Reviewerly/exHarmony huggingface dataset:
Variational Autoencoder Based Enhanced Behavior Characteristics Classification for Social Robot Detection

With the development of Internet and online social communication tools, malicious social bots have become a problem which cannot be ignored. They are intentionally manipulated by some organizations or people and always disseminate malicious information on the Internet, which greatly impact network environment. As a result, the detection of malicious social bots has become a hot topic in machine learning and many kinds of classification methods are widely used to detect social bots. However, the current classification methods are limited by the unbalanced dataset in which the samples of human users always outnumber samples of social bots. Given by the nature of binary classification problem of detecting social bots, the imbalance of the dataset greatly impacted the classification accuracy of the social bots. Aiming to promote the classification accuracy, this article has proposed the use of Variational Autoencoder (VAE) to generate samples of social bots basing on existing samples and thus mitigate the problem of imbalance dataset before feeding the original dataset into classifiers. In this way, the classification accuracy of social bots and normal human users can be efficiently improved basing on selected six categories of user account features. In order to verify the advantage of VAE in generating social bot samples, other traditional oversampling algorithm named SMOTE and SMOTE based SVM are also used to generate samples of social bots and compared in the contrast experiments. The experimental results indicated that the proposed method in this article has achieved better classification accuracy compared with other scenarios using the same datasets.
Passive Observer of Activities for Aging in Place Using a Network of RGB-D Sensors

Aging in place is a notion which supports the independent living of older adults at their own place of residence for as long as possible. To support this alternative living which can be in contrast to various other types of assisted living options, modes of monitoring technology need to be explored and studied in order to determine a balance between the preservation of privacy and adequacy of sensed information for better estimation and visualization of movements and activities. In this paper, we explore such monitoring paradigm on how a network of RGB-D sensors can be utilized for this purpose. This type of sensor offers both visual and depth sensing modalities from the scene where the information can be fused and coded for better protection of privacy. For this purpose, we introduce the novel notion of passive observer. This observer is only triggered by detecting the absence of movements of older adults in the scene. This is accomplished by classifying and localizing objects in the monitoring scene from both before and after the detection of movements. A deep learning tool is utilized for visual classification of known objects in the physical scene followed by virtual reality reconstructing of the scene where the shape and location of objects are recreated. Such reconstruction can be used as a visual summary in order to identify objects which were handled by an older adult in-between observation. The simplified virtual scene can be used, for example, by caregivers or monitoring personnel in order to assist in detecting any anomalies. This virtual visualization can offer a high level of privacy protection without having any direct visual access to the monitoring scene. In addition, using the scene graph representation, an automatic decision-making tool is proposed where spatial relationships between the objects can be used to estimate the expected activities. The results of this paper are demonstrated through two case studies.
Nuclei Segmentation and Detection using Deep Convolutional Neural Networks

Automatic segmentation of microscopic images is an important task in medical image processing and analysis. Nuclei detection is an important example of this task. Imagine a faster research process for almost every deadly disease prevalent today. Diseases like cancer, heart diseases, COVID-19, rare disorders, etc. We have all seen or know some people who are fighting these diseases every day. Identifying the cells' nuclei is the starting point for most analyses because most of the human body's trillions of cells contain a nucleus full of DNA, the genetic code that programs each cell. The identification of nuclei helps researchers understand the underlying process. They can measure how different samples react to a certain drug. But, the manual process of identifying the cell's nuclei is a tedious task. This work proposes the use of deep learning techniques to perform segmentation and identify the cell's nuclei for which we chose a modified CNN architecture, U-Net. The results obtained show that U-Net can be a promising approach to efficiently segment and identify the cell's nuclei.
Are Bots Humans? Analysis of Bot Accounts in 2019 Indian Lok Sabha Elections (Workshop Paper)

Social media platforms have taken political and cultural conversations to an online platform making them more accessible. Ability to anonymously post has allowed more people to participate fearlessly. However, this has also led to an opportunity to spread miss information and manipulative content. Political groups around the globe have used Bot accounts to help spread their preferred narrative online during elections. In the midst of 2019 Indian Lok Sabha Elections speculations were made about the presence of cyber-troops/IT Cells which operate fake accounts and push propaganda. Our finding suggests that a portion of Bot accounts seems to be operated by humans in the background. These accounts have a very distinct usage pattern on Twitter compared to legitimate human users. Our experiments also point out that only 1.3% of total interactions are directed from Humans to Bots, showing Bot accounts inability to gel well in the online social network.
Gated Heterogeneous Graph Representation Learning for Shop Search in E-commerce

