Reviewerly/exHarmony huggingface dataset:
Unsupervised deep quantization for object instance search

In this paper, we propose an unsupervised deep quantization (UDQ) method for object instance search. The UDQ utilizes product quantization to discover the underlying self-supervision information of the training data and iteratively exploits the self-supervision information to optimize features of the training data in an unsupervised fashion. The optimized features are further used to update the self-supervision information for the subsequent training procedure. We introduce two constraints, the separability constraint and the discriminability constraint, to encourage the features to satisfy a cluster structure which is essential for the effective supervision information generation with the product quantization. The UDQ is optimized with an iterative optimization strategy which guarantees that the features and the supervision information can be enhanced each other alternately in a unified model. Moreover, we develop three refinement strategies to refine features to obtain better supervision information for the model optimization. Experimental results on four datasets show the superiority of our UDQ over the state-of-the-art methods.
Toward the participatory human‐centred community an exploration of cyber‐physical public design for urban experience

With the development of information technology, designing urban experience faces an unprecedented challenge: the combination of cyber information and physical facilities. Cyber-Physical Social System (CPSS) forms the connection between virtual and physical objects, public and private, activeness and passiveness, leading an innovative way to explore human-centred design practice in the context of city and community. The challenges faced by urban communities and extend the design practice of SPSS in combination with public participation community design and Living Lab method. Through three practical cases and related evaluation feedbacks, this study presents a Cyber-Physical Public Design (CPPD) model of a human city. The CPPD model is constructed based on CPSS with the human-centred concept. By combining physical touch points with virtual information world, it is possible to collect data on residents’ lives and improve the city's public facilities and space design, rebuild the urban communities to better meet the needs of the public.
ARCalVR

With the development of ARKit and ARCore, mobile Augmented Reality (AR) applications have become popular. Our ARCalVR is a lightweight, open-source software environment to develop AR applications on Android devices, and it gives the programmer full control over the phone's resources. With ARCalVR, one can do 60fps marker-less AR on Android devices, including functionalities of more complex environment understanding, physical simulation, virtual object interaction and interaction between virtual objects and real environment.
Properties of syndrome distribution for blind reconstruction of cyclic codes

In the problem of blind reconstruction of channel codes, the receiver does not have the knowledge of the channel code used at the transmitter and the aim is to identify this unknown code from the received data. For the case of cyclic codes, typical blind reconstruction methods make use of some elementary properties of syndromes (remainders) of the received polynomials. The main aim of this paper is to provide a detailed analysis of the properties of the syndromes that could be useful to design more efficient blind reconstruction methods. Specifically, we prove that the syndrome distribution of the noise-free sequence can be either degenerate or uniform or restricted uniform. We also provide necessary and sufficient conditions for the syndrome distribution to be of a given type. For the noise-affected received sequence we identify additional structural properties exhibited by the syndrome distribution. Finally, we apply these results to analyze the performance of the existing blind reconstruction methods.
An Adaptive and Dynamic Dimensionality Reduction Method for Efficient Retrieval of Videos

In today’s multimedia age, Content Based Video Retrieval (CBVR) is a trending area and lot of research is being carried out in Video Surveillance, Big Data analysis and multimedia applications. Usage of multimedia data is becoming very common in day today life, content based video retrieval provides an effective mechanism for maintaining, managing and retrieving large number of videos efficiently as per user’s interest. The advancement in the technology is evolving and multimedia applications are gaining more importance so the performance of the CBVR system need to be high and accurate to fulfill user demands. The proposed paper focuses on surveying the different techniques for feature extraction and similarity computation for retrieving relevant videos. Feature extraction can be done using different techniques such as shot boundary detection, based on histogram, PCA Shift, Gist and SURF (Speeded up Robust Features) and Quadratic Equation are used for feature extraction and similarity computation. In CBVR technique the videos are retrieved from the large databases based on the given Input Query as an image, on processing this query the features from videos and the query image are extracted mainly the color, texture and shape. Once these features are extracted then a similarity between query and the videos is computed, ranking will be done based on the similarity score. The results interpret that SURF technique provides better results as compared to other techniques and the system has a retrieval performance of more than 70%.
Distributed Deterministic Asynchronous Algorithms in Time-Varying Graphs Through Dykstra Splitting

