Reviewerly/exHarmony huggingface dataset:
Development of a virtual shapemeter based on a shape control model for continuous annealing

Purpose Continuous annealing (CA) units usually lack a physical shapemeter; consequently, real-time display and closed-loop control of the strip shape are impossible to achieve. Design/methodology/approach A shape model for the CA process is established in this study. Specifically, a virtual shapemeter and closed-loop control system based on the advanced parameter acquisition system and information transmission of CA units are developed in C++ programming language. This system realises real-time dynamic shape display, closed-loop control and shape prediction by collecting raw data of steel coils and parameters during CA. Findings Field test results show that the shape predicted by the virtual shapemeter coincides with the measured shape by over 90 per cent, which fully meets the precision requirement of industrial applications. Originality/value Moreover, shape quality is effectively improved without increasing hardware investments.
Seasonality-Adjusted Conceptual-Relevancy-Aware Recommender System in Online Groceries

Conceptual relevancy - defined as how well a pair of products are related to each other - plays a significant role in online-grocery shopping behavior. When a customer goes grocery shopping, they first come up with a list of ingredients for recipes they may be interested in. A typical shopping list contains categories of products, many of them conceptually relevant to each other. However, the said user may be flexible on which exact product to purchase. For instance, a user may put `milk,' and `cheese' in their shopping list, but these may not refer to specific products such as `Great value 2% milk,' or `Kraft Singles American Slices.' Modern recommender systems, however, focus much more on how to identify specific items to recommend to customers, rather than the categories that may be relevant. Such an approach may lead the system to occasionally recommend outlier, or noise, ultimately violating conceptual relevancy. Moreover, many recommender systems ignore seasonal components; they assume that customers' shopping behavior is independent of the time of the year. However, conceptual relevancy between two products shifts over time. For instance, if a user is shopping for groceries in the middle of the winter, recommending particular products (say, barbecue-related products) may not be the best strategy even if the contextual (user's past interests, or item that the user is currently viewing) may suggest otherwise. In this paper, we introduce a novel strategy to enforce conceptual relevancy in online-grocery domain. Furthermore, recognizing that conceptual relevancy is heavily influenced by the time of the year, we propose a Bayesian-based seasonality algorithm to capture the drift in conceptual relevancy over time without having to re-train the whole model. The algorithm can be based on any of the popular approaches in recommender systems - either based on matrix factorization or that on neural networks. Through our experiments, we show that our seasonality framework can capture drifts in conceptual relevancy.
ForestNet – Automatic Design of Sparse Multilayer Perceptron Network Architectures Using Ensembles of Randomized Trees

In this paper, we introduce a mechanism for designing the architecture of a Sparse Multi-Layer Perceptron network, for classification, called ForestNet. Networks built using our approach are capable of handling high-dimensional data and learning representations of both visual and non-visual data. The proposed approach first builds an ensemble of randomized trees in order to gather information on the hierarchy of features and their separability among the classes. Subsequently, such information is used to design the architecture of a sparse network, for a specific data set and application. The number of neurons is automatically adapted to the dataset. The proposed approach was evaluated using two non-visual and two visual datasets. For each dataset, 4 ensembles of randomized trees with different sizes were built. In turn, per ensemble, a sparse network architecture was designed using our approach and a fully connected network with same architecture was also constructed. The sparse networks defined using our approach consistently outperformed their respective tree ensembles, achieving statistically significant improvements in classification accuracy. While we do not beat state-of-art results with our network size and the lack of data augmentation techniques, our method exhibits very promising results, as the sparse networks performed similarly to their fully connected counterparts with a reduction of more than 98% of connections in the visual tasks.
Navigation and Navigation Algorithms

The development of navigation technology stems from the need for direction and location identification of military and production activities in the early stages of human society. With the advancement of society and the improvement of productivity, the space for human activities has been expanding, which requires higher technical for navigation. Navigation under uncertainty is a challenging problem. When dealing with navigation problems in the case of complex dynamic environment information, intelligent revelation from nature can often play a very good role. This chapter briefly introduces the generation and definition of navigation and reviews the development history of navigation. Then, expound the basic concepts of natural heuristic navigation algorithms including ecosystem simulation algorithm, swarm intelligence algorithm, evolutionary algorithm, and artificial intelligence algorithm, and the principle and characteristics of the algorithm are summarized. Finally, prospect the development of the natural heuristic navigation algorithm and introduce its main application areas.
Blockchain Based Sharing of Security Information for Critical Infrastructures of the Finance Sector

