Reviewerly/exHarmony huggingface dataset:
Improving the Antinoise Ability of DNNs via a Bio-Inspired Noise Adaptive Activation Function Rand Softplus

Although deep neural networks (DNNs) have led to many remarkable results in cognitive tasks, they are still far from catching up with human-level cognition in antinoise capability. New research indicates how brittle and susceptible current models are to small variations in data distribution. In this letter, we study the stochasticity-resistance character of biological neurons by simulating the input-output response process of a leaky integrate-and-fire (LIF) neuron model and proposed a novel activation function, rand softplus (RSP), to model the response process. In RSP, a scale factor η is employed to mimic the stochasticity-adaptability of biological neurons, thereby enabling the antinoise capability of a DNN to be improved by the novel activation function. We validated the performance of RSP with a 19-layer residual network (ResNet) and a 19-layer visual geometry group (VGG) on facial expression recognition data sets and compared it with other popular activation functions, such as rectified linear units (ReLU), softplus, leaky ReLU (LReLU), exponential linear unit (ELU), and noisy softplus (NSP). The experimental results show that RSP is applied to VGG-19 or ResNet-19, and the average recognition accuracy under five different noise levels exceeds the other functions on both of the two facial expression data sets; in other words, RSP outperforms the other activation functions in noise resistance. Compared with the application in ResNet-19, the application of RSP in VGG-19 can improve a network's antinoise performance to a greater extent. In addition, RSP is easier to train compared to NSP because it has only one parameter to be calculated automatically according to the input data. Therefore, this work provides the deep learning community with a novel activation function that can better deal with overfitting problems.
Learning Laplacian Matrix from Bandlimited Graph Signals

In this paper, we present a method for learning an underlying graph topology using observed graph signals as training data. The novelty of our method lies on the combination of two assumptions that are imposed as constraints to the graph learning process: i) the standard assumption used in the literature that signals are smooth with respect to graph structure (i.e. small signal variation at adjacent nodes), with ii) the additional assumption that signals are bandlimited, which implies sparsity in the signals' representation in the spectral domain. The latter assumption affects the inference of the whole eigenvalue decomposition of the Laplacian matrix and leads to a challenging new optimization problem. The conducted experimental evaluation shows that the proposed algorithm to solve this problem outperforms a reference state-of-the-art method that is based only on the smoothness assumption, when compared in the graph learning task on synthetic and real graph signals.
Artificial neural network based wavelet transform technique for image quality enhancement

Image quality enhancement is an important image processing task, wherein denoising an image is essential for accurate image diagnoses, because the presence of noise in an image produces incorrect information. This paper proposes double density wavelet transform based intelligent techniques for image denoising. This hybrid technique combines wavelet and neural networking methods, performed and validated using different standard images and extant denoising methods. These images were degraded using a variety of noise levels to simulate actual noise degradation. The results reveal the high-peak signal-to-noise ratio of the proposed system, which is thus considered effective compared to state-of-the-art techniques.
On the predictive power of ARJI volatility forecasts for Bitcoin

Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility forecasts for Bitcoin returns over a daily horizon from 2013 to 2018. The empirical results indicate that the ARJI jump model can cope with the extreme price movements of Bitcoin, showing comparatively superior in-sample goodness-of-fit, as well as out-of-sample predictive performance. However, due to the excessive volatility swings on the cryptocurrency market, the realized volatility of Bitcoin prices is only marginally explained by the GARCH genre of employed models.
A Two-Stage Method for Online Signature Verification Using Shape Contexts and Function Features

As a behavioral biometric trait, an online signature is extensively used to verify a person's identity in many applications. In this paper, we present a method using shape contexts and function features as well as a two-stage strategy for accurate online signature verification. Specifically, in the first stage, features of shape contexts are extracted from the input and classification is made based on distance metric. Only the inputs passing by the first stage are represented by a set of function features and verified. To improve the matching accuracy and efficiency, we propose shape context-dynamic time warping (SC-DTW) to compare the test signature with the enrolled reference ones based on the extracted function features. Then, classification based on interval-valued symbolic representation is employed to decide if the test signature is a genuine one. The proposed method is evaluated on SVC2004 Task 2 achieving an Equal Error Rate of 2.39% which is competitive to the state-of-the-art approaches. The experiment results demonstrate the effectiveness of the proposed method.
Memory Effects in Quantum Metrology

