Reviewerly/exHarmony huggingface dataset:
Modified Sparse Representation Classification Framework for Online Writer Identification

In this paper, we propose a modified strategy using sparse representation to identify the authorship of online handwritten documents. The note-worthy aspect of this paper lies in the introduction of a-priori information for each dictionary atom, that in a way indicates its degree of importance with respect to the dynamic characteristics of the writer. The methodology behind obtaining this information for the dictionary atoms is based on the computation of entropy values over histograms that are generated from the sparse coefficients during the training phase. The prelearned values are incorporated on the traditional schemes of “max” and “mean” pooling of sparse codes, thereby resulting in a modified writer descriptor. Experiments performed with the proposed sparse classification strategy on the handwritten samples of the IAM and IBM-UB1 online handwriting databases demonstrate promising results.
Wavelet Method for Automatic Detection of Eye-Movement Behaviors

With the rapid development of eye tracking technology, eye movements have become more and more important in human-computer interaction. Generally, eye movements are classified into fixation, saccade, and smooth pursuit. Since the eye movements are natural and fast, contain important cues for human cognitive state and visual attention, the eye movement behaviors are difficult to detect and classify. In this paper, the novel eye-movement data filtering and eye-movement classification algorithm are proposed. The nonlinear wavelet threshold denoising method was used to smooth the eye-movement data and detect saccades in the presence of smooth pursuit movements, according to different eye-movement behaviors related to the different characteristics of wavelet detail coefficients. Experiments were conducted to compare the eye-movement signal analyzing algorithm based on wavelet with other algorithms. The results showed that the eye-movement data filtering algorithm based on wavelet performed better than the other eye-movement filters. Moreover, the classification algorithm based on wavelet can classify different eye-movement behaviors more accurately. Then, we used an eye tracking technology to record and analyze the user's eye movement during the test, so as to get the user's psychological and cognitive state.
A Reversible Watermark With a New Overflow Solution

Overflow problem is essential to reversible watermarking algorithms, as it would lead to great distortion or irreversibility if not properly handled.In general, the existing algorithms handle overflow problem with methods that take a lot changes to pixels, which consequently brings a possibly poor quality or a high complexity.In order to solve the overflow problem specifically with a higher quality and a lower complexity, and improve the security of watermark, we put forward a new reversible watermarking scheme.We take the strategy of twice embedding-first, the proposed scheme uses wavelet histogram shifting to embed the watermark information; second, a proposed low-distortion overflow processing algorithm (LDOPA) is implemented to process the overflow problem occurred during the first step, which iteratively scans the pixels, modifies the overflow pixels one by one, and embeds the modification record with its authentication information on the image.In addition, we apply logistic mapping, torus mapping, and CRC to improve the security of the watermark, allowing users to extract watermark only when they hold the correct password.The experimental results prove that the scheme realizes the complete reversibility of watermark and carrier image.Especially, the proposed LDOPA can be easily combined with the existing watermarking methods to further improve their performance.
Towards augmented proactive cyberthreat intelligence

In cyber crimes, attackers are becoming more inventive with their exploits and use more sophisticated techniques to bypass the deployed security system. These attacks are targeted and are commonly referred as Advanced Persistent Threats (APTs). The currently available techniques to tackle these attacks are mostly reactive and signature based. Security Information and Event Management (SIEM), a proactive approach is the best solution. However, the major problem with SIEM is tackling huge amount of data in real time that makes it a time consuming and tedious task for security analyst. The use of threat intelligence caters to such issue by prioritizing the level of threat. In this paper, we assign risk score and confidence value to each feed generated at our product “T-Eye platform”. On the basis of these values, we assign a severity score to each feed type. Severity score assigns a level to the threat means prioritize the threat. The results, we achieved for prioritizing the threat is more apparent and accurate. In addition, we optimize the rules of IBM-Q-Radar by using threat feeds generated at T-Eye platform. Furthermore, a huge amount of false positive alarms generated at IBM Q-Radar is reduced to a certain extent.
Self-Supervised Optical Flow Estimation by Projective Bootstrap

Dense optical flow estimation is complex and time consuming, with state-of-the-art methods relying either on large synthetic data sets or on pipelines requiring up to a few minutes per frame pair. In this paper, we address the problem of optical flow estimation in the automotive scenario in a self-supervised manner. We argue that optical flow can be cast as a geometrical warping between two successive video frames and devise a deep architecture to estimate such transformation in two stages. First, a dense pixel-level flow is computed with a projective bootstrap on rigid surfaces. We show how such global transformation can be approximated with a homography and extend spatial transformer layers so that they can be employed to compute the flow field implied by such transformation. Subsequently, we refine the prediction by feeding a second, deeper network that accounts for moving objects. A final reconstruction loss compares the warping of frame X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> with the subsequent frame X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t+1</sub> and guides both estimates. The model has the speed advantages of end-to-end deep architectures while achieving competitive performances, both outperforming recent unsupervised methods and showing good generalization capabilities on new automotive data sets.
Enrichment of ferrous iron in the bottom water of Lake Nyos

