Reviewerly/exHarmony huggingface dataset:
Web Usage Mining Issues in Big Data

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.
Hand-drawn Symbol Recognition of Surgical Flowsheet Graphs with Deep Image Segmentation

Perioperative data are essential to investigating the causes of adverse surgical outcomes. In some low to middle income countries, these data are computationally inaccessible due to a lack of digitization of surgical flowsheets. In this paper, we present a deep image segmentation approach using a U-Net architecture that can detect hand-drawn symbols on a flowsheet graph. The segmentation mask outputs are post-processed with techniques unique to each symbol to convert into numeric values. The U-Net method can detect, at the appropriate time intervals, the symbols for heart rate and blood pressure with over 99 percent accuracy. Over 95 percent of the predictions fall within an absolute error of five when compared to the actual value. The deep learning model outperformed template matching even with a small size of annotated images available for the training set.
Remote Special Education During Crisis: COVID-19 as a Case Study

Due to the COVID-19 pandemic, face-to-face teaching was banned, and more than one billion students did not return to school. While both teachers and students found themselves in a very challenging situation as they both need to quickly adapt to remote teaching and learning respectively, students with disabilities found themselves in even more difficult situations. This might be because they lack several assistive technologies at home that facilitate the learning process. The World Health Organization (WHO) pointed out that special attention should be paid to people with disabilities during the COVID-19 pandemic. Therefore, this study investigates the applied remote teaching methods of students with disabilities, as well as the faced challenges. In this context, a survey was created and sent out to several stakeholders (pedagogues, teachers, school directors, etc.) that are directly involved in special education to collect their inputs. Finally, based on the collected experiences, this study presents recommendations and best practices to facilitate remote teaching in crisis for students with disabilities.
A Large-scale Evaluation of the bitstream-based video-quality model ITU-T P.1204.3 on Gaming Content

The streaming of gaming content, both passive and interactive, has increased manifolds in recent years. Gaming contents bring with them some peculiarities which are normally not seen in traditional 2D videos, such as the artificial and synthetic nature of contents or repetition of objects in a game. In addition, the perception of gaming content by the user is different from that of traditional 2D videos due to its pecularities and also the fact that users may not often watch such content. Hence, it becomes imperative to evaluate whether the existing video quality models usually designed for traditional 2D videos are applicable to gaming content. In this paper, we evaluate the applicability of the recently standardized bitstream-based video-quality model ITU-T P.1204.3 on gaming content. To analyze the performance of this model, we used 4 different gaming datasets (3 publicly available + 1 internal) not previously used for model training, and compared it with the existing state-of-the-art models. We found that the ITU P.1204.3 model out of the box performs well on these unseen datasets, with an RMSE ranging between 0.38 - 0.45 on the 5-point absolute category rating and Pearson Correlation between 0.85 - 0.93 across all the 4 databases. We further propose a full-HD variant of the P.1204.3 model, since the original model is trained and validated which targets a resolution of 4K/UHD-1. A 50:50 split across all databases is used to train and validate this variant so as to make sure that the proposed model is applicable to various conditions.
Analysis of Phishing Website Detection Using CNN and Bidirectional LSTM

Phishing is a critical internet hazard and phishing losses progressively and it is caused by electronic means to deprive the users of sensitive information. Feature engineering is remaining essential for website-detection phishing solutions, although the quality of detection depends ultimately on previous knowledge of its features. Moreover, while the functionalities derived from different measurements are more precise, these characteristics take a lot of time to remove. This suggest a multidimensional approach to the detection of phishings focused on a quick detection mechanism through deep learning to overcome these limitations. The first step is to extract and use the character sequence features of the given URL for rapid classification through in-depth learning; this step does not include support from third parties or previous experience in phishing. It combine statistical URLs, web page code functions, website text features and easily categorise Profound learning in the second level on multidimensional functions. By the approach, the detection time of the threshold is shortened. The experimental results show that a rational adjustment of the threshold allows for the efficiency of the detection.
Crossover from a delocalized to localized atomic excitation in an atom–waveguide interface

