Reviewerly/exHarmony huggingface dataset:
Sleep Disorder Data Stream Classification Based on Classifiers Ensemble and Active Learning

Polysomnography (PSG) screening for obstructive sleep apnea (OSA) is time consuming. The OSA classification is very important for medical scientists and machine learning researchers. In the current work, we developed a classification method for electrocardiogram (ECG) data. The data set has two labels: sleep disorders or not. As a result, Active Learning is used as a classification technique. Data stream classification in a non-stationary environment is attaining more attention recently. It is a highly challenging task, since the concept drift and limited labeled data. Therefore, a classification model is needed to be struggling with concept drift detection and the need of labeled data. To solve these issues, we propose an efficient semi-supervised method in this paper which uses Active Learning to detect concept drift in an unsupervised way and Classifiers Ensemble to keep higher predictions combined with weighted majority voting. Experiments results on real-world and synthetic data-sets show the effectiveness of the proposed approach. For the initial experiment, we use an existing data set. The data set includes data for every 10 seconds, up to 6000 seconds, and 35 patients. We have used 80% of the data for training purposes and 20% of the data for testing purposes. Active Learning results show that our method can effectively detect OSA. The accuracy of the predicted result is 71%. Future research in this area will be to obtain data from hospitals and use our developed algorithms for OSA classification and prediction.
Error Analysis Applied to End-to-End Spoken Language Understanding

This paper presents a qualitative study of errors produced by an end-to-end spoken language understanding (SLU) system (speech signal to concepts) that reaches state of the art performance. Different studies are proposed to better understand the weaknesses of such systems: comparison to a classical pipeline SLU system, a study on the cause of concept deletions (the most frequent error), observation of a problem in the capability of the end-to-end SLU system to segment correctly concepts, analysis of the system behavior to process unseen concept/value pairs, analysis of the benefit of the curriculum-based transfer learning approach. Last, we proposed a way to compute embeddings of sub-sequences that seem to contain relevant information for future work.
On the variety generated by completions of representable relation algebras

Abstract Maddux recently defined the variety V generated by the completions of representable relation algebras. In this note, we observe that V is canonical, answering Maddux’s problem 1.1(3), and show that the variety of representable relation algebras is not finitely axiomatisable over V .
Removing DCT High Frequency on Feature Detector Repeatability Quality

This study evaluates five feature detectors popular used in 3-dimensional face recognition systems, where face images quality is improved by removing high frequencies at the Discrete Cosine Transform coefficient domain, because at this frequency contained noise. The evaluation is important to be done because the feature detector functions to look for keypoints on the face image needed in 3-dimensional face reconstruction from 2 dimensional face images, and th e accuracy of 3d face recognition depends on the success of the reconstruction. Several researches had been undertaken in 3D face recognition, among others by implementing feature detector to determine the same key point on two face images 2 dimensional at different angles to simulate epipolar geometry. However, the implementation of feature detector does not result in a maximum value of reliability, and thus improvements should be made, it could be on feature extraction and on feature selection stages. Discrete Cosine Transform algorithm is used as a feature extractor, which produced a coefficient consisting of low frequency, middle frequency and high frequency bands. A test of five feature extractors was carried out by removing the high frequency band from the coefficient as these frequencies. The face images were taken from ORL database and Head Pose Image Database, then data before and after the high frequency bands were removed are compared. SURF attained the highest F Score for both conditions, and on average, the value F score increases from the condition before the high frequency is removed and after it is removed from the DCT coefficient.
Argus: Traffic Behavior Based Prediction of Internet User Demographics through Hierarchical Neural Network

Predicting internet user demographics based on traffic behavior analysis can provide effective clues for the decision making of network administrators. Nonetheless, most of the existing researches overly rely on hand-crafted features, and they also suffer from the shallowness of information mining and the limitation in prediction targets. This paper proposes Argus, a hierarchical neural network solution to the prediction of Internet user demographics through traffic analysis. Argus is a hierarchical neural-network structure composed of an autoencoder for embedding and a fully-connected net for prediction. In the embedding layer, the high-level features of the input data are learned, with a customized regularization method to enforce their discriminative power. In the classification layer, the embeddings are converted into the label predictions of the sample. An integrated loss function is provided to Argus for end-to-end learning and architecture control. Argus has exhibited promising performances in experiments based on real-world dataset, where most of the metrics outperform those achieved by common machine learning techniques on multiple prediction targets. Further experiments reveal that the integrated loss function is capable of promoting Argus performance, and the contribution of a specific loss component during the training process is validated. Empirical settings for hyper parameters are given according to the experiments.
DIY Fraud Investigation and Access to Justice: A Case Study

