Reviewerly/exHarmony huggingface dataset:
Perceptual Image Compression with Block-Level Just Noticeable Difference Prediction

A block-level perceptual image compression framework is proposed in this work, including a block-level just noticeable difference (JND) prediction model and a preprocessing scheme. Specifically speaking, block-level JND values are first deduced by utilizing the OTSU method based on the variation of block-level structural similarity values between two adjacent picture-level JND values in the MCL-JCI dataset. After the JND value for each image block is generated, a convolutional neural network–based prediction model is designed to forecast block-level JND values for a given target image. Then, a preprocessing scheme is devised to modify the discrete cosine transform coefficients during JPEG compression on the basis of the distribution of block-level JND values of the target test image. Finally, the test image is compressed by the max JND value across all of its image blocks in the light of the initial quality factor setting. The experimental results demonstrate that the proposed block-level perceptual image compression method is able to achieve 16.75% bit saving as compared to the state-of-the-art method with similar subjective quality. The project page can be found at https://mic.tongji.edu.cn/43/3f/c9778a148287/page.htm.
Research on Automated Detection of Sensitive Information Based on BERT

Abstract With the booming of the Internet, Web public opinion plays an increasingly important role in the stability of the network community. Therefore, the sensitive information hidden on the Internet is likely to lead to unpredictable social impact. This paper focuses on the detection of Chinese sensitive information. First, we build a corpus to train the detection model. Secondly, we apply the Bert method to the detection problem. Then, many popular NLP methods are applied to this problem to show the progress of Bert in a sensitive information detection task. Finally, we got a sensitive information detection model based on BERT with a high F1 score of 97.31.
Crypt4GH: a file format standard enabling native access to encrypted data

The majority of genome analysis tools and pipelines require data to be decrypted for access. This potentially leaves sensitive genetic data exposed, either because the unencrypted data is not removed after analysis, or because the data leaves traces on the permanent storage medium.: We defined a file container specification enabling direct byte-level compatible random access to encrypted genetic data stored in community standards such as SAM/BAM/CRAM/VCF/BCF. By standardizing this format, we show how it can be added as a native file format to genomic libraries, enabling direct analysis of encrypted data without the need to create a decrypted copy.The Crypt4GH specification can be found at: http://samtools.github.io/hts-specs/crypt4gh.pdf.Supplementary data are available at Bioinformatics online.
An adaptive instinctive reaction strategy based on Harris hawks optimization algorithm for numerical optimization problems

An adaptive Harris hawks optimization (HHO) algorithm is proposed to solve the non-linearly constrained optimization problems. The algorithm, namely, instinctive reaction strategy based on HHO (IRSHHO), combines the instinctive reaction strategy (IRS) with the HHO algorithm. In IRSHHO, the increment of step length decreases with the increase in the iteration. Moreover, the energy of the prey is also considered to speed up the convergence in each iteration. The performance of the IRSHHO is investigated on five benchmark numerical examples. The results of IRSHHO provide very competitive results compared to other well-known algorithms. Furthermore, the IRSHHO is applied to optimize the auto drum fashioned brake problem. Results show that the IRSHHO can obtain the optimal solution with the braking efficiency factor that is increased by 27.872% from the initial design with the proposed IRSHHO method.
Multi Variant Handwritten Telugu Character Recognition Using Transfer Learning

Abstract Optical Character Recognition (OCR) has become one of the most important techniques in computer vision, given that it can easily obtain information from various images. However, existing OCR techniques cannot recognition Telugu literature characters (Handwritten Golusu Kattu writing) due to a lack of datasets and trained deep Convolutional Neural Networks (CNN). Since the Kakatiya Empire (12 th to 14 th century) the glorious era of Telugu literature spread across the region. Thereupon, several handwritten documents consist of ancient knowledge, health care tips, wealth information, and several land records written in Telugu Golusukattu writing. Therefore, getting that information has become a major problem because of a lack of expertise in Golusukattu writing skills in skills. In order to solve the above problem, we are proposing deep learning aided-OCR for Telugu literature.
Analysis of Contextual and Non-contextual Word Embedding Models for Hindi NER with Web Application for Data Collection

