Reviewerly/exHarmony huggingface dataset:
Human Face Detection and Recognition Protection System Based on Machine Learning Algorithms with Proposed AR Technology

This paper demonstrates the difference between two algorithms, the first one is “Template Matching” and another one is “Local Binary Pattern Histogram (LBPH).” The face identification and recognition security system prototype had implemented using the LBPH algorithm, Python, Raspberry Pi 3 Model B+ , and OpenCV technology. This concept introduces a system for identifying random faces with Haar classifier. This method does not search for individual matches like a biometric device, it compares people to all the same, instead provide matches based on first, second, and third findings in a static collection without having to deal with the databases. Similar to a CCTV, it can identify the persons; however, instead of storing a large amount of material, it contains just a small amount. However, the face detection rate of 90% had achieved when the LBPH method had used in bright lighting conditions. On the other hand, when the template matching method had used in this recognition method in bright light condition then the detection rate was 40%. A method has been proposed that would enable the successful recognition, visualization, and detection of the convicted person utilizing virtual platforms and 3D modeling of stored pictures, which might be innovative and used in the development of augmented reality (AR). Also, the detection performance of this device had analyzed that could detect the user’s face up to 15 m in proper lighting conditions without any hassle. This may be used for home and commercial surveillance, for identification, or in the event of an act of bank robbery, or for counterterrorism. Finally, the device had implemented with the LBPH algorithm and quite economical compare to another biometric security system.
Fault-Enabled Chosen-Ciphertext Attacks on Kyber

NIST’s PQC standardization process is in the third round, and a first final choice between one of three remaining lattice-based key-encapsulation mechanisms is expected by the end of 2021. This makes studying the implementation-security aspect of the candidates a pressing matter. However, while the development of side-channel attacks and corresponding countermeasures has seen continuous interest, fault attacks are still a vastly underdeveloped field.
FedIO: Bridge Inner- and Outer-hospital Information for Perioperative Complications Prognostic Prediction via Federated Learning

Perioperative complications are associated with increased patient morbidity and mortality, and result in substantial healthcare resource utilization. With the aim to facilitate medical decision-making and improve health outcomes, machine learning methods are used to train prediction models to inform healthcare professionals and patients about the risks, which require both inner- and outer-hospital information, e.g., daily performance and clinical tests. For sake of data security and privacy, the Hospital Information System (HIS) is usually isolated from the public Internet and the raw patient samples are forbidden to transfer directly, which limits the integration of inner-and outer-hospital information. In this paper, we propose a learning framework named FedIO which bridges Inner- and Outer-hospital information via vertical Federated Learning for perioperative complications prognostic prediction. Instead of transmitting data into one cloud center, FedIO leverages the locally kept data to train a prediction model, during which only the intermediate parameters are transmitted and integrated. Extensive experiments are conducted on real-world datasets and the results manifest that combining inner- and outer-hospital knowledge is better than either of them, and FedIO shows the same-level performance as the cloud-based methods but without sharing raw data.
Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

Due to the advanced developments of the Internet and information technologies, a massive quantity of electronic data in the biomedical sector has been exponentially increased. To handle the huge amount of biomedical data, automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative data. So, multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated information. This paper presents a Deep Learning based Attention Long Short Term Memory (DL-ALSTM) Model for Multi-document Biomedical Text Summarization. The proposed DL-ALSTM model initially performs data preprocessing to convert the available medical data into a compatible format for further processing. Then, the DL-ALSTM model gets executed to summarize the contents from the multiple biomedical documents. In order to tune the summarization performance of the DL-ALSTM model, chaotic glowworm swarm optimization (CGSO) algorithm is employed. Extensive experimentation analysis is performed to ensure the betterment of the DL-ALSTM model and the results are investigated using the PubMed dataset. Comprehensive comparative result analysis is carried out to showcase the efficiency of the proposed DL-ALSTM model with the recently presented models.
Determining Research Priorities for Astronomy Using Machine Learning

We summarize the first exploratory investigation into whether Machine Learning techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation using abstracts drawn from high impact astronomy journals may provide a leading indicator of future interest in a research topic. We show two topic metrics that correlate well with the high-priority research areas identified by the 2010 National Academies' Astronomy and Astrophysics Decadal Survey science frontier panels. One metric is based on a sum of the fractional contribution to each topic by all scientific papers ("counts") while the other is the Compound Annual Growth Rate of these counts. These same metrics also show the same degree of correlation with the whitepapers submitted to the same Decadal Survey. Our results suggest that the Decadal Survey may under-emphasize fast growing research. A preliminary version of our work was presented by Thronson et al. 2021.
A Framework for Pedestrian Attribute Recognition Using Deep Learning

