Reviewerly/exHarmony huggingface dataset:
Safe Semi-Supervised Fuzzy ${C}$ -Means Clustering

With the rapid increase in the number of collected data samples, semi-supervised clustering (SSC) has become a useful mining tool to find an intrinsic data structure with the help of prior knowledge.The common used prior knowledge includes pair-wise constraints and cluster labels.In the past decades, many relevant methods are proposed to improve clustering performance of SSC by mining prior knowledge.In general, the prior knowledge is assumed to be beneficial to yielding desirable results.However, one can gather inappropriate prior knowledge in some scenarios, such as wrong cluster labels.In this case, prior knowledge can result in degenerating clustering performance.Therefore, how to raise safe semi-supervised clustering (S3C) should be investigated.A main goal of S3C is that the corresponding result is never inferior to that of the corresponding unsupervised clustering part.To achieve the goal, we propose safe semi-supervised Fuzzy c-Means clustering (S 3 FCM) which is extended from traditional semi-supervised FCM (SSFCM).In our algorithm, wrongly labeled samples are carefully explored by constraining the corresponding predictions to be those yielded by unsupervised clustering.Meanwhile, the predictions of the other labeled samples should approach to the given labels.Therefore the labeled samples are expected to be safely explored through a balance between unsupervised clustering and SSC.From the reported clustering results on different datasets, we can find that S 3 FCM can yield comparable, if not the best, performance among different unsupervised clustering and SSC methods even if the wrong ratio achieves 20%.
Weighted Patch-based Manifold Regularization Dictionary Pair Learning model for facial expression recognition using Iterative Optimization Classification Strategy

This paper presents a novel method for solving Facial Expression Recognition (FER) task which integrates Manifold Regularization Dictionary Pair Learning and Intra-class Variation Reduced Feature (MRDPL-IVRF) model and Iterative Optimization Classification Strategy (IOCS). In the proposed framework, we firstly divide the facial image into overlapping patches. Weighted Patch-based Local Binary Patterns (WPLBP) is proposed for feature extraction by cascading the weighted LBP features from each patch, which highlights the informative facial regions. MRDPL-IVRF model is then adopted for robust sparse representation of facial features, where IVRFs are generated from the difference between query images and their corresponding approximation of each expression class, which can reduce the intra-class variations resulted by illumination and identity. Finally, IOCS is presented for expression classification which can explore the most unlikely class according to the maximum reconstruction residual. Different from traditional sparse coding classification scheme, our IOCS method iteratively removes irrelevant samples to obtain a more precise representation model, thus can further improve the recognition performance. The feasibility of our framework has been successfully tested on CK, CK+, SEFW, CMU-PIE and Multi-PIE databases. Furthermore, the experiments combining deep features shows that our model can be integrated with existing deep models and obtain better FER results.
On mixture periodic Integer-Valued <i>ARCH</i> models

This paper deals with the study of Mixture Periodic Integer-Valued Autoregressive Conditionally Heteroskedastic models. Some theoretical properties of the model, such as the first and the second moment periodically stationary conditions, are established. Moreover, closed-forms of these moments are, under these conditions, derived. The estimation is done by the maximum likelihood via the iterative EM algorithm and the performance of this method is shown via an intensive simulation study. A comparative real data study is performed on a Campylobacteriosis time series.
Chronos: Secure and Accurate Time-Stamping Scheme for Digital Files via Blockchain

It is common to certify when a file was created in digital investigations, e.g., determining first inventors for patentable ideas in intellectual property systems to resolve disputes. Secure time-stamping schemes can be derived from blockchain-based storage to protect files from backdating/forward-dating, where a file is integrated into a transaction on a blockchain and the timestamp of the corresponding block reflects the latest time the file was created. Nevertheless, blocks' timestamps in blockchains suffer from time errors, which causes the inaccuracy of files' timestamps. In this paper, we propose an accurate blockchain-based time-stamping scheme called Chronos. In Chronos, when a file is created, the file and a sufficient number of successive blocks that are latest confirmed on blockchain are integrated into a transaction. Due to chain quality, it is computationally infeasible to pre-compute these blocks. The time when the last block was chained to the blockchain serves as the earliest creation time of the file. The time when the block including the transaction was chained indicates the latest creation time of the file. Therefore, Chronos makes the file's creation time corresponding to this time interval. Based on chain growth, Chronos derives the time when these two blocks were chained from their heights on the blockchain, which ensures the accuracy of the file's timestamp. The security and performance of Chronos are demonstrated by a comprehensive evaluation.
A Luminance-aware Model of Judder Perception

