Reviewerly/exHarmony huggingface dataset:
Proxy Re-Encryption That Supports Homomorphic Operations for Re-Encrypted Ciphertexts

Homomorphic encryption (HE) is useful to analyze encrypted data without decrypting it. However, by using ordinary HE, a user who can decrypt a ciphertext that is generated by executing homomorphic operations, can also decrypt ciphertexts on which homomorphic evaluations have not been performed, since homomorphic operations cannot be executed among ciphertexts which are encrypted under different public keys. To resolve the above problem, we introduce a new cryptographic primitive called Homomorphic Proxy Re-Encryption (HPRE) combining the “key-switching” property of Proxy Re-Encryption (PRE) and the homomorphic property of HE. In our HPRE, original ciphertexts (which have not been re-encrypted) guarantee CCA2 security (and in particular satisfy non-malleability). On the other hand, re-encrypted ciphertexts only guarantee CPA security, so that homomorphic operations can be performed on them. We define the functional/security requirements of HPRE, and then propose a specific construction supporting the group operation (over the target group in bilinear groups) based on the PRE scheme by Libert and Vergnaud (PKC 2008) and the CCA secure public key encryption scheme by Lai et al. (CT-RSA 2010), and prove its security in the standard model. Additionally, we show two extensions of our HPRE scheme for the group operation: an HPRE scheme for addition and an HPRE scheme for degree-2 polynomials (in which the number of degree-2 terms is constant), by using the technique of the recent work by Catalano and Fiore (ACMCCS 2015).
High-Dimensional Clustering via Random Projections

This work addresses the unsupervised classification issue for high-dimensional data by exploiting the general idea of Random Projection Ensemble. Specifically, we propose to generate a set of low-dimensional independent random projections and to perform model-based clustering on each of them. The top B∗ projections, i.e., the projections which show the best grouping structure, are then retained. The final partition is obtained by aggregating the clusters found in the projections via consensus. The performances of the method are assessed on both real and simulated datasets. The obtained results suggest that the proposal represents a promising tool for high-dimensional clustering.
Domain Switch-Aware Holistic Recurrent Neural Network for Modeling Multi-Domain User Behavior

Understanding user behavior and predicting future behavior on the web is critical for providing seamless user experiences as well as increasing revenue of service providers. Recently, thanks to the remarkable success of recurrent neural networks (RNNs), it has been widely used for modeling sequences of user behaviors. However, although sequential behaviors appear across multiple domains in practice, existing RNN-based approaches still focus on the single-domain scenario assuming that sequential behaviors come from only a single domain. Hence, in order to analyze sequential behaviors across multiple domains, they require to separately train multiple RNN models, which fails to jointly model the interplay among sequential behaviors across multiple domains. Consequently, they often suffer from lack of information within each domain. In this paper, we first introduce a practical but overlooked phenomenon in sequential behaviors across multiple domains, i.e.,domain switch where two successive behaviors belong to different domains. Then, we propose aDomain Switch-Aware Holistic Recurrent Neural Network (DS-HRNN) that effectively shares the knowledge extracted from multiple domains by systematically handlingdomain switch for the multi-domain scenario. DS-HRNN jointly models the multi-domain sequential behaviors and accurately predicts the future behaviors in each domain with only a single RNN model. Our extensive evaluations on two real-world datasets demonstrate that \DCHRNN\ outperforms existing RNN-based approaches and non-sequential baselines with significant improvements by up to 14.93% in terms of recall of the future behavior prediction.
Study on Different Region-Based Object Detection Models Applied to Live Video Stream and Images Using Deep Learning

There is a plenty of very interesting problems in the field of computer vision, from the very basic image classification problem to 3d pose estimation problem. One among the many interesting problems is object detection, which is the computer capability to accurately identify the multiple objects present in the scene (image or video) with the bounding boxes around them and the appropriate labels indicating their class along with the confidence score indicating the degree of closeness with the class. In this work, we have discussed in detail different types of region-based object detection models applied on both live video stream and images.
CORALS

Identifying and recommending potential new customers for local businesses are crucial to the survival and success of local businesses. A key component to identifying the right customers is to understand the decision-making process of choosing a business over the others. However, modeling this process is an extremely challenging task as a decision is influenced by multiple factors. These factors include but are not limited to an individual's taste or preference, the location accessibility of a business, and the reputation of a business from social media. Most of the recommender systems lack the power to integrate multiple factors together and are hardly extensible to accommodate new incoming factors. In this paper, we introduce a unified framework, CORALS, which considers the personal preferences of different customers, the geographical influence, and the reputation of local businesses in the customer recommendation task. To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 state-of-the-art methods using two real-world datasets. The results demonstrate that CORALS outperforms all these baselines by a significant margin in most scenarios. In addition to identifying potential new customers, we also break down the analysis for different types of businesses to evaluate the impact of various factors that may affect customers' decisions. This information, in return, provides a great resource for local businesses to adjust their advertising strategies and business services to attract more prospective customers.
RSA public keys with inside structure: Proofs of key generation and identities for web-of-trust

