Reviewerly/exHarmony huggingface dataset:
Toward Computer Vision Systems That Understand Real-World Assembly Processes

Many applications of computer vision require robust systems that can parse complex structures as they evolve in time. Using a block construction task as a case study, we illustrate the main components involved in building such systems. We evaluate performance at three increasingly-detailed levels of spatial granularity on two multimodal (RGBD + IMU) datasets. On the first, designed to match the assumptions of the model, we report better than 90% accuracy at the finest level of granularity. On the second, designed to test the robustness of our model under adverse, real-world conditions, we report 67% accuracy and 91% precision at the mid-level of granularity. We show that this seemingly simple process presents many opportunities to expand the frontiers of computer vision and action recognition.
Towards Non-Visual Web Search

Speech-based user interfaces and, in particular, voice-activated digital assistants are gaining popularity. Assistants provide their users with an opportunity for hands-free interaction, and present an additional accessibility level for people who are blind. According to prior research, informational searches form a noticeable fraction of user interactions with the assistants. All major commercially available assistants handle factoid questions well by providing an answer that is quick, concise, and to-the-point. However, for complex information seeking intents, when a deeper exploration and multi-turn interaction may be required, the assistants often do not produce the desired results.
Detection of the leading player in handball scenes using Mask R-CNN and STIPS

In team sports scenes, recorded during training and lessons, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the leading player who performs the given handball technique such as a shooting at the goal, catching a ball or dribbling. This is a very challenging task for which, apart from an accurate object detector that is able to deal with cluttered scenes with many objects, partially occluded and with bad illumination, additional information is needed to determine the leading player. Therefore, we propose a leading player detector method combining the Mask R-CNN object detector and spatiotemporal interest points, referred to as MR-CNN+STIPs. The performance of the proposed leading player detector is evaluated on a custom sports video dataset acquired during handball training lessons. The performance of the detector in different conditions will be discussed.
Automation of Knowledge-Based Shared Services and Centers of Expertise

Automation of Knowledge-Based services is the natural evolution of Robotic Process Automation (RPA), fueled by new technologies, often categorized as Cognitive Automation (CA). The purposes of this research are to (1) explore the Expertise Shared Services and Centers of Expertise functions where automation of knowledge-based processes is most suitable and (2) understand the value drivers and primary tactical challenges in adopting a strategy for automating Knowledge-Based expertise services. In addition to a literature review conducted, we conducted in-depth interviews with selected executives and experts. We developed case studies to better understand how Software Bots can be deployed for automating Knowledge-Based Expertise Services in organizations when they transition to automation of knowledge-driven processes. Results of our research indicate that the majority of executives and experts are aware of the need for automating Knowledge-Based Expertise Services but most remain unclear regarding its value or how to invest in adoption of this new trend.
Preliminary results comparing thin-plate splines with finite element methods for modeling brain deformation during neurosurgery using intraoperative ultrasound

Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare two methods for modeling brain deformation, a simple thin plate spline method that interpolates displacements and a more complex finite element method (FEM) that models physical and geometric constraints of the brain and its material properties. Both methods are driven by the same set of displacements at locations surrounding the tumor. These displacements were derived from sets of corresponding matched features that were automatically detected using the SIFT-Rank algorithm. The deformation accuracy was tested using a set of manually identified landmarks. The FEM method requires significantly more preprocessing than the spline method but both methods can be used to model deformations in the operating room in reasonable time frames. Our preliminary results indicate that the FEM deformation model significantly out-performs the spline-based approach for predicting the deformation of manual landmarks. While both methods compensate for brain shift, this work suggests that models that incorporate biophysics and geometric constraints may be more accurate.
Integrating Enhanced Peer Assessment Features in Moodle Learning Management System

Peer assessment has increasingly proven its benefits for the learning process and several educational platforms have been proposed to support it. Rather than developing yet another standalone tool, in this paper we aim to integrate an existing Bayesian Network-based peer evaluation approach in a widely used learning management system, Moodle. This allows to capitalize both on the successful peer assessment model and on the broad range of educational functionalities provided by the learning management system. More specifically, we start from Moodle Workshop plugin, which supports the management of peer assessment sessions, and extend it with several features: i) support for student modeling based on Bayesian Network approach; ii) various metrics regarding the reliability of the computed models; iii) enhanced visualizations and comparisons of learner models and session results. The paper describes the solution proposed for extending the plugin in terms of mechanisms, pedagogical rationale, implementation and functionalities.
Detecting mammographically-occult cancer in women with dense breasts using deep convolutional neural network and Radon cumulative distribution transform

