Reviewerly/exHarmony huggingface dataset:
Distributed semi-supervised learning algorithms for random vector functional-link networks with distributed data splitting across samples and features

In this paper, we propose two manifold regularization (MR) based distributed semi-supervised learning (DSSL) algorithms using the random vector functional link (RVFL) network and alternating direction method of multipliers (ADMM) strategy. In DSSL problems, training data consisting of labeled and unlabeled samples are often large-scale or high-dimension and split across samples or features. These distributed data separately stored over a communication network where each node has only access to its own data and can only communicate with its neighboring nodes. In many scenarios, centralized algorithms cannot be applied to solve DSSL problems. In our previous work, we proposed a MR based DSSL algorithm, denoted as the D-LapWNN algorithm, to solve DSSL problems with distributed samples. It has been proved that the D-LapWNN algorithm, combining the wavelet neural network (WNN) with the zero-gradient-sum (ZGS) strategy, is an efficient DSSL algorithm with distributed samples or horizontally partitioned data. The drawback of the D-LapWNN algorithm is that the loss function of each node or agent over the communication network must be twice continuously differentiable. In order to extend our previous work and settle the corresponding drawback, we propose a horizontally DSSL (HDSSL) algorithm to solve DSSL problems with distributed samples. Then, we novelly propose a vertically DSSL (VDSSL) algorithm to solve DSSL problems with distributed features or vertically partitioned data. As far as we know, the VDSSL algorithm is the first work focusing on DSSL problems with distributed features. During the learning process of the proposed algorithms, nodes over the communication network only exchange coefficients rather than raw data. It means that the proposed algorithms are privacy-preserving methods. Finally, some simulations are given to show the efficiency of the proposed algorithms.
Study of identifying and managing the potential evidence for effective Android forensics

Since the advent of various IoT devices, the need for digital forensics for mobile devices that people use most closely in their daily lives has continued to grow. Besides, as Bring Your Own Device (BYOD) becomes the trend, devices store business-related information as well as privacy. Thus, mobile devices are becoming the most critical evidence of digital forensics. For practical mobile forensics, it is necessary to identify crime-related items among the many files inside the device accurately. Also, various user information for user behavior analysis from these files should be effectively extracted and managed as potential evidence to ensure integrity. This paper proposes an efficient forensics investigation method for mobile devices with Android OS, which holds the highest share in the world among mobile devices. In this paper, we studied data pre-processing (classification and identification of data), data analysis, evidence management, and Android data Taxonomy.
Security Considerations in Big Data Solutions Adoption: Lessons from a Case Study on a Banking Institution

Adoption of Big Data Solutions (BDS) must take into consideration several issues that may affect its successful implementation, such as the issue of security and privacy. This study aims to identify security-related considerations made by organizations adopting BDS through a single case study of a Malaysian banking institution. Three main themes derived were; technological, organizational and environmental security considerations. The sub-themes identified were: challenges in securing data, capability of legacy security mechanisms, managerial security awareness, top management support, SETA, security personnel skills, employees' perception on sensitivity of information assets, regulatory compliance, reputation of BDS vendors and environmental uncertainties. The findings of this study complement current big data, information security and technology adoption research domain. It may also provide organizations with relevant information in formulating security strategies required in a big data environment.
Online Signature Verification by Few-Shot Separable Convolution Based Deep Learning

Online Signature Verification (OSV) is a widely used biometric feature for recognized and authorized technique to authenticate a writers's distinctiveness and behavioral characteristic. Owing to huge intra-individual changeability, OSV is a challenging problem. Usage of online signatures in m-commerce and m-payment demands for light weight frameworks to classify a signature. The recent OSV models grounded on convolutional neural networks (CNN) and its variants are heavy weight and computationally intensive due to higher amount of parameters to learn. In this context, we put forward a CNN centric OSV framework which uses a stack of depthwise separable (DWS) convolution layers, which makes the framework light weight and enables the few shot learning for signature verification with quite higher accuracy compared to conventional deep learning models. To prove the robustness of our proposed framework, we performed exhaustive experimental evaluations with three standard datasets i.e. MCYT-100 (DB1), SUSIG-Visual corpus and SVC-2004-Task2. Experimental results confirm the efficiency of depthwise separable convolutions grounded OSV by realizing a lesser error rate as related to various current and state-of-the art OSV frameworks.
Talk to the Vehicle: Language Conditioned Autonomous Navigation of Self Driving Cars