Consider a setting where each vertex of a graph has a function, and communications can only occur between vertices connected by an edge. We wish to minimize the sum of these functions. For the case when each function is the sum of a strongly convex quadratic and a convex function, we propose a distributed version of Dykstra's algorithm. The computations to optimize the dual objective function can run asynchronously without a global clock, and in a distributed manner without a central controller. Convergence to the primal minimizer is deterministic instead of being probabilistic, and is guaranteed as long as, in each cycle, the edges where two-way communications occur connect all vertices. We also look at an accelerated algorithm, and an algorithm for the case when the functions on the nodes are not strongly convex.
An Empirical Study: ELM in Face Matching

Face data happens everywhere and face matching/verification is the must, such as it helps track criminals; unlock your mobile phone; and pay your bill without credit cards (e.g. Apple Pay). More often, in real world, grayscale image data are used since the color images require more storage. Gray level faces can be studied through two different features: edges and texture since spatial properties could be preserved. Such features could be used to classify faces from one another. Instead of using distance-based feature matching concept, in this paper, a fast machine learning classifier, which we call extreme learning machine (ELM) is used, where we have taken several different activation functions, such as tanh, sigmoid, softlim, hardlim, gaussian, multiquadric and inv_multiquadric. In our tests, five different publicly available datasets, such as Caltech, AR, ColorFERET, IndianFaces and ORL are used. For all activation functions, we have tested with and without feature selection techniques, and compared with the state-of-the-art results.
Overlap-add Windows with Maximum Energy Concentration for Speech and Audio Processing

Processing of speech and audio signals with time-frequency representations require windowing methods which allow perfect reconstruction of the original signal and where processing artifacts have a predictable behavior. The most common approach for this purpose is overlap-add windowing, where signal segments are windowed before and after processing. Commonly used windows include the half-sine and a Kaiser-Bessel derived window. The latter is an approximation of the discrete prolate spherical sequence, and thus a maximum energy concentration window, adapted for overlap-add. We demonstrate that performance can be improved by including the overlap-add structure as a constraint in optimization of the maximum energy concentration criteria. The same approach can be used to find further special cases such as optimal low-overlap windows. Our experiments demonstrate that the proposed windows provide notable improvements in terms of reduction in side-lobe magnitude.
Violator spaces vs closure spaces

The main goal of the paper is to make connections between two well-known, but, up to now, independently developed theories: the theory of violator spaces and the theory of closure spaces. Violator spaces were introduced by Matoušek et al. in 2008 as generalization of linear programming problems. The notion of closure arises in many disciplines, including topology, algebra, convexity analysis, logic etc. In this work, we investigate interrelations between violator spaces and closure spaces. We show that a violator mapping may be defined by a weak version of a closure operator. Interrelations between violator spaces and closure spaces give new insights on a number of well known findings. For example, we prove that violator spaces with a unique basis satisfy both the anti-exchange and the Krein–Milman properties. Finally, based on subsequent relaxations of the closure operator notion we introduce convex spaces as a generalization of violator spaces.
Next Generation Noise and Affine Invariant Video Watermarking Scheme Using Harris Feature Extraction

Digital watermarking always attracts the attention of researchers from early nineties to present scenario, in the ever changing world of digital creation, transmission and modification of information. In this paper we draw our attention towards a next generation of digital watermarking based on specific features of digital information using Harris corner detection (strongest) method. In this scheme first twenty strongest corner points obtained using Harris method are used as a reference for embedding watermark information and to retain the synchronization information after distortions. The scheme is time efficient as only twenty strongest corner points per frame are computed. Hence the proposed scheme is useful for time constrained applications. The experimental results confirms that the suggested scheme show a adequate level of resilience against the majority of image processing attacks together with affine transformations.
Distributed Join Algorithms on Multi-CPU Clusters with GPUDirect RDMA

In data management systems, query processing on GPUs or distributed clusters have proven to be an effective method for high efficiency. However, the high PCIe data transfer overhead between CPUs and GPUs, and the communication cost between nodes in distributed systems are usually bottleneck for improving system performance. Recently, GPUDirect RDMA has been developed and has received a lot of attention. It contains the features of the RDMA and GPUDirect technologies, which provides new opportunities for optimizing query processing.
Designing Technologies for Neurodiverse Users: Considerations from Research Practice