Lake Nyos is a meromictic lake with strong stratification separated into 4 distinct layers; shallow, intermediate, transitional, and bottom layers. This article discusses enrichment of Fe2+ in the bottom water in January 2003 when the effect of artificial degassing that started in January 2001 on the chemical structure of the lake was still minimal. In the bottom layer (205.5–209.5 m), Fe2+ and HCO3− are the major dissolved species and their concentrations increase sharply toward the bottom. Enrichment of Fe2+ and HCO3− is most likely caused by anaerobic microbial reduction of Fe(OH)3 precipitates at the water-sediment interface. The Fe(OH)3 precipitates sank to the bottom after atmospheric oxidation of Fe2+ dispersed by the degassing pipe. Lateritic hematite may also participate in the reduction reaction. Production of Fe2+ and HCO3− significantly increases the alkalinity and electric conductivity of the bottom-most water and is responsible for a small rise of pH between 205 m and 209.5 m, the bottom. The other dissolved species, e.g., Na+, K+, Ca2+, Mg2+, SiO2(aq) etc., result from the CO2-water-basalt interaction at low temperatures (25°∼30 °C) in the sub-lacustrine fluid reservoir that is believed to exist in the diatreme beneath the lake bottom, and they seep into the bottom layer.
Speaker identification using multi-modal i-vector approach for varying length speech in voice interactive systems

The development in the interface of smart devices has lead to voice interactive systems. An additional step in this direction is to enable the devices to recognize the speaker. But this is a challenging task because the interaction involves short duration speech utterances. The traditional Gaussian mixture models (GMM) based systems have achieved satisfactory results for speaker recognition only when the speech lengths are sufficiently long. The current state-of-the-art method utilizes i-vector based approach using a GMM based universal background model (GMM-UBM). It prepares an i-vector speaker model from a speaker's enrollment data and uses it to recognize any new test speech. In this work, we propose a multi-model i-vector system for short speech lengths. We use an open database THUYG-20 for the analysis and development of short speech speaker verification and identification system. By using an optimum set of mel-frequency cepstrum coefficients (MFCC) based features we are able to achieve an equal error rate (EER) of 3.21% as compared to the previous benchmark score of EER 4.01% on the THUYG-20 database. Experiments are conducted for speech lengths as short as 0.25 s and the results are presented. The proposed method shows improvement as compared to the current i-vector based approach for shorter speech lengths. We are able to achieve improvement of around 28% even for 0.25 s speech samples. We also prepared and tested the proposed approach on our own database with 2500 speech recordings in English language consisting of actual short speech commands used in any voice interactive system.
A smart coordinated temperature feedback controller for energy-efficient data centers

Energy cost plays a big role in the running cost of data centers and cutting it would get great economic and green benefits. This paper presents a coordinated MPC-based (Model Predictive Control) scheduling strategy called Co-ThermoRing to smartly reduce energy cost in data centers. Co-ThermoRing makes use of an online hierarchical feedback control mechanism which is composed by a global cluster-level temperature controller and many local node-level efficiency controllers. The inlet temperatures of all nodes can be kept balanced to minimize the heat recirculation by the temperature controller, and DVFS (Dynamic Voltage and Frequency Scaling) technology is adopted by each efficiency controller to utilize every computing node intelligently. This method can also contain temperature and utilization fluctuations with little performance degradation. With a TPC-W (Transaction Processing Performance Benchmark™ W) run in a real data center to simulate an online bookstore behavior our method is proved to have less energy consumption in normal cases and emergency cases compared with existing solutions such as LRHex, C-Oracle-F and ThermoRing.
Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things

The emergence of nontrivial embedded sensor units and cyber-physical systems and the Internet of Things has made possible the design and implementation of sophisticated applications where large amounts of real-time data are collected, possibly to constitute a big data picture as time passes. Within this framework, intelligence mechanisms based on machine learning, neural networks, and brain computing approaches play a key role to provide systems with advanced functionalities. Intelligent mechanisms are needed to guarantee appropriate performances within an evolving, time-variant environment, optimally harvest the available energy and manage the residual energy, reduce the energy consumption of the whole system, identify and mitigate occurrence of faults, and provide shields against cyberattacks. The chapter introduces the above aspects of intelligence, whose functionalities are needed to boost the next generation of cyber-physical and Internet of Things applications, and the smart world generation whose footprint is already around us.
Usability testing for a community college library website