We previously introduced the Radon Cumulative Distribution Transform (RCDT) as a novel image transformation to highlight the subtle difference between left and right mammograms to detect mammographically-occult (MO) cancer from women with dense breasts and negative screening mammograms. This study developed deep convolutional neural networks (CNN) as classifiers for estimating the probability of having MO cancer. We acquired screening mammograms of 333 women (97 unilateral MO cancer) with dense breasts and at least two consecutive mammograms and used the immediate prior mammograms, which radiologists interpreted as negative. We divided our dataset into a training, a validation, and a test set with ratios of 0.72:0.08:0.2. We applied RCDT on the left and right mammograms of each view. We applied inverse radon transform to represent the resulting RCDT images in the image domain. We then fine-tuned a VGG16 network pretrained on ImageNet using the resulting images per each view. Using the same images, we also developed a traditional classifier using handcrafted features per each view. The CNNs achieved areas under the receiver operating characteristic (AUC) curve of 0.74 and 0.69 for CC view and MLO view, respectively. The traditional classifiers from handcrafted features achieved AUCs of 0.5 and 0.64 for CC view and MLO view, respectively. We averaged the scores from the top three classifiers and achieved an AUC of 0.81 on the test set. In conclusion, we showed that inverse radon transformed RCDT images hold information to detect MO cancer and deep CNNs could learn such information.
Visual evidence for interpreting diagnostic decision of deep neural network in computer-aided diagnosis

Recent studies have reported that deep learning techniques could achieve high performance in medical image analysis such as computer-aided diagnosis (CADx). However, there is a limitation in interpreting the diagnostic decisions of deep learning due to the black-box nature. To increase confidence in the diagnostic decisions of deep learning, it is necessary to develop a deep neural network with the interpretable structure which could provide a reasonable explanation of diagnostic decisions. In this study, a novel deep neural network has been devised to provide visual evidence of the diagnostic decisions of CADx. The proposed deep network is designed to include a visual interpreter which could provide important areas as the visual evidence of the diagnostic decision in the deep neural network. Based on the observation that the radiologists usually make a diagnostic decision based on the lesion characteristics (the margin and the shape of masses), the visual interpreter provides visual evidence related with the margin and the shape, respectively. To verify the effectiveness of the proposed method, experiments were conducted on mammogram datasets. Experimental results show that the proposed method could provide more important areas as the visual evidence compared with the conventional visualization method. These results imply that the proposed visual interpretation method could be a promising approach to overcome the current limitation of the deep learning for CADx.
An Overview of Co-Clustering via Matrix Factorization

Co-clustering algorithms have been widely used for text clustering and gene expression through matrix factorization. In recent years, diverse co-clustering algorithms which group data points and features synchronously have shown their advantages over traditional one-side clustering. In order to solve the co-clustering problems, most existing methods relaxed constraints via matrix factorization. In this paper, we provide a detailed understanding of six co-clustering algorithms with different performance and robustness. We conduct comprehensive experiments in eight real-world datasets to compare and evaluate these co-clustering methods based on four evaluation metrics including clustering accuracy, normalized mutual information, adjusted rand index, and purity. Our findings demonstrate the strengths and weaknesses of these methods and provide insights to motivate further exploration of co-clustering methods and matrix factorization.
Access Control Visualization Using Triangular Matrices

The paper proposes an approach for visualization of access control systems based on triangular matrices. The approach is used for visualization of access control security model that based in methods of RBAC and Take-Grant. In comparison with regular access matrices, the sparseness of triangular matrices is less, and the approach is able to visualize nesting at the level of rights. The paper outlines a new triangular visualization model, its interpretation, management methods, and the results of experiments for visualization of an access control system in an IT company.
Cyclic Parameter Sharing for Privacy-Preserving Distributed Deep Learning Platforms

As datasets have been increased in sizes, parallelization strategies (i.e., model parallelization and data parallelization) are actively used for distributed deep learning training. In particular, data parallelization which partitions the input samples is commonly used to distributely train deep neural networks. However, if the data are sensitive in terms of security/privacy and the data have constraints on sharing among multiple computing nodes, it is hard to apply these conventional strategies. In this study, we propose a method that sequentially sharing models in cyclic order during training procedures in order to train deep neural networks while preserving the privacy of datasets. In addition, we discuss practical considerations that can be improved in two-folds, i.e., (i) dataset imbalance problem and (ii) efficient usage of computational resources of idle agents.
Workflow difference detection based on basis paths

