Reviewerly/exHarmony huggingface dataset:
A Novel Deep Neural Network CanNet for Malignant Detection

In recent years, breast cancer is causing the death of many women. The major reasons for breast cancer are an increase in age, dense breast tissue, alcoholism, body weight, and radiation exposure. If it is detected at an early stage, then the chances of survival increase. Machine Learning and deep learning can help us to detect breast cancer at initial stage. In this paper, we propose a novel deep neural network CanNet to detect cancer with high accuracy. CanNet has accuracy of 99% with 98.8% specmcity and 99.1% sensitivity. We also compared our experimental results with other state-of-the-art methods. The comparison shows that CanNet is more efficient than all other methods.
A Logic for Frege’s Theorem

Abstract It has been known for a few years that no more than Π11 ∆13 comprehension is needed for the proof of “Frege’s Theorem.” One can at least imagine a view that would regard Pi-1-1 comprehension axioms as logical truths but deny that status to any that are more complex—a view that would, in particular, deny that full second-order logic deserves the name. Such a view would serve the purposes of neo-logicists. It is, in fact, no part of my view that, say, Delta-3-1 comprehension axioms are not logical truths. What I suggest, however, is that there is a special case to be made on behalf of Pi-1-1 comprehension. Making the case involves investigating extensions of first-order logic that do not rely upon the presence of second-order quantifiers. A formal system for so-called “ancestral logic” is developed, and it is then extended to yield what I call “Arché logic.”
Personalized Ranking in eCommerce Search

We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context to augment a generic ranker optimized for conversion and relevance. We use a combination of latent features learned from item co-clicks in historic sessions and content based features that use item title and price. Personalization in search has been discussed extensively in the existing literature. The novelty of our work is combining and comparing content based and content agnostic features and showing that they complement each other to result in a significant improvement of the ranker. We experimentally show that our technique significantly outperforms a generic ranker in terms of Mean Reciprocal Rank (MRR). We also provide anecdotal evidence for the semantic similarity captured by the item embeddings on the eBay search engine.
A Cryptographic Confusion Primitive Based on Lotka–Volterra Chaotic System and Its Practical Applications in Image Encryption

New attacks on existing algorithms have revealed various weaknesses. To overcome these weaknesses, researchers are searching for new ones. In this study, a new image encryption algorithm has been proposed to realize this aim. The most obvious component of the proposed image encryption algorithm is the cryptographic substitution box structure used to provide the confusion requirement. The original aspect of the substitution box structure according to similar studies in the literature is that the design was realized by using Lotka–Volterra chaotic system. The analysis results showed that both the substitution box structure and the image encryption algorithm provide the necessary criteria for data security.
A Framework for Crowd-Sourced Exercise Data Collection and Processing

The prevalence of wearable mobile personal health devices and their everyday use enables great possibilities in the development of health-related applications and the research/studying of a plethora of behavioral habits and healthy/unhealthy lifestyle correlates. Strava is fitness-related social network, which is mainly concerned with running and cycling activities. Such platform contains a wealth of health data that, if accessed security and responsibly, can drive innovation in health informatics, urban planning based on exercise geolocations, and pattern recognition.In this paper, we present a generic framework for the collection of crowd-sourced exercise-related data. We present the overall architecture and the detailed implementation of how we collect, structure, prepare the data from crowd-sourced platform. The feasibility and efficacy of the proposed methods are demonstrated by building a prototype based on Strava.
Development of Senior Students’ Writing Skills in Genres of Academic Discourse Using Massive Open Online Courses

The role of foreign languages is constantly growing in the system of higher education in logical connection with the growing demands to foreign language competence for social success of the university graduates. In 2012, European commission issued a document called ‘Language competences for employability, mobility and growth’ (2012) underlining a new idea that language competences are directly connected with future employability. In the Russian system of higher education, we should also follow this direction and develop new models of effective teaching foreign languages. The paper presents the results of the senior students’ experimental training in the written academic discourse the competence in which is in high need in different spheres of labour market. In the paper, the authors consider the features and characteristics of the essay as one of the most popular genres of the written academic discourse. The authors developed and described three new models of teaching academic discourse with methodologically approved combination and integration of massive online courses (MOOCs) and traditional language teaching based on the theoretical study conducted in the paper: complementary, supporting and mixed, the latter was tested in the course of the experiment. The results of experimental training are discussed in this paper.
Automatic Sun Tracking Smart Wheel Chair with Real Time Navigation

