Reviewerly/exHarmony huggingface dataset:
High‐resolution depth measurements in digital microscopic surgery

Abstract Fully digital microscopes are becoming more and more common in surgical applications. In addition to high‐resolution stereoscopic images of the operating field, which can be transmitted over long distances or stored directly, these systems offer further potentials by supporting the surgical workflow based on their fully digital image processing chain. For example, the image display can be adapted to the respective surgical scenario by adaptive color reproduction optimization or image overlays with additional information, such as the tissue topology. Knowledge of this topology can be used for computer‐assisted or augmented‐reality‐guided microsurgical treatments and enables additional features such as spatially resolved spectral reconstruction of surface reflectance. In this work, a new method for high‐resolution depth measurements in digital microsurgical applications is proposed, which is based on the principle of laser triangulation. Part of this method is a sensor data fusion procedure to properly match the laser scanner and camera data. In this context, a strategy based on radial basis function interpolation techniques is presented to handle missing or corrupt data, which, due to the measuring principle, can occur on steep edges and through occlusion. The proposed method is used for the acquisition of high‐resolution depth profiles of various organic tissue samples, proving the feasibility of the proposed concept as a supporting technology in a digital microsurgical workflow.
Dimensionality Reduction Techniques: Comparative Analysis

It has been spotted an exponential growth in terms of dimension in real world data. Some example of higher dimensional data may includes speech signal, sensor data, medical data, criminal data and data related to recommendation process for different field like news, movies (Netflix) and e-commerce. To empowering learning accuracy in the area of machine learning and enhancing mining performance one need to remove redundant feature and feature not relevant for mining and learning task from this high dimension dataset. There exist many supervised and unsupervised methodologies in literature to perform dimension reduction. The objective of paper is to present most prominent methodologies related to the field of dimension reduction and highlight advantages along with disadvantages of these algorithms which can act as starting point for beginners of this field.
Combining Algebraic Effect Descriptions Using the Tensor of Complete Lattices

Algebras can be used to interpret the behaviour of effectful programs. In particular, we use Eilenberg-Moore algebras given over a complete lattices of truth values, which specify answers to queries about programs. The algebras can be used to formulate a quantitative logic of behavioural properties, specifying a congruent notion of program equivalence coinciding with a notion of applicative bisimilarity. Many combinations of effects can be interpreted using these algebras. In this paper, we specify a method of generically combining effects and the algebras used to interpret them. At the core of this method is the tensor of complete lattices, which combines the carrier sets of the algebras. We show that this tensor preserves complete distributivity of complete lattices. Moreover, the universal properties of this tensor can then be used to properly combine the Eilenberg-Moore algebras. We will apply this method to combine the effects of probability, global store, cost, nondeterminism, and error effects. We will then compare this method of combining effects with the more traditional method of combining equational theories using interaction laws.
Mitigating Man-in-the-Middle Attack in Digital Signature

We all are living in the digital era, where the maximum of the information is available online. The digital world has made the transfer of information easy and provides the basic needs of security like authentication, integrity, nonrepudiation, etc. But, with the improvement in security, cyber-attacks have also increased. Security researchers have provided many techniques to prevent these cyber-attacks; one is a Digital Signature (DS). The digital signature uses cryptographic key pairs (public and private) to provide the message's integrity and verify the sender's identity. The private key used in the digital signature is confidential; if attackers find it by using various techniques, then this can result in an attack. This paper presents a brief introduction about the digital signature and how it is vulnerable to a man-in-the-middle attack. Further, it discusses a technique to prevent this attack in the digital signature.
Internet of Things Traffic Characterization using flow and packet analysis

The "Internet of Things" (IoT) era is becoming a reality and the number of smart objects or devices connected to the Internet is rapidly increasing every year. Nevertheless, most IoT devices are known to be built with limited resources (CPU, memory, battery life etc.), thus not every security mechanism can be included in their development process. Given these properties of IoT devices, it became imperative for researchers and specialists in the information security field to understand how "normal" IoT traffic profile looks like to create a baseline for IoT environments behavior and define what characteristics can further be used as indicators for prevention and detection of cyber-attacks. In this paper, we proposed a methodology for distinguishing normal or legitimate traffic from abnormal or malicious traffic and identifying specific IoT environments network traffic characteristics from our analysis. This work is based on two existing public datasets that were examined in order to perform the analysis.
Development of a Blasting Vibration Monitoring System Based on Tri-axial Acceleration Sensor for Wireless Mesh Network Monitoring

