Reviewerly/exHarmony huggingface dataset:
Insider Threats

Insider threats refer to threats posed by individuals who intentionally or unintentionally destroy, exfiltrate, or leak sensitive information, or expose their organization to outside attacks. Surveys of organizations in government and industry consistently show that threats posed by insiders rival those posed by hackers, and that insider attacks are even more costly. Emerging U.S. government guidelines and policies for establishing insider threat programs tend to specify only minimum standards for insider threat monitoring, analysis, and mitigation programs. Arguably, one of the most serious challenges is to identify and integrate behavioral (sociotechnical) indicators of insider threat r isk in addition to cyber/technical indicators. That is, in focusing on data that are most readily obtained, insider threat programs most often miss the human side of the problem. This talk briefly describes research aiming to catalog human as well as technical factors associated with insider threat risk and summarizes several recent studies that seek to inform the development of more comprehensive, proactive approaches to insider threat assessment.
A versatile approach for constructing a domain knowledge graph for culture

ABSTRACT [Question] The construction of cultural knowledge base in digital library is in progress with the development of information technology. However, traditional cultural knowledge bases are facing the problems of high participation of manual operation and monotonous data source. This paper combines knowledge graph, a frontier technology of artificial intelligence, to explore a new method of building high‐quality, automated cultural knowledge base. [Method] Using the experimental method, we selected the knowledge of the Hundred Schools of Thought in ancient China. Because these knowledges are clearly interrelated, it is suitable to construct the knowledge graph of them. The knowledge graph can provide experience for the construction of other cultural knowledge bases. [Conclusion] At present, this paper has preliminarily completed the experimental design of the knowledge graph of the Hundred Schools of Thought, and provided the flowchart of the construction of cultural domain knowledge graph.
Video Face Clustering With Self-Supervised Representation Learning

Characters are a key component of understanding the story conveyed in TV series and movies. With the rise of advanced deep face models, identifying face images may seem like a solved problem. However, as face detectors get better, clustering and identification need to be revisited to address increasing diversity in facial appearance. In this paper, we propose unsupervised methods for feature refinement with application to video face clustering. Our emphasis is on distilling the essential information, identity , from the representations obtained using deep pre-trained face networks. We propose a self-supervised Siamese network that can be trained without the need for video/track based supervision, that can also be applied to image collections. We evaluate our methods on three video face clustering datasets. Thorough experiments including generalization studies show that our methods outperform current state-of-the-art methods on all datasets. The datasets and code are available at https://github.com/vivoutlaw/SSIAM .
Eye Movements and Human-Computer Interaction

Gaze provides an attractive input channel for human-computer interaction because of its capability to convey the focus of interest. Gaze input allows people with severe disabilities to communicate with eyes alone. The advances in eye tracking technology and its reduced cost make it an increasingly interesting option to be added to the conventional modalities in every day applications. For example, gaze-aware games can enhance the gaming experience by providing timely effects at the right location, knowing exactly where the player is focusing at each moment. However, using gaze both as a viewing organ as well as a control method poses some challenges. In this chapter, we will give an introduction to using gaze as an input method. We will show how to use gaze as an explicit control method and how to exploit it subtly in the background as an additional information channel. We will summarize research on the application of different types of eye movements in interaction and present research-based design guidelines for coping with typical challenges. We will also discuss the role of gaze in multimodal, pervasive and mobile interfaces and contemplate with ideas for future developments.
Observing LOD Using Equivalent Set Graphs: It Is Mostly Flat and Sparsely Linked

This paper presents an empirical study aiming at understanding the modeling style and the overall semantic structure of Linked Open Data. We observe how classes, properties and individuals are used in practice. We also investigate how hierarchies of concepts are structured, and how much they are linked. In addition to discussing the results, this paper contributes (i) a conceptual framework, including a set of metrics, which generalises over the observable constructs; (ii) an open source implementation that facilitates its application to other Linked Data knowledge graphs.
Automatic vehicle detection using spatial time frame and object based classification

