Reviewerly/exHarmony huggingface dataset:
Salient object detection network with multi-scale feature refinement and boundary feedback

Benefiting from the development of convolutional neural networks, salient object detection has achieved prominent progress in recent years. However, there are still two limitations when handling challenging scenarios. On the one hand, upsampling and pooling operations might cause blurry boundaries. On the other hand, multi-level features having different characteristics might incur fusion problems. To address these issues, we propose a novel pixel-wise salient object detection method equipped with multi-scale feature refinement and boundary feedback in this paper. Firstly, a feature interaction scheme is designed to depict the multi-scale representation of side-output features and the interactions between intra-layer features. Secondly, we design a context-aware feature refinement module to adaptively select useful side-output information for generating coarse saliency features. Finally, we obtain the predicted saliency maps with the help of extracted boundary information in a coarse-to-fine manner. Extensive experimental results on four benchmark datasets show that our proposed model performs favorably against state-of-the-art methods.
Can We Trust Theorem Provers for Industrial AI?

There are three technologies a modern AI-aware software engineer needs to know: data mining, theorem proving, and nonlinear optimization (also called search-based SE). While much of the current industrial AI activity is focused on data mining, these other technologies are starting to achieve prominence. Optimization technology is discussed in the editorial “A Watershed Moment for Search-Based Software Engineering” (see IEEE Software, July-August 2021). Here, we offer a tutorial on theorem proving (what it is, how to use it, how it can sometimes go wrong, and how to fix that).
Correcting Large Knowledge Bases Using Guided Inductive Logic Learning Rules

Domain-oriented knowledge bases (KBs) such as DBpedia and YAGO are largely constructed by applying a set of predefined extraction rules to the semi-structured contents of Wikipedia articles. Although both of these large-scale KBs achieve very high average precision values (above 95% for DBpedia (The estimated precision of those statements is 95% in the Data Set 3.9 of DBpedia) and YAGO), subtle mistakes in a few of the underlying extraction rules may still impose a substantial amount of mistakes derived from specific relations. By applying the same regular expressions to detect mistakes of range constraint of relation, some special features of negative statements are erased while mining the rules. For traditional rule-learning approaches based on Inductive Logic Programming (ILP), it is very difficult to correct these extraction mistakes from the Wikipedia, since they usually occur only in a relatively small subdomain of the relations’ arguments. In this paper, we thus propose a GILLearn, a Guided Inductive Logic Learning model, which iteratively asks for small amounts of feedback automatically over a given KB to learn a set of knowledge correction rules. The GILLearn introduces the guided information to rule augmentation and provides the respective metrics for the validation of corrected triples. Our experimental evaluation demonstrates that the proposed framework achieves the significant performance on the large knowledge bases.
Development and Analysis of an Improved Prototype within a Class of Bug-based Heuristic Path Planners

Path planning relies on free-space expansions. The Ray Path Finder (RPF) and RayScan algorithms solve the Euclidean shortest path problem. Both rely on free-space expansions by basing on the bug concept, which is to cast rays to a desired target and trace any obstructing contours. By combining heuristic costs, an optimal path can be found. In this work, a new and insightful analysis aiding future designs of this class of heuristic bug-based planners is provided. A rigorous proof for a modified Pledge in RPF is included. A corrected RPF prototype is designed to compare with Anya, a state-of-the-art free-space planner. The computational advantages of the heuristic bug-baed planners can offer are provided.
Thermal image enhancement using kurtosis based clipping histogram method

Enhancement is necessary to improve the visibility of the thermal or infrared images useful in the surveillance applications. As the thermal images will suffer with poor visibility, low contrast and low resolution it is difficult to enhance. In this paper, two new approaches are introduced for increasing the visibility of the low illumination thermal infrared images. The first method is based on the Histogram matching technique. The histogram of the low illumination input image is modified in accordance with the statistical parameters of reference image. Second approach is based on clipping the histogram using kurtosis and then bisecting the histogram using the median. For performance evaluation few images from OSU thermal dataset are used and compared with existing methods. The proposed technique performance is evaluated using various quality metrics such as PSNR, SSIM, Entropy and Absolute Mean Brightness Error (AMBE). The results have shown the improvement in the PSNR, SSIM and AMBE when compared with the mean based clipping. The performance of the proposed methods gave improved quantitative parameters in comparison with the state of the art methods.
Novel Fundamental Concepts beneath Quantum Photonics