Recently a variety of vibration monitoring devices based on MEMS (micro electro-mechanical system) 3-axis acceleration sensor has been introduced and is gradually replacing analog wire-type geophones for blasting vibration monitoring. Blasting vibration monitoring tasks generally require frequent movement of the monitoring devices. Since accurate device set along the vertical axis is essential at a new location, acceleration sensors sensitive to the gravitational acceleration are not suitable for accurate monitoring of the blasting vibration. In this study, the vibration monitoring system with a 3-axis MEMS acceleration sensor is developed for wireless mesh network monitoring. Individual monitoring units are equipped with an algorithm for reorientation along the direction of gravity once they are placed on a particular baseline. The algorithm aims at automatically adjusting the z-axis and resetting the zero offset value altered after each blasting vibration monitoring and relocation. With this feature, it shows individual unit can be applied as conventional portable devices as well. In addition, comparative studies are also carried out along with conventional units for 3-axis acceleration and primary frequency analysis. There are several advantages of the developed system. Firstly, this system has been designed for easy installation and wireless remote management to provide readings and alerts when the user-defined allowable limit is exceeded. Secondly, due to remote management, it can improve staff safety, reduce human resources, and save time and cost. Thirdly, this system can be positioned over a large area as each sensor can act as a repeater. Finally, multiple sensors can be installed to measure various locations monitoring at the same time. Furthermore, without the cables to interface with operations or accidental damage, this system improves safety and reduces maintenance costs. The readings from the multiple sensors deployed at target locations are transmitted to the management node connected to the PC. Thus, all the live data can be seen on the PC. This system is built to be deployed on mining and construction sites, tunnel, bridges, and other structures. The system is designed with the ultimate goal of understanding challenges and provide solutions to protect assets by the low-cost system with high accuracy and reliability.
Classification of Public Complaint Data in SMS Complaint Using Naive Bayes Multinomial Method

SMS Complaint is an electronic public complaint tool for reporting issues on government performance. Text mining classification utilized to determine the value of each complaint category. The SMS data in this study sourced from the SMS Complaint Service of Ambon City Government. There were 6 categories of classification, namely Public Service, Infrastructure, Bureaucracy, Health, Education, and Social. The classification performed to measure levels of accuracy of the Stemming process and non-Stemming process represented in Matrix with values of recall, precision, and f1 score. The methods used in the measurement were Naıve Bayes Multinomial. With the naıve Bayes method, an accuracy level with stemming of 91.38% obtained, and while the accuracy level without stemming was 90.73%. The result showed that the naıve Bayes method could be used effectively to predict complaint data through stemming.
Meteorological data outlier detection: a principal component approach

Meteorological modeling takes data captured from multiple sources that is then processed by data mining techniques to predict environmental changes. The most commonly used machine learning techniques for processing meteorological data are decision trees, rule-based methods, neural networks, naive Bayes, Bayesian belief networks, and support vector machines. These techniques require accurate data for effective models to be simulated. Meteorological datasets can contain outliers and errors that can significantly skew the accuracy of the generated models that are relied upon for many sectors of society including agriculture, natural disasters, and meteorological forecasting. This paper proposes a method to eliminate outliers from meteorological data to enhance the accuracy of models by applying a blind thresholding algorithm to the principal components (PCs) obtained from <i>L</i><sub>1</sub> and <i>L</i><sub>2</sub> norm Principal Component Analysis to identify and discard outliers in the dataset.
Deep Classification Consistency for Person Re-Identification

Person re-identification (Re-ID) has greatly benefited from utilizing features of different levels. Most methods draw support from elegant network designs and elaborate fusion modules. In this paper, different from employing multi-level feature fusion like mainstream skip connection methods, we propose a Deep Classification Consistency(DCC) layer which imposes classification consistency on deep features from different stages in CNNs. DCC regularizes the training process of networks. It profoundly changes the distribution of learned features, making them more discriminative and transferable. Extensive experiments on Market-1501, DukeMTMC-reID, and CUHK03 datasets prove that the proposed method has obtained competitive performances when compared with state-of-the-art approaches, especially those advanced methods merely based on metric learning.
Algorithmic Transparency of Large-Scale *AIDA Programs