We propose a construction of the modulus of the RSA public keys for decentralized public-key trust generation like Web-of-Trust, where the modulus has inside structure. The first function of the structure is that it enables a peer to verify that the owner of the public key certainly generated the corresponding secret key. The second function is to tie up the public key with the owner's identity as well as her guarantors identities. Our construction is based on the modified version of the Lenstra's algorithm, by which a related second public key and the identity strings are embedded into the RSA modulus. The second public key is of the elliptic-curve cryptosystem (ECC) which has almost equivalent security level to the RSA cryptosystem. Our construction is applicable to a peer-to-peer network equipped with the blockchain of "consortium" and "trusted" type to yield a flat network that does not depends on PKI of the X.509 type.
Arranging Student Scientific Research as an Educational Technology: The Experience of Regional Universities of Russia

Nowadays, student scientific research is a significant component for training specialists for Industry 4.0. However, many students do not feel like wanting much to participate in this kind of activity when studying at university. Therefore, there is a challenge in facing educators, i.e., to make a climate of learning in such a way so that learners would not only be able to acquire skills, competences, and proficiencies according to their major but also be able to promote their willingness for independent search for getting new knowledge and mastering research methods and practical techniques to solve scientific challenges and analyze various information flows. The purpose of the study is to develop a methodology for arranging student scientific research, which enables to provide with skilled professional employees. To accomplish the declared goal, the authors developed the online course The Basic Scientific Research , the methodology for involving students in scientific research; recommendations to include special subject matters in the content of academic course, for stimulating joint studies of educators and students; and the online course on training lecturers to the technology presented. The experiment was conducted from 2014 to 2018 among students of IT and economics at regional Russian universities. 242 students and 46 lecturers took part in it. Using the developed system by educators resulted in the following achievements: the students published more than 450 scientific articles, won 28 grants of various levels, and participated and became prize winners in more than 50 scientific research contests. One-third of the participants in the experiment received a merit semester scholarship for scientific research regularly. They participated and won competitions for the President of the Russian Federation grants, the Government of the Russian Federation. Business representatives gave top marks for graduate theses. The educational technology presented in the paper is prepared for scaling and be applied at universities.
A Study of User Experience in Knowledge-Based QA Chatbot Design

A chatbot is a computer program which can interact with humans through natural language. In this paper, we introduced Jiao Xiao Tong, an interactive question answering chatbot designed in assisting students and faculties in Shanghai Jiao Tong University to accomplish various campus activities such as setting remainders, answering school trivia questions, route navigation and etc. A two-part experiment was conducted to evaluate two aspects of chatbot user experience. The results show an 6.7% increase in user satisfaction rate using the chatbot in route navigation tasks and interests towards a more interactive approach. The study also suggests that users expect action cues when interacting with a chatbot. In this paper, we described a solution of knowledge-based QA chatbot design in a university context and discussed what level of optimizations was achieved and what remains to be done. This pilot study provides opportunities for further improvement on chatbots design and application.
Facial Component-Landmark Detection With Weakly-Supervised LR-CNN

In this paper, we propose a weakly supervised landmark-region-based convolutional neural network (LR-CNN) framework to detect facial component and landmark simultaneously. Most of the existing course-to-fine facial detectors fail to detect landmark accurately without lots of fully labeled data, which are costly to obtain. We can handle the task with a small amount of finely labeled data. First, deep convolutional generative adversarial networks are utilized to generate training samples with weak labels, as data preparation. Then, through weakly supervised learning, our LR-CNN model can be trained effectively with a small amount of finely labeled data and a large amount of generated weakly labeled data. Notably, our approach can handle the situation when large occlusion areas occur, as we localize visible facial components before predicting corresponding landmarks. Detecting unblocked components first helps us to focus on the informative area, resulting in a better performance. Additionally, to improve the performance of the above tasks, we design two models as follows: 1) we add AnchorAlign in the region proposal networks to accurately localize components and 2) we propose a two-branch model consisting classification branch and regression branch to detect landmark. Extensive evaluations on benchmark datasets indicate that our proposed approach is able to complete the multi-task facial detection and outperforms the state-of-the-art facial component and landmark detection algorithms.
Multi-scale sifting for mammographic mass detection and segmentation

