Reviewerly/exHarmony huggingface dataset:
EASC: An exception-aware semantic compression framework for real-world knowledge graphs

Knowledge graphs (KGs) have achieved great success in many real applications, and great efforts have been dedicated to constructing larger knowledge graphs. An obvious trend in KG construction is that the KGs become ever-increasingly bigger. However, we argue that constructing a KG by directly inserting more triples may harm the performance of the KG, and one possible solution is KG compression. In this paper, we propose an exception-aware semantic lossless compression framework EASC to compress a KG. Since many triples can be inferred from other triples with semantic rules, we remove the triples that can be inferred and store the rules and exception cases. Specifically, we formalize the lossless compression problem as a weighted set cover problem, which is NP-hard, and propose a semantic lossless compression algorithm to get an approximation result. We conduct extensive experiments on seven real-world large-scale KGs. The results show that EASC achieves state-of-the-art performance in semantic compression methods. Furthermore, by combining EASC as an independent module with syntactic compression methods, we achieve state-of-the-art performance in lossless compression methods.
Exploring Thai EFL pre-service teachers’ technology integration based on SAMR model

This study investigated how Thai EFL pre-service teachers (PSTs) use technology in their classrooms and the level of substitution, augmentation, modification, and redefinition (SAMR) they implement. Seven English education majors enrolled in the course “teaching internship 2” in their fourth year at a Thai university participated in the study. Due to COVID-19, the participants were trained to teach online, but during their teaching practicum, they were required to teach in a traditional on-site setting. Because of this novel arrangement, it was essential to investigate how these PSTs incorporate technology into the classroom. For the purpose of analyzing the process of educational technology integration, this study employed SAMR model. Observations and semi-structured interviews were used as data collection methods. Throughout the semester, observations were conducted twice. SAMR observation form was used to investigate how PSTs used technology in their teaching. Participants were asked to participate in an interview after each observation. The interviews included open-ended questions based on the conceptual framework of SAMR. Observation results show that technological tools were used to engage students, check comprehension, create teaching content, and evaluate students’ understanding. For the study, the participants were divided into three groups based on the extent of technological tools used. One participant was deemed to be in the substitution stage, five participants in the augmentation stage, and only one participant in the modification stage. The results of the interviews indicated that teacher motivation and the availability of ICT equipment, as well as familiarity with technological tools, played a significant role in PSTs’ integration of technology into the classroom.&lt;b&gt; &lt;/b&gt;The study’s conclusions are helpful in developing PSTs’ technology integration in EFL classrooms. SAMR model should be introduced to PSTs to let them critically reflect on and develop their own technology integration.
Hybrid Saliency-Based Visual Perception Model for Humanoid Robots

Recent years have seen an explosion of research on saliency detection and visual attention model development for humanoid robots. The bottom-up and top-down visual saliency detection models can be combined to develop hybrid visual attention for the interaction of the robot with humans. Most of the hybrid visual saliency models are not computationally economical for practical implementation on humanoid robots due to the high computation cost and complexity of their model. The main drawback of most of the visual attention models is that they can detect the salient object in the case of simple background in natural images, but cannot perform well in the case of images having cluttered and textured backgrounds. Most global contrast-based methods do not produce efficient results for images having multiple salient objects. The hybrid models based on local and global contrast-based methods have an issue that most background regions are predicted as salient regions. In this research paper, a hybrid stereo saliency model based on PC/BC-DIM neural network is presented which can efficiently detect salient objects for the simple, cluttered, and textured background images. The proposed model has added advantages of its simplicity, robustness, and execution on a CPU system due to which it is perfectly suited for realization on humanoid robots. The proposed saliency detection model can detect multiple salient objects. The mean absolute error (MAE) score for the hybrid saliency model is 0.22 and for the stereo saliency model, the MAE score is 0.375. The proposed model is computationally efficient and cost effective models for implementation on humanoid robots.
Optimized Focused Crawling for Web page Classification