This paper presents and discusses the perspectives of ten investigators experienced with design of technologies for and with neurodiverse users. Although the advances on emerging technologies improved their potential to assist users with neurodiverse needs, existing methods for participatory design, usability tests and evaluation have been created for, and validated with, able-bodied users. User-centered design methods are not always well-suited to meet the unique needs of neurodiverse individuals. Therefore, to involve neurodiverse users iteratively in the design process, investigators need to adapt traditional methods from HCI to successfully conduct user studies. Through an online questionnaire, we identified the experimental designs commonly adopted and the major problems investigators face during recruitment, data collection, analysis and design. Based on the analysis of the investigators’ experiences, we provide nine recommendations to conduct studies with neurodiverse users, aiming at engaging them as active participants front and center in the research and design process.
Multi-scale mapping of oil-sands in Anhembi (Brazil) using imaging spectroscopy

In this work, oil-sand outcrops of the Anhembi deposit located in the Paraná Basin, Brazil, were investigated using multi-scale imaging spectroscopy. The study incorporated VNIR–SWIR (400–2500 nm) spectroscopic data from imaging (i.e. sisuCHEMA) and nonimaging (i.e. FieldSpec-4) instruments in the lab, an AisaFENIX hyperspectral system on the ground and from the air, and WorldView-3 multispectral instrument. The aim was to assess the usefulness of emerging remote sensing technologies in characterizing hydrocarbon-bearing targets and understand the spatial variability of oil-sands at different scales using multi-source spectroscopic data. The bitumen content of the sands, estimated to be as high as 12 wt. %, was revealed to be unevenly distributed at all scales. Its distribution was shown to be controlled mainly by the clay proportion and permo-porosity of the strata, with the sand sheet facies corresponding to the highest bitumen contents. The ubiquitous clays, identified to be dominantly montmorillonite, were found to be intimately mixed with bitumen at all studied scales. The mean bitumen content was estimated to decrease from ˜ 6 to 4.5% while moving from small-, to deposit-scale as a consequence of the pixel aggregation effect and incremental clay contribution. The study showed that bitumen determination on the ground requires an imaging system with a high signal-to-noise ratio and good illumination conditions. Spectral denoising is also a crucial prerequisite for the extraction of coherent spectral information from the data. WV-3 data was proved capable of resolving HC’s feature at 1700 nm wavelength by its SWIR band-4 over targets encompassing >30% of the 7.5 m SWIR pixel; albeit it was not successful in determining the total bitumen content of the sands. This work indicated that HC signatures, particularly the one centered at 2300 nm, is consistent and comparable among scales, and upon employing properly calibrated data, could be used to confidently map the bitumen content of oil-sands at all imaging scales. A multi-scale spectroscopic approach can provide a complete picture of the variations in geologic targets and is able to fill the gap in scale differences across scales that when integrated, would add synergy and help reduce the uncertainties associated with ore grade estimation.
Reconstructing the past: the case of the Spadina Expressway

In order to build resilient systems that can be operational for a long time, it is important that analysts are able to model the evolution of the requirements of that system. The Evolving Intentions framework models how stakeholders’ goals change over time. In this work, our aim is to validate applicability and effectiveness of this technique on a substantial case. In the absence of ground truth about future evolutions, we used historical data and rational reconstruction to understand how a project evolved in the past. Seeking a well-documented project with varying stakeholder intentions over a substantial period of time, we selected requirements of the Toronto Spadina Expressway. In this paper, we report on the experience and the results of modeling this project over different time periods, which enabled us to assess the modeling and reasoning capabilities of the approach, its support for asking and answering ‘what if’ questions, and the maturity of the underlying tool support. We also demonstrate a novel process for creating time-based models through the construction and merging of scenarios.
Brokering Policies and Execution Monitors for IoT Middleware

Event-based systems lie at the heart of many cloud-based Internet-of-Things (IoT) platforms. This combination of the Broker architectural style and the Publisher-Subscriber design pattern provides a way for smart devices to communicate and coordinate with one another. The present design of these cloud-based IoT frameworks lacks measures to (i) protect devices against malicious cloud disconnections, (ii) impose information flow control among communicating parties, and (iii) enforce coordination protocols in the presence of compromised devices. In this work, we propose to extend the modular event-based system architecture of Fiege et al., to incorporate brokering policies and execution monitors, in order to address the three protection challenges mentioned above. We formalized the operational semantics of our protection scheme, explored how the scheme can be used to enforce BLP-style information flow control and RBAC-style protection domains, implemented the proposal in an open-source MQTT broker, and evaluated the performance impact of the protection mechanisms.
Anomalous occupancy sensor behavior detection in connected indoor lighting systems

