Reviewerly/exHarmony huggingface dataset:
Misty clouds—A layered cloud platform for online user anonymity in Social Internet of Things

Online services typically collect data that are explicitly provided by users and metadata that are implicitly inferred from users’ activity patterns. The proclaimed goal of metadata collection is to support the quality of service, thus enhancing the Social Internet of Things (SIoT). To most users, it is not obvious that metadata express people’s lives to a large degree. Traditionally, security has focused on protecting communication content rather than the metadata associated with it. This provides a thin layer of privacy protection. Thus, a demand exists for privacy-preserving technologies that prevent metadata collection and aggregation. This paper focuses on a service’s ability to observe communication metadata that can be exploited to learn users’ identities and behavior patterns. Thus, a novel system, Misty Clouds, is proposed as a platform for creating anonymous Internet connections to address both security and performance issues. The performance evaluation shows that the desired level of anonymity can be achieved with tolerable performance overheads. Through a comparison analysis, it is shown that the new algorithm outperforms an existing algorithm, Tor. Additionally, the features that can facilitate the growth of Misty Clouds into a holistic privacy-preserving platform are discussed. Furthermore, a user survey was conducted to study users’ perceptions and attitudes.
Linearization in Motion Planning under Uncertainty

Motion planning under uncertainty is essential to autonomous robots. Over the past decade, the scalability of such planners have advanced substantially. Despite these advances, the problem remains difficult for systems with non-linear dynamics. Most successful methods for planning perform forward search that relies heavily on a large number of simulation runs. Each simulation run generally requires more costly integration for systems with non-linear dynamics. Therefore, for such problems, the entire planning process remains relatively slow. Not surprisingly, linearization-based methods for planning under uncertainty have been proposed. However, it is not clear how linearization affects the quality of the generated motion strategy, and more importantly where to and where not to use such a simplification. This paper presents our preliminary work towards answering such questions. In particular, we propose a measure, called Statistical-distance-based Non-linearity Measure (SNM), to identify where linearization can and where it should not be performed. The measure is based on the distance between the distributions that represent the original motion-sensing models and their linearized version. We show that when the planning problem is framed as the Partially Observable Markov Decision Process (POMDP), the difference between the value of the optimal strategy generated if we plan using the original model and if we plan using the linearized model, can be upper bounded by a function linear in SNM. We test the applicability of this measure in simulation via two venues. First, we compare SNM with a negentropy-based Measure of Non-Gaussianity (MoNG) –a measure that has recently been shown to be a suitable measure of non-linearity for stochastic systems []. We compare their performance in measuring the difference between a general POMDP solver [] that computes motion strategies using the original model and a solver that uses the linearized model (adapted from [3]) on various scenarios. Our results indicate that SNM is more suitable in taking into account the effect that obstacles have on the effectiveness of linearization. In the second set of tests, we use a local estimate of SNM to develop a simple on-line planner that switches between using the original and the linearized model. Simulation results on a car-like robot with second order dynamics and a 4-DOFs and 6-DOFs manipulator with torque control indicate that our simple planner appropriately decides if and when linearization should be used.
A SCHEMATIC DEFINITION OF QUANTUM POLYNOMIAL TIME COMPUTABILITY

Abstract In the past four decades, the notion of quantum polynomial-time computability has been mathematically modeled by quantum Turing machines as well as quantum circuits. This paper seeks the third model, which is a quantum analogue of the schematic (inductive or constructive) definition of (primitive) recursive functions. For quantum functions mapping finite-dimensional Hilbert spaces to themselves, we present such a schematic definition, composed of a small set of initial quantum functions and a few construction rules that dictate how to build a new quantum function from the existing ones. We prove that our schematic definition precisely characterizes all functions that can be computable with high success probabilities on well-formed quantum Turing machines in polynomial time, or equivalently uniform families of polynomial-size quantum circuits. Our new, schematic definition is quite simple and intuitive and, more importantly, it avoids the cumbersome introduction of the well-formedness condition imposed on a quantum Turing machine model as well as of the uniformity condition necessary for a quantum circuit model. Our new approach can further open a door to the descriptional complexity of quantum functions, to the theory of higher-type quantum functionals, to the development of new first-order theories for quantum computing, and to the designing of programming languages for real-life quantum computer
Cinemacraft: exploring fidelity cues in collaborative virtual world interactions