Quantum metrology concerns estimating a parameter from multiple identical uses of a quantum channel. We extend quantum metrology beyond this standard setting and consider the estimation of a physical process with quantum memory, here referred to as a parametrized quantum comb. We present a theoretic framework of metrology of quantum combs, and derive a general upper bound of the comb quantum Fisher information. The bound can be operationally interpreted as the quantum Fisher information of a memoryless quantum channel times a dimensional factor. We then show an example where the bound can be attained up to a factor of 4. With the example and the bound, we show that memory in quantum sensors plays an even more crucial role in the estimation of combs than in the standard setting of quantum metrology.
Virtual Reality and Correctional Rehabilitation: A Game Changer

Virtual reality (VR) has been used for learning and treatment for decades. Few efforts have been made to incorporate this technology into correctional rehabilitation. Advances in VR systems, reductions in price, and the broader availability of the technology now make this effort more attainable. This article introduces readers to VR and explains how it can be used to enhance current efforts in offender treatment.
Representation Learning Using Convolution Neural Network for Acoustic-to-articulatory Inversion

Recent techniques employ end-to-end systems to learn relevant features for several speech related applications, including speech recognition, and speaker verification. In this work, we focus on the task of acoustic-to-articulatory inversion (AAI) for which we propose an end-to-end system that comprises a convolution neural network (CNN) and a bidirectional long short-term memory network (BLSTM). The aim of this work is to understand the nature of the features learnt by the end-to-end model and the importance of pre-emphasis in representation learning for AAI. Further, we propose a subject adaptation scheme to overcome the limitations of the availability of parallel acoustic-articulatory data to train an end-to-end AAI system. The AAI performance is evaluated with ~3.19 hours of acoustic-articulatory data collected from 8 subjects. Experiments reveal that, the frequency response of filters learnt by the CNN in the proposed system resembles those of the mel-scale, and hence, the performance of the proposed system (RMSE=1.47mm) is on par with that using mel-frequency cepstral coefficients (1.42mm) as features. Using pre-emphasis reduces RMSE by 0.13mm, and also the proposed adaptation scheme performs better than a subject-specific AAI model by an RMSE of 0.21mm despite of limited acoustic-articulatory data from a subject.
A fast hybrid clustering technique based on local nearest neighbor using minimum spanning tree

With rapid explosion of information, clustering emerged as an active research area for knowledge discovery. Most of the existing clustering algorithms become ineffective when inappropriate parameters are provided or applied on a dataset which consists of clusters of diverse shapes, sizes, and varying densities. To overcome these issues, many graph based hybrid clustering algorithms have been proposed but these algorithms first generate a complete graph of the dataset which takes O(N2) time where N is the number of data points which limits their application on large datasets. This paper proposes an algorithm namely a fast hybrid clustering technique based on local nearest neighbor using minimum spanning tree to reduce the computational overhead. In the first step, the algorithm partitions the dataset into large number of sub-clusters based on dispersion of data points to capture the geometry of clusters. After partitioning the dataset, a minimum spanning tree based on the centroids of each of the sub-clusters is constructed to identify the adjacent pairs. A novel merge method is proposed to find the genuine clusters by repeatedly merging the adjacent sub-clusters. The cohesion and intra-similarity are introduced to compute the level of dispersion of data points with respect to the centers of an adjacent pair and average edge weight of a sub-clusters respectively. The algorithm takes O(N3/2) time which is a N factor improvement over the popular hybrid clustering algorithms. Experimental analyses on both synthetic as well as gene expression datasets demonstrate that the proposed technique shows significant improvement over competing clustering algorithms in terms of execution time and improved cluster quality. Moreover, the proposed algorithm does not require any user defined parameters and it can estimate the number of clusters more accurately.
The Mutation and Injection Framework: Evaluating Clone Detection Tools with Mutation Analysis

