Reviewerly/exHarmony huggingface dataset:
Private Frequent Itemset Mining in the Local Setting

Set-valued data, which is useful for representing user-generated data, becomes ubiquitous in numerous online services. Service provider profits by learning patterns and associations from users' set-valued data. However, it comes with privacy concerns if these data are collected from users directly. This work studies frequent itemset mining from user-generated set-valued data meanwhile locally preserving personal data privacy. Under local d-privacy constraints, which capture intrinsic dissimilarity between set-valued data in the framework of differential privacy, we propose a novel privacy-preserving frequent itemset mining mechanism, called PrivFIM. It provides rigorous data privacy protection on the user-side and allows effective statistical analyses on the server-side. Specifically, each user perturbs his set-valued data locally to guarantee that the server cannot infer the user's original itemset with high confidence. The server can reconstruct an unbiased estimation of itemset frequency from these randomized data and then combines it with the Apriori-based pruning technique to identify frequent itemsets efficiently and accurately. Extensive experiments conducted on real-world and synthetic datasets demonstrate that PrivFIM surpasses existing methods, and maintains high utility while providing strong privacy guarantees.
A Secure Key Management Scheme for Hierarchical Access Control with Secret Key Validity Checking

Providing Security and privacy for resources in hierarchical access control is one of the fundamental security services in computer science. This service is becoming very complex to manage because of the growing number of users who have different access rights. Several key management schemes for dynamic hierarchical access control were proposed to solve the problem where higher level security classes can derive secret keys of all their corresponding successors. These schemes are used to generate, distribute and renew secret keys that are needed to encrypt data resources. In this paper, we propose a secure key management model to ensure that authorized and legitimate users have access only to resources to which they are entitled to. The proposed scheme is based on Elliptic Curve Cryptography. In this scheme, users are separated into security classes, each with different authorization level. For each security class, a specific encryption key is generated and assigned so that a class with higher authorization level can derive the key of any of its successors. Through the informal security analysis, our scheme is shown to be provably secure against both external and internal attacks. The proposed scheme provides an efficient solution to execute key generation and key derivation phases, and to overcome dynamic access control problems. In addition, our scheme makes it possible for users to verify the access to resources by running the key validity checking process to consider a derived secret key as a legitimate key before being used.
Exact solutions and convergence of gradient based dynamical systems for computing outer inverses

This paper investigates convergence properties of gradient neural network (GNN) and GNN-based dynamical systems for computing generalized inverses. The main results are exact analytical solutions for the state matrices of the corresponding GNN and GNN-based dynamical systems. The exact solutions are given in each time instant, which enables to express final results as appropriate limiting expressions. This enables more rigorous convergence analysis in terms of exact solutions. Finally, trajectories of state variables in considered dynamical systems can be generated by avoiding time-consuming numerical solving matrix differential equations inside the selected time interval. Using the fact that the stated dynamical systems are in the essence systems of linear equations, explicit solutions can be obtained using known techniques of ordinary differential equations. Main results of the paper are further transformations of obtained exact solutions using main properties of generalized inverses and linear algebra tools. It is important to mention that the convergence results are expressed in terms of expressions involving outer inverses. Several examples are presented and a closed-form expressions for the solutions are given and implemented in package Mathematica. These are compared with the numerical solutions obtained by Matlab Simulink implementation.
Intermittent human-in-the-loop model selection using cerebro

Deep learning (DL) is revolutionizing many fields. However, there is a major bottleneck for the wide adoption of DL: the pain of model selection , which requires exploring a large config space of model architecture and training hyper-parameters before picking the best model. The two existing popular paradigms for exploring this config space pose a false dichotomy. AutoML-based model selection explores configs with high-throughput but uses human intuition minimally. Alternatively, interactive human-in-the-loop model selection completely relies on human intuition to explore the config space but often has very low throughput. To mitigate the above drawbacks, we propose a new paradigm for model selection that we call intermittent human-in-the-loop model selection . In this demonstration, we will showcase our approach using five real-world DL model selection workloads. A short video of our demonstration can be found here: https://youtu.be/K3THQy5McXc.
Dynamic scene novel view synthesis via deferred spatio-temporal consistency