For better understanding of quantum photonics, five novel fundamental concepts are presented. Some typical quantum phenomena such as quantum tunneling, double-slit interference, delayed-choice experiment, quantum eraser experiment and the “uncertainty” relations have been properly re-interpreted and the advancement on full-cosmic-scope relativity has also been introduced.
Research on Risk and Supervision of Financial Big Data Application Based on Cloud Computing

With the continuous development of information technology and the coming of big data era, it is a key measure to promote the steady development of the financial industry to build a big data information system through cloud computing technology and further promote the financial credit risk supervision mechanism. Facing the problems of credit management in the traditional financial market and the credit risk problems caused by the virtual and diversified operation characteristics of Internet finance, it is of great significance to make full use of big data information technology to explore the financial credit risk supervision paradigm. Internet finance makes use of search engines, social networks, big data and cloud computing technology to achieve a breakthrough in the current financial operation and management system, improve market efficiency and optimize the allocation of resources, but the risks it brings cannot be ignored. This paper discusses various situations existing in Internet financial supervision, and puts forward effective solutions based on cloud computing technology, hoping to minimize the risks of Internet finance.
Steganalysis attacks resilient, imperceptible, blind, and keyed image hiding in ultra HD 4K video

A novel data hiding approach is being proposed in this article, camouflaging secret data within a very high-quality video object. The proposed algorithm utilizes Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) to hide image data, preprocessed using Arnold Transform. The novelty of this approach lies in the combination of five factors, which are – use of very high-resolution Ultra HD 4 K video as a cover object; Use of DCT coefficient in Scene Change Detection to identify cover frame; Scrambling the secret image by Arnold Transformation; Use of DCT-DWT in embedding and extraction process to that hide data in the blue plane of the RGB frame; and lastly, use of three different keys for data hiding to secure the proposed method against attacks, by adhering the Kerckhoff’s principle of a cryptosystem. Several quality metrics like Structural Content, Peak Signal-to-Noise Ratio, Normalized Cross-Correlation, Average Difference, Maximum Difference, and Normalized Absolute Error have been calculated to validate the imperceptibility. This method has also been validated against different steganalysis attacks like random cropping, rotating, resizing, noise addition, filtering, histogram attack, and lastly, compared with existing data hiding methods to prove superiority.
Improve Semantic Correspondence by Filtering the Correlation Scores in both Image Space and Hough Space

Semantic correspondence is an important and challenging task in computer vision due to background clutter, especially for analyzing images from the same category but with large intra-class variations. The methods using regularized Hough matching only consider global offset consensus to enhance geometric consistency, where the filtering in Hough space is usually used to aggregate the scores of the similar offsets. However, the matched objects between two images are usually located in a local region of the images. Therefore, in this paper, we propose an improved method for establishing semantic correspondence by introducing regional offset consensus, which is implemented by filtering the correlation scores in both Hough space and image space, where the filtering in image space is used to enforce the regional offset consensus. Experiments show that the proposed method can produce superior results than the state-of-the-art methods, when applied on four benchmark datasets including a large-scale SPair-71k dataset.
Amplification and Self-oscillations in Active Non-Foster Tank Circuit

This paper proposes non-Foster tank circuit that comprises both positive and negative R, L, C elements. It is shown that, apart from familiar effect of broadband dispersion cancellation, it is also possible to achieve amplification and self-oscillations rooted in negative reactive elements.
A proposed multi criteria indexing and ranking model for documents and web pages on large scale data

Due to the expansion of data, search engines encounter different obstacles for retrieving better relevant content to user's search queries. Consequently, various retrieval and ranking algorithms have been applied to satisfy the result's relevancy according to user's needs. Unfortunately, indexing and ranking processes face several challenges to achieve highly accurate results, since most of the existing indexes and ranking algorithms crawl documents and web pages based on limited number of criteria that satisfy user needs. So, this research studies and observes how search engines work and which factors contribute to higher rankings results. The research also proposes a Multi Criteria Indexing and Ranking Model (MCIR) based on weighted documents and pages which depend on one or more ranking factors, aiming at building a model that achieves high performance, better relevant pages, and the ability to index and rank both online/offline pages and documents. The MCIR model was applied on three different experiments to compare documents and pages results in terms of ranking scores, based on one or more criteria of user's preferences. The results of applying MCIR model proved that final pages ranking results depending on multi-criteria are better than using only one criterion, and some criteria have great effect on ranking results than other criteria. It was also observed that the MCIR model achieved high performance on indexing and ranking dataset up to 100 gigabytes.
Design Attributes for the Development of Digital Solutions: An Exploratory Study from the Perspective of Professionals Connected to Digital Transformation

