Reviewerly/exHarmony huggingface dataset:
GraphEvo: Characterizing and Understanding Software Evolution using Call Graphs

Understanding software evolution is an imperative prerequisite for software related activities such as testing, debugging, and maintenance. As a software system evolves, it increases in size and complexity, introducing new challenges of understating the inner system interactions and subsequently hinders the overall system comprehension. While tools that construct and visualize call graphs have been used to facilitate software comprehension, they are still limited to capturing the functionality of a single software system at a time. However, understanding the similarities and differences across multiple releases becomes an imperative and challenging task during software evolution. To this end, we present a tool, named GraphEvo, that focuses on automating the process of quantifying and visualizing the changes across multiple releases of a software system based on an information-theoretic approach to compare the call graphs. Specifically, GraphEvo can automatically (1) construct and visualize the call graph for one or more software releases, (2) calculate and display a set of graph-based metrics, and (3) construct color-coded call graphs to visualize system evolution. The main goal of GraphEvo is to assist software developers and testers in exploring and tracking software changes over time. We demonstrate the functionality of GraphEvo by analyzing and studying five real software systems throughout their entire lifespan. The tool, evaluation results, and a video demo are available at https://goo.gl/8edZ64.
Clustered Latent Dirichlet Allocation for Scientific Discovery

Topic modeling, a method for extracting the underlying themes from a collection of documents, is an increasingly important component of the design of intelligent systems enabling the sense-making of highly dynamic and diverse streams of text data related but not limited to scientific discovery. Traditional methods such as Dynamic Topic Modeling (DTM) do not lend themselves well to direct parallelization because of dependencies from one time step to another. In this paper, we introduce and empirically analyze Clustered Latent Dirichlet Allocation (CLDA), a method for extracting dynamic latent topics from a collection of documents. Our approach is based on data decomposition in which the data is partitioned into segments, followed by topic modeling on the individual segments. The resulting local models are then combined into a global solution using clustering. The decomposition and resulting parallelization leads to very fast runtime even on very large datasets. Our approach furthermore provides insight into how the composition of topics changes over time and can also be applied using other data partitioning strategies over any discrete features of the data, such as geographic features or classes of users. In this paper CLDA is applied successfully to seventeen years of NIPS conference papers (2,484 documents and 3,280,697 words), seventeen years of computer science journal abstracts (533,588 documents and 46,446,184 words), and to forty years of the PubMed corpus (4,025,976 documents and 386,847,695 words). On the PubMed corpus, we demonstrate the versatility of CLDA by segmenting the data by both time and by journal. Our runtime on this corpus demonstrates an ability to function on very large scale datasets.
Price-aware traffic splitting in D2D HetNets with cost-energy-QoE tradeoffs

With the advances in wireless technologies, there has been tremendous increase in user demand for resource-intensive mobile Internet services. This has been coupled with the limited capabilities of mobile devices and the high level of user expectations in terms of both quality of experience (QoE) and cost. An attractive enhancement technique is to utilize the co-existence of multiple interfaces in wireless devices to connect simultaneously to different access networks including cooperation over device-to-device (D2D) links. To this end, we present in this work optimized user-centric traffic splitting strategies in heterogeneous networks to achieve a high QoE level for video on demand applications with low cost and energy consumption for end users. We formulate a multi-objective optimization problem considering different pricing models with prediction whereby a user can estimate the links’ performance for future time slots and make suitable decisions accordingly. We evaluate the proposed strategies and demonstrate their effectiveness using parameters determined via experimental measurements to provide an evaluation under realistic operational conditions. Results provide useful insights on the tradeoffs between energy consumption, cost and quality of experience.
Traffic classification at the radio spectrum level using deep learning models trained with synthetic data