Abstract Access to justice for fraud victims remains an enduring problem in the UK. Law enforcement agencies have limited capacity and capabilities for delivering criminal justice. Civil justice is so expensive that it is only an option for those with deep pockets or lucky enough to find competent professionals who are willing to work under a conditional fee arrangement (no-win-no-fee). This article describes the progress of a fraud case from a victim’s perspective through both the criminal and civil justice systems in the UK. The experiences describe incompetence in law enforcement, dishonesty and incompetence amongst lawyers and other professionals, and fractured, self-centred justice systems that poorly serve the public in England and Wales.
Economic aspects of security decisions

This chapter provides basic elements for evaluating security investments based on a comparison between the costs and (hypothetical) benefits of a given security decision or alternative (namely, the cost–benefit analysis). The approach based on a comparison between the total present value of costs and the total present value of (hypothetical) benefits is explained through both qualitative and quantitative analyses of the relevant economic factors contributing to security decisions. Models and algorithms, such as the Borda algorithm, to possibly support decision-making, are explained with worked examples.
Convolutional Neural Networks for Realtime Multi-Faces Verification with Occlusion

The face is a major component of living creature that becomes a feature to distinguish between one and another living creature and also between living creature with inanimate objects such as statues. In the digital era nowadays, faces are used as objects for identification and verification. But the existence of occlusion in the form of glasses has a potential to influence the verification process carried out by the system. Therefore, a system will be build to perform a real time multi-faces verification processes with occlusion using Convolutional Neural Networks. We propose a "Siamese" architecture with 37 convolutional layer, 10 pooling layer, and 3 fully connected layer. On the training and testing we used Labeled Faces in the Wild (LFW) dataset. The image were taken from 5749 people. We also took 165 images from 11 people for testing with image size 96 × 96 for each images. The verification accuracy achieved for the proposed method is 98%.
The Centralized Fusion Unscented Kalman Filter for Nonlinear System with correlated noise

Based on the centralized fusion algorithm and the well-known Unscented Kalman Filter, a the centralized fusion Unscented Kalman Filter is presented for nonlinear multi-sensor systems with correlated noises. In this paper, the decorrelation method is used to solve the nonlinear system estimation problem with correlated noise. In order to improve the filtering accuracy, this paper adopts a centralized fusion method. The example is given to show the effectiveness of the proposed algorithm.
Eye Centre Localisation with Convolutional Neural Network Based Regression

This paper introduces convolutional neural network regression models based on the Inception-v3 and the DenseNet architectures for accurate and real-time eye centre localisation. At a normalised error of e <; 0.05, the proposed method yields an accuracy of 98.55% on the BioID dataset in a five-fold cross validation test, and 98.50% on the GI4E dataset in a cross-dataset validation test, outperforming the state-of-the-art methods. Both models, capable of running at 44 frames per second, demonstrate an excellent real-time performance. Not only is the proposed method highly accurate and efficient, it does not require invasive and expensive hardware, offering the potential for spawning applications in a wide variety of domains.
Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

Cloud services are used to achieve diverse computing needs such as cost, security, scalability, and availability. Acceleration evolution in the distributed and cloud domains is common for large and dynamic workflows deployment. Resources and task mapping depend on the user’s objectives such as reduction in cost or execution completion within the stipulated time in consideration with certain quality of services. Multiple virtual machine instances can be launched by defining different configurations such as operating system, server types, and applications. Though workflow scheduling is an NP-Hard problem, variety of decision-making techniques are available for optimum resource allocation. In this research paper, different algorithms are studied and compared with evolutionary approaches. Workflow scheduling using genetic algorithm is implemented and discussed. This paper aims to design a decision-making technique to optimize resources of cloud. It is an adaptive scheduling to maximize profit by reducing execution time. The approach implemented is useful to cloud service providers to maximize profit and resource efficiency in their services.
Person Identification by Walking Gesture Using Skeleton Sequences