In e-commerce search, vectorized matching is the most important approach besides lexical matching, where learning vector representations for entities (e.g., query, item, shop) plays a crucial role. In this work, we focus on vectorized search matching model for shop search in Taobao. Unlike item search, shop search is faced with serious behavior sparsity and long-tail problem. To tackle this, we take the first step to transfer knowledge from item search, i.e., leveraging items purchased under a query and the shops they belong to. Moreover, we propose a novel gated heterogeneous graph learning model (named GHL) to derive vector representations for entities. Both first-order and second-order proximity of queries and shops are exploited to fully mine the heterogeneous relationships. And to relieve long-tail phenomenon, we devise an innovative gated neighbor aggregation scheme where each type of entities (i.e., hot ones and long-tail ones) can benefit from the heterogeneous graph in an automatic way. Finally, the whole framework is jointly trained in an end-to-end fashion. Offline evaluation results on real-world data of Taobao shop search platform demonstrate that the proposed model outperforms existing graph based methods, and online A/B tests show that it is highly effective and achieves significant CTR improvements.
Classification for Text Data from the Power System Based on Improving Naive Bayes

After years of operation of the power system, a large amount of text data has been accumulated, and it is particularly important to analyze them such as the violation data. In this context, this paper introduced a novel classification method, Improving Naive Bayes Based on Improving Term Frequency-Inverse Document Frequency (ITF-IDF), which aims to categorize the text and reduce the costs of labor analysis. The classification of the violation data which including personal behavior, instrument, security activities, supervision and two-ticket data. To increase the classification accuracy, the proposed method improved the weight of Naive Bayes, namely ITF-TDF. In the experimental studies, the Improving Naive Bayes is evaluated on the test data of spam message which is a binary classification and the violation data from the power system as multi-classification, and is compared with the classifiers based on conventional Naive Bayes, the Logistic Regression and the Support Vector Machine (SVM), respectively. The results demonstrate that the proposed method has a better performance than the other methods.
Multimodal Medical Image Registration Based on a Hybrid Optimization Strategy

Image registration is a crucial task in medical applications and is perceived as an optimization problem which has an important interest in clinical diagnosis. In this work, we propose an optimization strategy based on a specific design of genetic algorithm combined with the gradient descent optimizer within multi-resolution scheme. The performance of the proposed method was tested and evaluated on real multimodal registration scenarios from the Retrospective Image Registration Evaluation (RIR) database. Our method results were compared with those of existing registration methods, they are accurate and effective.
Cryptanalysis and improvement of biometric based content distribution framework for digital rights management systems

Abstract Digital contents are utilized and transmitted over the public network. Thus, the evolution of an emphatic mechanism to ensure authorized access to digital content is the topmost priority of the multimedia industry. Digital right management (DRM) is a tool that aims to ensure authorized access multimedia content to the right holders. However, over the years, several other challenges associated with DRM systems have also emerged such as efficient computation, user privacy, the security of existing protocols. For the scalable distribute digital content, an efficient, secure and authorized content distribution protocol is required. This paper proposed a biometric‐based content technique for a DRM system, which enables to deliver content key among mutually authentic entities. The security of the proposed protocol has been proved under the widely recognized random oracle model, which indicates that the proposed protocol is provably secure under probabilistic polynomial time adversary. The proposed scheme achieves anonymity and unlinkability. Moreover, comparative analysis shows that the proposed scheme address efficiency along with security.
Nominal Compound Chain Extraction: A New Task for Semantic-Enriched Lexical Chain

Lexical chain consists of cohesion words in a document, which implies underlying structure of a text, and thus facilitates downstream NLP tasks. Nevertheless, existing work focuses on detecting the simple surface lexicons with shallow syntax associations, ignoring the semantic-aware lexical compounds as well as the latent semantic frames, (e.g., topic), which can be much more crucial for real-world NLP applications. In this paper, we introduce a novel task, Nominal Compound Chain Extraction (NCCE), extracting and clustering all the nominal compounds that share identical semantic topics. In addition, we model the task as a two-stage prediction (i.e., compound extraction and chain detection), which is handled via a proposed joint framework. The model employs the BERT encoder to yield contextualized document representation. Also HowNet is exploited as external resource for offering rich sememe information. The experiments are based on our manually annotated corpus, and the results prove the necessity of the NCCE task as well as the effectiveness of our joint approach.
Document Recommendation for Medical Training Using Learning to Rank

