Reviewerly/exHarmony huggingface dataset:
A Generative Model of Underwater Images for Active Landmark Detection and Docking

Underwater active landmarks (UALs) are widely used for short-range underwater navigation in underwater robotics tasks. Detection of UALs is challenging due to large variance of underwater illumination, water quality and change of camera viewpoint. Moreover, improvement of detection accuracy relies upon statistical diversity of images used to train detection models. We propose a generative adversarial network, called Tank-to-field GAN (T2FGAN), to learn generative models of underwater images, and use the learned models for data augmentation to improve detection accuracy. To this end, first a T2FGAN is trained using images of UALs captured in a tank. Then, the learned model of the T2FGAN is used to generate images of UALs according to different water quality, illumination, pose and landmark configurations (WIPCs). In experimental analyses, we first explore statistical properties of images of UALs generated by T2FGAN under various WIPCs for active landmark detection. Then, we use the generated images for training detection algorithms. Experimental results show that training detection algorithms using the generated images can improve detection accuracy. In field experiments, underwater docking tasks are successfully performed in a lake by employing detection models trained on datasets generated by T2FGAN.
Minimal Sensor Setup in Lower Limb Exoskeletons for Motion Classification based on Multi-Modal Sensor Data

Exoskeletons are considered to be a promising technology for assisting and augmenting human performance. A number of challenges related to design, intuitive control and interfaces to the human body must be addressed. In this paper, we approach the question of a minimal sensor setup for the realization of control strategies which take into account the actions currently performed by the user. To this end, we extend our previous work on online classifications of a human wearing a lower limb exoskeleton in two directions. First, we investigate the minimal number of sensors that should be attached to the exoskeleton to achieve a certain classification accuracy by investigating different sensor setups. We compare results of motion classification of 14 different daily activities such as walking forward and going upstairs using Hidden Markov Models. Second, we analyse the influence of different window sizes, as well as the classification performance of different motion types when training on multi- and single-subjects. Our results reveal that we can reduce our sensor setup significantly while achieving about the same classification performance.
Vec2graph: A Python Library for Visualizing Word Embeddings as Graphs

Visualization as a means of easy conveyance of ideas plays a key role in communicating linguistic theory through its applications. User-friendly NLP visualization tools allow researchers to get important insights for building, challenging, proving or rejecting their hypotheses. At the same time, visualizations provide general public with some understanding of what computational linguists investigate. In this paper, we present vec2graph: a ready-to-use Python 3 library visualizing vector representations (for example, word embeddings) as dynamic and interactive graphs. It is aimed at users with beginners’ knowledge of software development, and can be used to easily produce visualizations suitable for the Web. We describe key ideas behind vec2graph, its hyperparameters, and its integration into existing word embedding frameworks.
Standardization Efforts for Traditional Data Center Infrastructure Management: The Big Picture

Traditional data center infrastructure suffers from a lack of standard and ubiquitous management solutions. Despite the achieved advances, existing tools lack interoperability and are sometimes hardware dependent. Vendors are already actively participating in the specification and design of new standard software and hardware interfaces within different forums. Nevertheless, the complexity and variety of data center infrastructure components that includes servers, cooling, networking, and power hardware, coupled with the introduction of the software defined data center paradigm, led to the parallel development of a myriad of standardization efforts. In an attempt to shed light on recent works, we survey and discuss the main standardization efforts for traditional data center infrastructure management.
Demographic Factors of Cybersecurity Awareness in Bangladesh

The growing rate of cybercrime has turned into a crucial concern that makes people more afraid of cyberwar than a nuclear weapon and climate change. An alarming awareness level has been observed in this study which is not satisfactory. A significant percentage of the population is unaware of cybersecurity policies and practices. Even the relevant organizations, as well as the government, are not concerned about the issues of cybercrime. A significant variation is essential to enrich the cybersecurity policies and practices. Furthermore, it needs to be monitored and restructured with the pace of time. This pilot study is designed to consider a comprehensive investigation regarding the level of cybercrime awareness amongst Bangladeshi people. The survey has been carried out through both the online and offline responses against a well-prepared questionnaire. Additionally, T-test, mean score and ANOVA tests were accompanied for detailed analysis. Based on this study, it is recommended that the development and implementation of a massive cybersecurity awareness program are urgently required that would immune the citizens from cyber threats.
Developing Horizon Scanning Methods for the Discovery of Scientific Trends

In this application-oriented paper, we develop a methodology and a system for horizon scanning of scientific literature to discover scientific trends. Literature within a broadly defined field is automatically clustered and ranked based on topic and scientific impact, respectively. A method for determining the optimal number of clusters for the established Gibbs sampling Dirichlet multinomial mixture model (GSDMM) algorithm is proposed along with a method for deriving descriptive and distinctive words for the discovered clusters. Furthermore, we propose a ranking methodology based on citation statistics to identify significant contributions within the discovered subject areas.
Object Proposal Algorithms in the Wild: Are they Generalizable to Robot Perception?