Abstract An atom–waveguide system, which presents one of the quantum interfaces that enable strong couplings between light and atoms, can support tightly-confined guided modes of light. In this distinctive quantum interface, we theoretically investigate the crossover from a delocalized to localized atomic excitation under long-range dipole–dipole interactions and lattice disorders. Both localization lengths of the excitation distributions and power-law scalings of dissipative von Neumann entanglement entropy show signatures of this crossover. We further calculate numerically the level statistics of the underlying non-Hermitian Hamiltonian, from which as the disorder strength increases, the gap ratio decreases and the intrasample variance increases before reaching respective saturated values. The mean gap ratio in the deeply localized regime is close to the one from Poisson statistics along with a relatively large intrasample variance, whereas in the nondisordered regime, a significant level repulsion emerges. Our results provide insights to study the non-ergodic phenomenon in an atom–waveguide interface, which can be potentially applied to photon storage in this interface under dissipations.
Towards a common environment for learning scheduling algorithms

We propose a way to model and integrate HPC scheduling simulators into a popular Reinforcement Learning toolkit. We show experimentally that such an approach not only aids researchers being able to iterate faster by means of software reuse, but also to achieve state-of-the-art performance with 10x less interactions with the environment. We validate the simulation model's correctness by using unit tests, assertions and experimental comparisons. We also share an open source implementation of the model that will benefit researchers in resource management tasks assisted by Machine Learning.
CNN Network for Head Detection with Depth Images in cyber-physical systems

Head detection and localization is one of the most researched and demanding tasks in the field of computer vision, with wide applications in Cyber-Physical Systems (CPSs). It is also a key element in human-computer interaction, face analysis and other disciplines. In order to solve the multi-target head detection under the illumination change, complex environment, and head pose interference, we propose a novel head detection system which combines the perception of deep learning and the decision-making power of machine learning. This system classifies the images into head and non-head parts through a convolutional neural network. Then eliminate the false detection part and cluster out the midpoint of the head through the mean shift algorithm. We leverage both 2D and 3D geometric properties of a 2.5D depth images, and provide a multi-target detection method. Experiments on two challenging datasets, namely Pandora Dataset and watch-n-patch Dataset, show that our proposed method have higher localization precision than state-of-the-art approaches.
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems

Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the exact same training data could produce very different prediction results. People have relied on the state-of-the-art ensemble method to estimate prediction uncertainty. However, ensembles are expensive to train and serve for web-scale traffic systems.
A Gesture Elicitation Study for Selection of Nail Size Objects in a Dense and Occluded Dense HMD-VR.

Object selection is a fundamental and primary task in any Virtual Environment (VE). However, object selection in dense VEs presents challenges of inaccurate selection, higher error rates and increased task completion times. The challenges are further amplified when target objects are small as nail size, occluded and located at different distances from users. In this paper, we present a two-stage elicitation study for object selection in 4 dense VEs (2x2 selection conditions: the selection of near vs distant objects; with or without occlusion) in a Head-Mounted Display (HMD) based Virtual Reality (VR) platform. In study 1, we identified the most natural and intuitive gestures through a user-generated gesture study. In total, 737 gestures and 23, 29, 26 and 36 unique gestures were collected from 40 participants for mentioned VEs. We calculated an overall score to elucidate further a total of 3, 2, 3 and 3 gestures. In study 2, a new group of 33 participants rated these gestures on ease of performance, gesture appropriateness, body-part suitability and ranked them for user preferences and effort. Finally, we elucidate them to present one gesture each for each VE based on the results obtained from the study 2. We present and discuss the findings of preferences of upper body gestures, a strong emphasis on the confirmation, bringing targets within hands reach and non-occluded, accuracy over effort, scale manipulation of the VE and influence and adoption of WIMP and touch interfaces.
GRNet: Deep Convolutional Neural Networks based on Graph Reasoning for Semantic Segmentation

In this paper, we develop a novel deep-network architecture for semantic segmentation. In contrast to previous work that widely uses dilated convolutions, we employ the original ResNet as the backbone, and a multi-scale feature fusion module (MFFM) is introduced to extract long-range contextual information and upsample feature maps. Then, a graph reasoning module (GRM) based on graph-convolutional network (GCN) is developed to aggregate semantic information. Our graph reasoning network (GRNet) extracts global contexts of input features by modeling graph reasoning in a single framework. Experimental results demonstrate that our approach provides substantial benefits over a strong baseline and achieves superior segmentation performance on two benchmark datasets.
Quantum-Proof Lightweight McEliece Cryptosystem Co-processor Design

