Reviewerly/exHarmony huggingface dataset:
Security Approach for In-Vehicle Networking Using Blockchain Technology

Security is nonnegotiable key point for in-vehicle networking. However, all communication between Electrical Control Unites (ECU) still suffer from security drawbacks like highly processing time or preserving confidentiality, integrity and authenticity. In this paper, we propose an approach to assess the feasibility of a private Blockchain technology to overcome the aforementioned drawbacks. In this approach, we consider in-vehicle networking contains two parts, namely, central (or connected) gateway (cGW) and switches. cGW and switches are Blockchain nodes, wherein Blockchain consensus protocols are what keep all the nodes on a network synchronized with each other. The approach considers any communication type between ECUs as an individual event, which can be a transaction, data entry or application execution. A use case of secure communication between two ECUs is presented as an evaluation mechanism for securing in-vehicle networking using the proposed Blockchain approach.
Strong Typing, Swarm Enhancement, and Deep Learning Feature Selection in the Pursuit of Symbolic Regression-Classification

Symbolic Classification (SC), an offshoot of Genetic Programming (GP), can play an important role in any well rounded predictive analytics tool kit, especially because of its so called “WhiteBox” properties. Recently, algorithms were developed to push SC to the level of basic classification accuracy competitive with existing commercially available classification tools, including the introduction of GP assisted Linear Discriminant Analysis (LDA). In this paper we add a number of important enhancements to our basic SC system and demonstrate their accuracy improvements on a set of theoretical problems and on a banking industry problem. We enhance GP assisted linear discriminant analysis with a modified version of Platt’s Sequential Minimal Optimization algorithm which we call (MSMO), and with swarm optimization techniques. We add a user-defined typing system, and we add deep learning feature selection to our basic SC system. This extended algorithm (LDA++) is highly competitive with the best commercially available M-Class classification techniques on both a set of theoretical problems and on a real world banking industry problem. This new LDA++ algorithm moves genetic programming classification solidly into the top rank of commercially available classification tools.
Lithologic classification using multilevel spectral characteristics

Geological maps are commonly used to investigate the distribution of geological natural resources, such as minerals. However, the existing 1:200,000 geological map created in the 1990s for Guangxi, China, cannot support efficient investigation and interpretation of the geological surface changes. Therefore, we propose the application of remotely sensed multispectral imagery to update the existing 1:200,000 geological map at a scale of 1:100,000. To this end, the analysis of the spectral characteristics of six types of lithologies from the USGS spectral library and from actual measurements by Field Spec®4, ASD Inc. is conducted first. With the analyzed results of the spectral characteristics, the carbonate rock is separated from the other rocks using band ratios, and then the study area is separated carbonate and noncarbonate areas. In the noncarbonate area, five types of rocks, named, shale, marble, sandstone, granite, and basalt, are classified using supervised classification, in which the training data sets are from the 1:200,000 geographic map. The field verification of the classified results shows that a classification accuracy of 66% is reached, which meets the accuracy requirement for the creation of 1:100,000 geological maps on that basis of the standard formulated by China Geological Map Remote Sensing Interpretation technology. The 1:100,000 geological map created will be delivered to the Guangxi Geological Bureau, China, for applications by the geological and remote sensing communities.
Preserving activations in recurrent neural networks based on surprisal

Learning hierarchical abstractions from sequences is a challenging and open problem for recurrent neural networks (RNNs). This is mainly due to the difficulty of detecting features that span over long time distances with also different frequencies. In this paper, we address this challenge by introducing surprisal-based activation, a novel method to preserve activations and skip updates depending on encoding-based information content. The preserved activations can be considered as temporal shortcuts with perfect memory. We present a preliminary analysis by evaluating surprisal-based activation on language modeling with the Penn Treebank corpus and find that it can improve performance when compared to baseline RNNs and Long Short-Term Memory (LSTM) networks.
New Hybrid Method for Isogeny-Based Cryptosystems Using Edwards Curves