Detecting workflow difference is meaningful for many applications, e.g., in-vehicle system testing. However, current workflow difference detection methods stress on structural differences while ignoring the behavioral aspects of the workflow models. Due to the large number of possible behaviors for a workflow model, it is challenging to detect the behavioral difference. To address this problem, this paper introduces the idea of basis path testing from the software testing field, and presents an efficient method for detecting the workflow difference in terms of their behaviors. First, we compute the cyclomatic complexity according to the number of nodes and arcs in the workflow model. Then, based on the cyclomatic complexity we design an algorithm that can retrieve the basis paths from a tree structure converted from the workflow model. At last, the Kuhn–Munkres (KM) algorithm is used to map the independent paths from two workflow models and generate the optimal mapping that the overall distance is the minimum. The experimental evaluation shows that our proposed method has been proved feasible in terms of both efficiency and effectiveness.
DOORchain: Deep Ontology-Based Operation Research to Detect Malicious Smart Contracts

Blockchains have become of great vogue in different fields after the introduction of Bitcoin. There are some inherent problems that need to be solved. One of these problems is to ensure that secured transactions in blockchain are checked if they are malicious or not. This paper proposes DOORchain that combines three powerful approaches of detecting intrusions and maliciousness. They are Deep learning, Ontology, and Operation Research. This uses the advantage of constraints from operation research to formalize and detect network maliciousness, and ontology to detect behavioral maliciousness in particular. Then it feeds this formalization to deep learning in order to check if the transactions in blockchain are malicious or not. After applying the proposed DOORChain, the final results affirm that accuracy and recall are enhanced with a slight inescapable trade-off in precision.
On Cyber Crimes and Cyber Security

The world has become more advanced in communication, especially after the invention of the internet. A key issue facing today's society is the increase in cybercrime or e-crimes (electronic crimes), another term for cybercrime. Thus, e-crimes pose threats to nations, organizations, and individuals across the globe. It has become widespread in many parts of the world, and millions of people are victims of e-crimes. Given the serious nature of e-crimes, its global nature, and implications, it is clear that there is a crucial need for a common understanding of such criminal activity internationally to deal with it effectively. This chapter covers the definitions, types, and intrusions of e-crimes. It has also focused on the laws against e-crimes in different countries. Cybersecurity and searching methods to get secured are also part of the study.
Assessing the quantum-resistant cryptographic agility of routing and switching IT network infrastructure in a large-size financial organization

This paper provides exploratory research by determining the impact of quantum computing threat on modern routing and switching infrastructure components of IT network infrastructure in a large-size financial organization. We determine whether common routing and switching IT network infrastructure, including its hardware and software components, have enough cryptographic agility to accommodate the change of cryptographic algorithms to the ones that do not exhibit vulnerability to quantum computing and to the ones that are compliant with National Security Agency (NSA) Suite B set of protocols. We pinpoint upstream or downstream impacts of a change in the encryption algorithms across various IT network infrastructure components in terms of effort required to accomplish this transition. This study is among the first studies that investigate quantum-resistance cryptographic from the hardware perspectives of routing and switching technologies using diffusion of innovation theory. The study integrates enterprise governance to meet the challenges presented by quantum-computing with a focus on cryptographic agility. We offer an enterprise architecture framework for assessing the dependencies, costs, and benefits of IT infrastructure migration plan to meet the future challenges of quantum-resistant cryptographic. The analysis in this research can be used by IT managers to pre-plan for an upgrade of routing and switching infrastructure, assist in estimating the efforts required to perform these upgrades, and assist in selecting a vendor of routing and switching equipment from the perspective of cryptographic agility. With today's supercomputers, a computational task involved in breaking modern asymmetric cryptographic algorithms would be infeasible due to the required computational complexity. However, with progress in the development of quantum computing technology, firms are facing an increasing risk of potential security threats to existing encrypted data and secured transmission channels as the processing power of quantum computers continue to increase.
An algorithm for Morlet wavelet transform based on generalized discrete Fourier transform

Continuous wavelet transform (CWT) is a linear convolution of signal and wavelet function for a fixed scale. This paper studies the algorithm of CWT with Morlet wavelet as mother wavelet by using nonzero-padded linear convolution. The time domain filter, which is a non-causal filter, is the sample of wavelet function. By making generalized discrete Fourier transform (GDFT) and inverse transform for this filter, we can get a geometrically weighted periodic extension of the filter when evaluated outside its original support. From this extension of the time domain filter, we can get a causal filter. In this paper, GDFT-based algorithm for CWT, which has a more concise form than that of linear convolution proposed by Jorge Martinez, is constructed by using this causal filter. The analytic expression of the GDFT of this filter, which is essential for GDFT-based algorithm for CWT, is deduced in this paper. The numerical experiments show that the calculation results of GDFT-based algorithm are stable and reliable; the running speed of GDFT-based algorithm is faster than that of the other two algorithms studied in our previous work.
Robust filtering with randomly delayed measurements and its application to ballistic target tracking in boost phase