Due to the rapid advances in the development of quantum computers and their susceptibility to errors, there is a renewed interest in error correction algorithms. In particular, error correcting code-based cryptosystems have reemerged as a highly desirable coding technique. This is due to the fact that most classical asymmetric cryptosystems will fail in the quantum computing era. However, code-based cryptosystems are still secure against quantum computers, since the decoding of linear codes remains NP-hard even on these computing systems. One such code-based cryptosystem was proposed by McEliece. The classic McEliece cryptosystem uses binary Goppa code, which is known for its good code rate and error correction capability. However, its key generation and decoding procedures have a high computation complexity. In this work, we propose the design of a public-key encryption and decryption coprocessor based on a new variant of the McEliece cryptosystem. This co-processor takes advantage of non-binary Orthogonal Latin Square Code to achieve much smaller computation complexity and key size. We also propose a hardware-cost efficient, fully-parameterized FPGA-based implementation of the co-processor to perform fast encoding and decoding operations. When compared to an existing classic McEliece cryptosystem, we observe a speed up of about 3.3 ×.
Safe Speed Control and Collision Probability Estimation Under Ego-Pose Uncertainty for Autonomous Vehicle

In order for autonomous vehicles to become a part of the Intelligent Transportation Ecosystem, they are required to guarantee a particular level of safety. For that to happen safe vehicle control algorithms need to be developed, which include assessing the probability of a collision while driving along a given trajectory and selecting control signals that minimize this probability. In this paper, we propose a speed control system that estimates a collision probability taking into account static and dynamic obstacles as well as ego-pose uncertainty and chooses the maximum safe speed. For that, the planned trajectory is converted by the control system into control signals that form the input for the dynamic vehicle model. The model predicts the real vehicle path. The predicted trajectory is generated for each particle - a weighted hypothesis of the localization system about the vehicle pose. Based on the predicted particles' trajectories, the probability of collision is calculated, and a decision is made on the maximum safe speed. The proposed algorithm was validated on the real autonomous vehicle. The experimental results demonstrate that the proposed speed control system reduces the vehicle speed to a safe value when performing maneuvers and driving through narrow openings. Therefore the observed behavior of the system is mimicking a human driver behavior when driving in difficult and ambiguous traffic situations.
Facial expression recognition with polynomial Legendre and partial connection MLP

This paper presents a partially connected Multilayer Perceptron (PCM) neural network as an optimal new MLP with a supervised algorithm and three hidden layers to detect face emotions. Compared with the traditional MLP, the proposed network shows improvements in speed, accuracy, and computational time. Six emotions have been considered in this study, namely anger, surprise, fear, sadness, normal, and happiness. The image dataset with the fixed background is used to test and train the network. Canny edge detection algorithm is employed to separate regions of the face including eyes, mouth, and eyebrows from the background. A binary image is extracted to represent the areas of eye, mouth, and eyebrow. The feature extraction is carried out by Legendre coefficients of Legendre polynomials which are selected for the best accuracy. Results show that the partial network is faster and has a lower computational complexity compared with the full connection network. It also has a faster convergence with higher accuracy.
Text to photo-realistic image synthesis via chained deep recurrent generative adversarial network

Despite the promising progress made in recent years, automatically generating high-resolution realistic images from text descriptions remains a challenging task due to semantic gap between human-written descriptions and diversities of visual appearance. Most existing approaches generate the rough images with the given text descriptions, while the relationship between sentence semantics and visual content is not holistically exploited. In this paper, we propose a novel chained deep recurrent generative adversarial network (CDRGAN) for synthesizing images from text descriptions. Our model uses carefully designed chained deep recurrent generators that simultaneously recovers global image structures and local details. Specially, our method not only considers the logic relationships of image pixels, but also removes computational bottlenecks through parameters sharing. We evaluate our method on three public benchmarks: CUB, Oxford-102 and MS COCO datasets. Experimental results show that our method significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.
Detecting Spam Product Reviews in Roman Urdu Script

References In recent years, online customer reviews have become the main source to determine public opinion about offered products and services. Therefore, manufacturers and sellers are extremely concerned with customer reviews, as these can have a direct impact on their businesses. Unfortunately, there is an increasing trend to write spam reviews to promote or demote targeted products or services. This practice, known as review spamming, has posed many questions regarding the authenticity and dependability of customers’ review-based business processes. Although the spam review detection (SRD) problem has gained much attention from researchers, existing studies on SRD have mostly worked on datasets of English, Chinese, Arabic, Persian, and Malay languages. Therefore, the objective of this research is to identify the spam in Roman Urdu reviews using different classification models based on linguistic features and behavioral features. The performance of each classifier is evaluated in a number of perspectives: (i) linguistic features are used to calculate accuracy (F1 score) of each classifier; (ii) behavioral features combined with distributional and non-distributional aspects are used to evaluate accuracy (F1 score) of each classifier; and (iii) the combination of both linguistic and behavioral features (distributional and non-distributional aspects) are used to evaluate the accuracy of each classifier. The experimental evaluations demonstrated an improved accuracy (F1 score: 0.96), which is the result of combinations of linguistic features and behavioral features with the distributional aspect of reviewers. Moreover, behavioral features using distributional characteristic achieve an accuracy (F1 score: 0.86) and linguistic features shows accuracy (F1 score: 0.69). The outcome of this research can be used to increase customers’ confidence in the South Asian region. It can also help to reduce spam reviews in the South Asian region, particularly in Pakistan.
An Improved Sparse Hierarchical Lazy Theta* Algorithm for UAV Real-Time Path Planning in Unknown Three-Dimensional Environment