Named Entity Recognition (NER) is the process of taking a string and identifying relevant proper nouns in it. In this paper (All codes and datasets used in this paper are available at: https://github.com/AindriyaBarua/Contextual-vs-Non-Contextual-Word-Embeddings-For-Hindi-NER-With-WebApp .) we report the development of the Hindi NER system, in Devanagari script, using various embedding models. We categorize embeddings as Contextual and Non-contextual, and further compare them inter and intra-category. Under non-contextual type embeddings, we experiment with Word2Vec and FastText, and under the contextual embedding category, we experiment with BERT and its variants, viz. RoBERTa, ELECTRA, CamemBERT, Distil-BERT, XLM-RoBERTa. For non-contextual embeddings, we use five machine learning algorithms namely Gaussian NB, Adaboost Classifier, Multi-layer Perceptron classifier, Random Forest Classifier, and Decision Tree Classifier for developing ten Hindi NER systems, each, once with Fast Text and once with Gensim Word2Vec word embedding models. These models are then compared with Transformers based contextual NER models, using BERT and its variants. A comparative study among all these NER models is made. Finally, the best of all these models is used and a web app is built, that takes a Hindi text of any length and returns NER tags for each word and takes feedback from the user about the correctness of tags. These feed-backs aid our further data collection.
A new approach for the detection and analysis of phishing in social networks: the case of Twitter

Cybercriminals use Internet and social networks as a vector to launch phishing attacks in order to lure victims to disclose personal information. There are several methods for the detection and analysis of these attacks, among the most used are those based on machine learning. However, these methods suffer from a lack of precision in detecting phishing attacks. Therefore, it is necessary to propose new methods to improve the predictions of these attacks. In this article, we propone a three (03) step approach for the detection and analysis of phishing on Twitter, which can be applied to several other social networks. The first step is to browse a database called “Blacklist” in search of the suspicious URL (Uniform Resource Locator). Subsequently, we proceed to URL (Uniform Resource Locator) analysis leveraging machine learning techniques introducing new features. In this step the three following classifiers were used namely, Regression Logistics, SVM (Support vector Machine) and Random Forest. Afterwards, we added a module to analyze Twitter accounts, also based on machine learning, using user-related features to detect malicious twitters who are causing the phishing attacks. Finally, we tested our system on real data and then implemented it in the form of an application for the end users.
Decentralized Learning With Lazy and Approximate Dual Gradients

This paper develops algorithms for decentralized machine learning over a network, where data are distributed, computation is localized, and communication is restricted between neighbors. A line of recent research in this area focuses on improving both computation and communication complexities. The methods SSDA and MSDA (Scaman et al., 2017) have optimal communication complexity when the objective is smooth and strongly convex, and are simple to derive. However, they require solving a subproblem at each step, so both the required accuracy of subproblem solutions and total computational complexities are uncertain. We propose new algorithms that instead of solving a subproblem, run warm-started Katyusha for a small, fixed number of steps. In addition, when previous information is sufficiently useful, a local rule will decide even to skip a round of communication, leading to extra savings. We show that our algorithms are efficient in both computation and communication, provably reducing the communication and computation complexities of SSDA and MSDA. In numerical experiments, our algorithms achieve significant computation and communication reduction compared with the state-of-the-art.
Towards trustworthy blockchains: normative reflections on blockchain-enabled virtual institutions

Abstract This paper proposes a novel way to understand trust in blockchain technology by analogy with trust placed in institutions. In support of the analysis, a detailed investigation of institutional trust is provided, which is then used as the basis for understanding the nature and ethical limits of blockchain trust. Two interrelated arguments are presented. First, given blockchains’ capacity for being institution-like entities by inviting expectations similar to those invited by traditional institutions, blockchain trust is argued to be best conceptualized as a specialized form of trust in institutions. Keeping only the core functionality and certain normative ideas of institutions, this technology broadens our understanding of trust by removing the need for third parties while retaining the value of trust for the trustor. Second, the paper argues that blockchains’ decentralized nature and the implications and effects of this decentralization on trust issues are double-edged. With the erasure of central points, the systems simultaneously crowd out the pivotal role played by traditional institutions and a cadre of representatives in meeting their assigned obligations and securing the functional systems’ trustworthy performances. As such, blockchain is positioned as a technology containing both disruptive features that can be embedded with meaningful normative values and inherent ethical limits that pose a direct challenge to the actual trustworthiness of blockchain implementations. Such limits are proposed to be ameliorated by facilitating a shift of responsibility to the groups of people directly associated with the engendering of trust in the blockchain context.
Comparative experimental analysis of Docker container networking drivers