We propose a novel pipeline that blends encodings from natural language and 3D semantic maps obtained from visual imagery to generate local trajectories that are executed by a low-level controller. The pipeline precludes the need for a prior registered map through a local waypoint generator neural network. The waypoint generator network (WGN) maps semantics and natural language encodings (NLE) to local waypoints. A local planner then generates a trajectory from the ego location of the vehicle (an outdoor car in this case) to these locally generated waypoints while a low-level controller executes these plans faithfully. The efficacy of the pipeline is verified in the CARLA simulator environment as well as on local semantic maps built from real-world KITTI dataset. In both these environments (simulated and real-world) we show the ability of the WGN to generate waypoints accurately by mapping NLE of varying sequence lengths and levels of complexity. We compare with baseline approaches and show significant performance gain over them. And finally, we show real implementations on our electric car verifying that the pipeline lends itself to practical and tangible realizations in uncontrolled outdoor settings. In loop execution of the proposed pipeline that involves repetitive invocations of the network is critical for any such language-based navigation framework. This effort successfully accomplishes this thereby bypassing the need for prior metric maps or strategies for metric level localization during traversal.
Witnessing Secure Compilation

Compiler optimizations may break or weaken the security properties of a source program. This work develops a translation validation methodology for secure compilation. A security property is expressed as an automaton operating over a bundle of program traces. A refinement proof scheme derived from a property automaton guarantees that the associated security property is preserved by a program transformation. This generalizes known refinement methods that apply only to specific security properties. In practice, the refinement relations ("security witnesses") are generated during compilation and validated independently with a refinement checker. This process is illustrated for common optimizations. Crucially, it is not necessary to formally verify the compiler implementation, which is infeasible for production compilers.
DWnet: Deep-wide network for 3D action recognition

Action recognition plays an important role in human–robot cooperation and interaction. By recognizing human actions, robots can imitate or reproduce human actions and obtain skills. Recently, convolutional neural networks (CNNs) have been widely used to recognize actions based on 3D skeleton. Good performance has been achieved due to the approximation capability gained from the depth of the model. Unfortunately, in the mainstream deep structures, dropout and fully connected layers are usually used to classify actions. That is to say, ensemble is used to guarantee the recognition performance, which decreases the computational efficiency. In order to improve the computational efficiency, we propose in this paper a deep-wide network (DWnet) to recognize human actions based on 3D skeleton. Specifically, we modify the decision-making mechanism of the deep CNN with a shallow structure, which improves the computational efficiency. The state-of-the-art deep CNN is used to extract spatial–temporal features from the skeletal sequence. Then features are transformed into a higher dimensional feature space to obtain global information and classified by the modified decision making mechanism. Experiments on two skeletal datasets demonstrate the advantage of the proposed model on testing efficiency and the effectiveness of the novel model to recognize the action. The code has been publicly available at https://github.com/YHDang/DWnet.
A Deep Transfer Learning Approach to Document Image Quality Assessment

Document image quality assessment (DIQA) is an important process for various applications such as optical character recognition (OCR) and document restoration. In this paper we propose a no-reference DIQA model based on a deep convolutional neural network (DCNN), where the rich knowledge of natural scene image characterization of a previously-trained DCNN is exploited towards OCR accuracy oriented document image quality assessment. Following a two-stage deep transfer learning procedure, we fine-tune the knowledge base of the DCNN in the first phase and bring in a task-specific segment consisting of three fully connected (FC) layers in the second phase. Based on the fine-tuned knowledge base, the task-specific segment is trained from scratch to facilitate the application of the transferred knowledge on the new task of document quality assessment. Testing results on a benchmark dataset demonstrate that the proposed model achieves state-of-the-art performance.
Anti-Counterfeit on Medicine Detection Using Blockchain Technology

The goal of this project is to find whether a given medicine is fake or original using blockchain technology. Blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but also has the potential to disrupt other markets. It removes the need for trusted intermediaries, can facilitate faster transactions and add more transparency. A medical product is counterfeit when there is false representation in relation to its identity or source. In the case of medicines, the implications are more severe. This technology stops the entry of fake drugs into the supply chain, mainly the part between the manufacturer and consumer. The technology uses digital signature where each block gets a unique crypto id. This digital signature is provided for each block and it gives a strong control of ownership.
Amharic Text Image Recognition: Database, Algorithm, and Analysis

