Reviewerly/exHarmony huggingface dataset:
Perception Score: A Learned Metric for Open-ended Text Generation Evaluation

Automatic evaluation for open-ended natural language generation tasks remains a challenge. We propose a learned evaluation metric: Perception Score. It utilizes a pre-trained model and considers context information for conditional generation. Perception Score assigns a holistic score along with the uncertainty measurement. We conduct experiments on three open-ended conditional generation tasks and two open-ended unconditional generation tasks. Perception Score achieves state-of-the-art results on all the tasks consistently in terms of correlation with human evaluation scores.
Does Net-Speak Experience Interfere With the Processing of Standard Words? Evidence From Net-Speak Word Recognition and Semantic Decisions

The relationship between Chinese net-speak use and traditional literacy has rarely been discussed in the literature. In this study, we conducted two experiments to explore the effects of net-speak experience on word recognition and semantic decisions. A sample of senior middle school students was divided into a high-experience group and a low-experience group according to the students’ net-speak experience, and the Go/No-Go task (Experiment 1) was adopted to investigate the differences between the two groups in the recognition of pure net-speak words and standard words. The results showed that the response time (RT) of participants was longer for pure net-speak word recognition than for standard word recognition. In addition, for both types of words, the recognition RT of participants in the high-experience group was shorter than that of participants in the low-experience group, but the accuracy (ACC) of pure net-speak word recognition was higher. Taking dual semantic net-speak words with both net-speak meaning and traditional meaning as priming stimuli and standard words related to the two meanings as targets, a semantic decision task (Experiment 2) was used to explore the differences between the two groups in judgements of the semantic relationship between the target words and priming words. The results showed that the decision ACC of participants in the high-experience group for both kinds of meaning-related words was significantly higher than that of participants in the low-experience group. The decision RT of participants in the high-experience group was slightly shorter for net-speak meaning-related words than traditional meaning-related words. The decision RT and ACC of participants in the low-experience group for both kinds of meaning-related words were equivalent. This shows that for Chinese teenagers, net-speak use may not disturb the processing of standard words; on the contrary, it may enhance processing.
The influences of smartphones on students’ learning outcomes: A case study perspective

Although e-learning management-related research has been pursued in many parts of the world, there is dearth of research that integrates this concept with the increasing trend of smartphone usage for educational purposes.Furthermore, research in the area shows a general lack of contextualizing the concept in the Middle Eastern context.Therefore, the aim of this study is twofold: (i) to explore the everyday use and role of smartphones among university students in Kuwait; and (ii) to identify if there are any socio-demographic differences in smartphone usage among university students.A questionnaire was completed by 376 students in order to achieve the study objectives.The results showed that students frequently used their smartphones at home and that they sometimes used these phones in recreation places and on transportation, at university, and when walking.Also, they always used their smartphones to complete fourteen non-learning activities, such as making phone calls, using it as a watch and as an alarm clock, and ten learning activities, such as checking their exam schedules, checking class timetables, and checking their grades.Moreover, social networking, learning, privacy, and safety were important reasons offered by students for owning/using smartphones.Furthermore, the study found that at least one of eight socio-demographic variables (i.e., gender, marital status, major/subject, nationality, smartphone brand, using it for the first time, age range, and level of study) has an effect on at least one pattern of smartphone usage.Several recommendations were made based on these findings that may improve the effectiveness of smartphone usage among university students in Kuwait.
Bogdanov Map-based Permuted Double Image Encryption

Chaos-based image encryption schemes shuffle the position of the pixels (confuse), change their values (diffuse), to camouflage the identity of the original image. In this paper, a symmetric image cryptosystem based on permutation is proposed. Permutation, used both to change the position of the pixel and modify its value, is undertaken using the Bogdanov map. First, the input image is permuted using the Bogdanov map so that the pixel positions are changed. Thereafter, the resultant scrambled image is sliced into bit-planes which are again separately subjected to the Bogdanov map. The encrypted image is constructed by encrypting the scrambled bit-planes with the key generated using dyadic transform. The experimental results exhibited random behavior in the distribution of the pixel values of the encrypted image. The cryptosystem is simple and fast, as it is permutation-based and, secure, it may be used in real-time transmission.
E-commerce Optimization with the Implementation of Social Media and SEO Techniques to Boost Sales in Retail Business