Breast mass detection and segmentation are challenging tasks due to the fact that breast masses vary in size and appearance.In this work, we present a simultaneous detection and segmentation scheme for mammographic lesions that is constructed in a sifting architecture.It utilizes a novel region candidate selection approach and cascaded learning techniques to achieve state-of-the-art results while handling a high class imbalance.The region candidates are generated by a novel multi-scale morphological sifting (MMS) approach, where oriented linear structuring elements are used to sieve out the mass-like objects in mammograms including stellate patterns.This method can accurately segment masses of various shapes and sizes from the background tissue.To tackle the class imbalance problem, two different ensemble learning methods are utilized: a novel self-grown cascaded random forests (CasRFs) and the random under-sampling boost (RUSBoost).The CasRFs is designed to handle class imbalance adaptively using a probability-ranking based under-sampling approach, while RUSBoost uses a random under-sampling technique.This work is evaluated on two publicly available datasets: INbreast and DDSM BCRP.On INbreast, the proposed method achieves an average sensitivity of 0.90 with 0.9 false positives per image (FPI) using CasRFs and with 1.2 FPI using RUSBoost.On DDSM BCRP, the method yields a sensitivity of 0.81 with 3.1 FPI using CasRFs and with 2.9 FPI using RUSboost.The performance of the proposed method compares favorably to the state-of-the-art methods on both datasets, especially on highly spiculated lesions.
Event reconstruction using temporal pattern of file system modification

Nowadays, several digital forensic tools extract a lot of low-level information from different parts of the system. Constructing high-level information from low-level ones is very challenging. This study reconstructs high-level events by using the traces of applications that are found in the file system metadata. In this regard, an event reconstruction framework is proposed that determines which applications have been run on a compromised system. The proposed framework works in two phases. In the training phase, the signatures of various applications are constructed. The signature of an application is the temporal pattern of file system modification of the application. In the detection phase, at first, the temporal pattern of file system modification of the hard disk (TPFSM-D) of the compromised system is constructed. Then in order to determine whether a particular application has been run on the compromised system, the distance between the signature of the application and the TPFSM-D of the hard disk is calculated by using a proposed distance measure. Finally, a decision engine decides whether the application has been run on the compromised system. The proposed event reconstruction framework has been tested on different scenarios. The empirical results suggest that the framework is effective in reconstructing events.
On expansion and contraction of DL-Lite knowledge bases

Knowledge bases (KBs) are not static entities: new information constantly appears and some of the previous knowledge becomes obsolete. In order to reflect this evolution of knowledge, KBs should be expanded with the new knowledge and contracted from the obsolete one. This problem is well-studied for propositional but much less for first-order KBs. In this work we investigate knowledge expansion and contraction for KBs expressed in DL-Lite, a family of description logics (DLs) that underlie the tractable fragment OWL 2 QL of the Web Ontology Language OWL 2. We start with a novel knowledge evolution framework and natural postulates that evolution should respect, and compare our postulates to the well-established AGM postulates. We then review well-known model and formula-based approaches for expansion and contraction for propositional theories and show how they can be adapted to the case of DL-Lite. In particular, we show intrinsic limitations of model-based approaches: besides the fact that some of them do not respect the postulates we have established, they ignore the structural properties of KBs. This leads to undesired properties of evolution results: evolution of DL-Lite KBs cannot be captured in DL-Lite. Moreover, we show that well-known formula-based approaches are also not appropriate for DL-Lite expansion and contraction: they either have a high complexity of computation, or they produce logical theories that cannot be expressed in DL-Lite. Thus, we propose a novel formula-based approach that respects our principles and for which evolution is expressible in DL-Lite. For this approach we also propose polynomial time deterministic algorithms to compute evolution of DL-Lite KBs when evolution affects only factual data.
Semantic-driven multi-camera pedestrian detection

