Reviewerly/exHarmony huggingface dataset:
AniCode: authoring coded artifacts for network-free personalized animations

Time-based media are used in applications ranging from demonstrating the operation of home appliances to explaining new scientific discoveries. However, creating effective time-based media is challenging. We introduce a new framework for authoring and consuming time-based media. An author encodes an animation in a printed code and affixes the code to an object. A consumer captures an image of the object through a mobile application, and the image together with the code is used to generate a video on their local device. Our system is designed to be low cost and easy to use. By not requiring an Internet connection to deliver the animation, the framework enhances privacy of the communication. By requiring the user to have a direct line-of-sight view of the object, the framework provides personalized animations that only decode in the intended context. Animation schemes in the system include 2D and 3D geometric transformations, color transformation, and annotation. We demonstrate the new framework with sample applications from a wide range of domains. We evaluate the ease of use and effectiveness of our system with a user study.
Mining co-location patterns from distributed spatial data

Co-location patterns in spatial dataset are the interesting collection of dissimilar objects which are located in proximity. We keep similar objects in an entity set and maintain that no two objects in a co-location pattern belong to an entity set. Location proximity is based on Euclidean distance measure. However, algorithms for mining patterns in transactional datasets are not directly applicable to spatial datasets for mining co-location patterns. Conventional methods are not applicable to distributed temporal data and many applications generating spatial dataset are inherently distributive in nature. In this paper, a Map-Reduce based approach is proposed to find all co-location patterns from a spatial dataset distributed over nodes. This approach is modularized one and consists of four algorithms. With the first three algorithms in the first approach and by proposing an algorithm for dynamic datasets, this paper contains another approach for the co-location patterns set, that also updates in an incremental manner (not from scratch) whenever certain changes occur in the dataset. Experimental results on larger datasets are also presented.
Comparative study on the performance of neural networks for prediction in assisting visual servoing

This work compares the performance of three different models of neural networks in predicting the intermediary pose of a robot end effector for visual servoing tasks. Robotic applications in complicated and complex workspaces benefit from the use of non-touching sensor technology like vision. Visua l feedback control of a two camera robotic system combines the advantages of global and local visibilities of a fixed camera guiding the robot and end effector camera achieving convergence for pick and place tasks. Neural networks replace the control law for the visual guidance for targets initially not in the field of view of the eye-in-hand camera. Visual features collected by the eye-to-hand camera and the robot pose form input for the three types of networks, Multilayer Perceptron Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Elman Neural Network (ENN). The robot moves to the predicted pose, favorable for switching to Image Based Visual Servoing (IBVS) limiting the number of discrete events. Simulation studies and experimentation with an ABB make robot are performed for drawing conclusions regarding the network performance.
On the consistency of penalized MLEs for Erlang mixtures

In Yin and Lin (2016), a new penalty, termed as iSCAD penalty, is proposed to obtain the maximum likelihood estimates (MLEs) of the weights and the common scale parameter of an Erlang mixture model. In that paper, it is shown through simulation studies and a real data application that the penalty provides an efficient way to determine the MLEs and the order of the mixture. In this paper, we provide a theoretical justification and show that the penalized maximum likelihood estimators of the weights and the scale parameter as well as the order of mixture are all consistent.
Analysis of Speeches in Indian Parliamentary Debates

With the increasing usage of internet, more and more data is being digitized including parliamentary debates but they are in an unstructured format. There is a need to convert them into structured format for linguistic analysis. Much work has been done on parliamentary data such as Hansard, American congressional floor-debate data on various aspects but less on pragmatics. In this paper, we provide a dataset for synopsis of Indian parliamentary debates and perform stance classification of speeches i.e identifying if the speaker is supporting the bill/issue or is against it. We also analyze the intention of the speeches beyond mere sentences i.e pragmatics in the parliament. Based on thorough manual analysis of the debates, we developed an annotation scheme of 4 mutually exclusive categories to analyze the purpose of the speeches: to find out ISSUES , to BLAME, to APPRECIATE and for CALL FOR ACTION. We have annotated the dataset provided, with these 4 categories and conducted preliminary experiments for automatic detection of the categories. Our automated classification approach gave us promising results.
A Hybrid Filtering Approach for an Improved Context-aware Recommender System