The recent emergence of object proposal algorithms in the computer vision community shows great promise to addressing difficult problems in robotic such as object discovery and salient object detection. However, it is difficult to determine how these algorithms actually perform for real-world robot vision applications, because the standard evaluation protocol uses datasets which do not adequately account for real-world noise (motion blur, occlusion, etc.). We evaluated several state-of-the-art object proposal algorithms using naturalistic datasets from the robotics community, and found a substantial performance drop across all algorithms. This suggests that many object proposal algorithms are not as generalizable as the computer vision literature purports, which can have a significant impact on how they are applied to robotics. We also conducted a study on how each algorithm is influenced by specific kinds of real-world robot vision challenges, including variable brightness, gamma correction, Gaussian blur, and Gaussian noise. Our results provide insight into certain weaknesses of object proposal algorithms, which can be used to gauge how they might be suitable for different robotics applications. It is our intent that this work will motivate future research about how to design more flexible and robust object proposal algorithms for the robotics community.
Exploring Confidence Measures for Word Spotting in Heterogeneous Datasets

In recent years, convolutional neural networks (CNNs) took over the field of document analysis and they became the predominant model for word spotting. Especially attribute CNNs, which learn the mapping between a word image and an attribute representation, showed exceptional performances. The drawback of this approach is the overconfidence of neural networks when used out of their training distribution. In this paper, we explore different metrics for quantifying the confidence of a CNN in its predictions, specifically on the retrieval problem of word spotting. With these confidence measures, we limit the inability of a retrieval list to reject certain candidates. We investigate four different approaches that are either based on the network's attribute estimations or make use of a surrogate model. Our approach also aims at answering the question for which part of a dataset the retrieval system gives reliable results. We further show that there exists a direct relation between the proposed confidence measures and the quality of an estimated attribute representation.
Autonomic Resource Allocation Frameworks for Service-based Cloud Applications: A Survey

Cloud is a collection of heterogeneous-computing resources that are presented to the users on the payment basis. Cloud and the associated resources are dynamic in nature. The provisioning of resources in such dynamic environment is one of the critical issues in the cloud. Various Quality of Service (QoS) parameters contribute to the provisioning of appropriate resources in the cloud. A right proportion of resource provisioning is necessary to improve the performance of the system. To mention, energy wastage and cost increase due to over-provisioning, while, under-provisioning may seem to effect Service Level Agreements (SLA) and Service Quality (QoS). Hence, the virtual resources should be allocated accordingly to fulfil the current dynamic demand of various applications. This paper presents a deeper survey on the various resource allocation frameworks for service-based cloud applications.
Exploring Blockchain in Healthcare Industry

Healthcare is a crucial industry because it contains sensitive information of human health condition history then security and privacy are the main concern. Currently, Internet enables sharing of those records possible, but it poses a new risk like misuse of patient data as it is shared. Meanwhile in Indonesia one of the main problems that faced in healthcare industry is hospital integration of electronic medical recording. At the age of internet era, one of the hottest technology, that arise is blockchain due to it is astonishing features such as security, transparency, and traceability. Blockchain comes out as a holy grail technology that have great opportunity in the future and already implemented in several business industry. Many previous studies have implemented blockchain in Healthcare. The article examines the use of blockchain in healthcare system, that have been studied and reviewed intensively to see how this technology can be implemented in healthcare system in Indonesia, and to solve several Electronic Medical Record (EMR) problems such as security, privacy and interoperability.
An Accurate Neural Network for Cytologic Whole-Slide Image Analysis

