Reviewerly/exHarmony huggingface dataset:
Accelerating Side Channel Attack using Normalized Inter Class Variance

Correlation, Template, Machine Learning (ML) based attacks, etc. are well known Side Channel Attack (SCA) techniques widely used to extract the key of crypto systems. They exploit correlation between data under computation and corresponding power consumptions. In order to reduce the number of computations in key extraction, a power model free new technique is proposed to effectively compress the power traces of AES-128 running at FPGAs with the help of Normalized Inter Class Variance (NICV) values computed with publicly known information (cipher texts). This technique determines the end of encryption as Sample Index (SI) of power traces which has highest frequency for maxima of NICV values computed for lesser than all 16 bytes of cipher text. It estimates number of samples per round (NS) by subtracting two adjacent peak indexes of the power traces. Finally, it creates compressed traces by selecting 10% of NS samples around SI resulting in 98% compression. This technique has been validated by mounting Correlation Power Analysis (CPA) attack on several compressed power traces of AES algorithm running at Spartan 3E and Spartan 6 FPGAs. This will be extremely useful for Template and ML based attacks, where huge numbers of traces are required during the profiling (training) phase.
Move Real-Time Data Analytics to the Cloud: A Case Study on Heron to Dataflow Migration

Twitter is migrating its real-time data analytics infrastructure to the cloud. We propose a complete cloud migration procedure to tackle five identified challenges. Following the proposed solution, we successfully migrated a typical Heron job to Dataflow recently. This use case implements 1) control plane of provisioning and orchestration, 2) data plane of Heron to Beam programming model translation, 3) job IOs through replication and direct access/proxying, and 4) data validation by control test and metrics monitoring. The evaluation demonstrated the new Dataflow job matches the legacy Heron job in terms of performance and SLAs. Finally, we retrospect the migration process and identify future work on automated migration.
Text Summarization using TextRank for Knowledge Externalization from Indonesian Online Discussion Forums

Online discussion forum is one of digital media that contain many opinions about some issue. This research aims to use automatic text summarization technology for knowledge externalization from online discussion forums in Indonesia. The method that used in this research is the TextRank algorithm which is in summarizing the text gives quite good results. It is proven based on experiment and evaluation result using ROUGE-L evaluation to evaluate the similarity between manual summary and output summary from system. The experiment used 5 Indonesian Islamic online discussion forums. The summary results of 20 documents as sample data and 31 students carry out testing show that summary results are easy to understand. The test results obtained that the overall average value of Recall is 0.42 and Precision is 0.42. Simultaneously, the overall average F-score is 0.39 with a maximum F-score of 0.72 and a minimum F-score of 0.052. While the test for user understanding of the summary text results obtained, 44.52% very understand, 34.19% understand, and 16.13% understand enough. Meanwhile, students who stated that they did not understand the results of automatic text summary 2.26% and 0.97% stated that they did not understand.
Towards Designing a Mobile Stress Coping Assistant

Stress is a major public health concern and a severe threat to everyone.Facilitated by their powerful sensing capabilities, mobile devices may assist individuals in coping with stress.Building on existing studies and mobile apps supporting stress coping, we propose the design of a mobile coping assistant that uses multimodal sensor data to reduce its user's stress.Based on sensor data, a mobile coping assistant (1) warns the user about elevated stress, (2) delivers a fundamental understanding of why they are currently stressed, (3) recommends targeted coping strategies to encourage and train effective coping behavior, and (4) executes automated actions to reduce stress exposure.The presented design comprises an architecture, good practices for designing the architectural components, and an algorithm for selecting adequate coping actions and recommendations.A prototypical instantiation indicates opportunities and challenges.Future research should evaluate the short-and long-term effectiveness of mobile coping assistants in the field.
Identifying Sponsored Content in YouTube using Information Extraction

Information Extraction techniques can retrieve useful information from unstructured data that improve data analytics’ effectiveness and play a key role in the consumer decision-making process. The growth of sponsored content videos on social media increases the demand on knowing the effectiveness of the sponsored investment in the engagement results obtained. This study aims to analyze an approach to infer sponsored content in videos from top digital influencers on YouTube using knowledge acquisition techniques in audio transcriptions. A dataset with 34,563 videos, among 103 different YouTubers channels, was used in a model comprising six stages: data acquisition, preprocessing of documents, identification of candidate videos, manual transcriptions processing, automatic transcriptions processing, and tuple filtering. Despite we perceived several difficulties during the recognition of speech from YouTube videos, such as the absence of clear boundaries between words, the presence of two or more people talking in the video, and colloquial expressions, as a result, the approach identifies sponsored videos from their audio transcripts in a feasible process by identifying keywords, obtaining knowledge from tuples, and recognizing named entities.
Modeling Driver’s Visual Fixation Behavior Using White-Box Representations

