Reviewerly/exHarmony huggingface dataset:
Exploring frame segmentation networks for temporal action localization

Temporal action localization is an important task of computer vision. Though many methods have been proposed, it still remains an open question how to predict the temporal location of action segments precisely. Most state-of-the-art works train action classifiers on video segments pre-determined by action proposal. However, recent work found that a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. In this paper, we propose a Frame Segmentation Network (FSN) that places a temporal CNN on top of the 2D spatial CNNs. Spatial CNNs are responsible for abstracting semantics in spatial dimension while temporal CNN is responsible for introducing temporal context information and performing dense predictions. The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information. FSN is trained in an end-to-end manner, so the model can be optimized in spatial and temporal domain jointly. We also adapt FSN to use it in weakly supervised scenario (WFSN), where only video level labels are provided when training. Experiment results on public dataset show that FSN achieves superior performance in both frame-level action localization and temporal action localization.
Image analysis applied to quartz grain microtextural provenance studies

Every quartz grain surface is sculptured during time by many different processes. During the life of a grain, sediment transport, physical and chemical processes imprint specific microtextural features on their surface. State-of-the-art methods for analysis of the surface of quartz particles in Scanning Electron Microscope images are performed non-automatically by experts and essentially are dependent on operator proficiency. Here, we propose digital image processing-based method for automated microtextural analysis of grain's surface with the goal to classify the grain into specific classes corresponding to its genesis (last provenance cycle). The method is based on original classification scheme which extracts the textural features from the digital images of single grains in training database and creates Texton Dictionary for establishing the prototype of specific texture set characterizing given genesis class. The created Texton Dictionary then can be used for classification of a grain with unknown genesis with very good performances when tested on databases of grains with known origins.
Pitch-Synchronous Analysis of Human Voice

Objective Based on simultaneous voice and electroglottograph (EGG) signals, to gain a better understanding of human voice production process, to make pitch-synchronous segmentation of voice signals, and to make visual representations of pitch marks and timbre spectra with high resolution. Methods/Design The traditional spectrogram segments the voice signals with a process window of fixed size and fixed shift, then performs fast Fourier transformation after multiplied with a window function, typically a Hamming window. Then display power spectrum in both frequency and time. Pitch information and timbre information are mixed. The new design segments the signals into pitch periods, either using the derivatives of the EGG signals or based on the voice signals, then performs Fourier analysis to the segment of signals in each pitch period without using a window function. The pitch information and the timbre information are cleanly separated. The graphical representations of both pitch marks and timbre spectra exhibit high resolution and high accuracy. Results Detailed analysis of simultaneously acquired voice and EGG signals provides a more precise understanding of human-voice production process. The transient theory of voice production, proposed by Leonhard Euler in early 18th century, is substantiated with modern data. Based on the transient theory of voice production, a pitch-synchronous spectrogram software is developed, which makes a visual representation of pitch marks and timbre spectra. In addition, the timbre spectrum and the power evolution pattern in each pitch period can be displayed individually. Conclusions Simultaneously acquired voice and EGG signals indicates that each glottal closing triggers a decaying elementary wave in the vocal tract. A superposition of those elementary waves constitutes voice. Based on that concept and using EGG data, a pitch-synchronous voice signal processing method is developed. The voice signal is first segmented into pitch periods, then the two ends are equalized. Fourier analysis is applied to obtain the timbre spectrum of each pitch period. High resolution display of timbre spectrum is generated. The power evolution pattern in each pitch period is also displayed.
Anime Sketch Coloring with Swish-Gated Residual U-Net

