Reviewerly/exHarmony huggingface dataset:
Performance Analysis and Optimization in Privacy-Preserving Federated Learning

As a means of decentralized machine learning, federated learning (FL) has recently drawn considerable attentions. One of the prominent advantages of FL is its capability of preventing clients' data from being directly exposed to external adversaries. Nevertheless, via a viewpoint of information theory, it is still possible for an attacker to steal private information from eavesdropping upon the shared models uploaded by FL clients. In order to address this problem, we develop a novel privacy preserving FL framework based on the concept of differential privacy (DP). To be specific, we first borrow the concept of local DP and introduce a client-level DP (CDP) by adding artificial noises to the shared models before uploading them to servers. Then, we prove that our proposed CDP algorithm can satisfy the DP guarantee with adjustable privacy protection levels by varying the variances of the artificial noises. More importantly, we derive a theoretical convergence upper-bound of the CDP algorithm. Our derived upper-bound reveals that there exists an optimal number of communication rounds to achieve the best convergence performance in terms of loss function values for a given privacy protection level. Furthermore, to obtain this optimal number of communication rounds, which cannot be derived in a closed-form expression, we propose a communication rounds discounting (CRD) method. Compared with the heuristic searching method, our proposed CRD can achieve a much better trade-off between the computational complexity of searching for the optimal number and the convergence performance. Extensive experiments indicate that our CDP algorithm with an optimization on the number of communication rounds using the proposed CRD can effectively improve both the FL training efficiency and FL model quality for a given privacy protection level.
Distributed Fusion Filter for Nonlinear Multi-Sensor Systems With Correlated Noises

This paper is concerned with distributed fusion (DF) estimation problem for nonlinear multi-sensor systems with correlated noises. Based on a recursive linear minimum variance estimation (RLMVE) framework, a novel filter is developed. It is proved that the RLMVE-based filter and the existing de-correlated filter have the functional equivalence. Then, for multi-sensor cases, cross-covariance matrices between any two local filters are derived. Based on the RLMVE-based filter and cross-covariance matrices, a DF filter weighted by matrices is proposed in the sense of linear minimum variance. Finally, based on the existing de-correlated filter, the algorithm of cross-covariance for de-correlated systems and the DF algorithm weighted by matrices, a de-correlated DF filtering algorithm is proposed. An example verifies the effectiveness of the proposed RLMVE-based DF filter.
Optimization of mobile robot movement on a plane with finite number of repeller sources

The paper considers the problem of planning a mobile robot movement in a conflict environment, which is characterized by the presence of areas that impede the robot to complete the tasks. The main results of path planning in the conflict environment are considered. Special attention is paid to the approaches based on the risk functions and probabilistic methods. The conflict areas, which are formed by point sources that create in the general case asymmetric fields of a continuous type, are observed. A probabilistic description of such fields is proposed, examples of which are the probability of detection or defeat of a mobile robot. As a field description, the concept of characteristic probability function of the source is introduced; which allows us to optimize the movement of the robot in the conflict environment. The connection between the characteristic probability function of the source and the risk function, which can be used to formulate and solve simplified optimization problems, is demonstrated. The algorithm for mobile robot path planning that ensures the given probability of passing the conflict environment is being developed. An upper bound for the probability of the given environment passing under fixed boundary conditions is obtained. A procedure for optimizing the robot path in the conflict environment is proposed, which is characterized by higher computational efficiency achieved by avoiding the search for an exact optimal solution to a suboptimal one. A procedure is proposed for optimizing the robot path in the conflict environment, which is characterized by higher computational efficiency achieved by avoiding the search for an exact optimal solution to a suboptimal one. The proposed algorithms are implemented in the form of a software simulator for a group of ground-based robots and are studied by numerical simulation methods.
MassBrowser: Unblocking the Censored Web for the Masses, by the Masses

We have built and deployed MassBrowser as a fully operational system with end-user GUI software for major operating systems.Our system has been in the beta release mode for over a year with hundreds of invited users from major censored countries testing it on a daily basis.
XTransE: Explainable Knowledge Graph Embedding for Link Prediction with Lifestyles in e-Commerce