Driving is a highly dynamic task requiring extensive and fast processing of visuospatial information in brief time intervals, while experienced drivers can efficiently perform this task. Therefore, investigating the human-like visual fixation behavior shows much potential in developing the advanced driving assistance systems and efficient autonomous vehicles. Literature shows that driver’s visual fixation behavior is guided by a combination of top-down and bottom-up mechanisms. Currently, existing excellent computational frameworks rely heavily on the deep neural network’s strong feature extraction ability, which is inherently task-unaware. These black-box models are limited in strengthening the guidance of task-aware top-down factors in governing the fixation behavior. Furthermore, using the black-box model to fit the training data directly decreases its interpretability. In this paper, we encourage to adopt the white-box method. By researching the findings in the intersection of neuroscience and psychology, we propose to identify two white-box representations for driving separately: the information selection representation including salience, effort, expectancy, and value, and the driving task-aware representation highlighting sub-task and speed. We present a novel computational framework to incorporate all the above factors to predict driver’s visual fixation systematically. On the BDDA dataset, we have conducted the extensive quantitative evaluation and qualitative analysis to validate the effectiveness. Its performance surpasses the previous state-of-the-art by +11.3% in the Pearson’s Correlation Coefficient, especially by a large margin of +24.5% and +28.8% in the turning operations. The ablation study further demonstrates the influence of each component in our framework. Moreover, the trained model has been deployed on the DR(eye)VE dataset to verify the generalization ability, demonstrating the potential of domain adaptation.
Cluster Image Patches with Multiple Mutual Information in Unlabelled Whole-Slide Image

The massive annotation workload has always hindered the progress towards an automatic analysis of gigapixel whole-slide images. Histologically, individual patches from a constrained spatial region may share rich phenotypic information, where the morphological correlations have the potential to be mined for a grouping or clustering task. In this paper, we propose a clustering technique to extract multiple mutual information from histology images without prior domain knowledge. Specifically, our framework automatically localizes morphologically homogeneous patches within an extended solution space. Our novelty is an expanse and the pattern with which invariant information can be learnt, in contrast to the current literature of feature generation or parametric transformation within an individual patch. Additionally, structure-independent, the model may be applicable to any backbone convolutional neural network architectures. The empirical validation on The Cancer Genome Atlas (TCGA) datasets illustrates an observable margin of patch-level classification accuracy in comparison with state-of-the-art unsupervised approaches.
Supporting Test Case Design on Reasoning Scheme with Natural Language Processing Technique

The purpose of the study is to support test case design by introducing natural language processing techniques and reasoning schemes for designing. Sentences containing a logical relationship of implication (key sentences) are extracted from a specification document with natural language processing techniques, and supplementary explanation sentences for the key sentence (subordinate sentences) that complement parameters appeared in the key sentence are placed in Toulmin's reasoning scheme based on "similarity" to the key sentence. As a result, we succeeded in automatically generating test cases for product testing, and also in supporting to extract parameters for each test case.
An Encrypted Cloud Email Searching and Filtering Scheme Based on Hidden Policy Ciphertext-Policy Attribute-Based Encryption With Keyword Search

With the rapid growth of cloud email services, email encryption is beginning to be used more and more to alleviate concerns about cloud privacy and security. However, this increase in usage invites the problem of how to search and filter encrypted emails effectively. Searchable public key encryption is a popular technology to solve encrypted email searching, but encrypted email filtering is still an open problem. We propose an encrypted cloud email searching and filtering scheme based on hidden policy ciphertext-policy attribute-based encryption with keyword search as a new solution. It enables the recipient to search the encrypted cloud email keywords and allows the email filtering server to filter the encrypted email content when receiving the email, as the traditional email keyword filtering service. Our hidden policy scheme is constructed by composite order bilinear groups and proven secure by dual system encryption methodology. Our scheme can be applied to other scenarios such as file searching and filtering and has certain practical value.
Fusion of Deep Learning Features for Wild Animal Detection

