Reviewerly/exHarmony huggingface dataset:
Benchmarking Annotation Procedures for Multi-channel Time Series HAR Dataset

This contribution evaluates semi-automated annotation for generating high-quality data for multi-channel time series Human Activity Recognition. For this purpose, time series data that consists of Optical Motion Capturing and inertial measurements from on-body devices for industrial deployment is created, annotated, and revised by four individuals. The semi-automated annotation consists of predictions from a temporal convolutional neural-network, and manual revisions for generating high-quality and fine-grained annotations for Human Activity Recognition. This annotation approach reduces the annotation effort keeping similar quality as compared to manual annotation.
‘Un’-blocking the industry 4.0 value chain with cyber-physical social thinking

Integration of diverse technologies fuels business-model transformation, evolving new applications. Industry 4.0 utilizes emerging-technologies like IoT, AI to make next-generation manufacturing-processes more efficient and agile. Cyber physical systems (CPS) augmented with social, thinking technologies effectuate cyber physical social thinking systems (CPST). Distributed Ledger Technologies (DLT) like Blockchain are synergistic with architectural-elements of such industry-blueprints. Extant literature explores contextual-roles of distinct technology-components as-well-as their limited-combinations; clearly necessitating consideration of integrated-applications. This contemporary conceptual paper: (i) concisely analyses potential-synergies amongst Blockchain, CPST technologies; (ii) proposes novel integrated architecture; (iii) outlines underlying challenges, opportunities and performance considerations; bearing benefits beyond traditional-ecosystems to societal-stakeholders.
Towards Model-Agnostic Ensemble Explanations

Explainable Artificial Intelligence (XAI) methods form a large portfolio of different frameworks and algorithms. Although the main goal of all of explanation methods is to provide an insight into the decision process of AI system, their underlying mechanisms may differ. This may result in very different explanations for the same tasks. In this work, we present an approach that aims at combining several XAI algorithms into one ensemble explanation mechanism via quantitative, automated evaluation framework. We focus on model-agnostic explainers to provide most robustness and we demonstrate our approach on image classification task.
Two-Stage Merging Network for Describing Traffic Scenes in Intelligent Vehicle Driving System

Intelligent vehicle driving systems aim to control the driving behavior of a vehicle in real time without human intervention by perceiving and monitoring the surrounding environment. Describing images of traffic scenes automatically, which is one of the key problems of intelligent vehicle driving technology, has drawn attention since its inception. In recent years, a variety of automatic image description technologies have been proposed, among which the attention-based encoder-decoder framework achieved good results. In this paper we will discuss the fusing of a variety of information from multiple aspects of the images of traffic scenes. First, we will introduce visual attention, text attention and image topics attention which generates the weighted visual features, the attentive text information and the global image topics information respectively. We will then propose an adaptive two-stage merging network based on an encoder-decoder framework, which can fully integrate the three kinds of information in two stages, while automatically calculating the proportions of the information at each time step. Numerous experiments conducted on COCO2014 and Flickr30K datasets have demonstrated the effectiveness and advantages of the proposed method.
Fuzzy Rule-Based Trust Management Model for the Security of Cloud Computing

In the last few years, due to the benefit of solving large-scale computational problems, researchers have been developed multicloud infrastructures. The trust-related issue in multiclouds includes more complicated content and new problems. A new trust management framework for multicloud environments is proposed in this article. The proposed framework used a combination of objective and subjective trust values to calculate the cloud service provider’s trust values. This new framework can identify and rectify fake feedbacks from other feedbacks. Another advantage of this framework is applying fuzzy rules to calculate trust values. Two main components of the proposed framework are simulated in this paper. The simulation results confirm the important role of applied components. Also, this paper proposed a framework compared with other frameworks (feedback-based model, SLA-based model, and multicloud model). Simulation results show the proposed framework increased trust values rather than other models. Also, compared with other models, our framework gives better mean trust values.
Real-Time Selfie Video Stabilization

We propose a novel real-time selfie video stabilization method. Our method is completely automatic and runs at 26 fps. We use a 1D linear convolutional network to directly infer the rigid moving least squares warping which implicitly balances between the global rigidity and local flexibility. Our network structure is specifically designed to stabilize the background and foreground at the same time, while providing optional control of stabilization focus (relative importance of foreground vs. background) to the users. To train our network, we collect a selfie video dataset with 1005 videos, which is significantly larger than previous selfie video datasets. We also propose a grid approximation to the rigid moving least squares that enables the real-time frame warping. Our method is fully automatic and produces visually and quantitatively better results than previous real-time general video stabilization methods. Compared to previous offline selfie video methods, our approach produces comparable quality with a speed improvement of orders of magnitude. Our code and selfie video dataset is available at https://github.com/jiy173/selfievideostabilization.
Blockchain Based Decentralized Privacy Preserving in Edge Computing

