Reviewerly/exHarmony huggingface dataset:
Designing of a rigorous image retrieval system with amalgamation of artificial intelligent techniques and relevance feedback

Retrieving out the most comparable images from huge databases is the challenging task for image retrieval systems. So, there is a great need of constructing a capable and rigorous image retrieval system. In this implementation, an exclusive and competent Content based image retrieval (CBIR) system is schemed by the integration of Color moment (CM) and Local binary pattern (LBP). A hybrid feature vector is created by the combination of these two techniques through the process of normalization. This hybrid feature vector is given as the input to the intelligent classifiers i.e. Support vector machine (SVM) and Cascade forward back propagation neural network (CFBPNN). After that, Relevance feedback (RF) technique is applied so as to get the high level information in order to reduce the semantic gap. So, here two Artificial Intelligent CBIR models are proposed, first one is (Hybrid+SVM+RF) and second is (Hybrid+CFBPNN+RF) and their performance parameters are compared. The implementations are performed on two benchmark dataset Corel-1K and Oxford flower dataset which contains 1000 and 1360 images respectively. Different parameters are figured such as accuracy, precision, average retrieval time, recall etc. The average precision obtained for the first model is 93% with Corel 1K database and 91% with Oxford flower database. And similarly for the second model, it is 97% and 94% respectively which is higher than the first model. This implemented technique is validated on both the datasets and the attained results outperforms with other related s approaches.
The complexity of regex crosswords

In a typical regex crossword puzzle, one is given two non-empty lists 〈R1,…,Rm〉 and 〈C1,…,Cn〉 of regular expressions (regexes) over some alphabet, and the challenge is to fill in an m×n grid of characters such that the string formed by the ith row is in L(Ri) and the string formed by the jth column is in L(Cj), for all 1≤i≤m and 1≤j≤n. We consider a restriction of this puzzle where all the Ri are equal to one another and similarly the Cj. We consider a 2-player version of this puzzle, showing it to be PSPACE-complete. Using a reduction from 3SAT, we also give a new, simple proof of the known result that the existence problem of a solution for the restricted (1-player) puzzle is NP-complete. We also prove NP-completeness where all the row and column regular expressions are equal to each other.
Seeing in Extra Darkness Using a Deep-Red Flash

We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source. Our main observation is that in a dim environment, the human eye mainly uses rods for the perception of light, which are not sensitive to wavelengths longer than 620nm, yet the camera sensor still has a spectral response. We propose a novel modulation strategy when training a modern CNN model for guided image filtering, fusing a noisy RGB frame and a flash frame. This fusion network is further extended for video reconstruction. We have built a prototype with minor hardware adjustments and tested the new flash technique on a variety of static and dynamic scenes. The experimental results demonstrate that our method produces compelling reconstructions, even in extra dim conditions.
OR-Toffoli and OR-Peres Reversible Gates

The paper introduces new Toffoli and Peres reversible gates, which operate under disjunctive control, and shows their functionality based on the Barenco et al. quantum model. Both uniform and mixed polarity are considered for the controls. Rewriting rules are presented, which provide a reduction of the number of gates and quantum cost of reversible (sub) circuits using standard Toffoli or Peres gates. It is shown that in most cases a reduction in the number of CNOT gates is obtained, which is convenient when mapping reversible circuits to IBM-QX quantum computers.
Translating performative mediated art into virtual reality: A case study

Park benches are distinctive public spaces that invite a temporary pause for thought and time out from everyday activities and worldly preoccupations. Park Bench Sojourn is a multimodal arts project that explores the uniqueness and universality of these spaces and the kinds of experiences they foster. It asks what it means to be human; surrounded, as we are, by computer technologies and digital media, living lives that are perpetually ‘connected’ and dispersed through the cloud. It reflects on how our technologically determined lives and lifestyles conspire against us to find opportunities to stop, reflect and be witnesses to lived experience. It is a conceptually playful creative work that shares concerns for health and well-being arising from the contemporary mindfulness movement and the traditional practices and worldviews upon which mindfulness draws. The project is based around a range of experiential sojourns, which require participants to find a bench to sit on and then take a sojourn, or a number of sojourns from the project’s website, which may include audio, video, spoken word, or just listening. Other iterations of the project have included a multimedia gallery installation juxtaposing content from a variety of sojourns. Regardless of the format, context or specific content, the project explores ways in which we ‘perform’ ourselves and mediate experience via digital technologies. In this article, we describe the process of translating this mediated and performative artwork into a VR prototype and directions for future work.
Fuzzy TOPSIS-Based Cloud Model to Evaluate Cloud Computing Services