A novel method of feature extraction, based on feature fusion, is proposed in this paper. In object classification and detection models, more advantages of transfer learning can be easily extracted from feature fusion. In the proposed scheme, Firstly, features are extracted by using two deep learning models: DenseNet201 and ResNet101 and then a neighborhood component analysis (NCA) based feature selection method is applied to diminish the dimensionality of features. Then the features are fused by applying the concatenation process and finally, SVM is applied to classify the dataset. Experimental results on camera trap wild animal dataset, show that our method can classify the wild animals along with huge variation and extreme occlusion in position and size. Furthermore, we have attained an accuracy of 98.07% which is higher than the algorithm adopting single net and other existing state-of-art techniques.
Multi Layer Detection Framework for Spear-Phishing Attacks

With emails becoming prime communication medium across organizations, spear-phishing has become one of the most effective medium for attackers to breach enterprise network security. Latest targeted attacks which employ compromised email accounts for sending spear-phishing emails are almost impossible to detect using conventional security solutions. Attackers use contextual information of targeted entity to make the spear-phishing emails look legitimate. Convincing the victim to either click a malicious link or download and open a malicious attachment.The Detection of spear-phishing email becomes more challenging when it has to be done in an enterprise network scenario. Factors such as non-availability of training data, huge volumes of emails received and critical role of email in business operations makes detection a challenge. In the work presented in this paper we propose a multi-layer detection framework for the spear-phishing email detection. The proposed framework employs sentiment analysis, context-based behavior analysis along with deception technologies for the detection of spear-phishing emails.
Joint label refinement and contrastive learning with hybrid memory for Unsupervised Marine Object Re-Identification

Unsupervised object re-identification is a challenging task due to the missing of labels for the dataset. Many unsupervised object re-identification approaches combine clustering-based pseudo-label prediction with feature fine-tuning. These methods have achieved great success in the field of unsupervised object Re-ID. However, the inevitable label noise caused by the clustering procedure was ignored. Such noisy pseudo labels substantially hinder the model’s capability on further improving feature representations. To this end, we propose a novel joint label refinement and contrastive learning framework with hybrid memory to alleviate this problem. Firstly, in order to reduce the noise of clustering pseudo labels, we propose a novel noise refinement strategy. This strategy refines pseudo labels at clustering phase and promotes clustering quality by boosting the label purity. In addition, we propose a hybrid memory bank. The hybrid memory dynamically generates prototype-level and un-clustered instance-level supervisory signals for learning feature representations. With all prototype-level and un-clustered instance-level supervisions, re-identification model is trained progressively. Our proposed unsupervised object Re-ID framework significantly reduces the influence of noisy labels and refines the learned features. Our method consistently achieves state-of-the-art performance on benchmark datasets.
Fast Image Segmentation with Contextual Scan and Markov Chains

Transforming bi-dimensional images into mono-dimensional sequences with Peano scan (PS) allows using Hidden Markov Chains (HMCs) for unsupervised image segmentation. In some situations, such methods can be competitive compared to Hidden Markov Fields (HMFs) based ones, while being much faster. We propose enriching the HMC-PS model by introducing “contextual” Peano scan (CPS). It consists in associating to each index in the HMC obtained from PS, two observations on pixels which are neighbors of the pixel considered in the image, but are not its neighbors in the HMC. This gives three observations on each point of the Peano scan, which leads to a new HMC with a more complex structure, but whose prior and posterior laws are still Markovian. Therefore we can apply the usual parameter estimation method: Stochastic Expectation-Maximization (SEM), as well as study unsupervised segmentation Marginal Posterior Mode (MPM) so obtained. The CPS based supervised and unsupervised MPM are compared to the classic scan based HMC-PS and the HMF through experiments on artificial images. They improve notably the former, and can even compete with the latter.
Extractive Document Summarization of Text in Odia Language

Automatic text summarization is an important subfield in the area of Natural language processing (NLP). This article deals with automating the summarization of text documents in Odia language through computational means. The proposed model has an extractive approach. Clustering a machine learning approach has been efficiently utilized to achieve the set objective of getting a precise summary. Considering the morphological richness of the language linguistic features have been considered in determining the significance both at word level and sentence level. The model meticulously integrates both the machine learning and linguistic methods. The obtained result has been compared with manual summaries by human subjects. Experimental results show that the model has been successful in obtaining relatively important information removing redundant and less informative sentences. This work can be considered as a contribution in the computational development of Odia language.
STVM: Scattered Time Aware Energy Efficient Virtual Machine Migration in Cloud Computing