We consider the problem of classifying anomalous occupancy sensor behavior in connected indoor lighting systems. Anomalous occupancy sensor behavior may occur in the form of either a high number of false alarms (type-1 anomalies) or missed detection (type-2 anomalies). We consider a supervised machine learning approach to determine whether the detection signal of an occupancy sensor is normal, or exhibits type-1 or type-2 anomalies. We devise occupancy signal features in the time and frequency domains and employ a random forest classifier to perform 3-class classification. The proposed method is evaluated using motion sensor data from an office building, and is shown to have higher true positive rate and a lower false positive rate in comparison to an unsupervised k-means method and a random forest classifier with a single signal energy feature.
Rekeying-based Moving Target Defence Mechanism for Side-Channel Attacks

In this paper, we present a side-channel resilient moving target defence mechanism against power-/electromagnetic-based side-channel attacks. Recent countermeasures use fresh re-keying after every encryption/decryption process; this causes major overhead in synchronizing the communicating parties. In contrast to prior work, our mechanism integrates fresh re-keying and masking techniques at an interval, where these techniques are driven by the maximum number of side-channel leakage traces required towards a successful embedded attack. Hence, the mechanism tracks the effect of attacks on the number of traces, and consequently applies re-keying at suitable intervals to reduce the computational/communication overhead, while increasing the attack cost. The mechanism scalability was evaluated against an advanced attack model based on machine learning methods that reduces significantly the number of traces required for a successful attack under masking implementation.
Supporting Discussions About Forensic Bayesian Networks Using Argumentation

Bayesian networks (BNs) are powerful tools that are increasingly being used by forensic and legal experts to reason about the uncertain conclusions that can be inferred from the evidence in a case. Although in BN construction it is good practice to document the model itself, the importance of documenting design decisions has received little attention. Such decisions, including the (possibly conflicting) reasons behind them, are important for legal experts to understand and accept probabilistic models of cases. Moreover, when disagreements arise between domain experts involved in the construction of BNs, there are no systematic means to resolve such disagreements. Therefore, we propose an approach that allows domain experts to explicitly express and capture their reasons pro and con modelling decisions using argumentation, and that resolves their disagreements as much as possible. Our approach is based on a case study, in which the argumentation structure of an actual disagreement between two forensic BN experts is analysed.
Locomotion Solution for Stair Climbing Wheelchair with ER Fluid Based Control

Wheelchairs for people with disabilities are generally intended for movement on dedicated or arranged terrains. Stair climbing or uneven terrain is a real challenge. Current models use two methods to overcome this problem. The first method is to move with the mixed wheels/tracks. The tracks are used for movement on stairs or rough terrain and wheels in the rest of the cases. The second method uses multiple wheels to solve the staircase issue. In this paper the authors propose a wheelchair whose wheels have variable geometry. When the stairs or rough terrain type appears, the wheels change their geometry by adjusting to the ground. Practically the wheelchair wheel is made up of slices/spokes that can extend linearly and controlled, along the wheel's rays. Thus, the contour of the wheel can be modeled so that it becomes the negative of the ground, and the movement of the seat is made along some curves or linear trajectories. The actuator used for the wheel extension is a hybrid structure combining a spring-type actuator with a rheological hydraulic piston. For extension of wheel spokes the spring action is used. The retraction of the wheel elements is made by trough of the weight of the wheelchair. The control of the extension and retraction of the wheel spokes are made through by the control of a rheological piston. In the paper is also presented the control algorithm for movement to a known topometry field.
Incremental Mixture Importance Sampling With Shotgun Optimization

This article proposes a general optimization strategy, which combines results from different optimization or parameter estimation methods to overcome shortcomings of a single method. Shotgun optimization is developed as a framework which employs different optimization strategies, criteria, or conditional targets to enable wider likelihood exploration. The introduced shotgun optimization approach is embedded into an incremental mixture importance sampling algorithm to produce improved posterior samples for multimodal densities and creates robustness in cases where the likelihood and prior are in disagreement. Despite using different optimization approaches, the samples are combined into samples from a single target posterior. The diversity of the framework is demonstrated on parameter estimation from differential equation models employing diverse strategies including numerical solutions and approximations thereof. Additionally the approach is demonstrated on mixtures of discrete and continuous parameters and is shown to ease estimation from synthetic likelihood models. R code of the implemented examples can be found at https://github.com/BiljanaJSJ/IMIS-ShOpt. Supplementary materials for this article are available online.