Aging is a natural procedure prompt changes in quality and amount of muscles in skeletal, due to which muscle shortcoming and disability in moving of maturing populace. Crumbling pain in the arms and legs in the main cause of movement and the basic cause of disability in the spinal cord, cauda equina, or fringe neuropathy. As a result of seriousness and merits, electric wheelchairs are progressively requested in the recent era. Our proposed work is a smart electric wheelchair control by both joystick and a dependent user recognition voice system. To facilitate the patients in the outdoor environment an automatic sun-tracking solar plate is provided to charge the battery itself and automatically tracks sun position to provide not only power backup but also shadow to walk in sunlight. The navigation device is installed for providing real-time navigation which helps the user to select the shortest and easiest route. The proposed system is cost-effective of existing available in the market and also energy efficient.
Need for a Careful Comparison Between Hypotheses: Case Study of Epicycles

In this chapter, we illustrate the need for a careful comparison between different hypotheses on one of the most well-known historical examples—epicycles versus more modern techniques in celestial mechanics. Our conclusion is that, contrary to what one may read in modern astronomy and physics textbooks, epicycles were actually a very efficient tool, in some aspects foreseeing modern techniques such as Fourier series—while not exactly as efficient as Fourier series.
Detecting Uncertainty in Text using Multi-Channel CNN-TreeBiLSTM Network

Uncertainty in a text refers to the propositions that exhibit fuzziness in meaning. In this paper, we present a novel Multi-Channel Tree-LSTM model that integrates a relation aware self-attention along with multiple embeddings to automatically detect uncertainty cues in texts. We have evaluated the models with data sources across multiple domains that include bio-medical texts, privacy policies, and consumer reviews. Our preliminary analysis showed that the proposed model outperforms the existing baseline systems across all the domains.
Resilient Software Architecture Platform for the Individual Care Plan

The individual care plan (ICP) is a metamorphic being. The only steady reality that it maintains is its final objective, stated and explained in the previous chapter where the ICP is thoroughly introduced and debated. It is a fantastic beast, better described as a system of systems that is severely polymorphic due to its coverage both in level of care as well as sources of data to handle. Patient monitoring generates large volumes of data. There is the evident need of an advanced approach that can deal with these huge amounts of healthcare data extracted from various sources such as the wearable sensors, medical, and nursing records that are currently called big data. The purpose of this chapter is to introduce and discuss the software platform that is adequate to develop and deploy the system paying attention to the needs of high-availability, sensitive information security; service-level agreements for multiple healthcare interoperability; law and ruling conformance; as well as other technical and ethical aspects.
Bank Card Number Recognition based on Deep Learning

With the rapid development of the mobile Internet, mobile payment has become one of the most mainstream payment methods. At the same time, in life, many scenarios will involve the binding and identification of bank cards. Many existing recognition technologies have some shortcomings, such as insufficient accuracy. The system uses a deep learning end-to-end identification method to identify bank card numbers. End-to-end means that the input is the raw data and the output is the final result, allowing the neural network to automatically reduce the dimensions and learn features. Finally, the system uses a graphical interface to provide a good user-interactive interface.
Verification of Cryptographic Algorithms Based on the Use of Method Simulation Binary Random Sequences with Specified Statistical Properties

The problem of obtaining a set of samples to evaluate the quality of cryptographic algorithms through the use of statistical tests. Describes new features of binary Markov chains, taking into account the dependence of the probability of binary vectors of different lengths. The analytical expressions for the limits calculated according to ranges of values of binary probabilities of multidimensional random variables on the probability of binary random variables smaller dimension. The reasons of the need for additional “reject” procedure in the simulation implementations binary Markov processes. The method of directed enumeration values of probability distributions Markov ranks binary sequences, allowing to generate ergodic random binary sequence that allows you to completely abandon the “rejection” procedure. Presented algorithm implementing said method having reduced computational complexity compared to known algorithms Computing experiment to study the statistical properties of random binary sequences.
A model transformation approach to constructing agent-oriented design models for CPS/IoT systems

CPS (Cyber-Physical System) and IoT (Internet of Things) are keywords for representing recent software systems, which are composed of not only software components in the cyber space but also hardware devices in the physical space. Since CPS and IoT systems additionally contain hardware devices and interactions among their components comparing with traditional software systems, system design for such a CPS and IoT system tends to be considerably complicated. To deal with this complexity, designing such a complicated system as a multi-agent system (MAS) is a possible approach. This paper describes a model transformation method to construct a representative MAS design model for CPS and IoT systems. The method uses a source model that describes components and labeled relations between these components. We experimentally design a farm monitoring system in the real world. The result of the case study demonstrates that the proposed model transformation can systematically construct an agent-oriented design model.
O⁴-DNN: A Hybrid DSP-LUT-Based Processing Unit With Operation Packing and Out-of-Order Execution for Efficient Realization of Convolutional Neural Networks on FPGA Devices

