Reviewerly/exHarmony huggingface dataset:
Multi-Party Computation for Modular Exponentiation Based on Replicated Secret Sharing

In recent years, multi-party computation (MPC) frameworks based on replicated secret sharing schemes (RSSS) have attracted the attention as a method to achieve high efficiency among known MPCs. However, the RSSS-based MPCs are still inefficient for several heavy computations like algebraic operations, as they require a large amount and number of communication proportional to the number of multiplications in the operations (which is not the case with other secret sharing-based MPCs). In this paper, we propose RSSS-based three-party computation protocols for modular exponentiation, which is one of the most popular algebraic operations, on the case where the base is public and the exponent is private. Our proposed schemes are simple and efficient in both of the asymptotic and practical sense. On the asymptotic efficiency, the proposed schemes require O(n)-bit communication and O(1) rounds,where n is the secret-value size, in the best setting, whereas the previous scheme requires O(n2)-bit communication and O(n) rounds. On the practical efficiency, we show the performance of our protocol by experiments on the scenario for distributed signatures, which is useful for secure key management on the distributed environment (e.g., distributed ledgers). As one of the cases, our implementation performs a modular exponentiation on a 3,072-bit discrete-log group and 256-bit exponent with roughly 300ms, which is an acceptable parameter for 128-bit security, even in the WAN setting.
Temporal Interframe Pattern Analysis for Static and Dynamic Hand Gesture Recognition

Hand gesture, a common non-verbal language, is being studied for Human Computer Interaction. Hand gestures can be categorized as static hand gestures and dynamic hand gestures. In recent years, effective approaches have been applied to hand gesture recognition. However, almost all of the previous works only focus on either of the two categories instead of both, and none of them has used the temporal information on the recognition of static hand gestures.In this paper, we propose a three-level scheme to utilize the temporal interframe pattern on the recognition of both static and dynamic hand gestures. The first classifier assigns a class label to each frame of the video sequence that contains both static and dynamic hand gestures. The second classifier uses the temporal pattern of the class labels to correct the errors of the first classifier and to distinguish between static and dynamic hand gestures. The third classifier is then used to recognize dynamic hand gestures. We believe that we are the first to propose such a strategy. The extensive experiments showed promising performance and demonstrated the feasibility of using temporal interframe pattern to recognize dynamic hand gestures and to correct the errors in static hand gesture recognition.
Usability Testing of Data Entry Patterns Implemented According to Material Design Guidelines for the Web

Analysis of data entry design patterns in web application was presented in the paper. 22 design patterns were tested with different sets of input data. In order to facilitate the process of conducting usability tests, a web application was developed according to the Google’s Material Design principles. It contained all tested data entry design patters. Due to automating test results collection it was possible to perform the test with a larger number users. Usability tests were carried out with a group of 49 people. The group of participants consisted of people in different age, having different education, occupation and experience in using web applications. Based on the analysis of results a set of recommendations was formulated on the usage of selected data entry design patterns in web applications.
Non-Local Operational Anisotropic Diffusion Filter

High-frequency noise is present in several modalities of medical images. It originates from the acquisition process, scanner configurations, the scanned body, or to other external factors. This way, prospective filters are an important tool to improve image quality. In this paper, we propose a non-local weighted operational anisotropic diffusion filter and evaluate its effect on magnetic resonance images and on kV/CBCT radiotherapy images. We also provide a detailed analysis of non-local parameter settings. Results show that the new filter enhances previous local implementations and has potential application in radiotherapy treatments.
DimmStore

Memory can consume a substantial amount of power in database servers, yet memory power has received considerably less attention than CPU power. Memory power consumption is also highly non-proportional. Thus, memory power becomes even more significant in the common case in which a database server is either not completely busy or not completely full. In this paper, we study the application of two memory power optimization techniques - rank-aware allocation and rate-based layout - to database systems. By concentrating memory load, rather than spreading it out evenly, these techniques create and exploit memory idleness to achieve power savings. We have implemented these techniques in a prototype database system called DimmStore. DimmStore is part of a memory power testbed which includes customized hardware with direct power measurement capabilities, allowing us to measure the techniques' effectiveness. We use the testbed to empirically characterize the power saving opportunities provided by these techniques, as well as their performance impact, under YCSB and TPC-C workloads. Under simple YCSB workloads, power savings ranged up to 50%, depending on load and space utilization, with little performance impact. Savings were smaller, but still significant, for TPC-C, which has more complex data locality characteristics.
Industry-Driven Visual Analytics for Understanding Financial Timeseries Models

