Reviewerly/exHarmony huggingface dataset:
Sustainabot - Exploring the Use of Everyday Foodstuffs as Output and Input for and with Emergent Users

Mainstream digital interactions are spread over a plethora of devices and form-factors, from mobiles to laptops; printouts to large screens. For emergent users, however, such abundance of choice is rarely accessible or affordable. In particular, viewing mobile content on a larger screen, or printing out copies, is often not available. In this paper we present Sustainabot - a small robot printer that uses everyday materials to print shapes and patterns from mobile phones. Sustainabot was proposed and developed by and with emergent users through a series of co-creation workshops. We begin by discussing this process, then detail the open-source mobile printer prototype. We carried out two evaluations of Sustainabot, the first focused on printing with materials in situ, and the second on understandability of its output. We present these results, and discuss opportunities and challenges for similar developments. We conclude by highlighting where and how similar devices could be used in future.
GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid

Given sensor readings over time from a power grid consisting of nodes (e.g. generators) and edges (e.g. power lines), how can we most accurately detect when an electrical component has failed? More challengingly, given a limited budget of sensors to place, how can we determine where to place them to have the highest chance of detecting such a failure? Maintaining the reliability of the electrical grid is a major challenge. An important part of achieving this is to place sensors in the grid, and use them to detect anomalies, in order to quickly respond to a problem. Our contributions are: (1) Online anomaly detection: we propose a novel, online anomaly detection algorithm that outperforms existing approaches. (2) Sensor placement: we construct an optimization objective for sensor placement, with the goal of maximizing the probability of detecting an anomaly. We show that this objective has the property of submodularity, which we exploit in our sensor placement algorithm. (3) Effectiveness: Our sensor placement algorithm is provably near-optimal, and both our algorithms outperform existing approaches in accuracy by $$59\%$$ or more (F-measure) in experiments. (4) Scalability: our algorithms scale linearly, and our detection algorithm is online, requiring bounded space and constant time per update. Code related to this paper is available at: https://github.com/bhooi/gridwatch .
BoolTest: The Fast Randomness Testing Strategy Based on Boolean Functions with Application to DES, 3-DES, MD5, MD6 and SHA-256

The output of modern cryptographic primitives like pseudorandom generators and block or stream ciphers is frequently required to be indistinguishable from a truly random data. The existence of any distinguisher provides a hint about the insufficient confusion and diffusion property of an analyzed function. In addition to targeted cryptoanalysis, statistical tests included in batteries such as NIST STS, Dieharder or TestU01 are frequently used to assess the indistinguishability property. However, the tests included in these batteries are either too simple to spot the common biases (like the Monobit test) or overly complex (like the Fourier Transform test) requiring an extensive amount of data. We propose a simple, yet surprisingly powerful method called BoolTest for the construction of distinguishers based on an exhaustive search for boolean function(s). The BoolTest typically constructs distinguisher with fewer input data required and directly identifies the function’s biased output bits. We analyze the performance on four input generation strategies: counter-based, low hamming weight, plaintext-ciphertext block combination and bit-flips to test strict avalanche criterion. The BoolTest detects bias and thus constructs distinguisher in a significantly higher number of rounds in the round-reduced versions of DES, 3-DES, MD5, MD6 and SHA-256 functions than the state-of-the-art batteries. Finally, we provide a precise interpretation of BoolTest verdict (provided in the form of Z-score) about the confidence of a distinguisher found. The BoolTest clear interpretation is a significant advantage over standard batteries consisting of multiple tests, where not only a statistical significance of a single test but also aggregated decision over multiple, potentially correlated tests, needs to be correctly performed.
Description Language of Educational Content Structure: Possibilities of Modern Mathematics