Anime sketch coloring is to fill the color into the anime sketches to obtain the colorful anime images and it is a new research direction in deep learning technology. Currently, generative adversarial networks (GANs) have been used for anime sketch coloring and achieved some results. However, the colorful images generated by the anime sketch coloring methods based on GANs generally have poor coloring effects. In this paper, an efficient anime sketch coloring method based on swish-gated residual U-net (SGRU) is proposed to solve the above problems. In SGRU, the proposed swish layer and swish-gated residual blocks (SGRBs) effectively filter the information transmitted by each level and speed up the convergence of the network. The perceptual loss and the per-pixel loss are used to constitute the final loss of SGRU. The final loss function reflects the coloring results more realistically and can control the effect of coloring more effectively. SGRU can automatically color the sketch without providing any coloring hints in advance and can be trained end-to-end with the sketch and the corresponding color image. Experimental results show that our method performs better than other state-of-the-art coloring methods, and can achieve the colorful images with higher visual quality.
Customer data security and theft: a Malaysian organization’s experience

Purpose This study aims to identify weaknesses in current internal control systems in protecting customer data and the drivers that motivate employees to steal customer data and the impact of customer data theft on the organization. Design/methodology/approach A case study approach was taken to investigate and analyze internal control system weaknesses. One organization that involved investor and treasury services was selected as a case study in this research. A mixed method of data collection, specifically survey questionnaires and observations, was used. Findings This study revealed that employees are aware of the policy to protect customer data in their organization. Ironically, customer data theft still occurred despite the company having an internal control system. The main concern was the attitude of the employees to adhere to the policies in place, which becomes the major cause of internal control violation. Employees tend to ignore policies and standard operating procedures, providing opportunities for data theft and fraud to occur, although they realize this will result in a severe impact on the reputation of a company. Research limitations/implications The results provide further confirmation of the fraud triangle theory, i.e. opportunity on the possible causes of the data theft and fraud, supporting prior empirical research and surveys conducted by researchers and global professional firms on fraud. This study, however, was conducted on only one organization with limited participation from employees because of the sensitivity of the nature of the topic. Practical implications This study provided recommendations that can be a reference for companies and regulatory bodies in preventing customer data theft cases, such as regular training and awareness campaigns to the staff, stringent recruitment policies, close monitoring on the accessibility of customer data and continuous use of advanced technology to prevent a data breach. Originality/value This study is original, as it focuses on an organization that operates in the financial services industry, which is one of the most attacked sectors for data theft and cybercrime activity globally. Furthermore, this kind of research is rare in fraud literature, particularly in developing markets such as Malaysia. The findings of this study are inferred from the direct observation of the organizational and employee work environments, activities and behaviors, which are private and confidential and difficult to access by researchers for publication in academic journals.
Saccade latencies during a preferential looking task and objective scoring of grating acuity in children with and without visual impairments

We determined the latencies of orienting responses during a preferential looking task in children with normal vision and in children with visual impairments between 6 and 12 years old, and assessed the feasibility of scoring grating detection in these populations with video-based eye tracking.Children performed a computerized preferential looking test, while a remote eye tracker measured the children's eye movements. The stimuli consisted of a 2 × 2 grid, with three uniform grey fields and one target field consisting of a black-and-white square wave grating. The grating was presented randomly at one of the four locations. The spatial frequencies (1.05, 2.11 and 7.02 cyc/deg) were randomly interleaved, with 10 trials per spatial frequency. Three different methods were used to score the accuracy of the responses: (1) primary saccade ends on target, (1) gaze 50% of the presentation time on target, and (3) a combination of method 1 and 2 (i.e. primary saccade ends on target, and/or gaze 50% of the presentation time on target).The combined scoring method was most reliable to determine whether children could resolve the gratings. Children with visual impairments had significantly lower accuracies than children with normal vision with all three scoring methods. In addition, saccade latencies decreased with age and were significantly longer (62 ± 15 ms) in children with visual impairments.The use of eye tracking to assess grating detection with a preferential looking task in clinical populations provides valuable additional information, including objective detection measures and developmental delays in saccade latencies.
CBA: Contextual Quality Adaptation for Adaptive Bitrate Video Streaming

