Reviewerly/exHarmony huggingface dataset:
Checking temporal patterns of API usage without code execution

In theory, Application Programming Interfaces (APIs) allow developers to write client code that reuses library code without needing to know its internals. In practice, correctly using APIs requires respecting explicit and implicit constraints and usage temporal patterns. Ignoring such patterns could lead to errors and design flaws. These often cannot be detected prior to integration and system testing. We propose the Temporal Usage PAttern Checker (Tupac) for APIs, an interprocedural static analysis approach that can verify that client code conforms to temporal API usage patterns as it is being developed. We evaluated the effectiveness of our approach on 4 projects with 4 different APIs. Our evaluation shows that Tupac allows to accurately check the conformance of the client code to the patterns in under 1 second. This means that Tupac can realistically be deployed in an IDE without stalling the regular coding rhythm.
Remixing, Seeing, and Curating: Algorithms’ Influence on Human Creativity

In this doctoral research program, the influence of algorithms on creative perception and construction is investigated. First, I outline the need for a clear framework that aims to measure viewer-defined online creativity. To meet this need, I offer a 5-part mixed methods approach that synthesizes qualitative, quantitative, and visual data to construct a conception of online visual creativity. Then, I discuss applications of this framework to measure the impact of emerging technologies on human creativity. In a second study, I demonstrate the influence of TikTok's algorithm on the content being filmed by its users; in particular, I investigate the Duet feature as a site of remixing rather than creatively collaborating. In sum, algorithms are shown to influence creative consumers by both curating the content that one is exposed to and by guiding the content that is initially created. Next steps for my dissertation work are also discussed.
Enhancing Accelerometer-based Human Activity Recognition with Relative Barometric Pressure Signal

This paper proposes the use of relative barometric pressure sensors for enhancing the accuracy of human activity detection based on a tri-axial accelerometer. In our experiment, we employ a device consisting of a tri-axial accelerometer and a barometric pressure sensor on 12 subjects' waists while they are performing different sequences of activities of sitting, standing, walking, and lying. We also set another barometric pressure sensor on the wall as a reference for barometric pressure. We compare activity classification with features extracted from the following datasets 1) acceleration data, 2) acceleration and on-body barometric pressure data, and 3) acceleration, on-body barometric pressure, and the reference barometric pressure data. Three classifiers are compared, i.e., K-Nearest Neighbors (KNN), Decision Tree, and Random Forest. The results show that highest classification accuracy can be achieved when acceleration, on-body barometric pressure, and the reference barometric pressure information are used. This dataset provides the highest classification accuracy of 90.4% with the Random Forest algorithm.
Radically Simplifying Game Engines: AI Emotions &amp; Game Self-Evolution

Today, video games are a multi-billion-dollar industry, continuously evolving through the incorporation of new technologies and innovative design. However, current video game software content creation requires extensive and often-times ambiguous planning phases for developing aesthetics, online capabilities, and gameplay mechanics. Design elements can vary significantly relative to the expertise of artists, designers, budget, and overall game engine/software features and capabilities. Game development processes are often extensively long coding sessions, usually involving a highly iterative creative process, where user requirements are rarely provided. Therefore, we propose significantly simplifying game design and development with novel Artificial Cognition Architecture real-time scalability and dynamic emotion core. Rather than utilizing more static emotion state weighting emotion engines (e.g. ExAI), we leverage significant ACA research in successful implementation of analog neural learning bots with Maslowan objective function algorithms. We also leverage AI- based Artificial Psychology software which utilizes ACA's fine grained self-evolving emotion modeling in humanistic avatar patients for Psychologist training. An ACA common cognitive core provides the gaming industry with wider applications across video game genres. A modular, scalable, and cognitive emotion game architecture implements Non-Playable Character (NPC) learning and self-evolution. ACA models NPC's with fine grained emotions, providing interactive dynamic personality traits for a more realistic game environment and enables NPC self-evolution under the influence of both other NPC's and players. Furthermore, we explore current video game design engine architecture (e.g. Unity, Unreal Engine) and propose an ACA integration approach. We apply artificial cognition and emotion intelligence modeling to engender video games with more distinct, realistic consumer gaming experiences, while simultaneously minimizing software gaming development efforts and costs.
Malignant Web Sites Recognition Utilizing Distinctive Machine Learning Techniques