Timeseries models are used extensively in financial services, for example, to quantify risk and predict economics. However, analysts also need to comprehend the structure and behavior of these models to better understand and explain results. We present a methodology, derived from extensive industry experience, to aid explanation through integrated interactive visualizations that reveal model structure and behavior of constituent timeseries factors, thereby increasing understanding of the model, the domain and the sensitivities. Expert feedback indicates alignment with mental models.
Visual Surveillance of Human Activities via Gradient-Based Coverage Control on Matrix Manifolds

In this article, we address visual surveillance of human activities for a network of cameras with controllable orientations based on gradient-based coverage control techniques. We first formulate the problem as an optimization problem on the matrix manifold SO(3) and then derive the gradient for the cost function using a density function defined on the image plane of each camera. We then develop a real-time density estimation algorithm using computer vision techniques including a real-time pedestrian detection algorithm and examine its real-time feasibility through simulation. We finally demonstrate the complete algorithm including the gradient descent algorithm, the density estimation algorithm, image acquisition/processing and physical motion on a simulator built on a 3-D animation software and an experimental testbed.
Position-Free Hand Gesture Recognition Using Single Shot MultiBox Detector Based Neural Network

Hand Gesture Recognition plays an important role in noncontact human computer interaction. The traditional Recognition based on vision has specific requirements for the target location and equipment. We employ monocular camera to build a system which can detect the hand in any position of the screen and recognize the hand gesture. Single Shot MultiBox Detector (SSD) locates the hand position and Convolutional Neural Network (CNN) makes classification of the hand gestures. The gesture recognition performance of the proposed technique is 95.05% on Massey University gesture dataset, using CNN classifier.
Boosting implicit discourse relation recognition with connective-based word embeddings

Implicit discourse relation recognition is the performance bottleneck of discourse structure analysis. To alleviate the shortage of training data, previous methods usually use explicit discourse data, which are naturally labeled by connectives, as additional training data. However, it is often difficult for them to integrate large amounts of explicit discourse data because of the noise problem. In this paper, we propose a simple and effective method to leverage massive explicit discourse data. Specifically, we learn connective-based word embeddings (CBWE) by performing connective classification on explicit discourse data. The learned CBWE is capable of capturing discourse relationships between words, and can be used as pre-trained word embeddings for implicit discourse relation recognition. On both the English PDTB and Chinese CDTB data sets, using CBWE achieves significant improvements over baselines with general word embeddings, and better performance than baselines integrating explicit discourse data. By combining CBWE with a strong baseline, we achieve the state-of-the-art performance.
Fast Super-Resolution in MRI Images Using Phase Stretch Transform, Anchored Point Regression and Zero-Data Learning

Medical imaging is fundamentally challenging due to absorption and scattering in tissues and by the need to minimize illumination of the patient with harmful radiation. Common problems are low spatial resolution, limited dynamic range and low contrast. These predicaments have fueled interest in enhancing medical images using digital post processing. In this paper, we propose and demonstrate an algorithm for real-time inference that is suitable for edge computing. Our locally adaptive learned filtering technique named Phase Stretch Anchored Regression (PhSAR) combines the Phase Stretch Transform for local features extraction in visually impaired images with clustered anchored points to represent image feature space and fast regression based learning. In contrast with the recent widely-used deep neural network for image super-resolution, our algorithm achieves significantly faster inference and less hallucination on image details and is interpretable. Tests on brain MRI images using zero-data learning reveal its robustness with explicit PSNR improvement and lower latency compared to relevant benchmarks.
Multi-Task Learning of Depth from Tele and Wide Stereo Image Pairs

In this paper, we introduce the problem of estimating the real world depth of elements in a scene captured by two cameras with different field of views, where the first field of view (FOV) is a Wide FOV obtained by a lens with 1 × the optical zoom, and the second FOV is contained in the first FOV and corresponds to a tele zoom lens with 2 × the optical zoom. Traditional stereo matching techniques can estimate the stereo disparity, and hence the depth, in the overlapping FOV between both cameras only, which corresponds to the Tele FOV. We refer to the problem of estimating the disparity, or inverse depth, for the union of FOVs as `Tele-Wide disparity estimation'. We propose different deep learning solutions to establish baseline performances. We trained a single-image inverse-depth estimation (SIDE) network to estimate the inverse depth from the image corresponding to the Wide FOV only. We also trained a stereo image disparity estimation network to estimate the disparity for the overlapping Tele FOV only, and another tele-wide stereo matching network (TW-SMNet) for estimating the disparity for the union Wide FOV. We further propose an end-to-end multi-task tele-wide stereo matching deep neural network (MT-TW-SMNet) which attempts to do stereo matching in the overlapped Tele FOV and SIDE in the union Wide FOV. Experimental results on KITTI and the SceneFlow datasets establish baseline performances for the tele-wide stereo matching and demonstrate that multitask tele-wide stereo matching provides a reasonable solution to the Tele-Wide depth estimation problem.
Combining Sentiment Analysis Scores to Improve Accuracy of Polarity Classification in MOOC Posts

