Reviewerly/exHarmony huggingface dataset:
Robust Target Detection in Optical Scene Based on Multiple Reference Images

Target detection has a wide spectrum of promising applications in image processing. Several image matching techniques using features descriptors and detectors that can be used for target detection were introduced in the literature. These techniques achieve the detection task accurately in case of capturing the reference and scene images from the same sensor. On the other hand, the performance of these matching techniques is degraded if the scene and reference images were captured from different sensors because of different image transformations and deformations problems that occur. This paper introduces a robust technique that enhances the performance of the target detection. We argue that, the proposed technique differs significantly from many recent target detection techniques, as it is based mainly on a voting process that select the best matches between the reference images and the scene image. The proposed technique emphasizes the features of objects in multiple reference images with different perspective angles for enhancing the matching task. Experimental results with real images are used to illustrate the efficiency of this approach. The accuracy percentage for the proposed technique is 48.4615 %. The performance of the proposed technique outperforms the recent techniques and increases the resilience of the image matching task against different image transformations and deformations problems. Finally, the performance analysis is accomplished using three metrics: number of matches, execution time, and accuracy.
BDSS: Blockchain-Based Data Synchronization System

In view of the high-efficiency and security issues of data synchronization between cross-region remote cloud storage servers, such as the lack of consistency verification and the fact that most synchronization tools cannot achieve one-to-many data synchronization, a data synchronization system is proposed and implemented, and blockchain smart contract technology is introduced to achieve consistency verification of data synchronization between servers and traceability of data synchronization process; propose a data synchronization strategy based on Rsync and P2P to select the synchronization mode according to the threshold set by data synchronization system, reduce network overhead, and ultimately achieve efficient data synchronization; Experimental results show that: the blockchain-based data synchronization system can efficiently and securely achieve one-to-one or one-to-many data synchronization across regional servers.
Representing Cellular Lines with SVM and Text Processing

A main problem for predicting cell line interactions with chemical compounds is the lack of a computational representation for cell lines. We describe a method for characterizing cell lines from scientific literature. We retrieve and process cell line-related scientific papers, perform a document classification algorithm, and then obtain a description of the information space of each cell line. We have successfully characterized a set of 300+ cell lines.
Sequence Based Two-Factor Authentication (2FA) Method

Two-factor authentication (2FA) provides an extra layer of security by combining two different authentication factors that are unrelated (e.g., password and an authentication code sent to user’s phone). Despite its prevalence is growing across many online services to improve individual account security, its adoption rate remains low due to various reasons (e.g., usability, privacy concerns). Though there are many 2FA methods such as SMS, One-time password (OTP) and Time-based one-time password (TOTP), U2F (Universal 2nd Factor), Push notification, all of these 2FA methods require to have a phone or a device. However, users may not be willing to provide their phone numbers due to privacy concerns about sharing their phone numbers with companies, or they may not have a device for 2FA to work. We present a new type of 2FA method where users derive OTP from a pre-generated sequence of characters. This 2FA method eliminates the need to have a physical device to use 2FA both online and offline (e.g., no cell phone service is available). Also, it can be well integrated into existing password-based authentication systems.
Multi-Layered IoT System Design Towards End-to-End Secure Communication

An increasing amount of sensory data, often of confidential nature, is exchanged day by day: from the sensor and actuator layers over smart gateways to the business logic and analytics level. Robust yet efficient security measures play an essential role in this interaction. However, the complexity of securely connecting different building blocks of a distributed, multi-layered systems is considerable. Security methodologies are often applied at a late stage of system development, posing problems such as inappropriate security levels, performance issues, and longer time-to-market cycles. Addressing possible security properties already in the design phase of a security-critical system helps to mitigate these problems. In this paper, we discuss a distributed, multi-layered IoT data collection system that enables data aggregation and exchange from the embedded level up to different cloud instances while supporting end-to-end secured communication. The system was designed in the course of a case study where we used a design-space-exploration tool for identifying secure processes in regard to key management and distribution. Based on our analysis results, a distributed proof of concept was developed. Subsequently, the most critical processes of the individual layers were evaluated regarding security and execution speed.
Work-in-Progress: Enabling Edge-based Self-Navigation in Earthquake-Struck Zones