Motivated by the performance degradation of High-degree Cubature Kalman Filtering (HCKF) in coping with randomly delayed measurements in non-Gaussian system, a novel robust filtering named as Randomly Delayed High-degree Cubature Huber-based Filtering (RD-HCHF) is proposed in this paper. At first, the system model is re-written by the Bernoulli random variables to describe the randomly delayed measurements. Then, the Randomly Delayed HCKF (RD-HCKF) is derived based on the rewritten system model and 5th-degree spherical-radial cubature (SRC) rule. In order to enhance the robustness of the filter in glint noise case, the measurement update of RD-HCKF is modified by the Huber technique, which is essentially an M-estimator. Therefore, the proposed RD-HCHF is not only robust to the randomly delayed measurements, but also robust to the glint noise. In addition, the RD-HCHF is applied to the ballistic target tracking in boost phase, and the Gravity-Turn (GT) model is taken as the target model. Finally, the simulation is conducted and the tracking performance of RD-HCHF is compared with that of HCKF, RD-HCKF and High-degree Cubature Huber-based Filtering (HCHF). The results clearly confirm the superiority of the RD-HCHF.
Traffic sign detection method based on Faster R-CNN

Traffic sign detection is the pivotal technology of the traffic sign recognition system. In this article, a traffic sign detection method comes up based on Faster R-CNN deep learning framework. In this method, a convolution neural network is devoted to extract traffic sign image features automatically, and the extracted convolution feature map is sent into a Region Proposal Network (RPN) for foreground objects filtration and regression of bounding boxes. Then the proposed regions are mapped to the feature map, and the fixed-size proposal boxes via Region of Interest pooling layer (RoI). After that, we use the classification network to perform specific classification tasks and further compute the bounding box regression. The experiments performs on the German Traffic Sign Detection Benchmark (GTSDB) and the experimental results show that the method has effectiveness and robustness to different light, block, and motion.
DecMac: A Deep Context Model for High Efficiency Arithmetic Coding

Conventional lossless compression techniques that use look up table method tend to be inefficient. We propose a deep context model, named DecMac, which combines a three-layer LSTM with adaptive arithmetic coding for lossless compression. In order to capture much more context information for better predicting, we introduce a cycle connection to preserve the end of hidden states and reuse it as the initial states for the next batch. We evaluate our method on the text compression task, resulting in averaged 25% compressed size reduction over the state of the art PAQ, and averaged 45% reduction over GZIP and ZIP.
Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition

In this modern era, the use of computer technology and computing devices play significant role in every day human activities. From the disabled people perspective, there is huge demand to improve Human–Computer Interaction (HCI), to overcome their difficulty in using the standard interactive devices. Basically, HCI provides a way for humans to interact with a computer using a keyboard, a mouse, and other input devices in real-time. This paper proposes a novel assistive pointer device called Frontier Point method (FPM), which is based on a hand movement recognition technique. The proposed hand movement recognition technique primarily focuses on the direction of hand movement for dynamic recognition in real-time using least square fitting and virtual frame techniques. Next based on boundary values, such that if the hand crosses a boundary value of a given quadrant, then a SENDKEY stroke is generated that corresponds to that range. This method is implemented with the help of a depth sensor camera called Kinect. Kinect takes the RGB data and depth data of the human skeleton and generates coordinate information corresponding to specific body joints. Experiments were conducted in which different users were evaluated for their ability to navigate a PowerPoint presentation multiple times. Collectively, an average recognition time of 2.386 s was calculated with an average recognition rate of 97.37%.
Using Minimum Local Distortion to Hide Decision Tree Rules

The sharing of data among organizations has become an increasingly common procedure in several areas like banking, electronic commerce, advertising, marketing, health, and insurance sectors. However, any organization will most likely try to keep some patterns hidden once it shares its datasets with others. This article focuses on preserving the privacy of sensitive patterns when inducing decision trees. We propose a heuristic approach that can be used to hide a certain rule which can be inferred from the derivation of a binary decision tree. This hiding method is preferred over other heuristic solutions like output perturbation or cryptographic techniques-which limit the usability of the data-since the raw data itself is readily available for public use. This method can be used to hide decision tree rules with a minimum impact on all other rules derived.