Reviewerly/exHarmony huggingface dataset:
Demand characteristics challenge effects in embodiment and presence

Abstract The sensations to own and control a body as well as being located in a body describe the relation between ourselves and our body, termed embodiment. Embodiment plays a central role in our everyday actions. However, its assessment is challenging. Recent findings suggest that measures on embodiment are confounded by demand characteristics and suggestibility. To investigate the impact of demand characteristics on embodiment and presence, we compared results from an online experiment measuring participants’ expectations, to the same experiment in virtual reality (VR). In the online experiment, participants watched a video of a participant performing the VR experiment. In the VR experiment, participants performed a soap-bubble-kicking task, which allowed the feelings of embodiment and presence to arise. We manipulated temporo-spatial movement synchrony (Movement: synchronous, asynchronous) and avatar visibility (Visibility: visible, invisible). In addition, we assessed participants’ suggestibility with exercises. The introduced manipulations influenced the ratings in both experiments similarly. Embodiment ratings were additionally affected by suggestibility. Altogether, our results show that participants were aware of the research hypotheses, which indicates that demand characteristics confound embodiment and presence research alike. Overcoming challenges of demand characteristics is crucial to correctly interpret scientific results and to translate these results into applied settings.
Generating Talking Face With Controllable Eye Movements by Disentangled Blinking Feature

In virtual reality, talking face generation is committed to using voice and face images to generate real face speech videos to improve the communication experience in the case of limited user information exchange. In a real video, blinking is an action often accompanied by speech, and it is also one of the indispensable actions in real face speech videos. However, the current methods either do not pay attention to the generation of eye movements, or cannot control the blinking in the generated results. To this end, this article proposes a novel system which produces vivid talking face with controllable eye blinks driven by the joint features including identity feature, audio feature, and blink feature. In order to disentangle the blinking action, we designed three independent features to individually drive the main components in the generated frame, namely the facial appearance, mouth movements, and eye movements. Through the adversarial training of the identity encoder, we filter out the information of the eye state from the identity feature, thereby strengthening the independence of the blinking feature. We introduced the blink score as the leading information of the blink feature, and through training, the value can be consistent with human perception to form a complete and independent control of the eyes. Experimental results on multiple datasets show that our method can not only reproduce real talking faces, but also ensure that the blinking pattern and time are fully controllable.
Accuracy‐first and efficiency‐first privacy‐preserving semantic‐aware ranked searches in the cloud

Traditional term frequency-inverse document frequency model-based privacy-preserving ranked search schemes rarely consider the latent semantic meanings of documents and keywords. It is a challenge to design efficient semantic-aware ranked search (SRSE) schemes with privacy preservation. In this paper, two privacy-preserving SRSE schemes are developed for the cloud environments. The first scheme is the accuracy-first search scheme. In this scheme, the Latent Dirichlet Allocation topic model is adopted to generate the topic-based semantic information-embedded vectors for documents and queried keywords, which supports semantic-aware relevance measurement. The bisecting k-means clustering algorithm is used to build an accuracy-first filtering tree index (AFF-tree), and the AFF-tree-based search algorithm is proposed to achieve the accuracy-first ranked search. The second scheme is the efficiency-first search scheme. It performs a structure optimization on the AFF-tree, and a newly efficiency-first filtering tree index (EFF-tree) is designed. By using the EFF-tree, an anchor node-based search algorithm is designed to achieve the efficiency-first ranked search at the expense of a little decrease in search result precision. The secure inner product is used to perform privacy-preserving semantic-aware relevance measurement between documents and queried keywords in both schemes. To analyze the security of the proposed schemes, the game stimulation-based proof is presented. Experimental results show the better performance of the proposed schemes in search time cost.
Reliability-Aware and Energy-Efficient Workflow Scheduling in IaaS Clouds

