Reviewerly/exHarmony huggingface dataset:
Unsupervised Labeling and Extraction of Phrase-based Concepts in Vulnerability Descriptions

People usually describe the key characteristics of software vulnerabilities in natural language mixed with domain-specific names and concepts. This textual nature poses a significant challenge for the automatic analysis of vulnerabilities. Automatic extraction of key vulnerability aspects is highly desirable but demands significant effort to manually label data for model training. In this paper, we propose an unsupervised approach to label and extract important vulnerability concepts in textural vulnerability descriptions (TVDs). We focus on three types of phrase-based vulnerability concepts (root cause, attack vector, and impact) as they are much more difficult to label and extract than name- or number-based entities (i.e., vendor, product, and version). Our approach is based on a key observation that the same-type of phrases, no matter how they differ in sentence structures and phrase expressions, usually share syntactically similar paths in the sentence parsing trees. Therefore, we propose two path representations (absolute paths and relative paths) and use an auto-encoder to encode such syntactic similarities. To address the discrete nature of our paths, we enhance traditional Variational Auto-encoder (VAE) with Gumble-Max trick for categorical data distribution, and thus creates a Categorical VAE (CaVAE). In the latent space of absolute and relative paths, we further use FIt-TSNE and clustering techniques to generate clusters of the same-type of concepts. Our evaluation confirms the effectiveness of our CaVAE for encoding path representations and the accuracy of vulnerability concepts in the resulting clusters. In a concept classification task, our unsupervisedly labeled vulnerability concepts outperform the two manually labeled datasets from previous work.
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Vertical Federated Learning

Vertical federated learning has a great potential of driving a great variety of business cooperation among enterprises in many fields. In machine learning, decision tree ensembles such as gradient boosting decision trees (GBDT) and random forest are widely applied powerful models with high interpretability and modeling efficiency. However, state-of-art framework for decision tree ensembles in vertical federated learning frameworks adapt anonymous features to avoid possible data breaches, makes the interpretability of the model compromised.To address this issue in the inference process, in this paper, we firstly make a problem analysis about the necessity of disclosure meanings of feature to Guest Party in vertical federated learning. We protect data privacy and allow the disclosure of feature meaning by concealing decision paths and adapt a communication-efficient secure computation method for inference outputs. The advantages of Fed-EINI will be demonstrated through both theoretical analysis and extensive numerical results. We improve the interpretability of the model by disclosing the meaning of features while ensuring efficiency and accuracy.
Differentially Private Tripartite Intelligent Matching Against Inference Attacks in Ride-Sharing Services

In intelligent transportation systems, the key issue of the Ride-Sharing Service (RSS) is to find proper drivers for the passengers by Intelligent Matching (IM) of two or three objects, including the positions of drivers, the travel information of passengers, and the spots where passengers and drivers meet and separate. Unfortunately, the exposure of travel plans of passengers in the IM process due to inference attacks has raised concerns about the privacy violation. To resist the inference attacks, we propose a Differentially Private Tripartite IM (DPTIM) protocol for RSS. DPTIM is based on the tripartite IM process, which intelligently finds the suitable threshold to filter out the matched objects with satisfaction scores below the threshold, so as to provide the high average satisfaction score of matched passengers. Compared to existing relevant mechanisms, DPTIM is distinguished by the feature that it leverages the inference error and differential privacy techniques to prevent the prior-information-based inference attacks and constrain the posterior information leakage, while providing satisfactory matching results. Furthermore, DPTIM meets the personalized demand of location privacy by using the passenger-specific tolerance estimation on inference errors and the personalized privacy budget. Finally, we implement DPTIM on real-world datasets, and demonstrate the satisfactory performance of DPTIM in terms of the average satisfaction score of passengers, the anti-inference-attack capability, and the passenger-specific privacy requirement.
Action2video: Generating Videos of Human 3D Actions

