation scheme. The advantages of the proposed method are the suitability of the model for grey and vector-valued images, a small number of parameters involved, and a rather good speed of the algorithm. Experiments show that their approach outperforms the state-of-the-art algorithms in terms of segmentation accuracy while avoiding a dehazing preprocessing which reflects an extended CPU time.
Fine-Grained Cryptography Revisited

Fine-grained cryptographic primitives are secure against adversaries with bounded resources and can be computed by honest users with less resources than the adversaries. In this paper, we revisit the results by Degwekar, Vaikuntanathan, and Vasudevan in Crypto 2016 on fine-grained cryptography and show the constructions of three key fundamental fine-grained cryptographic primitives: one-way permutations, hash proof systems (which in turn implies a public-key encryption scheme against chosen chiphertext attacks), and trapdoor one-way functions. All of our constructions are computable in $$\mathsf {NC^1}$$ and secure against (non-uniform) $$\mathsf {NC^1}$$ circuits under the widely believed worst-case assumption $$\mathsf {NC^1}\subsetneq \mathsf{\oplus L/poly}$$.
Formal Verification of BNB Smart Contract

Blockchain is a decentralized cryptocurrency framework which has gained considerable attention in research and many industries. Ethereum is an implementation based on blockchain technology. Ethereum blockchain offers smart contracts, a piece of program codes which can execute and record the transactions in blockchain. Users can use smart contracts to create their own tokens based on ERC-20 standard, and token smart contracts have been used to manage and transfer tokens, carrying millions dollars worth of virtual currencies. However, keeping token smart contracts secure can be difficult. When smart contracts are deployed on Ethereum successfully, they cannot be changed. Due to the openness of Ethereum, both users and attackers can invoke contracts. So errors in smart contracts have led and will lead to a loss. For example, the DAO bug led to a 60 million US dollar loss in June 2016. Therefore, the researches on security of smart contracts are urgently important. In this paper, we use a formal approach to verify smart contracts. We apply formal verification on a concrete smart contract example, BNB contract, which is one of the most popular token contracts. We analyze the contract and discover the two attributes in the contract, one is transfer attribute involving the functions of transferring tokens operations, and the other is owner attribute which involves the authority of contract owner. We verify these two attributes respectively. We model the contract using SAPIC, the applied pi calculus and then verify by Tamarin prover. We specify the processes of modeling and verifying. The results show we cannot find an attack path. The research method reduces the complexity of verifying smart contracts and can be used to analyze complex contracts.