Background: The domain of context-aware recommender approaches has made a substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. Objective: There are generally three algorithms which can be used to include context and those are - pre-filter approach, post-filter approach and contextual modeling. Each of the algorithms has their own drawbacks if any single approach is chosen. The goal of this work is to identify and propose a new hybrid approach which can include contextual information to improve the current movie recommender systems. Method: Post evaluation of various patents related to recommender systems, the proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. Results: The performance of the proposed system is measured in terms of precision of the system and ranking of the recommended movies to the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to the user. Conclusion: With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach. The proposed system will be vital for movie ticketing brands for the promotional purposes and various online content providers to recommend the accurate movies to their users.
Do we need all the frequency components of a fringe signal to obtain position information in a vertical scanning wideband interferometer?

Using a wideband light source based vertical scanning interferometer, we can obtain the position information of an object from the peak value of the envelope of the interference fringe signal. For the reconstruction of the envelope of this signal, it is necessary to select its frequency components; however, the question that naturally arises is whether there is a need to select all the frequency components of the signal to determine the position of the envelope peak. Based on our proposed method and subsequent optical experiments, we find that the peak position of the envelope can be estimated using only the frequency pairs that have high signal-to-noise ratios. Thus, this observation can be applied to not only improve the measurement performance of wideband scanning interferometers, but also to enable more complex and accurate optical metrology.
A Novel and High-Performance Modular Square Scheme for Elliptic Curve Cryptography Over GF(${p}$ )

In this brief, we present a novel and high-performance modular squaring scheme with low complexity and a small hardware area for elliptic curve cryptography over GF(p). First, we develop a method to reduce half of partial products in a squaring operation by using the proposed same items merging and logic combination. Second, we propose the modular squaring scheme that can compress the partial products based on the method above and accomplish accumulation and reduction simultaneously. Third, we devise the implementation circuits for the proposed modular squaring scheme and then simplify the circuits by using the property of the prime number. Finally, we implement the circuits on different platform and the 0.13-μm CMOS ASIC implementation demonstrates that our design can perform a 256-bit modular squaring in 0.36-μs with 17 200 gates, which achieves a desirable balance between hardware resource and performance.
Half Difference Expansion Based Reversible Data Hiding Scheme for Medical Image Forensics

Background: Medical image authentication is an important area which attempts to establish ownership authentication and data authentication of medical images. Aims: In this paper, we propose a new reversible watermarking scheme based on a novel half difference expansion technique for medical image forensics. Methods: Conventional difference expansion based reversible watermarking scheme generates watermarked images with less visual quality, and the embedding rate was considerably less due to the high probability of overflow or underflow. In the proposed scheme, the quality of the watermarked image has been improved by modifying the traditional difference expansion based watermarking scheme, half of the difference between two pixels will be expanded during watermarking. The modification of pixels during watermarking is limited by expanding half of the pixel difference, which helps to obtain watermarked images with better visual quality and improved embedding rate due to less chance of overflow or underflow during watermarking. We also propose a tamper detection localization process to detect the tampered regions from the watermarked image. Results: Experimental study of the proposed scheme on the standard medical images from Osrix medical image data set shows that the proposed watermarking scheme outperforms the existing schemes in terms of visual quality of the watermarked image and embedding rate. Conclusion: The overhead related to location map and parity information need to be addressed in future works to improve the proposed scheme.
Correspondence edit distance to obtain a set of weighted means of graph correspondences

Given a pair of data structures, such as strings, trees, graphs or sets of points, several correspondences (also referred in literature as labellings, matchings or assignments) can be defined between their local parts. The Hamming distance has been largely used to define the dissimilarity of a pair of correspondences between two data structures. Although it has the advantage of being simple in computation, it does not consider the data structures themselves, which the correspondences relate to. In this paper, we extend the definitions of a recently presented distance between correspondences based on the concept of the edit distance, which we called Correspondence edit distance. Moreover, we present an algorithm to compute the set of weighted means between a pair of graph correspondences. Both the Correspondence edit distance and the computation of the set of weighted means are necessary for the calculation of a more representative prototype between a set of correspondences. In the validation section, we show how the use of the Correspondence edit distance increases the quality of the set of weighted means compared to using the Hamming distance.
Critical Infrastructure Protection and Cybersecurity