When coping with person identification problem, previous approaches either directly take raw RGB as inputs or use more sophisticated devices to capture other information. However, most of the approaches are sensitive to the changes of environment and different clothing, little variation may lead to failure identification. Recent research shows that “gait” (i.e., a person’s manner of walking) is a unique trait of a human being. Motivated by this, we propose a novel method to identify people by their gaits. In order to figure out the characteristic of individual gait, we are interested in utilizing skeletal information, which is more robust to the diversification of environment and appearance. To effectively utilize skeletal data, we analyze the spatial relationship of joints and transform the 3D skeleton coordinates into relative distances and angles between joints, and then we use a bidirectional long short-term memory neural network to explore the temporal information of the skeleton sequences. Results show that our proposed method can outperform previous methods on BIWI and IAS-Lab datasets by gaining 10.33% accuracy improvement on average.
A Deontic Logic for Programming Rightful Machines

A "rightful machine" is an explicitly moral, autonomous machine agent whose behavior conforms to principles of justice and the positive public law of a legitimate state. In this paper, I set out some basic elements of a deontic logic appropriate for capturing conflicting legal obligations for purposes of programming rightful machines. Justice demands that the prescriptive system of enforceable public laws be consistent, yet statutes or case holdings may often describe legal obligations that contradict; moreover, even fundamental constitutional rights may come into conflict. I argue that a deontic logic of the law should not try to work around such conflicts but, instead, identify and expose them so that the rights and duties that generate inconsistencies in public law can be explicitly qualified and the conflicts resolved. I then argue that a credulous, non-monotonic deontic logic can describe inconsistent legal obligations while meeting the normative demand for consistency in the prescriptive system of public law. I propose an implementation of this logic via a modified form of "answer set programming," which I demonstrate with some simple examples.
A Hybrid Recommender Method Based on Multiple Dimension Attention Analysis

With the development of the Internet and the popularity of Big Data, recommender systems have become an indispensable field due to its excellent ability to solve the problem of information overload. The existing recommender system mainly uses collaborative filtering to make recommendation by mining the interaction relationship between users and items. In order to better analyze the interaction relationship between users and items, a hybrid recommender method based on multiple dimension attention analysis is proposed. The idea is to fuse the embedded vectors of users and items into mapping vectors (or matrices) of different shapes through different methods, and learn the interactive relationship between users and items through the neural network model with attention mechanism. Experimental results show that compared with traditional analytical methods, multiple dimension analysis can more comprehensively explore the interaction between users and items, and the attention mechanism can greatly improve the analytical ability of the model.
Modelling of Monthly Clear Sky Solar Irradiation on Horizontal Surface in Denpasar City

This article discusses the monthly clear sky solar irradiation in Denpasar City from April to October, focusing on the effect of June Solstice. The primary data of daily solar irradiation on horizontal surface is taken using Lutron SPM-1116SDfrom 8 o'clock to 16 o'clock in minute basis. The data is filtered using second order polynomial fitting. Only data with adjusted R-square more than 0.9 is included. If it is needed, the upper envelope signal processing is taken before second order polynomial fitting. By integrating this data, we have daily solar irradiation data on horizontal surface in Watt hour per square meter and a precise model of monthly clear sky solar irradiation using third order polynomial.
Cancerous object detection using morphological region-based active contour in ultrasound images

Abstract Ultrasound is widely utilized in radiological screening to achieve early cancer investigations. Alongside this utilization, the need of object detection and segmentation techniques highly increases as crucial procedure of computer aided diagnosis tool. In addition, the industrial revolution 4.0 also demands automation and digitization in every technological development including computer aided diagnosis systems. This paper presents a morphological region-based active contour called MoRbAC to automatically detect the cancerous objects. Global level set active contour is initially employed to quick segment all objects in the whole image. Inhomogeneity in ultrasound images will lead to erroneous segmentation result. Hence, several morphology operations are taken to enhance it. The cancerous objects are then specifically detected by comparative calculation of area similarity. The proposed MoRbAC was tested in the real ultrasound images of breast and thyroid which validated by manual ground truth localization. Measurements of overlapping and disagreement regions are applied as quantitative indexes to demonstrate its performance. Average values of each validation has achieved i.e accuracy 98.58±0.89%, sensitivity 89.58±7.69%, specificity 99.58±0.11%, precision 95.58±2.77%, similarity 92.36±4.67%. In conclusion, the high performance of MoRbAC indicates its potential for practical applications in ultrasound computer aided diagnosis.
Beyond Virtual and Augmented Reality