Summary Traffic recognition is commonly done using deep packet inspection or packet‐based approaches. However, these methods require the listening device to be part of the network and raise privacy concerns. Traffic recognition models that operate directly at the spectrum level could, for instance, be used for smart spectrum management. To this extent, we present such an approach using deep learning methods. In particular, we present a convolutional neural network architecture that forms the basis for prediction models to recognize different transport protocols, burst traffic with different duty cycles, and different transmission rates. These models are trained with pure synthetic data to lighten the burden of data collection. As such, we validate recent successes in the area of robotics in the context of wireless networks. We compare the performance of two different datasets that contain spectrum images in either time or time‐frequency domain. Our evaluation shows that using time domain data results in an accuracy of at least 96% across all models. Time‐frequency information improves this accuracy even further. Furthermore, a validation with real‐life data shows that it is still possible to discriminate between different transmission rates with an accuracy of around 87%, while the detection of duty cycles and transport protocols takes place with accuracies of, respectively, around 73% and 78%. Finally, we also present a small‐scale real‐life prototype.
GRADI: Towards Group Recommendation Using Attentive Dual Top-Down and Bottom-Up Influences

Most of current group recommenders only consider the bottom-up influences, i.e., the preference of a group is greatly affected by the members in the group. For example, children usually dominate the preference of a family while senior experts often lead the preference of a professional group. However, in reality there also exist the top-down influences, i.e., a group inherently affects its every member, because a group often has some distinct themes which limit the preferences of all the members in the group. For instance, the members in a group for sports may prefer hiking and rock climbing, whereas the members in a group for entertainment would like to watch movies and play games. In other words, the influences between a group and its members are dual. To this end, this paper proposes a new model for Group Recommendation using Attentive Dual Influences (GRADI) that simultaneously explores both the bottom-up and top-down influences between a group and its members. The preference of a member in a group is represented as the group-specific member embedding by modeling the top-down influences from the group to the member. In addition, the preference of a group on a target item is represented as the item-specific group representation by considering the bottom-up influences from all the members to the group, where an attentive mechanism is developed to aggregate the preferences of all the members on a target item. Furthermore, GRADI investigates the interactions between groups and items with neural collaborative filtering. Results of extensive experiments conducted on two real-world datasets show that GRADI outperforms other state-of-the-art models.
Preliminary Research on Vehicle Speed Detection using Traffic Cameras

Recently, research on detection of abnormal vehicle behavior is getting increasing attention through various approaches, including interval speed measurement using radars, binocular surveillance cameras, and infrared speed sensors. In most areas in the U.S., such detection is still performed manually by the highway patrol. This paper presents a novel approach for detecting abnormal vehicle behavior using the traditional traffic surveillance cameras. Once the vehicle is within the camera field of view (FOV), its estimated speed is automatically detected based on designated references with respect to dashed white lines on roads, such that the speed of the vehicle is computed using techniques. The proposed method, if validated and facilitated, can largely reduce the cost of upgrading surveillance infrastructure as well as the extra human administrative burden.
A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.
Using Convolutional Neural Networks to Extract Keywords and Keyphrases: A Case Study for Foodborne Illnesses

Keywords and keyphrases are important for Natural Language Processing (NLP) applications such as document classification, information retrieval, and topic identification. They are also useful for capturing different classes of entities from content related to healthcare, biology, food science, and journalism fields. There are different approaches to extract keywords and keyphrases. Deep learning approaches have achieved high-performance results in terms of keywords and keyphrase extraction. However, among deep learning approaches, Convolutional Neural Network (CNN) potentials have not been fully explored as a technique for extracting keywords and keyphrases. In this work, we performed a comparative study using a benchmark dataset, the IEEE Xplore collection to test the CNN generalization ability in selecting keywords and keyphrases. In addition, we further collected a corpus in the field of foodborne illness outbreaks. We utilize this corpus to develop a CNN-based identification approach of keywords and keyphrases related to foodborne illnesses. Results were compared with several supervised (KEA, GuidedLDA) and unsupervised (LDA) machine learning algorithms. CNN outperformed these algorithms in selecting relevant keywords and keyphrases for foodborne illnesses. The findings of this study have also confirmed superiority of CNN-based algorithm for keyphrase extraction to other machine learning approaches.
DENAO: Monocular Depth Estimation Network with Auxiliary Optical Flow