If vital services and products are harmed by an attack or subject to a natural disaster, our economy and daily lives are likely to be impaired. We all depend on critical infrastructure sectors such as water, food, energy, transportation, and communications. In addition, our modern society depends on a complex network of interdependencies among services and products. If one essential service or product is weakened, other services and products may be affected. This is known as the cascade effect. Cybersecurity is especially important because hackers have the advantage over civilized society and they have become a serious threat to our way of life. Cybersecurity is also vital to protect critical infrastructure because sectors are often connected and controlled remotely over the Internet. Government and private industry work together to protect critical infrastructure, transportation systems, and borders.
Robust information unscented particle filter based on M‐estimate

Herein, inspired by the unscented particle filter (UPF), an improved particle filter (PF) using an unscented information filter based on M-estimate (UIF-BM) to approximate the importance density function (IDF) is designed. The filtering method is proposed to obtain an accurate state estimation for a non-linear discrete time dynamical system with non-Gaussian system noises and observation noises contaminated by some Gaussian impulsive noises. The PF framework is used as a common method to handle non-Gaussian system noises. In order to obtain a precise IDF, UIF based on M-estimate is used for heavy-tailed observation noises. Meanwhile, instead of the robust Gaussian filter, the robust IF can avoid the numerical problem that zero weight functions cannot be incorporated into the framework. The simulation results indicate the estimation accuracy and efficiency of the proposed filter. Compared with the UIF-BM, PF, and UPF, the superiority of the proposed filter against the non-ideal system and observation noises is obvious.
A Systematic Study of the Impact of Graphical Models on Inference-Based Attacks on AES

Belief propagation, or the sum-product algorithm, is a powerful and well known method for inference on probabilistic graphical models, which has been proposed for the specific use in side channel analysis by Veyrat-Charvillon et al. [14]. We define a novel metric to capture the importance of variable nodes in factor graphs, we propose two improvements to the sum-product algorithm for the specific use case in side channel analysis, and we explicitly define and examine different ways of combining information from multiple side channel traces. With these new considerations we systematically investigate a number of graphical models that "naturally" follow from an implementation of AES. Our results are unexpected: neither a larger graph (i.e. more side channel information) nor more connectedness necessarily lead to significantly better attacks. In fact our results demonstrate that in practice the (on balance) best choice is to utilise an acyclic graph in an independent graph combination setting, which gives us provable convergence to the correct key distribution. We provide evidence using both extensive simulations and a final confirmatory analysis on real trace data.
Recognition of Colored Face, Based on an Improved Color Local Binary Pattern

In this paper, a novel feature extraction method based on an improved color local binary pattern (LBP) is proposed for color face recognition. Firstly, in a given neighborhood of every pixel, we choose some sampling points from three color channels simultaneously and the numbers of the sampling points from every channel may be different. Secondly, we use a new rule to select the threshold which does not always locate in the geometrical center of the given neighborhood. Thirdly, in order to excavate the potential of the proposed sampling method, we use the [Formula: see text]-uniform LBP to obtain the binary code of each pixel. In addition, we embed the Hamming distance into our method for improving the recognition rate of the proposed method. For evaluating the performance of our method, we implement the proposed method and several related methods on five public face databases: FERET, CMU-PIE, Georgia, FEI and Asian databases. Experimental results show that our method possesses higher recognition rates and lower computational cost than other related color face recognition methods.
Constraint programming heuristics for configuring optimal products in multi product lines

Nowadays, complex application domains require configuring multi-product lines where product features and constraints among them are specified in several variability models. These variability models are enriched with inter-model constraints representing the existing relations among domain concerns, and with non-functional properties modeled as attributes attached to product features. Multiple techniques use constraint programming to automate the cumbersome task of manually configuring a suitable product. Currently, there are some proposals to improve the performance of constraint solvers when configuring single-model product lines, however configuration scenarios with multiple interrelated and attributed models are not yet targeted. This paper proposes and evaluates three search heuristics used to configure optimal products regarding multi-objective criteria. Results are compared against the default search strategy of the Choco constraint solver. We evaluated the performance for configuring optimal products in four state-of-the-art product lines and 130 generated variability models representing multi-product lines that scale up to 6400 features and 960 constraints. As a result, we observe that the proposed heuristics perform better than the default solver strategy when the variability models scale in terms of features. In contrast, the default strategy and one of the proposed heuristics perform better as the number of interdependencies between variability models increases.
From ds-Bounds for Cyclic Codes to True Minimum Distance for Abelian Codes