The chapter presents how Artificial Intelligence (AI) is combining with Virtual Reality and Augmented Reality. The evolution of AI is discussed, as well as its consequences. The chapter also provides an overview of the four types of intelligence in AI, and provides positive and negative impacts for society. The discussion on positive and negative effects of AI brings to light how it can help humans, but at the same time, how it can also transform society and firms. The rise of hybrid humans may be a turning point to develop a new global order, with new laws, justice, rights, and duties.
Research on Intellectual Property Protection of Industrial Innovation Under the Background of Big Data

The rise of big data industry has caused people’s attention to industrial innovation and legal protection of intellectual property. With the comprehensive and full protection of innovative industrial innovation data, the call for the right of commercial data has arisen. Based on the analysis of the current situation of intellectual property protection in China under the background of big data, this paper summarizes the new problems of intellectual property protection in software, Internet of Things and customized production caused by the development trend of manufacturing industry in industrial innovation. Finally, some suggestions are put forward to improve the software patent system and patent examination system for digital software intellectual property protection.
Adaptive Gabor algorithm for face posture and its application in blink detection

Gabor algorithm has been widely applied to the face image processing, especially in light interference. However, the conventional Gabor algorithm can not be applied well because of the limitation of the scale and direction of face images with different poses. In this paper, an adaptive Gabor algorithm for face posture is proposed, which adjusts the scale and direction of Gabor filter by judging the face posture, so as to speed up the extraction time of eye features. Taking blink detection as an example, the accuracy of EAR can be improved by 26%.
Fault tolerant non-linear techniques for scalar multiplication in ECC

Elliptic curve cryptography (ECC) has shorter key length than other asymmetric cryptography algorithms such as RSA with the same security level. Existing faults in cryptographic computations can cause faulty results. If a fault occurs during encryption, false information will be sent to the destination, in which case channel error detection codes are unable to detect the fault. In this paper, we consider the error detection in elliptic curve scalar multiplication point, which is the most important operation in ECC. Our technique is based on non-linear error detection codes. We consider an algorithm for scalar multiplication point proposed by Microsoft research group. The proposed technique in our methods has less overhead for additions (36.36%) and multiplications (34.84%) in total, compared to previous works. Also, the proposed method can detect almost 100% of injected faults.
Domain-Specific Search Engines for Investigating Human Trafficking and Other Illicit Activities

Web advertising related to human trafficking (HT) activity has been on the rise in recent years. Question answering over crawled sex advertisements to assist investigators in the real world is an important social problem involving many technical challenges. This article will describe the problem of domain-specific search (DSS), a specific set of technologies that can address these challenges, drawing on cutting-edge techniques developed over three years of DARPA-funded research conducted in collaboratively academic (e.g., the University of Southern California's Information Sciences Institute), government (e.g., NASA's Jet Propulsion Laboratory), and industrial (e.g., Uncharted) settings to assist analysts and investigative experts in the HT domain. Specifically, the article will describe the background, main principles and challenges of building a DSS in illicit domains and the general architecture of a DSS designed for illicit sex advertisements on the open web. The article will conclude with the scope of future research in this area.
An Improved Watershed Algorithm Based on k-Medoids in Cervical Cancer Images

The watershed algorithm is widely used in the field of the image segmentation, which can overcome the difficulty of image analysis caused by cell overlap. However, the result of the image segmentation with the watershed algorithm were often over-segmentated. To solve this problem, the k-medoids clustering algorithm was introduced to simplify the gradient image, which is preprocessed from the original image. The edge information of the original image was obtained by the Canny edge detection operator, and the target region template was calculated by the optimized initial segmentation. Then, the segmentation result was obtained. The improved algorithm was evaluated by the segmentation accuracy compared with the professional segmented pathology image. The results show that the improved watershed algorithm proposed in this paper has a specific advantage in alleviating the phenomenon of over-segmentation, and the target area appears more completely.
Lightweight Virtual Machine Mapping for Data Centers

Virtual machines (VM) are based on computer architectures for cloud based solutions and often provide functionality of a physical computing device. In cloud computing environment, processor sharing or virtualization over physical machines sometimes leads to communication instability. Optimized VM mapping, consolidation and representation is a critical operation in distributed systems and has a direct influence on performance optimization and resource management features of data centers. In this paper we presented a simple VM mapping scheme for data centers through the graph theoretic approach where cloud functions are elaborated in terms of nodes and edges.