Rapid evolution of cloud services led to the growth of many cloud services today. Selection of these cloud services has appeared as an important challenge for users and decision makers of an organization which further led to the evaluation of these services on the basis of the need of the users. Due to the availability of a huge number of cloud services having same functionalities in the plethora of cloud service providers, it becomes difficult to choose the suitable and optimal cloud service. Abundant articles have been already proposed in the last few years. But present literature procedure limited evaluation criteria while neglecting many decisive factors. In this paper, we proposed the formulation of this gap as a fuzzy TOPSIS-based model that evaluates cloud computing services. A case study on cloud computing services is worked to demonstrate the workability of the proposed model. Finally, the validation of the proposed model is done through sensitivity study for its robustness.
Aggregated Gaze Data Visualization Using Contiguous Irregular Cartograms

Gaze data visualization constitutes one of the most critical processes during eye-tracking analysis. Considering that modern devices are able to collect gaze data in extremely high frequencies, the visualization of the collected aggregated gaze data is quite challenging. In the present study, contiguous irregular cartograms are used as a method to visualize eye-tracking data captured by several observers during the observation of a visual stimulus. The followed approach utilizes a statistical grayscale heatmap as the main input and, hence, it is independent of the total number of the recorded raw gaze data. Indicative examples, based on different parameters/conditions and heatmap grid sizes, are provided in order to highlight their influence on the final image of the produced visualization. Moreover, two analysis metrics, referred to as center displacement (CD) and area change (AC), are proposed and implemented in order to quantify the geometric changes (in both position and area) that accompany the topological transformation of the initial heatmap grids, as well as to deliver specific guidelines for the execution of the used algorithm. The provided visualizations are generated using open-source software in a geographic information system.
Maritime Target Detection Based on Electronic Image Stabilization Technology of Shipborne Camera

During the detection of maritime targets, the jitter of the shipborne camera usually causes the video instability and the false or missed detection of targets. Aimed at tackling this problem, a novel algorithm for maritime target detection based on the electronic image stabilization technology is proposed in this study. The algorithm mainly includes three models, namely the points line model (PLM), the points classification model (PCM), and the image classification model (ICM). The feature points (FPs) are firstly classified by the PLM, and stable videos as well as target contours are obtained by the PCM. Then the smallest bounding rectangles of the target contours generated as the candidate bounding boxes (bboxes) are sent to the ICM for classification. In the experiments, the ICM, which is constructed based on the convolutional neural network (CNN), is trained and its effectiveness is verified. Our experimental results demonstrate that the proposed algorithm outperformed the benchmark models in all the common metrics including the mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean average precision (mAP) by at least -47.87%, 8.66%, 6.94%, and 5.75%, respectively. The proposed algorithm is superior to the state-of-the-art techniques in both the image stabilization and target ship detection, which provides reliable technical support for the visual development of unmanned ships.
Human Bas-Relief Generation From a Single Photograph

We present a semi-automatic method for producing human bas-relief from a single photograph. Given an input photo of one or multiple persons, our method first estimates a 3D skeleton for each person in the image. SMPL models are then fitted to the 3D skeletons to generate a 3D guide model. To align the 3D guide model with the image, we compute a 2D warping field to non-rigidly register the projected contours of the guide model with the body contours in the image. Then the normal map of the 3D guide model is warped by the 2D deformation field to reconstruct an overall base shape. Finally, the base shape is integrated with a fine-scale normal map to produce the final bas-relief. To tackle the complex intra- and inter-body interactions, we design an occlusion relationship resolution method that operates at the level of 3D skeletons with minimal user inputs. To tightly register the model contours to the image contours, we propose a non-rigid point matching algorithm harnessing user-specified sparse correspondences. Experiments demonstrate that our human bas-relief generation method is capable of producing perceptually realistic results on various single-person and multi-person images, on which the state-of-the-art depth and pose estimation methods often fail.
Semi-supervised Heterogeneous Defect Prediction with Open-source Projects on GitHub