Sentiment analysis is a set of techniques that deal with the verification of sentiment and emotions in written texts. This introductory work aims to explore the combination of scores and polarities of sentiments (positive, neutral and negative) provided by different sentiment analysis tools. The goal is to generate a final score and its respective polarity from the normalization and arithmetic average scores given by those tools that provide a minimum of reliability. The texts analyzed to test our hypotheses were obtained from forum posts from participants in a massive open online course (MOOC) offered by Universidade Aberta de Portugal, and were submitted to four online service APIs offering sentiment analysis: Amazon Comprehend, Google Natural Language, IBM Watson Natural Language Understanding, and Microsoft Text Analytics. The initial results are encouraging, suggesting that the average score is a valid way to increase the accuracy of the predictions from different sentiment analyzers.
High-Accuracy Automatic Person Segmentation with Novel Spatial Saliency Map

In this work, we propose a high-efficiency person segmentation system that achieves high segmentation accuracy with a much smaller CNN network. In this approach, key-point detection annotation is incorporated for the first time and a novel spatial saliency map, in which the intensity of each pixel indicates the likelihood of forming a part of the human and reflects the distance from the body, is generated to provide more spatial information. Additionally, a lightweight automatic person segmentation network is proposed, which is small and efficient for person segmentation by leveraging atrous convolution. The experimental results prove that an image pyramid resizing augmentation can also improve efficiency. Our proposed segmentation method achieves an accuracy of 94.06% on the person segmentation dataset built in this work, which exceeds the results of previous state-of-the-art methods in accuracy and efficiency.
Graph Filtering of Time-Varying Signals over Asymmetric Wireless Sensor Networks

In many applications involving wireless sensor networks (WSNs), the observed data can be modeled as signals defined over graphs. As a consequence, an increasing interest has been witnessed to develop new methods to analyze graph signals, leading to the emergence of the field of Graph Signal Processing. One of the most important processing tools in this field is graph filters, which can be easily implemented distributedly over networks by means of cooperation among the nodes. Most of previous works related to graph filters assume the same connection probability in both link directions when transmitting an information between two neighboring nodes. This assumption is not realistic in practice due to the typical random link asymmetry in WSNs caused by interferences and background noise. This paper proposes solutions to cope with the problem of graph filtering of noisy time-varying input graph signals over random time-varying asymmetric WSNs. We design an extension to node-variant graph filters that can provide a trade-off between the expected error and variance, by optimizing the filter coefficients adaptively, resulting in an accurate graph filtering. Numerical experiments carried out over different random WSNs illustrate the efficiency of the proposed solutions.
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing

Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. In this paper, we develop improved techniques for defending against adversarial examples. First, we propose an enhanced defense technique denoted Attention and Adversarial Logit Pairing (AT + ALP), which encourages both attention map and logit for the pairs of examples to be similar. When being applied to clean examples and their adversarial counterparts, AT + ALP improves accuracy on adversarial examples over adversarial training. We show that AT + ALP can effectively increase the average activations of adversarial examples in the key area and demonstrate that it focuses on discriminate features to improve the robustness of the model. Finally, we conduct extensive experiments using a wide range of datasets and the experiment results show that our AT + ALP achieves the state of the art defense performance. For example, on 17 Flower Category Database, under strong 200-iteration Projected Gradient Descent (PGD) gray-box and black-box attacks where prior art has 34 and 39% accuracy, our method achieves 50 and 51%. Compared with previous work, our work is evaluated under highly challenging PGD attack: the maximum perturbation ϵ ∈ {0.25, 0.5} i.e. L∞ ∈ {0.25, 0.5} with 10-200 attack iterations. To the best of our knowledge, such a strong attack has not been previously explored on a wide range of datasets.
A Secure Image Encryption Architecture Based on Pseudorandom Number Generator and Chaotic Logistic Map

Communication is essential to every society, and securely exchanging data are critical in the age of digital data. The image has been the center of interest for security; chaotic maps image encryption become widely used in recent years. In our study, authors assessed the pixel rearrangement (permutation) and pixel dissemination (diffusion) stage used in cipher image established on designed scheme Pseudo-random number Generators (PRNGs) and Logistic chaotic map. Logistic maps used to generate seed value for PRNG and two-permutation stage and PRNGs used in diffusion operation. The proposed design through experimental tests demonstrate decent Statistical output, and high security for our image encryption schema, Our PRNGs passed NIST SP 800-22, and TestU01 practical randomness test suggests PRNGs have suitable statistical property.
Image Preprocessing Techniques for Facial Expression Recognition with Canny and Kirsch Edge Detectors

