Reviewerly/exHarmony huggingface dataset:
Designing Physical-Digital Workspaces to Support Globally Collaborative Work

Abstract This paper examines some aspects of physical-digital workspaces, focusing on multi-user, multi-touch technologies and how different workspaces impact collaboration. We introduce the concept of globally collaborative work. We chose to use case studies completed by groups of students in an engineering course to test different workspace modalities: the use of a large multi-touch table top in conjunction with a multi-touch board (vertical), the use of tablets with the multi-touch board, and finally the multi-touch board alone. The evaluation criteria are based on modes of interaction which emerge during globally collaborative work sessions: individual work, communication, coordination, cooperation and collaboration. We hypothesized that the workspaces would influence collaborative activity, expecting to see higher rates of collaboration in the table top environment than in the other two modalities studied. However, results showed less co-building and more cooperative work, as students divided their work and later attempted to negotiate a coherent product built on individual contributions. Lastly, we share a few design recommendations based on these results.
Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes

Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets.
Deep Foreground Segmentation using Convolutional Neural Network

This paper proposes foreground segmentation algorithm powered by the convolutional neural network. The task requires CNN network to extract features from given image and upsample the image to segment background and foreground. The proposed algorithm consists of two networks. VGG-16 based CNN is used to extract the feature from the given image. The feature maps are upsampled using deconvolution network. The upsample image is segmented with sigmoid and threshold to get background foreground information. The proposed method is tested on all the categories of the change detection dataset. The dataset consists of 11 challenging categories such as dynamic background, bad weather, camera jitter, low frame rate, etc. The proposed method has been compared with state of the art foreground detection algorithms to prove effectiveness.
Measuring Performances of a White-Box Approach in the IoT Context

The internet of things (IoT) refers to all the smart objects that are connected to other objects, devices or servers and that are able to collect and share data, in order to “learn” and improve their functionalities. Smart objects suffer from lack of memory and computational power, since they are usually lightweight. Moreover, their security is weakened by the fact that smart objects can be placed in unprotected environments, where adversaries are able to play with the symmetric-key algorithm used and the device on which the cryptographic operations are executed. In this paper, we focus on a family of white-box symmetric ciphers substitution–permutation network (SPN)box, extending and improving our previous paper on the topic presented at WIDECOM2019. We highlight the importance of white-box cryptography in the IoT context, but also the need to have a fast black-box implementation (server-side) of the cipher. We show that, modifying an internal layer of SPNbox, we are able to increase the key length and to improve the performance of the implementation. We measure these improvements (a) on 32/64-bit architectures and (b) in the IoT context by encrypting/decrypting 10,000 payloads of lightweight messaging protocol Message Queuing Telemetry Transport (MQTT).
Generation of a Quadphoton via Seventh‐Order Nonlinear Susceptibility

Abstract With a wide range of potential applications, the generation of nonclassical multiphoton and number states has attracted renewed interest recently. Here, a correlated quadphoton with seventh‐order nonlinear susceptibility is reported in which all four photons are simultaneously created via an eight‐wave mixing (EWM) process in an atomic medium. The efficiency of the pure EWM process is low, which results from the small order of magnitude of the seventh‐order nonlinear susceptibility (the quadphoton correlation with four periods corresponding to 8 channels and 12 resonance positions). But the EWM process has a strong dressing effect. It not only increases the nonlinear susceptibility, but also produces 15 or 16 resonance positions in the dressing state picture. In addition, with the energy conservation of these positions, 12 coherent channels can be generated in this system. According to the interference between multichannels of the quadphoton, the quadphoton coincidence count appears as a damped Rabi oscillation, which has eight or ten oscillation periods. If only the nonlinear optical response is considered, the coincidence counts of the quadphoton behave as a damped Rabi oscillation with periods that can be controlled by the dressing field. These outcomes may contribute to a new promising method for quantum communication.
Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads

The edge computing paradigm has recently attracted research efforts coming from different application domains. However, evaluating an edge platform or algorithm is impeded by the lack of suitable benchmarks. We propose a methodology for characterizing edge workloads from different application domains. It is a first step towards defining workloads to be included in a future edge benchmarking suite. We evaluate the methodology on three use cases and find that defining a common and standard set of workloads is plausible.
Person Re-Identification Based on Two-Stream Network With Attention and Pose Features