Nowadays, more and more workflow applications with different computing requirements are migrated to clouds and executed with cloud resources. Workflow scheduling becomes a critical problem in the cloud environment, which focuses on meeting various quality of service (QoS) constraints. Workflow reliability and energy consumption are two essential parts in clouds and minimizing energy consumption for scheduling workflow with the reliability constraint is a challenging issue. In response to the challenge, we propose a workflow scheduling algorithm named REWS to reduce energy consumption and satisfy workflow reliability constraints. In REWS, a new sub-reliability constraint prediction strategy is adopted to break down the workflow reliability constraint to task sub-reliability constraints and the effectiveness of this strategy is proved. Moreover, an update method is adopted to adjust the task sub-reliability constraint for reducing energy consumption. In addition, a brief system framework which consists of five parts: workflow analyzer, reliability decomposer, resource manager, workflow scheduler and feedback processer is built to support the algorithm implementation of REWS. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed REWS approach. The results demonstrate the superiority of REWS as compared with the state-of-the-art algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Workflow scheduling is a challenging issue in emerging trends of the cloud environment that focuses on satisfying various QoS constraints. In this paper, we investigate a reliability-aware and energy-efficient workflow scheduling problem in cloud computing. A novel workflow scheduling algorithm called REWS, is designed to reduce the energy consumption and meet the workfolw reliability constraint. The basic idea of REWS is to divide the workflow reliability constraint into task sub-reliability constraints and schedule tasks with an energy-efficient scheduling strategy. We conduct the experiments to evaluate the proposed REWS and the results demonstrate that REWS outperforms the state-of-the-art algorithms.
The Sky-Status Climatology of Greece: Emphasis on Sunshine Duration and Atmospheric Scattering

The aim of this work is the study of the sky conditions climatology over Greece based on the diffuse-fraction (kd) limits, for clear, kd ∈ [0, 0.26]; intermediate, kd ∈ (0.26, 0.78); and overcast, kd ∈ (0.78, 1) skies. kd is, therefore, used here to characterise the sky conditions over a site. Its values are estimated from diffuse and global horizontal solar irradiances the typical meteorological years of 43 selected Greek sites. The kd values in each specific range are equivalent to sunshine durations (SSDs) under the particular sky conditions. Annual, seasonal, and intra-annual variations in SSDs are estimated with regression equations to fit their means. Clear skies comprise 33%, intermediate 40%, and overcast 27% of the time in a year. kd, as an atmospheric scattering index (ASI), shows dependence on the sites’ geographical latitude: best-fit lines mean ASIs are derived showing no trend, while overcast skies show a slight negative trend. A comparison of the clear-sky SSDs for four Greek sites from the Global Climate Data and one site from the Academy of Sciences of Moldova with those derived from kd shows a remarkable difference. A new methodology is developed that results in much smaller differences. Finally, maps of the annual SSDs and ASIs are derived for Greece.
Investigating toxicity changes of cross-community redditors from 2 billion posts and comments

This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the same community, across multiple communities, and over time. As for the toxicity detection performance, the BERT model achieved a 91.27% classification accuracy and an area under the receiver operating characteristic curve (AUC) score of 0.963 and outperformed several baseline machine learning and neural network models. The user behavior toxicity analysis showed that 16.11% of users publish toxic posts, and 13.28% of users publish toxic comments. However, results showed that 30.68% of users publishing posts and 81.67% of users publishing comments exhibit changes in their toxicity across different communities, indicating that users adapt their behavior to the communities' norms. Furthermore, time series analysis with the Granger causality test of the volume of links and toxicity in user content showed that toxic comments are Granger caused by links in comments.
Greedy structure learning from data that contain systematic missing values

Abstract Learning from data that contain missing values represents a common phenomenon in many domains. Relatively few Bayesian Network structure learning algorithms account for missing data, and those that do tend to rely on standard approaches that assume missing data are missing at random, such as the Expectation-Maximisation algorithm. Because missing data are often systematic, there is a need for more pragmatic methods that can effectively deal with data sets containing missing values not missing at random. The absence of approaches that deal with systematic missing data impedes the application of BN structure learning methods to real-world problems where missingness are not random. This paper describes three variants of greedy search structure learning that utilise pairwise deletion and inverse probability weighting to maximally leverage the observed data and to limit potential bias caused by missing values. The first two of the variants can be viewed as sub-versions of the third and best performing variant, but are important in their own in illustrating the successive improvements in learning accuracy. The empirical investigations show that the proposed approach outperforms the commonly used and state-of-the-art Structural EM algorithm, both in terms of learning accuracy and efficiency, as well as both when data are missing at random and not at random.
Hot-started NAS for Task-specific Embedded Applications