Abstract In the current worldwide situation, pedestrian detection has reemerged as a pivotal tool for intelligent video-based systems aiming to solve tasks such as pedestrian tracking, social distancing monitoring or pedestrian mass counting. Pedestrian detection methods, even the top performing ones, are highly sensitive to occlusions among pedestrians, which dramatically degrades their performance in crowded scenarios. The generalization of multi-camera setups permits to better confront occlusions by combining information from different viewpoints. In this paper, we present a multi-camera approach to globally combine pedestrian detections leveraging automatically extracted scene context. Contrarily to the majority of the methods of the state-of-the-art, the proposed approach is scene-agnostic, not requiring a tailored adaptation to the target scenario–e.g., via fine-tuning. This noteworthy attribute does not require ad hoc training with labeled data, expediting the deployment of the proposed method in real-world situations. Context information, obtained via semantic segmentation, is used (1) to automatically generate a common area of interest for the scene and all the cameras, avoiding the usual need of manually defining it, and (2) to obtain detections for each camera by solving a global optimization problem that maximizes coherence of detections both in each 2D image and in the 3D scene. This process yields tightly fitted bounding boxes that circumvent occlusions or miss detections. The experimental results on five publicly available datasets show that the proposed approach outperforms state-of-the-art multi-camera pedestrian detectors, even some specifically trained on the target scenario, signifying the versatility and robustness of the proposed method without requiring ad hoc annotations nor human-guided configuration.
Optimizing Multi-Granularity Region Similarity for Person Re-Identification

Person re-identification (re-id) is one of the hottest research topics due to its great value in video analysis applications, such as indoor security and road surveillance. It has been verified as beneficial for re-id to joint global and local features in the recent literature. However, most existing methods usually extract the features of the global region or divide the whole image into several parts without considering the alignment of different parts, which are not discriminating or robust to the complex scenarios. In this paper, we propose a novel method that optimizes multi-granularity similarity fusion based on the coarse region and the fine region. We extract the global feature representations of the coarse region and the local feature representations of the fine region. Instead of using the pose estimation method, we align the local parts by calculating the similarity between the local parts, which is optimized by the refined longest path. The extensive experiments on four challenging datasets are carried out and indicate that our method has achieved the state-of-the-art performances. For instance, on the VIPeR dataset, our method achieves a 67.25% rank-1 matching rate, outperforming the state-of-the-art approaches by a large margin.
Remote Sensing Image Haze Removal Using Gamma-Correction-Based Dehazing Model

Haze is evident in most remote sensing (RS) images, particularly for the RS scenes captured in inclement weather, which severely hinders image interpretation. In this paper, two simple yet effective visibility restoration formulas are proposed for RGB-channel RS (RRS) images and multi-spectral RS (MSRS) images, respectively. More specifically, a robust gamma-correction-based dehazing model (RGDM) is first defined, which can better address the non-uniform illumination problem in hazy images. Then, the scene albedo restoration formula (SARF) used for the RRS images is obtained by imposing the existing prior knowledge on this RGDM, which enables us to simultaneously eliminate the interferences of haze and non-uniform illumination. In subsequence, according to Rayleigh’s law, an expanded restoration formula (E-SARF) is further developed for MSRS data. Using the proposed E-SARF, the spatially varying haze in each band can be thoroughly removed without using any extra information. The experiments are conducted on the challenging RRS and MSRS images, including images with non-uniform illumination, non-uniform haze distribution, and heavy haze. The results reveal that the SARF and the E-SARF are superior to most other state-of-the-art techniques in terms of both the recover quality and the implementation efficiency.
Robust auto tool change for industrial robots using visual servoing

This work presents an automated solution for tool changing in industrial robots using visual servoing and sliding mode control.The robustness of the proposed method is due to the control law of the visual servoing, which uses the information acquired by a vision system to close a feedback control loop.Furthermore, sliding mode control is simultaneously used in a prioritized level to satisfy the constraints typically present in a robot system: joint range limits, maximum joint speeds and allowed workspace.Thus, the global control accurately places the tool in the warehouse, but satisfying the robot constraints.The feasibility and effectiveness of the proposed approach is substantiated by simulation results for a complex 3D case study.Moreover, real experimentation with a 6R industrial manipulator is also presented to demonstrate the applicability of the method for tool changing.
A Speed Regression Using Acceleration Data in a Deep Convolutional Neural Network

A speed estimation method using the acceleration data measured by a wearable device during exercising on a treadmill is described in this paper. The moving speed is estimated by a regression algorithm that implements a deep convolutional neural network (CNN) model. This model is trained on a set of test speeds for walking and running conditions. The mean square error between the inferred speeds and test speeds is minimized during the training to optimize the internal model parameters. The speed inference mean error is shown to be accurate within 7% and 18% of the actual running and walking test speeds, respectively. The deep CNN model parameters, e.g., data sample size, convolution kernel size, and fully connected layer sizes, are optimized for inference accuracy and sized to enable a compact hardware design. The feasibility of designing a wearable device that can infer speed from the acceleration measurement for wearable applications is demonstrated by device simulation.