Reviewerly/exHarmony huggingface dataset:
Tumor Segmentation by a Self-Organizing-Map based Active Contour Model (SOMACM) from the Brain MRIs

Segmentation of tumors from the brain Magnetic Resonance Images (MRIs) is very important for the analysis and right treatment. Tumors treated at early stages improve the survival time. This paper proposes an advanced method named SOMACM which is a combination of Self -Organizing -Map (SOM) and Active Contour Model (ACM) for the efficient segmentation of brain MRIs to detect tumors. ACM is an energy-based segmentation method and treats the segmentation as an optimization issue. It can model complex shapes and handles topological changes in the object boundary. The customary ACMs rely upon the intensities of the pixels and are very vulnerable to parameter tuning hence it is very difficult to segment the images of distinct pixel intensities. ACMs will evolve from the object boundary for the images consisting of Intensity Inhomogeneity (IIH). Neural Networks (NNs) are exceptionally compelling in processing the images of inhomogeneities. Furthermore, image segmentation can be done by NNs without the use of an objective function. The proposed SOMACM method works by precisely incorporating the global information extracted from the weights of the trained SOM neurons which helps in modeling complex shapes and distinct intensity distributions. It can handle images with noise, intensity similarity and IIH. The proposed segmentation technique is not sensitive to parameter tuning. The outcomes of the proposed SOMACM demonstrate the improved accuracy in the segmentation results of different types of tumor images, in contrast with the individual SOM, ACM, Fuzzy-C- Means (FCM), Particle Swarm Optimization (PSO) and Probabilistic Neural Networks (PNN) segmentation methods.
On the investigation of activation functions in gradient neural network for online solving linear matrix equation

In this paper, we investigate different activation functions (AFs) on convergence performance of a gradient-based neural network (GNN) for solving linear matrix equation, AXB+X=C. It is observed that, by employing different AFs, i.e., linear, power-sigmoid, sign-power, and general sign-bi-power functions, the presented GNN model can achieve different convergence performance. More specifically, if linear function is employed, the GNN model can achieve exponential convergence; if the power-sigmoid function is employed, superior convergence can be achieved as compared to the linear case; while if the sign-power and general sign-bi-power functions are employed, the GNN model can achieve finite- and fixed-time convergence, respectively. Detailed theoretical proofs are offered to demonstrate these facts. Besides, the exponential convergence rate and the upper bounds of finite and fixed convergence time are also theoretically estimated. Finally, two illustrative examples are performed to further substantiate the aforementioned theoretical results and the effectiveness of the presented GNN model for solving the linear matrix equation.
Smart FIR: Securing e-FIR Data through Blockchain within Smart Cities

Electronic First Information Report (e-FIR) is a basic document filed to the police stations by a victim or someone on his/her behalf when a cognizable offense such as murder, kidnapping, rape, theft, etc. is committed. In the e-FIR database, the offense's record can be compromised due to its centralized nature, and further the intentional registration of false e-FIR can occur. Thus, data integrity and transparency are key concerns in e-FIR database. In this paper, e-FIR data integrity and false registration appended with police stations in a centralized database are addressed via a consensus-based distributed blockchain solution, as an integral part of a smart city environment. Specifically, a smart contract based intelligent framework has been utilized to explore the potential of Ethereum blockchain in providing integrity to e-FIR data stored in a police station's database. Local database is interfaced with Ethereum blockchain using Web3 Remote Procedure Call (RPC) protocol. Multiple simulations have been performed to evaluate the performance of the proposed framework. Our results show a trade-off between different hashing algorithm security level for the offenses data and number of transactions stored in a single block on blockchain ledger.
Strategies for successful implementation of mobile phone library services

The study attempts to investigate the preparedness of University of Cape Coast Library to implement mobile technology-based library service. The study involved six management members from the library and fifteen students comprising ten undergraduate and five postgraduate students. Interview guide was used as a tool for data collection. The interview guide was grouped under four headings: perception of mobile library service on campus, students' proficiency and strategies for successful implementation and imminent challenges. The data collected was qualitative in nature. The study shows that Almost every student had at least one mobile device which is a smartphone and can be used to access library service. Though proficiency level among students was high, there would still be the need for library management to train users to fully appreciate the use of these gadgets in accessing library services. Students and management expressed their willingness to patronise such services. They also showed positive perception regarding mobile phone-based library services. The recommendation made after the study was that students should be educated to know the benefits that come with the use of mobile device to access library services while library personnel should be adequately trained for such services. Management should allocate more resources for successful implementation the resources.
Reinforcement Learning Based Decision Making of Operational Indices in Process Industry Under Changing Environment

