Reviewerly/exHarmony huggingface dataset:
An Embedded System for Gray Matter Segmentation of PET-Image

Das, Khakon Khorat, Dipankar Sharma, Samarendra KumarThis paper presents a complete embedded system for segmentation of gray matter of the human brain from Positron emission tomography (PET) image using 8-bit embedded Atmel microcontroller operating at low power and a frequency of 16 MHz. Hardware implementation of the embedded system for gray matter segmentation (ESGMS) of PET image is chosen as hardware design is more efficient by adopting the principles of software development. The ideology of parallelism achieved by the sixth level of software pipelining and two levels of dedicated hardware pipelining. Using lifting technique, the wavelet function is generated and denoised the same simultaneously, generally known as second-generation wavelet. The proposed architecture reduced number of memories through in-place approach as the resultant of the operands is overwritten on the operand location. For realization, Haar wavelet is used as mother wavelet in the design which is compiled lifting technique to perform split and update. Haar mother wavelet with the lifting scheme procures the denoised image for future operations. The wavelets confer a better output image, which is established with altering different statistical measures. With our proposed system, we can obtain peak signal to noise ratio (PSNR) about 107 dB. For segmentation, unsupervised clustering algorithm (K-mean) is used and each segment is superimposed with different colors. The gray matter of the human brain in the PET image is leveled using pink color.
Bayesian nonparametric test for independence between random vectors

A nonparametric approach for testing independence among groups of continuous random variables is proposed. Gaussian-centered multivariate finite Polya tree priors are used to model the underlying probability distributions. Integrating out the random probability measure, a tractable empirical Bayes factor is derived and used as the test statistic. The Bayes factor is consistent in the sense that it tends to infinity under the alternative, and zero under the null. A p-value is then obtained through a permutation test based on the observed Bayes factor. Through a series of simulation studies, the performance of the proposed approach is examined and compared to several existing approaches based on the power of the test as well as the observed Bayes factor. Lastly, the proposed method is applied to a set of real data in ecology.
Research on Image Encryption Algorithm Based on Wavelet Transform and Qi Hyperchaos

In this paper, the Wavelet transform is used to decompose the image into low-frequency components, horizontal components, vertical components, and diagonal components. Then, the low-frequency components are scrambled by constructing index sequences, obtaining the scrambled image by wavelet reconstruction. The chaotic sequence generated by the logistic chaotic system is used to encrypt the scrambled image, and then multiple sequences generated by Qi hyperchaos are used for the second encryption of images. The decryption process is the inverse of encryption. The encryption scheme is simulated on MATLAB, and the security analysis is carried out by key space, key sensitivity, histogram, information entropy, pixel correlation, etc., and compared with other algorithms, it is concluded that the algorithm can be applied to images encryption.
A kernel based learning method for non-stationary two-player repeated games

Repeated games is a branch of game theory, where a game can be played several times by the players involved. In this setting, players may not always play the optimal strategy or they may be willing to engage in collaboration or other types of behavior which might lead to a higher long-term profit. Since the same game is repeated for several rounds, and considering a scenario with complete information, it is possible for a player to analyze its opponent's behavior in order to find patterns. These patterns can then be used to predict the opponent's actions. Such a setting, where players have mutual information about past moves and do not always play in equilibrium, leads naturally to non-stationary environments, where the players can frequently modify their strategies in order to get ahead in the game. In this work, we propose a novel algorithm based on a string kernel density estimation, which is capable of predicting the opponent's actions in repeated games and can be used to optimize the player's profit over time. The prediction is not limited to the next round action. It can also be used to predict a finite sequence of future rounds, which can be combined with a lookahead search scheme with limited depth. In the experiments section, it is shown that the proposed algorithm is able to learn and adapt rapidly, providing good results even if the opponent also adopts an adaptive strategy.
Dual Watermarking for Colour Images’ Copyright and Authentication Using DWT Technique

The watermarking technique is a very vital method to identify the originality of the object. This paper describes the dual watermarking method for the digital images which is invisible. In this technique, the watermark is added in host image, and specifically, the dual watermarks provide more security compared with the single one. The robust and fragile watermarks are used in this scheme for image authenticity and the copyright protection purpose. Firstly, the colour image is converted into YCbCr space separated into each space; the Y colour space is used as the embedding watermark section. The DWT is applied to the Y space from which the LL section is embedded with the watermark. The improved values of PSNR and MSE can define the quality of image.
Multiple similarity collaborative filtering recommendation among users