Typically, high accuracy in deep learning is achieved by large dataset in pixel-wise labeling for segmentation or image-level labeling for classification. However, in biomedical domain, the challenge is not only the availability of image data itself, but also the acquisition of relevant annotations for these images from clinicians. In this work, we propose a novel two-stage architecture to jointly perform the tasks of detection, segmentation and classification of abnormal cells and cancer. Compared with one-step detection for all the catalogues, we combine the advantage of image-level and pixel-level labeling in our deep learning based framework. We use the detection of lesions in cervical clinical dataset as a case study for performance evaluation. In the first stage, a hybrid ResNet and U-Net architecture is designed to predict three catalogues of nuclei, cytoplasm and background with pixel-wise labeled segmentation map. In the second stage, a residual learning based model is applied to the identified nuclei for subtype classification. Confirmed with cytotechnologist, the proposed model is estimated to efficiently deduct more than 90% annotation burden compared with pixel-wise labeling approach. Moreover, the proposed two-stage approach model outperforms one-stage neural network in segmentation and classification for objects with high similarities in appearance. Our collected real-life clinical cytology images and the source code in the experiments are provided in https://github.com/SJTU-AI-GPU/TwoStageCellSegmentation.1
Study of Recurrent Neural Network models used for evaluating Numerical Expressions

Numerical expression evaluation from sentences using Natural Language processing is a well-known problem domain. Evaluation of noise-free mathematical expressions has been done previously to assess the ability of the recurrent neural network to solve numerical expressions. However, the evaluation of numerical expressions when present in noisy format, i.e., operator and operands are represented “in-word” and mixed numerical format, are not well nourished till now. This paper presents a comparative study to analyze the ability of sequence-to-sequence neural network based Encoder-Decoder models to calculate the numerical output of the noisy expressions. We have treated the problem as a sequence-to-sequence based classification problem instead of a regression problem as considered earlier, to deal with the noisy data. Performance of three different Encoder-Decoder models namely Unidirectional, Bidirectional, and Attention based Encoder-Decoder LSTM models are used to evaluate the noisy expressions. It has been experimentally observed that Bidirectional and Attention based Encoder-Decoder LSTM models perform better than Unidirectional Encoder-Decoder model and the sequence-to-sequence model.
Humans Optional? Automatic Large-Scale Test Collections for Entity, Passage, and Entity-Passage Retrieval

Manually creating test collections is a time-, effort-, and cost-intensive process. This paper describes a fully automatic alternative for deriving large-scale test collections, where no human assessments are needed. The empirical experiments confirm that automatic test collection and manual assessments agree on the best performing systems. The collection includes relevance judgments for both text passages and knowledge base entities. Since test collections with relevance data for both entity and text passages are rare, this approach provides a cost-efficient way for training and evaluating ad hoc passage retrieval, entity retrieval, and entity-aware text retrieval methods.
Hiding Text In Color Image Using YC<sub>b</sub>C<sub>r</sub> Color Model: An Image Steganography approach

For the security related issues over internet two main techniques are used first is Cryptography and second is Steganography. Both are basically used for data security. Cryptography transforms the data from one form to another form while steganography hide data in an image in such a way that it cannot be detected by human eyes. This paper introduces an image steganography method using YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> color model based on Least Significant Bit (LSB). In this paper proposed method convert the image from RGB to YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> color space then secret data is hidden inside YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> color space using least significant bit and after hiding the data, convert it back to RGB color space. The proposed technique is evaluated by objective analysis. Different techniques of cryptography are compared using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). It is observed that the proposed method has high PSNR and low MSE which shows the proposed approach is very efficient to hide data in an image.
Quantum One Time Password with Biometrics

Use of One Time Passwords (OTP) have proved to be more secure method, than one factor authentication and is therefore currently utilised in online transactions. However, it authenticates the device rather than the user. Also, number of fraud cases is being highlighted owing to the inherent insecurity in classical methods of OTP generation and communication. The study in this paper utilises mathematical concepts of the Quantum Entanglement property to generate Quantum OTP (QOTP), for authenticating the user based on its biometrics. The proposed methodology enhances security in two factor authentication methods, as per mathematically proven properties of Quantum Cryptography.
IoT-Based Anonymous Authentication Protocol Using Biometrics in Smart Homes

The smart home is increasingly being considered as one of the Internet of Things applications. With the development of mobile networks and the speed of data transfer, the expansion of smartphones and increasing of interest in raising the safety factor in personal life, many companies have entered the smartphone market. With the help of the Internet of things portal, it is possible to monitor wireless sensors and use non-computerized alert systems. Therefore, it is essential to secure such a system to create a sense of relaxation in the lives of users and homeowners to deal with possible occurrences. The integration of technologies for the automation of home affairs with the Internet of things means that all physical objects can be accessed on cyberspace; therefore, the concerns raised by users about the lack of privacy and security are a serious argument that science and technology should answer. This paper proposes an anonymous secure framework in connected smart home environments, using solely lightweight operations. The proposed framework in this paper provides efficient authentication and key agreement, and enables devices (identity and data) anonymity and unlinkability. It is demonstrated that computation complexity of the proposed framework is low as compared to the existing schemes, while security has been significantly improved. The proposed scheme assures us that even if the home owner's device is attacked, the rest of the system will continue to work properly.
Genetic Algorithm-Based Feature Selection and Optimization of Backpropagation Neural Network Parameters for Classification of Breast Cancer Using MicroRNA Profiles