We aim to tackle the interesting yet challenging problem of generating videos of diverse and natural human motions from prescribed action categories. The key issue lies in the ability to synthesize multiple distinct motion sequences that are realistic in their visual appearances. It is achieved in this paper by a two-step process that maintains internal 3D pose and shape representations, action2motion and motion2video. Action2motion stochastically generates plausible 3D pose sequences of a prescribed action category, which are processed and rendered by motion2video to form 2D videos. Specifically, the Lie algebraic theory is engaged in representing natural human motions following the physical law of human kinematics; a temporal variational auto-encoder is developed that encourages diversity of output motions. Moreover, given an additional input image of a clothed human character, an entire pipeline is proposed to extract his/her 3D detailed shape, and to render in videos the plausible motions from different views. This is realized by improving existing methods to extract 3D human shapes and textures from single 2D images, rigging, animating, and rendering to form 2D videos of human motions. It also necessitates the curation and reannotation of 3D human motion datasets for training purpose. Thorough empirical experiments including ablation study, qualitative and quantitative evaluations manifest the applicability of our approach, and demonstrate its competitiveness in addressing related tasks, where components of our approach are compared favorably to the state-of-the-arts.
M<sup>2</sup>: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M2 does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M2 significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.
Hierarchical Semantic Structure Preserving Hashing for Cross-Modal Retrieval

Cross-modal hashing has become a vital technique in cross-modal retrieval due to its fast query speed and low storage cost in recent years. Generally, most of the priors supervised cross-modal hashing methods are flat methods which are designed for non-hierarchical labeled data. They treat different categories independently and ignore the inter-category correlations. In practical applications, many instances are labeled with hierarchical categories. The hierarchical label structure provides rich information among different categories. To rationally take use of category correlations, hierarchical cross-modal hashing is proposed. However, existing methods intend to preserve instance-pairwise or class-pairwise similarities, which cannot fully explore the semantic correlations among different categories and make the learned hash codes less discriminative. In this paper, we propose a deep cross-modal hashing method named hierarchical semantic structure preserving hashing (HSSPH), which directly exploits the label hierarchy information to learn discriminative hash codes. Specifically, HSSPH learns a set of class-wise hash codes for each layer. By augmenting class-wise codes with labels, it generates layer-wise prototype codes which reflect the semantic structure of each layer. In order to enhance the discriminative ability of hash codes, HSSPH supervises the hash codes learning with both labels and semantic structures to preserve the hierarchical semantics. Besides, efficient optimization algorithms are developed to directly learn the discrete hash codes for each instance and each class. Extensive experiments on two benchmark datasets show the superiority of HSSPH over several state-of-the-art methods.
Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback

Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.
NOWinBRAIN: a Large, Systematic, and Extendable Repository of 3D Reconstructed Images of a Living Human Brain Cum Head and Neck

Despite the tremendous development of various brain-related resources, a large, systematic, comprehensive, extendable, and beautiful repository of 3D reconstructed images of a living human brain expanded to the head and neck is not yet available. I have created such a novel repository and populated it with images derived from a 3D atlas constructed from 3/7 Tesla MRI and high-resolution CT scans. This web-based repository contains 6 galleries hierarchically organized in 444 albums and sub-albums with 5,156 images. Its original features include a systematic design in terms of multiple standard views, modes of presentation, and spatially co-registered image sequences; multi-tissue class galleries constructed from 26 primary tissue classes and 199 sub-classes; and a unique image naming syntax enabling image searching based solely on the image name. Anatomic structures are displayed in 6 standard views (anterior, left, posterior, right, superior, inferior), all views having the same brain size, and optionally with additional arbitrary views. In each view, the images are shown as sequences in three standard modes of presentation, non-parcellated unlabeled, parcellated unlabeled, and parcellated labeled. There are two types of spatially co-registered image sequences (imitating image layers and enabling animation creation), the appearance image sequence (for standard views) and the context image sequence (with a growing number of tissue classes). Color-coded neuroanatomic content makes the brain beautiful and facilitates its learning and understanding. This unique repository is freely available and easily accessible online at www.nowinbrain.org for a wide spectrum of users in medicine and beyond. Its future extensions are in progress.
Chinese named-entity recognition via self-attention mechanism and position-aware influence propagation embedding