Due to posture, blurring, occlusion, and other problems, person re-identification(Re-ID) remains a challenging task at present. In this paper, we combine the advantages of pose estimation and attention mechanism to better solve these problems with better performance, which combines pose and attention with two-stream network. Our proposed method mainly consists of two parts. 1) Spatial Features with Fusion Multi-Layer Features and Attention: the same pedestrian presents different gestures under different camera angles, indicating that the simple spatial information is no longer reliable. Therefore, it becomes important to distinguish view invariant features from multiple semantic levels. As a consequence, we fusion the mid-level and high-level features, and then correlate global information through self-attention. Due to fusion the mid-level and high-level features, semantic information is more abundant, which enables the attention mechanism to better focus on the important areas of the picture; 2) Aggregation Attention Stream and Pose Estimation Stream Features: although self-attention mechanism can automatically pay attention to the important areas of the image, it may pay too much focus on the prominent parts of the body and ignore the edge information of the body. Hence, the guidance of pedestrian posture is needed to make self-attention better able to pay attention to all parts of the body. Finally, we use bilinear pooling aggregates the features of two-stream as the final features. We do not use any data enhancement and re-ranking methods to achieve the $rank=1$ accuracy of 93.3% and 85.5% in Market1501 and DukeMTMC-reID datasets, respectively, which indicates the effectiveness of our method.
Geometrical and morphometrical tools for the inclusion analysis of metallic alloys

The mechanical and use properties of metal alloys depend on several factors, including the amount and the geometry of impurities (inclusions). In this context, image analysis enables these inclusions to be studied from digital images acquired by various systems such as optical/electron microscopy or X-ray tomography. This paper therefore aims to present some geometrical and morphometrical tools of image analysis, in order to characterize inclusions in metal alloys. To achieve this quantification, many geometrical and morphometrical features are traditionally used to quantitatively describe a population of objects (inclusions). Integral geometry, via Minkowski’s functionals (in 2D: area, perimeter, Euler-Poincaré number), has been particularly investigated in image analysis. Nevertheless, they are sometimes insufficient for the characterization of complex microstructures (such as aggregates/agglomerates of objects). Other quantitative parameters are then necessary in order to discriminate or group different families of objects. In particular, shape diagrams are mathematical representations in the Euclidean plane for studying the morphology (shape) of objects, regardless of their size. In addition, this representation also makes it possible to analyze the evolution from one shape to another. In conclusion, image analysis using integral geometry and shape diagrams provide efficient tools with known mathematical properties to quantitatively describe inclusions (providing separate information on size and shape). The geometrical characteristics of these inclusions could thereafter be related to the mechanical properties of the metal alloys.
morph-GraphQL: GraphQL Servers Generation from R2RML Mappings (S)

REST has become in the last decade the most common manner to provide web services, yet it was not originally designed to handle typical modern applications (e.g., mobile apps).GraphQL was released publicly in 2015 and since then has gained momentum as an alternative approach to REST.However, generating and maintaining GraphQL resolvers is not easy.First, a domain expert has to analyse a dataset, design the corresponding GraphQL schema and map the dataset to the schema.Then, a software engineer (e.g., GraphQL developer) implements the corresponding GraphQL resolvers in a specific programming language.In this paper we present an approach that generates GraphQL resolvers from declarative mappings specification in the W3C Recommendation R2RML, hence, can be used both by a domain expert as without the need to involve software developers to implement the resolvers, and by software developers as the initial version of the resolvers to be implemented.Our approach is implemented in morph-GraphQL.
Characteristics Identification of Myeloblast Cell Using K-Means Clustering for Uncontrolled Images

Blood cells are one of the most important parts in humans.One type of blood cells that play an important role in a leukemia diagnosis is leukocyte cells.There are some types of leukocyte i.e. myeloblast, lymphoblast, monoblasts and erythroblasts.One method of measuring leukocyte cell abnormalities is by examination of the morphology of leukocyte cells covering the area, circumference and diameter of leukocyte cells.In this research will be identified morphology of myeloblast cells by using K-means clustering method.The observed control variable is a characteristic of myeloblast cell that includes diameter, contour, and uniformity of object, amount, and cell density.While the observed data is uncontrolled image (noise) with RGB color format.The experiment showed promising results for further development.
Fast single fringe-pattern processing with graphics processing unit