Abstract [Objective] Through the analysis of multiple similarity among users, the problem that the traditional user based collaborative filtering algorithm only uses a single similarity and leads to the decline of recommendation quality is solved. [Method] The original single similarity calculation formula is improved, and the multiple similarity calculation formula is put forward, on this basis, the multiple similarity prediction score is calculated. [Result] By comparison with the traditional user based collaborative filtering algorithm, the method put forward in this paper has outstanding effect. [Limited] Users’ interests will change with time, so time information should be included in the calculation. [Conclusion] From the experiment, we can find that the improved method has better recommendation quality than traditional methods.
Urban response towards tropical cyclone using twitter in Indonesia

Abstract Tropical Cyclone Cempaka struck the southern coast of Java Island in November in 2017, causing floods and landslides in Yogyakarta, Wonogiri, Pacitan, Ponorogo, and the surrounding areas. These disasters devastated urban and rural areas and disrupted community livelihoods as they disconnected inter-city road access, flooded agricultural land, and damaged public facilities. During these hazardous events, many people actively left a “trace” on social media, especially Twitter. This research was designed to describe urban respond using Twitter during Tropical Cyclone Cempaka (TCC). The research data sources were tweets filtered with five keywords with geotagging. After the preprocessing stage, a total of 457 tweets were categorized as “urban” and “rural”. Then, the content was analyzed qualitatively by identifying the most frequently appearing words, the number of response (like, reply, and retweet), and the accounts mentioned on the tweets. The results showed that urban response to these catastrophic events was positive by posting words of prayer and sympathy, disseminating information for raising awareness of disasters, and organizing fundraising for the victims. Turned out that those able to respond was not mainly urban dwellers, there were many responds which based at rural areas. It was marked by similar words that mostly appeared in two comparative areas.
A Pairing-Less Identity-Based Blind Signature with Message Recovery Scheme for Cloud-Assisted Services

The rapid growing big data enforces many organizations to shift their data and services like digital right management, e-payment, and e-voting systems to the cloud. In such cloud-assisted services, the blind signature scheme could be one of the cryptographic tools, which provides the integrity of data and user anonymity. It allows the user to ask the signer for signing on message without disclosing any information about the content to the signer. Since several blind signature schemes have been proposed, but due to the expensive computation and bandwidth cost, they are impractical for the cloud-assisted as well as Internet-based environment. In this paper, we propose a new provable secure identity-based blind signature scheme with message recovery (IDBS-MR) using the elliptic curve cryptography. The proposed IDBS-MR scheme does not transmit the message with the signature while the message is recovered during verification round; hence it has the least message-signature length. The security analysis shows that the proposed IDBS-MR scheme is secured against existential forgery attack under the adaptive chosen message and ID attacks (EF-ID-CMA) under the assumption of solving the ECDL problem, and random oracle model (ROM) and achieves blindness property. The performance analysis shows that our scheme is efficient as compared to related existing schemes.
Compression of Dynamic Graphs Generated by a Duplication Model

Abstract We continue building up the information theory of non-sequential data structures such as trees, sets, and graphs. In this paper, we consider dynamic graphs generated by a full duplication model in which a new vertex selects an existing vertex and copies all of its neighbors. We ask how many bits are needed to describe the labeled and unlabeled versions of such graphs. We first estimate entropies of both versions and then present asymptotically optimal compression algorithms up to two bits. Interestingly, for the full duplication model the labeled version needs $$\Theta (n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Θ</mml:mi><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> bits while its unlabeled version (structure) can be described by $$\Theta (\log n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Θ</mml:mi><mml:mo>(</mml:mo><mml:mo>log</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> bits due to significant amount of symmetry (i.e. large average size of the automorphism group of sample graphs).
Reflections on the Adoption of Virtual Adaptive Learning Tool for Industrial Training

Ensuring proper education for employees represents a key factor in most of the industrial sectors. Several researches have been conducted in order to test the efficiency of new educational methods for improving their flexibility and effectiveness. At the same time, the introduction of emergent technologies, such as Virtual Reality (VR), is offering new solutions for industries for both reducing cost of training programmes and for better tailoring those on the needs of the users. Based on a first cross-industry reflection, the paper analyses how the combination of adaptive learning methods with VR could bring substantial benefits to companies (railway and process industry) in replacing the actual training programmes based on traditional learning approaches. Firstly, the paper offers a short overview on the features of adaptive learning and VR. Secondly, a consideration on the main requirements on railway sector and process industry is conducted in order to link them with constrains appeared during the current training programmes. Finally, possible solutions based on the previous findings are highlighted in order to sketch a possible working framework for creating a Virtual Reality Adaptive Learning Tool. Our research, thereby, shows how long-term strategic educational policies can impact the performance of the industries.
A Taxonomy and Survey of Data Partitioning Algorithms for Big Data Distributed Systems