The pedestrian attribute recognition task is becoming more popular daily because of its significant role in surveillance scenarios. As the technological advances are significantly more than before, deep learning came to the surface of computer vision. Previous works applied deep learning in different ways to recognize pedestrian attributes. The results are satisfactory, but still, there is some scope for improvement. The transfer learning technique is becoming more popular for its extraordinary performance in reducing computation cost and scarcity of data in any task. This paper proposes a framework that can work in surveillance scenarios to recognize pedestrian attributes. The mask R-CNN object detector extracts the pedestrians. Additionally, we applied transfer learning techniques on different CNN architectures, i.e., Inception ResNet v2, Xception, ResNet 101 v2, ResNet 152 v2. The main contribution of this paper is fine-tuning the ResNet 152 v2 architecture, which is performed by freezing layers, last 4, 8, 12, 14, 20, none, and all. Moreover, data balancing techniques are applied, i.e., oversampling, to resolve the class imbalance problem of the dataset and analysis of the usefulness of this technique is discussed in this paper. Our proposed framework outperforms state-of-the-art methods, and it provides 93.41% mA and 89.24% mA on the RAP v2 and PARSE100K datasets, respectively.
ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce

Existing studies have made a great endeavor in predicting users’ potential interests in items by modeling user preferences and item characteristics. As an important indicator of users’ satisfaction and loyalty, repeat purchase behavior is a promising perspective to extract insightful information for community e-commerce. However, the repeated purchase behaviors of users have not yet been thoroughly studied. To fill in this research gap from the perspective of repeated purchase behavior and improve the process of generation of candidate recommended items this research proposed a novel approach called ReRec (Repeat purchase Recommender) for real-life applications. Specifically, the proposed ReRec approach comprises two components: the first is to model the repeat purchase behaviors of different types of users and the second is to recommend items to users based on their repeat purchase behaviors of different types. The extensive experiments are conducted on a real dataset collected from a community e-commerce platform, and the performance of our model has improved at least about 13.6% compared with the state-of-the-art techniques in recommending online items (measured by F-measure). Specifically, for active users, with w=1 and NUA∈5,25, the results of ReRec show a significant improvement (at least 50%) in recommendation. With α and σ as 0.75 and 0.2284, respectively, the proposed ReRec for unactive users is also superior to (at least 13.6%) the evaluation indicators of traditional Item CF when NUB∈6, 25. To the best of our knowledge, this paper is the first to study recommendations in community e-commerce.
Fine-tuned Xception for Image Classification on Tiny ImageNet

Image classification has been one of the most widely topic in artificial intelligence, deep models need larger datasets and powerful hardware to improve the highperformance classification. ImageNet Challenge was started in 2010 to classify 100,000 test images into 1000 different classes. Tiny ImageNet challenge is similar to ImageNet challenge, where images are taken from the standard ImageNet and resized to be 64x64. In this paper a fine-tuned Xception to classify images into the 200 classes is presented using the standard Tiny ImageNet dataset, the down-sampling (64x64) of images and the low similarity inter-class makes feature extraction and classification difficult and more challenging. We used a transfer learning algorithm to fine-tune the Xception architecture using the Extreme version of the Inception module to achieve a high validation accuracy of 65.14%.
A machine learning toolkit for CRISM image analysis

Hyperspectral images collected by remote sensing have played a significant role in the discovery of aqueous alteration minerals, which in turn have important implications for our understanding of the changing habitability on Mars. Traditional spectral analyzes based on summary parameters have been helpful in converting hyperspectral cubes into readily visualizable three channel maps highlighting high-level mineral composition of the Martian terrain. These maps have been used as a starting point in the search for specific mineral phases in images. Although the amount of labor needed to verify the presence of a mineral phase in an image is quite limited for phases that emerge with high abundance, manual processing becomes laborious when the task involves determining the spatial extent of detected phases or identifying small outcrops of secondary phases that appear in only a few pixels within an image. Thanks to extensive use of remote sensing data and rover expeditions, significant domain knowledge has accumulated over the years about mineral composition of several regions of interest on Mars, which allow us to collect reliable labeled data required to train machine learning algorithms. In this study we demonstrate the utility of machine learning in two essential tasks for hyperspectral data analysis: nonlinear noise removal and mineral classification. We develop a simple yet effective hierarchical Bayesian model for estimating distributions of spectral patterns and extensively validate this model for mineral classification on several test images. Our results demonstrate that machine learning can be highly effective in exposing tiny outcrops of specific phases in orbital data that are not uncovered by traditional spectral analysis. We package implemented scripts, documentation illustrating use cases, and pixel-scale training data collected from dozens of well-characterized images into a new toolkit. We hope that this new toolkit will provide advanced and effective processing tools and improve community’s ability to map compositional units in remote sensing data quickly, accurately, and at scale.
Feature separation and double causal comparison loss for visible and infrared person re-identification