Digital technology plays a significant role in everyday life, being increasingly integrated at home, work and leisure activities. Its pervasiveness has been changing consumption habits and social interactions. As a result, the number of digital platforms and services grew exponentially and it is expected to continue to expand in future years. In a world that offers a wide range of digital products to people, it is crucial to develop meaningful products. Taking this into account, it seems important to focus on understanding which attributes should be considered in the development process of digital platforms and services to work towards optimal solutions for society. A sentence completion task was employed to uncover the motivations, emotions and beliefs of a small sample of individuals who work for a Digital Transformation company. The results highlighted the significance of content, usability, aesthetics, customization and trust. Furthermore, a reflection on the themes suggests that usability is foundational, that content and aesthetics arouse interest and customization elicits positive reactions. Remarkably, it was found that a lack of trust causes discomfort and may negatively impact the use of digital solutions.
Recognizing Human Actions in Video using Motion History Image and Deep Learning

Action recognition from videos has become one of the coveted fields in recent years because of its potential applications. In particular, action recognition methods utilizing deep learning are found to be effective. In this paper we use spatio-temporal context of videos to recognize the actions in videos using motion history images. The effectiveness of the proposed approach is demonstrated on a new dataset with typical payment related actions.
Intersectional Approaches for Supporting Casual Language and Culture Learning in Immigrant Families

In multigenerational immigrant families, everyone is a lifelong learner. Grandparents must learn to foster social connection with their grandchildren despite language and culture barriers, while grandchildren seek to learn their heritage language and culture to better connect with their grandparents. Educational support tools for this context are sparse as language learning apps are often Eurocentric in design and do not fit in the existing routines of immigrant families. In my research, I design educational tools for marginalized immigration populations, such as apps for learning language, preserving family stories, and sharing culture. I design for people at the margins, which requires interdisciplinary intensive approaches. After providing an overview of my research, I present my ongoing project to build a storytelling tool for immigrant families called CrossRoads. CrossRoads employ human-centered approaches that are better suited for uncovering the needs and practices of marginalized immigrant populations. I design and evaluate a tool that fits within the routines of immigrant grandparents and grandchildren by employing the familiar activity of storytelling. I discuss preliminary findings, and implications of my work in the development of educational technology for supporting marginalized, lifelong learners in casual contexts.
Inheritance metrics feats in unsupervised learning to classify unlabeled datasets and clusters in fault prediction

Fault prediction is a necessity to deliver high-quality software. The absence of training data and mechanism to labeling a cluster faulty or fault-free is a topic of concern in software fault prediction (SFP). Inheritance is an important feature of object-oriented development, and its metrics measure the complexity, depth, and breadth of software. In this paper, we aim to experimentally validate how much inheritance metrics are helpful to classify unlabeled data sets besides conceiving a novel mechanism to label a cluster as faulty or fault-free. We have collected ten public data sets that have inheritance and C&K metrics. Then, these base datasets are further split into two datasets labeled as C&K with inheritance and the C&K dataset for evaluation. K-means clustering is applied, Euclidean formula to compute distances and then label clusters through the average mechanism. Finally, TPR, Recall, Precision, F1 measures, and ROC are computed to measure performance which showed an adequate impact of inheritance metrics in SFP specifically classifying unlabeled datasets and correct classification of instances. The experiment also reveals that the average mechanism is suitable to label clusters in SFP. The quality assurance practitioners can benefit from the utilization of metrics associated with inheritance for labeling datasets and clusters.
A Light SRGAN for Up-Scaling of Low Resolution and High Latency Images

In the past few years Single Image Super-Resolution (SISR) has been one of the most researched topics in the field of AI. Super-Resolution Generative Adversarial Nets in short SRGAN paved the way to achieve Super-Resolution (SR) of images while hallucinating a lot of details. Deriving from the main components from SRGAN, i.e. Architecture, Loss and Adversarial nature, we have refined a model that works for very small images, and tries to make out as much information as possible in a short amount of time. The main things being focused are to create a fast Generator which also tries to keep a good SSIM score with the ground truth images, tries to recover as much of the information from relative pixels and also gets close enough to benchmark performance with as limited resources as possible. The core objective of having a simple, fast and light model, is not only to enlarge images but fill in as many missing details as it can from simple pixels, to fully defined and distinct features within that image that might have double or quadruple resolution than the Low-Resolution Images.
Research on State Evaluation Method of Fire Control System Based on Improved K_ means Algorithm

