Reviewerly/exHarmony huggingface dataset:
Prostate cancer trajectory-map: clinical decision support system for prognosis management of radical prostatectomy

Prostate cancer has a low mortality rate and requires persistent treatment; however, treatment decisions are challenging. Because prostate cancer is complex, the outcomes warrant thorough follow-up evaluation for appropriate treatment. Electronic health records (EHRs) do not present intuitive information. This study aimed to develop a Clinical Decision Support System (CDSS) for prognosis management of radical prostatectomy.We used data from 5,199 prostate cancer patients from three hospitals' EHRs in South Korea, comprising laboratory results, surgery, medication, and radiation therapy. We used open source R for data preprocessing and development of web-based visualization system. We also used R for automatic calculation functionalities of two factors to visualize the data, e.g., Prostate-Specific Antigen Doubling Time (PSADT), and four Biochemical Recurrence (BCR) definitions: American Society of Therapeutic Radiology and Oncology (ASTRO), Phoenix, consecutive PSA > 0.2 ng/mL, and PSA > 0.2 ng/mL.We developed the Prostate Cancer Trajectory Map (PCT-Map) as a CDSS for intuitive visualization of serial data of PSA, testosterone, surgery, medication, radiation therapy, BCR, and PSADT.The PCT-Map comprises functionalities for BCR and PSADT and calculates and visualizes the newly added patient data automatically in a PCT-Map data format, thus optimizing the visualization of patient data and allowing clinicians to promptly access patient data to decide the appropriate treatment.
Enhancement Spatial Transformer Networks for Text Classification

This paper introduces a 2D transformation based framework for arbitrary-oriented text detection in natural scene images. We present the localization networks within Spatial Transformer Networks (STN), which are designed to generate proposals with text orientation affine information including translation, scaling and rotation. This information will then be adapted as learning parameters to make the proposals to be fitted into the text regular form in terms of the orientation more accurately. Localization network is proposed to project arbitrary-oriented proposals to a feature map for a text region classifier. Compared with any previous text detection systems, this work ensures the relationship between the learning parameters, which can lead to a better approximation for orientation. As a result, this new layer greatly enhances the training accuracy. Moreover, the design and implementation can be easily deployed in the current systems built upon the standard CNNs architecture.
Sparse multiplication for skew polynomials

Consider the skew polynomial ring L[x; σ], where L is a field and σ is an automorphism of L of order r. We present two randomized algorithms for the multiplication of sparse skew polynomials in L[x;σ].
Simultaneous Perturbation Stochastic Approximation for Few-Shot Learning

Few-shot learning is an important research field of machine learning in which a classifier must be trained in such a way that it can adapt to new classes which are not included in the training set. However, only small amounts of examples of each class are available for training. This is one of the key problems with learning algorithms of this type which leads to the significant uncertainty. We attack this problem via randomized stochastic approximation. In this paper, we suggest to consider the new multi-task loss function and propose the SPSA-like few-shot learning approach based on the prototypical networks method. We provide a theoretical justification and an analysis of experiments for this approach. The results of experiments on the benchmark dataset demonstrate that the proposed method is superior to the original prototypical networks.
Behavior Modeling for Cybersecurity

A significant percentage of cyber security incidents can be prevented by changing human behaviors. The humans in the loop include the system administrators, software developers, end users and the personnel responsible for securing the system. Each of these group of people work in a given context and are affected by both soft factors such as management influences and workload and more tangible factors in the real world such as errors in procedures and scanning devices, faulty code or the usability of the systems they work with.
On Bayesian consistency for flows observed through a passive scalar

We consider the statistical inverse problem of estimating a background fluid flow field $\mathbf{v}$ from the partial, noisy observations of the concentration $\theta$ of a substance passively advected by the fluid, so that $\theta$ is governed by the partial differential equation \begin{equation*}\frac{\partial}{\partial t}{\theta}(t,\mathbf{x})=-\mathbf{v}(\mathbf{x})\cdot\nabla\theta(t,\mathbf{x})+\kappa\Delta\theta(t,\mathbf{x}),\quad\theta(0,\mathbf{x})=\theta_{0}(\mathbf{x})\end{equation*} for $t\in[0,T],T>0$ and $\mathbf{x}\in\mathbb{T}^{2}=[0,1]^{2}$. The initial condition $\theta_{0}$ and diffusion coefficient $\kappa$ are assumed to be known and the data consist of point observations of the scalar field $\theta$ corrupted by additive, i.i.d. Gaussian noise. We adopt a Bayesian approach to this estimation problem and establish that the inference is consistent, that is, that the posterior measure identifies the true background flow as the number of scalar observations grows large. Since the inverse map is ill-defined for some classes of problems even for perfect, infinite measurements of $\theta$, multiple experiments (initial conditions) are required to resolve the true fluid flow. Under this assumption, suitable conditions on the observation points, and given support and tail conditions on the prior measure, we show that the posterior measure converges to a Dirac measure centered on the true flow as the number of observations goes to infinity.
A Blockchain Based Approach to Resource Sharing in Smart Neighbourhoods