Purpose This paper details a usability testing case study on a simplified homepage for [Library]. The usability testing was completed in Spring 2017 to meet the needs of diverse user groups and shifting trends in Web design and development. At the conclusion of the usability testing, recommendations for change informed the design decisions and a new homepage was implemented in October 2018. Design/methodology/approach The researcher performed eight usability tests with a combination of the different library user types; full-time faculty, students, an administrator and members of the public. The usability test consisted of 13 specific tasks. After testers completed the tasks, users filled out a 30-question Likert-scale questionnaire and answered a set of 8 open-ended questions. Findings This paper discusses the recommendations for change which the researcher discovered at the conclusion of the usability testing period. The research found the need to improve and include specific navigational, visual and easy-to-use elements to best meet the needs of the users in the usability tests. Changes were ranked and implemented on a scale of catastrophic to cosmetic. Research limitations/implications As websites, technology and user preferences continually evolve, the homepage will need to be tested for usability again in the next several years. Researchers are encouraged to adapt the methods to their own institutions. Practical implications This paper discusses findings specific to [Library], which in turn has proved to increase usage of certain features and functions by the user community. Originality/value This is the first time usability testing has been done for the [Library’s] website. It was the first time the design of the homepage was informed by real user preference. This paper is valuable to those looking to create a simple, easy-to-use homepage that best benefits their own unique community of users.
Low-Rank Projection Learning via Graph Embedding

With robustness to various corruptions, it is the local geometrical relationship among data that plays an important role in the recognition/clustering task of subspace learning (SL). However, a lot of previous SL methods cannot take into consideration both of the local neighborhood and the robustness, which results in poor performance in image classification and feature extraction. In this paper, a robust SL method is proposed to solve the feature extraction problem, named as Low-Rank Projection Learning via Graph Embedding (LRP-GE). The proposed algorithm enjoys two merits. First, it preserves the local neighborhood information among data by introducing the graph embedding (GE). Second, it alleviates the impact of noise and corruption by learning a robust subspace based on the low-rank projection. We cast the problem as a convex optimization problem and provide an iterative solution that can be solved efficiently in polynomial time. Extensive experiments performed on four benchmark data sets demonstrate that the proposed method performs favorably against other well-established SL methods in image classification.
A Space Efficient Minimum Spanning Tree Approach to the Fuzzy Joint Points Clustering Algorithm

The fuzzy joint points (FJPs) method is a neighborhood-based clustering method that uses a fuzzy neighborhood relation and eliminates the need for a parameter. Even though the fuzzy neighborhood-based clustering methods are proven to be fast enough, such that tens of thousands of data can be handled under a second, the space complexity is still a limiting factor. In this study, a minimum spanning tree based reduced space FJP algorithm is proposed. The computational experiments show that the reduced space algorithm enables the method to be used for much larger data sets.
Re-Revisiting Learning on Hypergraphs: Confidence Interval, Subgradient Method, and Extension to Multiclass

We revisit semi-supervised learning on hypergraphs. Same as previous approaches, our method uses a convex program whose objective function is not everywhere differentiable. We exploit the non-uniqueness of the optimal solutions, and consider confidence intervals which give the exact ranges that unlabeled vertices take in any optimal solution. Moreover, we give a much simpler approach for solving the convex program based on the subgradient method. Our experiments on real-world datasets confirm that our confidence interval approach on hypergraphs outperforms existing methods, and our subgradient method gives faster running times when the number of vertices is much larger than the number of edges. Our experiments also support that using directed hypergraphs to capture causal relationships can improve the prediction accuracy. Furthermore, our model can be readily extended to capture multiclass learning.
Multiview Semantic Representation for Visual Recognition

Due to interclass and intraclass variations, the images of different classes are often cluttered which makes it hard for efficient classifications. The use of discriminative classification algorithms helps to alleviate this problem. However, it is still an open problem to accurately model the relationships between visual representations and human perception. To alleviate these problems, in this paper, we propose a novel multiview semantic representation (MVSR) algorithm for efficient visual recognition. First, we leverage visually based methods to get initial image representations. We then use both visual and semantic similarities to divide images into groups which are then used for semantic representations. We treat different image representation strategies, partition methods, and numbers as different views. A graph is then used to combine the discriminative power of different views. The similarities between images can be obtained by measuring the similarities of graphs. Finally, we train classifiers to predict the categories of images. We evaluate the discriminative power of the proposed MVSR method for visual recognition on several public image datasets. Experimental results show the effectiveness of the proposed method.
Flexible recovery of uniqueness and immutability