Virtualization allows for more efficient hardware usage by allowing several instances of Operating Systems (OSs) or Virtual Machines (VMs) to run on a physical server. Containers are a subset of lightweight virtualization and reduce the overhead of virtualizing an entire OS by sharing the server's OS with the virtualized instances. Moreover, containers work closer to hardware than VMs and are similar to Linux processes, however, this limits connectivity freedom since processes do not have access to network addressing. To resemble container's communication to that of conventional networks we use network drivers. Studies show that in processing- or memory-bound scenarios, containers perform better than VMs, but in network-bound scenarios they achieve less performance. This work analyzes performance of networking implementations for Docker in different container allocations and workload scenarios.
T-RexNet—A Hardware-Aware Neural Network for Real-Time Detection of Small Moving Objects

This paper presents the T-RexNet approach to detect small moving objects in videos by using a deep neural network. T-RexNet combines the advantages of Single-Shot-Detectors with a specific feature-extraction network, thus overcoming the known shortcomings of Single-Shot-Detectors in detecting small objects. The deep convolutional neural network includes two parallel paths: the first path processes both the original picture, in gray-scale format, and differences between consecutive frames; in the second path, differences between a set of three consecutive frames is only handled. As compared with generic object detectors, the method limits the depth of the convolutional network to make it less sensible to high-level features and easier to train on small objects. The simple, Hardware-efficient architecture attains its highest accuracy in the presence of videos with static framing. Deploying our architecture on the NVIDIA Jetson Nano edge-device shows its suitability to embedded systems. To prove the effectiveness and general applicability of the approach, real-world tests assessed the method performances in different scenarios, namely, aerial surveillance with the WPAFB 2009 dataset, civilian surveillance using the Chinese University of Hong Kong (CUHK) Square dataset, and fast tennis-ball tracking, involving a custom dataset. Experimental results prove that T-RexNet is a valid, general solution to detect small moving objects, which outperforms in this task generic existing object-detection approaches. The method also compares favourably with application-specific approaches in terms of the accuracy vs. speed trade-off.
Robust Meta Network Embedding Against Adversarial Attacks

Recent studies have shown that graph mining models are vulnerable to adversarial attacks. This paper proposes a robust meta network embedding framework, RoMNE, which improves the robustness of multiple network embedding on adversarial noisy networks while preserving the utility on original clean ones. First, we propose a generic meta learning based multiple network embedding model that can quickly adapt it to new embedding tasks on a variety of network data with only a small number of parameter and training updates. Second, Gumbel estimator and Gaussian smoothing techniques are introduced to implement differentiable approximation for optimizing non-differential objective of effective adversarial attacks. Last but not least, the adversarial attack and defense models are integrated into a dynamic adversarial training model. The competition of two models helps the latter be robust to adversarial attacks.
Evolving Gaussian Processes and Kernel Observers for Learning and Control in Spatiotemporally Varying Domains: With Applications in Agriculture, Weather Monitoring, and Fluid Dynamics

Monitoring and modeling large-scale stochastic phenomena with both spatial and temporal (spatiotemporal) evolution by using a network of distributed sensors is a critical problem in many control applications (see "Summary"). Consider, for example, a team of robots that has the task of destroying herbicide- resistant weeds on a farm (see Figure 1 and "Key Control Problems in Agriculture"). This team must predict weed growth across the whole farm to make intelligent, coordinated decisions [1].
Implementation of Blockchain based P2P Energy Trading Platform

As the development of smart cities, energy management systems have been changing from centralized management systems to distributed energy management systems for better energy efficiency. In the distributed energy management systems, while producing the energy from distributed users, there can be two types of users such that 1) users who have surplus energy generations and 2) users who lack energy generations compared to their demands. In this paper, to alleviate such imbalance of energy generation between distributed users, a peer to peer (P2P) based energy trading platform is proposed. Specifically, blockchain is one of emerging solutions in which transactions can be made reliably to achieve P2P transactions without any centralized broker intervention. Correspondingly, we implement the P2P energy trading platform under Ethereum's smart contract for reliable trading. The energy generated from distributed users at the proposed platform can be traded by utilizing the characteristics of Decentralize Application. Specifically, we provide the details of implementations of the proposed platform, which includes both hardware platform and software platform. Further, we establish a web page and an mobile application for monitoring the transaction information such as transaction details and energy prices, which can enhance users' accessibility. Finally, the demonstration of process of energy trading via web interface is represented.
Evaluation and Analysis of Selective Deployment of Power Optimizers for Residential PV Systems