Despite the provided conveniences, privacy issues are emerging because data sharing is trending to improve the service performances. It is essential for end devices to share local data to central authorities such as edge servers for global aggregation.
Gender identification on Twitter

Abstract To determine the author of a text's gender, various feature types have been suggested (e.g., function words, n ‐gram of letters, etc.) leading to a huge number of stylistic markers. To determine the target category, different machine learning models have been suggested (e.g., logistic regression, decision tree, k nearest‐neighbors, support vector machine, naïve Bayes, neural networks, and random forest). In this study, our first objective is to know whether or not the same model always proposes the best effectiveness when considering similar corpora under the same conditions. Thus, based on 7 CLEF‐PAN collections, this study analyzes the effectiveness of 10 different classifiers. Our second aim is to propose a 2‐stage feature selection to reduce the feature size to a few hundred terms without any significant change in the performance level compared to approaches using all the attributes (increase of around 5% after applying the proposed feature selection). Based on our experiments, neural network or random forest tend, on average, to produce the highest effectiveness. Moreover, empirical evidence indicates that reducing the feature set size to around 300 without penalizing the effectiveness is possible. Finally, based on such reduced feature sizes, an analysis reveals some of the specific terms that clearly discriminate between the 2 genders.
University Innovative Networking in Digital Age: Theory and Simulation

The relevance of the chosen research topic is justified by the need to find the tools for remote collaboration of various educational teams in the process of the university development as an open university. The methods of research are methods of system analysis. The issues for the optimal self-adjustment of evolutionary process at smart university by using Hurwitz criteria and indicators are considered. The procedure of the smart university capacity evaluation is proposed. The study is based on the main methodological hypothesis: the implementation of smart technologies and environment will improve the quality of economic collaboration and cooperation between universities in the conditions of digital environment and challenges for innovation.
Fool Me Twice: Entailment from Wikipedia Gamification

We release FOOLMETWICE (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game.Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using "shortcuts" compared to other entailment datasets.Players are presented with two tasks.The first task asks the player to write a plausible claim based on the evidence from a Wikipedia page.The second one shows two plausible claims written by other players, one of which is false, and the goal is to identify it before the time runs out.Players "pay" to see clues retrieved from the evidence pool: the more evidence the player needs, the harder the claim.Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and results in higher quality data for the entailment and evidence retrieval tasks.We open source the dataset and game code.
Coarse-grained decomposition and fine-grained interaction for multi-hop question answering

In recent years, question answering (QA) and reading comprehension (RC) has attracted much attention, and most research on QA has focused on multi-hop QA task which requires connecting multiple pieces of evidence scattered in a long context to answer the question. The key to the multi-hop QA task is semantic feature interaction between documents and questions, which is widely processed by Bi-directional Attention Flow (Bi-DAF), but Bi-DAF generally captures only the surface semantics of words in complex questions, and fails to capture implied semantic feature of intermediate answers, as well as ignoring parts of contexts related to the question and failing to extract the most important parts of multiple documents. In this paper, we propose a new model architecture for multi-hop question answering by applying two completion strategies:(1) Coarse-Grained complex question Decomposition (CGDe) strategy is introduced to decompose complex questions into simple ones without any additional annotations; (2) Fine-Grained Interaction (FGIn) strategy is introduced to explicitly represent each word in documents and extract more comprehensive and accurate sentences related to the inference path. The above two strategies are combined and tested on the SQuAD and HotpotQA datasets, and the experimental results show that our method outperforms state-of-the-art baselines.
The Ghost Commit Problem When Identifying Fix-Inducing Changes: An Empirical Study of Apache Projects