In this paper, we propose O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> -DNN, a high-performance FPGA-based architecture for convolutional neural network (CNN) accelerators relying on operation packing and out-of-order (OoO) execution for DSP blocks augmented with LUT-based glue logic. The high-level architecture is comprised of a systolic array of processing elements (PEs), supporting output stationary dataflow. In this architecture, the computational unit of each PE is realized by using a DSP block as well as a small number of LUTs. Given the limited number of DSP blocks in FPGAs, the combination (DSP block and some LUTs) provides more computational power obtainable through each DSP block. The proposed computational unit performs eight convolutional operations on five input operands where one of them is an 8-bit weight and the others are four 8-bit input feature (IF) maps. In addition, to improve the energy efficiency of the proposed computational unit, we present an approximate form of the unit suitable for neural network applications. To reduce the memory bandwidth as well as increase the utilization of the computational units, a data reusing technique based on the weight sharing is also presented. To improve the performance of the proposed computational unit further, an addressing approach for computing the partial sums out-of-order is proposed. The efficacy of the architecture is assessed using two FPGA devices executing four state-of-the-art neural networks. Experimental results show that this architecture leads to, on average (up to), 2.5× (3.44×) higher throughput compared to a baseline structure. In addition, on average (maximum of), 12% (40%) energy efficiency improvement is achievable by employing the O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> -DNN compared to the baseline structure.
An Efficient Text Labeling Framework Using Active Learning Model

Electronic medical discharge summaries provide a wealth of information. Extracting useful structured information from such unstructured text is challenging. However, supervised machine learning (ML) algorithms can achieve good performance in extracting useful relations between different entities. To use supervised ML techniques, huge annotated datasets are required. Annotating manually is very expensive and time taking due to the requirement of domain experts for annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. Active learning leverages the advantage of training the classifier with a limited number of samples but achieving maximum performance. This strategy not only saves time but also decreases the annotation cost involved. Active learning works well with datasets where annotation cost is high, and training a decent classifier with the available annotated dataset is a requirement. The key factor for an active learning model's success is its selection of samples that needs annotation. The more informative the samples are, the less time it takes to train the supervised model with high accuracy. Thus, the query strategy in sample selection plays a vital role in the AL process. In this study, we aim to develop a novel query strategy to select the most informative samples from the dataset that can eventually accelerate the supervised model's performance. The query strategy is designed using deep reinforcement learning techniques like actor-critic. The performance of the sample selection strategy is determined by finding the accuracy of the model after a predefined number of iterations.
User-guided 3D reconstruction using multi-view stereo

We present a user-guided system for accessible 3D reconstruction and modeling of real-world objects using multi-view stereo. The system is an interactive tool where the user models the object on top of multiple selected photographs. Our tool helps the user place quads correctly aligned to the photographs using a multi-view stereo algorithm. This algorithm in combination with user-provided information about topology, visibility, and how to separate foreground from background, creates favorable conditions in successfully reconstructing the object.
Data Security Frameworks for Mobile Cloud Computing: A Comprehensive Review of the Literature

Using a mobile Internet connection, Mobile Cloud Computing (MCC) customers can access the powerful computational and storage resources made available by MCC service providers. However, the MCC environment is open to security attacks, especially at the more exposed mobile device (MD) end, as the low resourced MD may not be able to implement sufficiently advanced security protection mechanisms. A number of security frameworks addressing MCC data security issues have been proposed in the extant literature. This paper reviews a selection of MCC data security frameworks and identifies challenges and directions for further work. These include the development of an MCC data protection framework that provides a comprehensive protection against the threat spectrum and across all layers of the MCC infrastructure, by adequately addressing the fundamental security requirements.
Reasoning with Ontologies

This chapter considers the notion of a formal ontology, which is a conceptual vocabulary equipped with a logical semantics. Three families of knowledge representation and reasoning formalisms that put ontologies at the core of any knowledge base are presented, namely: description logics, conceptual graphs and existential rules. We present the main knowledge constructs and dialects of these families, as well as the main reasoning problems with their complexity. We highlight the relationships between these families and compare them from an expressivity viewpoint.
Mathematical Analysis of DCN-Based Super-Resolution