The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of detecting irregularly shaped obstacles such as cracks, puddles, and debris on the streets. In this paper, we present an edge-based self-navigation vehicle that can detect unique obstacles in earthquake-struck sites and discuss the performance and energy impact of various neural network structures, edge platforms, and optimizations. To enable vehicles to safely navigate earthquake-struck sites, we compiled a new image database of various earthquake impacted regions and developed semantic segmentation models that identify obstacles unique to earthquake-sites. The models are tested on an edge-based car platform. To our best knowledge, this is the first study that identifies unique challenges and discusses the performance and energy impact of edge-based self-navigation vehicles for earthquake-struck zones.
A Survey of High-utility Itemsets Mining

Abstract Data mining is of significance for finding useful information in massive data. Frequent itemsets mining (FIM ) and high-utility itemsets mining(HUIM) are extremely common and wide application in research and real life. For one thing, HUIM algorithm focuses on utility, which is more practical. It can be used to find high profit goods, items with user’s preference, etc. For another, the difference between utility and frequency determines that HUIM and FIM algorithms are different. In order to introduce HUIM algorithms in the round, this paper showed typical HUIM algorithms for static data and stream data separately in section 2 and section 3. Meanwhile, section 2 partitioned algorithms based on candidates generation and threshold. Section 3 showed algorithms in terms of window model which is necessary to stream data mining. Lastly, this paper made a conclusion of referred HUIM algorithms and proposed some research prospects for this work.
Android XML Layouts – A study of Viewgroups and Views for UI Design in Android Application

The main objective of this paper is to tell about the Android platform, which is designed for mobile devices, and android based mobile app development, which helps developers to develop various applications, which are now being used by the user all across the world. Android developers make use of layout with the help of which users can get a good user interface for an android application. This paper will mostly focus on the types of layouts and views that developers use to design the user interface of an android application. This paper will also discuss extensible mark-up language (XML) which is the lightweight mark-up language used for designing the layouts.
Set-Up of a Method for People-Counting Using Images from a UAV

WeParisi, Daniel R.Giribet, Juan I.Pose, Claudio D.Mas, Ignacio present a new method for obtaining the positions of each person attending an outdoor gathering. From this information it is possible to calculate the density field and the number of people. A dual-camera (visible + infrared (IR)) is mounted on an unmanned aerial vehicleUnmanned aerial vehicles applications (UAV). In this work, we only use the IR channel and present an initial set-up and calibration of the system along with the characterization of a small group of people.
The Framework of Consensus Equilibria for Gap Games in Blockchain Ecosystems

The goal of this paper is to establish the general framework of consensus equilibria for Mining Pool Games in Blockchain Ecosystems, and in particular to explain the stable in the sense for the existence of consensus equilibria related to mining gap game’s behaviors by using one new concept called “Consensus Games” under the environment of Blockchain Ecosystems, where, the Blockchain Ecosystem mainly means the economic activities by taking into the account of three fundamental factors which are “Expenses, Reward Mechanism and Mining Power” for the work on blockschain by applying the key consensus called “Proof of Work” due to Nakamoto in 2008 and related ones.
CellIdNet: Automatic Cell Instance Segmentation and Classification in Bone Marrow Examination with Double Backbone Networks

Bone marrow examination plays an important role in the diagnosis of a number of diseases, including leukemia, multiple myeloma, etc. However, the examination is slowed down by complicated manual work which requires professional knowledge. In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in bone marrow examination. The process is mainly composed of two steps, segmentation and classification. Double backbone networks structure is employed to show better result. A two-step training strategy is also proposed. Mask-Region-Convolutional Neural Networks(Mask-RCNN) was used for detection and image segmentation to extract cells and then common architectures in convolutional neural networks pre-trained on ImageNet, such as Dense Convolutional Network and Deep Residual Network(ResNet), were fully experimented and compared in classification. Result of the first stage shows high efficiency to meet application requirements. For cell classification, InceptionV3 shew the best precision of 93% in five cell lines and ResNet-152 achieved 82% in twelve classes. This structure improves nearly three times of accuracy of a single Mask-RCNN.
A Hybrid Approach Towards Two Stage Bengali Question Classification Utilizing Smart Data Balancing Technique