This paper introduces a dataset for an exotic, but very interesting script, Amharic. Amharic follows a unique syllabic writing system which uses 33 consonant characters with their 7 vowels variants of each. Some labialized characters derived by adding diacritical marks on consonants and or removing part of it. These associated diacritics on consonant characters are relatively smaller in size and challenging to distinguish the derived (vowel and labialized) characters. In this paper we tackle the problem of Amharic text-line image recognition. In this work, we propose a recurrent neural network based method to recognize Amharic text-line images. The proposed method uses Long Short Term Memory (LSTM) networks together with CTC (Connectionist Temporal Classification). Furthermore, in order to overcome the lack of annotated data, we introduce a new dataset that contains 337,332 Amharic text-line images which is made freely available at http://www.dfki.uni-kl.de/~belay/. The performance of the proposed Amharic OCR model is tested by both printed and synthetically generated datasets, and promising results are obtained.
Jeffries-Matusita distance as a tool for feature selection

The following topics are dealt with: learning (artificial intelligence); pattern classification; feature selection; data mining; recurrent neural nets; decision making; feature extraction; support vector machines; query processing; neural nets.
Monocular Depth Estimation in New Environments With Absolute Scale

In this work we propose an unsupervised training method that finetunes a single image depth estimation CNN towards a new environment. The network, which has been pretrained on stereo data, only requires monocular input for finetuning. Unlike other unsupervised methods, it produces depth estimations with absolute scale - a feature that is essential for most practical applications, yet has mostly been overlooked in the literature. First, we show how our method allows adapting a network trained on one dataset (Cityscapes) to another (KITTI). Next, by splitting KITTI in subsets, we show the sensitivity of pretrained models to a domain shift. We then demonstrate that, by finetuning the model using our method, it is possible to improve the performance on the target subset, without using stereo or any form of groundtruth depth and with preservation of the correct absolute scale.
Forecasting the Trends and Patterns of Crime in Bangladesh using Machine Learning Model

Last few years in Bangladesh, the crime rate has increased rapidly. Hence it is an essential task to analyze and predict the crime so that the authority can minimize or prevent the crimes easily. In this situation, machine learning can perform a notable role to reveal the crime trends and patterns of Bangladesh. Here, various machine learning regression models i.e. linear regression, polynomial regression, and random forest regression are used to forecast the trends and patterns of crime in Bangladesh. Dataset used in this research is available for the public which is gathered from the Bangladesh police's website. The dataset comprises record about various crime types i.e. dacoity, robbery, kidnapping, murder, women & child repression, theft, burglary, arms act, explosive, narcotics, and smuggling of Bangladesh. Firstly, training of regression models is done on the training dataset. After completion of the training, forecasting of crime is performed on the test data by the different regression models. Then we compare the forecasting results with the actual results and calculate the model evaluation metrics for the different applied regression models. After comparing the result, it is possible to find out the best-suited regression model for the crime-related data among all the applied regression models. Finally, it is observed that polynomial and random forest regression are better to predict the crime trends and patterns than the linear regression.
Hypercomplex-valued recurrent correlation neural networks

Recurrent correlation neural networks (RCNNs), introduced by Chiueh and Goodman as an improved version of the bipolar correlation-based Hopfield neural network, can be used to implement high-capacity associative memories. In this paper, we extend the bipolar RCNNs for processing hypercomplex-valued data. Precisely, we present the mathematical background for a broad class of hypercomplex-valued RCNNs. Then, we address the stability of the new hypercomplex-valued RCNNs using synchronous and asynchronous update modes. Examples with bipolar, complex, hyperbolic, quaternion, and octonion-valued RCNNs are given to illustrate the theoretical results. Finally, computational experiments confirm the potential application of hypercomplex-valued RCNNs as associative memories designed for the storage and recall of gray-scale images.
A Survey on K-Means Clustering for Analyzing Variation in Data

Most of the times data for certain task seems to be varying due constant changes made to method of data collection as well as due to inclusion of new parameters related to the task. This may result in false conclusion derived from data generated and might lead to failure in task or degradation in the standard of activity related to that task which is being monitored from that data. Clustering is basically the grouping of similar kind of data wherein each cluster consist of data with some similarities. Whereas most of the data is unstructured or semi-structured, and that’s where unsupervised K-means Clustering method plays role to convert the data into structured one’s for clustering. This paper consist of K-means clustering method which is being used to keep an eye on such variations which are occurring in data generated for a task when certain changes are incorporated in technique to track this data.
A Decentralized Model for Spatial Data Digital Rights Management

