Reviewerly/exHarmony huggingface dataset:
A Proposed Context-Awareness Taxonomy for Multi-data Fusion in Smart Environments: Types, Properties, and Challenges

this paper presents a new taxonomy for the context-awareness problem in data fusion. It interprets fusing data extracted from multiple sensory datatypes like images, videos, or text. Any constructed smart environment generates big data with various datatypes which are extracted from multiple sensors. This big data requires to fuse with expert people due to the context-awareness problem. Each smart environment has specific characteristics, conditions, and roles that need to expert human in each context. The proposed taxonomy tries to cure this problem by focusing on three dimensions classes for data fusion, types of generated data, data properties as reduction or noisy data, or challenges. It neglects the context domain and introduces solutions for fusing big data through classes in the proposed taxonomy. This taxonomy is presented from studying sixty-six research papers in various types of fusion, and different properties of data fusion. This paper presents new research challenges of multi-data fusion.
Generic Sensor Model for Object Detection Algorithms Validation

In the artificial intelligence era of perception algorithms being used in the state-of-the-art Advance Driver Assistance Systems, algorithm validation is not an easy task, mainly due to the amount of sensor data that must be processed at once. To provide the highest possible safety level of the solution, algorithm performance must be assessed in various difficult conditions. To address this problem, a novel Generic Sensor Model algorithm is presented, which can be directly incorporated in performance analysis of the perception algorithm in highly occluded driving scenarios. In this paper, the Generic Sensor Model algorithm is comprehensively described and its usefulness and robustness are proven with a set of experiments.
Online Sequential Ensembling of Fuzzy Systems

Evolving fuzzy systems (EFS) have enjoyed a wide attraction in the community to handle learning from data streams in an incremental, single-pass and transparent manner. The main concentration so far lied in the development of approaches for single EFS models. Forgetting mechanisms have been used to increase their flexibility, especially to adapt quickly to changing situations such as drifting data distributions. These, however, require a forgetting factor steering the degree of timely outweighing older learned concepts. Furthermore, as being pure supervised incremental methods, they typically assume that target reference values are immediately available without any delays. In this paper, we propose a new concept of learning fuzzy systems from data streams, which we call sequential ensembling. It is able to model the recent dependencies in streams on a chunk-wise basis: for each new incoming chunk, a new fuzzy model is trained from scratch and added to the ensemble (of fuzzy systems trained before). The point is that a new chunk can be used for establishing a new fuzzy model as soon as the target values are available. This induces i.) flexibility for respecting the actual system delay in receiving target values (by setting the chunksize adequately) and ii.) fast drift handling possibilities. The latter are realized with specific prediction techniques for new data chunks based on the sequential ensemble members trained so far over time, for which we propose four different variants. These include specific spatial and timely uncertainty concepts. Finally, in order to cope with large-scale and (theoretically) infinite data streams within a reasonable amount of prediction time, we demonstrate a concept for pruning past ensemble members. The results based on two data streams show significantly improved performance compared to single EFS models in terms of a better convergence of the accumulated chunk-wise ahead prediction error trends over time. This is especially true in the case of abrupt and gradual drifts appearing in the target concept, where the sequential ensemble (especially due to recent weak members) is able to react more flexibly and quickly than (more heavy) single EFS models. In the case of input space drifts and new operating conditions, the more advanced prediction schemes, which include uncertainty weighing concepts, can significantly outperform standard averaging over all members' outputs.
Manifold Learning for Hand Drawn Sketches

It is a challenge issue to recognize and comprehend hand drawn sketches for various applications such as image retrieval and image-based modeling. In this work, we propose an unsupervised learning framework to obtain the manifold of hand drawn sketches. We use DCT (Discrete Cosine Transform) to exact the feature of preprocessed sketch images from Quick Draw Dataset and adopt LLP (Locality Preserving Projections) to calculate the 2-D manifold of these sketches. Experiment result in flower sketches demonstrates the proposed approach is suitable to represent the manifold of hand drawn sketch.
Big Data Mining on Rainfall Data

India is a country with leading economy driven by agriculture, which is majorly depended on the climatic changes. Rainfall events in India have diversified irregular pattern across the country, which makes it more complex and challenging to mine. Rainfall data comes under the meteorological data which is spatiotemporal in nature. The meteorological data is increasing day by day, to handle that huge amount of data imposing a wide range of challenges for storage and analysis. Big data technologies, like Hadoop Distributed File System (HDFS), Hbase, and many query processing tools for processing data like Hive and Pig are popularly known to handle such type of data. But these tools and techniques individually are inadequate for mining data efficiently. We have reviewed the research in the area of mining rainfall data and identified the gaps in the existing approaches.
Image Segment Classification Using CNN