This paper describes a general approach where Learning-to-Rank (LtR) algorithms are used to suggest training materials, such as videos, simulations, tutorials, and articles to students based on their mistakes while using an e-learning platform. In particular, the method is discussed and implemented for MedSim, computer-based medical simulations that replicate clinical scenarios for medical students. While interacting with a virtual patient, the student may ask questions, do medical examinations or therapy, and eventually arrive at a final diagnosis. The basic idea of the approach is to generate a query based on the mistakes that a student makes while interacting with a virtual patient. Based on this query, the LtR algorithm then provides the student with suggestions for training material to review. We evaluated the approach with different LtR algorithms and different queries and dataset sizes (Haridas et al. in Educ. Inf. Technol. 1–19, 2020 []). We studied the extensibility of the trained models by adding new training material to the test set. The accuracy is measured using NDCG@10. The results of the evaluation suggest that a training size of around 1000 is sufficient to obtain accurate results. The comparison of algorithms suggests that listwise algorithms perform better than pair-wise algorithms, also that in particular, LambdaMart performs very well on this problem.
QKD in Support of Secured P2P and P2MP Key Exchange for Low-Latency 5G Connectivity

A quantum-secured packetized optical fronthaul segment is thoroughly discussed. We present an extensive study on the integration of a Discrete Variable-Quantum Key Distribution (DV-QKD) link supporting the Advanced Encryption Standard-256 (AES-256) encryption of packetized fronthaul operating at 10Gbps. Secure key rates exceeding the 1Kbps and short rotation times down to 1.4s are reported for Point-to-Point (P2P) topologies by considering the latency budget of 5G fronthaul connectivity. For the multi-user environment, the Bob stations implementation of quantum layer is adapted to satisfy the connectivity needs of Point-to-Multipoint (P2MP) scenario, allowing for successful distribution of AES-256 keys to N=16 5G terminal nodes with ultra-low attack success probabilities of less than 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-60</sup> .
Grano-GT: A granular ground truth collection tool for encrypted browser-based Internet traffic

Modern network traffic classification puts much attention toward producing a granular classification of the traffic, such as at the application service level. However, the classification process is often impaired by the lack of granular network traffic ground truth. Granular network traffic ground truth is critical to provide a benchmark for a fair evaluation of modern network traffic classification. Nevertheless, in modern network traffic classification, existing ground truth tools only managed to build the ground truth at the application name level at most. Application name level granularity is quickly becoming insufficient to address the current needs of network traffic classification and therefore; this paper presents the design, development and experimental evaluation of Grano-GT, a tool to build a reliable and highly granular network traffic ground truth for encrypted browser-based traffic at the application name and service levels. Grano-GT builds on four main engines which are packet capture, browser, application and service isolator engines. These engines work together to intercept the application requests and combine them with the support of temporal features and cascading filters to produce reliable and highly granular ground truth. Preliminary experimental results show that Grano-GT can classify the Internet traffic into respective application names with high reliability. Grano-GT achieved an average accuracy of more than 95% when validated using nDPI at the application name level. The remaining 5% loss of accuracy was primarily due to the unavailability of signatures in nDPI. In addition, Grano-GT managed to classify application service traffic with significant reliability and validated using the Kolmogorov-Smirnov test.
Ramanujan Graphs for Post-Quantum Cryptography

Abstract We introduce a cryptographic hash function based on expander graphs, suggested by Charles et al. ’09, as one prominent candidate in post-quantum cryptography. We propose a generalized version of explicit constructions of Ramanujan graphs, which are seen as an optimal structure of expander graphs in a spectral sense, from the previous works of Lubotzky, Phillips, Sarnak ’88 and Chiu ’92. We also describe the relationship between the security of Cayley hash functions and word problems for group theory. We also give a brief comparison of LPS-type graphs and Pizer’s graphs to draw attention to the underlying hard problems in cryptography.
Neuro-Symbolic Artificial Intelligence: Application for Control the Quality of Product Labeling

The paper presents the implementation of an intelligent decision support system (IDSS) to solve a real manufacturing problem at JSC “Savushkin Product”. The proposed system is intended to control the quality of product labeling, based on neuro-symbolic artificial intelligence, namely integrating deep neural networks and semantic models. The system perform localization and recognition of images from a high-speed video stream and is based on several deep neural networks. Semantic networks fulfill intelligent processing of recognition results in order to generate final decision as regards the state of the production conveyor. We demonstrate the performance of the proposed technique in the real production process. The main contribution of this paper is a novel view at the creation of a real intelligent decision support system, which combines bio inspired approach, namely neural networks and conventional technique, based on a knowledge base.
Multi-user Dynamic Symmetric Searchable Encryption for Attribute-Value Type Database in Cloud Storage