Data partitioning is a backbone of distributed systems that boost the performance of big data applications, especially in distributed systems. In past years, many data partitioning algorithms have been developed which had improved the big data management and its processing for the real-time applications of the big data stores. Furthermore, the feature of “elasticity” to the data partitioning has removed the need for human interaction while handling the big data applications on the distributed system during the high workloads and skews. In this survey, a taxonomy is proposed that characterizes and classifies various types of data partitioning algorithms, which will help to identify the current limitations in the state of the art and will extend the state of the art to improve the enhancements for the effective and efficient performance of the big data stores on distributed systems. The taxonomy not only highlights the design, the similarities, and the differences within state of the art for different types of data partitioning algorithms but also identifies the areas that need further research.
Improving Flow Scheduling Scheme With Mix-Traffic in Multi-Tenant Data Centers

Data centers need low-latency fabrics. Several flow scheduling schemes have been proposed to minimize the Flow Completion Time (FCT) based on Shortest Job First (SJF) heuristic. However, to mimic SJF, previous proposals sacrifice the generality (e.g., pFabric requires special hardware) or sacrifice the performance to guarantee the generality (e.g., PIAS loses some of pFabric's performance). Especially, in multi-tenant data centers, traffic patterns from different applications are mixed together and vary over time, thereby creating even more challenges. In this paper, we investigated that the performance of information-agnostic scheme could be further improved by leveraging the unique characteristics of different traffic types. Based on this investigation, we present Traffic Prediction based Flow Scheduling (TPFS), aiming at achieving near-optimal performance and good generality in multi-tenant data centers with the mix-traffic pattern. To achieve near-optimal performance, we design a two-stage machine learning algorithm to first automatically cluster flows with the similar flow size distribution and then predict the priorities of flows based on the clustering results. Besides, we implement TPFS in virtual switches, which exerts fine-grained flow scheduling over the arbitrary network stacks of tenants. Testbed evaluation and simulations show that TPFS outperforms the previous information-agnostic flow scheduling scheme PIAS and greatly reduces the tail latency of the network.
Application Research of CNN Accelerator Design Based on FPGA in ADAS

Abstract In recent years, with the rapid development of artificial intelligence, various deep learning algorithms represented by Convolutional Neural Networks(CNN) have been widely used in various fields, showing their unique advantages. However, as we need to solve the problem, in order to obtain higher accuracy, the depth of the network is deeper and deeper, and the structure is more and more complex. In some scenarios with high real-time requirements, it is difficult to meet the application requirements. Aiming at the characteristics of a large number of multiply-accumulate operations in CNN, this paper proposes a pipelined FPGA parallel acceleration design module and studies the application in Advanced Driver Assistance System(ADAS). Altera DE1 SOC FPGA development board is our hardware implementation tool. Take 36×36 image input as an example. The running time of CNN in the FPGA at 100MHz clock frequency is 2.812ms. This time is enough for real-time Advanced Driver Assistance System(ADAS) requirements.
An adaptive stacked hourglass network with Kalman filter for estimating <scp>2D</scp> human pose in video

Abstract One of the main challenges in computer science and image processing is 2D human pose estimation. Specifically, occlusion and in particular occlusion of human joints caused by camera angle are of paramount importance. In this paper, a new highly accurate network was proposed that can estimate 2D human poses in video images using deep learning. We employ the Single Shot MultiBox Detector network to detect the centre position of each human within a video frame and then use the stacked hourglass network to estimate the 2D human pose. We approximate the human motion as a linear motion between different frames in a certain period; and optimize the human centres based on the local outlier factor and Kalman filters. The same method is applied to optimize the human pose estimations in video, which can address the inaccurate prediction caused by human joints occlusion. The proposed adaptive network is tested using the two well‐known benchmarks for human pose estimation (MPII and Joint‐annotated Human Motion Data Base datasets), and we also generate some 2D human pose estimating qualitative results of single and multiple people in Internet videos. The experimental results show that the proposed network has strong practicability and can achieve high accuracy on adaptive estimating the 2D human pose in video.
Comparison Performance of Decision Tree Classification Model for Spam Filtering with or without the Recursive Feature Elimination (RFE) Approach