At present, the rapid development of convolutional neural networks (CNNs or ConvNets) as a tool for image detection and classification encourages us to implement them more widely in CBIR systems. This paper compares the transfer learning architecture for selected pre-trained CNNs: AlexNet, GoogleNet, VGG16 and VGG19 with the simple ConvNet training from scratch. We have analysed three solvers (optimisation methods): the stochastic gradient descent with the momentum (SGDM) optimizer, the RMSProp optimizer and the ADAM optimization method for both models with and without transfer learning. We concentrated on (3 × 3), (7 × 7) and (9 × 9) sizes of convolutional filters, respectively for each of the above mentioned ConvNets.
Face Recognition Based on Human Sketches Using Fuzzy Minimal Structure Oscillation in the SIFT Domain

Through this paper we present a new algorithm with the help of Scale Invariant Feature Transformation (SIFT) along with fuzzy $$ m_{X}^{*} $$ oscillation. We propose a fuzzy based similarity measurement technique i.e. fuzzy $$ m_{x}^{*} $$ oscillation for providing a better precision in face recognition area. First, apply SIFT for finding key points from sketch and digital images and then select the key points for feature extraction. After feature extraction fuzzy $$ m_{X}^{*} $$ oscillation based classification are used for these values. For experiment we have considered two scenarios which are described in the beginning of Sect. 4. So using this proposed algorithm we will easily able to identify the correct image as an output of face sketch to photo matching. Accuracy of our algorithm describes this work piece concerned on fuzzy $$ m_{X}^{*} $$ oscillation has achieved its aim of recognizing the face sketches in the SIFT domain.
Malaria Detection System Using Convolutional Neural Network Algorithm

Malaria is a disease caused when a female Anopheles mosquito bites. There are over 200 million cases recorded per year with more than 400,000 deaths. Current methods of diagnosis are effective; however, they work on technologies that do not produce higher accuracy results. Henceforth, to improve the prediction rate of the disease, modern technologies need to be performed for obtain accurate results. Deep learning algorithms are developed to detect, learn, and determine the containing parasites from the red blood smears. This chapter shows the implementation of a deep learning algorithm to identify the malaria parasites with higher accuracy.
Digital pathology as a platform for primary diagnosis and augmentation via deep learning

Digital technology has the potential of reshaping both clinical medicine and medical research. In pathology, the increasingly widespread adoption of whole slide imaging (WSI) technology has fueled this transition. The benefits are obvious in such matters as education, peer review, archival retrieval, intra- and extrainstitutional consultation, and ultimately primary diagnosis in most aspects of diagnostic anatomic pathology. Of equal importance is the use of WSI to assist in both the integration of advanced state-of-the-art imaging methodologies and the use of deep learning strategies to inform both diagnostic and therapeutic decision-making. For all of these, important issues relating to standardization, validation, and implementation must be addressed to maximize gains in information, management, image sharing, and image analysis/interpretation.
FAST Based Video Stabilization with Preserved Intentional Motion and Smear Removal

Visual sequence recording device may have unintentional jitter motion due to the unwanted movements in camera platform or hand motions. This unintentional motion causes video instability. These unstabilized videos have poor quality and jittery frame motion, hence not suitable for viewing or any other post processing applications like visual tracking etc. Thus, digital video stabilization is of prime importance. A fast algorithm is proposed in this paper for digital video stabilization. Algorithm is also capable of preserving intentional camera motion intelligently and removes smear introduced by unintentional camera motions. Global Motion Vector (GMV) is estimated using Features from Accelerated Segment Test (FAST). Proposed video processing algorithm considers only two number of frames at a time for computation and compensation of global camera motion, which results in fast execution. Hundred strongest robust features are tracked for video stabilization against any translation as well as rotational disturbances, resulting in robust online stabilization. Performance of the proposed algorithm is evaluated on a number of videos w.r.t. ground truth and output stabilized video has improved quality factor of up to 18 dB.
Player Locomotion in Virtual Reality Games