In e-Commerce, we are interested in deals by lifestyle which will improve the diversity of items shown to users. A lifestyle, an important motivation for consumption, is a person’s pattern of living in the world as expressed in activities, interests, and opinions. In this paper, we focus on the key task for deals by lifestyle, establishing linkage between items and lifestyles. We build an item-lifestyle knowledge graph to fully utilize the information about them and formulate it as a knowledge graph link prediction task. A lot of knowledge graph embedding methods are proposed to accomplish relational learning in academia. Although these methods got impressive results on benchmark datasets, they can’t provide insights and explanations for their prediction which limit their usage in industry. In this scenario, we concern about not only linking prediction results, but also explanations for predicted results and human-understandable rules, because explanations help us deal with uncertainty from algorithms and rules can be easily transferred to other platforms. Our proposal includes an explainable knowledge graph embedding method (XTransE), an explanation generator and a rule collector, which outperforms traditional classifier models and original embedding method during prediction, and successfully generates explanations and collects meaningful rules.
Interactions Between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF

To fulfill the tasks of human-robot interaction (HRI), how to detect the specific human (SH) becomes paramount. In this paper, the deep learning approach by the integration of Single-Shot Detection, FaceNet, and Kernelized Correlation Filter (SSD-FN-KCF) is developed. From the outset, the SSD is employed to detect the human up to 8m using the RGB-D camera with 320 × 240 resolution. Afterward the omnidirectional mobile robot (ODMR) is driven to the neighborhood of 2.5~3.0 m such that the depth image can accurately estimate the detected human's pose. Subsequently, the ODMR is commanded to the vicinity of 1.0m and the orientation inside -60~60° with respect to the optical axis to identify whether he/she is the SH by the FaceNet. To reduce the computation time of the FaceNet and extend the SH's tracking, the KCF is employed to achieve the task of HRI (e.g., human following). Based on the image processing result, the required pose for searching or tracking (specific) human is accomplished by the image-based adaptive finite-time hierarchical constraint control. Finally, the experiment with the SH, who is far from and on the backside of the ODMR, validates the effectiveness and robustness of the proposed approach.
Time and Individual Duration in Genetic Programming

This paper presents a new way of measuring complexity in variable-size-chromosome-based evolutionary algorithms. Dealing with complexity is particularly useful when considering bloat in Genetic Programming. Instead of analyzing size growth, we focus on the time required for individuals' fitness evaluations, which correlates with size. This way, we consider time and space as two sides of a single coin when devising a more natural method for fighting bloat. We thus view the problem from a perspective that departs from traditional methods applied in Genetic Programming. We have analyzed first the behavior of individuals across generations, taking into account their fitness evaluation times, thus providing clues about the general practice of the evolutionary process when modern parallel and distributed computers are used to run the algorithm. This new perspective allows us to understand that new methods for bloat control can be derived. Moreover, we develop from this framework a first proposal to show the usefulness of the idea: to group individuals in classes according to computing time required for evaluation, automatically accomplished by parallel and distributed systems without any change in the underlying algorithm, when they are only allowed to breed within their classes. Experimental data confirms the strength of the approach: using computing time as a measure of individuals' complexity allows control of the natural size growth of genetic programming individuals while preserving the quality of solutions in both the parallel and sequential versions of the algorithm.
Optimal Transmission of Multi-Quality Tiled 360 VR Video by Exploiting Multicast Opportunities

In this paper, we would like to investigate fundamental impacts of multicast opportunities on efficient transmission of a 360 VR video to multiple users in the cases with and without transcoding at each user. We establish a novel mathematical model that reflects the impacts of multicast opportunities on the average transmission energy in both cases and the transcoding energy in the case with user transcoding, and facilitates the optimal exploitation of transcoding-enabled multicast opportunities. In the case without user transcoding, we optimize the transmission resource allocation to minimize the average transmission energy by exploiting natural multicast opportunities. The problem is nonconvex. We transform it to an equivalent convex problem and obtain an optimal solution using standard convex optimization techniques. In the case with user transcoding, we optimize the transmission resource allocation and the transmission quality level selection to minimize the weighted sum of the average transmission energy and the transcoding energy by exploiting both natural and transcoding- enabled multicast opportunities. The problem is a challenging mixed discrete-continuous optimization problem. We transform it to a Difference of Convex (DC) programming problem and obtain a suboptimal solution using a DC algorithm. Finally, numerical results demonstrate the importance of effective exploitation of transcoding-enabled multicast opportunities in the case with user transcoding.
Collaborative Learning for Extremely Low Bit Asymmetric Hashing

Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression. Nevertheless, existing approaches could hardly guarantee a satisfactory performance with the extremely low-bit (e.g., 4-bit) hash codes due to the severe information loss and the shrink of the discrete solution space. In this article, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collaborative Learning</i> strategy that is tailored for generating high-quality low-bit hash codes. The core idea is to jointly distill bit-specific and informative representations for a group of pre-defined code lengths. The learning of short hash codes among the group can benefit from the manifold shared with other long codes, where multiple views from different hash codes provide the supplementary guidance and regularization, making the convergence faster and more stable. To achieve that, an asymmetric hashing framework with two variants of multi-head embedding structures is derived, termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of training and querying. Extensive experiments on three benchmark datasets have been conducted to verify the superiority of the proposed MAH, and have shown that the 8-bit hash codes generated by MAH achieve 94.3 percent of the MAP <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>1.</label> Mean Average Precision (MAP) </fn> score on the CIFAR-10 dataset, which significantly surpasses the performance of the 48-bit codes by the state-of-the-arts in image retrieval tasks.
Fast Head Pose Estimation via Rotation-Adaptive Facial Landmark Detection for Video Edge Computation

The human head pose estimation is an important and challenging problem, which provides the estimation of the head posture in 3D space from 2D image. It is a crucial technique for face recognition, gaze estimation, facial attribute recognition, etc. However, fast head pose estimation executing on the terminal for video edge computation has many challenges due to the computational complexity of the existing algorithms. In this paper, we propose a fast head pose estimation method based on a novel Rotation-Adaptive facial landmark detection powered by Local Binary Feature (RALBF). The landmark detection method is structured through fusing the prior of the rotation information provided by the Progressive Calibration Networks (PCN) face detector to a Local Binary Feature (LBF) based landmark detection method, which improves the robustness against head pose variations and simultaneously keep the computing efficiency. RALBF is trained and tested on 300W dataset and AFLW2000 dataset, it is verified by the accuracy evaluation that RALBF performs better than LBF. To improve the speed of head pose estimation, the 68, 51 and 10 landmarks distribution schemes are explored and compared on speed and accuracy. In the 10 landmarks scheme, the head pose estimation running once only takes 8.3ms on Intel i7-6700HQ CPU and takes 21.8ms on HiSilicon SoC Hi3519AV100, and the average error of Euler angle is 5.9973° when the face yaw angle is between ±35° on AFLW2000 3D dataset. Experiments demonstrate our approach performing well on real scenes.
See Clearly in the Distance: Representation Learning GAN for Low Resolution Object Recognition

Identifying tiny objects with extremely low resolution is generally considered a very challenging task even for human vision, due to limited information presented inside the object areas. There have been very limited attempts in recent years to deal with low-resolution recognition. The existing solutions rely on either generating super-resolution images or learning multi-scale features. However, their performance improvement becomes very limited, especially when the resolution becomes very low. In this paper, we propose a Representation Learning Generative Adversarial Network (RL-GAN) to generate super image representation that is optimized for recognition. Our solution deals with the classical vision task of object recognition in the distance. We evaluate our idea on the challenging task of low-resolution object recognition. Comparison of experimental results conducted on public and our newly created WIDER-SHIP datasets demonstrate the effectiveness of our RL-GAN, which improves the classification results significantly, with 10-15% gain on average, compared with benchmark solutions.
Threat Analysis of Poisoning Attack Against Ethereum Blockchain

In recent years, blockchain technology has witnessed remarkable developments in its application to crypto assets (cryptocurrency) considering not only function storing values but also extension of the smart contract and anonymity improvement. Ethereum is a blockchain that features the smart contract and there is a data space, where programs can be freely stored, on the blockchain. However, pollution of such data space can jeopardize the existence of Ethereum. In this study, we analyze the fact that the malicious files that are stored in the data space of Ethereum and discuss "blockchain poisoning attacks" that significantly contaminate the blockchains by embedding malicious data at a relatively lower cost. We try to tackle Ethereum-specific risks which are not mentioned in previous study. In addition, we empirically examine the possibility of a poisoning attack on a private blockchain network.
Multidimensional Group Recommendations in the Health Domain

Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients’ welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy, and the psycho-emotional status of the patients. Exploiting those dimensions, we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating preference scores. We experimentally show that our approach can perform better recommendations to small group of patients for useful information documents.
A Patient-Centric Interoperable Framework for Health Information Exchange via Blockchain

To design a blockchain-based framework for the cross-enterprise document sharing (XDS) and integration of electronic health records (EHR). In compliance with the integrated healthcare enterprise (IHE) profile, the XDS of EHRs were sent to the EHR cloud repository from each registered healthcare providers or by users themselves, and their link was shared and incorporated into a blockchain framework by using the same identifier for the same patient. In our proposed system, we used the link of XDS repository to hash the XDS EHR with SHA-256, which is a string of 64 HEX characters (32 bytes), and embossed it with a timestamp to indicate when the document was created or recorded in the blockchain. Then, the interested stakeholders verified the authenticity of the XDS EHR document by taking the hash of the document and matching it with the hash value stored in the Blockchain. A decentralized, non-tamperable, and encrypted storage framework was prototyped to enable the integration and sharing of XDS EHR. In this prototype, patient-centric EHR of different healthcare providers could be accessed in the blockchain dashboard of health records. The proposed blockchain framework provides a solution for the longitudinal storage of patient EHR and for sharing it in a secure and patient-centric way.
Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters

The international classification of diseases (ICD) is a widely used tool to describe patient diagnoses. At University Medical Center Utrecht (UMCU), for example, trained medical coders translate information from hospital discharge letters into ICD-10 codes for research and national disease epidemiology statistics, at considerable cost. To mitigate these costs, automatic ICD coding from discharge letters would be useful. However, this task has proven challenging in practice: it is a multi-label task with a large number of very sparse categories, presented in a hierarchical structure. Moreover, existing ICD systems have been benchmarked only on relatively easier versions of this task, such as single-label performance and performance on the higher “chapter” level of the ICD hierarchy, which contains fewer categories. In this study, we benchmark the state-of-the-art ICD classification systems and two baseline systems on a large dataset constructed from Dutch cardiology discharge letters a t UMCU hospital. Performance of all systems is evaluated for both the easier chapter-level ICD codes and single-label version of the task found in the literature, as well as for the lower-level ICD hierarchy and multi-label task that is needed in practice. We find that state-of-the-art methods outperform the baseline for the single-label version of the task only. For the multi-label task, the baselines are not defeated by any state-of-the-art system, with the exception of HA-GRU, which does perform best in the most difficult task on accuracy. We conclude that practical performance may have been somewhat overstated in the literature, although deep learning techniques are sufficiently good to complement, though not replace, human ICD coding in our application.
Modeling Dynamic Missingness of Implicit Feedback for Sequential Recommendation

Implicit feedback is widely used in collaborative filtering methods for sequential recommendation. It is well known that implicit feedback contains a large number of values that are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">missing not at random</i> (MNAR); and the missing data is a mixture of negative and unknown feedback, making it difficult to learn users’ negative preferences. Recent studies modeled <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exposure</i> , a latent missingness variable which indicates whether an item is exposed to a user, to give each missing entry a confidence of being negative feedback. However, these studies use static models and ignore the information in temporal dependencies among items, which seems to be an essential underlying factor to subsequent missingness. To model and exploit the dynamics of missingness, we propose a latent variable named “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user intent</i> ” to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process. The resulting framework captures the dynamic item missingness and incorporates it into matrix factorization (MF) for recommendation. We further extend the proposed framework to capture the dynamic preference of users, which results in a unified framework that is able to model different evolution patterns of user intent and user preference. We also explore two types of constraints to achieve a more compact and interpretable representation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user intents</i> . Experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommender systems.
Quantum measurements of sums

A method is proposed that allows one to infer the sum of the values of an observable taken during contacts with a pointer state. Hereby, the state of the pointer is updated while contacted with the system and remains unchanged between contacts while the system evolves in time. After a prescribed number of such contacts, the position of the pointer is determined by means of a projective measurement. The outcome is specified in terms of a probability distribution function for unitary and Markovian dissipative dynamics and compared with the results of the same number of generalized Gaussian measurements of the considered observable. As a particular example, a qubit is considered with an observable contacting to the pointer that does not commute with the system Hamiltonian.