Estimating depth from multi-view images captured by a localized monocular camera is an essential task in computer vision and robotics. In this study, we demonstrate that learning a convolutional neural network (CNN) for depth estimation with an auxiliary optical flow network and the epipolar geometry constraint can greatly benefit the depth estimation task and in turn yield large improvements in both accuracy and speed. Our architecture is composed of two tightly-coupled encoder-decoder networks, i.e., an optical flow net and a depth net, the core part being a list of exchange blocks between the two nets and an epipolar feature layer in the optical flow net to improve predictions of both depth and optical flow. Our architecture allows to input arbitrary number of multiview images with a linearly growing time cost for optical flow and depth estimation. Experimental result on five public datasets demonstrates that our method, named DENAO, runs at 38.46fps on a single Nvidia TITAN Xp GPU which is 5.15X $\sim$ ∼ 142X faster than the state-of-the-art depth estimation methods Meanwhile, our DENAO can concurrently output predictions of both depth and optical flow, and performs on par with or outperforms the state-of-the-art depth estimation methods and optical flow methods.
Image Generation Method Based on Improved Condition GAN

The Generated Adversarial Network (GAN) is commonly used to learn to generate a wide variety of images. The Wasserstein GAN improves the stability of GAN, but there are also deficiencies that do not have controllable conditions. This paper proposes an improved GAN network model, which we call CWGAN. CWGAN achieves the goal of improving the training stability and controllability of GAN by adding condition information to WGAN generators and discriminators. The experiment results show that CWGAN improves the training stability, solves the problem of gradient disappearance, and produces images more clearly, and there is no obvious mode collapse problem.
Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection

Anomaly detection has become a popular topic in many domains because anomalies can provide valuable information. Recently, ensemble learning has been applied to improve the generalization ability of existing anomaly detection methods. In an anomaly ensemble framework, diversity is essential for building a powerful ensemble method to obtain impressive performance. However, most studies use heuristic techniques to improve the diversity of ensembles, and it generally leads to limited diversity. To obtain improved diversity, we propose a diversity aware-based sequential ensemble method (called D-SEM) for anomaly detection. Specifically, our proposed method divides the ensemble diversity into two parts: sample diversity and model diversity. For sample diversity, we introduce the subsampling technique to implement preliminary generation of diverse datasets for training. For model diversity, we design an ensemble-based optimization model to learn base classifiers with improved diversity. Furthermore, an unsupervised diversity measure is proposed to quantitatively assess diversity and an anomaly pruning strategy is utilized to successively eliminate pseudo-anomalies. Based on the inclusion of sample diversity and model diversity, the proposed D-SEM method obtains better generalization ability for anomaly detection. The experimental results based on real-world datasets suggest that the proposed method has superior performance compared with various state-of-the-art methods.
Similarity hashing for charged particle tracking

The tracking of charged particles produced in high energy collisions is particularly challenging. The combinatorics approach currently used to track tens of thousands of particles becomes inadequate as the number of simultaneous collisions increase at the High Luminosity Large Hadron Collider (HLLHC). We propose to reduce the complexity of tracking in such dense environments with the use of similarity hashing. We use hashing techniques to separate the detector space into buckets. The particle purity of these buckets is increased using Approximate Nearest Neighbors search. The bucket size is sufficiently small to significantly reduce the complexity of track reconstruction within the buckets. We demonstrate the use of the proposed approach on a public dataset of simulated collisions. The performance evaluation shows a significant speed improvement over the current technique and a further understanding of charged particles structure.
Ultracompact eye and pupil tracking device using VCSEL arrays and position sensitive detector

Eye tracking is one of the essential technologies for near-eye display systems, providing natural and involuntary humanmachine interfaces for application scenes of AR, VR, and MR. In this paper, novel type of an eye-tracking device is proposed, which consists of VCSEL array and PSD for realizing a triangulation system, and operates non-video based and non-mechanical. The PSD detects spatial position of a laser beam spot emitted from a certain light source and reflected from the surface of the cornea, relating to microscopic eye orientation. While, the spatially-aligned light emitters, i.e., VCSEL array, play an important role to expand detection angle of eye movement because slight change of light-incident angle on the cornea due to the difference of light-emitter positions transforms the corresponding angle into spatial information folded on a light receiving surface of the PSD. Such operations denote detections of coarse and fine movements of the eye, respectively. A notable feature of this device is that faint light from each light emitter identifies the position of laser beam spot with the help of the lock-in detection technique. The operation principle has been evaluated numerically and experimentally, and a prototype eye-tracking module with the size enough small to be mounted on glass frames has been demonstrated. These results have suggested possibility for realizing eye tracker with battery-powered driving, ultimate low processing load and data transfer amount that are our final speculation for this device.
Saliency-Based Diver Target Detection and Localization Method

