Reviewerly/exHarmony huggingface dataset:
Hierarchical identity-based inner product functional encryption

• Hightlights • The identity of the decryptor in the inner product encryption can be specified precisely. • The identity has a hierarchical structure and supports delegating secret key. • The security is proven under the standard model and not the RO model, so it is more practical. • Theoretical and empirical analysis shows that the program is efficient. Inner product functional encryption (IPFE), as a novel cryptographic primitive, can be classified into public-key IPFE (PK-IPFE) and secret-key IPFE (SK-IPFE). PK-IPFE has the following functions: an encryptor generates the ciphertext by encrypting a vector x → , and a decryptor decrypts the ciphertext and obtains the inner product 〈 x → , y → 〉 if it holds the secret key related to vector y → . The general IPFE cannot describe the hierarchical structure. The superior cannot use the general IPFE to issue the delegating keys for the subordinates, which complicates key management. We first propose a new primitive, hierarchical identity-based PK-IPFE (HID-PK-IPFE). HID-PK-IPFE has the following features. First, the identity of the decryptor can be specified when encrypting. Second, the identity of the recipient has a hierarchical structure. In the hierarchical tree, the upper-level users can generate the private key of the lower-level users; that is, our scheme has a delegation function. We formalize the selective chosen plaintext attack model of HID-PK-IPFE, propose the scheme in the standard model, and prove the security of the scheme on the basis of d -DBDHE assumption. We conducted two sets of simulations. We set the maximum depth of the identity hierarchy to 10, and increased the vector length from 10 to 15. We then listed the time spent by each algorithm in these two ranges. The simulation results show that the running time of each algorithm of our HID-PK-IPFE is completely acceptable in actual application scenarios.
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting

Analyzing the worst-case performance of deep neural networks against input perturbations amounts to solving a large-scale non-convex optimization problem, for which several past works have proposed convex relaxations as a promising alternative. However, even for reasonably-sized neural networks, these relaxations are not tractable, and so must be replaced by even weaker relaxations in practice. In this work, we propose a novel operator splitting method that can directly solve a convex relaxation of the problem to high accuracy, by splitting it into smaller sub-problems that often have analytical solutions. The method is modular, scales to very large problem instances, and compromises of operations that are amenable to fast parallelization with GPU acceleration. We demonstrate our method in bounding the worst-case performance of large convolutional networks in image classification and reinforcement learning settings, and in reachability analysis of neural network dynamical systems.
Cross-Protocol Unfairness between Adaptive Streaming Clients over HTTP/3 and HTTP/2: A Root-Cause Analysis

With the introduction of HTTP/3, whose transport is no longer the traditional TCP protocol but the novel QUIC protocol, research for solutions to the unfairness of Adaptive Streaming over HTTP (HAS) has become more challenging. In other words, because of different transport layers, the HTTP/3 may not be available for some networks and the clients have to use HTTP/2 for their HAS applications instead. Therefore, the scenario in which HAS over HTTP/3 (HAS/3) competes against HTTP/2 (HAS/2) must be considered seriously. However, there has been a shortage of investigations on the performance and the origin of the unfairness in such a cross-protocol scenario in order to produce proper solutions. Therefore, this paper provides a performance evaluation and root-cause analysis of the cross-protocol unfairness between HAS/3 and HAS/2. It is concluded that, due to differences in the congestion control mechanisms of QUIC and TCP, HAS/3 clients obtain larger congestion windows, thus requesting higher video bitrates than HAS/2. As the problem lies in the transport layer, existing client-side ABR-based solutions for the unfairness from the application layer may perform suboptimally for the cross-protocol case.
VRGQ

While much of the research on graph analytics over large power-law graphs has focused on developing algorithms for evaluating a single global graph query, in practice we may be faced with a stream of queries. We observe that, due to their global nature, vertex specific graph queries present an opportunity for sharing work across queries. To take advantage of this opportunity, we have developed the VRGQ framework that accelerates the evaluation of a stream of queries via coarsegrained value reuse. In particular, the results of queries for a small set of source vertices are reused to speedup all future queries. We present a two step algorithm that in its first step initializes the query result based upon value reuse and then in the second step iteratively evaluates the query to convergence. The reused results for a small number of queries are held in a reuse table. Our experiments with best reuse configurations on four power law graphs and thousands of graph queries of five kinds yielded average speedups of 143×, 13.2×, 6.89×, 1.43×, and 1.18×.
Adaptive Histogram Equalization based Approach for SAR Image Enhancement: A Comparative analysis