In order to improve the accuracy of tank fire control system state evaluation, a new method based on improved K-means is proposed, which takes a certain type of tank fire control system gyroscope group as the research object. The method of evaluating the gyroscope group state of fire control system based on means clustering algorithm. This method adopts the basic principle of K-means algorithm to reduce the subjective factors in the evaluation process. Principal component analysis is used to extract data features, and then the initial clustering center is selected by minimum variance to reduce the randomness of the initial clustering center and improve the accuracy. In view of the lack of data samples, the bootstrap small sample statistical method is integrated. Finally, benchmark sample data and test data are used to verify the feasibility and accuracy of the method.
Mastering the effects of air turbulence in optical component interferometric metrology

Air turbulence is a major environmental factor that degrades the precision of optical part interferometric metrology. For large optics with a long cavity, turbulence effects become critical. Reducing those effects by protecting the cavity with mechanical means is necessary, though not sufficient. Averaging wavefronts is a simple, classic, and efficient remedy. However averaging leaves unanswered the following issues: How many wavefronts should be averaged? What is the resulting uncertainty on the parameters of interest (RMS, PTV...)? What is the risk for a parameter to be over tolerance? For addressing those issues, the Xonox X-fringe® interferometry software is fitted with an advanced "Average Statistics" function. Reading a given number of phase data maps, the function computes the average phase map and estimates the significant statistical characteristics of the turbulence, namely its space and time auto-correlation function (ACF), as well as the ACF of the sample average. The latter provides a covariance matrix, applied to Monte-Carlo random trial simulations. On each of those random trials, a parameter of interest is computed. For a high number of trials, the frequency histogram of this parameter tends towards its probability density. Finally, this density yields confidence intervals of the parameter (i.e., uncertainty with a confidence level), and above all, the risk that the parameter could be over tolerance. The user can create templates defining, for a given set of Zernike, the parameter of interest (PTV, RMS, Strehl), or that of the residual wavefront. The software evaluates the signal-to-noise ratio and the measurement reliability.
CoSpeak: Peer Feedback on Voice Stories to Inform Learning Spoken English

English is officially recognized in India as a means of public communication as it plays an important role in academia, government affairs etc. However, only 10.6% of Indians speak some English according to the 2011 Census report. In this project, primary research was conducted in the context of teaching English to class 12 students from regional language medium schools. The context was understood, difficulties faced in learning, aspects of learning and teaching were identified. The topic of speaking English in daily conversations was prioritized. Existing solutions were analyzed. A design brief consisting of goals based on the research was formulated. Ideas were brainstormed and the final direction was consolidated based on the feedback received. CoSpeak, an interactive mobile application using voice recognition and utilizing collaborative learning was designed iteratively. An evaluation with the teacher and students was conducted using a Wizard of Oz type prototype and future possibilities were identified.
Business Process Re-Engineering: Strategies for Health Management Services in Puskesmas

Puskesmas has several operational problems that disrupt and even hampering the health service delivery to the patients in certain occasional practices. It is often occurred regularly in the daily routine such as damaged medical devices, the limited number of doctors, the process of taking drugs that take a long time, and damaged drugs’ condition. To improve the service and solve the problems, Puskesmas can conduct iterative and incremental processes to the health activities through certain adjustments such as business process re-engineering (BPR). It aims to generate competitive and sustainable processes by increasing the effectiveness and efficiency lead to quality improvement, costs’ reduction, simplifying the life cycle. This study wants to identify the current situation and analyze the targeted condition of health management services in Puskesmas through informal interviews and field observations. It will provide the targeted business process reference models with the changes of certain activity routines and several actors.
Feature Enhancement and Multi-scale Cross-Modal Attention for RGB-D Salient Object Detection

RGB-D Salient Object Detection (SOD) methods utilize the information provided by the depth map to assist detection. To make effective use of depth information, existing methods face two challenges: (1) How to obtain valuable cross-modal complementary information. (2) How to reduce the negative effects of unreliable depth maps for RGB-D SOD. To prevent unreliable depth maps from interfering with the detection results, we design a feature enhancement module (FEM). The module first uses a feature guidance unit (FGU) to guide the depth feature to restore the saliency information, and then uses a feature selection unit (FSU) to obtain the higher quality depth feature. Finally, the representation of depth features is deeply enhanced through progressive steps of guidance and selection. To obtain valuable cross-modal complementary information, we design a multi-scale cross-modal attention module (MCA). We first use multiple parallel dilated convolutional layers to capture global and local information, and then we design a cross-modal attention unit (CAU) to generate cross-modal attention maps. These maps can eliminate redundant information and suppress background interference. Quantitative and qualitative experiments on five datasets demonstrate the superiority of the proposed approaches.