Sharing resources is a growing trend in today’s world. Sharing allows larger groups of individuals to benefit from available resources, hence optimizing the resource consumption. Sharing however demands a level of trust and control on the outcome: one needs to have confidence that a shared item will be used according to a prior agreement. In this paper we rely on geographic proximity of users to provide as an initial level of trust, and use this trust to develop a platform for sharing digital goods in a geographic neighbourhood. We require the platform to allow users to express their preferences and be confident about the sharing outcome. Our proposed platform uses a permissioned blockchain that is maintained by network providers whose subscribers are in the same geographic area, and use attribute-based access control to specify and control accesses to the shared items. Our proposed system uses smart contracts to enforce the conditions of access. We analyze security of the proposed design and discuss directions for future work.
Adversarial defense via the data-dependent activation, total variation minimization, and adversarial training

We improve the robustness of Deep Neural Net (DNN) to adversarial attacks by using an interpolating function as the output activation. This data-dependent activation remarkably improves both the generalization and robustness of DNN. In the CIFAR10 benchmark, we raise the robust accuracy of the adversarially trained ResNet20 from $ \sim 46\% $ to $ \sim 69\% $ under the state-of-the-art Iterative Fast Gradient Sign Method (IFGSM) based adversarial attack. When we combine this data-dependent activation with total variation minimization on adversarial images and training data augmentation, we achieve an improvement in robust accuracy by 38.9$ \% $ for ResNet56 under the strongest IFGSM attack. Furthermore, We provide an intuitive explanation of our defense by analyzing the geometry of the feature space.
Emotional Interactive Simulation System of English Speech Recognition in Virtual Context

With the development of virtual scenes, the degree of simulation and functions of virtual reality have been very complete, providing a new platform and perspective for teaching design. Firstly, the hidden Markov chain model is used to perform emotion recognition on English speech signals. English speech emotion recognition and speech semantic recognition are essentially the same. Hidden Markov style has been widely used in English speech semantic recognition. The experiments of feature extraction and pattern recognition of speech samples prove that Hidden Markovian has higher recognition rate and better recognition effect in speech emotion recognition. Secondly, combining the human pronunciation model and the hearing model, by analyzing the impact of the glottis feature on the human ear hearing-model feature, the research application of the English speech recognition emotion interactive simulation system uses the glottis feature to compensate the human ear, hearing feature is proposed by compensated English speech recognition, and emotion interaction simulation system is used in the English speech emotion experiment, which has obtained a high recognition rate and showed excellent performance.
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

With the explosive growth of e-payment industry, online transaction fraud has become one of the biggest challenges for the business. The historical behavior information of users provides rich information for digging into the users' fraud risk. While considerable efforts have been made in this direction, a long-standing challenge is how to effectively exploit user's behavioral information and provide explainable prediction results. In fact, the value variations of same field from different events and the interactions of different fields within one event have proven to be strong indicators of fraudulent behaviors. In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the inter- and intra-event information among users’ behavior sequence from dual perspectives, i.e., field value variations and field interactions simultaneously for fraud detection. The proposed model is deployed in Alipay's risk management system, which provides real-time fraud detection service for e-commerce platforms. Experimental results on industrial data under various scenarios in the platform clearly demonstrate that our model achieves significant improvements compared with various state-of-the-art baseline models. Moreover, the \model~could also give an insight into the explanation of the prediction results from dual perspectives.
SARDAM: Service Assistant Robot for Daily Activity Monitoring

In this work, we propose an autonomous monitoring system for the daily routine of an elderly person. SARDAM (Service Assistant Robot for Daily Activity Monitoring), which is the name of this system, uses a humanoid robot as a key element that carries out a direct interaction with the user. The purpose of SARDAM is to keep the user active as long as possible by suggesting, and monitoring, a series of daily tasks and healthy habits according to the prescription of a health professional, in order to reduce the early appearance of cognitive and motor impairment. In the current version of SARDAM we use the NAO humanoid robot, which performs a natural interaction with the user through vision and speech libraries. To assure the appropriate execution of the user&rsquo;s daily tasks, a module for emotion detection has been incorporated in order to propose corrective tasks according to the detected emotion. SARDAM was tested in a scenario with a real user, getting successful results and positive opinions from them that encourage further work.
SickZil-Machine: A Deep Learning Based Script Text Isolation System for Comics Translation