Optical interferometric techniques provide noncontact, full-field, and high-precision measurements that are very attractive in various research and application fields. Single fringe-pattern processing (SFPP) is often required when measuring fast phenomena, which contain multiple steps including noise removal, phase demodulation, and unwrapping. However, several difficulties are encountered during SFPP, among which the processing time is of interest due to the increasing computational load brought by the large amount and high-resolution fringe patterns in recent years. In this paper, we propose a general and complete graphics processing unit (GPU)-based SFPP framework to perform a systematic discussion on SFPP acceleration. Typical methods from the spatial domain, the transform-based, and the path-related are chosen to have a variety of methods in the framework for better parallelization demonstration, namely, coherence-enhancing diffusion for denoising, spiral phase quadrature transform for demodulation, and quality-guided phase unwrapping. To the best of our knowledge, this is the first time a complete GPU-based framework has been proposed for SFPP. The advantages of performing the analysis and parallelization in framework level are demonstrated, where processing redundancy can be identified and reduced. The proposed framework can be used as an example to demonstrate the GPU-based parallelization in SFPP. Methods in the framework can be replaced but the framework level analysis, the parallel design, and the involved functions are always good references. Experiments are performed on simulated and experimental fringe patterns to demonstrate the effectiveness of the proposed work and achieve at most 29.8 times speedup compared with CPU-based sequential processing.
Application Guided Image Quality Estimation Based on Classification

Image Quality (IQ) plays significant role for both human vision and artificial vision applications. This last decade, the number of camera increases exponentially, but the exploitation of the information depends on the quality of these acquired images, and sequences. A large number of objective blind IQ assessment (OBIQA) methods were proposed which find a global IQ estimation regardless the application. In this work, we assume that is impossible that such a method can be applied to all applications, however, for a given problem an objective model of the quality can be provided. Consequently, this paper addresses this issue for an automatic vehicle type classification application and proposes a novel OBIQA based classification approach. The proposed method first extracts a set of selective image features, then learns to classify images accordingly. In other terms, it aims to prevent misclassification and localize the source of poor images. Experiments show that it performs better than the state-of-the-art methods and can be used for similar applications.
Learning Bias-Free Representation for Large-Scale Person Re-Identification

Person re-identification (re-ID) aims to match the same person across disjointed cameras. In practice, misalignment is one of the key problems that limits the re-ID accuracy. There are many causes, such as detection errors from automatic detectors, human pose changes and relative movement between people and cameras. However, most current automatic re-ID solutions are still limited in mitigating such misalignments. In this paper, we propose that the misalignment scenarios in re-ID can be divided into three basic types. To further study their effects, we visualize their negative impacts in the learned embedding space and compare the changes in the re-ID accuracy. To address these specific misalignment problems, we design three subnetworks to correct the corresponding misalignments and discuss the performance gains. Moreover, by integrating all subnetworks into one unified structure, we propose a bias-free representation learning method. Unlike previous approaches that mainly focus on one specific kind of misalignment, our proposed method can eliminate the feature bias that is introduced by multiple misalignment problems. Systematic visualizations and comparisons are conducted to demonstrate that our method can efficiently correct the feature bias during the representation learning step. Evaluations on three large-scale datasets show that our proposed bias-free representation learning method can outperform the state-of-the-art methods.
Machine Translation with parfda, Moses, kenlm, nplm, and PRO

We build parfda Moses statistical machine translation (SMT) models for most language pairs in the news translation task.We experiment with a hybrid approach using neural language models integrated into Moses.We obtain the constrained data statistics on the machine translation task, the coverage of the test sets, and the upper bounds on the translation results.We also contribute a new testsuite for the German-English language pair and a new automated key phrase extraction technique for the evaluation of the testsuite translations.
Improved Online Sequential Extreme Learning Machine: A New Intelligent Evaluation Method for AZ-Style Algorithms