In this paper, we develop a technique to extend any bound for the minimum distance of cyclic codes constructed from its defining sets (ds-bounds) to Abelian (or multivariate) codes through the notion of B-apparent distance. We also study conditions for an Abelian code to verify that its B-apparent distance reaches its (true) minimum distance. Then, we construct some codes as an application.
Dual‐scale weighted structural local sparse appearance model for object tracking

It is a great challenge to develop an effective appearance model for robust visual tracking due to various interfering factors, such as pose change, occlusion, background clutter etc. More and more visual tracking methods tend to exploit the local appearance model to deal with the above challenges. In this study, the authors present a simple yet effective weighted structural local sparse appearance model, which can better describe the target appearance information through patch‐based generative weight. To further improve the robustness of tracking, they implement this appearance model on two‐scale patches. The two derived appearance models are then combined to form a collaborative model to play their advantages. Extensive experiments on the tracking benchmark dataset show that the proposed method performs favourably against several state‐of‐the‐art methods.
Comparison of altered mineral information extracted from ETM+, ASTER and Hyperion data in Águas Claras iron ore, Brazil

Locations used to validate extraction results from remote sensing images are typically ground rock samples. However, remotely sensed image pixels are 30 m2 with mixing spectra. Rock samples cannot fully represent extraction results on an image. Here, alteration information associated with the Águas Claras, Brazil iron deposit was analysed using a Landsat Enhanced Thematic Mapper plus (ETM+), a Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion data, all with 30 m resolution. With improvements in spectral resolution, extracted results have changed from the alteration information to altered rocks and minerals. The accuracy of alteration information has improved continuously as data extracted from these sources verify one another. Minerals extracted using Hyperion corresponded to altered assemblages identified from ASTER. Both were consistent with alteration information extracted from ETM+ at the same location. Alteration information extracted from three remote images were located within ore formations, and controlled by the north-east trending faults and matched well with known deposits. Information extraction based on the integrated application of multi-source remote sensing data could compensate for deficiencies in using single data. These results can verify one another to improve data reliability particularly in the areas inaccessible and lacking sufficient field confirmation.
Ensembles of Nested Dichotomies with Multiple Subset Evaluation

A system of nested dichotomies (NDs) is a method of decomposing a multiclass problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Many methods have been proposed to perform this split, each with various advantages and disadvantages. In this paper, we present a simple, general method for improving the predictive performance of NDs produced by any subset selection techniques that employ randomness to construct the subsets. We provide a theoretical expectation for performance improvements, as well as empirical results showing that our method improves the root mean squared error of NDs, regardless of whether they are employed as an individual model or in an ensemble setting.
Survey of amplitude‐aided multi‐target tracking methods

An amplitude-aided multi-target tracking (MTT) exploits amplitude as well as spatial features for MTT. Compared to MTT using a spatial feature only, it can maintain its performance even in densely cluttered environment because more accurate association is achieved by using both features for likelihood evaluation between tracks and measurements. Therefore, the goal of this study is to review the state-of-the-art amplitude-aided MTT methods and compare each other extensively. For a fair comparison, a unified MTT framework is developed, and various methods are implemented and compared based on the same framework. On the challenging visual MTT datasets, the implemented methods are evaluated for several aspects such as accuracy, speed, sensitivity, and stability. Moreover, this study presents a summary of the extensive evaluation and guideline to select an appropriate method for amplitude-aided tracking.
Entanglement concentration for photon systems assisted with single photons

The entanglement concentration protocols (ECPs) are presented for concentrating arbitrary partially entangled Bell-like state, N-photon GHZ state and N-photon W state, respectively, and the corresponding maximally entangled state can be obtained finally with a certain probability. Besides, compared with other ECPs, the accurate coefficients of the initial partially entangled state in our ECPs do not need to know in advance. Moreover, current ECPs can be repeated to acquire a higher success probability. The devices used in the schemes are linear which leads to flexible operations and improves the performance greatly in the present experiment and our ECPs may be useful in the long-distance quantum information processing.