E-commerce is said to be the selling and buying of goods and services through an electronic network called the internet. The survival of each of the business in this era is highly dependable on the sales. Further, if the business wants to earn the highest sales then it is important to have a powerful SEO (Search Engine Optimization) strategy. The research is aiming at e-commerce optimization with the implementation of social media and the SEO techniques for boosting sales in the retail business. It is helping in increasing the sales of the retail online businesses. E-commerce optimization with the implementation of SEO techniques and social media in the retail businesses are helping in boosting sales. Primary and secondary research methods were used in this research process. The secondary research will be executed by reviewing different kinds of literature. For the primary research, the survey technique will be used for conducting quantitative research. The results illustrate that the implementation of social media and the SEO techniques are helping in boosting the sales of the retail business. Social media can attain maximum customers and could influence their purchases. It is important for the e-commerce business to increase traffic with the help of social media strategy.&#x0D; From the analyzed results, it can be said that social media implementation with SEO techniques is essential for boosting sales in the retail e-commerce businesses. E-commerce optimization is also essential for improving sales along with these elements.
CURRENT CHALLENGES IN OPERATIONAL VERY HIGH RESOLUTION LAND-COVER MAPPING

Abstract. Many land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are not sufficient for designing better products, adapted to real-case applications, operational constraints, and the generation of services, built upon these core layers. In this paper, we review the main requirements and the recent changes in remote sensing for the specific case of very high resolution land-cover mapping. We also comment current and evaluate challenges for the optimal exploitation of Earth Observation images with the aim of automatically generating maps tailored to specific end-users’ needs. We advocate for more challenging large-scale benchmarks and for human-in-the-loop solutions.
DENSE MATCHING COMPARISON BETWEEN CLASSICAL AND DEEP LEARNING BASED ALGORITHMS FOR REMOTE SENSING DATA

Abstract. Deep learning and convolutional neural networks (CNN) have obtained a great success in image processing, by means of its powerful feature extraction ability to learn specific tasks. Many deep learning based algorithms have been developed for dense image matching, which is a hot topic in the community of computer vision. These methods are tested for close-range or street-view stereo data, however, not well studied with remote sensing datasets, including aerial and satellite data. As more high-quality datasets are collected by recent airborne and spaceborne sensors, it is necessary to compare the performance of these algorithms to classical dense matching algorithms on remote sensing data. In this paper, Guided Aggregation Net (GA-Net), which belongs to the most competitive algorithms on KITTI 2015 benchmark (street-view dataset), is tested and compared with Semi-Global Matching (SGM) on satellite and airborne data. GA-Net is an end-to-end neural network, which starts from an stereo pair and directly outputs a disparity map indicating the scene’s depth information. It is based on a differentiable approximation of SGM embedded into a neural network, performing well for ill-posed regions, such as textureless areas, slanted surfaces, etc. The results demonstrate that GA-Net is capable of producing a smoother disparity map with less errors, particularly for across track data acquired at different dates.
Sample-efficient benchmarking of multiphoton interference on a boson sampler in the sparse regime

Verification of a quantum advantage in the presence of noise is a key open problem in the study of near-term quantum devices. In this work, we show how to assess the quality of photonic interference in a linear optical quantum device (boson sampler) by using a maximum likelihood method to measure the strength at which various noise sources are present in the experiment. This allows us to use a sparse set of samples to test whether a given boson-sampling experiment meets known upper bounds on the level of noise permissible to demonstrate a quantum advantage. Furthermore, this method allows us to monitor the evolution of noise in real time, creating a valuable diagnostic tool. Finally, we observe that sources of noise in the experiment compound, meaning that the observed value of the mutual photon indistinguishability, which is the main imperfection in our study, is an effective value taking into account all sources of error in the experiment.
Parallelization of Partitioning Around Medoids (PAM) in K-Medoids Clustering on GPU

K-medoids clustering is categorized as partitional clustering. K-medoids offers better result when dealing with outliers and arbitrary distance metric also in the situation when the mean or median does not exist within data. However, k-medoids suffers a high computational complexity. Partitioning Around Medoids (PAM) has been developed to improve k-medoids clustering, consists of build and swap steps and uses the entire dataset to find the best potential medoids. Thus, PAM produces better medoids than other algorithms. This research proposes the parallelization of PAM in k-medoids clustering on GPU to reduce computational time at the swap step of PAM. The parallelization scheme utilizes shared memory, reduction algorithm, and optimization of the thread block configuration to maximize the occupancy. Based on the experiment result, the proposed parallelized PAM k-medoids is faster than CPU and Matlab implementation and efficient for large dataset.
Enhanced Latent Semantic Indexing Using Cosine Similarity Measures for Medical Application