With the development of Web technology, the Internet has become a stage for wide scope of criminal scheme including spam promotion, budgetary fraud, Web page defacement, etc. Because of the fast development of the Internet, Web sites have become the interloper's fundamental objective. As the quantity of Web pages expands, the malicious Web pages are moreover extending and the attack is dynamically gotten progressed. The existing approaches like blacking listing and dynamic analysis approaches are time and resources intensive, hence these methodologies are not adequate to classify the Web sites as malignant or benign. The proposed research work develops a lightweight classification framework to recognize malicious Web pages effectively using distinctive supervised machine learning classifiers like Naïve Bayes, K-nearest neighbors, random forest, AdaBoost, and some distinct relevant URL features. So here, some URL-based features are extracted for the detection of malicious and benign Web sites. We have experimented the test results using Python environment and found the random forest achieves highest classification accuracy of 98.7%.
Gesture of Interest: Gesture Search for Multi-Person, Multi-Perspective TV Footage

In real-world datasets, specifically in TV recordings, videos are often multi-person and multi-angle, which poses significant challenges for gesture recognition and retrieval. In addition to being of interest to linguists, gesture retrieval is a novel and challenging application for multimedia retrieval. In this paper, we propose a novel method for spatio-temporal gesture retrieval based on visual and pose information which can retrieve similar gestures in multi-person scenes through continuous shots. The attention-aware features, extracted from human pose key-points, together with a sophisticated pre-processing module, alleviate the susceptibility of gesture retrieval to background noise and occlusion. We have evaluated our method on a subset of the NewsScape Dataset. Our experimental results demonstrate the effectiveness of the proposed method in retrieving similar results in occluded scenes as measured by the quality of the top 5 results.
Scaling Single-Image Super-Resolution Training on Modern HPC Clusters: Early Experiences

Deep Learning (DL) models for super-resolution (DLSR) are an emerging trend in response to the growth of ML/DL applications requiring high-resolution images. DLSR methods have also shown promise in domains such as medical imaging, surveillance, and microscopy. However, DLSR models are extremely computationally demanding, and require unreasonably long training times on modern Volta GPUs. In our experiments, we observed only 10.3 images/second on a single Volta GPU for training EDSR, a state-of-the-art DLSR model for single-image super-resolution. In comparison, a Volta GPU can process 360 images/second while training ResNet-50, a state-of-the-art model for image classification. Therefore, we believe supercomputers provide a good candidate to speed up DLSR model training. In this paper, we select EDSR as the representative DLSR PyTorch model. Further, we introduce Horovod-based distributed EDSR training. However, we observed poor default EDSR scaling performance on the Lassen HPC system at Lawrence Livermore National Laboratory. To investigate the performance degradations, we perform exhaustive communication profiling. These profiling insights are then used to optimize CUDA-Aware MPI for DLSR models by ensuring advanced MPI designs involving CUDA IPC and registration caching are properly applied by DL frameworks. We present a comprehensive scaling study of EDSR with MVAPICH2-GDR and NCCL up to 512 GPUs on Lassen. We demonstrate an improvement in scaling efficiency by 15.6% over default Horovod training, which translates to a 1.26× speedup in training performance.
Discovery of Burglary Hotspots and Extraction of Their Features

Investigation of the underline contributing factors to burglaries within a geographical area, is essential in finding solutions. Such investigation for a geographical area in the southeast of Georgia is the subject of this paper. Our goal is bifold: (a) discovering the burglary hotspots for the area and (b) extracting the features associated with each given hotspot. Such features are the underlying contributing factors for each hotspot, which differ from one hotspot to the next. Data sources used for meeting the goal are cleansed city Police reports, United States Census, Google maps, and the City Ordinances. The discovery of the hotspots is accomplished by using the Optics clustering technique and feature extraction is performed by an association analysis.
Online one pass clustering of data streams based on growing neural gas and fuzzy inference systems

Abstract The clustering of big data streams has become a challenging task due to time and space constraints of the hardware and decreasing accuracy when the dimensionality of input data grows in time. In this paper, fuzzy growing neural gas is introduced, an online fuzzy approach for clustering data streams based on the growing neural gas algorithm, by adopting more restrictive criteria for selecting the winner nodes in the topological graph constructed at each iteration of the algorithm. The algorithm is tested on public datasets, and the results show improvements over existing clustering methods.
Evaluating the Impact of Wide-Angle Lens Distortion on Learning-based Depth Estimation