Structure from motion (SfM) enables us to reconstruct a scene via casual capture from cameras at different viewpoints, and novel view synthesis (NVS) allows us to render a captured scene from a new viewpoint. Both are hard with casual capture and dynamic scenes: SfM produces noisy and spatio-temporally sparse reconstructed point clouds, resulting in NVS with spatio-temporally inconsistent effects. We consider SfM and NVS parts together to ease the challenge. First, for SfM, we recover stable camera poses, then we defer the requirement for temporally-consistent points across the scene and reconstruct only a sparse point cloud per timestep that is noisy in space–time. Second, for NVS, we present a variational diffusion formulation on depths and colors that lets us robustly cope with the noise by enforcing spatio-temporal consistency via per-pixel reprojection weights derived from the input views. Together, this deferred approach lets us generate novel views for dynamic scenes without requiring challenging spatio-temporally consistent reconstructions nor training complex models on large datasets. We demonstrate our algorithm on real-world dynamic scenes against classic and more recent learning-based baseline approaches.
Learning Navigation Policies for Mobile Robots in Deep Reinforcement Learning with Random Network Distillation

Learning navigation policies considers the task of training a model that can find collision-free paths for mobile robots, where various Deep Reinforcement Learning (DRL) methods have been applied with promising results. However, the natural reward function for the task is usually sparse, i.e., obtaining a penalty for the collision and a positive reward for arriving the target position, which makes it difficult to learn. In particular, for some complex navigation environments, it is hard to search a collision-free path by the random exploration, which leads to a rather slow learning speed and solutions with poor performance. In this paper, we propose a DRL based approach to train an end-to-end navigation planner, i.e, the policy neural network, that directly translates the local grid map and the relative goal of the robot into its moving actions. To handle the sparse reward problem, we augment the normal extrinsic reward from the environment with intrinsic reward signals measured by random network distillation (RND). In specific, the intrinsic reward is calculated by two different networks from RND, which encourages the agent to explore a state that has not been seen before. The experimental results show that by augmenting the reward function with intrinsic reward signals by RND, solutions with better performance can be learned more efficiently and more stably in our approach. We also deploy the trained model to a real robot, which can perform collision avoidance in navigation tasks without any parameter tuning. A video of our experiments can be found at https://youtu.be/b1GJrWfO8pw.
KDM to UML Model Transformation for Quantum Software Modernization

Thanks to the last engineering advances, quantum computing is gaining an increasing importance in many sectors that will be benefited from its superior computational power. Before achieving all those promising benefits, companies must be able to combine their classical information systems and the new quantum software to operate with the so-called hybrid information systems. This implies, at some point of such a modernization process, that hybrid information systems will have to be (re)designed. UML can be used for defining abstract design models, not only for the classical part as done before, but also for the quantum software in an integrated manner. This paper proposes a model transformation for generating UML models that represents quantum circuits as activity diagrams. Thanks to the usage of UML, these designs are technological-independent which contributes to the modernization of hybrid information systems. The outgoing UML models are compliant with a vast amount of design tools and might be understood by a big community.
Enhanced Speaker-Aware Multi-Party Multi-Turn Dialogue Comprehension

Multi-party multi-turn dialogue comprehension brings unprecedented challenges in handling complicated scenarios, as the co-occurrence of multiple speakers causes complexity and inconsistency. As a result of the multiple participation, the shift of speaker roles and crisscrossed discourse relations among utterances hinder reading comprehension. Motivated by this, we further integrate the enhancements of speaker-related features for dialogue comprehension performance. This work proposes a novel model with enhancement from both sides of speaker roles and speaker-aware relations. At the token level, we apply a speaker mask for attention, while at the discourse level, we utilize heterogeneous graph networks for comprehensive speaker-aware discourse clues. Experimental results show that our <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> nhanced <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> peaker- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ware method (ESA) helps achieve state-of-the-art performance on the Molweni dataset, as well as significant improvements on the FriendsQA dataset. We find that our method makes steady improvements on stronger backbones. Analysis shows that our model enhances the connections between utterances and their own speakers and captures the speaker-aware discourse relations. Discussions on data features and error cases are presented, and a visualized case is displayed. The findings reveal the importance of speaker-aware signals in dialogue comprehension.
A Universal Lossless Compression Method applicable to Sparse Graphs and heavy-tailed Sparse Graphs