Recent advances in quality adaptation algorithms leave adaptive bitrate (ABR) streaming architectures at a cross-roads: When determining the sustainable video quality one may either rely on the information gathered at the client vantage point or on server and network assistance. The fundamental problem here is to determine how valuable either information is for the adaptation decision. This problem becomes particularly hard in future Internet settings such as Named Data Networking (NDN) where the notion of a network connection does not exist. In this paper, we provide a fresh view on ABR quality adaptation for QoE maximization, which we formalize as a decision problem under uncertainty, and for which we contribute a sparse Bayesian contextual bandit algorithm denoted CBA. This allows taking high-dimensional streaming context information, including client-measured variables and network assistance, to find online the most valuable information for the quality adaptation. Since sparse Bayesian estimation is computationally expensive, we develop a fast new inference scheme to support online video adaptation. We perform an extensive evaluation of our adaptation algorithm in the particularly challenging setting of NDN, where we use an emulation testbed to demonstrate the efficacy of CBA compared to state-of-the-art algorithms.
Optical Asymmetric Cryptosystem Based on Kronecker Product, Hybrid Phase Mask and Optical Vortex Phase Masks in the Phase Truncated Hybrid Transform Domain

This research work proposes a novel asymmetric scheme by utilizing the hybrid phase truncated fractional Fourier and Gyrator transform with the secure enhancement by adding Kronecker product as a key. In this cryptosystem, the encryption keys constitute Hybrid Phase Mask along with the Optical Vortex Phase Mask and Kronecker product as the other keys. The Hybrid Phase Mask is formed by the combination of a secondary image and random phase mask. The phase truncated parts during encryption process are reserved as decryption keys with the inverse Kronecker product. The proposed method comprises of more obscure keys for upgraded security and to defend different attacks. In support of the technique proposed, the results under the effect of various attacks are presented. The efficiency and robustness of the presented cryptosystem has been examined and demonstrated by simulation in MATLAB (R2014a) with different performance parameters.
Decision Support for Security-Control Identification Using Machine Learning

[Context & Motivation] In many domains such as healthcare and banking, IT systems need to fulfill various requirements related to security. The elaboration of security requirements for a given system is in part guided by the controls envisaged by the applicable security standards and best practices. [Problem] An important difficulty that analysts have to contend with during security requirements elaboration is sifting through a large number of security controls and determining which ones have a bearing on the security requirements for a given system. This challenge is often exacerbated by the scarce security expertise available in most organizations. [Principal ideas/results] In this paper, we develop automated decision support for the identification of security controls that are relevant to a specific system in a particular context. Our approach, which is based on machine learning, leverages historical data from security assessments performed over past systems in order to recommend security controls for a new system. We operationalize and empirically evaluate our approach using real historical data from the banking domain. Our results show that, when one excludes security controls that are rare in the historical data, our approach has an average recall of $$\approx $$ 95% and average precision of $$\approx $$ 67%. [Contribution] The high recall – indicating only a few relevant security controls are missed – combined with the reasonable level of precision – indicating that the effort required to confirm recommendations is not excessive – suggests that our approach is a useful aid to analysts for more efficiently identifying the relevant security controls, and also for decreasing the likelihood that important controls would be overlooked.
Compare less, defer more: scaling value-contexts based whole-program heap analyses

The precision of heap analyses determines the precision of several associated optimizations, and has been a prominent area in compiler research. It has been shown that context-sensitive heap analyses are more precise than the insensitive ones, but their scalability continues to be a cause of concern. Though the value-contexts approach improves the scalability of classical call-string based context-sensitive analyses, it still does not scale well for several popular whole-program heap analyses. In this paper, we propose a three-stage analysis approach that lets us scale complex whole-program value-contexts based heap analyses for large programs, without losing their precision.
FORA: An OWO based framework for finding outliers in web usage mining