Question classification (QC) is the primary step of the Question Answering (QA) system. Question Classification (QC) system classifies the questions in particular classes so that Question Answering (QA) System can provide correct answers for the questions. Our system categorizes the factoid type questions asked in natural language after extracting features of the questions. We present a two stage QC system for Bengali. It utilizes one dimensional convolutional neural network for classifying questions into coarse classes in the first stage. Word2vec representation of existing words of the question corpus have been constructed and used for assisting 1D CNN. A smart data balancing technique has been employed for giving data hungry convolutional neural network the advantage of a greater number of effective samples to learn from. For each coarse class, a separate Stochastic Gradient Descent (SGD) based classifier has been used in order to differentiate among the finer classes within that coarse class. TF-IDF representation of each word has been used as feature for the SGD classifiers implemented as part of second stage classification. Experiments show the effectiveness of our proposed method for Bengali question classification.
Using Applicability to Quantifying Octave Resonance in Deep Neural Networks

Features in a deep neural network are only as robust as those present in the data provided for training. The robustness of features applies to not just the types of features and how they apply to various classes, known or unknown, but also to how those features apply to different octaves, or scales. Neural Networks trained at one octave have been shown to be invariant to other octaves, while neural networks trained on large robust datasets operate optimally at only the octaves that resonate best with the learned features. This may still discard features that existed in the data. Not knowing the octave a trained neural network is most applicable to can lead to sub-optimal results during prediction due to poor preprocessing. Recent work has shown good results in quantifying how the learned features in a neural network apply to objects. In this work, we follow up on work in feature applicability, using it to quantify which octaves the features in a trained neural network resonate best with.
Semi-automatic RDFization Using Automatically Generated Mappings

Most data available on the Web do not conform to the RDF data model. A number of tools/approaches have been developed to encourage the transition to RDF. Manual and automatic tools/approaches tend to be complex and rigid. On the other hand, semi-automatic tools can hide and automate complex tasks while enhancing flexibility by solicitating human experts for decision making purposes. In this paper, we describe a semi-automatic approach to facilitate the transformation of heterogeneous semi-structured data to RDF. The originality of our approach is its ability to generate exhaustive descriptions using entities from several ontologies without requiring end-users to have a knowledge of ontologies. We provide an implementation of our approach and demonstrate its use using a real dataset from an open data portal.
Challenging stylometry: The authorship of the baroque play <i>La Segunda Celestina</i>

Abstract The aim of this study was to verify the possibility of Sor Juana Inés de la Cruz authoring the anonymous part of the baroque play La Segunda Celestina, commissioned to Agustín de Salazar, and left unfinished after his death. This is a first systematic stylometric study on this problem and a baroque hispanoamerican text. In our study, we faced building a balanced corpus from few available resources, and took extensive evaluation measures to deal with unclear stylometric signals. We use a variety of established attribution and verification methods, and introduce a novel evaluation procedure of examining historic texts with scarce corpora. The results support Sor Juana’s authorship, and unravel new connections between her and other authors of the time, showing, still undermined, powerful impact of her works on the epoch. The solutions adopted in solving methodological problems of such a complex task show how stylometry can overcome similar challenges.
Feature fused breast cancer detection

Breast cancer is dreadful, fatal and a widespread disease now. Mammogram is a commonly used imaging technique for screening. Deep learning is applied in clinical dataset for medical imaging. Images are affected by quality factors. To diagnose more accurately pre-processing methods are required. Here denoising is done by wiener filter and image enhancement is performed by contrast limited adaptive histogram equalization. As medical image segmentation has an important role in computer aided diagnosis, a hybrid segmentation technique, thresholding and morphological operation is done. Image feature analysis is done by a convolutional neural network and geometric and texture feature analysis is done by multi-layer perceptron. A multi-modal feature fusion technique which merge low-level features with high-level features is applied. Late multimodal fusion technique implemented combines the output from individual classifiers and differentiates benign and malignant growth. In the performed work AlexNet architecture shows an improved accuracy in classifying.
Word-serial unified and scalable semi-systolic processor for field multiplication and squaring

This paper exhibits a word-serial unified and scalable semi-systolic processor core for concurrently executing both multiplication and squaring operations over GF(2k). The processor is extracted by applying a chosen non-linear scheduling and projection functions to the dependency graph of the adopted bipartite multiplication-squaring algorithm. It has the advantage of sharing the data-path resources between the two operations leading to considerable savings in both space and power resources. Also, the processor’s scalability nature provides the designer with higher flexibility to manage the processor size as well as its execution time. The acquired ASIC synthesis results of the explored word-serial multiplier-squarer architecture and the reported competing word-serial multiplier architectures indicate that the developed design significantly outperforms the competing ones in terms of area and consumed energy at the word-size of 32-bits. Therefore, the explored architecture is more suited for realizing cryptographic primitives in all resource-constrained embedded applications operating at this word-size.
Architectures in parametric component-based systems: Qualitative and quantitative modelling