The research presented in this paper explores the contribution of avatar fidelity to social interaction in virtual environments and how sensory fusion can improve these interactions. Specifically, we vary levels of interaction fidelity to investigate how responsiveness and behavioural realism affect people’s experience of interacting with virtual humans. This is accomplished through the creation of Cinemacraft, a technology-mediated immersive platform for collaborative human–computer interaction. Cinemacraft leverages a voxel game engine similar to Minecraft to facilitate collaborative interaction in a virtual 3D world and incorporates sensory fusion to improve the fidelity of real-time collaboration. The primary hypothesis of the study is that embodied interactions result in a higher degree of presence, and that sensory fusion can improve the quality of presence and co-presence. We tested our hypothesis through a user-study of 24 participants. Based on suggestions from existing literature, we sidestep the uncanny valley effect through the use of low fidelity avatars (a la Minecraft) and identify cues that impact users ratings of presence, co-presence and successful collaboration. The findings and ensuing data in this research can be applied to produce a more compelling platform for live collaborative interactions, performances, and empathetic storytelling. This research contributes to the field of immersive, collaborative interaction by making transparent the platform, methodology, instruments and code accessible for team members with less technological expertise, as well as developers aspiring to use interactive 3D media to promote further experimentation and conceptual discussions.
ZOOpt: a toolbox for derivative-free optimization

Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt (Zeroth Order Optimization) toolbox that provides efficient derivative-free solvers and is designed easy to use. ZOOpt provides single-machine parallel optimization on the basis of python core and multi-machine distributed optimization for time-consuming tasks by incorporating with the Ray framework -- a famous platform for building distributed applications. ZOOpt particularly focuses on optimization problems in machine learning, addressing high-dimensional and noisy problems such as hyper-parameter tuning and direct policy search. The toolbox is maintained toward a ready-to-use tool in real-world machine learning tasks.
Grammar-Based Compression of Unranked Trees

We introduce forest straight-line programs (FSLPs for short) as a compressed representation of unranked ordered node-labelled trees. FSLPs are based on the operations of forest algebra and generalize tree straight-line programs. We compare the succinctness of FSLPs with two other compression schemes for unranked trees: top dags and tree straight-line programs of first-child/next sibling encodings. Efficient translations between these formalisms are provided. Finally, we show that equality of unranked trees in the setting where certain symbols are associative and/or commutative can be tested in polynomial time. This generalizes previous results for testing isomorphism of compressed unordered ranked trees. An extended abstract of this paper appeared in Gascóon et al. (2018).
On multiplicative decompositions of polynomial sequences, III

In two earlier papers we studied the multiplicative decomposability of polynomial sequences $\{f(x):x\in \mathbb Z ,f(x) \gt 0\}$. Here we extend this problem by considering also sequences which can be obtained from sequences of this type by changing “not
A Coloring Algorithm for Disambiguating Graph and Map Drawings

Drawings of non-planar graphs always result in edge crossings. When there are many edges crossing at small angles, it is often difficult to follow these edges, because of the multiple visual paths resulted from the crossings that slow down eye movements. In this paper we propose an algorithm that disambiguates the edges with automatic selection of distinctive colors. Our proposed algorithm computes a near optimal color assignment of a dual collision graph, using a novel branch-and-bound procedure applied to a space decomposition of the color gamut. We give examples demonstrating this approach in real world graphs and maps, as well as a user study to establish its effectiveness and limitations.
Data Mining: Clustering

This article presents a broad overview of the main clustering methodologies. It is accomplished by introducing the clustering problem and the key elements characterizing it. In particular, we describe different distance and similarity measures which can be used in a clustering method. Then, we introduce a categorization of the clustering methods and describe some relevant algorithms belonging to each category. In order to contextualize the presented methods, we provide a section reporting some relevant application cases of the clustering algorithms in different domains. Finally, we discuss about measures and criteria which have been commonly adopted for clustering evaluation.
Applying Exponential Family Distribution to Generalized Extreme Learning Machine

The learning algorithm of an extreme learning machine (ELM) has two fundamental steps: 1) random nonlinear feature transformation and 2) least squares learning. Since the probabilistic interpretation for a sample by the least squares method follows a Gaussian distribution, there are two limitations in ELM caused by the second step: 1) it may be inaccurate to handle binary classification problems, since the output of a binary dataset has a distribution far from Gaussian and 2) it may have difficulties in dealing with nontraditional data types (such as count data, ordinal data, etc.), which also do not follow Gaussian distribution. In order to solve the above-mentioned problems, this paper proposes a generalized ELM (GELM) framework by applying the exponential family distribution (EFD) to the output layer node of ELM. It simplifies the design of ELM models for task-specific output domains with different data types. We propose a unified learning paradigm for all the models under this GELM framework with different distributions in EFD, and prove that traditional ELM is a special instance of GELM by setting the output distribution as a Gaussian distribution (GELM-Gaussian). We also prove that the training of GELM-Gaussian can be finished in one iteration, in this case, GELM-Gaussian does not slow down the training speed of traditional ELM. Besides, we propose the kernel version of GELM, which can also be concretized to different models by applying different EFDs. Experimental comparisons demonstrate that GELM can give more accurate probabilistic interpretation to binary classification and GELM has a great potential in dealing with a broader range of machine learning tasks.
Consistent multiple graph matching with multi-layer random walks synchronization

