Reviewerly/exHarmony huggingface dataset:
Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filters

In this paper, we investigate the empirical impact of orthogonality regularization (OR) in deep learning, either solo or collaboratively. Recent works on OR showed some promising results on the accuracy. In our ablation study, however, we do not observe such significant improvement from existing OR techniques compared with the conventional training based on weight decay, dropout, and batch normalization. To identify the real gain from OR, inspired by the locality sensitive hashing (LSH) in angle estimation, we propose to introduce an implicit self-regularization into OR to push the mean and variance of filter angles in a network towards 90° and 0° simultaneously to achieve (near) orthogonality among the filters, without using any other explicit regularization. Our regularization can be implemented as an architectural plug-in and integrated with an arbitrary network. We reveal that OR helps stabilize the training process and leads to faster convergence and better generalization.
Overcoming the Tradeoff Between Accuracy and Compactness in Decision Diagrams for Quantum Computation

Quantum computation promises to solve many hard or infeasible problems substantially faster than classical solutions. The involvement of big players like Google, IBM, Intel, Rigetti, or Microsoft furthermore led to a momentum which increases the demand for automated design methods for quantum computations. In this context, decision diagrams for quantum computation provide a major pillar as they allow to efficiently represent quantum states and quantum operations which, otherwise, have to be described in terms of exponentially large state vectors and unitary matrices. However, current decision diagrams for the quantum domain suffer from a tradeoff between accuracy and compactness, since: 1) small errors that are inevitably introduced by the limited precision of floating-point arithmetic can harm the compactness (i.e., the size of the decision diagram) significantly and 2) overcompensating these errors (to increase compactness) may lead to an information loss and introduces numerical instabilities. In this article, we describe and evaluate the effects of this tradeoff which clearly motivates the need for a solution that is perfectly accurate and compact at the same time. More precisely, we show that the tradeoff indeed weakens current design automation approaches for quantum computation (possibly leading to corrupted results or infeasible run-times). To overcome this, we propose an alternative approach that utilizes an algebraic representation of the occurring complex and irrational numbers and outline how this can be incorporated in a decision diagram which is suited for quantum computation. Evaluations show that-at the cost of an overhead which is moderate in many cases-the proposed algebraic solution indeed overcomes the tradeoff between accuracy and compactness that is present in current numerical solutions.
Semi-Supervised Pedestrian Instance Synthesis and Detection With Mutual Reinforcement

We propose a GAN-based scene-specific instance synthesis and classification model for semi-supervised pedestrian detection. Instead of collecting unreliable detections from unlabeled data, we adopt a class-conditional GAN for synthesizing pedestrian instances to alleviate the problem of insufficient labeled data. With the help of a base detector, we integrate pedestrian instance synthesis and detection by including a post-refinement classifier (PRC) into a minimax game. A generator and the PRC can mutually reinforce each other by synthesizing high-fidelity pedestrian instances and providing more accurate categorical information. Both of them compete with a class-conditional discriminator and a class-specific discriminator, such that the four fundamental networks in our model can be jointly trained. In our experiments, we validate that the proposed model significantly improves the performance of the base detector and achieves state-of-the-art results on multiple benchmarks. As shown in Figure 1, the result indicates the possibility of using inexpensively synthesized instances for improving semi-supervised detection models.
A Novel Graph-Based Program Representation for Java Code Plagiarism Detection

Source code plagiarism is a long-standing issue in undergraduate computer science education. Identifying instances of source code plagiarism is a difficult and time-consuming task. To aid in its identification, many automated tools have been proposed to find indications of plagiarism. However, prior works have shown that common source code plagiarism detection tools are susceptible to plagiarism-hiding transformations. In this paper a novel graph-based representation of Java programs is presented which is resilient to plagiarism-hiding transformations. This graph is titled the Program Interaction Dependency Graph (PIDG) and represents the interaction and transformation of data within a program, and how this data interacts with the system. To show the effectiveness of this graph, it is evaluated on a data set of simulated source code plagiarism. The results of this evaluation indicate the PIDG is a promising means of representing programs in a form that is resilient to plagiarism.
Deep optical character recognition: a case of Pashto language