The Vector Space Model (VSM) is widely used in data mining and Information Retrieval (IR) systems as a common document representation model. However, there are some challenges to this technique such as high dimensional space and semantic looseness of the representation. Consequently, the Latent Semantic Indexing (LSI) was suggested to reduce the feature dimensions and to generate semantic rich features that can represent conceptual term-document associations. In fact, LSI has been effectively employed in search engines and many other Natural Language Processing (NLP) applications. Researchers thereby promote endless effort seeking for better performance. In this paper, we propose an innovative method that can be used in search engines to find better matched contents of the retrieving documents. The proposed method introduces a new extension for the LSI technique based on the cosine similarity measures. The performance evaluation was carried out using an Arabic language data collection that contains 800 medical related documents, with more than 47,222 unique words. The proposed method was assessed using a small testing set that contains five medical keywords. The results show that the performance of the proposed method is superior when compared to the standard LSI
<i>NSVD</i>: <u>N</u>ormalized <u>S</u>ingular <u>V</u>alue <u>D</u>eviation Reveals Number of Latent Factors in Tensor Decomposition

Tensor decomposition has been shown, time and time again, to be an effective tool in multi-aspect data mining, especially in exploratory applications where the interest is in discovering hidden interpretable structure from the data.In such exploratory applications, the number of such hidden structures is of utmost importance, since incorrect selection may imply the discovery of noisy artifacts that do not really represent a meaningful pattern.Albeit extremely important, selection of this number of latent factors, also known as low-rank, is very hard, and in most cases, practitioners and researchers resort to ad-hoc trial-and-error, or assume that somehow this number is known or is given via domain expertise.There has been a considerable amount of prior work that proposes heuristics for selecting this low rank.However, as we argue in this paper, the state-of-the-art in those heuristic methods is rather unstable and does not always reveal the correct answer.In this paper, we propose the Normalized Singular Value Deviation (NSVD), a novel method for selecting the number of latent factors in Tensor Decomposition, that is based on principled theoretical foundations.We extensively evaluate the effectiveness of NSVD in synthetic and real data and demonstrate that it yields a more robust, stable, and reliable estimation than state-of-the-art.
Analysis of DNS Security Threats on campus network

Abstract DNS, as the entrance to Internet application access, is the only way for all business access. Normal services must access services through DNS, and malicious programs also need to be connected through DNS. In this paper, we focus on the analysis of campus network egress data using traffic analysis technology. And we pay attention to DNS-related data, hoping to find useful information about network attack. Since we collected data for about a month (mainly data about DNS data), we found many attacks and a large number of computer viruses. They are hidden in the normal data stream, but can be recognized by DNS data.
A Hybrid Path Planning Method Based on Articulated Vehicle Model

Due to the unique steering mechanism and driving characteristics of the articulated vehicle, a hybrid path planning method based on the articulated vehicle model is proposed to meet the demand of obstacle avoidance and searching the path back and forth of the articulated vehicle. First, Support Vector Machine (SVM) theory is used to obtain the two-dimensional optimal zero potential curve and the maximum margin, and then, several key points are selected from the optimal zero potential curves by using Longest Accessible Path (LAP) method. Next, the Cubic Bezier (CB) curve is adopted to connect the curve that satisfies the curvature constraint of the articulated vehicle between every two key points. Finally, Back and Forth Rapidly-exploring Random Tree with Course Correction (BFRRT-CC) is designed to connect paths that do not meet articulated vehicle curvature requirements. Simulation results show that the proposed hybrid path planning method can search a feasible path with a 90-degree turn, which meets the demand for obstacle avoidance and articulated vehicle back-and-forth movement.
Fast 3D Shape Measurement of Transparent Glasses by Sequential Thermal Fringe Projection

We present an approach for measuring the shape of transparent glasses, which enables us to significantly reduce the comparatively long measurement time while increasing the measurement accuracy. Instead of using area-like patterns, we irradiate the object to be measured successively in a locally strongly restricted area with considerably higher irradiance.
Hybrid calibration method for improving 3D measurement accuracy of structured light systems

In structured-light systems, the lens distortions of the camera and the projector reduce the measurement accuracy when calibrated as a standard stereo-vision system. The conventional compensation via distortion coefficients reduces the error, but still leaves a significant residual. Recently, we proposed a hybrid calibration procedure that leverages the standard calibration approach to improve measurement accuracy. This hybrid procedure consisted of building a pixel-wise phase-to-coordinate mapping based on adjusted 3D data obtained from the standard stereo-vision method. Here, we show experimentally that the measurement accuracy can be significantly improved, even using the linear pinhole model and linear mapping functions. We then move to consider the nonlinear model to improve the measurement accuracy further. Encouraging results show that this new calibration method increases the measurement accuracy without requiring elaborate calibration procedures or sophisticated ancillary equipment.
Deinterleaving pulse trains with DBSCAN and FART