The heterogeneous defect prediction (HDP) technique can predict defects in a target company using heterogeneous metric data from external company, which has received substantial research attention. However, existing HDP methods assume that source data is labeled but labeling data is expensive. Semi-supervised defect prediction technique can perform defect prediction with few labeled data. In this paper, we investigate a new problem — semi-supervised HDP (SHDP). To solve this problem, we propose a new approach named cost-sensitive kernel semi-supervised correlation analysis (CKSCA) as a solution of SHDP problem. It introduces unified metric representation and canonical correlation analysis to make the data distributions of different company projects more similar. CKSCA also designs a cost-sensitive kernel semi-supervised discriminant analysis mechanism to utilize the limited labeled data and sufficient real-life unlabeled data from different companies. Besides we collect lots of open-source projects from GitHub website to construct a new large-scale unlabeled dataset called GITHUB dataset. It contains 26,407 modules and is greater than each public project dataset. It has been public online and can be extended continuously. Experiments on the GITHUB dataset and other public datasets indicate that unlabeled GITHUB data can help prediction model improve prediction performance, and CKSCA is effective and efficient for solving SHDP problem.
Curriculum Learning based approaches for robust end-to-end far-field speech recognition

Performance of Automatic Speech Recognition (ASR) systems is known to suffer considerable degradation when exposed to Far-Field speech data capture. Consequently, far-field ASR has received considerable attention in recent years. Motivated by our recent work using Curriculum Learning (CL) based strategies to improve Speaker Identification (SID) under noisy and degraded conditions, this study proposes a novel approach to improve far-field ASR using CL based approaches. Specifically, we propose using a CL based approach for training a Bidirectional Long Short Term Memory (BLSTM) based ASR network trained using the Connectionist Temporal Classification (CTC) objective function. We initiate the training with comparatively easier near-field data, and include more diverse (difficult) far-field data progressively in the later stages of training. These proposed approaches are shown to significantly outperform the baseline BLSTM ASR system, and offer relative reductions in WERs of up to +7.3% and +10.1% for the dev and eval sets of the AMI far-field voice capture corpus.
Credit Card Fraud Detection using Apache Spark Analysis

Credit card fraud is an ever-growing threat to the financial industry. High dependence on internet technology leads to an increase in fraudulent transactions. Nowadays, banking sector is the most vulnerable for attacks as most of the transactions are done through credit cards and net banking. Many techniques have been proposed to tackle this problem. This paper mainly focus on recognizing fraudulent transactions by analyzing the previous set of transaction records. This research work attempts to integrate big data analytics along with machine algorithms for fast detection of large real-time data. This paper gives a brief comparison of some of the machine learning techniques using Big data.
The impact of digital technology on learning motivation and learning modes

The new era creates new opportunities and poses a number of challenges for education to adapt to the emerging prospects of digital transformation of educational activities. The article provides a brief analysis of domestic and foreign works on the problems of digitalization in education. It is shown that, along with the benefits offered by the digital space, there are many problems of its use in educational activities. The solution of these problems is primarily associated with the training of teachers to use new opportunities for the organization and implementation of distance learning forms, the development of their digital competencies. At the same time, it has been shown that in both foreign and domestic universities, students in their majority prefer contact forms of classes and want a live dialogue with teachers, despite their rather high digital literacy. Therefore, the emerging digital didactics is very important to determine the ratio of traditional and distance learning will be able to provide a comfortable learning environment and a high level of training of future specialists. These and other questions can be answered by digital didactics, which has not yet been developed by scientists and educational practitioners.
Enhanced Human Activity Recognition based on Activity Tracker Data Using Secure Homomorphic Encryption Techniques

The fitness tracker data for human activity recognition is playing an important role in improving health and monitoring the daily movements of human such as health care and fitness. This detects human activities such as step count, sleeps rate, calories burned, and person mood for the data collected by the activity tracker device and mainly helps in identifying the Apnea-Hypopnea Index (AHI) which is caused due to Obstructive Sleep Apnea. Medical data is very susceptible for many external threats that affect its security and privacy. The privacy and security of the data shared across the medical field is very difficult to maintain. To overcome these problems, Homomorphic Encryption techniques are proposed, which is used for privacy preserving out-sourced storage and computation. The encrypted technique helps in protecting sensitive data from outsourced. The two Homomorphic techniques are Gorti's Enhanced Encryption Scheme and Carmichael's Encryption Scheme that are used to encrypt the fitness tracker data. The computation is made with the encrypted data without revealing the original data to providers. The analysis is made on the encrypted data by computing the AHI to predict the disease based on the range given in AHI. Thus, security of the data is maintained. The performance of the schemes is measured based on the time taken for encryption and decryption of the medical data and analysis performed on the encrypted data.
Sparse Multi-Path Corrections in Fringe Projection Profilometry