Nowadays, flood is the most frequently occurring natural disaster among all-natural disasters. A flood is a disaster that causes huge causalities. Recovery from these huge causalities is a very serious concern for developing countries. This research work focuses on flush food, which is a very common disaster that occurs due to heavy rainfall or some manmade reasons. An Image of the flooded area provides useful information for taking necessary precautions in that particular situation. If the captured image is of poor quality, then isn't provide sufficient information. This paper considers the Synthetic Aperture Radar (SAR) images of the flooded area and it is mainly focused on increasing the image's visual quality. There are various image enhancement techniques available; this paper has used the histogram-based image quality improvement technique done by the Histogram Equalization (HE) method. To defeat the limitation of HE, another enhancement approach named Adaptive Histogram Equalization (AHE) is also used. In many cases, HE is increasing the quality of the image, but on the other side over brightness is causes the loss of some useful background information. In the AHE technique, the image is divided into small blocks: an equalization function is applied on each block. Both the techniques are compared on the various images of the flooded area, and the result shows that AHE provides better results.
MNCF: Prediction Method for Reliable Blockchain Services under a BaaS Environment

Blockchain is an innovative distributed ledger technology that is widely used to build next-generation applications without the support of a trusted third party. With the ceaseless evolution of the service-oriented computing (SOC) paradigm, Blockchain-as-a-Service (BaaS) has emerged, which facilitates development of blockchain-based applications. To develop a high-quality blockchain-based system, users must select highly reliable blockchain services (peers) that offer excellent quality-of-service (QoS). Since the vast number of blockchain services leading to sparse QoS data, selecting the optimal personalized services is challenging. Hence, we improve neural collaborative filtering and propose a QoS-based blockchain service reliability prediction algorithm under BaaS, named modified neural collaborative filtering (MNCF). In this model, we combine a neural network with matrix factorization to perform collaborative filtering for the latent feature vectors of users. Furthermore, multi-task learning for sharing different parameters is introduced to improve the performance of the model. Experiments based on a large-scale real-world dataset validate its superior performance compared to baselines.
Detection of normal and slow saccades using implicit piecewise polynomial approximation

The quantitative analysis of saccades in eye movement data unveils information associated with intention, cognition, and health status. Abnormally slow saccades are indicative of neurological disorders and often imply a specific pathological disturbance. However, conventional saccade detection algorithms are not designed to detect slow saccades, and are correspondingly unreliable when saccades are unusually slow. In this article, we propose an algorithm that is effective for the detection of both normal and slow saccades. The proposed algorithm is partly based on modeling saccadic waveforms as piecewise-quadratic signals. The algorithm first decreases noise in acquired eye-tracking data using optimization to minimize a prescribed objective function, then uses velocity thresholding to detect saccades. Using both simulated saccades and real saccades generated by healthy subjects and patients, we evaluate the performance of the proposed algorithm and 10 other detection algorithms. We show the proposed algorithm is more accurate in detecting both normal and slow saccades than other algorithms.
Domain-oriented Language Modeling with Adaptive Hybrid Masking and Optimal Transport Alignment

Motivated by the success of pre-trained language models such as BERT in a broad range of natural language processing (NLP) tasks, recent research efforts have been made for adapting these models for different application domains. Along this line, existing domain-oriented models have primarily followed the vanilla BERT architecture and have a straightforward use of the domain corpus. However, domain-oriented tasks usually require accurate understanding of domain phrases, and such fine-grained phrase-level knowledge is hard to be captured by existing pre-training scheme. Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge. But meanwhile, there is a risk of introducing noise due to the lack of groundtruth word-level alignment. To address the issues, we provide a generalized domain-oriented approach, which leverages auxiliary domain knowledge to improve the existing pre-training framework from two aspects. First, to preserve phrase knowledge effectively, we build a domain phrase pool as auxiliary knowledge, meanwhile we introduce Adaptive Hybrid Masked Model to incorporate such knowledge. It integrates two learning modes, word learning and phrase learning, and allows them to switch between each other. Second, we introduce Cross Entity Alignment to leverage entity association as weak supervision to augment the semantic learning of pre-trained models. To alleviate the potential noise in this process, we introduce an interpretableOptimal Transport based approach to guide alignment learning. Experiments on four domain-oriented tasks demonstrate the superiority of our framework.
A Deep Learning Study On Understanding Banglish and Abbreviated Words Used in Social Media