An abundant number of clone detection tools have been proposed in the literature due to the many applications and benefits of clone detection. However, there has been difficulty in the performance evaluation and comparison of these clone detectors. This is due to a lack of reliable benchmarks, and the manual efforts required to validate a large number of candidate clones. In particular, there has been a lack of a synthetic benchmark that can precisely and comprehensively measure clone-detection recall. In this paper, we present a mutation-analysis based benchmarking framework that can be used not only to evaluate the recall of clone detection tools for different types of clones but also for specific kinds of clone edits and without any manual efforts. The framework uses an editing taxonomy of clone synthesis for generating thousands of artificial clones, injects into code bases and automatically evaluates the subject clone detection tools following the mutation analysis approach. Additionally, the framework has features where custom clone pairs could also be used in the framework for evaluating the subject tools. This gives the opportunity of evaluating specialized tools for specialized contexts such as evaluating a tool's capability for the detection of complex Type-4 clones or real world clones without writing complex mutation operators for them. We demonstrate this framework by evaluating the performance of ten modern clone detection tools across two clone granularities (function and block) and three programming languages (Java, C and C#). Furthermore, we provide a variant of the framework that can be used to evaluate specialized tools such as for large gaped clone detection. Our experiments demonstrate confidence in the accuracy of our Mutation and Injection Framework when comparing against the expected results of the corresponding tools, and widely used real-world benchmarks such as Bellon's benchmark and BigCloneBench. We provide features so that most clone detection tools that report clones in the form of clone pairs (either in filename/line numbers or filename/tokens) could be evaluated using the framework.
Estimation of Muscle Scores of Live Pigs Using a Kinect Camera

The muscle grading of livestock is a primary component of valuation in the meat industry. In pigs, the muscularity of a live animal is traditionally estimated by visual and tactile inspection from an experienced assessor. In addition to being a time-consuming process, scoring of this kind suffers from inconsistencies inherent to the subjectivity of human assessment. On the other hand, accurate, computer-driven methods for carcass composition estimation, such as magnetic resonance imaging (MRI) and computed tomography scans (CT-scans), are expensive and cumbersome to both the animals and their handlers. In this paper, we propose a method that is fast, inexpensive, and non-invasive for estimating the muscularity of live pigs, using RGB-D computer vision and machine learning. We used morphological features extracted from the depth images of pigs to train a classifier that estimates the muscle scores that are likely to be given by a human assessor. The depth images were obtained from a Kinect v1 camera which was placed over an aisle through which the pigs passed freely. The data came from 3246 pigs, each having 20 depth images, and a muscle score from 1 to 7 (reduced later to 5 scores) assigned by an experienced assessor. The classification based on morphological features of the pig's body shape-using a gradient boosted classifier-resulted in a mean absolute error of 0.65 in tenfold cross-validation. Notably, the majority of the errors corresponded to pigs being classified as having muscle scores adjacent to the groundtruth labels given by the assessor. According to the end users of this application, the proposed approach could be used to replace expert assessors at the farm.
You `Might' Be Affected

Data breaches place affected individuals at significant risk of identity theft. Yet, prior studies have shown that many consumers do not take protective actions after receiving a data breach notification from a company. We analyzed 161 data breach notifications sent to consumers with respect to their readability, structure, risk communication, and presentation of potential actions. We find that notifications are long and require advanced reading skills. Many companies downplay or obscure the likelihood of the receiver being affected by the breach and associated risks. Moreover, potential actions and offered compensations are frequently described in lengthy paragraphs instead of clearly listed. Little information is provided regarding an action's urgency and effectiveness; little guidance is provided on which actions to prioritize. Based on our findings, we provide recommendations for designing more usable and informative data breach notifications that could help consumers better mitigate the consequences of being affected by a data breach.
Audible Panorama

As 360 deg cameras and virtual reality headsets become more popular, panorama images have become increasingly ubiquitous. While sounds are essential in delivering immersive and interactive user experiences, most panorama images, however, do not come with native audio. In this paper, we propose an automatic algorithm to augment static panorama images through realistic audio assignment. We accomplish this goal through object detection, scene classification, object depth estimation, and audio source placement. We built an audio file database composed of over $500$ audio files to facilitate this process. We designed and conducted a user study to verify the efficacy of various components in our pipeline. We run our method on a large variety of panorama images of indoor and outdoor scenes. By analyzing the statistics, we learned the relative importance of these components, which can be used in prioritizing for power-sensitive time-critical tasks like mobile augmented reality (AR) applications.
Using Holograms for visualizing and interacting with educational content in a Teaching Factory

The Teaching Factory concept utilizes advanced delivery mechanisms and high-grade industrial didactic equipment to offer education content. It connects remotely located engineering teams with teams of students in the classroom or laboratory so as work together in a common engineering project in manufacturing. This work investigates the potential of using Holograms as key technology for delivering educational content to the classroom side. The holographic system enables the visualization of complex 3D models in real-size dimensions enabling concurrent 3D visualization of the models to a team of students. A prototype has been implemented that offers the collaboration tools to both sides of the Teaching Factory setup. On the classroom or laboratory side, interaction is enabled through gesture recognition upon a low-cost finger tracking device, while on the factory side interaction as well as visualization is possible using tablet or mobile devices. Both sides can simultaneously interact with 3D object being displayed in holographic and mobile displays. Additional interaction functionality such as display annotations, tree view of the geometrical object and content management is offered though the mobile app.
Augmented Reality Views for Occluded Interaction

