Reviewerly/exHarmony huggingface dataset:
The FAIR Data Point: Interfaces and Tooling

ABSTRACT While the FAIR Principles do not specify a technical solution for ‘FAIRness’, it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that would simplify the creation of FAIR-compliant resources. The FAIR Data Point is a metadata repository that follows the DCAT(2) schema, and utilizes the Linked Data Platform to manage the hierarchical metadata layers as LDP Containers. There has been a recent flurry of development activity around the FAIR Data Point that has significantly improved its power and ease-of-use. Here we describe five specific tools—an installer, a loader, two Web-based interfaces, and an indexer—aimed at maximizing the uptake and utility of the FAIR Data Point.
A new feature for handwritten signature image description based on local binary patterns

Objectives. The problem of describing the invariant features of a digital image of handwritten signature that describes the distribution of its local features is considered. The formation of fundamentally new approach to the calculation of such features is described. Methods. Digital image processing methods are used. First an image is converted into a binary representation, then its morphological and median filtering is performed. Then using the method of principal components, the image is rotated to give the signature a horizontal orientation. A rectangle describing the signature is cut out, then it is scaled to the template of a certain size. In the article the template of 300×150 pixels was used. Then the border of the signature is formed. Local binary patterns are calculated from its binary contour, i.e. each pixel is assigned a number from 0 to 255, which describes the location of the edge pixels in 3×3 neighborhood of each pixel. A histogram of calculated patterns for 256 intervals is formed. The first and last intervals are discarded because they correspond to all black and white pixels in the neighborhood and are not informative. The remaining 254 numbers of the array form new local features of the signature. Results. The studies were performed on the bases of digitized signatures TUIT and CEDAR containing true and fake signatures of 80 persons. The accuracy of correct verification of signatures on these bases was about 78 % and 70 %. Conclusion. The possibility of using the proposed possibilities for solving the problems of verifying the authenticity of handwritten signatures has been experimentally confirmed.
C-BDCLSTM: A false emotion recognition model in micro blogs combined Char-CNN with bidirectional dilated convolutional LSTM

In recent years, researchers are enthusiastic about the field of sentiment computing. They pay attention to normal emotion recognition, but little to abnormal emotion recognition. In social network, users are more and more abnormal emotions, which is easy to generate in the network public opinion. However, in order to achieve different personal purposes, some users with ulterior motives often express some abnormal emotions (such as false emotion) in social networks. Recognizing false emotion correctly and effectively has great significance in analyzing users' original views on goods, movies or hot events on various websites as well as the guidance of public opinion. In this paper, we define the false emotion by extending the definition of normal emotion recognition and propose the task of false emotion recognition based on the six-element basic emotion combined with the idea of abnormal emotion detection. We construct a Char-CNN and bidirectional dilated convolutional LSTM combined model (denoted to C-BDCLSTM) for the task of false emotion recognition in micro blogs. This model is divided into RNN and CNN units, in which RNN unit includes embedding layer, BiLSTM layer, dilation layer, attention mechanism layer, and CNN unit includes convolution pooling layer and multiple filters of different sizes. By combined RNN and CNN units, C-BDCLSTM model will learn the six emotions of micro blog comments and the polarity of micro blog content, and then carry out decision-level fusion to finally complete the task of false emotion recognition. Due to the lack of false emotion recognition data set, we specially annotate and provide a data set about the false emotion recognition of micro blog contents and micro blog comments, which includes a total of ten thousand pieces of record. The experiment results demonstrate that C-BDCLSTM outperform other baselines (BiLSTM, Char-CNN, TextRCNN, LSTM-Attention, Transformer). The F1 score of our proposed C-BDCLSTM model is 0.7259 for the six emotions classification on the data set, and the F1 score of the twelve classification including false emotion is 0.6989. In addition, we also propose an evaluation index FFS for false emotion recognition, and the FFS score of our proposed C-BDCLSTM model is 0.7013.
Specialized re-ranking: A novel retrieval-verification framework for cloth changing person re-identification