Programming in pictures is an approach where pictures and moving pictures are used as super-characters to represent the features of computational algorithms and data structures, as well as for explaining the models and application methods involved. *AIDA is a computer language that supports programming in pictures. This language and its environment have been developed and promoted as a testbed for various innovations in information technology (IT) research and implementation, including exploring the compactness of the programs and their adaptive software systems, and obtaining better understanding of information resources. In this paper, new features of the environment and methods of their implementation are presented. They are considered within a case study of a large-scale module of a nuclear safety analysis system to demonstrate that *AIDA language is appropriate for developing efficient codes of serious applications and for providing support, based on folding/unfolding techniques, enhancing the readability, maintainability and algorithmic transparency of programs. Features of this support and the code efficiency are presented through the results of a computational comparison with a FORTRAN equivalent.
Learning to Rank for Blind Image Quality Assessment

We propose a blind image quality assessment (BIQA) model using learning to rank (LTR) method in order to overcome the defects of the existing BIQA model. In this model, we first simplify the problem of predicting exact image quality score with the classification of image quality preference and we then use feature fusion and multiple kernel learning based on group lasso (MKLGL) methods to train a classifier. Finally, based on the voting strategy, a simple and effective image quality prediction model is constructed. Experimental results on the existing IQA datasets show that our proposed method is highly consistent with the subjective perception of human eyes and is superior to existing BIQA methods. In addition, our method is highly scalable.
Vision: What If They All Die? Crypto Requirements For Key People

The question above seems absurd but it is what a Bank has to ask to its suppliers to meet the European Central Bank (ECB) regulations on the continuity of critical business functions. The bank has no intention of mingling in the daily work of the supplier (that’s the whole purpose of outsourcing). Nor the supplier has any intention to make available to the bank the keys of its kingdom (it is actually forbidden to do so by the very same regulations). We need a way to do so only when the hearts of the key people stop beating. In this paper, we discuss whether recent advances in cryptography (secret sharing and MPC, time-lock puzzles, etc.) can replace the classical approach based on human redundancy.
3D Visualization Design of Realtime Position Tracker Based On Ultra-Wideband Device

To optimize the level of security of company's asset, it needs to keep track on the location and status of asset s in real-time. Companies to maintain their assets' data usually use conventional information systems for asset management. Unfortunately, they have a shortcoming that the data are mostly presented textually or numerically, while human perceives visual information better than those in textual format do. On the other hand, while a 3D map can visualize environment in its natural shape, an ordinary 3D map cannot dynamically update its images when the real object is moved. This study aims to build a 3D map that can facilitate monitoring the displacement of each company's asset and keep track of its status. Ultra-Wideband (UWB) devices as references for movement are installed in every corner of the building. Trackers are attached to each asset to be the target of tracking. Information on the tracker movement data will be visualized in 3D. So, this application is expected to produce real-time display of asset movements in 3D. The application that has been built, has succeeded to display 3D indoor maps and the movements of the assets in real-time.
Money/Code/Space

Abstract Chapter 2 provides a theoretical discussion of money, code, and space to foreground the emergence of Bitcoin as a radical response to existing economic structures. Using the history of central banking and software production, Bitcoin is compared to traditional modes of centralised governance to outline some of the political context of algorithmic decentralisation. In doing so, the binary of centralised and decentralised is rendered reductive and thus impotent for describing digital networks because of the inescapable complexity inherent within them. Instead, the concept of obligatory passage points is adapted into a framework for understanding (de)centralisation in algorithmic networks. This provides an understanding of money/code/space that encapsulates the cultural and economic messiness of cryptocurrencies and blockchain technology that can be used for bringing places of power to the forefront of related academic scholarship.
A Non-cooperative Game Approach for the Dynamic Modeling of a Sailing Match Race

This research introduces a spatio-temporal planning framework whose objective is to simulate a sailing yacht match race. The race is a duel in which strategy and tactics play a major role as sailors continuously have to take decisions according to wind variations and opponent's locations and actions. We introduce a decision-aid framework based on a stochastic game approach grounded on an action-oriented model that replicates yachts' behaviors. The objective is to replicate as closely as possible the respective behaviors and navigation decisions taken by yachts competitors. The proposed formalism has been implemented and is illustrated by a sample race example .
A Hardware/Software Co-Design of K-mer Counting Using a CAPI-Enabled FPGA