In modern conditions of intensive growth and differentiation of scientific knowledge, continuing reforms of educational systems, the pedagogical society is constantly facing the problem of justification of the content of education and its structuring. One of the tasks referring to this problem is a task of development of the description language of the educational content structure. Research objective consists of justification of the possibility of use of fractal geometry statements for the description of the educational content structure. As a main research method we have chosen a theoretic methodological analysis of scientific works concerning a development of an educational content theory and describing empirically stated defining peculiarities of its structure. Comparison of the description language of the educational content structure accepted in pedagogy and the description methods of structure of objects researched by the fractal geometry. The hypothesis about fractal nature of the educational content structure has been formed and proved immediately by means of the empiric material: the content structure of education and its “through” branches has a mosaic nature consisting of elements with different qualities (for example, mathematic, scientific and humanitarian disciplines or: basic, vocational and polytechnic education. The mosaic elements create a multistage system and are characterized by essentially divergent proportions. With the increasing of density or weight (with respect to “size”) of elements of bearers of some properties we can state that the element of a larger proportion consisting of smaller elements expresses mainly the given property (so it plays a key role). The general picture looks like a mosaic board consisting of elements made themselves as mosaic pictures. This procedure occurs again on several levels. This description presents properly an idea of a multifractal formed by means of overlapping fractals, because the iteration procedure of creation of geometrical fractals looks on every step as a mosaic picture. The article may be useful for educationists researching the questions concerning the educational content and its structure and also for practitioners selecting the training documentation while developing different levels of educational programs.
A multi-view object tracking using triplet model

Object tracking is very important in intelligent systems, such as video surveillance, automatic drive and traffic security. Background subtraction algorithm is a mature method for foreground object extraction, but it may be affected by complicated background or the change of object shape. So in this paper, a novel object tracking method is proposed using a triplet model. First, BING feature is used to find some potential object proposals. Then, we construct a triplet model for each potential object. The triplet of the same object between two consecutive frames is considered similar. Finally, object tracking can be achieved by computing feature difference of triplets. Experimental results show that our method can achieve object tracking effectively and in real time.
A study on a new criterion for minimum-energy perfect control in the state-space framework

In this article, an advanced study concerning the energy cost of the perfect control algorithm is provided. An application of different nonunique matrix inverses into perfect control law has resulted in remarkable influence on both control and state signals. Following the newly obtained issues, covering the minimum-energy behavior, a new related criterion is proposed here. Based on deterministic norm we can, in a simple way, estimate the crucial energy performance. Simulation examples made in MATLAB/Simulink environment show the high potential of a new approach considered in the article.
Semantic Nighttime Image Segmentation with Synthetic Stylized Data, Gradual Adaptation and Uncertainty-Aware Evaluation.

Most progress in semantic segmentation reports on daytime images taken under favorable illumination conditions. We instead address the problem of semantic segmentation of nighttime images and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night via labeled synthetic images and unlabeled real images, both for progressively darker times of day, which exploits cross-time-of-day correspondences for the real images to guide the inference of their labels; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, designed for adverse conditions and including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, which comprises 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 151 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark to perform our novel evaluation. Experiments show that our guided curriculum adaptation significantly outperforms state-of-the-art methods on real nighttime sets both for standard metrics and our uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals that selective invalidation of predictions can lead to better results on data with ambiguous content such as our nighttime benchmark and profit safety-oriented applications which involve invalid inputs.
Blockchain Based Data Trust Sharing Mechanism in the Supply Chain

Currently, the trust and privacy problem is an obstacle during data sharing process in supply chain. To solve it, this paper proposes a blockchain based data trust sharing mechanism in the supply chain. Firstly, we design the ar-chitecture to introduce the system framework, service process and data model for data trust sharing. Secondly, we implement the blockchain-based supply chain platform, consisting of account management module and data request processing module with open data index name extension (ODINE). At last, we state an use case to analyze this platform.
Self-Adaptation of Software Using Automatically Generated Control-Theoretical Solutions

Control theory has contributed a set of foundational techniques to handle “change” at runtime in software applications. These techniques however have fundamental limitations as well: (i) they require the development and understanding of mathematical models; (ii) synthesizing solutions is often done on a per-problem basis, discouraging flexibility and generality. Software engineering, as a discipline, has always aimed at finding reusable and modular solutions. The combination of the desire to apply formally grounded control-theoretical principles and reuse existing solutions has motivated research on the topic of automatically generated control solutions. This research aims at designing control strategies in an automated way from data that qualifies the given problem at hand. This chapter provides an overview of the research topic of automatically generated control-theoretical solutions, explaining the key research contributions and paving the way for future research.
Fusing Thermopile Infrared Sensor Data for Single Component Activity Recognition within a Smart Environment