Most computer vision research focuses on narrow angle lenses and is not adapted to super-wide-angle (aka spherical) lenses. This is mainly because current neural networks are not designed or trained to interpret the significant barrel distortion that is introduced in the captured image by such wide angle lenses. As these lenses capture a half-sphere or a section of sphere on the object space, barrel distortion appears when the image is projected on a 2D flat image sensor. By controlling this distortion at the lens design stage, camera designers can create some areas with augmented resolution [26]. In this work, we present an analysis of the impact of such augmented resolution on computer vision algorithm accuracy, using the problem of single image depth estimation as a case study. To this end, 360° panorama datasets are warped to simulate different wide-angle lens datasets, which are then used to train identical neural networks. Each lens presents specific areas of the image with augmented resolution using spatially-varying non-linear distortion. We show that this property leads to better local accuracy in depth estimation. We also demonstrate that considering lens manufacturing improves performance when tested on realistic lenses, especially in the area of augmented resolution. We further show that this property helps to locally come closer to performances obtained on perspective images without cropping the field of view.
Nonverbal behavior of interviewers influences the competence ratings of observers in recruitment interviews: a study investigating social influence using 360-degree videos with virtual reality and 2D screen displays

Abstract This study examined whether an interviewer’s nonverbal behavior influences observers’ competence ratings in a recruitment interview using 360-degree videos experienced with immersive virtual reality (VR-cardboard) and 2D screen displays. Participants ( n = 110) observed a recruitment interview and assessed three competences of the applicant (behavior in a team, customer care, and sales skill). We used a 2 × 2 design with the nonverbal behavior (positive vs. negative) of the interviewer and display type (VR-cardboard vs. 2D screen display) as between-subjects factors. After observing interview sequences and providing competence ratings, participants also rated different aspects of immersion using the augmented reality immersion questionnaire (ARI; Georgiou and Kyza in Int J Hum Comput Stud 98: 24–37, 2017) and their overall satisfaction with the experience. For two of the three competences (customer care and behavior in a team), we found that observers gave higher competence ratings when the interviewer’s nonverbal behavior was positive compared to when it was negative. This social influence effect was similar for 360-degree videos experienced with immersive VR and 2D screen displays. VR resulted in higher immersion than 2D screen displays regarding the dimensions of flow and presence . Our results suggest that the ARI questionnaire can be used to reliably assess 360-degree videos experienced with immersive VR and 2D screen displays.
Improving Training time in Capsule Neural Network

Chiadika Electrical & Computer Engr Mathematics department Electrical & Computer Engr Brunel University, Seoul National University Brunel University, London, United Kingded the Attention Routing CapsuleNet (AR CapsNet) as proposed by Jaewoong Choi et al. in Attention Routing between Capsules. The AR-CapsNet is an enhanced version of CapsNet which, uses a new and different routing and activation function. The unique routing style is Attention routing which, is simply capsules been routed, with the help an attention module and a fast-forward pass but, what is most important is that the spatial information is kept, which is the primary reason behind Capsules. Primarily, the in-built interpretation of the dynamic routing is finding a focal point of the prediction capsules. As well known, emphasis on preserving a vector orientation is what activation functions and its variant deal majorly on; the activation function used is capsule activation because it focuses is on how a capsule-scale activation function performs. The model was trained on the MNIST and CIFAR-10 datasets and classification tasked against AR CapsNet and CapsuleNet. The model showed almost accuracy with less training parameters and less training time.
Designing an Educational Virtual Reality Application to Learn Ergonomics in a Work Place

In this paper we describe the process of designing an educational Virtual Reality (VR) application for university medical students to learn how to set up a work place ergonomically correct. We focus on commercially available hardware and a commercial game engine. This was a collaboration between medical experts and VR experts to achieve the ideal results. This use case builds on research of 3D user interfaces (UI) to create an UI system that novice VR-users can use intuitively. Next steps include a user study to compare immersive and non-immersive versions, as well as learn effects in VR.
Expressiveness within Sequence Datalog

Motivated by old and new applications, we investigate Datalog as a language for sequence databases. We reconsider classical features of Datalog programs, such as negation, recursion, intermediate predicates, and relations of higher arities. We also consider new features that are useful for sequences, notably, equations between path expressions, and "packing''. Our goal is to clarify the relative expressiveness of all these different features, in the context of sequences. Towards our goal, we establish a number of redundancy and primitivity results, showing that certain features can, or cannot, be expressed in terms of other features. These results paint a complete picture of the expressiveness relationships among all possible Sequence Datalog fragments that can be formed using the six features that we consider.
Diagnosis of Facial Skin Disease in Expert System Using Fuzzy Sugeno Method

Abstract Structure of human skin is the largest structure in the human body. Skin has many function for human body. The importance of skin care is done to provide beauty to the skin, especially facial skin care, how important facial care can be seen by number of beauty clinics that are used to help treat skin beauty. Various kinds of facial skin problems, one of problem were facial acne which can interfere with beauty of facial skin. Most people oftenly uneducated how to treat acne which is often inappropriate. Usually, sufferers will go directly to the doctor or beauty clinic. Problem often occurs is high cost of consulting with experts and long distance between user’s location and nearest doctor’s clinic, so needed an application that can help users to be able to consult about skin diseases on the face. In these researches, researchers focused on problem of acne on facial skin by implementing an expert system using Sugeno fuzzy method. Results obtained from these researches is kind of acne and severity based on the symptoms input by user. This application expert system implemented with mobile app and having an accuracy rate of 75% from 100% by testing results of experts and results by system.
REST API performance comparison of web applications based on JavaScript programming frameworks