This chapter discusses the challenges of virtual reality (VR) locomotion and outlines a number of available solutions that allow the exploration of fictional worlds. Existing games are sometimes ported to VR, be it to promote the title or to build on the success of the original game. Locomotion in VR has a huge impact on the failure or success of a game. A well-chosen technique is able to enhance the player experience and help players to immerse even more in the fictional worlds. In contrast to delicate finger recognition required for the aforementioned tapping gesture, such arm swings can be easily tracked via mainstream VR controllers. The described arc-based teleportation, although being recommended by hardware manufacturers, is still relatively uncommon in VR games. The chapter discusses a broad spectrum of possibilities to move in VR, ranging from stationary approaches relying on gamepads to advanced redirected walking techniques that trick our perception to enable unrestricted natural walking in a limited space.
SSAM: A Haskell Parallel Programming STM Based Simple Actor Model

Abstract As computers with manycore architecture are being widely spread, parallel programming becomes a pending issue. While parallel programming has been a challenging issue, Haskell is known to be of the best available one. The immutability and declarative nature of Haskell suit well the need for parallelism. In our previous work, we implemented the SAM, a parallel programming model based on Haskell. In this paper, we propose the SSAM, an updated version of SAM, by solving the performance overhead of SAM socket. We discuss the architecture of SSAM and its major functions and show the 14% scalability upgrade in a performance test.
Codes with locality from cyclic extensions of Deligne–Lusztig curves

Recently, Skabelund defined new maximal curves which are cyclic extensions of the Suzuki and Ree curves. Previously, the now well-known GK curves were found as cyclic extensions of the Hermitian curve. In this paper, we consider locally recoverable codes constructed from these new curves, complementing that done for the GK curve. Locally recoverable codes allow for the recovery of a single symbol by accessing only a few others which form what is known as a recovery set. If every symbol has at least two disjoint recovery sets, the code is said to have availability. Three constructions are described, as each best fits a particular situation. The first employs the original construction of locally recoverable codes from curves by Tamo and Barg. The second yields codes with availability by appealing to the use of fiber products as described by Haymaker, Malmskog, and Matthews, while the third accomplishes availability by taking products of codes themselves. We see that cyclic extensions of the Deligne–Lusztig curves provide codes with smaller locality than those typically found in the literature.
Chinese Sentence Compression Algorithm Based on Deep Analysis of Sentence Hierarchy in Multiple Application Scenarios

In recent years, people have paid more and more attention to how to obtain high-value information from massive data, and the demand for efficiency and accuracy has also increased. As a basic technology to solve this problem, sentence compression technology can be widely used in machine translation, question answering system, and generating abstracts. Given that the proposed strategy for Chinese sentence compression is less than English sentence compression, the actual value of Chinese-related sentence compression is self-evident. In the current practical context, the problems encountered in sentence compression are as follows: First, the traditional sentence compression methods are mostly based on parallel corpora of "original sentences-compressed sentences". Parallel corpora are usually difficult to build or obtain. The corpus is even more scarce. Second, sentence compression is relatively rigid, and you cannot choose a flexible compression strategy based on the application. Based on the above practical background, this paper proposes a Chinese sentence compression algorithm that does not rely on parallel corpus and deep analysis of sentence hierarchy in multiple application scenarios. The three compression methods are compared: manual compression, only compression using rules, and compression based on deep analysis of sentence hierarchies. The experimental results show that the Chinese sentence compression algorithm based on deep analysis of sentence hierarchies can be used to some extent according to different application scenarios, reducing or increasing the compression ratio flexibly and appropriately within the acceptable range to obtain a better / slightly lower amount of information and grammatical retention. The compression effect under the comprehensive index is significantly better than the previous two compression methods, and can bring more flexible and realistic compression effects according to the actual application scenario.
Blockchain-based Content-oriented Surveillance Network

For the reason of public security, there are many surveillance actives taken places in either open areas or small areas. During an outburst of public eventuality, it is necessary to surveil public individuals. Based on the great number of populations nowadays, it is critical to propose efficient and secured data delivery network for surveillance networks. There are many different methods to surveil and secure an area, such as cameras and radio wave scanners. The modern surveillance networks are usually based on video content deliveries which consumes much network bandwidth and data security performance. Because it needs to efficiently deliver and protect the data generated by different devices. We propose a content-oriented IoT surveillance network, currently under development, which aims to identify dangerous individuals with various sensors and track the individuals through areas. This paper describes the concept of simulated passive imaging and identifying for conceal objects, various sensors association for person tracking and its traffic volume reduction. The data security in the network uses trust verification concept from blockchain technology. There are simulation and experiment to prove the work valid.
A Novel Unsupervised Dead-value Detection Method for Monitoring Indicators in Data Center