This paper presents an automated vehicle identification and classification method from traffic videos. The proposed method unlike other traditional methods combines the multiple time spatial frames to detect moving objects. These moving objects are the potential vehicles however there may be some o ther moving objects also. Therefore to further improve the accuracy of the proposed method, the moving objects are classified using object oriented classification scheme. The identification of vehicles from traffic videos plays an important role in Intelligent Transport systems (ITS). A virtual line is placed on each frame such that the objects crossing this line are the desired moving objects. The object based classifier makes use of fuzzy rules based on features like area, perimeter, and elongation and so on. These fuzzy rules are used to classify them into vehicle and non-vehicle classes. The second level of classification further classifies the vehicles into two wheeler, four wheeler and six wheeler vehicles. The method can be appropriately used for traffic surveillance as it also computes the speed of vehicles using the time spatial frames. The proposed method is applied on traffic videos of multiple time lengths. A comparative study of the proposed method with the existing methods reveals that the proposed work has higher accuracy. The motion detection, vehicle classification and speed of computation make this method best suited for many ITS applications like traffic surveillance and other similar applications.
Multimodal Classification of Urban Micro-Events

In this paper we seek methods to effectively detect urban micro- events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these events are difficult to identify in most data sources. However, by using citizen sensing to gather data, detecting them becomes feasible. The data gathered by citizen sensing is often multimodal and, as a consequence, the in- formation required to detect urban micro-events is distributed over multiple modalities. This makes it essential to have a classifier ca- pable of combining them. In this paper we explore several methods of creating such a classifier, including early, late and hybrid fusion as well as representation learning using multimodal graphs. We evaluate performance in terms of accurate classification of urban micro-events on a real world dataset obtained from a live citizen re- porting system. We show that a multimodal approach yields higher performance than unimodal alternatives. Furthermore, we demon- strate that our hybrid combination of early and late fusion with multimodal embeddings outperforms our other fusion methods.
Learning Local Similarity with Spatial Relations for Object Retrieval

Many state-of-the-art object retrieval algorithms aggregate activations of convolutional neural networks into a holistic compact feature, and utilize global similarity for an efficient nearest neighbor search. However, holistic features are often insufficient for representing small objects of interest in gallery images, and global similarity drops most of the spatial relations in the images. In this paper, we propose an end-to-end local similarity learning framework to tackle these problems. By applying a correlation layer to the locally aggregated features, we compute a local similarity that can not only handle small objects, but also capture spatial relations between the query and gallery images. We further reduce the memory and storage footprints of our framework by quantizing local features. Our model can be trained using only synthetic data, and achieve competitive performance. Extensive experiments on challenging benchmarks demonstrate that our local similarity learning framework outperforms previous global similarity based methods.
An Intelligent System for the Classification of Lung Cancer Based on Deep Learning Strategy

This study intends to develop an easy-to-use software using deep learning algorithms classification of benign and malignant lung nodules with high accuracy. In modelling phase Keras library and convolutional neural networks model are used. To construct user interface, shiny web framework is utilized. Experimental findings showed that the convolutional neural network model has high classification performance metrics of benign and malignant lung nodules on computed tomography images. As future research, it will be aimed to develop new software for medical images of other types of definitely diagnosed diseases by using appropriate deep learning models. The developed software is available at: biostatapps.inonu.edu.tr/AKSY/.
Completeness of Abstract Domains for String Analysis of JavaScript Programs

Completeness in abstract interpretation is a well-known property, which ensures that the abstract framework does not lose information during the abstraction process, with respect to the property of interest. Completeness has been never taken into account for existing string abstract domains, due to the fact that it is difficult to prove it formally. However, the effort is fully justified when dealing with string analysis, which is a key issue to guarantee security properties in many software systems, in particular for JavaScript programs where poorly managed string manipulating code often leads to significant security flaws. In this paper, we address completeness for the main JavaScript-specific string abstract domains, we provide suitable refinements of them, and we discuss the benefits of guaranteeing completeness in the context of abstract-interpretation based string analysis of dynamic languages.
No-Reference Quality Assessment of Stereoscopic Videos With Inter-Frame Cross on a Content-Rich Database