The perceived discrepancy between continuous motion as seen in nature and frame-by-frame exhibition on a display, sometimes termed judder, is an integral part of video presentation. Over time, content creators have developed a set of rules and guidelines for maintaining a desirable cinematic look under the restrictions placed by display technology without incurring prohibitive judder. With the advent of novel displays capable of high brightness, contrast, and frame rates, these guidelines are no longer sufficient to present audiences with a uniform viewing experience. In this work, we analyze the main factors for perceptual motion artifacts in digital presentation and gather psychophysical data to generate a model of judder perception. Our model enables applications like matching perceived motion artifacts to a traditionally desirable level and maintain a cinematic motion look.
Optimal privacy control for transport network data sharing

In the era of smart cities, Internet of Things, and Mobility-as-a-Service, the need for private operators to willingly share data with public agencies is greater than ever before. However, it is still problematic for private operators to share data with the public due to risks to competitive advantages. A privacy control algorithm is proposed to overcome this key obstacle for private operators sharing complex network-oriented data objects. The algorithm is based on information-theoretic k-anonymity where an operator's tour data is used in conjunction with performance measure accuracy controls to synthesize a set of alternative tours with diffused probabilities for sampling during a query. The algorithm is proven to converge sublinearly toward constrained maximum entropy under certain asymptotic conditions with measurable optimality gap. Computational experiments verify the applicability to multi-vehicle fleet tour data; confirm that reverse engineered parameters from the diffused data results in controllable sampling error; and tests conducted on a set of realistic routing records from travel data in Long Island, NY, demonstrate the use of the methodology from both the adversary and user perspectives.
Reinforcement learning using continuous states and interactive feedback

Research in intelligent systems field has led to different learning methods for machines to acquire knowledge, among them, reinforcement learning (RL). Given the problem of the time required to learn how to develop a problem, using RL this work tackles the interactive reinforcement learning (IRL) approach as a way of solution for the training of agents. Furthermore, this work also addresses the problem of continuous representations along with the interactive approach. In this regards, we have performed experiments with simulated environments using different representations in the state vector in order to show the efficiency of this approach under a certain probability of interaction. The obtained results in the simulated environments show a faster learning convergence when using continuous states and interactive feedback in comparison to discrete and autonomous reinforcement learning respectively.
Multi Workflow Fair Scheduling Scheme Research Based on Reinforcement Learning

In this study, aiming to optimize the multi-workflow scheduling order, in which tasks submitted at different time require different service quality, we present a fair multi-workflow scheduling scheme based on reinforcement learning. Firstly we design a dynamic priority-driven algorithm, in order to set the initial state of the task priority according to the type of cloud workflow and service quality on the one hand, and on the other hand, to adjust the tasks priority dynamically while scheduling so as to avoid violating the Service Level Agreement by delaying the workflow provisioning. Secondly, we design a fine-grained cloud computing model and apply the reinforcement-learning based scheduling algorithm to balance the cluster loads. Finally the experimental results prove the effectiveness of this scheme.
Trust-Aware and Fast Resource Matchmaking for Personalized Collaboration Cloud Service

In data-intensive cloud collaboration services with tens of thousands of users and million-level resources, means of providing personalized and trust-aware services quickly and simultaneously is a challenging issue. In this paper, we propose Per-trust, a trust-aware and fast resource matchmaking scheme for personalized QoS guaranteeing in collaboration cloud service. First, an integrated and trust-aware service broking architecture is proposed across the collaborative cloud computing environment; this architecture can provide trust computing and personalized resource matchmaking capacities. Then, a resource clustering method is proposed based on the multidimensional properties of cloud resources; this method can accurately, quickly meet the personalized requirements of users. Finally, an innovative algorithm is proposed for the trust computing of service resources based on real-time and dynamic monitoring of data, thereby quickly and effectively providing trust-aware resource matchmaking. Different from existing methods, which focus only on QoS and trust issues, our approach adds a resource clustering step before QoS and trust evaluation. Three key components are organically combined, namely, service broking architecture, resource clustering, and security and QoS-related trust computing. To the best of our knowledge, this paper is the first to construct an integrated solving scheme for cloud resource matchmaking that can simultaneously satisfy the trustworthiness and personalization required by users. Theoretical and experimental results verify the effectiveness of the proposed scheme.
Investigating Clarinet Articulation Using a Physical Model and an Artificial Blowing Machine