We present an imperative object calculus where types are annotated with qualifiers for aliasing and mutation control. There are two key novelties with respect to similar proposals. First, the type system is very expressive. Notably, it adopts the recovery approach, that is, using the type context to justify strengthening types, greatly improving its power by permitting to recover uniqueness and immutability properties even in presence of other references. This is achieved by rules which restrict the use of such other references in the portion of code which is recovered. Second, execution is modeled by a non standard operational model, where properties of qualifiers can be directly expressed on source terms, rather than as invariants on an auxiliary structure which mimics physical memory. Formally, this is achieved by the block construct, introducing local variable declarations, which, when evaluated, play the role of store.
Efficient Profiled Attacks on Masking Schemes

Side-channel adversaries represent real-world threats against (certified and uncertified) cryptographic devices. Masking schemes represent prevailing countermeasures to reduce the success probabilities of side-channel attacks. However, masking schemes increase the implementation cost in terms of power consumption, clock cycles, and random number generation. Investigation of tools evaluating the degree of resilience of cryptographic devices using masking (against side-channel attacks) represents an important aspect in certification procedures (e.g., common criteria, FIPS 140-2, and EMVco). Several side-channel evaluation techniques exist such as template attacks and machine learning-based attacks. In this paper, we formalize results obtained in side-channel attacks community when targeting masked implementations. We report theoretical as well as practical results of parametric and non-parametric side-channel attacks on masking schemes. The theoretical part reports results based on a simulation of the execution of software devices, while the practical part focuses on actual leakages measured during the execution of a software implementation in three different contexts (that contain different levels of noise).
The Capacity of Anonymous Communications

We consider the communication scenario where K transmitters are each connected to a common receiver with an orthogonal noiseless link. One of the transmitters has a message for the receiver, who is prohibited from learning anything in the information theoretic sense about which transmitter sends the message (transmitter anonymity is guaranteed). The capacity of anonymous communications is the maximum number of bits of desired information that can be anonymously communicated per bit of total communication. For this anonymous communication problem over a parallel channel with K transmitters and one receiver, we show that the capacity is 1/K, i.e., to communicate 1 bit anonymously, each transmitter must send a 1 bit signal. Furthermore, it is required that each transmitter has at least 1 bit correlated randomness (that is independent of the messages and is not available to the receiver) per message bit and the size of correlated randomness at all K transmitters is at least K - 1 bits per message bit.
Robust pixelwise saliency detection via progressive graph rankings

In this paper, we propose a novel saliency detection method based on superpixel-to-pixel level optimization. First, we segment the input image into superpixels under four scales. For each scale, we construct a k-regular basic graph with these superpixels as nodes. Furthermore, we enlarge the basic graph with virtual absorbing nodes and utilize absorbing Markov chain ranking to calculate background-based saliency. Second, for each scale, we obtain robust foreground queries from the previous result, and use manifold ranking to obtain foreground-based saliency. Third, a regularized random walk ranking based on the pixelwise graph for each scale is used to diffuse the saliency values among pixels. Finally, we obtain four saliency maps for the input image and integrate them together for the final saliency map. Extensive experiments on several challenging datasets reveal that the proposed method performs better in terms of precision, recall and F-measure values. Despite complex backgrounds, our method performs better in detecting small and/or multiple salient objects than other state-of-the-art methods as a whole.
Fast active learning for hyperspectral image classification using extreme learning machine

Owing to undulating and complexity of the earth's surface, obtaining the training samples for remote sensing data is time-consuming and expensive. Therefore, it is highly desirable to design a model that uses as few labelled samples as possible and reducing the computational time. Several active learning (AL) algorithms have been proposed in the literature for the classification of hyperspectral images (HSIs). However, its performance in terms of computational time has not been focused yet. Here, the authors have proposed AL approach based on extreme learning machine (ELM) that effectively decreases the computational time while maintaining the classification accuracy. Further, the effectiveness of the proposed approach has been depicted by comparing its performance with state-of-the-art AL algorithms in terms of classification accuracy and computational time as well. The ELM-based AL with different query strategies were conducted on two HSI data sets. The proposed approach achieves the classification accuracy up to 90% which is comparable to support vector machine-based AL approach but effectively reduces the computational time significantly by 1000 times. Thus, the proposed system shows the encouraging results with adequate classification accuracy while reducing the computation time drastically.