Breast cancer is one of the most common types of cancer found in women. Breast cancer mortality increases every year because it has not found an appropriate early detection method. MicroRNA can be used as a potential biomarker, because the profile of the microRNA feature in breast cancer will decrease or increase the value of expression compared to normal conditions. But because of the thousands of types of microRNA that make up breast cancer, a lot of money is needed to detect it entirely. Backpropagation Artificial Neural Network Method has good performance in generalization, so it is suitable to be used as a method for classification with many features. The classification results from the neural network model will be more accurate if the parameters used can be optimized precisely. Genetic algorithms can be used to optimize backpropagation neural network parameters as well as feature selection, because of its global search characteristics. This study aims to compare the performance of backpropagation artificial neural networks optimized parameters as well as feature selection using genetic algorithms (GABPNN_ FS) with backpropagation artificial neural networks optimized using genetic algorithms without feature selection (GABPNN). The results showed that the GABPNN had better results with an error value of 0.016115. But GABPNN_ FS has a faster average process duration of 53.2689 seconds. The best individual chromosome translation results on GABPNN_ FS for breast cancer classification based on microRNA profile are random state = 6098, learning rate = 0.7, number of neuron hidden = 6, and selected features = 707 features that produce accuracy, sensitivity, and specificity ie 97.50 %, 99.00% and 96.00%.
Pyramid feature adaptation for semi-supervised cardiac bi-ventricle segmentation

Cardiac bi-ventricle segmentation (BVS) is an essential task for assessing cardiac indices, such as the ejection fraction and volume of the left ventricle (LV) and right ventricle (RV). However, BVS is extremely challenging due to the high variability of the bi-ventricle structure and lack of labeled data. In this paper, we propose a pyramid feature adaptation based semi-supervised method (PABVS) for cardiac bi-ventricle segmentation. The PABVS first extracts the multiscale pyramid features of bi-ventricle structure to cope with the high variability of bi-ventricle structure. Then, a weighted pyramid feature adaptation strategy is proposed to ensure a smooth feature space among labeled data and unlabeled data. In particular, the PABVS performs weighted feature adaptation at each level of a multiscale pyramid feature based on adversarial learning. It gives less importance to outlier feature layers of labeled data and more importance to representative layers. The experimental results on magnetic resonance images show that our proposed PABVS can achieve Dice values 0.915 for EpiLV with 40% labeled data and the Dice values 0.976 for EpiLV with all labeled data, which outperforms mainstream semi-supervised methods. This endows our PABVS with great potential for the effective clinical application of BVS.
Knowledge Graph Data Management: Models, Methods, and Systems

With the rise of artificial intelligence, knowledge graphs have been widely considered as a cornerstone of AI. In recent years, an increasing number of large-scale knowledge graphs have been constructed and published, by both academic and industrial communities, such as DBpedia, YAGO, Wikidata, Google Knowledge Graph, Microsoft Satori, Facebook Entity Graph, and others. In fact, a knowledge graph is essentially a large network of entities, their properties, semantic relationships between entities, and ontologies the entities conform to. Such kind of graph-based knowledge data has been posing a great challenge to the traditional data management theories and technologies. In this paper, we introduce the state-of-the-art research on knowledge graph data management, which includes knowledge graph data models, query languages, storage schemes, query processing, and reasoning. We will also describe the latest development trends of various database management systems for knowledge graphs.
An Efficient Accelerator for Sparse Convolutional Neural Networks

In this paper, we propose a sparse convolutional neural network accelerator design on FPGAs. Similar to the DNNWEAVER architecture, our accelerator uses two-level hierarchy: multiple Processing Units (PUs) and each PU comprises a set of Processing Elements (PEs). To address the irregularity of sparse neural networks, we introduce a novel sparse dataflow for sparse CNN computing as well as weight merging method to balance the computation load on different PUs for better overall efficiency. We implement our design with 32 PUs and 14 PEs in each PU. When compared with DNNWEAVER on VGG16 network, our accelerator achieves 3.49× speedup and 3.05× energy saving on average when running at 150MHz on a Xilinx ZC706 board and reaches the speed of 400 GOPS.