Chinese Named Entity Recognition (NER) has received extensive research attention in recent years. However, Chinese texts lack delimiters to divide the boundaries of words, and some existing approaches cannot capture the long-distance interdependent features. In this paper, we propose a novel end-to-end model for Chinese NER. A new global word boundary detection approach is designed to capture the semantic dependency via a self-attention mechanism to represent character embedding by assigning compatible weights for each character in a sentence. To improve the representation ability of Chinese named-entity boundaries, we introduce position-aware influence propagation with the Gaussian kernel for each character, which combines convergence propagation and radiation propagation. Convergence propagation mainly measures the influence of surrounding characters on the target character. The purpose of radiation propagation is to measure the range of influence of the target character on surrounding characters. The proposed method has been evaluated and shown to offer strong performance in two Chinese NER datasets: MSRA and PFR.
Eliminating Harmful Joins in Warded Datalog+/−

We provide a rewriting technique of Warded Datalog+/− settings to sustain decidability and data tractability of reasoning tasks in the presence of existential quantification and recursion. To achieve this behaviour in practice, reasoners implement specialized strategies which exploit the theoretical bases of the language to control the effects of recursion, ensuring reasoning termination with small memory footprint. However, as a necessary condition for such exploitation, the setting is required to be in a “normalized form”, essentially without joins on variables affected by existential quantification. We present the Harmful Join Elimination, a normalization algorithm of Warded Datalog+/− that removes such “harmful” joins, supporting the tractability of the reasoning task as well as the full expressive power of the language. The algorithm is integrated in the Vadalog system, a Warded Datalog+/− -based reasoner that performs ontological reasoning in complex scenarios.
Technical and Economic Analysis of Modernization of Solar Power Plant: A Case Study from the Republic of Cuba

The problem of increasing the efficiency of existing power plants is relevant for many countries. Solar power plants built at the end of the 20th century require, as their shelf lives have now expired, not only the replacement of the solar modules, but also the modernization of their component composition. This is due to the requirements to improve the efficiency of power plants to ensure the expansion of renewable energy technologies. This article presents a technical and economic analysis of the choice of solar power plant modernization method, which consists of (1) a method for calculating the amount of power generation; (2) the modeling of solar power plants under specific climatic conditions; (3) the analysis of electricity generation using different types of PV modules and solar radiation trapping technologies in Matlab/Simulink; and (4) the technical and economic analysis of a 2.5 MW solar power plant in the Republic of Cuba (in operation since 2015), for which four different modernization options were considered. All the scenarios differ in the depth of modernization; the results of the analysis were compared with the existing plant. The results of the study showed that the different modernization scenarios respond differently to changes in the inputted technical and economic parameters (cost per kWh, inflation rate, losses, and power plant efficiency). The maximum NPV deviations among the considered scenarios are: a 1% increase in inflation reduces NPV by 2%; a decrease in losses from 20% to 10% increases the NPV by 2.5%; a change in cost from EUR 0.05 to EUR 0.1 increases the NPV by more than 3.5 times. The dependence of the economic results was also tested as a function of three factors: solar module efficiency, inflation, and the price per 1 kWh. It was found that the greatest influence on the NPV of the proposed model is the price per 1 kWh. Based on this analysis, an algorithm was developed to choose the most effective scenario for the conditions of the Republic of Cuba for the modernization of the existing power plants.
Affinity Propagation Clustering for Older Adults Daily Routine Estimation

This work proposes a system that allows estimating and monitoring daily routine changes in a sensorized home through Machine Learning and Affinity Propagation clustering techniques. Older adults often have low-activity and rather routine lives, which means that these routines can be an indicator of their physical and cognitive state in order to lead an independent life and healthy ageing. Therefore, it is important to be able to generate precise routines, as well as to monitor them, to trigger alarms in case of significant variations. This proposal defines routines based on the time spent in each of the monitored rooms. The daily time in each room is estimated trough a Bluetooth Low Energy-based indoor localization system. The localization is obtained through the Bluetooth received signal strength, which is processed with different supervised algorithms and fused with the acceleration measured by the mobile receiver, obtaining an accuracy above 96 %. From these data, the sample has been synthetically expanded to generate four different routines, on which the proposed algorithm based on Principal Component Analysis and Affinity Propagation clustering has been tested, obtaining very promising results.
Application and Significance of Geological, Geochemical, and Geophysical Methods in the Nanpo Gold Field in Laos