Cloth changing person re-identification(Re-ID) can work under more complicated scenarios with higher security than normal Re-ID and biometric techniques and is therefore extremely valuable in applications. Meanwhile, the wide range of appearance flexibility results in more similar-looking, confusing images, which is the weakness of the widely used retrieval methods. In this work, we shed light on how to handle these similar images. Specifically, we propose a novel retrieval-verification framework. Given an image, the retrieval module will search for a shot list of similar images quickly. Our proposed verification network will then compare the probe image with these candidate images by contrasting local details for their similarity scores. An innovative ranking strategy is also introduced to achieve a good balance between retrieval and verification results. Comprehensive experiments are conducted to show the effectiveness of our framework and its capability in improving the state-of-the-art methods remarkably on both synthetic and realistic datasets.
Investigating machine learning attacks on financial time series models

Machine learning and Artificial Intelligence (AI) already support human decision-making and complement professional roles, and are expected in the future to be sufficiently trusted to make autonomous decisions. To trust AI systems with such tasks, a high degree of confidence in their behaviour is needed. However, such systems can make drastically different decisions if the input data is modified, in a way that would be imperceptible to humans. The field of Adversarial Machine Learning studies how this feature could be exploited by an attacker and the countermeasures to defend against them. This work examines the Fast Gradient Signed Method (FGSM) attack, a novel Single Value attack and the Label Flip attack on a trending architecture, namely a 1-Dimensional Convolutional Neural Network model used for time series classification. The results show that the architecture was susceptible to these attacks and that, in their face, the classifier accuracy was significantly impacted.
Towards Complex Document Understanding By Discrete Reasoning

Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language, which is an emerging research topic for both Natural Language Processing and Computer Vision. In this work, we introduce a new Document VQA dataset, named TAT-DQA, which consists of 3,067 document pages comprising semi-structured table(s) and unstructured text as well as 16,558 question-answer pairs by extending the TAT-QA dataset. These documents are sampled from real-world financial reports and contain lots of numbers, which means discrete reasoning capability is demanded to answer questions on this dataset. Based on TAT-DQA, we further develop a novel model named MHST that takes into account the information in multi-modalities, including text, layout and visual image, to intelligently address different types of questions with corresponding strategies, i.e., extraction or reasoning. Extensive experiments show that the MHST model significantly outperforms the baseline methods, demonstrating its effectiveness. However, the performance still lags far behind that of expert humans. We expect that our new TAT-DQA dataset would facilitate the research on deep understanding of visually-rich documents combining vision and language, especially for scenarios that require discrete reasoning. Also, we hope the proposed model would inspire researchers to design more advanced Document VQA models in future. Our dataset will be publicly available for non-commercial use at https://nextplusplus.github.io/TAT-DQA/.
SDABE: Efficient Encryption in Decentralized CP-ABE using Secret Sharing

Attribute-based Encryption (ABE) as a relatively new extension of public-key cryptography, introduces a new scheme of one to many encryption, compared to one to one scheme in the traditional public-key cryptography. It defines the users based on their attributes and provides fine-grained access control based on those attributes. As an improved version of ABE, decentralized ABE (DABE) aimed to remove the dependency between attribute authorities and any centralized entity in the system. Due to its heavy operations, DABE includes resource extensive computations based on the used attributes in the access policies and is not efficient in executing its different algorithms. This prevents its implementations or results in execution with significant delay on some resource-constrained devices. In this paper, we proposed a model to securely distribute the heavy computations of DABE in the encryption algorithm using secret sharing and let the user only perform some multiplications based on the received results. When the bottleneck is the exponentiation, our model can perform better. We defined and proved the correctness and security of our model. Additionally, our performance analysis showed notable improvements in terms of efficiency.
A Novel Wireless Interference Identification and Scheduling Method based on Convolutional Neural Network

Wireless interference identification plays a key role in improving the performance of mobile communication systems in terms of empowering smarter scheduling. This paper proposes to apply the convolutional neural network (CNN) to identification of wireless interference, by constructing a novel multi-level identifier which works on three different time granularities and combines the results. Exploiting the powerful feature extraction ability of CNN, the proposed approach can identify and locate 7 types of interference with high accuracy, and an adaptive threshold is calculated based on the identification result for smart scheduling. Simulation results verify that the proposed multi-level method can improve the accuracy of interference identification significantly, and achieve smart scheduling as well as increase the throughput of the network.
Hybrid Deep Learning Model for Sarcasm Detection in Indian Indigenous Language Using Word-Emoji Embeddings