Handling outliers are one of the primary concerns of today’s data mining techniques. The concept of outliers, it’s handling, and diagnosis is context specific and varies according to the field of application. The existence of outliers while mining web data is inevitable by virtue of unique characteristic features exhibited by a typical web user. As the output of a regression algorithm is always different from the actual value, it poses a challenge to the knowledge workers and researchers about the notion of an outlier in such cases. In this paper, we propose to develop the concept of an outlier with respect to regression analysis of any Web-based dataset. A framework to find outliers in the output of a regression algorithm is being formulated with the help of Ordered Weighted operators. The underlying idea is to find an error rectification value, ϵ, that will work, in association with the predicted value from the regression model and then help to distinguish an outlier. This will, in addition, also provide a possible range of deviation from the predicted output. A case study on a web dataset is being done to show the usefulness of the proposed approach.
Producing Printability: Articulation Work and Alignment in 3D Printing

Three-dimensional printing is widely celebrated as enabling open design and manufacturing practice. With easy-to-use techniques such as automated modeling, fabrication machines ostensibly help designers turn ideas into fully fledged objects. Prior HCI literature focuses on improving printing through optimization and by developing printer and material capabilities. This paper expands such considerations by asking, how do 3D printing practitioners understand and create “printability?” And how might HCI better support the work that holds together printing workflows and changing ecosystems of materials and techniques? We conducted studies in two sites of open design: a technology firm in Silicon Valley, California and a makerspace in Stockholm, Sweden. Deploying workshops and interviews, we examine how practitioners negotiate the print experience, revealing a contingent process held together by trial and error exploration and careful interventions. These insights point to the value of tools and processes to support articulation work, what Strauss and colleagues have called the acts of fitting together people, tasks, and their ordering to accomplish an overarching project. We show that despite the sought-after efficiencies of such manufacturing, 3D printing entails articulation work, particularly acts of alignment, exposing messy modes of production carried out by a varied cast of practitioners, machines, and materials.
User tailored colorization using automatic scribbles and hierarchical features

Fully automatic image colorization is extensively developed using a large number of examples. However, this kind of methods need further detailed revision if users require fine colorization for delicate objects. This paper proposes a novel scribble-based colorization scheme which automatically generates scribbles and propagates color information from representative pixels within homogeneous superpixels. The scribbles are placed in the regions with minimal entropy to ensure color propagation from representative pixels with high confidence. To avoid color bleeding and under colorization, colors are propagated under a robust similarity metric using a set of hierarchical features, where the low level features consist of quaternion Gabor phases and the high level features are extracted using VGG-19 pre-trained deep model. Experimental results on open dataset demonstrate that the proposed colorization scheme can achieve more consistent results with the users' purposes.
Detecting and Adapting to Users’ Cognitive and Affective State to Develop Intelligent Musical Interfaces

In musical instrument interfaces, such as piano keyboards, the player’s communication channels may be limited by the expressivity and resolution of input devices, the expressivity of relevant body parts, and human attention bottlenecks. In this chapter, we consider intelligent musical interfaces that can measure cognitive or affective states implicitly in real-time to allow musically appropriate adaptations by the system without conscious effort on the part of the user. This chapter focuses on two specific areas in music where the detection of cognitive and affective states has been applied to interaction design for music: musical learning (including learning instruments or pieces of music) and musical creativity (including composing and improvisation). The motivation, theory, and technological basis for work of this kind are discussed. Relevant existing work is considered. The design and evaluation of two systems of this kind for musical learning and musical creativity implemented by the authors is presented and critiqued.
Pixel-Value-Ordering Based Reversible Data Hiding with Adaptive Texture Classification and Modification

Pixel-value-ordering (PVO) is a new technique for the high-fidelity reversible data hiding (RDH). It contains a process of sorting the pixels of a block by their values at first and then embedding into data bits into the maximum or minimum pixels of a block. In this paper, we propose to modify the pixel blocks differently according to how smooth they are, and embed the adequate number of bits into different types of blocks. The pixel blocks are first classified into five types based on the local complexity. The maximum pixels of the most smooth block will be embedded at most four bits, and the less smooth ones are embedded with a lower number of bits. The block classification is dynamically adjusted to achieve the adaptive embedding with the best trade-off between the capacity and the embedding distortion. Experimental results show that the proposed method can give a better performance over the previous PVO-based methods.