Studying radar pulses and looking for certain patterns is critical in order to assess the threat level of the environment around an antenna. In order to study the electromagnetic pulses emitted fro ...
SKOS Tool: A Tool for Creating Knowledge Graphs to Support Semantic Text Classification

Knowledge graphs are being increasingly adopted in industry in order to add meaning to data and improve the intelligence of data analytics methods. Simple Knowledge Management System (SKOS) is a W3C standard for representation of knowledge graphs in a web-native and machine-understandable format. This paper introduces SKOS Tool; a web-based application developed at the Engineering Informatics Lab at Texas State University. It can be used for creating knowledge graphs and concept schemes based on the SKOS standard. The main feature and functions of SKOS Tool are described in this paper. Beyond creating knowledge graphs, SKOS Tool has additional features that can be used to support semantic document classification based on the Bag of Concepts technique. To demonstrate the utilities of SKOS Tool, a use case related to classifications of manufacturing suppliers with Medical Grade Polymer Tubing capabilities is presented.
Artificial Intelligence Based Face Mask Detection System

With the advent of COVID-19 pandemic, use of mask is mandatory as per WHO/ ICMR guidelines to avert spread of CORONA virus. The post lockdown period has seen increase in cases day by day as people have now stepped out of their home to resume their work and recreational activities. Wearing mask all the time has still not found an enduring place in our day to day routine practices. It is a natural human tendency to be complacent and to remove mask while talking, working or after prolong use just use to relax and breathe properly. Thus not only risking own life but also of others who might have come in contact with the person during the period when he/she was not wearing mask. Presently the inspection of people with/ without mask is being done manually and visually by sentries/ guards present at entry/ exit points. Guards/ Sentries cannot be stationed at every place to keep a check on such people who remove their mask and roam around without restraint once they have been scrutinized at the entry gate. In the proposed system, efforts have been made in inspecting people with/ without mask automatically with the help of Computer vision and Artificial Intelligence. This module detects the face of the individual, identifies whether he/she is wearing mask or not and raises an alarm if the person is detected without wearing mask.
Image and Video Understanding for Recommendation and Spam Detection Systems

Image and video-based content has become ever present in a variety of domains like news, entertainment and education. Users typically discover and engage with content via search and recommendation systems. It is also important to serve high quality data to users by filtering out irrelevant or harmful content. Thus, there is an increasing need to leverage the rich information in image and video content in order to power systems for search and recommendation. At the same time, the effectiveness and efficiency of these systems has been accelerated by the availability of large-scale labeled datasets and sophisticated deep learning-based models.
A Methodology for Joint Data Cleaning of Solar Photovoltaic Generation and Solar Irradiation

A good solar power photovoltaic generation forecast depends on good quality time series data from measurements of global horizontal irradiation and solar generation. However, measurement system failures and errors in data handling can corrupt data records with gaps and outliers that undermine forecasting accuracy. Therefore, it is important that the fitting of solar energy prediction models must be preceded by a data analysis in order to detect and correct measurement errors. This paper presents the main features of an approach for the joint data cleaning of solar photovoltaic generation and solar irradiation. The methodology comprises 5 chained steps and consists in the combined treatment of global horizontal irradiation and solar photovoltaic generation data using statistical techniques, data mining algorithms and reanalysis data with the purpose of correcting outliers, replacing incorrect values and filling data gaps. The application of the proposed approach is illustrated with a real system presenting good performance.
MCMIA: Model Compression Against Membership Inference Attack in Deep Neural Networks.

Deep learning or deep neural networks (DNNs) have nowadays enabled high performance, including but not limited to fraud detection, recommendations, and different kinds of analytical transactions. However, the large model size, high computational cost, and vulnerability against membership inference attack (MIA) have impeded its popularity, especially on resource-constrained edge devices. As the first attempt to simultaneously address these challenges, we envision that DNN model compression technique will help deep learning models against MIA while reducing model storage and computational cost. We jointly formulate model compression and MIA as MCMIA, and provide an analytic method of solving the problem. We evaluate our method on LeNet-5, VGG16, MobileNetV2, ResNet18 on different datasets including MNIST, CIFAR-10, CIFAR-100, and ImageNet. Experimental results show that our MCMIA model can reduce the attack accuracy, therefore reduce the information leakage from MIA. Our proposed method significantly outperforms differential privacy (DP) on MIA. Compared with our MCMIA--Pruning, our MCMIA--Pruning \& Min-Max game can achieve the lowest attack accuracy, therefore maximally enhance DNN model privacy. Thanks to the hardware-friendly characteristic of model compression, our proposed MCMIA is especially useful in deploying DNNs on resource-constrained platforms in a privacy-preserving manner.