Reviewerly/exHarmony huggingface dataset:
Secure Data Sharing and Customized Services for Intelligent Transportation Based on a Consortium Blockchain

In view of the security risks and centralized structure of traditional intelligent transportation system, we propose a novel scheme of secure data sharing and customized services based on the consortium blockchain (DSCSCB).The ciphertext-policy attribute-based proxy re-encryption algorithm has the function of keyword searching by dividing the key into an attribute key and a search key, which not only solves the problem that proxy re-encryption algorithm cannot retrieve data, but also realizes data sharing and data forwarding.Moreover, the algorithm effectively controls the access permission of data, and provides a secure communication environment for the vehicular ad-hoc network (VANET).Service sectors, such as insurance companies, the traffic police and maintenance suppliers, obtain the corresponding ciphertext and then apply the smart contract to provide customized services for the onboard unit after decryption.Security analysis and performance evaluation demonstrate that our scheme not only meets the requirements of data sharing in the security and confidentiality, but also has obvious advantages in the overhead of computing and communication.
Blockchain-Based Reputation Management for Task Offloading in Micro-Level Vehicular Fog Network

With the widespread adoption of the internet of things (IoT) technologies towards building a smart city, connected devices often offload computation tasks to nearby edge locations (base stations) to reduce overall computation and network delay.However, serving an ever-increasing number of end devices at these traditional edge locations is becoming impossible, subsequently making them fail to deliver the agreed quality of service to all requesting devices.However, the backend cloud data center is available to serve these requests but incurred additional communication delay, thus, unsuitable for delay-sensitive applications.Furthermore, the fact that the underlying network is inherently ad hoc which makes it prone to malicious nodes affecting its overall performance.In this work, we propose a secure fog computing paradigm where roadside units (RSUs) are used to offload tasks to nearby fog vehicles based on repute scores maintained at a distributed blockchain ledger.The experimental results demonstrate a significant performance gain in terms of queuing time, end-to-end delay, and task completion rate when compared to the baseline queuing-based task offloading scheme.
High Precision Open-World Website Fingerprinting

Traffic analysis attacks to identify which web page a client is browsing, using only her packet metadata - known as website fingerprinting (WF) - has been proven effective in closed-world experiments against privacy technologies like Tor. We want to investigate their usefulness in the real open world. Several WF attacks claim to have high recall and low false positive rate, but they have only been shown to succeed against high base rate pages. We explicitly incorporate the base rate into precision and call it r-precision. Using this metric, we show that the best previous attacks have poor precision when the base rate is realistically low; we study such a scenario (r = 1000), where the maximum r-precision achieved was only 0.14.To improve r-precision, we propose three novel classes of precision optimizers that can be applied to any classifier to increase precision. For r = 1000, our best optimized classifier can achieve a precision of at least 0.86, representing a precision increase by more than 6 times. For the first time, we show a WF classifier that can scale to any open world set size. We also investigate the use of precise classifiers to tackle realistic objectives in website fingerprinting, including different types of websites, identification of sensitive clients, and defeating website fingerprinting defenses.
Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces

Age and gender predictions of unfiltered faces classify unconstrained real-world facial images into predefined age and gender. Significant improvements have been made in this research area due to its usefulness in intelligent real-world applications. However, the traditional methods on the unfiltered benchmarks show their incompetency to handle large degrees of variations in those unconstrained images. More recently, Convolutional Neural Networks (CNNs) based methods have been extensively used for the classification task due to their excellent performance in facial analysis. In this work, we propose a novel end-to-end CNN approach, to achieve robust age group and gender classification of unfiltered real-world faces. The two-level CNN architecture includes feature extraction and classification itself. The feature extraction extracts feature corresponding to age and gender, while the classification classifies the face images to the correct age group and gender. Particularly, we address the large variations in the unfiltered real-world faces with a robust image preprocessing algorithm that prepares and processes those faces before being fed into the CNN model. Technically, our network is pretrained on an IMDb-WIKI with noisy labels and then fine-tuned on MORPH-II and finally on the training set of the OIU-Adience (original) dataset. The experimental results, when analyzed for classification accuracy on the same OIU-Adience benchmark, show that our model obtains the state-of-the-art performance in both age group and gender classification. It improves over the best-reported results by 16.6% (exact accuracy) and 3.2% (one-off accuracy) for age group classification and also there is an improvement of 3.0% (exact accuracy) for gender classification.
Julia language in machine learning: Algorithms, applications, and open issues

Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields. Currently, the programming languages most commonly used to develop machine learning algorithms include Python, MATLAB, and C/C ++. However, none of these languages well balance both efficiency and simplicity. The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity. This paper summarizes the related research work and developments in the applications of the Julia language in machine learning. It first surveys the popular machine learning algorithms that are developed in the Julia language. Then, it investigates applications of the machine learning algorithms implemented with the Julia language. Finally, it discusses the open issues and the potential future directions that arise in the use of the Julia language in machine learning.
Capsule Network on Font Style Classification

Verification of font style followed in a file is a difficult task to classify. An artificial intelligence based algorithm network can effectively perform this task in reduced time. Capsule network is one among such algorithm and an emerging technique implemented for so many classification process with limited datasets. The proposed font style classification algorithm is enforced with Capsule Network (CapsNet) algorithm for executing the font style classification task. The proposed method is confirmed by classifying times new roman, Arial black and Algerian font style in English letters along with the performance evaluation in terms of accuracy and confusion matrix parameters. The proposed network structure is also compared with the existing Naive Bayes (NB), Decision Tree (DT) and K nearest neighbor (KNN) algorithms for comparative study and the evaluation result indicates that the proposed font style classification model based on CapsNet is classifying the images with better accuracy, F1 score and Gmean.
A burst-based unsupervised method for detecting review spammer groups

With the development of e-commerce, online shopping has become a part of people's life. As customers often refer to online product reviews for shopping, sellers often collude with review spammers in writing fake reviews to promote or demote target products. In particular, spammers working in groups are more harmful than individual attacks. To detect such spammer groups, previous researchers proposed some frequent item mining based algorithms and graph-based algorithms. In this paper, we propose a method called GSDB (Group Spam Detection algorithm based on review Burst). Our algorithm first locates target products attacked by spammers by detecting the abnormality of product rating distribution. As group spammers usually post many fake reviews within a short period, we design a burst-based algorithm that discovers candidate spammer groups in reviewbursts using the Kernel Density Estimation algorithm. As some innocent reviewers may coincidently review during the burst period, we formulate a variety of individual spam indicators to measure the spamicity of the reviewers to isolate the candidate spammer groups. Finally, we design a series of group spam indicators to measure and classify the spamicity of spammer groups. Experimental results show that our proposed GSDB algorithm outperforms state-of-the-art algorithms.
Improving Quality Education through Better Working Conditions of Academic Institutes

Education has become inevitable for every country and specifically for every person to proceed in with the appropriate pace. Life is really difficult to be lived without essential amount of education. Many countries are giving attention towards this cause and are making strategies and policies to improve the quality of education in the country. For better quality of education, working conditions of the educational institutes is very important. In addition, faculty retention and employability are very crucial in this regard. In the current study, the aim of the author is to study the impact of working conditions on quality education in Indonesia in the presence of mediation of faculty retention and employability. For this purpose, data was collected from 293 faculty members of the educational institutes of Indonesia. To analyze the gathered data, confirmatory factor analysis CFA and structural equation modeling approaches have been used. The results of data analysis have shown that the impact of working condition on quality education is insignificant but the mediating role of employability and faculty retention are significant in this regard. In the last section of the study, several theoretical, practical and policy making implications have been discussed by the author. In addition, some limitations and future research indications have also been described.
Hamiltonian simulation in the low-energy subspace

Abstract We study the problem of simulating the dynamics of spin systems when the initial state is supported on a subspace of low energy of a Hamiltonian H . This is a central problem in physics with vast applications in many-body systems and beyond, where the interesting physics takes place in the low-energy sector. We analyze error bounds induced by product formulas that approximate the evolution operator and show that these bounds depend on an effective low-energy norm of H . We find improvements over the best previous complexities of product formulas that apply to the general case, and these improvements are more significant for long evolution times that scale with the system size and/or small approximation errors. To obtain these improvements, we prove exponentially decaying upper bounds on the leakage to high-energy subspaces due to the product formula. Our results provide a path to a systematic study of Hamiltonian simulation at low energies, which will be required to push quantum simulation closer to reality.
Real Life Human Movement Realization in Multimodal Group Communication Using Depth Map Information and Machine Learning