Recent security incidents in the finance sector have demonstrated the importance of sharing security information across financial institutions, as a means of mitigating risks and boosting the early preparedness against relevant attacks. However, financial institutions are in several cases reluctant to share security information beyond what is imposed by applicable regulations. In this paper, we introduce a blockchain-based solution for sharing security information in a decentralized way, which boosts security and trust in the information sharing process. We also illustrate how the information that is shared across financial institutions can serve as a basis for collaborative security services such as risk assessment.
Protection Against Side-Channel Attacks on Multifusion Zero-Permission Sensors using Differential Privacy

The last decade has witnessed significant growth in Mobile Crowdsensing (MCS) applications. These applications exploit the variety of embedded sensors in our smartphones. Access to some of these sensors does not require explicit permission from the user and is known as zero-permission sensors (ex: accelerometer and gyroscope). Even though these sensors have important applications in context-awareness and activity recognition, they can be exploited using side-channel attacks. In this paper, we study the privacy vulnerabilities arising from zero-permission motion sensors. We implement a realistic and comprehensive attack model using machine learning algorithms and show that an attack can be conducted with high accuracy. Accordingly, we propose a novel defense model based on differential privacy to address these privacy vulnerabilities while ensuring the functionality of the apps is not affected. We evaluate the proposed defense model using a collected dataset and show its potential in preserving the user's privacy.
Building Blocks of Negotiating Agents for Healthcare Data

The healthcare market demands advanced, flexible, and secure solutions for personal health data sharing. In our paper, we present preliminary work that proposes a distributed infrastructure of negotiating agents for the healthcare domain. This infrastructure will support healthcare stakeholders to share and access patient health data in a secure way, thus providing benefits for patients and their treatment. Distributed ledger technologies and smart contracts can be considered as a basis for negotiations between distributed agents that carry health-related data. We present an overview of related work and outline the research methodology.
Conceptual Content of Digital Society

This article is dedicated to study of the conceptual field of digital society. Relying upon the method of definitional analysis, the authors identified the main categories used in the definitions: subject; characteristics/features of the subject; actions of the subject; field of action of the subject; object; characteristics/features of the object; actions of the object; field of action of the object; environment. Presentation of definitions as formula charts allowed revealing deeper relationships rooted in the definitions and discovering functions not lying on the surface. Irrespective of peculiarities of meanings of the subjects used in the definitions, we can distinguish information and society as two main subjects. All actions of society and information actually come down to three functions: operations with information; operations with society; operations with material labor. As a result of the study, we concluded that society and information perform the same number of functions; they are identical; both society and information have a subjective-objective nature, since their actions are aimed at both the external environment and themselves. Numerous, concretized, detailed actions, primarily, with information, are a significant element of the conceptual content of information society. Based on the frequency parameter, we concluded that the main emphasis in the definitions is placed on developing non-material labor, which further leads to the change of such areas of society as social, political, economic, cultural, information and communication. During the study the systemic components of the concept “digital field” were identified, namely, axiological, evolutionary, educational, historical, subject and instrumental.
Low-Latency Pairing Processor Architecture Using Fully-Unrolled Quotient Pipelining Montgomery Multiplier

Various kinds of cryptosystems with advanced functions, such as ID-based and attribute-based cryptography, are expected as the solution for problems in the IoT era. Pairing computation is considered to be one of the promising components to construct such advanced cryptosystems. However, the computation time of the pairing, which involves the processing of complicated algebraic calculations, is a major obstacle for achieving the practical application. Previous studies in hardware implementation pairings mainly aim to improve the performance of the area-time product. While these studies prefer the usage of the resource-restricted environment, some server-side applications, such as attribute-based encryption for video streaming, require the implementation of low latency instead of the area-time product. This study proposes a low-latency pairing processor architecture and its optimal pipeline scheduling. The proposed architecture is implemented on Xilinx Virtex-6 ML605 and Virtex Ultrascale+ VCU118 board, and we confirmed that these implementations complete single pairing computation within 153 μs and 61 μs, respectively.
Visual Counting of Traffic Flow from a Car via Vehicle Detection and Motion Analysis

Visual traffic counting so far has been carried out by static cameras at streets or aerial pictures from sky. This work initiates a new approach to count traffic flow by using populated vehicle driving recorders. Mainly vehicles are counted by a camera moves along a route on opposite lane. Vehicle detection is first implemented in video frames by using deep learning YOLO3, and then vehicle trajectories are counted in the spatial-temporal space called motion profile. Motion continuity, direction, and detection missing are considered to avoid multiple counting of oncoming vehicles. This method has been tested on naturalistic driving videos lasting for hours. The counted vehicle numbers can be interpolated as a flow of opposite lanes from a patrol vehicle for traffic control. The mobile counting of traffic is more flexible than the traffic monitoring by cameras at street corners.
Engineering a Parallel Δ-stepping Algorithm