Visible and infrared cross-modal person re-identification (VI-ReID) is the task of retrieving person images from visible images and infrared images. In the past, most VI-ReID algorithms focused only on learning common representations of different modes. In contrast, we extract identity-related features from different modalities and filter out identity-independent interference, and we let our network learn the domain unchanged as a more effective feature representation. In this paper, a novel end-to-end feature separation and double causal comparison loss framework for VI-ReID (FSDCC) is proposed to address cross-modality ReID tasks. We first separate the features using the feature separation module (FSM) to obtain strong identity-related and weak identity-related features. Then, double causal comparison loss is used to guide the model training. This process effectively reduces the influence of identity-irrelevant information such as occlusion and background, and finally achieves enhanced expression of identity-relevant features. Simultaneously, we combine identity loss and weighted regularization TriHard loss in a progressive joint training manner. Additionally, to enhance the CNN's ability to extract global semantic information and better establish the connection between two pixels with a certain distance on the image, we propose CNS non-local neural network (CNS non-local), finally improving VI-ReID accuracy. Extensive experiments on two cross-modality datasets demonstrate that the proposed method outperforms current state-of-the-art by a large margin, achieving rank-1/mAP accuracy 87.18%/79.10% on the RegDB dataset, and 68.79%/65.72% on the SYSU-MM01 dataset.
GeoSPARQL query support for scientific raster array data

This paper presents the design of a GeoSPARQL query processing solution for scientific raster array data, called GeoLD. The solution enables the implementation of SPARQL endpoints on top of OGC standard Web Coverage Processing Services (WCPS). Thus, the semantic querying of scientific raster data is supported without the need of specific raster array functions in the language. To achieve this, first Coverage to RDF mapping solutions were defined, based on the well-known W3C standard mappings for relational data. Next, the SPARQL algebra is extended with a new operator that delegates part of the GeoSPARQL query in WCPS services. Query optimization replaces those parts of the SPARQL query plan that may be delegated to a WCPS service by instances of such new WCPS operator. A first prototype has been implemented by extending the ARQ SPARQL query engine of Apache Jena. Petascope was used as the WCPS implementation on top of the Rasdaman raster array database. An initial evaluation with real meteorological data shows, as it was initially expected, that the approach outperforms an existing reference relational database based GeoSPARQL implementation.
Shadowed-set-based three-way clustering methods: An investigation of new optimization-based principles

Shadowed set approximation form a cornerstone for the explainable decision advice provided by shadowed C means (SCM) clustering in unsupervised learning. Due to its advantage of managing uncertainty in fuzzy clustering, it has been used in data classification. Existing SCM clustering method requires the overall amount of uncertainty associated with a fuzzy cluster ck to be preserved in its boundary region. This requirement may suffer serious risk of having high number of unclassified patterns, especially when the uncertainty in ck is very high. Consequently, the ensuing clustering may not adequately maximize the inter-cluster separation necessary for achieving optimum cluster validity results. To tackle this problem, this paper considers new SCM clustering methods arising from (i) trade-off between uncertain and certain regions, which is necessary for refraining from making uncertain classification as much as possible, (ii) measure of sharpness balance, which helps to leverage on the location of a pattern from borderline and identify included or excluded patterns by means of their location from borderline, (iii) measure of gradualness balance, which exploits the degree of transition of a pattern out of or into ck. Each method comes with some advantages. For instance, the first and third methods may minimize the overall amount of unclassified patterns. To provide an overall evaluation of the performance of the proposed methods, a comparative study with some other shadowed set-based optimization methods are involved by considering some data sets from UCI Machine Learning repository. Friedman testing followed by Holm-Bonferroni testing are also exploited to provide statistical analysis on the performance significance of the compared methods.
Attention Augmented ConvLSTM for Environment Prediction