The latest advancements in the evolution of depth map information’s has paved way for interesting works like object recognition sign detection and human movement detection etc. The real life human movement detection or their activity identification is very challenging and tiresome. Since the real life activities of the humans could be of much interest in almost all areas, the subject of identifying the human activities has gained significance and has become a most popular research field. Identifying the human movements /activities in the public places like airport, railways stations, hospital, home for aged become very essential due to the several benefits incurred form the human movement recognition system such as surveillance camera, monitoring devices etc. since the changes in the space and the time parameters can provide an effective way of presenting the movements, yet in the case of natural color vision, as the flatness is depicted in almost all portions of images. So the work laid out in the paper in order to identify the human movement in the real life employs the space and the time depth particulars (Spatial-Temporal depth details –STDD) and the random forest in the final stage for movement classification. The technology put forth utilize the Kinect sensors to collecting the information’s in the data gathering stage. The mechanism laid out to identify the human movements is test with the MATLAB using the Berkley and the Cornell datasets. The mechanism proposed through the acquired results proves to deliver a better performance compared to the human movements captured using the normal video frames.
The Cascade Transformer: an Application for Efficient Answer Sentence Selection

Large transformer-based language models have been shown to be very effective in many classification tasks.However, their computational complexity prevents their use in applications requiring the classification of a large set of candidates.While previous works have investigated approaches to reduce model size, relatively little attention has been paid to techniques to improve batch throughput during inference.In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers.Each ranker is used to prune a subset of candidates in a batch, thus dramatically increasing throughput at inference time.Partial encodings from the transformer model are shared among rerankers, providing further speed-up.When compared to a state-of-the-art transformer model, our approach reduces computation by 37% with almost no impact on accuracy, as measured on two English Question Answering datasets.
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach

Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. It is an important research topic which has been widely studied in e-Commerce and relation learning. There are two main limitations in existing attribute value extraction methods: scalability and generalizability. Most existing methods treat each attribute independently and build separate models for each of them, which are not suitable for large scale attribute systems in real-world applications. Moreover, very limited research has focused on generalizing extraction to new attributes.
A Simple and Effective Unified Encoder for Document-Level Machine Translation

Most of the existing models for documentlevel machine translation adopt dual-encoder structures.The representation of the source sentences and the document-level contexts 1 are modeled with two separate encoders.Although these models can make use of the document-level contexts, they do not fully model the interaction between the contexts and the source sentences, and can not directly adapt to the recent pre-training models (e.g., BERT) which encodes multiple sentences with a single encoder.In this work, we propose a simple and effective unified encoder that can outperform the baseline models of dualencoder models in terms of BLEU and ME-TEOR scores.Moreover, the pre-training models can further boost the performance of our proposed model.
OFF-eNET: An Optimally Fused Fully End-to-End Network for Automatic Dense Volumetric 3D Intracranial Blood Vessels Segmentation

Intracranial blood vessels segmentation from computed tomography angiography (CTA) volumes is a promising biomarker for diagnosis and therapeutic treatment in cerebrovascular diseases. These segmentation outputs are a fundamental requirement in the development of automated decision support systems for preoperative assessment or intraoperative guidance in neuropathology. The state-of-the-art in medical image segmentation methods are reliant on deep learning architectures based on convolutional neural networks. However, despite their popularity, there is a research gap in the current deep learning architectures optimized to address the technical challenges in blood vessel segmentation. These challenges include: (i) the extraction of concrete brain vessels close to the skull; and (ii) the precise marking of the vessel locations. We propose an Optimally Fused Fully end-to-end Network (OFF-eNET) for automatic segmentation of the volumetric 3D intracranial vascular structures. OFF-eNET comprises of three modules. In the first module, we exploit the up-skip connections to enhance information flow, and dilated convolution for detailed preservation of spatial feature map that are designed for thin blood vessels. In the second module, we employ residual mapping along with inception module for speedy network convergence and richer visual representation. For the third module, we make use of the transferred knowledge in the form of cascaded training strategy to gradually optimize the three segmentation stages (basic, complete, and enhanced) to segment thin vessels located close to the skull. All these modules are designed to be computationally efficient. Our OFF-eNET, evaluated using 70 CTA image volumes, resulted in 90.75% performance in the segmentation of intracranial blood vessels and outperformed the state-of-the-art counterparts.
Feature Projection for Improved Text Classification