With the wide application of stereoscopic video technology, the quality of stereoscopic video has attracted people's attention. Objective stereoscopic video quality assessment (SVQA) is highly challenging, but essential, particularly the no-reference (NR) SVQA method, where reference information is not needed and a large number of samples are required for training and testing sets. However, as far as we know, there are only a few samples in the established stereo video database, which is unsuitable for NR quality assessment and seriously hampers the development of NR-SVQA method. For these difficulties that we encountered, we carry out a comprehensive subjective evaluation of stereoscopic video quality in our newly established TJU-SVQA databases that contain various contents, mixed resolution coding and symmetrically/asymmetrically distorted stereoscopic videos. Furthermore, we propose a new inter-frame cross map to predict the objective quality scores. We compare and analyze the performance of several state-of-the-art 2D and 3D quality evaluation methods on our new databases. The experimental results on our established databases and a public database demonstrate that the proposed method can robustly predict the quality of stereoscopic videos.
Local List Recovery of High-Rate Tensor Codes and Applications

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 18 December 2017Accepted: 10 May 2019Published online: 22 October 2019Keywordslocal decoding, list decoding, list recvoeryAMS Subject Headings94B35Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
MP3 steganalysis based on joint point-wise and block-wise correlations

With the growing attention on multimedia security, various MP3 steganographic and steganalytic algorithms have been proposed increasingly. However, the existing MP3 steganalysis is lack of good universality and detection performance. To address this problem, we devise an effective MP3 steganalytic algorithm based on joint point-wise and block-wise correlations of quantified modified discrete cosine transfer coefficients matrix. On the one hand, a universal rich high pass filtering module for MP3 steganalysis is deployed to boost the sensitiveness of the algorithm to the subtle signal brought by the data hiding. On the other hand, based on the principle of MP3 encoding and the characteristics of MP3 steganography, multi-scale correlations measure module is introduced, which is used to measure the changes of point-wise, 2 × 2 block-wise, and 4 × 4 block-wise correlations due to steganography separately. Additionally, customized feature optimization, including truncated threshold selection and high pass filters measure, is performed based on the properties of MP3 steganography. Experimental results illustrate that our proposed algorithm can be applied to various MP3 steganographic algorithms, bitrates, duration, and relative payloads. Detection accuracies are promoted by more than 20% averagely compared with state-of-the-art methods.
GAN Flexible Lmser for Super-resolution

Existing single image super-resolution (SISR) methods usually focus on Low-Resolution (LR) images which are artificially generated from High-Resolution (HR) images by a down-sampling process, but are not robust for unmatched training set and testing set. This paper proposes a GAN Flexible Lmser (GFLmser) network that bidirectionally learns the High-to-Low (H2L) process that degrades HR images to LR images and the Low-to-High (L2H) process that recovers the LR images back to HR images. The two directions share the same architecture, added with the gated skip connections from the H2L-net to the L2H-net in order to enhance information transferring for super-resolution. In comparison with several related state-of-the-art methods, experiments demonstrate that not only GFLmser is the most robust method on images of unmatched training set and testing set, but also its performance on real-world face LR images is best in PSNR and reasonably good in FID.
The Case for Haptic Props: Shape, Weight and Vibro-tactile Feedback

The use of haptic props in a virtual environment setting is purported to improve both user immersion and task performance. While the efficacy of various forms of haptics has been tested through user experiments, this is not the case for hand-held tool props, an important class of input device with both gaming and non-gaming applications. From a cost and complexity of implementation perspective it is also worth investigating the relative benefits of the different types of passive and active haptics that can be incorporated into such props.
Convolutional-recurrent Neural Network for Age and Gender Prediction from Speech

This paper deals with age prediction and gender distinction for Interactive Voice Response system. The proposed algorithm uses a convolutional-recurrent neural network. A public dataset is used for training and testing the proposed algorithms. Gender is recognized with average error below 1.55%, while the probability of speakers' age to be correctly classified into three age groups is higher than 80% on average. Considering the information in the datasets, these results are very promising in order to justify it applicability in the considered systems. The implemented solution does not depend on the length of the input signal.
Towards Smart Sports Exercises

In this paper we introduce the first results from the multidisciplinary Smart Sports Exercises project. Building on a thorough background and the aims of the project, we present our approach and discuss the outcomes of a context analysis and the first design iterations. We propose a model spanning the known 'Augmenting to Transforming' sports dimension, and add a dimension spanning 'Skill-specific to Integrative' exercises, and finish with the description and rationale for six interactive concepts that will form the basis for the next phase of the project.
Predicting Traffic Accidents with Event Recorder Data