Over the past decades, text recognition technologies have focused immensely on noncursive isolated scripts. A text recognition system for the cursive Pashto script will serve as a great contribution, allowing the traditional, cultural, and educational Pashto literature to be converted into machine-readable form. We propose the use of deep learning architectures based on the transfer learning for the recognition of Pashto ligatures. For recognition analysis and evaluation, the ligature images in the dataset are preprocessed by data augmentation techniques, i.e., negatives, contours, and rotated to increase the variation of each sample and size of the original dataset. Rich feature representations are automatically extracted from the Pashto ligature images using deep convolution layers of the convolution neural network (CNN) architectures using fine-tuned approach. Pretrained CNN architectures: AlexNet, GoogleNet, and VGG (VGG-16 and VGG-19) are used for classification by feeding the extracted features to a fully connected layer and a softmax layer. The proposed deep transfer-based learning has achieved phenomenal recognition rates for Pashto ligatures on benchmark FAST-NU Pashto dataset. An accuracy of 97.24%, 97.46%, and 99.03% is achieved using AlexNext, GoogleNet, and VGGNet architectures, respectively.
Ring-based finger tracking using capacitive sensors and long short-term memory

We present a ring-shaped interaction device, called PeriSense, utilizing capacitive sensing in order to enable finger tracking. The finger angles and its adjacent fingers are sensed by measuring capacitive proximity between electrodes and human skin. To map the capacitive measurements to the finger angles, we use long short-term memory (LSTM). By wearing the ring on the middle finger, the angles of the index, middle and ring finger can be determined from the capacitive measurements. We collected sample data from 17 users using a Leap Motion camera, which provided the reference data. In a leave-one-user-out cross-validation test, we revealed a mean absolute error of 13.02 degrees over all finger angles. The motion of the little finger and the thumb are out of the capacitive measurement range or covered by the index and ring finger, respectively. With natural finger movements, the LSTM estimates the angles of the little finger and thumb based on the movement pattern of the other fingers.
A Statistical Approach to Graduate Admissions’ Chance Prediction

In the current scenario, grad students often experience difficultyChakrabarty, Navoneel in choosing a proper institution for pursuing masters based on their academic performances. Although there are many consultancy services and Web applications suggesting students, institutions in which they are most likely to get admitted. But, not always the decisions are staunch since there are different kinds of students with different portfolios and performancesChowdhury, Siddhartha in their academic careers and institution selection is done on the basis of historical admissions’ data. This study aims to analyze a student’s academic achievements as well as university ratingRana, Srinibas and give the probability of getting admission in that university, as output. The gradient boosting regressor model is deployed, which accomplished a $${R^2}$$-score of 0.84 eventually surpassing the performance of the state-of-the-art model. In addition to $${R^2}$$-score, other performance error metrics like mean absolute error, mean square error, and root mean square error are computed and showcased.
Exploring NAT Detection and Host Identification Using Machine Learning

The usage of Network Address Translation (NAT) devices is common among end users, organizations, and Internet Service Providers. NAT provides anonymity for users within an organization by replacing their internal IP addresses with a single external wide area network address. While such anonymity provides an added measure of security for legitimate users, it can also be taken advantage of by malicious users hiding behind NAT devices. Thus, identifying NAT devices and hosts behind them is essential to detect malicious behaviors in traffic and application usage. In this paper, we propose a machine learning based solution to detect hosts behind NAT devices by using flow level statistics (excluding IP addresses, port numbers, and application layer information) from passive traffic measurements. We capture a large dataset and perform an extensive evaluation of our proposed approach with four existing approaches from the literature. Our results show that the proposed approach could identify NAT behaviors and hosts not only with higher accuracy but also demonstrates the impact of parameter sensitivity of the proposed approach.
Self-Contained Stylization via Steganography for Reverse and Serial Style Transfer

Style transfer has been widely applied to give real-world images a new artistic look. However, given a stylized image, the attempts to use typical style transfer methods for de-stylization or transferring it again into another style usually lead to artifacts or undesired results. We realize that these issues are originated from the content inconsistency between the original image and its stylized output. Therefore, in this paper we advance to keep the content information of the input image during the process of style transfer by the power of steganography, with two approaches proposed: a two-stage model and an end-to-end model. We conduct extensive experiments to successfully verify the capacity of our models, in which both of them are able to not only generate stylized images of quality comparable with the ones produced by typical style transfer methods, but also effectively eliminate the artifacts introduced in reconstructing original input from a stylized image as well as performing multiple times of style transfer in series.
An Energy Optimal Path-Planning Scheme for Quadcopters in Forests