Partial shading is widely considered to be a limiting factor in the performance of photovoltaic (PV) systems applied in urban environments. Modern system architectures, combined with per module deployment of power electronics, have been used to improve performance, especially at heterogeneous irradiance conditions, but they come with a high investment cost. In this paper, another approach is used to evaluate the selective deployment of power optimizers (SDPO), which can operate with a variety of string inverters and can be retrofitted in PV systems suffering from high shading losses. A combination of modelling and outdoor field testing showed the benefits and drawbacks of SDPOs in a variety of shading scenarios. Results suggest that there is an energy yield increase of 1–2% on an annual basis compared to that of a reference system. The exact level of increase depends on the shading patterns and combination scenarios used in this paper.
Neural Machine Translation for Semantic-Driven Q&amp;A Systems in the Factory Planning

Shorter lifecycles, increasing product variance and the integration of new products and technologies into existing factories lead to a high complexity in today’s factory planning. In order to master this complexity, many companies attempt to improve their processes by using digitalization tools. This generates enormous amounts of data, which are currently only partially managed centrally in the company. In order to simplify the associated difficulties regarding the access to information, semantic technologies for the generation and application of knowledge graphs (KG) are currently being investigated intensively by research and industry. To retrieve information - stored in the KG - query languages such as SPARQL are used which require knowledge regarding the structure of the KG as well as a profound knowledge of query languages. To also enable non-expert users to retrieve the stored information in the KG, an intuitive user interface for question answering (Q&A) is needed. In this context, we propose a translation model using artificial neural networks (ANN), which translates questions in german into the query language SPARQL. Due to a lack of suitable datasets, we first developed a method to automatically generate synthetic data sets for training the translation model based on existing ontologies. Based on similar approaches from research, we develop an ANN architecture for the translation model. The method for data generation and the ANN were tested and validated at a german car manufacturer using a knowledge graph. It was shown that the developed architecture is particularly suitable for our field of application.
Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification

Aspect-based sentiment classification aims at determining the corresponding sentiment of a particular aspect. Many sophisticated approaches, such as attention mechanisms and Graph Convolutional Networks, have been widely used to address this challenge. However, most of the previous methods have not well analyzed the role of words and long-distance dependencies, and the interaction between context and aspect terms is not well realized, which greatly limits the effectiveness of the model. In this paper, we propose an effective and novel method using attention mechanism and graph convolutional network (ATGCN). Firstly, we make full use of multi-head attention and point-wise convolution transformation to obtain the hidden state. Secondly, we introduce position coding in the model, and use Graph Convolutional Networks to obtain syntactic information and long-distance dependencies. Finally, the interaction between context and aspect terms is further realized by bidirectional attention. Experiments on three benchmarking collections indicate the effectiveness of ATGCN.
Hyper Explanations for Feature-Model Defect Analysis

Proprietary formats, missing analysis tools, the lack of continuous toolchains and their complexity itself impair the maintainability of industrial variability models, making them prone to defects. Also, automated analysis of variability models is still not common in industry. To gain detailed information about the defects in a proprietary variability model, it can be converted into a standardized feature model to apply automated analyses that expose present defects. In this paper, however, we exclusively handle and evaluate defects of type dead feature. Resolving those defects can be challenging, as their cause can be complex, especially for large feature models. To mitigate this, an explanation can be generated which identifies feature model parts that are involved in a specific defect. Although those explanations provide valuable information, handling a high number of defects at the same time remains a tedious task, as there is no prioritization that states which defect is to be handled first to achieve the best progress. In this paper, we propose a concept to automatically derive that prioritization by deriving a hyper explanation that aggregates the information provided by the explanations of all individual defects. Hyper explanations allow to derive a prioritization not only for defects but also for defect-creating constraints. We apply our concept to industrial variability models and discussed the results with domain experts, which leads to an unexpected conclusion: The models contain intentionally created dead features. We further evaluate the usage of intentionally dead features in industry and discuss how to handle them together with actual defects.