The SZZ approach for identifying fix-inducing changes traces backwards from a commit that fixes a defect to those commits that are implicated in the fix. This approach is at the heart of studies of characteristics of fix-inducing changes, as well as the popular Just-in-Time (JIT) variant of defect prediction. However, some types of commits are invisible to the SZZ approach. We refer to these invisible commits as “Ghost Commits.” In this paper, we set out to define, quantify, characterize, and mitigate ghost commits that impact the SZZ algorithm during its mapping (i.e., linking defect-fixing commits to those commits that are implicated by the fix) and filtering phases (i.e., removing improbable fix-inducing commits from the set of implicated commits). We mine the version control repositories of 14 open source Apache projects for instances of mapping-phase and filtering-phase ghost commits. We find that (1) 5.66–11.72 percent of defect-fixing commits of defect-fixing commits only add lines, and thus, cannot be mapped back to implicated commits; (2) 1.05–4.60 percent of the studied commits only remove lines, and thus, cannot be implicated in future fixes; and (3) that no implicated commits survive the filtering process of 0.35–14.49 percent defect-fixing commits. Qualitative analysis of ghost commits reveals that 46.5 percent of 142 addition-only defect-fixing commits add checks (e.g., null-ness or emptiness checks), while 39.7 percent of 307 removal-only commits clean up (unused) code. Our results suggest that the next generation of SZZ improvements should be language-aware to connect ghost commits to implicated and defect-fixing commits. Based on our observations, we discuss promising directions for mitigation strategies to address each type of ghost commit. Moreover, we implement mitigation strategies for addition-only commits and evaluate those strategies with respect to a baseline approach. The results indicate that our strategies achieve a precision of 0.753, improving the precision of implicated commits by 39.5 percentage points.
Noncyclic nonadiabatic holonomic quantum gates via shortcuts to adiabaticity

High-fidelity quantum gates are essential for large-scale quantum computation. However, any quantum manipulation will inevitably affected by noises, systematic errors and decoherence effects, which lead to infidelity of a target quantum task. Therefore, implementing high-fidelity, robust and fast quantum gates is highly desired. Here, we propose a fast and robust scheme to construct high-fidelity holonomic quantum gates for universal quantum computation based on resonant interaction of three-level quantum systems via shortcuts to adiabaticity. In our proposal, the target Hamiltonian to induce noncyclic non-Abelian geometric phases can be inversely engineered with less evolution time and demanding experimentally, leading to high-fidelity quantum gates in a simple setup. Besides, our scheme is readily realizable in physical system currently pursued for implementation of quantum computation. Therefore, our proposal represents a promising way towards fault-tolerant geometric quantum computation.
Package Recommendation with Intra- and Inter-Package Attention Networks

With the booming of online social networks in the mobile internet, an emerging recommendation scenario has played a vital role in information acquisition for user, where users are no longer recommended with a single item or item list, but a combination of heterogeneous and diverse objects (called a package, e.g., a package including news, publisher, and friends viewing the news). Different from the conventional recommendation where users are recommended with the item itself, in package recommendation, users would show great interests on the explicitly displayed objects that could have a significant influence on the user behaviors. However, to the best of our knowledge, few effort has been made for package recommendation and existing approaches can hardly model the complex interactions of diverse objects in a package. Thus, in this paper, we make a first study on package recommendation and propose an Intra- and inter-package attention network for Package Recommendation (IPRec). Specifically, for package modeling, an intra-package attention network is put forward to capture the object-level intention of user interacting with the package, while an inter-package attention network acts as a package-level information encoder that captures collaborative features of neighboring packages. In addition, to capture users preference representation, we present a user preference learner equipped with a fine-grained feature aggregation network and coarse-grained package aggregation network. Extensive experiments on three real-world datasets demonstrate that IPRec significantly outperforms the state of the arts. Moreover, the model analysis demonstrates the interpretability of our IPRec and the characteristics of user behaviors. Codes and datasets can be obtained at https://github.com/LeeChenChen/IPRec.
G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE

Traditional supervised machine learning classifiers are challenged to learn highly skewed data distributions as they are designed to expect classes to equally contribute to the minimization of the classifiers cost function. Moreover, the classifiers design expects equal misclassification costs, causing a bias for overrepresented classes. Different strategies have been proposed to correct this issue. The modification of the data set has become a common practice since the procedure is generalizable to all classifiers. Various algorithms to rebalance the data distribution through the creation of synthetic instances were proposed in the past. In this paper, we propose a new oversampling algorithm named G-SOMO. The algorithm identifies optimal areas to create artificial data instances in an informed manner and utilizes a geometric region during the data generation process to increase their variability. Our empirical results on 69 datasets, validated with different classifiers and metrics against a benchmark of commonly used oversampling methods show that G-SOMO consistently outperforms competing oversampling methods. Additionally, the statistical significance of our results is established.
Diverse part attentive network for video-based person re-identification