Neural architecture search (NAS) has proven its worth in discovering new neural networks. Combining the possibility to satisfy multiple objectives in one search, it is especially useful for getting the most out of embedded devices with limited resources. However, research into small and efficient neural networks precedes NAS. We investigate the influence of combining this pre-existing knowledge with NAS techniques, for which we propose to hot-start the NAS search with a human-designed optimal network. Our experiments show that doing so speeds up the NAS process significantly, but the resulting optimal model at the end is only marginally better. Since embedded devices are often used for a specific task, we also explore the impact of using a task-specific dataset in the NAS process. Our experiments demonstrate that for a constrained problem, a smaller network can be found as compared to a general problem.
Continual Learning with Transformers for Image Classification

In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn new concepts without forgetting what has been learnt in the past. This phenomenon is known as catastrophic forgetting and it is often difficult to prevent due to practical constraints, such as the amount of data that can be stored or the limited computation sources that can be used. Moreover, training large neural networks, such as Transformers, from scratch is very costly and requires a vast amount of training data, which might not be available in the application domain of interest. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning, but this needs complex tuning to balance the growing number of parameters and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we validate in the computer vision domain a recent solution called Adaptive Distillation of Adapters (ADA), which is developed to perform continual learning using pre-trained Transformers and Adapters on text classification tasks. We empirically demonstrate on different classification tasks that this method maintains a good predictive performance without retraining the model or increasing the number of model parameters over the time. Besides it is significantly faster at inference time compared to the state-of-the-art methods.
Artistic Style Novel View Synthesis Based on A Single Image

Recent progress in 3D display technologies has raised the demand for stylized 3D digital content. Previous approaches either perform style transfer on stereoscopic image pairs or reconstruct 3D environment with multiple view images. In this paper, we propose a novel view stylization framework that can convert a single 2D image into multiple stylized views. It is a two-stage solution that contains view synthesis and neural style transfer. We estimate dense optical flow between the source and novel views so that the style transfer model can produce consistent results. Experimental results show that our method significantly improves the consistency among views compared to the baseline method.
Shfl-BW

Weight pruning in deep neural networks (DNNs) can reduce storage and computation cost, but struggles to bring practical speedup to the model inference time. Tensor-cores can significantly boost the throughput of GPUs on dense computation, but exploiting tensor-cores for sparse DNNs is very challenging. Compared to existing CUDA-cores, tensor-cores require higher data reuse and matrix-shaped instruction granularity, both difficult to yield from sparse DNN kernels. Existing pruning approaches fail to balance the demands of accuracy and efficiency: random sparsity preserves the model quality well but prohibits tensor-core acceleration, while highly-structured block-wise sparsity can exploit tensor-cores but suffers from severe accuracy loss.
Quantitative Risk Assessment of Threats on SCADA Systems Using Attack Countermeasure Tree

SCADA systems are one of the critical infrastructures and face many security threats. Attackers can control SCADA systems through network attacks, destroying the normal operation of the power system. It is important to conduct a risk assessment of security threats on SCADA systems. However, existing models for risk assessment using attack trees mainly focus on describing possible intrusions rather than the interaction between threats and defenses. In this paper, we comprehensively consider intrusion likelihood and defense capability and propose a quantitative risk assessment model of security threats based on attack countermeasure tree (ACT). Each leaf node in ACT contains two attributes: exploitable vulnerabilities and defense countermeasures. An attack scenario can be constructed by means of traversing the leaf nodes. We set up six indicators to evaluate the impact of security threats in attack scenarios according to NISTIR 7628 standard. Experimental results show the attack probability of security threats and high-risk attack scenarios in SCADA systems. We can improve defense countermeasures to protect against security threats corresponding to high-risk scenarios. In addition, the model can continually update risk assessments based on the implementation of the system’s defensive countermeasures.
Person Re-Identification in Multi-Camera Environment

The task of re-identifying the same person in several video/image sequences obtained from multiple camera viewpoints and angles is known as person Re-Identification (Re-ID). Person re-identification in a multi-camera system remains a difficult task. Researchers have done a lot of work on people re-id models, but each has its own set of limitations and needs improvement. The complexity of the architecture used also plays a significant role in re-identification in real-time scenarios. We demonstrate using a CNN-based feature extraction framework to re-identify persons in multi-camera environments based on their appearance. We present an approach for performing Re-Idtasks using a less dense CNN-backed model that employs Cross-input Neighborhood Difference (CND). The Re-ID model is trained and tested experimentally on the CUHK03 dataset, which contains diverse data. In this study, we look at the Re-ID task as a binary comparing task that determines if a given pair of images is similar or not. In comparison to state-of-the-art approaches, we demonstrate our proposed method on the CUHK03 dataset and attain rank-1 accuracy of 86.6 percent.
Detecting non-natural language artifacts for de-noising bug reports