Spam filters have become an important tool for ISP(Internet service Provider) to address the growing spam in cyberspace. Spam filtering is a program used to detect spam, prevent it and enter users' mailboxes. With the rapid growth of Internet technology, email communication is becoming increasingly important in people's daily lives. In addition, the amount of spam contained in emails has increased considerably in recent years. In this study, the authors will compare the performance of the decision tree classification models using or not using Recursive Feature Elimination (RFE) in spam filtering. The results of this test will be compared with the results of level accuracy, recall, precision, and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> Score using or not using the RFE method. where in the results of this level measurement, the model used can be a reference or solution to filter spam correctly, especially through the use of supervised learning data.
Calibration of paracatadioptric cameras based on sphere images and pole-polar relationship

With a paracatadioptric camera, according to the projective antipodal properties of the space sphere in the unit sphere model, two infinity points and a symmetric axis can be obtained. On the image plane, the image of the symmetric axis is the vanishing point’s polar with respect to the image of the absolute conic (IAC), and an infinity point is orthogonal to the polar direction of a circle. Thus, we obtain three vanishing points orthogonal to each other. This study extends research into the internal sphere parameters and the vanishing point in a paracatadioptric camera. The results of tests confirm the feasibility and effectiveness of the proposed calibration algorithms.
The Library: A Non-Intrusive Gaze Directed Virtual Reality Animation

In recent years, Cinematic Virtual Reality (CVR) has emerged as a unique form of VR storytelling. While narrative strategies for this new medium are still being explored, one often discussed issue is in guiding the viewer to look where the filmmaker intended. Conventional cinematic techniques such as sound and visual cues have been adopted to direct the viewer's attention. However, this dispersed viewer's attention might suggest an unexplored territory for storytelling in VR and immersive media in general. My current creative practice-led research explores how dispersed viewer's attention could be used to navigate a branching and spatial narrative structure. The Library is a work in progress, an animated VR experience that reimagines Jorge Luis Borges' Library of Babel. The story progresses according to how the viewer's gaze reacts to the narrative events that are organised both temporally and spatially, all the while without the viewer being aware of the gaze interaction mechanism. This paper presents some of the strategies that have emerged in the production of the creative work, which could lead to further explorations of a non-intrusive gaze directed interactive immersive cinema.
The Undersampling Effects on RANDSHUFF Oversampling Algorithms

Randshuff (Random Shuffle Oversampling Techniques for Qualitative Data) is one of an oversampling algorithm which appropriate for nominal attributes. Randshuff uses IVDM (Interpolated Value Difference Metric) distance calculation and crossover with random shuffle technique. Although Randshuff can overcome the problems on minority data, but the problems on majority data are ignored. The problem arises where majority data contain distribution complexity problems such as small disjuncts, overlap and noise. There are two kinds of undersampling concepts: informed undersampling and simple random undersampling. Tomeks links, Edited Nearest neighbors (ENN) and Near Miss are informed undersampling state of the art methods. Meanwhile, Random Undersampling (RUS) is simple random undersampling method. So, evaluations of both undersampling concepts on Randshuff are needed to be conducted. The experiments were evaluated on five public datasets. The results show that RUS as simple random undersampling and Near Miss as informed under sampling improve recall, f-measure and g-mean performance on Randshuff algorithm.
CONDENSE: A Moving Target Defense Approach for Mitigating Cache Side-Channel Attacks

Common consumer electronic devices (including smartphones, smart TVs, personal computers, etc.) feature increasingly complex processors for the required computations. These processors typically feature caches, which substantially improve the devices' performance, but may also be prone to side-channel attacks, wherein an attacker measures and analyzes the side-channel data in order to deduce private information. In this article, we explore CONDENSE: Configurability-Driven Security as a viable low-overhead technique for mitigating security vulnerabilities in caches. CONDENSE takes advantage of runtime reconfigurable caches to provide a moving target defense against side-channel attacks, while also optimizing the cache's energy consumption. Experimental results reveal that employing cache configurability as a defense mechanism is promising and warrants further studies.
Descriptors for Human Activity Recognition

This article presents some filtration process on a public human activity dataset called `EJUST-ADL-l`. It consists of four types of 3D IMU sensory signals: User acceleration, angular velocity, rotation displacement, and gravity for 14 activities of daily living ADLs measured by a wearable smart watch. The EJUST-ADL-l dataset contains mainly activities of communication, feeding, transferring, and personal grooming. The data is filtered by using several descriptors. The descriptors are constructed using different combinations of the following signal features: The minimum, maximum, median, mode, range (maximum-minimum), mean, standard deviation, entropy, and the autocorrelation function up to a certain lag and taking these values as representative features of the given signal. Experiments show which descriptor achieves highest accuracy on the random-forest based model.