For equipment monitoring, monitoring data are continuously generated, and when the monitoring data remain constant abnormally, we believe that these are dead values. Dead values are common and dead value detection is critical to subsequent data driven intelligent analysis for equipment. However, there is no specify work to study the dead value detection problem, to the best of our knowledge. In addition, due to challenges such as confusing profiles of dead values, the huge amount of monitoring data, and nonstationarity, existing anomaly detection methods are invalid. In this paper, we propose an effective dead value detection method consisting of two steps: dead value scoring and dead value detecting. In our evaluation, we analyze the monitoring data of equipment in the data center, and summarize six representative monitoring indicators with dead values as dataset. The evaluation experiments indicate the proposed dead value detection method achieves an average F1 score of 0.93, significantly outperforming the best performing baseline detection approaches by 117.5% on average.
The ‘Relatedness’ Perspective in Compliance Management of Multi-business Firms

This paper tries to closely look at compliance knowledge relatedness and IT relatedness based on Tanriverdi’s ‘relatedness’ concept. Also, this paper’s main focus lies on how knowledge relatedness and IT relatedness influence compliance performance through compliance knowledge exploitation. The present study conducted a full-scale survey and finalized questionnaire was sent to compliance managers of 187 Korean multi-business firms. This study found (1) the impact of compliance knowledge relatedness on compliance performance, (2) the mediating role of knowledge exploitation on the relationship between compliance knowledge relatedness and compliance performance, and (3) the interaction effect of IT relatedness and compliance knowledge relatedness on knowledge exploitation. This paper contributes to both academic and business world by widening applicability of theories and providing guidelines conducive to improved compliance performance of corporations.
A Comparative Performance of Sorting Algorithms: Statistical Investigation

This paper is aimed at a comparative study of several sorting algorithms with the purpose of evaluating their relative performance when the input array is randomly generated from zero-truncated binomial and poisson distributions. The sorting algorithms to be investigated are quick sort, k-sort, heap sort and merge sort, all having the same average case complexity of O(NlogN). In practical statistical cases, sometimes it is not possible to record or know about the occurrences as they are limited to values which exist above or below a given limit or within a particular range. Such situations result in truncated distributions. The relative performance of the above-mentioned sorting methods was compared on the basis of ‘execution time’ and the ‘number of swapping’ for both the average and worst cases of different algorithms.
Visualization Of The Traditional House Architecture Of Belek Sembalun Lawang Village By Using 3D Animation

Abstract the development of information technology is currently progressing, multimedia is one of the right way to facilitate the delivery of information in the form of audiovisual form of 3D animation. Utilizing multimedia to convey information is as one way in an effort to publish the traditional architecture of the Lombok area, especially the traditional house of Belek Sembalun Lawang Village in the form of 3D animation using blender software that can be used as a medium of learning. Belek Village is a large village/village parent which was first created consisting of seven houses that are the forerunner of Sembalun Village. By using 3D animation is expected to provide solutions and alternatives in the process of introduction and learning about the traditional architecture of Lombok traditional house especially the traditional house of Belek Village, so that in the process of learning and recognition becomes more unique and interesting, as well as learning media and introduction of traditional house of Belek Village, 3D animation can also provide an advantage in publication efforts and documentation of history and culture of Sembalun Lawang Villag.
On Generating Cancelable Biometric Templates Using Visual Secret Sharing

Cancelable Biometrics have been gaining popularity due to security and privacy concerns of an individual’s Biometric image(s). The main objective in Cancelable Biometrics is to generate templates which are non-invertible in nature and result from repetitive distortion of the Biometric image. In this paper, we propose a novel framework for generating Cancelable Biometric templates using Visual Secret Sharing. In the proposed scheme, n different shares are generated corresponding to one Biometric image with the help of $$n-1$$ other images called Cover images. The generated Secret Shares are stored in a distributed manner instead of the original Biometric image. For generating n shares, we propose three different methods (M1) One Biometric image and $$n-1$$ randomly chosen natural gray images (M2) One Biometric image with $$n-1$$ randomly permuted version of Biometric image (M3) Both Secret image and Cover images are randomly permuted version of the Biometric image. To show the efficacy of the proposed approach, we have used the publicly available IIT Delhi Iris database (version 1.0). The performance of these three approaches have been compared in terms of average Co-relation Coefficient, False Accept Rate (FAR), False Reject Rate (FRR) and Genuine Accept Rate (GAR), True Error Rate (TER) and True Success Rate (TSR). The experimental results show that M3 performs best among the proposed methods in terms of all the performance measures and in qualitative terms.