A time-domain physical model is presented that is capable of simulating a variety of articulation techniques in single-reed woodwind instruments and suitable for real-time sound synthesis. Due to the nonlinear nature of the excitation mechanism, an energy-based approach is adopted for the construction of the numerical scheme in order to ensure algorithm stability. To validate the model, measurements are carried out using an artificial blowing machine. The construction of the machine, including a sensor-equipped reed and mouthpiece as well as an automated artificial tongue and lip, is described in detail. By adjusting the motion of the tongue, the blowing machine can generate audio signals corresponding to portato and staccato articulation. These signals are resynthesised following an inverse modelling approach based on the presented physical model, during which model parameters are estimated. All estimated parameters lie in a physically feasible range and may be used for sound synthesis and sound analysis applications.
Face Verification and Recognition for Digital Forensics and Information Security

In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.
Visualization and Exploration of Deep Learning Networks in 3D and Virtual Reality

Understanding of artificial intelligence (AI) systems becomes more important as their use cases in real-world applications growth. Today’s AI systems are increasingly complex and ubiquitous. They will be responsible for making decisions that directly affect individuals. Explainable AI can potentially help by explaining actions, decisions and behaviours of AI systems to users. Our approach is to provide a tool with an interactive user interface in 3D. The tool visualizes deep learning network layers and allows interactive exploration on different levels of detail. The visualization then improves transparency and opacity of AI systems for experts and non-experts.
Multi-level diversity promotion strategies for Grammar-guided Genetic Programming

Grammar-guided Genetic Programming (G3P) is a family of Evolutionary Algorithms that can evolve programs in any language described by a context-free grammar. The most widespread members of this family are based on an indirect representation: a sequence of bits or integers (the genotype) is transformed into a string of the language (the phenotype) by means of a mapping function, and eventually into a fitness value. Unfortunately, the flexibility brought by this mapping is also likely to introduce non-locality phenomena, reduce diversity, and hamper the effectiveness of the algorithm. In this paper, we experimentally characterize how population diversity, measured at different levels, varies for four popular G3P approaches. We then propose two strategies for promoting diversity which are general, independent both from the specific problem being tackled and from the other components of the Evolutionary Algorithm, such as genotype–phenotype mapping, selection criteria, and genetic operators. We experimentally demonstrate their efficacy in a wide range of conditions and from different points of view. The results also confirm the preponderant importance of the phenotype-level analyzes in diversity promotion.
The Labeled Multi-Bernoulli Filter for Jump Markov Systems Under Glint Noise

This paper proposes a novel labeled multi-Bernoulli (LMB) filter for jump Markov systems (JMS) to track the multiple maneuvering objects under glint noise. By modeling the glint noise as a Student's t-distribution and using the variational Bayesian method to acquire the approximate state distribution, we present an efficient implementation of the LMB filter with joint prediction and update for JMS. Simulation results illustrate that the proposed filter outperforms the existing filters for multi-object tracking under glint noise.
Compliance Checking of Open Source EHR Applications for HIPAA and ONC Security and Privacy Requirements

Electronic Health Record (EHR) applications are digital versions of paper-based patient's health information. They are increasingly adopted to improved quality in healthcare, such as convenient access to histories of patient medication and clinic visits, easier follow up of patient treatment plans, and precise medical decision-making process. EHR applications are guided by measures of the Health Insurance Portability and Accountability Act (HIPAA) to ensure confidentiality, integrity, and availability. Furthermore, Office of the National Coordinator (ONC) for Health Information Technology (HIT) certification criteria for usability of EHRs. A compliance checking approach attempts to identify whether or not an adopted EHR application meets the security and privacy criteria. There is no study in the literature to understand whether traditional static code analysis-based vulnerability discovered can assist in compliance checking of regulatory requirements of HIPAA and ONC. This paper attempts to address this issue. We identify security and privacy requirements for HIPAA technical requirements, and identify a subset of ONC criteria related to security and privacy, and then evaluate EHR applications for security vulnerabilities. Finally propose mitigation of security issues towards better compliance and to help practitioners reuse open source tools towards certification compliance.
A Gamified Mobile Augmented Reality System for the Teaching of Astronomical Concepts