Researches on computer games for Go, Chess, and Japanese Chess stand out as one of the notable landmarks in the progress of artificial intelligence. AlphaGo, AlphaGo Zero, and AlphaZero algorithms, which are called AlphaZero style (AZ-style) algorithms in some literature [1], have achieved superhuman performance by using deep reinforcement learning (DRL). However, the unavailability of training details, expensive equipment used for model training, and the low evaluation accuracy resulted by slow self-play training without expensive computing equipment in practical applications have been the defects of AZ-style algorithms. To solve the problems to a certain extent, the paper proposes an improved online sequential extreme learning machine (IOS-ELM), a new evaluation method, to evaluate chess board positions for AZ-style algortihm. Firstly, the theoretical principles of IOS-ELM is given. Secondly, the study considers Gomoku as the application object and uses IOS-ELM as the evaluation method for AZ-style's board positions to discuss the loss in the training process and hyperparameters affecting performance in detail. Under the same experimental conditions, the proposed method reduces the training parameters by 14 times, training time to 15%, and error of evaluation by 13% compared with the board evaluation network used in original AZ-style algorithms.
BLOCKCHAIN FOR IMPROVED SAFETY OF SMART BUILDINGS

In this paper, we propose a blockchain powered automated self-incited fire detection sensors connected to smart buildings.This solution is based on utilizing Ethereum smart contracts to coordinate and monitor the interaction between the service providers, smart connected devices, and the end users.The smart contracts monitor the status of the fire detecting sensors; interact with a service provider according to pre-negotiated contract terms and conditions, and carries out the maintenance task in an automated fashion.When a failure occurs within the device, an internal sensor which indicates the failure generates a request for service on the blockchain.This transaction informs the service provider about the nature of the failure, identifying the specific device.The service provider can respond to the service request in a timely manner.Upon completion, the sensor reading indicates the completion of the maintenance/repair work carried out and can release payment to the service provider on the blockchain.Moreover, the proposed solution automates the payment process between the client and the service provider.
NOVEL LEARNING STRATEGY BASED ON GENETIC PROGRAMMING FOR CREDIT CARD FRAUD DETECTION IN BIG DATA

Due to the growing volume of monetary transactions on the internet, credit card fraud poses many challenging issues for banks and financial institutions, thus forcing them to continuously improve their fraud detection systems.However, current fraud detection techniques are far from accurate and fail to minimize the false alarm rates, which can cause inconvenience and dissatisfaction for customers in e-banking system.Furthermore, there are several factors that decrease the performance of Fraud Detection Systems (FDS), such as skewed distribution, concept drift, supports real time detection, large amount of data etc.In this paper, we address the highly imbalanced class issue faced by a FDS when working with big data and propose a novel method of generation of data set's minority class based on K-means clustering method and genetic algorithm to improve classification performance in credit card fraud detection.In our experiments, we apply the proposed approach to synthetic unbalanced credit card fraud data set to demonstrate its effectiveness by means of the most appropriate performance measures for fraud detection purposes.
Improving Word Embeddings Using Kernel PCA

Word-based embedding approaches such as Word2Vec capture the meaning of words and relations between them, particularly well when trained with large text collections; however, they fail to do so with small datasets.Extensions such as fastText reduce the amount of data needed slightly, however, the joint task of learning meaningful morphology, syntactic and semantic representations still requires a lot of data.In this paper, we introduce a new approach to warm-start embedding models with morphological information, in order to reduce training time and enhance their performance.We use word embeddings generated using both word2vec and fastText models and enrich them with morphological information of words, derived from kernel principal component analysis (KPCA) of word similarity matrices.This can be seen as explicitly feeding the network morphological similarities and letting it learn semantic and syntactic similarities.Evaluating our models on word similarity and analogy tasks in English and German, we find that they not only achieve higher accuracies than the original skip-gram and fastText models but also require significantly less training data and time.Another benefit of our approach is that it is capable of generating a high-quality representation of infrequent words as, for example, found in very recent news articles with rapidly changing vocabularies.Lastly, we evaluate the different models on a downstream sentence classification task in which a CNN model is initialized with our embeddings and find promising results. Word (frequency) Modelk-nearest neighbors (sorted by similarity) three (114,775) Skip-gram four, five, seven, eight, runways KPCA skip-gram four, seven, five, eight, nine fastText four, five, seven, zero, eight KPCA fastText seven, five, four, eight, nine history (12,623) Skip-gram overview, timeline,