The geographical location in the globe is usually searched by following the geographical map. The same resemblance works for the Web search engine. The main job of the Web search engine is to get relevant Web pages from World Wide Web (WWW) for its end user. These Web pages are retrieved by Web crawlers (automated program). The Web crawling process starts with the seed list of URLs managed in queue. The Web page against each seed URL is fetched one after another. The hyperlinks on the Web pages are extracted and further added in the queue if relevant to the archiving task. This process continues until the required number of URLs are fetched. Focused crawler is one of the types of Web crawler that extracts only those Web pages that are relevant to the topic specified by the user. A topic is usually specified by keywords or some exemplary documents on which focused Web crawler decides whether a Web page is relevant to the topic or not. In focused Web crawlers automated Web page classification is used to classify the relevant and irrelevant Web pages. In this paper we have used a modified Genetic Algorithm (GA) based automated web page classifier. In this method keywords are used as feature set for the classification process. The crawled web pages are labeled as relevant or irrelevant. The best features are selected by the Genetic algorithm using Cosine Similarity function. These extracted features are used by classifier for the classification of relevant Web pages. Using the keywords as a feature, better precision, recall, accuracy and F1 scores are achieved.
Systematic Literature Review: Evaluating Effects of Adversarial Attacks and Attack Generation Methods

Advancement in Artificial Intelligence (AI) aims to train the Machine Learning (ML) Models in such a way that they would be able to take decisions spontaneously, however on the other side attackers attempt to manipulate the results generated by these models which makes the application of these models difficult in security-critical areas including classification of medical images, autonomous system installed in vehicles, street lights, malware detection and identification of a person as criminal or innocent. Advancement in research proved the vulnerability of these classifiers due to adversarial attacks that can alter their result in training as well as in testing phase of the model. Causative attacks are the training phase attacks whereas the attacks done at testing phase are the exploratory attacks. This systematic literature Review (SLR) is conducted to gain in-depth knowledge of adversarial attacks, which is the most effective type of exploratory attack, parameters on which these attacks are based, along with the most conventional methods of generating adversarial attacks.
Vision-Based Velocity Estimations for Autonomous Mobile Robots

Nowadays, the deployment of autonomous mobile robots (AMRs) in warehouses and factories is becoming increasingly prevalent. Various sensors are often fitted on the robot to identify adjacent objects for navigation. In order to maintain the performance in a dynamic environment, the robot must be able to predict the velocity of surrounding obstacles before a local path can be generated. In this work, a vision-based velocity estimation technique is proposed using a pattern matching technique via LabVIEW machine vision. The focus is on estimating the velocity of oncoming obstacles which can be either a robot, a human, or both. Three real-time experiments were conducted to evaluate the performance of the approach. From the experiments, the proposed technique results in average estimation errors of no greater than 0.8° for angle, and 0.41cm/s for speed for single obstacle detections. For multiple obstacle detections, average errors of no greater than 1.42° for angle, and 2cm/s for speed were obtained. Based on the recorded numerical results, the AMR is able to make decision when the obstacle is at least 39cm away from it, which is sufficient to avoid collision.
An Applied Artificial Intelligence Aided Technique for Effective Classification of Breast Cancer

Among Women, Breast cancer is one of the maximum occurring diseases. Many women die every year because of breast cancer globally. Early prediction and diagnosis of this disease can prevent death in the end. The survival rate increases on detecting breast cancer early as better treatment can be provided. Development in prediction and diagnosis is necessary for the life of people. A higher amount of accuracy in the prediction of breast cancer is necessary for the treatment aspects and also for the survivability of patients. It is apparent that there are different techniques available in breast cancer detection but machine learning algorithms can bring a large contribution to the process of prediction and early diagnosis of breast cancer. In this study, we use a Wisconsin dataset which was collected from a scientific dataset of 569 breast cancer. Out of 569 patients, 63% were diagnosed with benign and 37% were diagnosed with malignant cancer. The benign tumor grows slowly and does not spread. We apply five machine learning algorithms to this dataset and train a model for predicting malignant and benign tissues (BCs). Algorithms are K-Nearest neighbor, Support vector machine, Decision tree, Deep learning, and Random-forest respectively. The effectiveness of these algorithms is evaluated in terms of accuracy, F measure, confusion matrix, and specificity. By comparing the results deep learning classifier gives the highest accuracy and outclass all the other classifiers by attaining an accuracy of 9S.l3%. SVM gives an accuracy of 97.66% whereas KNN gives an accuracy of 95.79% etc.
Resource Availability Forecasting for Federated Clouds