Automated sarcasm detection is deemed as a complex natural language processing task and extending it to a morphologically-rich and free-order dominant indigenous Indian language Hindi is another challenge in itself. The scarcity of resources and tools such as annotated corpora, lexicons, dependency parser, Part-of-Speech tagger, and benchmark datasets engorge the linguistic challenges of sarcasm detection in low-resource languages like Hindi. Furthermore, as context incongruity is imperative to detect sarcasm, various linguistic, aural and visual cues can be used to predict target utterance as sarcastic. While pre-trained word embeddings capture the meanings, semantic relationships and different types of contexts in the form of word representations, emojis can also render useful contextual information, analogous to human facial expressions, for gauging sarcasm. Thus, the goal of this research is to demonstrate the use of a hybrid deep learning model trained using two embeddings, namely word and emoji embeddings to detect sarcasm. The model is validated on a Hindi tweets dataset, Sarc-H, manually annotated with sarcastic and non-sarcastic labels. The preliminary results clearly depict the importance of using emojis for sarcasm detection, with our model attaining an accuracy of 97.35% with an F-score of 0.9708. The research validates that automated feature engineering facilitates efficient and repeatable predictive model for detecting sarcasm in indigenous, low-resource languages.
A Probabilistic Framework for Modeling the Detection Capability of Smartphone Networks in Earthquake Early Warning

Abstract Earthquake early warning systems based on smartphone networks are emerging as complementary systems to the more expensive systems based on scientific-grade instruments. Hence, there is a need to better understand their detection capabilities. This article introduces a probabilistic framework for modeling the interaction between a smartphone network and seismic events to provide estimates of the detection probability for a given earthquake and to assess how the network geometry affects the detection delay. The framework was used to study the detection capability of the first operational smartphone-based earthquake early warning system implemented by the Earthquake Network (EQN) initiative, which started in 2013 and has issued more than 5500 warnings in 25 countries. The analysis showed that the probability of detection of an earthquake depends on the interaction between the network geometry and the earthquake parameters and that the detection probability is greatly affected by the population spatial distribution. Countries that benefit most from the EQN initiative are those without large gaps in the geographic distribution of their population.
Using Machine Learning Techniques with Incomplete Polarity Datasets to Improve Earthquake Focal Mechanism Determination

Abstract Earthquake focal mechanisms are traditionally produced using P-wave first-motion polarities and commonly require well-recorded seismicity. A recent approach that is less dependent on high signal-to-noise exploits similar waveforms to produce relative polarity measurements between earthquake pairs. Utilizing these relative polarity measurements, it is possible to produce composite focal mechanisms for clusters within microseismic sequences using regional networks. However, missing or low-confidence polarity measurements still limit our ability to calculate high-quality composite focal mechanisms. Here, we replaced unreliable polarity measurements with estimates using iterative random forests, an unsupervised ensemble machine learning method. Using the imputed (“replaced”) polarity data, we then categorically clustered the events into families. As a case study, we applied this modified composite mechanism workflow to a multistation template matched catalog of an earthquake swarm that occurred during 2020 near the Maacama fault in northern California. We found that our modified methodology produced higher-quality earthquake families and improved composite focal mechanisms, with fault-plane uncertainties &amp;lt;35° for 94% of the families compared with 34% of families using the previous methodology.
Forensic analysis of the Xiaomi Mi Smart Sensor Set

Forensic investigations in the Internet of Things (IoT) are becoming ever more important as the number of cyberincidents that occur in it continues to rise. As a result, it has become necessary to determine how to proceed in performing examinations in this environment, as its novelty and new characteristics demand the development of new forensic solutions that can guarantee a complete and efficient analysis. The approach followed until now by the research community has been to examine the most popular IoT devices and systems with the aim of gaining an insight into what data can be extracted from them, how to extract these data, and what limitations an investigator may encounter in the process. Following this idea, this article, apart from studying the state of the art of IoT forensic investigations, details the examination process for the "Xiaomi Mi Smart Sensor Set" smart home kit, emphasizing the acquisition and analysis of the three main types of forensic evidence: non-volatile memory, volatile memory and network traffic. In particular, we extract and list the useful forensic artifacts that can be obtained from this kit, describing their purpose and location.
Combining multi-view ensemble and surrogate lagrangian relaxation for real-time 3D biomedical image segmentation on the edge