As the main part of the Indosinian metallogenic province in the eastern part of the Tethys metallogenic domain, Southeast Asia has experienced multiple stages of tectonic magnetic activities accompanied by the formation of rich mineral resources. However, due to the undeveloped economy, low degree of geological work, dense vegetation cover, and lack of obvious prospecting marks, traditional geological prospecting work in the area is not optimal. Consequently, the combination of high-precision geophysics and geochemistry has become an important method of looking for ore bodies deep underground in this area. The Nanpo gold deposit is a hydrothermal gold deposit that occurs in the Indosinian felsic volcanic rock body, and its mineralization is closely related to felsic magmatism. This study carried out comprehensive geophysical and geochemical exploration methods of soil geochemical survey, induced polarization (IP) survey, and audio-frequency magnetotelluric (AMT) survey. Based on the characteristics of geophysical and geochemical anomalies, geological inference, and interpretation, the integrated geophysical and geochemical prospecting criteria of the ore area have been determined: The large-scale and overlapping Au-Ag-Cu anomaly area in the host felsic magmatic rocks (mainly diorite, monzodiorite and granodiorite) is a favorable metallogenic area. Two anomalies, P1–H1 and P3–H6, with the best metallogenetic conditions and the deepest extensions of the known ore bodies, were further selected as engineering verification targets. After the study of the drill core, gold (mineralized) bodies consistent with the anomalies were found, indicating that the combined method is suitable for the exploration of mineral resources in this area, and the prospecting effect is good. At the same time, the metallogenic prediction shows that the deep part of the mining area still has great metallogenic prospects and prospecting potential. The characteristics of geophysical and geochemical anomalies and prospecting experience in the study area can provide references for the prospecting of hydrothermal gold deposits in the Luang Prabang–Loei structural belt.
A Novel Approach for Security in Cloud Data Storage Using AES-DES-RSA Hybrid Cryptography

In today’s world, everything is running online and a lot of data are transferred over the internet, so data security had become an important issue. Even if one doesn’t send a file, hackers can get into the systems and access private data. There exist numerous encryption techniques using symmetric and asymmetric cryptographic technique. We have built a hybrid cryptographic system using parts of AES, DES and RSA encryption algorithms to take advantage of the benefits of both algorithms and overcome their weaknesses for the security of data stored on the cloud. Using this hybrid cryptosystem, the data stored on the cloud can be accessed or retrieved as per the user’s request without the direct access to the server computer. But the major concern regarding storage of data on cloud is the security, which is here implemented using the proposed hybrid cryptographic system. Finally, the algorithm is compared to existing AES, DES and RSA algorithms.
Secure deduplication schemes for content delivery in mobile edge computing

Since the emergence of the mobile edge computing (MEC) paradigm, data leakage has become a serious threat against edge computing users, thwarting the further applications of MEC. Previous studies concentrating on data storage security and deduplication for conventional cloud computing paradigm cannot be simply adapted to edge computing because a central coordinator (for example, a cloud server) with a global view is not always available in MEC. To tackle this problem, we take the particular properties of MEC into consideration and propose secure data deduplication schemes for three MEC settings (i.e., centralized, semi-distributed and distributed settings). All of our schemes can provide secure data storage, retrieval, sharing, and deduplication. Through theoretical analysis, we prove the security of our schemes against typical attacks in outsourced data storage. Experimental results with a real-world deployment environment have showed that our schemes can guarantee service quality of MEC effectively.
Clustering Algorithms: Taxonomy, Comparison, and Empirical Analysis in 2D Datasets

Because of the abundance of clustering methods, comparing between methods and determining which method is proper for a given dataset is crucial. Especially, the availability of huge experimental datasets and transactional and the emerging requirements for data mining and the like needs badly for clustering algorithms that can be applied in various domains. This paper presents essential notions of clustering and offers an overview of the significant features of the most common representative clustering algorithms of clustering categories presented in a comparative way. More specifically the study is based on the numerical type of the data that the algorithm supports, the shape of the clusters, and complexity. The experiments were done using nine clustering algorithms representing the common clustering categories on eight 2D clustered datasets differ in the clusters’ shapes and density of the data points. Furthermore, the comparison was done from the point of view seven performance measures.