To provide accurate activity recognition within a smart environment, visible spectrum cameras can be used as data capture devices in solution applications. Privacy, however, is a significant concern with regards to monitoring in a smart environment, particularly with visible spectrum cameras. Their use, therefore, may not be ideal. The need for accurate activity recognition is still required and so an unobtrusive approach is addressed in this research highlighting the use of a thermopile infrared sensor as the sole means of data collection. Image frames of the monitored scene are acquired from a thermopile infrared sensor that only highlights sources of heat, for example, a person. The recorded frames feature no discernable characteristics of people; hence privacy concerns can successfully be alleviated. To demonstrate how thermopile infrared sensors can be used for this task, an experiment was conducted to capture almost 600 thermal frames of a person performing four single component activities. The person’s position within a room, along with the action being performed, is used to appropriately predict the activity. The results demonstrated that high accuracy levels, 91.47%, for activity recognition can be obtained using only thermopile infrared sensors.
Image Super-Resolution Reconstruction Method Based on Global and Local Residual Learning

In the image super-resolution (SR) algorithm, some more effective methods are obtained by convolutional neural networks (CNN). However, at present, there are two main problems in the SR algorithm using CNN: First, due to the degradation of the image, it is easy to cause partial loss of image details in a very deep network. Second, although the depth network is very powerful, when performing efficient nonlinear mapping from low-resolution (LR) input images to high-resolution (HR) target images, a large number of parameters are required, which easily causes learning difficulties. Therefore, in this paper, combined global residual learning (GRL) and local residual learning (LRL), we propose a new method to effectively obtain image details. Especially, using the stacked local residual block (LRB) structure for nonlinear mapping the parameters of CNN can be reduced and the image degradation problem can be overcome effectively. Due to the high correlation between LR images input and HR images output in the network, we can use the skip-connection method to reconstruct of HR images from most of the LR information. The experimental results show that the proposed image SR algorithm is effective.
Document-based and term-based linear methods for pseudo-relevance feedback

Query expansion is a successful approach for improving Information Retrieval effectiveness. This work focuses on pseudo-relevance feedback (PRF) which provides an automatic method for expanding queries without explicit user feedback. These techniques perform an initial retrieval with the original query and select expansion terms from the top retrieved documents. We propose two linear methods for pseudo-relevance feedback, one document-based and another term-based, that models the PRF task as a matrix decomposition problem. These factorizations involve the computation of an inter-document or inter-term similarity matrix which is used for expanding the original query. These decompositions can be computed by solving a least squares regression problem with regularization and a non-negativity constraint. We evaluate our proposals on five collections against state-of-the-art baselines. We found that the term-based formulation provides high figures of MAP, nDCG and robustness index whereas the document-based formulation provides very cheap computation at the cost of a slight decrease in effectiveness.
Speed Up Bilateral Filtering via Sparse Approximation on a Learned Cosine Dictionary

The edge-preserving bilateral filter (BF) is a widely used smoothing tool in many applications. However, its brute-force implementation depends on the size of the box window. The shortcoming causes BF time-consuming for the image processing task with a large window. To make the computational complexity irrelevant to the window size, sparse approximations of the filtering kernels are calculated on a learned cosine dictionary by two steps. First, all possible frequencies are learned (estimated) from the filtering kernel to compose a cosine dictionary. Then, the sparse approximation is conducted on the learned dictionary to seek the optimal cosine approximation for both the range and spatial kernels. By making use of the one-dimensional cosine approximation for the range kernel, the BF is transformed into spatial convolutions. Subsequently, by employing the two-dimensional cosine approximation for the spatial kernel, spatial convolutions are decomposed into box filters of which the computational complexity is O(1). To the best of our knowledge, our approach is the first method that adaptively constructs a cosine dictionary according to the input kernel. This merit guarantees the best filtering accuracy and efficiency. These advantages are corroborated by several carefully designed experiments.
A 3D measurement method with accurate boundaries based on mutation feature detection of laser stripes