In classification, there are usually some good features that are indicative of class labels.For example, in sentiment classification, words like good and nice are indicative of the positive sentiment and words like bad and terrible are indicative of the negative sentiment.However, there are also many common features (e.g., words) that are not indicative of any specific class (e.g., voice and screen, which are common to both sentiment classes and are not discriminative for classification).Although deep learning has made significant progresses in generating discriminative features through its powerful representation learning, we believe there is still room for improvement.In this paper, we propose a novel angle to further improve this representation learning, i.e., feature projection.This method projects existing features into the orthogonal space of the common features.The resulting projection is thus perpendicular to the common features and more discriminative for classification.We apply this new method to improve CNN, RNN, Transformer, and Bert based text classification and obtain markedly better results.
Re-translation versus Streaming for Simultaneous Translation

There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available.We study a related problem in which revisions to the hypothesis beyond strictly appending words are permitted.This is suitable for applications such as live captioning an audio feed.In this setting, we compare custom streaming approaches to re-translation, a straightforward strategy where each new source token triggers a distinct translation from scratch.We find retranslation to be as good or better than stateof-the-art streaming systems, even when operating under constraints that allow very few revisions.We attribute much of this success to a previously proposed data-augmentation technique that adds prefix-pairs to the training data, which alongside wait-k inference forms a strong baseline for streaming translation.We also highlight re-translation's ability to wrap arbitrarily powerful MT systems with an experiment showing large improvements from an upgrade to its base model.
Gamma distribution-based sampling for imbalanced data

Imbalanced class distribution is a common problem in a number of fields including medical diagnostics, fraud detection, and others. It causes bias in classification algorithms leading to poor performance on the minority class data. In this paper, we propose a novel method for balancing the class distribution in data through intelligent resampling of the minority class instances. The proposed method is based on generating new minority instances in the neighborhood of the existing minority points via a gamma distribution. Our method offers a natural and coherent approach to balancing the data. We conduct a comprehensive numerical analysis of the new sampling technique. The experimental results show that the proposed method outperforms the existing state-of-the-art methods for imbalanced data. Concretely, the new sampling technique produces the best results on 12 out of 24 real life as well as synthetic datasets. For comparison, the SMOTE method achieves the top score on only 1 dataset. We conclude that the new technique offers a simple yet effective sampling approach to balance data.
Generation of optical Schrödinger cat states by generalized photon subtraction

We propose a high-rate generation method of optical Schr\"{o}dinger's cat states. Thus far, photon subtraction from squeezed vacuum states has been a standard method in cat-state generation, but its constraints on experimental parameters limit the generation rate. In this paper, we consider the state generation by photon number measurement in one mode of arbitrary two-mode Gaussian states, which is a generalization of conventional photon subtraction, and derive the conditions to generate high-fidelity and large-amplitude cat states. Our method relaxes the constraints on experimental parameters, allowing us to optimize them and attain a high generation rate. Supposing realistic experimental conditions, the generation rate of cat states with large amplitudes ($|\alpha| \ge 2)$ can exceed megacounts per second, about $10^3$ to $10^6$ times better than typical rates of conventional photon subtraction. This rate would be improved further by the progress of related technologies. Ability to generate non-Gaussian states at a high rate is important in quantum computing using optical continuous variables, where scalable computing platforms have been demonstrated but preparation of non-Gaussian states of light remains as a challenging task. Our proposal reduces the difficulty of the state preparation and open a way for practical applications in quantum optics.