Computation of the single-source shortest path (SSSP) is a fundamental primitive in many network analytics tasks. With the increasing size of networks to be analysed, there is a need for efficient tools to compute shortest paths, especially on the widely adopted shared-memory multicore architectures. The Δ-stepping algorithm, that trades-off the work efficiency of Dijkstra's algorithm with the parallelism offered by the Bellman-Ford algorithm, has been found to be among the fastest implementations on various parallel architectures. Despite its widespread popularity, the different design choices in implementing the parallel Δ-stepping algorithm are not properly understood and these design choices can have a significant impact on the final performance. In this paper, we carefully compare two different implementations of the Δ-stepping algorithm for shared-memory multicore architectures: (i) a static workload assignment where the nodes are assigned to threads at the beginning of the algorithm and only the assigned thread can relax edges leading to a node and (ii) a dynamic workload assignment where the nodes are dynamically allocated to threads at the time of bucket relaxation. Based on an extensive empirical study on a range of graph classes, edge density and weight distributions, we show that while the more intuitive and widely used approach of dynamically balanced workload suits dense power-law graphs well, the static partitioning approach outperforms this more intuitive approach on a wide range of graph classes. Our findings can guide a network analyst in selecting the best parallel implementation of the Δ-stepping algorithm for a given analytics task and a given graph class.
Improving Bees-based Imputation using Nearest Neighbor for Heuristic Function in Imputing Data

Data imputation is a necessary task to solve missing value problem for better data mining result. The current data imputation with Bees algorithm contains several random procedures including instance selection and feature selection, and the randomness causes inconsistency and swinging result in iteration. Thus, this work proposes to solve them by applying a heuristic function in those procedures from using importance score in selecting attribute to handle and probability in selecting correlated value. These calculations provide the bees with a guidance direction; thus, there are less random processes and should lower inconsistent and swinging results from randomness. From evaluation, the proposed Bees-based imputation obtained higher accuracy than the previous Bees-based and Genetic algorithm-based imputation method from all data sets for all missing data percentage between 10% to 50%. The best improvement in accuracy for 23% in average was found in SPECT data set which consists of only binary type values. For the data sets with values mixing of binary and category type, the proposed method gained about 3-7% improvement in average.
An advisable facial semantic characterization based on Axiomatic Fuzzy Set theory and information granules

With the ever-increasing popularity of social networks, a colossal amount of images are being uploaded to the digital world including human faces, and semantic facial descriptors would be very useful to describe some facial action units, which can be further used for affection analysis. In this paper, a novel facial semantic characterization method via Axiomatic Fuzzy Set (AFS) clustering scheme and information granules is proposed. Firstly, semantic descriptions for each facial component are extracted in the framework of the AFS theory. Then in order to further characterize the semantics obtained by the AFS clustering, the information granules are created around the constructed prototypes, that can specify the merits of the semantic facial descriptors. Multiple experiments on the Multi-PIE and BU-4DFE facial databases demonstrate that the proposed facial semantic characterization method not only can obtain more accurate and interpretable descriptions, but also the results are much closer to human perception.
Net Promoter Sentiment Classifier Using OHPL-ALL

Net Promotor Score is an important business measurement process where customers are surveyed and asked to rate their likelihood of recommending the company's products and/or services. In many applications, customers are asked to respond on an 11-point ordinal scale of 0 to 10. In developing the score, the data are reformulated into a labelled 3 class scale (0-6: Detractor, 7-8: Passive and 9-10: Promoter). [1] Many companies that choose to use Net Promoter Score as a core management metric integrate the measurement into all phases of the company and seek every opportunity to assess company performance in terms of likelihood to promote the company. In addition to a variety of survey opportunities, the ability to score comments in survey, social media and blogs with promoter rating may provide an additional valuable source of business insight. Even on a three-point scale, Net Promoter is an ordinal classification problem. A number of successful algorithms, that develop ordinal classifiers have been developed. [2] None of the top performing classifiers can be used for applications like text classification or image classification, since they don't employ deep learning. Any appropriate strategy must utilize the ordering information of classes without imposing a strong continuous assumption or fixed spacing assumption on the ordinal classes. In this paper, we use a novel Deep Learning methodology called OHPLnet (Ordinal Hyperplane Loss Network) that is specifically designed for data with ordinal classes. [3] The algorithm is used to develop predictions of the eleven classes, that may be used in the standard Net Promoter Score generation process.
Multi-View Unsupervised Feature Selection with Dynamic Sample Space Structure