Real-time 3D biomedical image segmentation is always preferred considering the exponentially growing medical imaging data for the past decade. Recently deep learning has significantly boosted the performance of automatic medical image segmentation with high computation and memory requirements, especially for 3D biomedical images. Meanwhile, the privacy and security of patient data have always been the primary concern in medical applications among hospitals and clinics, and there also exists some applications which need real-time processing in clinic practice. Thus, 3D biomedical image segmentation is typically required to be performed locally (i.e. on the edge) with limited computation and memory resources. In this paper, we propose to combine multi-view ensemble and Surrogate Lagrangian relaxation (SLR) for real-time 3D biomedical image segmentation on the edge. Instead of directly dealing with 3D biomedical images, our segmentation conducts on the three 2D domains of the 3D images with an ensemble strategy. In addition, Surrogate Lagrangian relaxation is proposed to compress the model to enable high efficiency and real-time processing. Experiments on a typical edge Nvidia GPU show that our method achieves real-time processing which is 1.5× faster with an improvement of 9% on accuracy compared with single-view models. It also saves 26× computational resources and 6× memory resources compared to 3D segmentation models.
The Influence of Gaming Experience and Optic Flow on Simulator Sickness: Insights from a Driving Simulator Study

Simulator sickness can increase dropout rates and bias participant behavior in driving simulator experiments. It has been suggested that participants experienced with simulator driving are less susceptible to simulator sickness. The authors hypothesized that gaming experience may likewise reduce the susceptibility to simulator sickness. It was also proposed to decrease optic flow in the virtual driving scene to reduce simulator sickness. The preceding assumptions were investigated in a driving simulator experiment with N=26 participants. Results indicate that gamers are less susceptible to simulator sickness compared to non-gamers. Regarding optic flow, only descriptive tendencies could be observed in terms of lower simulator sickness with reduced optic flow. Future studies should be aimed at investigating how gaming experience can be used in order to reduce simulator sickness in driving simulators. The effect of optic flow should be reconsidered in a larger experiment.
Simulator Fidelity Influences the Sense of Presence in Driving Simulators

Driving simulator studies often face the problem that traffic situations cannot be represented sufficiently realistically, which reduces the sense of presence and can influence the driving behavior of the participants. However, factors influencing the sense of presence in driving simulators have yet poorly been investigated. We conducted a within-subjects experiment with N=50 participants comparing presence between a lower- and a higher-fidelity simulator in different driving scenarios. Results indicate that a higher sense of presence can be achieved using a higher-fidelity simulator. Due to differences in both the visualization and the motion systems of the two simulators, the differences cannot yet be attributed to individual factors on the simulator side. The exact relation of presence and driving behavior likewise remains unclear and should be the subject of further investigation.
The potential of spectral unmixing method applied to PRISMA hyperspectral images in the identification of Li minerals: an evaluation for prospecting purposes

Different remote sensing methods already applied have proven efficient in identifying pegmatites, but the high number of the false positives and the size of the study areas involved, make the location of new points of interest for exploration a difficult task. In order to develop and evaluate more autonomous tools for localization of new points of interest, this study aims to apply the Envi Spectral Hourglass Wizard (SHW) algorithm and spectral analysis, both applied on PRISMA hyperspectral images, to determine mineral distribution in St. Austell greisen deposit, a Li exploration target located at Cornwall, UK. The SHW finds endmembers within the dataset to map their location and sub-pixel abundance. This processing workflow is composed of several steps: (i) MNF (Minimum Noise Fraction) Transform; (ii) PPI (Pixel Purity Index); (iii) n-D space visualizer, allowing the extraction of the endmembers and; (iv) the SAM (Spectral Angle Mapper) classification algorithm, which classifies the image creating a class for each collected endmember. The classification results show the potential of the method to indicate the presence of Li minerals being able to identify Kaolinite and map the distribution and abundance of Topaz, Tourmaline and Biotite. This approach is highly valuable for the Li mining industry.
Is VR game training more effective for hospitality employees? A longitudinal experiment

The purpose of this study was to explore the effectiveness of virtual reality (VR) game training compared to both in-person demonstration and traditional video training. Combining the constructivist learning theory with the cognitive-affective theory of learning, the study designed a longitudinal lab experiment in the context of restaurant frontline worker training. The results showed that VR game training led to more favorable brand attitudes but less knowledge retention than the other two training methods, both immediately after training and four weeks later. The contradictory results might be explained by higher arousal elicited by VR game training, which has a negative mediating effect on knowledge retention. On the other hand, pleasure positively mediates the effect of training method on brand attitude and knowledge retention. In addition, intrinsic motivation showed a significant moderating role only in delayed effects of training. The study findings have significant implications for both academia and professionals.