With facial expressions, humans can interconnect and relay information and feelings between one another. Recognizing emotions involves the four key stages namely facial expression recognition, preprocessing, feature extraction and classification. The facial images have more information than necessary; the images’ background noise can also impact automated expression recognition. To resolve unnecessary information and background noise, filtering and edge detection algorithms were used during the preprocessing phase. The edge detectors were used in facial expression recognition to highlight frequent facial components, locating sharp discontinuities and filtering less important data. Key edge detectors including Sobel, Prewitt, Differences of Gaussian, Laplacian of Gaussian, Roberts, Kirsch and Canny Edge detector were used for preprocessing of facial expression images. Viola Jones facial detection algorithm and local feature extraction algorithms, local directional patterns as well as k-nearest neighbor algorithms are used for image detection, feature extraction and classification respectively. The best results were based on the Cohn-Kanade database (CK+) with local directional patterns, canny edge detector and k-nearest neighbor.
N-RPN: Hard Example Learning For Region Proposal Networks

The region proposal task is to generate a set of candidate regions that contain an object. In this task, it is most important to propose as many candidates of ground-truth as possible in a fixed number of proposals. In a typical image, however, there are too few hard negative examples compared to the vast number of easy negatives, so region proposal networks struggle to train on hard negatives. Because of this problem, networks tend to propose hard negatives as candidates, while failing to propose ground-truth candidates, which leads to poor performance. In this paper, we propose a Negative Region Proposal Network(nRPN) to improve Region Proposal Network(RPN). The nRPN learns from the RPN's false positives and provide hard negative examples to the RPN. Our proposed nRPN leads to a reduction in false positives and better RPN performance. An RPN trained with an nRPN achieves performance improvements on the PASCAL VOC 2007 dataset.
Criteria for Learning without Forgetting in Artificial Neural Networks

Task progressive learning without catastrophic forgetting using artificial neural networks (ANNs) has demonstrated viability and promise. Due to the large number of ANN hyper-parameters, a model already trained over a group of tasks can further learn a new task without forgetting the previous tasks. Several algorithms have been proposed for progressive learning, including synaptic weight consolidation, ensemble, rehearsal, and sparse coding. One major problem with such methods is that they fail to detect the congestion in the ANN shared parameter space to indicate the saturation of the existing network and its inability to add new tasks using progressive learning. The detection of such saturation is especially needed to avoid the catastrophic forgetting of old trained task and the concurrent loss in their generalization quality. In this paper, we address such problem and propose a methodology for ANN congestion detection. The methodology is based on computing the Hessian of the ANN loss function at the optimal weights for a group of previously learned tasks. Since the Hessian calculation is compute-intensive, we provide a set of approximation heuristics that are computationally efficient. The algorithms are implemented and analyzed in the context of two cloud network security datasets, namely, UNSW-NB15 and AWID, as well as the MNIST image recognition dataset. Results show that the proposed congestion metrics give an accurate assessment of the ANN progressive learning capacity for these various datasets. Furthermore, the results show that models that have more features exhibit higher congestion thresholds and are therefore more amenable to progressive learning.
Line Following Robot Using Image Processing

A line following robot is automated device programmed to follow specific path. Some of existing techniques used in controlling line following robot are by IR sensor, LDR sensors, etc. This research work aims to develop image processing-based line following robot. A camera is used to obtain image of track and then it is converted into bitmap image. Least Square Method is used to follow the predefined path. By calculating slope of line, angle by which robot should turn is determined. Thus, by using image processing technique line following robot is guided along the desired path.
A Cloud Server Oriented FPGA Accelerator for LSTM Recurrent Neural Network

Long Short-Term Memory network (LSTM) is the most widely used recurrent neural network architecture. It plays an important role in a number of research areas, such as language modeling, machine translation and image captioning. However, owing to its recurrent nature, general-purpose processors like CPUs and GPGPUs can achieve limited parallelism while consuming high power energy. FPGA accelerators can outperform general-purpose processors with flexibility, energy-efficiency and more delicate optimization capabilities for the recurrent based algorithms. In this paper, we present the design and implementation of a cloud-oriented FPGA accelerator for LSTM. Different from most of previous works designed for embedded systems, our FPGA accelerator transfers data sequences from and to the host server through PCIe and performs multiple time series predictions in parallel. We optimize both the on-chip computation and the communication between the host server and the FPGA board. We perform experiments to evaluate the overall performance as well as the computation and the PCIe communication efforts. The results show that the performance of our implementation is better than the CPU-based and other hardware-based implementations.