Cloud federation has enabled organizations to adopt collaborative services for sharing data and workloads across various platforms. Induction of federation members may require some verifications and predictions related to capacity and capability of these members for compensating such types of workloads. However, the nature of federated services require stringent methods to keep track of dynamically forming resource clusters for forecasting their behavior. Recent literature has mostly focused on the applicability of forecasting algorithms based on static datasets with little or no applicability to real time scenarios. Proposed research has utilized a real world application of Clouds4Coordination (C4C) federation system. A resource forecasting strategy using two well-known algorithms, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) has been proposed for collaborative clouds used in Architecture, Engineering and Construction (AEC) industry. The results have shown that no single algorithm is sufficient enough to deal with dynamic scenarios of cloud federation. Moreover, the selection of algorithm is highly dependent upon the type and duration of prediction required i.e. short term or long term as required by the user.
Techno-Economic Analysis &amp; Optimal Sizing for Rooftop Photovoltaics Utilizability: A Case Study for UET Taxila

This study intends to demonstrate the feasibility of Rooftop Photovoltaics (RTPV), which have become somewhat more significant due to technological, financial and ecological advantages, with lower levelized cost of energy (LCOE). Two techniques, Particle Swarm Optimization (PSO) and Photovoltaic System (PVsyst) have been used to determine the proposed RTPV size and its cost factor while accounting for shading effects and environmental factors. Results are contrasted, which influence the choice of the ideal system size, economic viability and energy efficiency for the RTPV system. This particular study was carried out at the main campus of the public sector institute; University of Engineering & Technology (UET), located in Taxila. Its befitting location is beneficial for the study as it is situated in the northern Pakistan. Numerous other variables, such as the RTPV system’s life expectancy, performance ratio (PR), and carbon footprint reduction, are also investigated. These make adoption of RTPV systems for public sector buildings more rational and practicable choice.
Number Recognition System-based Virtual Sketch with Hand Gestures Using Attentional Convolutional Network

Advances in computer technology in the modern era have proliferated. Computer technology has had a significant impact on human life. The relationship between humans and computers, known as Human-Computer Interactions (HCI), can be done non-contact or virtual; one example is Hand Gesture Recognition. However, the development of hand gesture recognition has several challenges, especially in increasing the performance of its glory. Several methods are used to improve the performance of hand gesture recognition, such as the convolutional neural network, which still needs improvement. In this paper, we conducted a number recognition system-based virtual sketch with a hand gesture using an Attentional Convolutional Network with an accuracy value obtained in the non-real-time of 83.9% and real-time accuracy is 90%.
Robot Path Planning via Harris Hawks Optimization: A Comparative Assessment

Path planning is the task of finding a safe and efficient path for a mobile robot to navigate through an environment while taking into account factors such as energy efficiency and time constraints. This study compares the performance of the Harris Hawks optimization (HHO)-based method with other competing swarm intelligence algorithms such as Whale optimization Algorithm, Sine Cosine Algorithm, and Multi-Verse optimizer Algorithm. By including safety and path length in the cost function, and imposing a constraint on the processing time, simulation results demonstrate that the HHO-based path planning method is able to drive the robot towards the target with the lowest path cost compared to the rest.
Improved segmentation of damages on high-resolution coating images using CNN-based ensemble learning

The automation of inspection processes in aircraft engines comprises challenging computer vision tasks. In particular, the inspection of coating damages in confined spaces with hand-held endoscopes is based on image data acquired under dynamic operating conditions (illumination, position and orientation of the sensor, etc.). In this study, 2D RGB video data is processed to quantify damages in large coating areas. Therefore, the video frames are pre-processed by feature tracking and stitching algorithms to generate high-resolution overview images. For the subsequent analysis of the whole coating area and to overcome the challenges posed by the diverse image data, Convolutional Neural Networks (CNNs) are applied. In a preliminary study, it was found that the image analysis is advantageous when executed on different scales. Here, one CNN is applied on small image patches without down-scaling, while a second CNN is applied on larger down-scaled image patches. This multi-scale approach raises the challenge to combine the predictions of both networks. Therefore, this study presents a novel method to increase the segmentation accuracy by interpreting the network results to derive a final segmentation mask. This ensemble method consists of a CNN, which is applied on the predictions of the given patches from the overview images. The evaluation of this method comprises different pre-processing techniques regarding the logit outputs of the preceding networks as well as additional information such as RGB image data. Further, different network structures are evaluated, which include own structures specifically designed for this task. Finally, these approaches are compared against state-of-the-art network structures.
Surpassing spectator qubits with photonic modes and continuous measurement for Heisenberg-limited noise mitigation