Advances in Next Generation Sequencing (NGS) technologies have caused the proliferation of genomic applications to detect DNA mutations and guide personalized medicine. These applications have an enormous computational cost due to the large amount of genomic data they process. Although leveraging FPGAs can improve the processing time of such amount of data, the limited memory capacity of FPGAs often restricts the potential gains. To overcome this limitation, IBM CAPI (Coherent Accelerator Processor Interface) supported platforms provide FPGAs with direct access to the CPU memory. This paper proposes a hardware/software co-design for k-mer counting, one of the most time-consuming phases of genomic applications. The proposed co-design targets CAPI-enabled FPGAs and is integrated into SMUFIN, a state-of-the-art reference-free method for finding DNA mutations. Results show that the proposed co-design outperforms the CPU-only design by a factor of 2.14×, it consumes 2.93× less energy, and it requires 1.57× less memory.
Success factors affecting digital literacy training initiatives led by local community organizations

Abstract This paper describes an in‐progress research study investigating the factors affecting the success of digital literacy training initiatives led by local community organizations, including public libraries. Data were collected from two public libraries and five community organizations in two cities in Canada. Data collection comprised interviews with 14 administrators, six training instructors, 20 clients of training programs, the analysis of training documents, observations of six training sessions, and a survey administered to 20 clients. The study involved two rounds of data analysis. The first round is complete. The second round is currently underway and its results, in addition to those from the first round, will be disseminated at the ASIS&amp;T Annual Meeting. Goals of the study are to identify factors affecting the success of digital literacy training opportunities led by local community organizations, and to elicit recommendations for practice for local community organizations to follow.
About Topics of Interest to I&amp;M [Life after graduation]

Reports on the major topics of interest to those in the instrumentation and measurement industry. Presents a listing of the top 20 searched terms in IEEE Xplore in 2020 as well as the top 10 publication topics from the IEEE Transactions on Instrumentation and Measurement in 2020. Suggests that such term and topic analyses offers a good opportunity to determine future areas of research and development and to find inspiration for further exploration.
Designing for Interaction in Outdoor Winter Sports

Winter sports define a wide variety of different activities, which are adopted especially in northern countries. The winter context sets special requirements for designing interactive systems, as the activities are typically conducted in cold temperatures, with heavier clothing and equipment than summer sports. This chapter describes the authors’ experiences when designing, prototyping, and evaluating computational enhancements for use in outdoor winter sports. The chapter presents four case studies addressing different winter sports, namely cross-country skiing, downhill skiing, snowboarding, and ice skating. As a conclusion, we discuss the common themes, challenges, and lessons learnt from the case studies as a whole.
A Progressive Non-discriminatory Intensity Equalization Algorithm for Face Analysis

Illumination plays a major role in the determination of image quality in an uncontrolled environment. Shadow casting, poor contrast and poor intensity are of particular interest in facial analysis. These problems are characterized by the nature of face image from uncontrolled environment. Several attempts have been made to enhance image quality; however, existing enhancement methods are limited in the specificity for facial expression analysis, thus resulting into non-uniform pixel brightness. This paper presents a novel method which is referred to as an intensity equalizer that handles objects of an image in singleton, pixelates the objects in this case a face image, employs the HSV color model for intensity value separation of every pixel, computes Gaussian probability density function and transforms every pixel with the best local minimum difference of mean intensity and variance of saturation across the object. Experimental result shows that the proposed model is invariant to shadow casting and gives moderate contrast on face image irrespective of input’s contrast class while preserving the global intensity information and enhancing local pixel information.
Bitcoin-Based Anti-collusion Fair Payments for Outsourcing Computations in Cloud Computing

As an attractive cloud computing model, outsourcing computation often has a fair payment issues. In cloud computing, resource-constrained users outsource tasks and pay for them. However, the traditional outsourcing computation model can hardly resist the threat of collusion between calculators. In this paper, we propose a new scheme of Bitcoin-based anti-collusion fair payments for outsourcing computations (BAPay), give the architecture of BAPay, security requirements, and describe the design details. Besides, the scheme guarantees that an honest calculator will be paid for doing the calculations, regardless of the outsourcer’s behaviour. In addition, the security analysis shows that BAPay achieves completeness and fairness.
Information Security in Software Systems

Abstract The rapid progress of Information and Communication Technology (ICT) continuously intensifies the interest in the two disciplines of security and privacy. This chapter introduces the most important concepts of information security, which essentially include the three major security mechanisms identification, authentication and authorization. These mechanisms are used to obtain the most important security goals, namely confidentiality, integrity, availability and non-repudiation, which are standardized by many international organisations. The next section deals with the possible attacks against distributed systems, which are mainly the Denial-of-Service (DoS) and the Sybil attack. The chapter then concludes by introducing one possible defense mechanism against such attacks, which is now publicly known as Proof-of-Work (PoW).