Diver target automatic detection is indispensable for underwater defense systems, such as the unmanned harbor surveillance system. It is a very challenging task due to various poses and intensity features of diver target. In addition, the background noise in sonar images is complex, which also makes the task more difficult. In this paper, we propose a diver detection method based on saliency detection for sonar images. On the basis of studying the characteristics of diver sonar images, we first decompose the original sonar image and perform median filtering on it, which can significantly improve the quality of the sonar image saliency map. We employ saliency detection technique based on frequency analysis to segment the acoustic highlight region from its surroundings. This segmentation region roughly locates the diver target and generates a region of interest (ROI). We then extract the acoustic shadow region in ROI, which contributes to furtherly improve the localization accuracy. Finally, we merge the segmented highlight region and the extracted acoustic shadow region and compute the minimum outer rectangle of the merged region. Experimental results validate that the proposed method can well detect and locate the diver target, and it can also satisfy the demands of real-time application, and there is almost no false alarm in this method.
Neuro-fuzzy assessment of pupil performance based on e-learning platform implementation

Learning and teaching process for the both, learners and teachers, could be challenging task since every learner has different cognitive preconditions.Hence every learner should have different learning material and different learning paths in order to achieve given learning goals.In this article was made an e-learning platform for teacher and learners based on object-oriented approach.In the platform every learner could has personalized learning contents based on the learners' background.The personalized learning contents could be achieved by different learning paths for each learner.Object-oriented technique by unified modelling language (UML) was performed for the modelling process of the learning platform.Afterwards the pupil performance was analyzed after the introduction of the e-learning platform by computational intelligence approach.
URL-based Phishing Detection using the Entropy of Non-Alphanumeric Characters

Phishing is a type of personal information theft in which phishers lure users to steal sensitive information. Phishing detection mechanisms using various techniques have been developed. Our hypothesis is that phishers create fake websites with as little information as possible in a webpage, which makes it difficult for content- and visual similarity-based detections by analyzing the webpage content. To overcome this, we focus on the use of Uniform Resource Locators (URLs) to detect phishing. Since previous work extracts specific special-character features, we assume that non-alphanumeric (NAN) character distributions highly impact the performance of URL-based detection. We hence propose a new feature called the entropy of NAN characters for URL-based phishing detection. Experimental evaluation with balanced and imbalanced datasets shows 96% ROC AUC on the balanced dataset and 89% ROC AUC on the imbalanced dataset, which increases the ROC AUC as 5 to 6% from without adopting our proposed feature.
Parsimonious Bayesian Filtering in Markov Jump Systems With Applications to Networked Control

We consider the problem of controlling the precision of the multiple-model multiple-hypothesis filter with Gaussian mixture reduction. The controller adaptively chooses the number of hypotheses kept by the filter to (sub)optimally seek a tradeoff between filter precision and computational effort. In order to quantify the approximation error due to hypotheses truncation, the controller employs probability divergence measures such as f-divergences and the Wasserstein divergence. The proposed solution is tested on the problem of estimating the states of a networked control system with packet drops on the controller-actuator channel. Theoretical results demonstrate that our strategy leads to a divergence between the true Bayes posterior and the truncated one that remains bounded over time. Numerical results show a good improvement with respect to truncation with a constant number of hypotheses, specially as the number of modes increases and so does the problem dimensionality.
Systematic literature review of empirical studies on mental representations of programs

Programmers are frequently tasked with modifying, enhancing, and extending applications. To perform these tasks, programmers must understand existing code by forming mental representations. Empirical research is required to determine the mental representations constructed during program comprehension to inform the development of programming languages, instructional practices, and tools. To make recommendations for future work a systematic literature review was conducted that summarizes the empirical research on mental representations formed during program comprehension, how the methods and tasks have changed over time, and the research contributions. The data items included in the systematic review are empirical studies of programmers that investigated the comprehension and internal representation of code written in a formal programming language. The eligibility criteria used in the review were meant to extract studies with a focus on knowledge representation as opposed to knowledge utilization. The results revealed a lack of incremental research and a dramatic decline in the research meaning that newly developed or popularized languages and paradigms have not been a part of the research reviewed. Accordingly, we argue that there needs to be a resurgence of empirical research on the psychology of programming to inform the design of tools and languages, especially in new and emerging paradigms.