The translation of comics (and Manga) involves removing text from a foreign comic images and typesetting translated letters into it. The text in comics contain a variety of deformed letters drawn in arbitrary positions, in complex images or patterns. These letters have to be removed by experts, as computationally erasing these letters is very challenging. Although several classical image processing algorithms and tools have been developed, a completely automated method that could erase the text is still lacking. Therefore, we propose an image processing framework called ‘SickZil-Machine’ (SZMC) that automates the removal of text from comics. SZMC works through a two-step process. In the first step, the text areas are segmented at the pixel level. In the second step, the letters in the segmented areas are erased and inpainted naturally to match their surroundings. SZMC exhibited a notable performance, employing deep learning based image segmentation and image inpainting models. To train these models, we constructed 285 pairs of original comic pages, a text area-mask dataset, and a dataset of 31,497 comic pages. We identified the characteristics of the dataset that could improve SZMC performance. SZMC is available at: https://github.com/KUR-creative/SickZil-Machine .
The Reverse construction of complete (k, n)- arcs in PG(2,q) where q=2,4,8 related with linear codes

The aim of this work is to study The reverse construction of complete (k, n)- arcs in PG(2,q) where q=2,4,8 is related to linear codes, and n = q, q-1, ..., 2. And n = q, q-1, ... By removing points from the complete arc (K, n) to get a full arc (K, m) where m < n.
Benchmark for Performance Evaluation of SHACL Implementations in Graph Databases

Due to the rise in the commercial usage of knowledge graphs, the validation of graph-based data has gained importance over the past few years in the field of Semantic Web. In spite of this trend, the number of graph databases that support W3C’s validation specification Shapes Constraint Language (SHACL) can still be regarded as low, and best practices for their SHACL implementations performance evaluation are lacking. In this paper, we propose a benchmark for performance evaluation of SHACL implementations and present an evaluation of five common graph databases using the benchmark.
Innovative Techniques for Student Engagement in Cybersecurity Education

Use of technology has become a part of our everyday life. New innovative technologies are getting developed and everyone is getting dependent on it. Governments, businesses, and individuals use systems, networks, and the Internet to access, process, and store and share their huge amount of data. This data is getting compromised by malicious people/cyber criminals and hackers causing enormous loss. One of the major hindrances in tackling the cybercrimes is lack of skilled professionals in the information security sector. There exists a huge gap in demand and supply of these professionals. This gap is only going to get wider in the future if we do not take adequate steps now. This paper discusses the challenges in the cybersecurity teaching and lists out the different teaching methodologies. The paper highlights some of the innovative teaching techniques which the educational institutions can adopt to make cybersecurity education more exciting and meaningful. The idea is to generate interest among the students to think and make cybersecurity as a career option.
Academic Blockchain: An Application of Blockchain Technology in Education System

Blockchain is a decentralized technology that manages the constantly growing list of records called blocks linked together using cryptography. Blockchain is a public distributed database that holds encrypted ledgers with high security and has the ability to transform various fields including educational field. Blockchain eliminates intermediary bodies which maintain a private database of records and makes all records public with increased transparency thereby reducing the data management cost. The process involved in blockchain is more democratic, transparent, and efficient and therefore, it has the potential to disrupt multiple industries. As the Information contained in blockchain is tamper-proof and due to the digital immutability feature, blockchain has the ability to disrupt education industry as well. This paper describes how educational industry experts can use this technology as a transparent and secured system for sharing academic records and student’s achievements. Although the implementation of blockchain technology in education field is at its infancy stage, this paper proposes Academic Blockchain and implements the core functionality of providing student records in academic blockchain thereby facilitating stakeholders in education field the ease of access. The challenges this technology puts forward and the opportunities it provides is all that is needed to revolutionize our digital world.
Cyber-Systemics, Systemic Governance and Disruption of the Criminal Law

Criminal law regulators face difficulties in adapting to technological change. They must often operate in environments of significant uncertainty, with changing policy aims and legislative provisions that fail to ‘move with the times’. Rather than engaging with robust, let alone radical theoretical examination of their actions and structure, regulatory organisations struggle to enforce laws in communities affected by technological or systemic change, often leading to claims of overcriminalisation, inadequacy, regulatory overreach or inconsistency. This article suggests that dealing with disruptive criminality solely through legal instruments is a policy failure. Instead, a radical new framework is proposed, embedded in cybernetics (a transdiscplinary approach to exploring regulatory systems). Such a framework — systemic governance — offers a substantially altered way of managing regulatory relationships that resists disruptive change and challenges regulators to find new ways of engaging with the population they seek to influence.
Correlative study and analysis for hidden patterns in text analytics unstructured data using supervised and unsupervised learning techniques