Along with the resistance against quantum computers, isogeny-based cryptography offers attractive cryptosystems due to small key sizes and compatibility with the current elliptic curve primitives. While the state-of-the-art implementation uses Montgomery curves, which facilitates efficient elliptic curve arithmetic and isogeny computations, other forms of elliptic curves can be used to produce an efficient result. In this paper, we present the new hybrid method for isogeny-based cryptosystem using Edwards curves. Unlike the previous hybrid methods, we exploit Edwards curves for recovering the curve coefficients and Montgomery curves for other operations. To this end, we first carefully examine and compare the computational cost of Montgomery and Edwards isogenies. Then, we fine-tune and tailor Edwards isogenies in order to blend with Montgomery isogenies efficiently. Additionally, we present the implementation results of Supersingular Isogeny Diffie–Hellman (SIDH) key exchange using the proposed method. We demonstrate that our method outperforms the previously proposed hybrid method, and is as fast as Montgomery-only implementation. Our results show that proper use of Edwards curves for isogeny-based cryptosystem can be quite practical.
Understanding Music Interaction, and Why It Matters

This is the introductory chapter of a book dedicated to new research in, and emerging new understandings of, music and human-computer interaction—known for short as music interaction. Music interaction research plays a key role in innovative approaches to diverse musical activities, including performance, composition, education, analysis, production and collaborative music making. Music interaction is pivotal in new research directions in a range of activities, including audience participation, interaction between music and dancers, tools for algorithmic music, music video games, audio games, turntablism and live coding. More generally, music provides a powerful source of challenges and new ideas for human-computer interaction (HCI). This introductory chapter reviews the relationship between music and human-computer interaction and outlines research themes and issues that emerge from the collected work of researchers and practitioners in this book.
Information-Theoretic Feature Selection Using High-Order Interactions

Feature selection is one of the major challenges in machine learning. In this paper, we focus on mutual information based methods, which attracted a significant attention in recent years. A clear limitation of the most existing methods is that they usually take into account only low-order interactions between features (up to 3rd order). We propose a novel criterion which takes into account both 3-way and 4-way interactions and can be naturally extended to the case of higher order terms. The basic component of our criterion is interaction information which is a measure of interaction strength derived from information theory. We show that our method is able to find interactions which remain undetected when using standard methods. We prove some theoretical properties of the introduced criterion and interaction information.
Frustratingly Easy Trade-off Optimization Between Single-Stage and Two-Stage Deep Object Detectors

There are mainly two types of state-of-the-art object detectors. On one hand, we have two-stage detectors, such as Faster R-CNN (Region-based Convolutional Neural Networks) or Mask R-CNN, that (i) use a Region Proposal Network to generate regions of interests in the first stage and (ii) send the region proposals down the pipeline for object classification and bounding-box regression. Such models reach the highest accuracy rates, but are typically slower. On the other hand, we have single-stage detectors, such as YOLO (You Only Look Once) and SSD (Singe Shot MultiBox Detector), that treat object detection as a simple regression problem, by taking an input image and learning the class probabilities and bounding box coordinates. Such models reach lower accuracy rates, but are much faster than two-stage object detectors. In this paper, we propose and evaluate four simple and straightforward approaches to achieve an optimal trade-off between accuracy and speed in object detection. All the approaches are based on separating the test images in two batches, an easy batch that is fed to a faster single-stage detector and a difficult batch that is fed to a more accurate two-stage detector. The difference between the four approaches is the criterion used for splitting the images in two batches. The criteria are the image difficulty score (easier images go into the easy batch), the number of detected objects (images with less objects go into the easy batch), the average size of the detected objects (images with bigger objects go into the easy batch), and the number of detected objects divided by their average size (images with less and bigger objects go into the easy batch). The first approach is based on an image difficulty predictor, while the other three approaches employ a faster single-stage detector to determine the approximate number of objects and their sizes. Our experiments on PASCAL VOC 2007 show that using image difficulty compares favorably to a random split of the images. However, splitting the images based on the number objects divided by their size, an approach that is frustratingly easy to implement, produces even better results. Remarkably, it shortens the processing time nearly by half, while reducing the mean Average Precision of Faster R-CNN by only $$0.5\%$$ .
Ethical and Socially-Aware Data Labels

Many software systems today make use of large amount of personal data to make recommendations or decisions that affect our daily lives. These software systems generally operate without guarantees of non-discriminatory practices, as instead often required to human decision-makers, and therefore are attracting increasing scrutiny. Our research is focused on the specific problem of biased software-based decisions caused from biased input data. In this regard, we propose a data labeling framework based on the identification of measurable data characteristics that could lead to downstream discriminating effects. We test the proposed framework on a real dataset, which allowed us to detect risks of discrimination for the case of population groups.
Tumour parcellation and quantification (TuPaQ): a tool for refining biomarker analysis through rapid and automated segmentation of tumour epithelium