Large amounts of data on accidents are continually being collected by dashboard cameras (dashcams). In this paper, we address the problem of predicting the occurrence of accidents: Our goal is to predict when accidents will occur based on stored dashcam data and analysis of live video streams. We propose a survival analysis model for predicting the event occurrence time. The occurrence of accidents involves changes in the situation of own car and surroundings. Therefore, the hazard function of the proposed model is modeled by a convolutional recurrent neural network that can capture it from high-dimensional time-series information, i.e., video. Another characteristic of our model is its incorporation of location data because how likely the events are to occur strongly depends on location. Our model can predict accidents by simultaneously considering video and location data. Experiments on real-world event recorder data show that our model can more accurately predict accident occurrences than baseline models.
Comparison of Person Tracking Algorithms Using Overhead View Implemented in OpenCV

As computer based technologies are growing rapidly, new problems are arising, which need serious and urgent attentions. Person tracking is one of the typical computer vision problem which is the center of interest for researchers working on surveillance systems (industries, shopping malls, educational institutions and hospitals etc. In this research work, a top view camera has been installed with a wide angle lens able to cover a wide area and more information for surveillance and visual monitoring purposes. Some of the common issues that are addressed in this research work are occlusion, sudden change in movement, tracking standstill body, abrupt change in direction, varying lightening conditions and differentiating a person from other objects. In this paper, different tracking algorithms are compared on a newly developed dataset using OpenCV. These algorithms are pre-implemented in the popular OpenCV library. The results and efficiency of these algorithms on a new data set are also discussed. All these algorithms give different results on overhead and frontal view video sequences.
Normalization of unconstrained handwritten words in terms of Slope and Slant Correction

In offline handwritten text slope (or skew) and slant are inevitably introduced, but to varying degrees depending on several factors, such as the writing style, speed and mood of the writers. Therefore slope and slant detection in offline handwritten text and their subsequent correction have become the critical pre-processing steps for document analysis and retrieval systems to neutralize the variability of writing styles and to improve the performance of word and character recognition systems. In this paper, we present new methods that use two novel core-region detection techniques to estimate both the slope and slant angles of offline handwritten word images. Also we prepare multilingual datasets comprised of both real and synthetic handwritten word images, along with ground truth information related to the slope and slant of each word, to address the lack of standard datasets for this research. These datasets of Bangla, Devanagari and English words along with the code are made publicly available. Extensive experimental results prove the efficacy of the proposed methods compared to contemporary state-of-the-art methods. Moreover, the methods are robust, efficient, and easily implementable. (The code and datasets are available at: https://scholarworks.boisestate.edu/saipl/)
A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis

Breast cancer is a current problem that causes the death of many women. In this work, we test meta-heuristics applied to the segmentation of mammographic images. Traditionally, the application of these algorithms has a direct relationship with optimization problems; however, in this study, its implementation is oriented to the segmentation of mammograms using the Dunn index as an optimization function, and the grey levels to represent each individual. The update of grey levels during the process results in the maximization of the Dunn’s index function; the higher the index, the better the segmentation will be. The results showed a lower error rate using these meta-heuristics for segmentation compared to a well-adopted classical approach known as the Otsu method.
The Implementation of a Power Efficient BCNN-Based Object Detection Acceleration on a Xilinx FPGA-SoC

This paper focuses on the power efficient design on the FPGA SoC for the object detection system based on Binary Convolutional Neural Network (BCNN). Especially, for the small IoT devices, such as an intelligent dash-cam, computer vision system installed on an unmanned aerial vehicle, the power consumption could be a significant factor of the performance and scalability. However, the optimized FPGA design has limitations to reduce the overall power consumption amount. We focus on the design of the FPGA Accelerator as well as the effective design of the peripherals including CPU. In our proposed FPGA-SoC design, it supports not only FPGA but also CPU and the peripheral component can be supported by additional virtual memory system for reducing the processing time. Overall customization including customized BCNN, virtual memories for CPU and FPGA part allows our testbed to achieve low power consumption without speed degradation. Our testbed is based on customized YOLOv2 which consists of applied binary and half precision convolution, and pipeline-based architecture with accelerated hardware design on the target device. The target device used in this paper is the Xilinx ZYNQ-SoC based PYNQ Z-1 board. Our proposed system achieves 15.15 frames per second (FPS) and 1.45 watts of power dissipation. Our result shows that our design technique is effective for real-time object detection and low power system.