Safe and proactive planning in robotic systems generally requires accurate predictions of the environment. Prior work on environment prediction applied video frame prediction techniques to bird’s-eye view environment representations, such as occupancy grids. ConvLSTM-based frameworks used previously often result in significant blurring of the predictions, loss of static environment structure, and vanishing of moving objects, thus hindering their applicability for use in safety-critical applications. In this work, we propose two extensions to the ConvLSTM architecture to address these issues. We present the Temporal Attention Augmented ConvLSTM (TAAConvLSTM) and Self-Attention Augmented ConvLSTM (SAAConvLSTM) frameworks for spatiotemporal occupancy grid prediction, and demonstrate improved performance over baseline architectures on the real-world KITTI and Waymo datasets. We provide our implementation at https: //github.com/sisl/AttentionAugmentedConvLSTM.
Aspect-Based Sentiment Analysis Using Graph Convolutional Networks and Co-attention Mechanism

Aspect-based sentiment analysis (ABSA) refers to classifying the sentiment polarity of a specific aspect in a sentence. Recently, attention-based deep learning approaches are proposed to capture the semantic information and achieve satisfying results. However, due to the significance of syntactic structure, syntactic information is also analyzed for ABSA. As such, this work proposes a model that integrates the graph convolution network (GCN) and the co-attention mechanism to deal with the aspect-based information and remove the noise from unrelated context words. Both the semantic information and the syntactic information are conveyed by the representation for sentiment analysis. Experimental results show our model achieves a better working performance, which establishes a strong evidence of the capability.
Analyzing Learning-based Encrypted Malware Traffic Classification with AutoML

Malware traffic classification is an essential pillar of network intrusion detection systems. The explosive growth of traffic encryption makes it infeasible to classify malware traffic with port-based or signature-based approaches. Nowadays, researchers and industrial developers turn to learning-based approaches for encrypted malware traffic classification, mining the statistical patterns of traffic behaviors. However, different machine learning models with different hyper-parameters can be used, and one can hardly explain why a learning approach works or not. To alleviate this problem, this paper conducts encrypted malware traffic classification with the automated machine learning (AutoML) approach which contains 7 representative models in the pipeline and realizes automated hyper-parameter tuning and model assembling. Experimenting on real-world encrypted malware traffic, this paper analyzes the performance of the ensemble model of AutoML and how each model performs in detail to understand its contribution. Moreover, the analysis of AutoML feature selection shows discriminant features on encrypted malware traffic especially TLS metadata related. The concrete experiments and analysis give insight to the following studies on encrypted malware traffic classification.
LICALITY—Likelihood and Criticality: Vulnerability Risk Prioritization Through Logical Reasoning and Deep Learning

Security and risk assessment aims to prioritize detected vulnerabilities for remediation in a computer networking system. The widely used expert-based risk prioritization approach, e.g., Common Vulnerability Scoring System (CVSS), cannot realistically associate vulnerabilities to the likelihood of exploitation. The CVSS metrics are calculated from static formulas, and cannot easily integrate attackers’ motivations and capabilities w.r.t. the network environmental factors. To address this issue, this paper proposes LICALITY, a vulnerability risk prioritization system. LICALITY captures the attacker’s preference on exploiting vulnerabilities through a threat modeling method, and learns threat attributes that contribute to the exploitation of vulnerability. LICALITY creatively uses a neuro-symbolic model, with neural network (NN) and probabilistic logic programming (PLP) techniques, to learn such threat attributes. The risk of vulnerability is assessed from the criticality of exploitation and the likelihood of exploitation. LICALITY consolidates these two measurements by using a logic reasoning engine. In the evaluation, the historical threat and future threat are from real attack scenarios. The results reveal that LICALITY reduces the vulnerability remediation work of the future threat required by the CVSS by a factor of 2.89 in the first case study and by a factor of 1.85 in the second case study. Such future threats are identified as the top routinely exploited vulnerabilities and the APT attack chained vulnerabilities reported in the Cybersecurity and Infrastructure Security Agency (CISA) alerts.
Beyond screen time: Using head-mounted eye tracking to study natural behavior

Head-mounted eye tracking is a new method that allows researchers to catch a glimpse of what infants and children see during naturalistic activities. In this chapter, we review how mobile, wearable eye trackers improve the construct validity of important developmental constructs, such as visual object experiences and social attention, in ways that would be impossible using screen-based eye tracking. Head-mounted eye tracking improves ecological validity by allowing researchers to present more realistic and complex visual scenes, create more interactive experimental situations, and examine how the body influences what infants and children see. As with any new method, there are difficulties to overcome. Accordingly, we identify what aspects of head-mounted eye-tracking study design affect the measurement quality, interpretability of the results, and efficiency of gathering data. Moreover, we provide a summary of best practices aimed at allowing researchers to make well-informed decisions about whether and how to apply head-mounted eye tracking to their own research questions.