Day by Day, the trend of using social media(SM) has increased among the people. With the increasing rate, the use of Banglish(Merge of Bangla and English) and shortcut words has also being increased. In this research, Banglish and Shortcut words have been fully converted to English. For this type of conversation, we have used the CNNs method and the method consist of multiple layer and these layers are connected to each other. This method is considered to be the best method because it does not need any feature extraction. The convolutional neuron network is used in many areas such as image and pattern recognition, speech recognition, natural language processing and video analysis. In this research we have used CNN method because we have decided to use computer vision as some of the words are close to each other and at first we need to convert all the shortcut words into images for pre-processing the data. With the help of CNN method searched for the Banglish and shortcut words that people use for their daily conversation. At first we find the different representative of a single word and then we converted those shortcut and Banglish words to main word using CNNs method.
Information Technology in Teaching Future Pop Vocalists to Promote Their Creativity at the University

One of the urgent problems in the system of training students, whose profession is connected with creativity, is learning how to present their creative achievements to society. The lack of demand for creativity, the impossibility of demonstrating it leads even great musicians to lose motivation to create works, to translate them on the stage and even to depression. In this regard, at present, courses are being introduced into the system of training musicians and artists at universities to promote their creativity and the possibilities of its presentation to the public. This promotion is extremely important for the system of training pop vocalists, whose creativity, associated with current and innovative trends in song fashion, should soon reach the consumer and be properly appreciated by him. Today, university teachers where pop vocalists are trained to include topics related to the promotion of the future artist’s creativity in certain disciplines. However, being carried away by the problems of management and the problems of quality of performance, teachers’ pay insufficient attention to such modern means of promoting creativity as information technology. The purpose of this article is to show that minimum of knowledge about information technologies, including social networks, programs, music platforms, video editors, which will allow an aspiring pop vocalist to successfully promote their creativity. The presented information technologies were introduced into the process of preparing future pop performers during one semester and showed their effectiveness. The main result of the experiment was the emergence of more invitations of students to new and interesting projects.
Emergency Communication Application for Speech and Hearing-Impaired Citizens

Citizens worldwide with mutism and hearing loss, converse differently than the rest of the population. Preferring a gestural mode of communication, they use sign languages which are established natural languages with an extensive vocabulary and syntax. Nevertheless, despite having equal status to spoken languages, society prominently caters to the majority of citizens who are capable of speaking and processing spoken languages. As a result, sign language speakers face difficulties with social aggregation. The Sri Lankan citizens with mutism and hearing loss obtain improving benefits with the advancement of technology; however, accessing emergency services remains a challenge as they can only be accessed verbally using a phone call. Project `Wadhan' enables citizens with mutism and hearing loss to request for emergency services in real-time without depending on an intermediary. This cross-platform mobile application will convert Sri Lankan Sign Language (SSL) statements to voice and vice-versa, which allows a seamless conversation between the SSL speaker and the emergency operator. The application includes SSL Gesture Recognition, Sinhala Text-to-Speech (TTS), Sinhala Speech Recognition, and SSL Animation Generation, thus resulting in an emergency communication application that can be used by SSL users in dire situations.
Comparative study for Stylometric analysis techniques for authorship attribution

A text is a meaningful source of information. Capturing the right patterns in written text gives metrics to measure and infer to what extent this text belongs or is relevant to a specific author. This research aims to introduce a new feature that goes more in deep in the language structure. The feature introduced is based on an attempt to differentiate stylistic changes among authors according to the different sentence structure each author uses. The study showed the effect of introducing this new feature to machine learning models to enhance their performance. It was found that the prediction of authors was enhanced by adding sentence structure as an additional feature as the f1_scores increased by 0.3% and when normalizing the data and adding the feature it increased by 5%.
Using Pareto simulated annealing to address algorithmic bias in machine learning

Abstract Algorithmic bias arises in machine learning when models that may have reasonable overall accuracy are biased in favor of ‘good’ outcomes for one side of a sensitive category, for example gender or race. The bias will manifest as an underestimation of good outcomes for the under-represented minority. In a sense, we should not be surprised that a model might be biased when it has not been ‘asked’ not to be; reasonable accuracy can be achieved by ignoring the under-represented minority. A common strategy to address this issue is to include fairness as a component in the learning objective. In this paper, we consider including fairness as an additional criterion in model training and propose a multi-objective optimization strategy using Pareto Simulated Annealing that optimizes for both accuracy and underestimation bias. Our experiments show that this strategy can identify families of models with members representing different accuracy/fairness tradeoffs. We demonstrate the effectiveness of this strategy on two synthetic and two real-world datasets.
SP-BTM: A Specific Part-of-speech BTM for Service Clustering