Two-third of the data generated by the internet is unstructured text in the form of e-mails, audio, video, pdf files, word documents, text documents. Extraction of these unstructured text patterns using mining techniques achieve quick access to outcomes. Textual data available at online contains different patterns and when those huge incoming unstructured data enters into the system creates a problem while organising those documents into meaningful groups. This paper discusses document classification using supervised learning by focusing on the concept-based algorithm and also deals with the hidden patterns in the documents using unsupervised clustering technique and topic-based modelling for the analysis and improvement of systematic arrangement of documents by applying k-means and LDA algorithm. Finally, this presents comparative study and importance of clustering than classification for unstructured documents.
Convolutional Neural Network Classifier with Fuzzy Feature Representation for Human Activity Modelling

Human activity recognition is concerned with identifying the specific movement of a person based on sensor data. In recent years, many different techniques have been proposed for modelling and recognising human activities, with a specific focus on the development of approaches to classifying human activities using deep learning techniques. The research presented in this paper proposes a fuzzy feature representation approach to represent occupancy sensor data, along with a Convolutional Neural Network Classifier (CNNC) for human activity modelling and recognition. Sensory data gathered from a home environment are converted into occupancy data representing human activities and then fuzzified before being fed as inputs into the CNNC. The learning capability of CNNC allows the model to learn the relationship between the fuzzified inputs and their corresponding output activities during training mode. The relations learned in the trained CNNC model are then used to identify human activity patterns and classify these when the testing dataset is applied. The proposed method is evaluated using a dataset representing activities of daily living for a single user gathered from a real-home environment.
Maximizing Employee Compliance with Cybersecurity Policies

IT executives are well aware that formal policies are fundamental to combatting insider cybersecurity threats but struggle to achieve adequate levels of policy compliance from employees. This article identifies which practices for promoting compliance are effective and ineffective and provides actionable recommendations for maximizing employee compliance. Following these recommendations will enable organizations to derive more value from their cybersecurity investments and reduce the financial, reputational and legal risks associated with data breaches and network intrusions.
Sign Language Translation in Urdu/Hindi Through Microsoft Kinect

Abstract Communication is the foremost and basic necessity of a person. The ability to speak without any problem is a boon; nevertheless, woefully we have often seen people around us who are the victim of speech or hearing impairments. Thus, the ‘Sign Language’ (SL) appears as an alternate standard language that is globally understood and adopted by the deaf community. Though SL is a communication tool, but in practice, we still see most of the normal people do not understand the SL properly; thus, it is again a problem in communication between a speech/hearing impaired and normal persons. In this regard, many research attempts have been made to resolve this problem via wearable technology and other different paradigms of computational solutions. However, almost all of them focused on English or western languages, which provide a trivial resolution for the people of the Indian subcontinent. Thus, in this paper, we propose a solution to address this issue by employing Kinect Motion Sensor for detecting the signs (in SL) through hand movements and gestures and translating them in Urdu/Hindi language, which is readable and audible to normal persons. The proposed system is developed on a feature-based model; hence, we stored signs in a dictionary/training set for the development of the baseline system; however, the users can customize the system by adding their sign patterns in the future.
Distributed Time-varying Kalman Filter Design and Estimation over Wireless Sensor Networks Using OWA Sensor Fusion Technique

In this paper, a novel estimation procedure is proposed, which consists of designing a distributed class of time-varying Kalman filter based on wireless sensor networks topology along with a new sensor fusion method. The proposed technique is employed to estimate the states and outputs of a linear time-varying system with a high level of accuracy. Both the dynamics of the system and the measurements are assumed to be contaminated by external noises. The notion of Orness and Ordered Weighted Averaging (OWA) operator technique are utilized to fuse the estimation of the sensors. O'Hagan method, along with the gradient descent method, is employed to find the optimal weights. In the introduced approach, OWA weights are learned for each observation such that they efficiently minimize the estimation error for that particular observation. This will result in an outstanding high accurate sensor fusion outcome. In addition, two optimistic and pessimistic exponential OWA operators are used and compared together to achieve a pre-specified level of Orness. The simulation results are shown on a given linear time-varying system to verify the effectiveness of the proposed sensor fusion distributed filtering design method.