We rely on our sight when manipulating objects. When objects are occluded, manipulation becomes difficult. Such occluded objects can be shown via augmented reality to re-enable visual guidance. However, it is unclear how to do so to best support object manipulation. We compare four views of occluded objects and their effect on performance and satisfaction across a set of everyday manipulation tasks of varying complexity. The best performing views were a see-through view and a displaced 3D view. The former enabled participants to observe the manipulated object through the occluder, while the latter showed the 3D view of the manipulated object offset from the object's real location. The worst performing view showed remote imagery from a simulated hand-mounted camera. Our results suggest that alignment of virtual objects with their real-world location is less important than an appropriate point-of-view and view stability.
A Hybrid Technique To Detect Botnets, Based on P2P Traffic Similarity

The botnet has been one of the most common threats to the network security since it exploits multiple malicious codes like worm, Trojans, Rootkit, etc. These botnets are used to perform the attacks, send phishing links, and/or provide malicious services. It is difficult to detect Peer-to-peer (P2P) botnets as compare to IRC (Internet Relay Chat), HTTP (HyperText Transfer Protocol) and other types of botnets because of having typical features of the centralization and distribution. To solve these problems, we propose an effective two-stage traffic classification method to detect P2P botnet traffic based on both non-P2P traffic filtering mechanism and machine learning techniques on conversation features. At the first stage, we filter non-P2P packages to reduce the amount of network traffic through well-known ports, DNS query, and flow counting. At the second stage, we extract conversation features based on data flow features and flow similarity. We detected P2P botnets successfully, by using Machine Learning Classifiers. Experimental evaluations show that our two-stage detection method has a higher accuracy than traditional P2P botnet detection methods.
ForceRay

Smartphones are used predominantly one-handed, using the thumb for input. Many smartphones, however, have grown beyond 5". Users cannot tap everywhere on these screens without destabilizing their grip. ForceRay (FR) lets users aim at an out-of-reach target by applying a force touch at a comfortable thumb location, casting a virtual ray towards the target. Varying pressure moves a cursor along the ray. When reaching the target, quickly lifting the thumb selects it. In a first study, FR was 195 ms slower and had a 3% higher selection error than the best existing technique, BezelCursor (BC), but FR caused significantly less device movement than all other techniques, letting users maintain a steady grip and removing their concerns about device drops. A second study showed that an hour of training speeds up both BC and FR, and that both are equally fast for targets at the screen border.
Learning Temporal Features for Detection on Maritime Airborne Video Sequences Using Convolutional LSTM

In this paper, we study the effectiveness of learning temporal features to improve detection performance in videos captured by small aircraft. To implement this learning process, we use a convolutional long short-term memory (LSTM) associated with a pretrained convolutional neural network (CNN). To improve the training process, we incorporate domain-specific knowledge about the expected size and number of boats. We carry out three tests. The first searches the best sequence length and subsampling rate for training and the second compares the proposed method with a traditional CNN, a traditional LSTM, and a gated recurrent unit (GRU). The final test evaluates our method with the already published detectors in two data sets. Results show that in favorable conditions, our method's performance is comparable to other detectors but, on more challenging environments, it stands out from other techniques.
Sensing Posture-Aware Pen+Touch Interaction on Tablets

Many status-quo interfaces for tablets with pen + touch input capabilities force users to reach for device-centric UI widgets at fixed locations, rather than sensing and adapting to the user-centric posture. To address this problem, we propose sensing techniques that transition between various nuances of mobile and stationary use via postural awareness. These postural nuances include shifting hand grips, varying screen angle and orientation, planting the palm while writing or sketching, and detecting what direction the hands approach from. To achieve this, our system combines three sensing modalities: 1) raw capacitance touchscreen images, 2) inertial motion, and 3) electric field sensors around the screen bezel for grasp and hand proximity detection. We show how these sensors enable posture-aware pen+touch techniques that adapt interaction and morph user interface elements to suit fine-grained contexts of body-, arm-, hand-, and grip-centric frames of reference.