Noise is an ever-present challenge to the creation and preservation of fragile quantum states. Recent work suggests that spatial noise correlations can be harnessed as a resource for noise mitigation via the use of spectator qubits to measure environmental noise. In this work we generalize this concept from spectator qubits to a spectator mode: a photonic mode which continuously measures spatially correlated classical dephasing noise and applies a continuous correction drive to frequency-tunable data qubits. Our analysis shows that by using many photon states, spectator modes can surpass many of the quantum measurement constraints that limit spectator qubit approaches. We also find that long-time data qubit dephasing can be arbitrarily suppressed, even for white noise dephasing. Further, using a squeezing (parametric) drive, the error in the spectator mode approach can exhibit Heisenberg-limited scaling in the number of photons used. We also show that spectator mode noise mitigation can be implemented completely autonomously using engineered dissipation. In this case no explicit measurement or processing of a classical measurement record is needed. Our work establishes spectator modes as a potentially powerful alternative to spectator qubits for noise mitigation.
Factual to Virtual Visualization

The purpose of this study is to explore the formation of image output in computer system from the user input information. The study seeks to answer the research question How does the image displayed on the computer screen and how appearance of image changed? The goal is to analyze transformation of real world picture to computerized output image in a very simple method by explaining all major components involved in this process in detail with diagrams. Further by using some mathematical concepts algorithms are derived and implemented to transform the images to change their shapes, sizes etc As there is a very near context between computer science and mathematics. Systems works completely based on the algorithms written to perform the particular task, algorithms are designed based on the mathematical concepts and are implemented by the programming languages in computer. Paper concentrates on the graphic capabilities and potentials of the digital computer. It presents an abstract idea of system that, how computer works internally to create and represent the simple graphical elements and images which human can see and understand. Paper presents all the components and related concepts with suitable examples from input information to graphical output produced by computer. After by getting the concept of image formation the next step is to know that how transformation of image takes place by changing original image. This paper illustrates four types of transformations with example translation, rotation, scaling, and shearing. Paper explores about display devices, different types of display methods, programming tools and finally drawing of a simple line in computer screen using a simple line drawing algorithm. Further it elaborates the concept of pixels, resolution, aspect ratio, persistency, different types of transformations etc.,
Electricity Generation using Solar Power

Today energy is the main inspiration for socio-economic development. But due to incremental rate of environmental concern renewable energy provide a significant interest. This alternative power source is continuously achieving greater popularity due to continuous reduction in fossil fuels. It is the energy comes from sun, wind, rain etc. Among the non-conventional, renewable energy sources, solar energy affords great potential for conversion into electric power. Maximizing power output from a solar system is desirable to increase efficiency. In order to maximize power output, needs to keep the panels aligned with the sun. This paper deals with the electricity generation using solar power. The proposed system ensures the optimization of the conversion of solar energy into electricity by properly orienting the panel in accordance with the position of the sun. The operation of the paper is based on a Stepper motor intelligently moves a panel according to the light intensity of the sun sensing by light sensor
Unsupervised anomaly detection for industrial manufacturing using multiple perspectives of free falling parts

We present an extension of our automatic anomaly detection approach for the quality inspection of industrially manufactured parts. The sample under test is imaged from different perspectives simultaneously while it is in free fall to reduce inspection time and minimize part handling. Despite using a diffuse reflecting hollow sphere to achieve the best possible conditions for all camera perspectives, small artifacts from reflections on highly reflecting test specimens and drop shadows appear in the images. The presence of these artifacts leads to the appearance of type I errors. To address this issue, the state-of-the-art for anomaly detection PatchCore1 is modified to handle multiple perspectives at first. Second, a weighting step is added to the image evaluation pipeline. For this, the pose of the test sample is estimated, which is subsequently used to calculate a weight matrix per image. The weights correspond to the local viewing angle of the camera on the sample’s surface because the artifacts occur mainly at steep viewing angles. In addition, two datasets are created to evaluate the proposed approach containing sample data with single and multiple perspectives. The results show that the developed pipeline outperforms PatchCore and the original free-fall inspection setup algorithm. It reaches 95.9% AUROC for Object one and 85.7% AUROC for Object two on validation of multi-perspective datasets. Moreover, combining the proposed approach and the free-fall inspection algorithm improves the results for Object two, achieving 98% AUROC. The conducted experiments allow us to conclude that this approach has the potential to further increase robustness toward various anomalies and artifacts.