In this paper we present the first version of the PlanetarySystemGO project, an evolution of SolarSystemGO, a Mobile Augmented Reality game previously created with the Unity3D/Vuforia game engine. While both projects aim at providing awareness of several Astronomy concepts, for PlanetarySystemGO a web based back-end system was implemented, developed under the. NET technology, that allows the instructor to manage several aspects of the game. Augmented Reality (AR) is a technology that elevates the possibilities of current Mobile Applications by superposing digital content to real world objects. The mobile devices that run these applications are increasingly powerful regarding their computational resources, allowing widespread adoption of the AR paradigms. The system presented can be enjoyed by players of all ages, but our main target are school children and young adults, as an application tool or even for learning purposes. A set of field tests was conducted, validating the presented design, and allowing continuous improvement of the system.
An Empirical Study on API-Misuse Bugs in Open-Source C Programs

Today, large and complex software is developed with integrated components using application programming interfaces (APIs). Correct usage of APIs in practice presents a challenge due to implicit constraints, such as call conditions or call orders. API misuse, i.e., violation of these constraints, is a well-known source of bugs, some of which can cause serious security vulnerabilities. Although researchers have developed many API-misuse detectors over the last two decades, recent studies show that API misuses are still prevalent. In this paper, we provide a comprehensive empirical study on API-misuse bugs in open-source C programs. To understand the nature of API misuses in practice, we analyze 830 API-misuse bugs from six popular programs across different domains. For all the studied bugs, we summarize their root causes, fix patterns and usage statistics. Furthermore, to understand the capabilities and limitations of state-of-the-art static analysis detectors for API-misuse detection, we develop APIMU4C, a dataset of API-misuse bugs in C code based on our empirical study results, and evaluate three widely-used detectors on it qualitatively and quantitatively. We share all the findings and present possible directions towards more powerful API-misuse detectors.
Randomized GPU Algorithms for the Construction of Hierarchical Matrices from Matrix-Vector Operations

Randomized algorithms for the generation of low rank approximations of large dense matrices have become popular methods in scientific computing and machine learning.In this paper, we extend the scope of these methods and present batched GPU randomized algorithms for the efficient generation of low rank representations of large sets of small dense matrices, as well as their generalization to the construction of hierarchically low rank symmetric H 2 matrices with general partitioning structures.In both cases, the algorithms need to access the matrices only through matrix-vector multiplication operations which can be done in blocks to increase the arithmetic intensity and substantially boost the resulting performance.The batched GPU kernels are adaptive, allow nonuniform sizes in the matrices of the batch, and are more effective than SVD factorizations on matrices with fast decaying spectra.The hierarchical matrix generation consists of two phases, interleaved at every level of the matrix hierarchy.A first phase adaptively generates low rank approximations of matrix blocks through randomized matrix-vector sampling.A second phase accumulates and compresses these blocks into a hierarchical matrix that is incrementally constructed.The accumulation expresses the low rank blocks of a given level as a set of local low rank updates that are performed simultaneously on the whole matrix allowing high-performance batched kernels to be used in the compression operations.When the ranks of the blocks generated in the first phase are too large to be processed in a single operation, the low rank updates can be split into smaller-sized updates and applied in sequence.Assuming representative rank k, the resulting matrix has optimal O(kN ) asymptotic storage complexity because of the nested bases it uses.The ability to generate an H 2 matrix from matrix-vector products allows us to support a general randomized matrix-matrix multiplication operation, an important kernel in hierarchical matrix computations.Numerical experiments demonstrate the high performance of the algorithms and their effectiveness in generating hierarchical matrices to a desired target accuracy.
A Traceable Ring Signature Scheme Based on Coding Theory

Traceable ring signatures are a variant of ring signatures which allows the identity of a user to be revealed, when it signs two different messages with respect to the same group of users. It has applications in e-voting and in cryptocurrencies, such as the well-known Monero. We propose the first traceable ring signature scheme whose security is based on the hardness of the Syndrome Decoding problem, a problem in coding theory which is conjectured to be unsolvable by both classical and quantum algorithms. To construct the scheme, we use a variant of Stern's protocol and, by applying the Fiat-Shamir transform to it in an ingenious way, we obtain a ring signature that allows traceability. We prove that the resulting protocol has the standard security properties for traceable ring signatures in the random oracle model: tag-linkability, anonymity and exculpability. As far as we know, this is the first proposal for a traceable ring signature scheme in the post-quantum setting.