Cloud computing offers services to large number of consumers, applications are hosted through large scale datacenters. Virtualization technology provides virtual servers from the underlying physical resources. Cloud service providers objective is to utilize the computing resources efficiently. Achieving energy efficiency gains more attention in these days, the existing cloud service managements are based on centralized architecture. This paper proposes a decentralized method for virtual machine consolidation and migration. Scattered time aware virtual machine migration method has been designed and implemented, which provides energy efficient virtual machine management solution by handling over-utilized and under-utilized physical machines. The efficacy of algorithm is evaluated through simulation using cloudsim toolkit.
DuFeNet: Improve the Accuracy and Increase Shape Bias of Neural Network Models

Abstract In image classification field, existing work tends to modify the network structure to obtain higher accuracy or faster speed. However, some studies have found that the neural network usually has texture bias effect, which means that the neural network is more sensitive to the texture information than the shape information. Based on such phenomenon, we propose a new way to improve network performance by making full use of gradient information. The dual features network (DuFeNet) is proposed in this paper. In DuFeNet, one sub-network is used to learn the information of gradient features, and the other is a traditional neural network with texture bias. The structure of DuFeNet is easy to implement in the original neural network structure. The experimental results clearly show that DuFeNet can achieve better accuracy in image classification and detection. It can increase the shape bias of the network adapted to human visual perception. Besides, DuFeNet can be used without modifying the structure of the original network at lower additional parameters cost.
High Security User Authentication Based on Piezoelectric Keystroke Dynamics Applying to Multiple Emotional Responses

With the rapid development of touch sensing technology, keystroke authentication has been proved to be a more secure and reliable identification method. However, emotion has a big influence on users’ keystroke habits, a condition not considered in previous studies, yet greatly affect the accuracy of keystroke authentication. In this article, we propose a new strategy for keystroke authentication applicable to multiple emotional states based on piezoelectric touch panel, which provides precise force data and enables a higher detection accuracy. In our experiment, keystroke samples composed by force and time features of legitimate user and intruders in different emotions were collected by a piezoelectric touch screen. Four typical machine learning algorithms were used for authentication. Finally, the result was demonstrated, that Random Forest classifier achieved an accuracy of 96.40%, a FAR of 1.02% and a FRR of 8.82%, which is significantly better than the result when only emotion-stable keystroke samples were used to train classifiers. The proposed strategy improves the practicability of keystroke authentication technique in real world application.
Cybersecurity Knowledge Graph Improvement with Graph Neural Networks

Cybersecurity Knowledge Graphs (CKGs) help in aggregating information about cyber-events. CKGs combined with reasoning and querying systems such as SPARQL enable security researchers to look up information about past cyberevents that is helpful in understanding future cyber-events or drawing similarity with a known cyber-event recorded in a CKG. CKGs have assertions in the form of semantic triples. The triples describe a relationship between a subject and object, both of which are cybersecurity entities. The quality of information present in the CKG depends on the data source. Since data sources can have varying degrees of reliability, we need a score that should help us benchmark the veracity of the CKG assertions. Verifying the information asserted in the CKG is a challenging task. In this paper, we describe a novel method that associates a score with the semantic triples asserted in the CKG using deep learning. We use semantic triples that we know are correct, in a supervised machine learning algorithm that produces the output for each relationship. In particular, we use Graph Convolutional Neural Networks (GCN) on a dataset of CKGs that can be used to ascertain the scores for each semantic triple.
On the application of time delay embedding for the data driven discovery of nonlinear systems from partial state information

Studying physical phenomena through data has become increasingly easy because of the abundance of information and improved computational infrastructure. However, the ability to understand and predict the behavior of such systems using limited data, owing to various reasons such as corrupted/unusable data or just the inability to capture certain types of information due to lack of sensors is emerging to be a central challenge in data science. In this paper, we propose to develop a technique that can reconstruct the entire dynamics of a nonlinear system from limited information. It is based on the method of time-delay embedding which is justified by Takens theorems and has demonstrated, with reasonable success, the reconstruction of low dimensional, non-linear systems purely from scalar time series. In this paper, we present initial ideas, methods, and observations.