Immunohistochemistry (IHC) is an essential component of biomarker research in cancer. Automated biomarker quantification is hampered by the failure of computational algorithms to discriminate 'negative' tumour cells from 'negative' stromal cells. We sought to develop an algorithm for segmentation of tumour epithelium in colorectal cancer (CRC), irrespective of the biomarker expression in the cells.We developed tumour parcellation and quantification (TuPaQ) to segment tumour epithelium and parcellate sections into 'epithelium' and 'non-epithelium'. TuPaQ comprises image pre-processing, extraction of regions of interest (ROIs) and quantification of tumour epithelium (total area occupied by epithelium and number of nuclei in the occupied area). A total of 286 TMA cores from CRC were manually annotated and analysed using the commercial halo software to provide ground truth. The performance of TuPaQ was evaluated against the ground truth using a variety of metrics. The image size of each core was 7000 × 7000 pixels and each core was analysed in a matter of seconds. Pixel × pixel analysis showed a sensitivity of 84% and specificity of 95% in detecting epithelium. The mean tumour area obtained by TuPaQ was very close to the area quantified after manual annotation (r = 0.956, P < 0.001). Moreover, quantification of tumour nuclei by TuPaQ correlated very strongly with that of halo (r = 0.891, P < 0.001).TuPaQ is a very rapid and accurate method of separating the epithelial and stromal compartments of colorectal tumours. This will allow more accurate and objective analysis of immunohistochemistry.
Theoretical learning guarantees applied to acoustic modeling

In low-resource scenarios, for example, small datasets or a lack in computational resources available, state-of-the-art deep learning methods for speech recognition have been known to fail. It is possible to achieve more robust models if care is taken to ensure the learning guarantees provided by the statistical learning theory. This work presents a shallow and hybrid approach using a convolutional neural network feature extractor fed into a hierarchical tree of support vector machines for classification. Here, we show that gross errors present even in state-of-the-art systems can be avoided and that an accurate acoustic model can be built in a hierarchical fashion. Furthermore, we present proof that our algorithm does adhere to the learning guarantees provided by the statistical learning theory. The acoustic model produced in this work outperforms traditional hidden Markov models, and the hierarchical support vector machine tree outperforms a multi-class multilayer perceptron classifier using the same features. More importantly, we isolate the performance of the acoustic model and provide results on both the frame and phoneme level, considering the true robustness of the model. We show that even with a small amount of data, accurate and robust recognition rates can be obtained.
Fostering Social Interaction in Playful Cities

This paper describes different types of activities/challenges designed for social interaction, while discussing the performance of such challenges using the mobile digital game “Secrets of the South” ( http://secretsofthesouth.tbm.tudelft.nl/ , Secrets of the South). The game was played as part of a scientific meeting, with participants from 25 to 62 years of age and a varying degree of cultural differences. The presentation and discussion of the results of the gameplay provide insights on the appropriateness of the different challenges for social interaction in a playful city. Directions for future work for such challenge designs are presented.
Column Distances of Convolutional Codes Over &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;${\mathbb Z}_{p^r}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;

Maximum distance profile codes over finite nonbinary fields have been introduced and thoroughly studied in the last decade. These codes have the property that their column distances are maximal among all codes of the same rate and degree. In this paper, we aim at studying this fundamental concept in the context of convolutional codes over a finite ring. We extensively use the concept of p-encoder to establish the theoretical framework and derive several bounds on the column distances. In particular, a method for constructing (not necessarily free) maximum distance profile convolutional codes over Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> r is presented.
Building a Newborn Screening Information Management System from Theory to Practice

Information Management Systems are the central process management and communication hub for many newborn screening programs. In late 2014, Newborn Screening Ontario (NSO) undertook an end to end assessment of its information management needs which resulted in a project to develop a flexible IS Information Systems (IS) ecosystem and related process changes. This enabled NSO to better manage its current and future work-flows and communication needs. An idealized vision of a Screening Information Management System (SIMS) was developed that was refined into enterprise and functional architectures. This was followed by the development of technical specifications, user requirements and procurement. In undertaking a holistic full product lifecycle redesign approach, a number of change management challenges were faced by NSO across the entire program. Strong leadership support and full program engagement are key for overall project success. It is anticipated that improvements in program flexibility and the ability to innovate will outweigh the efforts and costs.
Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment

Manifold models of image features abound in computer vision. We present a novel approach that combines unsupervised computation of representative manifold-valued features, called labels, and the spatially regularized assignment of these labels to given manifold-valued data. Both processes evolve dynamically through two Riemannian gradient flows that are coupled. The representation of labels and assignment variables are kept separate, to enable the flexible application to various manifold data models. As a case study, we apply our approach to the unsupervised learning of covariance descriptors on the positive definite matrix manifold, through spatially regularized geometric assignment.
Weak algebraic information systems and a new equivalent category of DOM of domains

In this paper, concepts of generalized algebraic information system (in short, GA-inf) and weak algebraic information system (in short, wA-inf) are introduced. Relationships among several algebraicities of information systems are explored. Main results are: (1) A dcpo D is an algebraic domain iff D is represented by an algebraic information system (in short, A-inf), and iff D is represented by a GA-inf; (2) every A-inf is a GA-inf and a wA-inf; (3) every C-inf S ( D , B ) induced by a domain with a basis B is a wA-inf; (4) the category WAINF of wA-infs and approximable mappings is equivalent to the category DOM of domains and Scott continuous functions; (5) every wA-inf S satisfying Condition (MT) is a retract of w S (the information structure induced by S ) in category WAINF of wA-infs and approximable mappings. Particularly, C-inf S ( D , B ) induced by a domain D with a basis B is a retract of A-inf w S ( D , B ) in category WAINF .
Profiling the publish/subscribe paradigm for automated analysis using colored Petri nets

UML sequence diagrams are used to graphically describe the message interactions between the objects participating in a certain scenario. Combined fragments extend the basic functionality of UML sequence diagrams with control structures, such as sequences, alternatives, iterations, or parallels. In this paper, we present a UML profile to annotate sequence diagrams with combined fragments to model timed Web services with distributed resources under the publish/subscribe paradigm. This profile is exploited to automatically obtain a representation of the system based on Colored Petri nets using a novel model-to-model (M2M) transformation. This M2M transformation has been specified using QVT and has been integrated in a new add-on extending a state-of-the-art UML modeling tool. Generated Petri nets can be immediately used in well-known Petri net software, such as CPN Tools, to analyze the system behavior. Hence, our model-to-model transformation tool allows for simulating the system and finding design errors in early stages of system development, which enables us to fix them at these early phases and thus potentially saving development costs.
The finitely axiomatizable complete theories of non-associative arrow frames

Arrow logic is a modal logic that is designed to talk about objects that can be illustrated as arrows. In this article, we consider the non-associative arrow logic NAL. We list all the finitely axiomatizable, complete and consistent theories of NAL. This gives an answer to the open problem, posed by I. Németi, addressing the atomicity of the free algebras of the class NA of non-associative relation algebras. We use the method of games as introduced to the fields of logic and algebra by R. Hirsch and I. Hodkinson. We also give a simple proof for the known fact that NAL is decidable through the finite model property.
Marketing model of promotion of remote education by modern university

Purpose The purpose of this paper is to substantiate the necessity of marketing of remote education by the example of leading universities of Rostov Oblast of modern Russia and to develop the marketing model of promotion of remote education by the modern university. Design/methodology/approach The authors use the method of economic statistics and regression and correlation analysis. Based on the data of the single information system of the Russian Federation in the sphere of purchases, the authors selected orders of the leading universities of Rostov Oblast for access to the internet for 2017, which are the indicator of their modernization on the basis of new information and communication technologies. Based on the data of the Ministry of Education and Science of the Russian Federation, the authors determined the incomes of the leading universities of Rostov Oblast in 2017 and their positions in the regional ranking of effectiveness of activities of universities of Rostov Oblast. These data became a statistical basis for compiling the regression curve, which reflects regression and correlation of expenditures for access to the internet and total expenditures of the leading universities of Rostov Oblast in 2017. Findings It is concluded that modernization on the basis of new information and communication technologies (primarily, the internet) stimulates the growth of modern university’s competitiveness. Due to this, remote education is a perspective direction of development and increase of effectiveness of modern university’s activities. The necessity for marketing of remote education is predetermined by its lower value as compared to traditional education and, accordingly, lower demand for educational services that are provided in the remote form. Originality/value The authors’ marketing model of promotion of remote education by modern university is developed and presented. The advantage of this model is harmonization of commercial and non-commercial goals and priorities of modern university’s activities, due to which balance of its social function in the economic system and its entrepreneurial function, which consists in the necessity for providing self-financing and maximization of the received profit, are ensures.