Graphical data arises naturally in several modern applications, including but not limited to internet graphs, social networks, genomics and proteomics. The typically large size of graphical data argues for the importance of designing universal compression methods for such data. In most applications, the graphical data is sparse, meaning that the number of edges in the graph scales more slowly than <tex>$n^{2}$</tex>, where <tex>$n$</tex> denotes the number of vertices. Although in some applications the number of edges scales linearly with <tex>$n$</tex>, in others the number of edges is much smaller than <tex>$n^{2}$</tex> but appears to scale superlinearly with <tex>$n$</tex>. We call the former sparse graphs and the latter heavy-tailed sparse graphs. In this paper we introduce a universal lossless compression method which is simultaneously applicable to both classes. We do this by employing the local weak convergence framework for sparse graphs and the sparse graphon framework for heavy-tailed sparse graphs.
Controlled Quantum Network Coding Without Loss of Information

Quantum network coding is used to solve the congestion problem in quantum communication, which will promote the transmission efficiency of quantum information and the total throughput of quantum network. We propose a novel co... | Find, read and cite all the research you need on Tech Science Press
A secure data collection strategy using mobile vehicles joint UAVs in smart city

Recruiting mobile vehicles (MVs) has been proved as an effective and low-cost strategy to collect data from sensing devices (SDs) in the smart city. However, few works consider data security when using MVs as data mules. In our previous work, we have proposed a Consistent Trust Verification for MVs (CTV-MV), which builts trust through recommendation relationships, but this method was vulnerable to collusion attack, that is, multiple MVs provided consistent fake data to deceive the system. Therefore, a Cross Trust Verification for MVs joint UAVs (CTV-MVU) data collection strategy is proposed in this paper, where the Unmanned Aerial Vehicles (UAVs) are deployed to collect data from specific SDs which are used as baseline data to realize trust reasoning mechanism. Besides, the UAVs can also sense the SDs that are difficult to be collected by MVs due to their limitations of coverage, and thus improve the data collection ratio. Furthermore, a Trust Priority Recruitment (TPR) strategy for CTV-MVU is also proposed to prioritize the recruitment of high-trust MVs. Experiment results show that the proposed CTV-MVU strategy outperforms the CTV-MV one in terms of the excellent ratio, trust, data collection ratio, and robustness.
Readmission Prediction with Knowledge Graph Attention and RNN-Based Ordinary Differential Equations

Predicting the readmission risk within 30 days on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in healthcare domain. Deep-learning-based models are recently utilized to address this task since those models can relatively improve prediction performance and work as decision aids, which helps reduce unnecessary readmission and recurrence risk. However, existing prediction models, limited by fuzzy relevance of patient data, are unable to get higher prediction accuracy due to data noise generated by patients with different disease types. To solve this problem, we propose an end-to-end model called GROM, which integrates knowledge graph to alleviate the interference of data noise generated in the processing of irregularity dynamic clinical data with neural ordinary differential equation (ODE). The experimental results show that our model achieved the highest average precision and proved that the graph attention mechanism is suitable to improve performance of predicting the risk of readmission within 30 days.
Improving Scalability And Security Medical Dataset Using Recurrent Neural Network And Blockchain Technology

Nowadays, dealing with medical data is very difficult task. Because medical dataset contains very sensible details. And also handling of this crucial data is another difficult task. While dealing and handling with such data’s, data leakage problem may occur which paves the way to unstable the cloud environment. Existing approaches enables blockchain technology over cloud environment by processing large amounts of raw data which in turn reduces the overall network performance. In order to overcome these issues, we suggest a blockchain technology combined with Recurrent Neural Networks. The main objectives of the proposed system are two folded. Initially, we classify the data into high priority and low priority using Recurrent Neural Network (RNN) to improve the performance and scalability. Second, the blockchain technology is applied over high priority data to enhance the security of the sensitive information. Then, the low priority data is stored into a separate log file for later use. Comparison results have shown that the proposed system outperforms existing systems.
Uncertainty Interpretation of the Machine Learning Survival Model Predictions

A method for interpreting uncertainty of predictions provided by machine learning survival models is proposed. It is called UncSurvEx and aims to determine which features of an analyzed example lead to uncertain predictions of an explainable black-box survival model. One of the ideas behind the proposed method is to approximate the uncertainty measure of a local black-box survival model prediction by the uncertainty measure of the Cox proportional hazards model at the local area around a test example. The linear relationship between covariates and predictions in the corresponding Cox model allows determining quantitative impacts of covariates on the uncertainty measure. A specific certainty measure of the survival function, taking into account the most uncertain survival function, is introduced to interpret the prediction uncertainty. The L2-norm is used to compute the distance between survival functions. The method leads to an unconstrained non-convex optimization problem which is solved by means of the well-known Broyden–Fletcher–Goldfarb–Shanno algorithm. A lot of numerical experiments demonstrate the uncertainty interpretation method.
Dynamic Feature Subset Selection for Occluded Face Recognition