We address the correspondence search problem among multiple graphs with complex properties while considering the matching consistency. We describe each pair of graphs by combining multiple attributes, then jointly match them in a unified framework. The main contribution of this paper is twofold. First, we formulate the global correspondence search problem of multi-attributed graphs by utilizing a set of multi-layer structures. The proposed formulation describes each pair of graphs as a multi-layer structure, and jointly considers whole matching pairs. Second, we propose a robust multiple graph matching method based on the multi-layer random walks framework. The proposed framework synchronizes movements of random walkers, and leads them to consistent matching candidates. In our extensive experiments, the proposed method exhibits robust and accurate performance over the state-of-the-art multiple graph matching algorithms.
Bound and Conquer: Improving Triangulation by Enforcing Consistency

We study the accuracy of triangulation in multi-camera systems with respect to the number of cameras. We show that, under certain conditions, the optimal achievable reconstruction error decays quadratically as more cameras are added to the system. Furthermore, we analyze the error decay-rate of major state-of-the-art algorithms with respect to the number of cameras. To this end, we introduce the notion of consistency for triangulation, and show that consistent reconstruction algorithms achieve the optimal quadratic decay, which is asymptotically faster than some other methods. Finally, we present simulations results supporting our findings. Our simulations have been implemented in MATLAB and the resulting code is available in the supplementary material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPAMI.2019.2939530.
Efficient Oblivious Data Structures for Database Services on the Cloud

Database-as-a-service (DBaaS) allows the client to store and manage structured data on the cloud remotely. Despite its merits, DBaaS also brings significant privacy issues. Existing encryption techniques (e.g., SQL-aware encryption) can mitigate privacy concerns, but they still leak information through access patterns, which are vulnerable to statistical inference attacks. Oblivious Random Access Machine (ORAM) can seal such leakages; however, the recent studies showed significant challenges on the integration of ORAM into databases. That is, the direct usage of ORAM on databases is not only costly but also permits very limited query functionalities. In this paper, we propose new oblivious data structures called Oblivious Matrix Structure (OMAT), which allow tree-based ORAM to be integrated into database systems in a more efficient manner with diverse query functionalities supported. OMAT provides special ORAM packaging strategies for table structures, which not only offers a significantly better performance but also enables a broad range of query types that may not be efficient in existing frameworks. On the other hand, OTREE allows oblivious conditional queries to be performed on tree-indexed databases more efficiently than existing techniques. We implemented our proposed techniques and evaluated their performance on a real cloud database with various metrics, compared with state-of-the-art counterparts.
Adaptive neighbourhood for locally and globally tuned biogeography based optimization algorithm

Biogeography Based Optimization (BBO) is a population based metaheuristic algorithm using the idea of migration and mutation operation of species for solving complex optimization problems. BBO has demonstrated good performance on various unconstrained and constrained benchmark functions. It has also been applied to real world optimization problems of type linear or nonlinear, nominal or ordinal as well as mixed variables. But, it is realized that adaptation of the intensification and diversification for solving complex optimization problems are challenging tasks. To cope with these challenges, we develop a novel migration model for BBO which inherits features of the nearest neighbour of the local best individual to be migrated along with a global best individual of the pool. Furthermore to select the local best individual for the habitat to be migrated an adaptive local topological structure has been used. We name it as "Adaptive Neighbourhood for Locally and Globally Tuned Biogeography Based Optimization algorithm (ANLGBBO)". This maintains the balance between intensification and diversification i.e., improve solution by exploiting the accumulated search space and exploring the large space by identifying regions with high quality solutions. We have carried out an extensive numerical evaluation and comparisons for experimental tests using twenty benchmark functions with different features to measure the efficiency of the algorithm. The experimental study confirms ANLGBBO draws clear line of other variants of BBO algorithms in terms of population diversity and establish the accuracy of global optimal solution.
A Secure Exception Mode for Fault-Attack-Resistant Processing