The copyright of data is a key point that needs to be solved in spatial data infrastructure for data sharing. In this paper, we propose a decentralized digital rights management model of spatial data, which can provide a novel way of solving the existing copyright management problem or other problems in spatial data infrastructure for data sharing. An Ethereum smart contract is used in this model to realize spatial data digital rights management function. The InterPlanetary File System is utilized as external data storage for storing spatial data in the decentralized file system to avoid data destruction that is caused by a single point of failure. There is no central server in the model architecture, which has a completely decentralized nature and it makes spatial data rights management not dependent on third-party trust institutions. We designed three spatial data copyright management algorithms, developed a prototype system to implement and test the model, used the smart contract security verification tool to check code vulnerabilities, and, finally, discussed the usability, scalability, efficiency, performance, and security of the proposed model. The result indicates that the proposed model not only has diversified functions of copyright management compared with previous studies on the blockchain-based digital rights management, but it can also solve the existing problems in traditional spatial data infrastructure for data sharing due to its characteristics of complete decentralization, mass orientation, immediacy, and high security.
“One part politics, one part technology, one part history”: Racial representation in the Unicode 7.0 emoji set

Emoji are miniature pictographs that have taken over text messages, emails, and Tweets worldwide. Although contemporary emoji represent a variety of races, genders, and sexual orientations, the original emoji set came under fire for its racial homogeneity: minus two “ethnic” characters, the people emoji featured in Unicode 7.0 were represented as White. This article investigates the set of circumstances that gave rise to this state of affairs, and explores the implications for users of color whose full participation in the emoji phenomenon is constrained by their exclusion. This project reveals that the lack of racial representation within the emoji set is the result of colorblind racism as evidenced through two related factors: aversion to, and avoidance of, the politics of technical systems and a refusal to recognize that the racial homogeneity of the original emoji set was problematic in the first place.
Improving Biomedical Information Extraction with Word Embeddings Trained on Closed-Domain Corpora

Named Entity Recognition (NER) systems allow complex concepts extraction and text mining from Natural Language documents. Actually, NER systems based on Deep Learning (DL) approaches are able to reach state-of-the-art performances when applied to general domain texts. On the other hand, the performances of these systems decrease when they are applied to texts that belong to specific domains, such as the biomedical one. In particular, Biomedical NER (B-NER) is a crucial task for the automatic analysis of medical documents, such as Electronic Health Records (EHRs), in order to support the work of physicians and researchers. Thus, new approaches are required to boost B-NER systems performances. In this paper we analyze the behaviour of a B-NER DL architecture specifically devoted to Italian EHRs, focusing on the contribution of different Word Embeddings (WEs) models used as input text representation layer. The achieved results show the substantial contribution of WEs trained on a closed domain corpus exclusively formed by documents belonging to the biomedical domain. The resulting improvements are comparable with the ones obtained using the most recent and complex neural language models, such as ELMo or BERT, which have a much higher computational complexity if compared with classic WEs approach.
Hybrid Training Data for Historical Text OCR

Current optical character recognition (OCR) systems commonly make use of recurrent neural networks (RNN) that process whole text lines. Such systems avoid the task of character segmentation necessary for character-based approaches. A disadvantage of this approach is a need of a large amount of annotated data. This can be solved by sing generated synthetic data instead of costly manually annotated ones. Unfortunately, such data is often not suitable for historical documents particularly for quality reasons. This work presents a hybrid approach for generating annotated data for OCR at a low cost. We first collect a small dataset of isolated characters from historical document images. Then, we generate historical looking text lines from the generated characters. Another contribution lies in the design and implementation of an OCR system based on a convolutional-LSTM network. We first pre-train this system on hybrid data. Afterwards, the network is fine-tuned with real printed text lines. We demonstrate that this training strategy is efficient for obtaining state-of-the-art results. We also show that the score of the proposed system is comparable or even better in comparison to several state-of-the-art systems.
Discovering Relevant Reviews for Answering Product-Related Queries

With the increasing popularity of e-commerce, the number of product-related queries generated by customers is growing. Answering these queries manually in real time is infeasible, and so automatic question-answering systems can be immensely helpful. Product queries are, however, very different from open-domain questions: they tend to be product-specific and the answers they demand can be very subjective. Previous research suggests that reviews are a valuable resource for answering product queries, but a key challenge is the language mismatch between user queries and reviews. To address this, we propose two neural models that discover relevant reviews for answering product queries. We demonstrate that our best model produces strong performance, outperforming state-of-the-art systems by consistently finding the most relevant reviews for product queries.