Three-dimensional scanning by means of structured light illumination is an active imaging technique involving projecting and capturing a series of striped patterns and then using the observed warping of stripes to reconstruct the target object’s surface through triangulating each pixel in the camera to a unique projector coordinate corresponding to a particular feature in the projected patterns. The undesirable phenomenon of multi-path occurs when a camera pixel simultaneously sees features from multiple projector coordinates. Bimodal multi-path is a particularly common situation found along step edges, where the camera pixel sees both a foreground and background surface. Generalized from bimodal multi-path, this paper examines the phenomenon of sparse or N-modal multi-path as a more general case, where the camera pixel sees no fewer than two reflective surfaces, resulting in decoding errors. Using fringe projection profilometry, our proposed solution is to treat each camera pixel as an underdetermined linear system of equations and to find the sparsest (least number of paths) solution by taking an application-specific Bayesian learning approach. We validate this algorithm with both simulations and a number of challenging real-world scenarios, demonstrating that it outperforms state-of-the-art techniques.
P‐111: Halo guides the design of backlight partition in Mini‐LED Display

Applying high dynamic range(HDR) to mini‐LED backlight is the current research interest of display. The partition control technology for backlight of mini LED is adopted to improve image contrast, however it can not avoid the introduction of halo, which affects the image quality and needs to be solved. Therefore, this article firstly proposes a method to evaluate halo; Then we studied the brightness distribution of the halo from the perspective of the backlight and the module. We also put forward some factors that affect the size of the halo. It is hoped that the design of backlight partitions can be guided through the study of halo.
Analysis of COVID-19 evolution based on testing closeness of sequential data

A practical algorithm has been developed for closeness analysis of sequential data that combines closeness testing with algorithms based on the Markov chain tester. It was applied to reported sequential data for COVID-19 to analyze the evolution of COVID-19 during a certain time period (week, month, etc.).
Leveraging Models to Reduce Test Cases in Software Repositories

Given a failing test case, test case reduction yields a smaller test case that reproduces the failure. This process can be time consuming due to repeated trial and error with smaller test cases. Current techniques speed up reduction by only exploring syntactically valid candidates, but they still spend significant effort on semantically invalid candidates. In this paper, we propose a model-guided approach to speed up test case reduction. The approach trains a model of semantic properties driven by syntactic test case properties. By using this model, we can skip testing even syntactically valid test case candidates that are unlikely to succeed. We evaluate this model-guided reduction on a suite of 14 large fuzzer-generated C test cases from the bug repositories of two well-known C compilers, GCC and Clang. Our results show that with an average precision of 77%, we can decrease the number of removal trials by 14% to 61%. We observe a 30% geomean improvement in reduction time over the state of the art technique while preserving similar reduction power.
Towards Adaptive Robotic Tutors in Universities: A Field Study

Learning in university setting includes the challenging task of self-motivation of the learner. The use of social robots has been shown to support the learner in their social learning process. In this paper, we address the motivation of learners in terms of self-determination theory as a theoretical framework to address need satisfaction. To this end, we conducted a field study using an adaptive robotic tutor that supports learners in exam preparation using an online learning session. With the aim to not only benefit motivation, but also academic success, we draw from research in social robotics in education as well as from adaptive tutoring, to create an adequate learning scenario. Adaptation is realized by a simple content and learner model and resulted in a significantly higher perceived use of the tutoring compared to a control condition. Our results also showed descriptive benefits such as increased perceived tutor quality, need satisfaction and motivation resulting from the adaptive tutoring. Finally, we found significantly better exam performance with the robotic tutor in the adaptive or non-adaptive version relative to students not participating in the robotic tutoring.
The Cybersecurity Skills Survey: Response to the 2020 SIM IT Trends Study

The Cybersecurity Skills Survey was developed and implemented to respond to the high-demand for cybersecurity professionals, noted by the findings of the 2017, 2018, 2019, and 2020 SIM (Society for Information Management) IT Trends and Issues Study. These findings were based upon input from over 1,000 IT leaders representing 37 SIM Chapters. The goals of the cybersecurity skills survey were to identify: (1) What technical skills are needed for entry-level professionals in cybersecurity jobs? (2) What professional skills are needed for entry level professionals in cybersecurity jobs? (3) What technical skills are needed for early-career professionals in cybersecurity jobs? and (4) What professional skills are needed for early-career professionals in cybersecurity jobs? The survey findings provide key insights into in-demand skills and "difficult-to-find" competencies. This paper reports on 99 responses captured from IT leaders representing the SIM Chapters in St. Louis, Austin, Milwaukee, and Phoenix, and is part of an ongoing data collection effort to include the Chapters in Tampa, Los Angeles, Houston, Chicago, and Boston. Dialogues between academic professionals and industry leaders in each of these professional communities are contributing to meeting the demand for talented cybersecurity graduates.