Abstract Textual documents produced in the software engineering process are a popular target for natural language processing (NLP) and information retrieval (IR) approaches. However, issue tickets often contain artifacts such as code snippets, log outputs and stack traces. These artifacts not only inflate the issue ticket sizes, but also can this noise constitute a real problem for some NLP approaches, and therefore has to be removed in the pre-processing of some approaches. In this paper, we present a machine learning based approach to classify textual content into natural language and non-natural language artifacts at line level. We show how data from GitHub issue trackers can be used for automated training set generation, and present a custom preprocessing approach for the task of artifact removal. The training sets are automatically created from Markdown annotated issue tickets and project documentation files. We use these generated training sets to train a Markdown agnostic model that is able to classify un-annotated content. We evaluate our approach on issue tickets from projects written in C++, Java, JavaScript, PHP, and Python. Our approach achieves ROC-AUC scores between 0.92 and 0.96 for language-specific models. A multi-language model trained on the issue tickets of all languages achieves ROC-AUC scores between 0.92 and 0.95. The provided models are intended to be used as noise reduction pre-processing steps for NLP and IR approaches working on issue tickets.
A parallel based evolutionary algorithm with primary-auxiliary knowledge

The development of hybrid algorithms or the application of multiple strategies is one of the focal points for research on improving evolutionary algorithms. However, since most of the evolutionary equations of different algorithms can be transformed into each other, it is difficult to change the established properties of the given algorithm through a hybrid algorithm based on a mixture of evolutionary equations. In addition, multi-strategy methods tend to adopt the best strategy for the current local domain through greedy strategies in the solution process, which does not ensure validity in the global domain. Recently, Federated Learning has achieved remarkable results in machine learning, where the idea of model independence, parallelism and data sharing can essentially compensate for the weaknesses of hybrid and multi-strategy algorithms. Inspired by the idea of Federated Learning, this paper proposes an evolutionary algorithm named as parallel based Evolutionary Algorithm with primary-auxiliary knowledge. Specifically, a Spark-based primary-auxiliary knowledge model is developed, with different evolutionary algorithms used on each parallel sub-model. Then, an effective topological knowledge (individual) migration method is devised, which enables the primary knowledge model to learn the best knowledge from different auxiliary knowledge models through a topological structure. In this way, the best knowledge on the auxiliary knowledge models can be transferred to the primary knowledge model. Through a test conducted on the CEC2013 test set, it can be found out that the proposed algorithm clearly outperforms the 10 algorithms compared, which demonstrates the excellent performance of our proposed parallel based evolutionary algorithm with primary-auxiliary knowledge.
Integrating Artificial Intelligence and Blockchain for Enabling a Trusted Ecosystem for Healthcare Sector

The advances in the information technology and communication technologies have fueled the digitization process in almost all sectors. Healthcare is one of such sectors where we could witness a rapid paradigm shift for the last couple of years. The major share of medical data that are generated from sources such as patient health records, clinical trials, patient-generated data such as forum posts, and social network data. There are several advantages due to this digitization but at the same time, it poses several challenges as well. One major challenge is on the storage and efficient retrieval of the digital healthcare data and another one is to capture the data in such a way that the sensitive medical data will not be tampered with. Thus, building a credible and trustworthy ecosystem for efficiently capturing and storing sensitive healthcare data is the need of the hour in the healthcare sector. Exponential technologies such as Artificial Intelligence (AI) and Blockchain are being used to develop innovative healthcare applications and solutions to tackle the issues mentioned. The credible data capturing infrastructure that is introduced with the famous currency, Bitcoin, now changing the way various industries and business function. The tamper-proof nature of blockchain can store the data that no one can erase but with the consent of the patient and a machine learning model trained on this data may have several potential applications in healthcare. This chapter proposes a new framework for building a trusted data management platform for the healthcare domain using blockchain that can be integrated with artificial intelligence techniques to build machine learning models. The authors believe that such a model will be useful to build several semantic knowledge discovery applications in the healthcare sector. This chapter will also discuss the future research dimensions combining AI and blockchain for enabling a trusted healthcare ecosystem.