The plant-wide production process is composed of multiple unit processes, in which the operational indices of each unit process are assigned and adjusted according to product quality, yield, and actual operating modes. Due to the changing operational conditions of the production process, the operational indices cannot be effectively adjusted by most of the model-based methods or evolutionary computation. In this article, the decision making of operational indices is formulated as a continuous state, continuous action reinforcement learning (RL) problem and a model-free RL algorithm is proposed, which learns a decision policy to determine the operational indices according to the actual operational conditions. Different from the existing methods, this article presents a multiactor networks ensemble algorithm and an actor-critic framework with stochastic policy to avoid falling into local optimums. The relatively overall optimal policy is obtained by extracting the results of parallel training of multiactor networks, which guarantees the optimality of the obtained policy. In addition, by using the experience replay, it is particularly valuable to effectively deal with the problem that lacking of sampling data in the model-free RL. Simulation studies are conducted on actual data of a mineral processing plant and the results demonstrate the effectiveness of the proposed algorithm.
User Interfaces in Dark Mode During Daytime – Improved Productivity or Just Cool-Looking?

Applications are increasingly coming equipped with a so-called dark mode. Our observation is that many computer enthusiasts are under the impression that dark mode in a way is better than the traditional light mode. This study sets out to explore this belief by observing if dark mode indeed poses any improvements in terms of productivity and quantity of errors over light mode. A controlled experiment was designed involving a visually intensive text entry task using a virtual keyboard with an unfamiliar layout. The results indicate that there were no differences between dark mode and light mode in terms of productivity and quantity of errors.
A Fast, Two-dimensional Gaussian Process Method Based on Celerite: Applications to Transiting Exoplanet Discovery and Characterization

Abstract Gaussian processes (GPs) are commonly used as a model of stochastic variability in astrophysical time series. In particular, GPs are frequently employed to account for correlated stellar variability in planetary transit light curves. The efficient application of GPs to light curves containing thousands to tens of thousands of data points has been made possible by recent advances in GP methods, including the celerite method. Here we present an extension of the celerite method to two input dimensions where, typically, the second dimension is small. This method scales linearly with the total number of data points when the noise in each large dimension is proportional to the same celerite kernel and only the amplitude of the correlated noise varies in the second dimension. We demonstrate the application of this method to the problem of measuring precise transit parameters from multiwavelength light curves and show that it has the potential to improve transit parameters measurements by orders of magnitude. Applications of this method include transit spectroscopy and exomoon detection, as well a broader set of astronomical problems.
Explaining the Attributes of a Deep Learning Based Intrusion Detection System for Industrial Control Networks

Intrusion detection is only the initial part of the security system for an industrial control system. Because of the criticality of the industrial control system, professionals still make the most important security decisions. Therefore, a simple intrusion alarm has a very limited role in the security system, and intrusion detection models based on deep learning struggle to provide more information because of the lack of explanation. This limits the application of deep learning methods to industrial control network intrusion detection. We analyzed the deep neural network (DNN) model and the interpretable classification model from the perspective of information, and clarified the correlation between the calculation process of the DNN model and the classification process. By comparing the normal samples with the abnormal samples, the abnormalities that occur during the calculation of the DNN model compared to the normal samples could be found. Based on this, a layer-wise relevance propagation method was designed to map the abnormalities in the calculation process to the abnormalities of attributes. At the same time, considering that the data set may already contain some useful information, we designed filtering rules for a kind of data set that can be obtained at a low cost, so that the calculation result is presented in a more accurate manner, which should help professionals lock and address intrusion threats more quickly.
Side-Scan Sonar Image Synthesis Based on Generative Adversarial Network for Images in Multiple Frequencies

The side-scan sonar (SSS) is a critical sensor device used to explore underwater environments in the deep sea. Gathering SSS data, however, is an expensive and time-consuming task because it requires sensor towing and involves complicated field operations. Recently, deep learning has been making advances rapidly in the field of computer vision. Benefiting from this development, generative adversarial networks (GANs) have been demonstrated to produce realistic synthetic data of various types, including images and acoustics signals. In this letter, we propose a GAN-based semantic image synthesis model based on GAN that can generate high-quality SSS images at a low cost in less time. We evaluate the proposed model using both shallow and deep water SSS data sets that include a diverse range of imaging conditions. such as high and low sonar operating frequencies and different landscapes. The experimental results show that the proposed method can effectively generate synthesized SSS data characterized by the shape and style of real data, thereby demonstrating its promising potential for SSS data augmentation in diverse SSS relevant machine learning tasks.
Myrror: a platform for holistic user modeling