Cloud storage services allow a data owner to share their data with each other as a group. Typically, the data owner outsources files to the cloud server, and some authorized users can access and edit the files subsequently. In reality, the most widely-used format of files in such a cloud-based collaboration system is attribute-value type one, e.g., an excel spreadsheet, where each file corresponds to an item and multiple attribute values. Each attribute value can be considered as a keyword, and a type of attribute values “describes” an attribute. The database generated by such files is called the attribute-value type database. Authorized users can search outsourced files by either keywords or attributes such that they can access the files flexibly. However, due to the sensitivity of the outsourced files, the data owner always encrypts the file before outsourcing, which precludes users from searching target files. This problem could be mitigated by utilizing DSSE, but it only supports searching over ciphertexts by keywords, and thereby is unsatisfactory for the attribute-value database. Furthermore, DSSE schemes built on a single-user setting are out of alignment with the practical applications. In this paper, we propose a multi-user DSSE scheme for attribute-value type database based on blind storage, dubbed MDSSE, such that authorized users can search over files by either keywords or attributes, while the confidentiality of the files is ensured. We analyze MDSSE in terms of security and efficiency, which proves that MDSSE is secure against various attacks with high efficiency in terms of communication and computation costs.
A Reinforced Semi-supervised Neural Network for Helpful Review Identification

It is crucial to recommend helpful product reviews to consumers in e-commercial service, as the helpful ones can promote consumption. Existing methods for identifying helpful reviews are based on the supervised learning paradigm. The capacity of supervised methods, however, is limited by the lack of annotated reviews. In addition, there is a serious distributional bias between the labeled and unlabeled reviews. Therefore, this paper proposes a reinforced semi-supervised neural learning method (abbreviated as RSSNL) for helpful review identification, which can automatically select high-related unlabeled reviews to help training. Concretely, RSSNL composes with a reinforced unlabeled review selection policy and a semi-supervised pseudo-labeling review classifier. These two parts train jointly and integrate together based on the policy gradient framework. Extensive experiments on Amazon product reviews verify the effectiveness of RSSNL for using unlabeled reviews.
Blockchain Based Distributed Package Management Architecture

Application management in Linux based operating systems is managed by package managers. The package repositories of most Linux distributions provide software through central servers. While having a central server provides a unique verified and maintained version, servers often separate official and community packages, and users frequently use community packages. However, some highly demanded community packages may not be stored in official repositories. While central servers are open to a single point of failure, using community packages without any security check may be insecure. In this paper, we introduce the idea of a distributed package management architecture for Linux systems based on blockchain technology. In the proposed system, package providers are considered as miners, building a package is mining, and proof-of-work is reproducible packaging by the miners. The blockchain keeps the hash of the package contents. Creating and testing a package will return to the packager as reliability scores. This system will have the following advantages. Packages will be verified through consensus, single points of failure will be eliminated ensuring availability, packages are stored in a distributed network. The goal is to keep the operating system alive with a decentralized, reliable, and transparent blockchain-based package management system.
Discovering HTTPSified Phishing Websites Using the TLS Certificates Footprints

With the recent rise of HTTPS adoption on the Web, attackers have begun "HTTPSifying" phishing websites. HTTPSifying a phishing website has the advantage of making the website appear legitimate and evading conventional detection methods that leverage URLs or web contents in the network. Further, adopting HTTPS could also contribute to generating intrinsic footprints and provide defenders with a great opportunity to monitor and detect websites, including phishing sites, as they would need to obtain a public-key certificate issued for the preparation of the websites. The potential benefits of certificate-based detection include: (1) the comprehensive monitoring of all HTTPSified websites by using certificates immediately after their issuance, even if the attacker utilizes dynamic DNS (DDNS) or hosting services; this could be overlooked with the conventional domain-registration-based approaches; and (2) to detect phishing websites before they are published on the Internet. Accordingly, we address the following research question: How can we make use of the footprints of TLS certificates to defend against phishing attacks? For this, we collected a large set of TLS certificates corresponding to phishing websites from Certificate Transparency (CT) logs and extensively analyzed these TLS certificates. We demonstrated that a template of common names, which are equivalent to the fully qualified domain names, obtained through the clustering analysis of the certificates can be used for the following promising applications: (1) The discovery of previously unknown phishing websites with low false positives and (2) understanding the infrastructure used to generate the phishing websites. We use our findings on the abuse of free certificate authorities (CAs) for operating HTTPSified phishing websites to discuss possible solutions against such abuse and provide a recommendation to the CAs.