Laser scanning measurement plays an important role in detecting the parameters of parts in a workshop. However, the existing methods and measurement instruments are unable to accurately calculate the 3D coordinates of large-scale part boundaries in an on-site complex environment. This paper proposes a 3D measurement method with accurate boundaries based on the mutation feature detection of laser stripes. First, drawing on the type analysis of light stripe images in the target boundary, the corresponding ideal boundary models are built. Next, an adaptive classification mechanism is designed in order to achieve rapid recognition and classification of the mutation features of laser stripes. Then, a boundary endpoint laser stripe detection method based on mutation features is presented as a way to obtain sub-pixel boundary endpoint locations. Finally, simulations and lab experiments are conducted using a standard part to validate the accuracy of the proposed method. In the analysis of results between the measured data and the reference distance, the proposed method demonstrates a high measuring accuracy, with a root mean square of the relative error of 0.024%. This measuring accuracy satisfies the requirements of field measurement. In addition, a measurement experiment with a curved surface part is carried out, which further verifies the feasibility and effectiveness of the proposed method.
Active contour model based on variable exponent p-Laplace equation for image segmentation

In this paper, we propose a region-based active contour model for image segmentation. By combining the region fitting energy based on coefficient of variation with the variable exponent p-Laplace energy, the proposed method can perform well in segmenting complex images. The region fitting energy conducts the evolving curve to reach the boundaries of the objects, and the p-Laplace energy can handle the topological changes and extract the boundaries accurately. In order to eliminate the re-initialization step, an augmented Lagrangian method is employed to solve the optimization problem. The results of experiments on synthetic and real images demonstrate that our method can successfully segment complex object boundaries, and it is robust to noise and not sensitive to the initial position of contours.
Analysis of Mobile Environment for Ensuring Cyber-Security in IoT-Based Digital Forensics

The Internet of things (IoT) is the collection of smart objects, which it collects the natural phenomena from environment, senses the data and transmits them using machine to machine (M2M) communication. In most of the existing system, providing efficient security in IoT mobile environment is a major concern. The attackers can easily exploit the vulnerability, eavesdrop and masquerade the sensitive information in the network. Because of this, there is a need to provide efficient security analysis in IoT environment. In this work, the enhanced security mechanism is proposed which uses efficient digital forensics tools to identify the threats and we demonstrated how to reconstruct the past events with forensics tools in order to provide digital evidence for legal proceedings.
Method to determine which quantum operations can be realized with linear optics with a constructive implementation recipe

The evolution of quantum light through linear optical devices can be described by the scattering matrix $S$ of the system. For linear optical systems with $m$ possible modes, the evolution of $n$ input photons is given by a unitary matrix $U=\varphi_{m,M}(S)$ given by a known homomorphism, $\varphi_{m,M}$, which depends on the size of the resulting Hilbert space of the possible photon states, $M$. We present a method to decide whether a given unitary evolution $U$ for $n$ photons in $m$ modes can be achieved with linear optics or not and the inverse transformation $\varphi_{m,M}^{-1}$ when the transformation can be implemented. Together with previous results, the method can be used to find a simple optical system which implements any quantum operation within the reach of linear optics. The results come from studying the adjoint map bewtween the Lie algebras corresponding to the Lie groups of the relevant unitary matrices.
An association-based evolutionary ensemble method of variable selection

Variable selection is a vital problem in expert systems and in knowledge discovery. However, traditional variable selection methods achieve low accuracies on large and complex datasets because they do not consider the importance of variable combinations. A variable combination is a variable set in which the combined variables fit better than do other, redundant variables, but each of which, considered individually, may fit worse. Therefore, an association-based evolutionary ensemble variable selection method for multiple linear regression is proposed. Both greedy algorithms and stochastic algorithms are applied under an ensemble framework. Instead of ranking variables according to selection frequency, the associations among variables in local solutions are considered to identify important variable combinations. Experiments show that the proposed approach has a competitive performance for variable selection and outperforms some classic methods on large-scale datasets. The study is important for automated feature discovery for complex systems with multiple combined input variables.
Blockchain Technology for Luggage Tracking

Lost luggage is one of the main fears when boarding a plane, especially on long flights. Around 10,000 suitcases are lost every day at airports around the world. The proposed solution is to locate much faster and more efficiently the lost object. There are currently a multitude of players involved in this process, who could synchronize by sharing information, which would save airlines costs. This way, the customer and the airline can know where the luggage is at any time.