In this article, we present a platform that allows the creation of a comprehensive representation of the user that we call a holistic user model (HUM). Such a representation is based on the intuition that users' personal data take different forms and come from several heterogeneous sources. Accordingly, we designed a pipeline that: (1) extracts personal data from three examples of important classes of such sources, namely social networks, wearable devices and smartphones; (2) processes these data through natural language processing and machine learning techniques; (3) stores the output of such processing in a user model that encodes different aspects of people's life, such as demographic data, interests, affect values, social relations, activities and physical states. The resulting representation is made available to the user and to external developers. In the first case, a web interface allows the user to browse through her own personal data and to consult different facets of her HUM, in order to improve her self-awareness. In the latter, holistic user profiles are exposed through a REST interface and can be exploited by third-party applications to provide personalized services based on HUMs. In the experimental session, we evaluated usability and acceptability of the HUM in a user study which investigated how people were willing to use it. The results confirmed the effectiveness of our design choices and built the foundations for future usage of these profiles in personalized applications.
Smart Attendance System using QR Code

In higher education institutions, student participation in the classroom is directly related to their academic performance. However, the majority of student attendance registration is still conventionally done, which is tedious and time-consuming, especially for those courses that involve large numbers of students. Over the years, attendance management has been conducted manually at most of the universities. To overcome the manual attendance issues, we proposed and implemented a smart attendance system with the aim to encourage the potential use of the Quick Response (QR) code as a future attendance management system, to track and record student attendance in lectures and exercises for all relevant courses, as an aim of this paper.
Challenges in Energy-Efficient Deep Neural Network Training with FPGA

In recent years, it is highly demanding to deploy Deep Neural Networks (DNNs) on edge devices, such as mobile phones, drones, robotics, and wearable devices, to process visual data collected by the cameras embedded in these systems. In addition to the model inference, training DNNs locally can benefit model customization and data privacy protection. Since many edge systems are powered by batteries or have limited energy budgets, Field-Programmable Gate Array (FPGA) is commonly used as the primary processing engine to satisfy both demands in performance and energy-efficiency. Although many recent research papers have been published on the topic of DNN inference with FPGAs, training a DNN with FPGAs has not been well exploited by the community. This paper summarizes the current status of adopting FPGA for DNN computation and identifies the main challenges in deploying DNN training on FPGAs. Moreover, a performance metric and evaluation workflow are proposed to compare the FPGA-based systems for DNN training in terms of (1) usage of on-chip resources, (2) training efficiency, (3) energy efficiency, and (4) model performance for specific computer vision tasks.
Shortcut Gestures for Mobile Text Editing on Fully Touch Sensitive Smartphones

While advances in mobile text entry enable smartphone users to type almost as fast as on hardware keyboards, text-heavy activities are still not widely adopted. One reason is the lack of shortcut mechanisms. In this article, we determine shortcuts for text-heavy activities, elicit shortcut gestures, implement them for a fully touch-sensitive smartphone, and conduct an evaluation with potential users. We found that experts perform around 800 keyboard shortcuts per day, which are not available on smartphones. Interviews revealed the lack of shortcuts as a major limitation that prevents mobile text editing. Therefore, we elicited gestures for the 22 most important shortcuts for smartphones that are touch-sensitive on the whole device surface. We implemented the gestures for a fully touch-sensitive smartphone using deep learning and evaluated them in realistic scenarios to gather feedback. We show that the developed prototype is perceived as intuitive and faster than recent commercial approaches.
Co-creating Persona Scenarios with Diverse Users Enriching Inclusive Design

In this article, we will examine personas as methodological approach and review some critiques about how its use may omit or stereotype users with disabilities or even restrict user involvement. We review previous persona creation methods and compare it to our approach where we involve diverse users directly in the personas creation process, to ensure more grounded personas. This approach has recently been refined in a project where we are building a tool aiming to give citizens more control over their health information. We discuss our experiences and offer some experience based guidelines for using our method.
CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multiview Data Association

Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment. Multiway data association methods provide a mechanism to improve alignment accuracy of pairwise associations and ensure their consistency. However, existing methods that solve this computationally challenging problem are often too slow for real-time applications. Furthermore, some of the existing techniques can violate the cycle consistency principle, thus drastically reducing the fusion accuracy. This article presents the consistent lifting, embedding, and alignment rectification (CLEAR) algorithm to address these issues. By leveraging insights from the multiway matching and spectral graph clustering literature, CLEAR provides cycle-consistent and accurate solutions in a computationally efficient manner. Numerical experiments on both synthetic and real datasets are carried out to demonstrate the scalability and superior performance of our algorithm in real-world problems. This algorithmic framework can provide significant improvement in the accuracy and efficiency of existing discrete assignment problems, which traditionally use pairwise (but potentially inconsistent) correspondences. An implementation of CLEAR is made publicly available online.
Structural breaks and trend awareness-based interaction in crypto markets

This study aims to test multiple structural breaks using Bai-Perron methodology for the crypto markets. We analyze return and volatility (proxied by absolute and squared returns) series of Bitcoin and the following crypto assets: Bitcoin Cash, DASH, Ethereum, IOTA, Litecoin, NEO, XRP. In the analysis, we consider the BTC markets of those 7 altcoins as well as the USD markets. Empirical findings indicate existence of statistically significant structural changes in terms of returns and volatility. Return and volatility series share different dynamics. On the other hand, time series quoted in BTC perform different structural change behavior than the ones quoted in USD. We also observed a clustering of breakpoints in the periods of February–March 2017 and December 2017–March 2018. For the logarithmic returns, in the mean of BTCUSD and series quoted in BTC no structural breaks are available. The mean of logarithmic returns of DASH, ETH and LTC quoted in USD have structural changes corresponding to the beginning of the downtrend in USD price series of the relevant assets. Absolute returns revealed more of structural breaks in terms of volatility than squared returns. Except for IOTA, volatility last longer in the series quoted in USD than the ones quoted in BTC. For the volatility series of ETH, LTC and NEO, regimes in the time series quoted in BTC is affected by the trend change in the USD quoted price series of the asset. We have defined this regime change and trend change relationship as “trend awareness-based interaction”. We also found a specific trend change pattern in the time series. In comparison to the time series of altcoins, BTCUSD is the first one which has the trend change almost in all cases. Besides, the time series of BCH, ETH, LTC and NEO (and DASH partly) follows a specific trend change order as the following: first in USD quoted time series of the altcoin and then in BTC quoted ones, while the time series of IOTA and XRP indicate the opposite.
Efficient and Lightweight Data Streaming Authentication in Industrial Control and Automation Systems

The industrial control and automation systems have played an increasingly important role in critical manufacturing processes. In such systems, many Internet of Things devices continuously collect large number of streaming data for real-time processing. Verifiable data streaming (VDS) addresses such authenticity issue for streaming data, but most VDS schemes are not efficient and lightweight, do not support range querying, and cannot be used in practice. To improve the efficiency and achieve a verifiable range query in data streaming, we present here a new primitive, namely, a chameleon authentication tree with prefixes (PCAT), which is extended from the PBTree and chameleon authentication tree. Our scheme is not only lightweight but also supports dynamic expansion and verifiable range query in data streaming, making it more suitable for resource-constrained devices. We separate the PCAT's algorithms into the following phases: initialization, data appending, query, and verification. Our analyses prove that the PCAT satisfies all the security requirements of VDS. Moreover, an efficiency analysis and performance evaluation demonstrate that our scheme not only supports lightweight data streaming authentication but also has high efficiency, which means that the PCAT is easier to apply in the industrial control and automation systems.
Digital Signage by Real-Time Gender Recognition From Face Images

Digital signage is a new advertising strategy using smart multimedia screens, which carries out the dynamic customization of the promotional content according to the customers who are looking at the monitor. Gender recognition from face images is among the most popular applications for digital signage, since it allows to select in real-time advertising spots customized for males or females. In this paper, we propose a system which implements this solution using a smart camera mounted above the monitor, dedicated to gender recognition in real-time, and a component that dynamically modifies the content projected on the screen according to the gender of the audience. The computer vision algorithm is designed to be as fast as effective, since the whole processing chain must be performed in real-time in order to avoid missing people passing in front of the screen. We evaluated the performance of the proposed solution on a standard dataset for gender recognition in the wild and in a real fair, obtaining a gender recognition accuracy of 94.99% and 92.70%, respectively, that is very relevant in such unconstrained scenarios. In addition, the method is able to process 5 fps on a smart camera and, thus, it can be used in a digital signage application.