Attention mechanisms have achieved success in video-based person re-identification (re-ID). However, current global attentions tend to focus on the most salient parts, e.g., clothes, and ignore other subtle but valuable cues, e.g., hair, bag, and shoes. They still do not make full use of valuable information from diverse parts of human bodies. To tackle this issue, we propose a Diverse Part Attentive Network (DPAN) to exploit discriminative and diverse body cues. The framework consists of two modules: spatial diverse part attention and temporal diverse part attention. The spatial module utilizes channel grouping to exploit diverse parts of human bodies including salient and subtle parts. The temporal module aims to learn diverse weights for fusing learned features. Besides, this framework is lightweight, which introduces marginal parameters and computational complexities. Extensive experiments were conducted on three popular benchmarks, i.e. iLIDS-VID, PRID2011 and MARS. Our method achieves competitive performance on these datasets compared with state-of-the-art methods.
Automated Feature Engineering and Hyperparameter optimization for Machine Learning

Machine learning (ML) has proven to be important in many parts of our lives and has been beneficial to us. Nevertheless, designing a highly performant Machine Learning system for a specific application requires high knowledge and proficiency, thus hindering its application in many areas. Data pre-processing, feature engineering, and hyperparameter optimization influence the performance of models immensely, thus automating the same will reduce the burden of amateur users and save time for a lot of Machine Learning experts who are experimenting. In this paper, we implement the methods for automating the data pre-processing, feature processing, and hyperparameter optimization. The methods are exclusively implemented for tabular data which is in the textual formal used for supervised learning. We evaluate, compare and summarize the results on the basis of execution time and various evaluation metrics for the respective tasks. Finally, the best set of features and hyperparameters for the model to train on are returned. The overall performance of combinations of hyperparameter optimization and feature processing techniques has been evaluated on various datasets. With the help of the results, the most optimum technique for feature engineering and hyperparameter optimization are deduced.
Hand Gesture Detection and Recognition Using Spectrogram and Image Processing Technique with a Single Pair of Ultrasonic Transducers

This paper presents an effective signal processing scheme of hand gesture recognition with a superior accuracy rate of judging identical and dissimilar hand gestures. This scheme is implemented with the air sonar possessing a pair of cost-effective ultrasonic emitter and receiver along with signal processing circuitry. Through the circuitry, the Doppler signals of hand gestures are obtained and processed with the developed algorithm for recognition. Four different hand gestures of push motion, wrist motion from flexion to extension, pinch out, and hand rotation are investigated. To judge the starting time of hand gesture occurrence, the technique based on continuous short-period analysis is proposed. It could identify the starting time of the hand gesture with small-scale motion and avoid faulty judgment while no hand in front of the sonar. Fusing the short-time Fourier transform spectrogram of hand gesture to the image processing techniques of corner feature detection, feature descriptors, and Hamming-distance matching are the first-time, to our knowledge, employed to recognize hand gestures. The results show that the number of matching points is an effective parameter for classifying hand gestures. Based on the experimental data, the proposed scheme could achieve an accuracy rate of 99.8% for the hand gesture recognition.
Super-resolution of remotely sensed data using channel attention based deep learning approach

Remote Sensing image super-resolution aims to improve the spectral and/or spatial resolution of the satellite imageries. In order to improve the performance of the CNN-based super-resolution methods, increasing the depth of the network is commonly used. However, this increases computational complexity and training difficulties only with small improvement of the performance. Meanwhile, the CNN kernels treat all the channels equally and cannot take the advantage of the abundant high-frequency information contained in the low-resolution images. To address these problems, Channel attention is one of the mechanisms and has been proven to be useful in many tasks. In this research, we proposed a channel attention-based framework for Remote Sensing Image Super-resolution (CARS) by constructing a novel residual channel attention block (RCAB) to further extract the features. In addition, a densely residual channel attention block (RCAB+) and densely residual spatial attention block (RSAB) were proposed to improve the performance. We adopted a post-upsampling architecture to reduce the computational complexity and time cost. Moreover, transfer learning strategy (CARS+T) was introduced to further improve the SR performance and proved to generate finer edge details. Experimentally, our proposed CARS, CARS_SA and CARS+T achieved competitive quantitative and qualitative results both on Data Fusion Contest Dataset and Pleiades Dataset that we created.