Motivated by the seriousness of local forests infestation by Pine Processionary Moths, in this paper we devise a method to completely yet optimally scan a forest. More precisely, given a forest map, an optimal path-planning algorithm that is adequate for a drone to visit all of the trees, in an energy optimal manner, is developed. The pre-map of the trees and terrain are modeled so that they are embedded within the optimal trajectory planning. Optimal control theory is then used to generate a trajectory and path between each pair of trees. Having robustly defined the tree-to-tree travel cost, an adjacency matrix is constructed. Taking recourse to Graph Theory and Integer Linear Programming along with Sub-Tour Elimination Constraints, the routes are chosen to tour the entire forest and return to the base in an energy optimal fashion.
<i>CPA</i>: Accurate <i>C</i>ross-<i>P</i>latform Binary <i>A</i>uthorship Characterization Using LDA

Binary authorship characterization refers to the process of identifying stylistic characteristics that are related to the author of an anonymous binary code. The aim is to automate the laborious and error-prone reverse engineering task of discovering information related to the author(s) of binary code. This paper presents CPA, a novel approach for characterizing the authors of program binaries. Instead of using generic features such as n-grams, CPA proposes a set of new features based on collections of various aspects of author style, including author code traits, code structure characteristics, and author expertise in solving coding tasks. It employs the Latent Dirichlet Allocation (LDA) algorithm to generate author style signatures to help identify similar author style characteristics in other binaries. We evaluated CPA on large datasets extracted from selected opensource C/C++ projects in GitHub and Google Code Jam events, and it successfully attributed a large number of authors with a significantly higher F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score: around 91% when the number of authors was 1,500. In addition, the false positive rate was low, around 1.5%. When the code was subjected to refactoring techniques or code transformation or was processed using different compilers/compilation settings, there was no significant drop in accuracy, demonstrating the robustness of our tool. Finally, in the case of code written by multiple authors, CPA was able to identify the authors with a high F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score, around 89%.
OPAL: An extensible framework for ontology‐based program analysis

Summary The syntactic information of a program can be represented as resource description framework (RDF) triples called program triples . We propose an extensible static analysis framework, called OPAL—Ontology‐based Program AnaLysis. The framework enables formal representation of external knowledge, such as usage knowledge of libraries and domain knowledge. Utilizing this knowledge and the program triples, we compute the semantic information, called static trace of the program. It is generated through path‐sensitive intraprocedural traversal of the program. We approximate information in case of loops by unrolling them a fixed number of times. The main contribution of the framework is to store the static trace as RDF triples called semantic triples . They are described using the Program Analysis ontology proposed in this article. The program triples and the semantic triples are together called consolidated program triples . These triples are stored and used to accelerate the execution of client‐analyses specified by the end user. In the framework, a client‐analysis is specified by a set of conjunctive expressions that use SPARQL (W3C RDF query language) queries. The framework is effective for the client‐analyses that warrant sound and approximate information. The effectiveness is assessed first, using two source‐code‐analyses that require only the program triples, and then 10 intraprocedural path‐sensitive analyses that require the consolidated program triples. Using NPB and SPEC 2006 benchmarks, we achieve an improvement in the conciseness of analysis specifications. Also, the execution time using OPAL is competitive to LLVM's clang for individual analysis and outperforms clang over a series of analyses because of the reuse of consolidated program triples.
IoTSpot: Identifying the IoT Devices Using their Anonymous Network Traffic Data

The Internet of Things (IoT) has been erupting the world widely over the decade. Smart home owners and smart building managers are increasingly deploying IoT devices to monitor and control their environments due to the rapid decline in the price of IoT devices. The network traffic data produced by these IoT devices are collected by Internet Service Providers (ISPs) and telecom providers, and often shared with third-parties to maintain and promote user services. Such network traffic data is considered “anonymous” if it is not associated with identifying device information, e.g., MAC address and DHCP negotiation. Extensive prior work has shown that IoT devices are vulnerable to multiple cyber attacks. However, people do not believe that these attacks can be launched successfully without the knowledge of what IoT devices are deployed in their houses. Our key insight is that the network traffic data is not anonymous: IoT devices have unique network traffic patterns, and they embedded detailed device information. To explore the severity and extent of this privacy threat, we design IoTSpot to identify the IoT devices using their “anonymous” network traffic data. We evaluate IoTSpot on publicly-available network traffic data from 3 homes. We find that IoTSpot is able to identify 19 IoT devices with F1 accuracy of 0.984. More importantly, our approach only requires very limited data for training, as few as 40 minutes. IoTSpot paves the way for operators of smart homes and smart buildings to monitor the functionality, security and privacy threat without requiring any additional devices.
Ad-Hoc Collaboration Space for Distributed Cross Device Mobile Application Development