Service clustering has played a great role in some fundamental service computing domain, such as service discovery, service selection and service recommendation. Inferring topics from short service descriptions is an important but challenge task for service clustering. BTM (Biterm Topic Model) performs well in discovering latent relevance semantic from short text. But choosing two words indiscriminately to form a biterm may affect the accuracy of topic modeling, because the distinct words are not equally indicative in presenting service topics. Therefore, a SP-BTM is proposed in this paper which only chooses the words with specific parts-of-speech to form biterms for topic modeling. By some experiments on real world dataset, it is verified that nouns, verbs and adjectives are benefit to topic representation and the SP-BTM can improve the effectiveness of service clustering.
Cutmix Dual Branch Network for Person Re-Identification

The performance of deep learning methods for person re-identification (Re-ID) is influenced by overfitting problem. To improve the generalization ability, most methods pay attention to generating and utilizing new samples. However, generated samples focus on object occlusion but neglect pedestrian occlusion, while triplet loss fails to preserve all identity information on samples with multiple pedestrians. In this paper, we propose the CutMix dual branch network (CDBN) to relieve these problems. CutMix is introduced to this model, and it is responsible for generating new samples with pedestrian occlusion. CutMix is verified complementary to image erasing strategy for Re-ID. Besides, a generalized triplet loss called CutMix triplet loss (CTP) is employed on samples augmented by CutMix with consideration of identity information from multiple pedestrians, making CDBN robust to two kinds of occlusions. Extensive experiments on two bench-marks demonstrate the strengths of CDBN, which is superior to state-of-the-art methods.
Smart University: Development of Analytical Management System Based on Big Data

In contrast to the existing publications, in this research, the analytical system of smart university management based on big data is considered. Smart components of a smart university generate a very large amount of data continuously and all this data should be taken into account when making management decisions. At the same time, the article does not regard the structure essence of the smart university management system, but rather the analytical management system. The main attention is focused on the smart university information infrastructure. It allows you to use the advantages of intelligent technologies, such as big data. The novelty of the solutions and the practical significance of the research consist in the author's models application for supporting managerial decision-making based on big data and a graphical model and the information infrastructure in the analytical system of smart university management. Mathematical and graphical methods are used for modeling. The experience of managing Russian universities based on big data is also considered.
On Image Preprocessing Methods for Underwater Image Matching

Deep seafloor explorations have numerous scientific applications in the fields of archeology, geology and biology et al. To obtain large-scale detailed visual information, it is needed to stitch multiple adjacent images into a photo-mosaic. Here we focus on image matching which is a key step in photo stitching application. Due to the complexity of underwater imaging environment such as light attenuation, non-uniform illumination and scattering, the images taken in deep water are degraded greatly and the performance of classical methods for image matching is reduced which seriously affect subsequent image alignment. In this paper, we try to investigate three underwater image preprocessing methods to increase the number of matched feature points. These three methods include color channel selection, lighting compensation and contrast enhancement. The experiment results show some encouraging conclusions. More feature points are matched in blue or green channel than red one. By proper inhomogenous lighting compensation and/or linear contrast enhancement, the matched points also increase significantly. When the pre-processes methods are used in combination, the number of matched features is improved 2.7 times statistically.
Towards GDPR Enforcing Blockchain Systems

This paper gives an overview of current research areas considering GDPR and blockchain. It is shown that GDPR is often seen as a problem, limiting blockchain use cases. However, approaches towards more data protection for the data subjects based on blockchain technology emerge. In this paper, we evaluate a first step towards a GDPR enforcing blockchain by using a combination of smart contracts within Hyperledger Fabric, evaluating if a joint controllership agreement is in place. Such agreement is required for joint controller to process personal data. Based on this rather simple use case evaluation, it is discussed that a combination of the different research areas around GDPR and blockchain should be further evaluated and combined, aiming to GDPR enforcing blockchain systems.
A Comparative Study on Distributed File Systems

Distributed File Systems are mainstays for the way massive amounts of data is stored. The emergence of Hadoop File Systems, Google File Systems and Network File Systems have changed the course of how data is managed in servers and has its own implications on Cloud Computing and Big Data management. Each file system offers its own advantages and challenges in terms of performance, fault-tolerance, consistency, scalability and availability. This opens an open debate on how these can be taken up for implementation. The choice of a feature available with each one of them has their own metrices that differentiates them from other file systems. This paper looks at a comparative study on the file systems and proposes the criterion behind the choice of selection of a specific file system. The study also explores the advantages of using a file system and the benefits and disadvantages associated with them.