One of the key aspects in component-based design is specifying the software architecture that characterizes the topology and the permissible interactions of the components of a system. To achieve well-founded design there is need to address both the qualitative and non-functional aspects of architectures. In this paper we study the qualitative and quantitative formal modelling of architectures applied on parametric component-based systems, that consist of an unknown number of instances of each component. Specifically, we introduce an extended propositional interaction logic and investigate its first-order level which serves as a formal language for the interactions of parametric systems. Our logics achieve to encode the execution order of interactions, which is a main feature in several important architectures, as well as to model recursive interactions. Moreover, we prove the decidability of equivalence, satisfiability, and validity of first-order extended interaction logic formulas, and provide several examples of formulas describing well-known architectures. We show the robustness of our theory by effectively extending our results for parametric weighted architectures. For this, we study the weighted counterparts of our logics over a commutative semiring, and we apply them for modelling the quantitative aspects of concrete architectures. Finally, we prove that the equivalence problem of weighted first-order extended interaction logic formulas is decidable in a large class of semirings, namely the class (of subsemirings) of skew fields.
Emblem Recognition: Cultural Coaching Software via Hand Gestures

Negotiations and casual exchanges that involve different cultures may often lead to misunderstandings and eventually adverse outcomes. These conflicts can be easily avoided if both parties are familiarized with each other’s culture. This paper proposes a novel way of tackling this problem by using computer vision and identifying hand gestures that are particular to a culture. Such gestures, also known as emblems, are used frequently during interaction. There is a certain complexity to emblems, and their identification could provide vital information during communication if recognized accurately. An example for this case is the “thumbs up” gesture, where the thumb is extended while the other fingers are curled up. This gesture, while positive in the West, can be insulting in other countries. The implemented computer vision software was able to detect five distinct hand gestures that had one specific meaning in the United States, as well as other connotations in other countries. This helped inform participants with residency in the United States about additional meanings of familiar hand gestures that are commonly used in North America. The main purpose of culturally-sensitive technology is to help a user get acquainted with socio-cultural particularities of another group. Possible applications are discussed further, such as serving as a cultural interpreter during a diplomatic or business exchange or aiding in cultural sensibility training for tourists traveling to a new country.
The Method of Fuzzy Comprehensive Evaluation on Radar Jamming Effect Based on Variable Trapezoidal Membership Function

The exploit of fuzzy mathematics in evaluating the radar jamming effect plays an important role in the field of ECM. The key to applying the fuzzy comprehensive evaluation method is to determine an appropriate membership function. However, the current membership function is usually represented by an accurate function model. In this paper, the weakness in the evaluation method adopted unique and static traditional fuzzy membership function such as less information and monotonous contents is pointed out. A variable membership function is proposed, which extends the concept of the traditional membership function. A simulation example is given to illustrate that the results can reflect the changing trend of the jamming effect.
Signal Complexity Measures Based on Ising Model

Following the principle of “Just decode like encode,” we address using two-dimensional Ising model to represent signal complexity. Under choosing an apt phase transition temperature for Li chaos equation, we first distinguish the periodicity versus chaotic states in weak chaos equation, then do three classifications on the state of periodicity, chaos, or random in Lorenz and Chua equations and in Python random instructions. The results show that the classic phase diagrams are distinguishable, and the new measure coined average energy ratio of initial input energy to average output energy to be more than 70% of the correct recognition rate. The Curie temperature features in the new measure. This case study on signal complexity measure may add fresh leaves for chaos criteria tree.
Improving High-Meets-Low technique to generate odd-variable resilient Boolean functions with currently best nonlinearity

This paper studies the construction of resilient Boolean functions in odd variables with strictly almost optimal (SAO) nonlinearity. Based on High-Meets-Low technique, we present a new construction to obtain Boolean functions with higher resiliency order and currently best known nonlinearity. It is shown that this method can achieve a better tradeoff between resiliency and nonlinearity than those of the previous research results.