Real-time path planning in the complex and unknown three-dimensional environments is a very challenging topic for the unmanned aerial vehicle (UAV) systems. In this paper, we propose an improved sparse hierarchical lazy theta* (SHLT*) algorithm to plan a near-optimal path in real-time and efficiently in unknown 3D space. In the proposed SHLT* algorithm, the sparse hierarchical framework is adopted to divide the whole 3D space into different levels, wherein the kinematic constraints of UAV are utilized to filter the unsatisfactory successor nodes, thus ensure the feasibility of the generated path. On this basis, an adaptive lazy theta* algorithm is proposed to find the shortest path in each level, wherein the dynamic heuristic factors are applied in different levels so as to accelerate the path generation and improve the accuracy of the obtained path. Besides, path smoothing procedures are also designed to enhance the smoothness of the generated path. Simulation results demonstrate that the proposed SHLT* algorithm can plan a near-optimal path and significantly outperforms the reference algorithm on the performance in terms of path length and path smoothness.
TOPOSCH: Latency-Aware Scheduling Based on Critical Path Analysis on Shared YARN Clusters

Balancing resource utilization and application QoS is a long-standing research topic in cluster resource management. Big data YARN clusters need to co-schedule diverse workloads on shared resources including batch processing jobs, streaming jobs, and other long-running applications such as web services, database services, etc. Current resource managers are only responsible for resource allocation among applications/jobs but completely unaware of runtime QoS requirements of interactive and latency-sensitive applications. Prior works to maximize the QoS of monolithic applications ignore inherent dependencies and temporal-spatio performance variability of components, characteristics of distributed applications primarily driven by microservices. In this paper, we present Toposch, a new resource management system to adaptively co-locate batch tasks and microservices by harvesting runtime latency. In particular, Toposch tracks full footprints of every request across microservices over time. A latency graph is periodically generated for identifying victim microservices through an end-to-end latency critical path analysis. We then exploit per-microservice and per-node risk assessment to gauge the visible resources to the capacity scheduler in YARN. Execution of batch tasks are adaptively throttled or delayed, thereby avoiding latency increase due to node over-saturation. TOPOSCH is integrated with YARN and experiments show that the latency of DLRAs can be reduced by up to 39.8% against the default capacity scheduling in YARN.
A Mixed Appearance-based and Coding Distortion-based CNN Fusion Approach for In-loop Filtering in Video Coding

With the success of the convolutional neural networks (CNNs) in image denoising and other computer vision tasks, CNNs have been investigated for in-loop filtering in video coding. Many existing methods directly use CNNs as powerful tools for filtering without much analysis on its effect. Considering the in-loop filters process the reconstructed video frames produced from a fixed line of video coding operations, the coding distortion in the reconstructed frames may share similar properties that can be learned by CNNs in addition to being a noisy image. Therefore, in this paper, we first categorize the CNN based filtering into two types of processes: appearance-based CNN filtering and coding distortion-based CNN filtering, and develop a two-stream CNN fusion framework accordingly. In the appearance-based CNN filtering, a CNN processes the reconstructed frame as a distorted image and extracts the global appearance information to restore the original image. In order to extract the global information, a CNN with pooling is used first to increase the receptive field and up-sampling is added in the late stage to produce pixel-level frame information. On the contrary, in the coding distortion-based filtering, a CNN processes the reconstructed frame as blocks with certain types of distortions by focusing on the local information to learn the coding distortion resulted by the fixed video coding pipeline. Finally, the appearance-based filtering stream and the coding distortion-based filtering stream are fused together to combine the two aspects of CNN filtering, and also the global and local information. To further reduce the complexity, the similar initial and last convolutional layers are shared over two streams to generate a mixed CNN. Experiments demonstrate that the proposed method achieves better performance than the existing CNN-based filtering methods, with 11.26% BD-rate saving under the All Intra configuration.