In last few years, a tremendous increase has been observed in the usage of portable electronic devices including smart phones, tablets, laptops, and wearables. These devices are produced by different manufacturers and work on different platforms. People surrounded by these devices need to interact with them during the meeting, presentation, class room and lots of other collaborative activities to share and receive information across the devices. Recent research trends lead towards better utilization of these mobile devices by connecting them together, whereas the interaction among these devices is still device centric and is dependent on expensive fixed software and hardware infrastructure. However, ad-hoc settings, where fixed infrastructure services do not exist, or may suspend the interaction across these devices, require specialized collaborative space. This research presents an architectural framework, named Ad-hoc Collaborative Space (ACS) that provides an abstraction layer by hiding the complexities of ad-hoc environment thus resulting into reduced application development time by providing the easy to use API's. The experimental evaluation based on different operating parameters shows that the proposed framework efficiently manages service registration, service discovery, synchronization, and connectivity between different devices.
Automatic Fusion of Satellite Imagery and AIS data for Vessel Detection

Being able to fuse information coming from different sources, such as AIS and satellite images is of major importance for maritime domain awareness, for example, to locate and identify vessels that may have purposely turned off their AIS transponder, preventing illegal activities. This paper presents a fully-automatic method for fusing AIS data and SAR satellite images for vessel detection. The proposed framework is based on the automatic annotation of satellite images by correlating them with AIS data producing train and test datasets which are provided as input to a convolutional neural network (CNN). The CNN was trained to detect the presence of ships in sectors of the image. Our automatic process allows the neural network to learn on a large amount of data, without the need for hand-labelled datasets. Our neural network, trained on our automatically-generated test set of images, achieved an accuracy of 88&#x0025; at ship detection, and an area under ROC curve of 94,6&#x0025;. Our estimate of real world accuracy is about 86-90&#x0025;.
Image Haze Removal By Adaptive CycleGAN

We introduce our machine-learning method to remove the fog and haze in image. Our model is based on CycleGAN, an ingenious image-to-image translation model, which can be applied to de-hazing task. The datasets that we used for training and testing are creatd according to the atmospheric scattering model. With the change of the adversarial loss from cross-entropy loss to hinge loss, and the change of the reconstruction loss from MAE loss to perceptual loss, we improve the performance measure of SSIM value from 0.828 to 0.841 on the NYU dataset. With the Middlebury stereo datasets, we achieve an SSIM value of 0.811, which is significantly better than the baseline CycleGAN model.
Reinforcement learning-based collision avoidance: impact of reward function and knowledge transfer

Abstract Collision avoidance for robots and vehicles in unpredictable environments is a challenging task. Various control strategies have been developed for the agent (i.e., robots or vehicles) to sense the environment, assess the situation, and select the optimal actions to avoid collision and accomplish its mission. In our research on autonomous ships, we take a machine learning approach to collision avoidance. The lack of available ship steering data of human ship masters has made it necessary to acquire collision avoidance knowledge through reinforcement learning (RL). Given that the learned neural network tends to be a black box, it is desirable that a method is available which can be used to design an agent's behavior so that the desired knowledge can be captured. Furthermore, RL with complex tasks can be either time consuming or unfeasible. A multi-stage learning method is needed in which agents can learn from simple tasks and then transfer their learned knowledge to closely related but more complex tasks. In this paper, we explore the ways of designing agent behaviors through tuning reward functions and devise a transfer RL method for multi-stage knowledge acquisition. The computer simulation-based agent training results have shown that it is important to understand the roles of each component in a reward function and the various design parameters in transfer RL. The settings of these parameters are all dependent on the complexity of the tasks and the similarities between them.
Object counting method based on dual attention network

The challenging problem that the authors solved in this study is to precisely estimate the number of objects in an image. Combining the spatial attention mechanism and pyramid structure, a novel atrous pyramid attention module is introduced to extract precise dense multi-scale features for object counting. Also, a global attention feature module is designed to enhance the ability of the network to learn feature representation based on channel attention mechanism. Combining the proposed atrous pyramid attention module and global attention feature module, a novel object counting method based on a dual attention network is established in this study. The experiments on public vehicle counting dataset including TRANCOS and crowd counting dataset including Mall and Shanghitech_A datasets demonstrate the proposed method achieves competitive performance, and the ablation study verifies the structure rationality of the designed modules.