Accurate recognition of person identity is a critical task in civil society for various application and different needs.There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc.Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method.However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc.In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded faces.The proposed DFSS methods are based on resilient algorithms attempting to minimize the negative influence of confusing and low-quality features extracted from occluded areas by either exclusion or weight reduction.Principal Component Analysis and Gabor filtering based approaches are initially used for face feature extraction, then the proposed DFSS methods are dynamically enforced.This is leading to more effective feature representation and an improved recognition performance.To validate their effectiveness, multiple experiments are conducted and the performance of different methods is compared.The experimental work is carried out using AR database and Extended Yale Face Database B. The obtained results of face identification and verification show that both proposed DFSS methods outperform the standard (static) use of the whole number of features and the equal feature weights.
Thermal building upgrade with off-grid PV system: a Polish case

Abstract Nowadays, one of the basic requirements for thermally upgraded buildings involves limitation in CO 2 emission even by over 90%. To fulfil these criteria, it is necessary to use alternative energy sources and photovoltaics constitutes a reasonable option for this. This paper addresses an analysis of the efficiency and profitability of a photovoltaic system located in the geometric center of Europe-Poland, where the intensity of solar irradiation is not very high compared to other European countries. The difference of total solar radiation density between Poland and Malta is 49.2%, from analysis based on SolarGIS base. The PV Lighthouse calculator was used for global power density and photon current examination for a Polish city and locations of the highest and the lowest solar radiation values, Malta and Finland, respectively. This case study concerns a thermally upgraded building; a gas boiler was replaced by a heat pump supported by an off-grid PV system. To achieve a reduction in CO 2 emission of 90%, it is necessary to install 182 PV cells, which generates high investment costs. An investment is entirely profitable with 70% of funding with Simple Pay Back Time, SPBT~7 years although Net Present Value, NPV&gt;0; Internal Rate of Return, IRR=10.6%.
Using Blockchain Technology in Mobile Network to create decentralized Home Location Registry (HLR)

Blockchain can mean many things to many people.It is a set of protocols and encryption technologies for securely storing data on a distributed network for the developers.It is a distributed ledger for business and finance and the technology underlying the explosion of new digital currencies.For technologists, it is the driving force behind the next generation of the internet.On the other hand, it is a transformational technology facilitating large-scale human progress in previously unimagined ways for the rest of the people, a tool for radically reshaping society and economy.Some view it as a disruptive technology that can be the source of a great deal of fraud, illegal activity, where others see opportunities to bring into existing systems by providing decentralization, transparency, and efficiency.This complex technological, economic, and social phenomenon has been the subject of fervent debate.It calls into question what might have been seen to be established parameters of the modern world like currency, economics, trust, value, and exchange.It is a revolutionary new computing paradigm and one of the most significant, fundamental digital platforms' advances since the internet.It is an emergent technology experiencing very rapid evolution, and so is our understanding of what it is and what it can be.This paper is subject to the use of Blockchain concepts in mobile networks to strengthen the Home Location Registry (HLR) database and make it decentralized for secure transactions and in banking and financial centers.Blockchain also holds potential implications for global commerce.It could make trade more efficient by removing the manual and paper-based processes and introducing streamlined and automated processes.
Hardware Support for Low-Cost Memory Safety

Programs written in C/C++ are vulnerable to memory-safety errors like buffer-overflows and use-after-free. While several mechanisms to detect such errors have been previously proposed, they suffer from a variety of drawbacks including poor performance, imprecise or probabilistic detection of errors, or requiring invasive changes to the binary-layout or source-code. Consequently, memory-safety errors continue to exist in production-software and are a principal cause of security problems.In our project at IBM, we worked on a minimally-invasive and low-cost hardware-based bounds-checking framework for preventing out-of-bounds accesses and use-after-free errors. The key idea is to re-purpose "unused bits" in a pointer to store an index into a bounds-information table that can be used to catch out-of-bounds errors and use-after-free errors without any change to the binary layout. Using this bounds-checking framework, we implement a design for preventing Out-of-Bounds accesses and Use-After-Free for heap-objects, that are responsible for the majority of memory-safety errors in the wild.