Although DCN-based super-resolution (DCN-SR) techniques have shown impressive performance, the working mechanism has not been completely understood and DCN-SR methods still produce some artefacts. In this paper, we analyze the working mechanisms of DCN-SR methods. We derive mathematical formulations of the DCN-SR methods and provide some experimental analyses, which show that the effective receptive fields of the DCN-SR methods are considerably smaller than the theoretical receptive fields. Based on the mathematical formulations, experiments were performed. The results indicates that current DCN-SR methods may have some fundamental problems and new types of DCN structures are needed for reliable super-resolution performance.
LAD: Learning Access Control Polices and Detecting Access Anomalies in Smart Environments

The domain of access control has long suffered from a lack of expressiveness in specifying access control policies. Recent approaches have leveraged contextual fingerprinting to formulate access control frameworks for both generating and enforcing access control policies. However, effectively and automatically identifying the context attributes relevant for access has proven challenging and cumbersome. An approach that shows promise in supporting more expressive and easy-to-use attribute-based access control relies on recent advances in continuous neighbor discovery protocols and low cost wireless communication technologies such as Bluetooth Low Energy (BLE). These technologies have created opportunities to build smart environments that can seamlessly and inexpensively provide rich contextual data. These capabilities have the potential to enable new transparent and automatic approaches to defining and evaluating access control policies for mobile users and for detecting anomalous access patterns in smart environments. In this paper, we present the LAD framework that uses raw contextual data available via technologies such as BLE to derive real-time attributes defined by the presence of mobile and static nodes in the nearby environment. Based on user interactions in these environments, our framework learns appropriate access control policies and enforces these policies based on attributes that change in real-time as users move in the smart environment.
Identification of Human &amp; various objects through Image Processing based system

The objective of this paper is to image processing based system. This paper identification of humans through the image proceeding development technique by an image-based database system. The techniques involve using of identification of human object monitoring detection development IDEs and adequate APIs to have desired functionalities. We monitoring the human activities for deflection through the surveillance system and recognition through the image process system. We proposed machine learning algorithms such as SVM and Naïve bayed to classification various activities recognition. We implemented this technique using Matlab programming.
Low-Rank Regularized Multimodal Representation for Micro-Video Event Detection

Currently, micro-videos are becoming one of the most representative products in the new media age.Although the length of micro-videos is limited to cater to the fast pace of life and are beneficial for rapid distribution, micro-videos are usually recorded in specific scenarios and tend to convey relatively complete events.To more accurately obtain the event types of micro-videos to facilitate potential applications, we propose a low-rank regularized multimodal representation method for micro-video event detection.To solve the less descriptive power of each modality, the latent common representation of micro-videos is obtained by exploiting complementarity among modalities.A considerable gain in accuracy on this basis can be achieved by further considering the low-rank constraint for the lowest-rank intrinsic representation and a flexible label-relaxation strategy for mappings between representations and their correspondences.A newly constructed micro-video dataset is used to verify the advantages of our proposed model.The experimental results demonstrated the superior performance of our proposed method compared with stateof-the-art methods.
Masked Face Detection Based on Locally Nonlinear Feature Fusion

Realizing that features with strong discrimination are both needed for the generation and discrimination of candidate regions in the masked face detection, LNFF-Net (Locally Nonlinear Feature Fusion-based Network) is proposed. To highlight the feature from the face region and suppress the background region, this method nonlinearly fuses the visual saliency map and the heat map, which is extracted from a light fully convolutional network (FCN). On the other hand, through transferring the Fast R-CNN based multi-objective detection to single masked face detection, the structure of candidate region discrimination using convolutional network is optimized. Experimental results show that the proposed algorithm has better detection accuracy than other method.
Fine-Grained Entity Typing with Hierarchical Inference

Fine-Grained Entity Typing is a task to infer context-related fine-grained types to an entity mention. Recent models do not explicitly consider the hierarchical structure of the type set in type inference. Some state-of-the-art models transform the problem from a multi-label classification to a single-label classification, which results in the confirmation bias. Therefore, we propose a neural model that can effectively capture entity information from the context and the mention aspects and hierarchical inference, which hierarchically infers types layer by layer in type set. In the optimization process, we also introduce a penalty term that can effectively alleviate the side effect of the confirmation bias and label noise introduced by distant supervision. Experiments on benchmark datasets show that our model achieves state-of-the-art results in micro-F1 scores.