Fault attacks are a known threat to secure embedded implementations. We propose a generic technique to detect and react to fault attacks on embedded software. The countermeasure combines a micro-architecture extension in hardware with a secure trap in software. The combined extension leads to a secure exception mode to handle fault attacks. The microprocessor hardware uses a low-level hardware checkpointing mechanism to recover from fault injection. A high-level secure trap in software then enables an application-specific response. The trap is user-defined and can be co-developed with the application. The combination of hardware fault detection and recovery, with a high-level fault response policy in software leads to significantly lower overhead when compared to traditional redundancy-based techniques in hardware or software. We demonstrate a prototype implementation of the proposed secure exception mode. The prototype is based on a modified LEON3 processor and it is able to detect and respond to setup-time violation attacks. We have realized the design in a 180 nm standard cell ASIC with integrated memory. Using several driver application examples, we characterize the software and hardware overhead of the proposed solution, and we compare it to the conventional redundancy-based solutions. In our understanding this is the first proof-in-silicon processor to offer a comprehensive secure exception mode against fault-injection attacks.
Verify heaps via unified model checking

This paper addresses the problem of verifying heap evolution properties of pointer programs. To this end, a new unified model checking approach with MSVL (Modeling, Simulation and Verification Language) and PPTLSL is presented. The former is an executable subset of PTL (Projection Temporal Logic) while the latter is an extension of PPTL (Propositional Projection Temporal Logic) with separation logic. MSVL is used to model pointer programs, and PPTLSL to specify heap evolution properties. Technically, on one hand, models of MSVL programs are characterized by Normal Form Graphs (NFGs). On the other hand, PPTLSL is equisatisfiably reduced to its subset which can reuse the decision procedure of PPTL. Our technique is able to deal with a variety of pointer structures such as linked lists and composite structures. In addition, we implement a prototype tool by using an SMT solver as the verification engine in order to demonstrate our approach.
Nonintrusive Smartphone User Verification Using Anonymized Multimodal Data

Smartphone user verification is important as personal daily activities are increasingly conducted on the phone and sensitive information is constantly logged. The commonly adopted user verification methods are typically active, i.e., they require a user's cooperative input of a security token to gain access permission. Though popular, these methods impose heavy burden to smartphone users to memorize, maintain, and input the token at a high frequency. To alleviate this imposition onto the users and to provide additional security, we propose a new nonintrusive and continuous mobile user verification framework that can reduce the frequency required for a user to input his/her security token. Using tailored Hidden Markov Models and sequential likelihood ratio test, our verification is built on low-cost, readily available, anonymized, and multimodal smartphone data without additional effort of data collection and risk of privacy leakage. With extensive evaluation, we achieve a high rate of about 94 percent for detecting illegitimate smartphone uses and a rate of 74 percent for confirming legitimate uses. In a practical setting, this can translate into 74 percent of frequency reduction of inputting a security token using an active authentication method with only about 6 percent risk of miss detection of a random intruder, which is highly desirable.
Mathematical Approaches for Medical Image Registration

Image registration is the process of overlaying images to be analyzed and it is a crucial step where everywhere information from several images has to be combined. They can be taken at different times, from different viewpoints, and by different sensors. In medical imaging, registration enables to combine data from multiple modalities such as CT, MR, SPECT, or PET to obtain complete information about the patient. It can help to monitor tumor growth, to facilitate treatment verification, to improve interventions, or to compare patient’s data to anatomical atlases. The importance of image registration methods grows with growing number of different types of data acquisition devices and their increasing availability. There are many registration methods and to be able to decide which method is the most appropriate for a given task and a dataset it is important to take into account issues inherent to medical imaging, related to data types, to an acquisition process as well as to required accuracy and sensitivity to errors. One universal method applicable under all conditions is unrealistic due to the variability of the image registration task. Main approaches together with their pros and cons will be introduced together with discussion about limitations and promising research directions.
Learning to Synthesize and Manipulate Natural Images

Humans are avid consumers of visual content. Every day, people watch videos, play games, and share photos on social media. However, there is an asymmetry-while everybody is able to consume visual data, only a chosen few are talented enough to express themselves visually. For the rest of us, most attempts at creating realistic visual content end up quickly "falling off" what we could consider to be natural images. In this thesis, we investigate several machine learning approaches for preserving visual realism while creating and manipulating photographs. We use these methods as training wheels for visual content creation. These methods not only help users easily synthesize realistic photos but also enable previously not possible visual effects.