The aim of the work was to compare two technologies for creating server applications based on the JavaScript programming language. For the purposes of the research, two test applications were created. The first one was built on the basis of the Express programming framework and the second one on the basis of the Hapi framework. The client part of both applications was prepared using the React library. The client and server parts communicated with each other by means of REST API – the universal HTTP interface. The client application sent requests to the server application which then performed basic operations on the MongoDB basis and returned the result. As part of the work, an experiment consisting of four scenarios was developed. In each scenario, a different type of data was taken into consideration: a string of characters, an array, an object and an array of objects. The research focused on the efficiency aspect – measuring the response time of requests during GET, POST, PUT and DELETE operations. The tests were performed on two computers and the measurements were made in two ways: using a single code embedded in test applications and using the Postman tool. The obtained results, after averaging and analyzing them allowed for the conclusion that the Express framework proved to be more efficient than Hapi due to the shorter response time of requests. Only in the scenario where operations with large datasets were performed was the response time of requests at a similar level.
Machine Learning approach for Item-basedMovie Recommendation using the most relevant similarity techniques

The recommendation system based on correlations of users' interest is mostly generated by the Collaborative Filtering approach. The collaborative filtering technique is capable of providing better predictions when there is enough data. Find out item similarity and user similarity using ratings is an important part of collaborative filtering. These similarities are measured for rating prediction for a better recommendation. There are several algorithms for calculating the similarity. Different similarities are used in previous studies for item-based and user-based recommendations. As there are different similarities used, it is difficult to choose which one is suitable for the desired recommendation. In this work, we present item-based filtering for movie recommendations and apply the most used similarity techniques which are Pearson correlation, Cosine similarity, Spearman Rank correlation. We implement them on the same dataset. Then we have applied these similarity techniques in the same metrics of the dataset for comparing them and choose the similarity techniques that provide better accuracy.
Classification of Liver Diseases in CT-scan Images based on Support Vector Machine and Texture Analysis

Liver is the largest organ present in the human body which plays a vital role in many bodily functions such as metabolizing drugs, clearing toxins from the blood, and producing blood proteins and bile to aid digestion. On other hand, there are many liver diseases such as hepatoma, cirrhosis, hepatitis. Liver diseases are amongst the leading causes of death worldwide. Hence there is a need to detect and control these diseases at early stages. Computer aided diagnostics helps to detect diseases using medical imaging technologies and it serves as the second opinion to physicians in disease diagnosis. In this study, we have developed a Computer aided diagnostics (CAD) system which extracts the liver from computed tomography images and detects the abnormality present in it. Region of interest i.e Active contour method is applied for segmentation and segmented images are used to extract prominent features based on ROI texture and these features are used for classification of abnormalities such as Hepatic Cyst, Hepatic Abscess, and HCC. Classification is done using Support Vector Machine. The experimental result shows the method can identify diseased patients and assist medical physicians in diagnosis.
ArtMatch: Classifying Famous Paintings and Matching Them with Children’s Artwork

This paper represents the progress of an ongoing effort to develop a mobile app called ArtMatch, which will help kids develop a passion for art. The proposed app will deepen children's appreciation for famous pieces of artwork while enhancing their creativity. By taking an image of artwork created by the user, ArtMatch will show the most similar painting done by a famous artist, using AI and deep learning technology. The app works through a program that clusters images based on their design and style. Throughout this process, the user will see the effectiveness of his/her painting through their match with well-known paintings. The program's present version can generate accurate matches for several works done by young children.
Applying the Histogram of Oriented Gradients to Recognize Arabic Letters

the aim of this paper is to recognize the Arabic handwriting letters by using histogram of oriented gradients (HOG). We collected 2240 letters by 8 people, each person wrote 28 alphabet letter 10 times. First of all we resize All 2240 hand writing letter of Arabic Alphabet as images(pre-processing) after that extract these images by using one of feature extraction methods which is histogram of oriented gradients (HOG) .For classification, the K-Nearest Neighbor (KNN) is used. The results are shown by using 1120 images in the one case and 2240 images in the second case and evaluate these results with the confusion matrix. Other cases we used leave one out (LOO), 2-fold classification and leave one out cross validation. The best fully performance of HOG was with leave one out technique because of the ability of HOG algorithm to capture the shape of letter in the image according to its edges (gradients).