With the rapid growth of complex high-dimensional sparse data and the limitation of a single perspective, there is an increasing demand for new methods of feature selection from multiple perspectives. The feature selection method based on minimum regression is usually learning projection matrix, which is lack of theoretical explanation to evaluate the importance of features. Moreover, these methods cannot find the global and sparse solutions of the projection matrix. In this paper, we propose a new multi-view unsupervised feature selection method, which can learn the global and sparse solutions of the projection matrix. The new method extends the least squares regression model by adjusting the regression coefficients in least squares regression by using a set of scale factors for feature ranking. It shows that the new model can learn global and sparse solutions. In addition, the introduction of the scale factor provides a theoretical explanation for why we can use the projection matrix to sort features. In order to optimize the new model, a simple and effective algorithm for proof convergence is proposed. By classifying and clustering the data after feature selection, we can see the superiority of our algorithm.
An Alternative Diffie-Hellman Protocol

The Diffie–Hellman protocol, ingenious in its simplicity, is still the major solution in protocols for generating a shared secret in cryptography for e-trading and many other applications after an impressive number of decades. However, lately, the threat from a future quantum computer has prompted successors resilient to quantum computer-based attacks. Here, an algorithm similar to Diffie–Hellman is presented. In contrast to the classic Diffie–Hellman, it involves floating point numbers of arbitrary size in the generation of a shared secret. This can, in turn, be used for encrypted communication based on symmetric cyphers. The validity of the algorithm is verified by proving that a vital part of the algorithm satisfies a one-way property. The decimal part is deployed for the one-way function in a way that makes the protocol a post-quantum key generation procedure. This is concluded from the fact that there is, as of yet, no quantum computer algorithm reverse engineering the one-way function. An example illustrating the use of the protocol in combination with XOR encryption is given.
Smart Case Mining Based on Membrane Clustering

Case Based Reasoning is a method for solving problems based on previous experience. In some cases large numbers of cases are available representing the experience to draw from. Dealing with large numbers of potentially noisy cases introduces challenges to do with storage capacity and similarity matching but can also lead to sub-optimal solutions. This paper proposes an algorithm for generating a reduced set of highly representative constructed cases, to use as the Smart Case Base in replacement of the original large training data set. The goal of using the reduced set of cases is to address the problems linked to the original Case Base size as well as to maintain or to improve upon generalization accuracy for problem solving. The algorithm makes use of membrane clustering, an evolutionary computing approach for organizing data in large search spaces. The performance of the algorithm has been evaluated using public benchmark data sets.
Efficiency Enhancements for Practical Techniques for Searches on Encrypted Data

More often, organizations see the need of outsourcing their data storages in meeting the operational and financial objectives. In doing so, the data is encrypted before storing with a third party data service provider. This creates a need to extract only the required data and performing efficient searches on encrypted data at a remote third party storage has become an interesting problem. In this paper we discuss an implementation results of an existing symmetric searchable encryption scheme where every word in a text file is treated as a keyword for search. We identify search time and space efficiency as practical issues and propose an enhanced scheme with keyword separation, an implementation and finally arrive at close to 90% reduction in extra space usage and more than 90% improvement in search time compared to the original scheme.
Dynamic Collaborative Visualization Ecosystem to Support the Analysis of Large-Scale Disparate Data

There is no one display device or software package that is ideally suited for interactively visualizing related nonspatial, 1D, 2D, 3D, and 4D datasets. This is a major drawback, as the benefits of interactive visualization and advanced display technologies cannot be brought to bear. The Dynamic Collaborative Visualization Ecosystem (DynaCoVE) framework addresses this limitation by unifying SciVis, InfoVis, and display technology tools. Pre-existing packages are wrapped as DynaCoVE clients. This entails tracking user interactions with their GUI's and also automatically updating them in response to cues from the DynaCoVE server. The DynaCoVE server then acts as a message broker between many collaborating clients. A messaging protocol was defined based on ZeroMQ's majordomo protocol to enable efficient communication between clients and the server. Thus, visualization software packages that were never intended to be used together can be linked to perform cross display visual analytics. The system is intended to be data and user centric while remaining software, algorithm and display agnostic. This is accomplished in part by providing a common metavisualization graph interface to setup cross-display visualizations. To date DynaCoVE clients for Looker, VisIt, and VTK have been tested together with traditional monitors, tiled displays, and an immersive CAVE-type system.