Reviewerly/exHarmony huggingface dataset:
Empirical Study of Password Strength Meter Design

Computer password was first used at the Massachusetts Institute of Technology around 1960 when researchers built a large-scale time-sharing computer called CTSS (Compatible Time Sharing System). There are many purposes where regular users require different passwords whenever they send and receive emails, do online shopping and numerous other activities on the internet. Surprisingly since the invention of the password, it has not been capable to protect the user accounts until now. There is no problem in using the similar password, but different passwords are often difficult to remember and mistakes can creep in rather easily. Many users do not know what kind of passwords should be chosen which will be strong enough to thwart all sorts of fraudulent activities. Thus, most passwords are not secure as they should be, and the users could become targets of attacks at any time. This research attempt, after a thorough literature review and in-depth empirical study, developed a software plug-in called ‘Password Strength Meter’, which can be used to visually inform the user about the durability of their chosen password and an estimate on the timeframe it may take to break the password using standard cracking mechanism. The output of this empirical study has been widely appreciated by the users who have tested the developed software, stating that the confidence on their chosen password increases significantly while using this tool to form a password.
Medical Image Enhancement and Deblurring

One of the most common image artifacts is blurring. Blind methods have been developed to restore a clear image from blurred input. In this paper, we introduce a new method which optimizes previous works and adapted with medical images. Optimized non-linear anisotropic diffusion was used to reduce noise by choosing constants correctly. After de-noising, edge sharpening is done using shock filters. A novel enhanced method called Coherence-Enhancing shock filters helped us to have strong sharpened edges. To obtain a blur kernel, we used the coarse-to-fine method. In the last step, we used spatial prior before restoring the unblurred image. Experiments with images show that combining these methods may outperform previous image restoration techniques in order to obtain reliable accuracy.
LiDAR-Stereo

Obtaining accurate depth information is vital for real world 3D perception, and a combination of different sensors is frequently used for better performance. In this paper, we propose an approach for high-precision depth estimation by jointly utilizing information acquired from stereo cameras and LiDAR sensors. More specifically, we design novel convolutional neural network (CNN) architecture with two novel units: monocular completion unit and stereo fusion unit, and the complementary properties of sparse LiDAR and dense stereo images are fully explored in a boosting manner. Extensive experiments on both synthetic and real datasets have shown that the proposed approach achieves significant improvements over state-of-the-art techniques in terms of both subjective and objective evaluations.
Improving the Licensing Workflow at a Major Research University Library

A license agreement is negotiated when libraries enter into business relationships with content providers for access to electronic resources. The following discusses the changes implemented at The University of Alabama Libraries to improve workflow procedures and communication regarding the licensing process. Addressed within are aspects of license negotiations as well as suggestions to maximize the efficiency of tracking and managing licensing and the other business documents that reside as part of the process.
Image Segmentation using k-means Clustering and Otsu’s Thresholding with Classification Method for Human Intestinal Parasites

Abstract Helminth is one of the intestinal parasites that may cause harm and death to human. It is very important to have a system that is capable of assisting the technologist in investigating of fecal samples. In this paper, an automatic classification process is proposed to detect the different types of helminth eggs from fecal samples by using image processing technique. 50 samples of Ascaris Lumbricoides Ova (ALO) and Trichuris Trichiura Ova (TTO) are tested. First, these images undergo partial contrast stretching (PCS) technique to enhance the target images. Next, RGB and HSV color model have been compared in order to identify which color component is able to ease the segmentation process. S component shows a good results with high contrast between the target and the unwanted region. Then, Otsu’s thresholding and k-means clustering are compared in order to to select the most suitable image processing method to be used in classification procedure. k-means clustering shows a better results compared to Otsu’s thresholding. In classification process, area and size have been chosen as the feature to extract for the classification. The ratio for successfully detected ALO species is 84% while TTO is 76%.
Analysis of Fuel Pump Skimming Devices

Payment card fraud is a growing problem in the United States. Credit and debit card numbers are harvested from automated devices such as fuel dispensers, Point of Sale (POS) terminals, and Automated Teller Machines (ATMs) in a process known as "skimming". Skimming requires the installation of malicious hardware on (or inside of) the targeted device. As such, it serves as an example of a physical cyber threat. In this paper, we provide an overview of payment card skimming in general, before narrowing our focus to payment card skimming occurring at fuel pumps. We then reverse engineer skimmers which law enforcement has harvested from fuel pumps in Florida. We consider only those skimmers which use Bluetooth to exfiltrate the "skimmed" payment card data. The goals of our research are to analyze the internal operation of the skimmers and determine if they can be disabled wirelessly, without requiring the fuel pump cabinet to be opened.
Scalable Approach to Enhancing ICS Resilience by Network Diversity

Network diversity has been widely recognized as an effective defense strategy to mitigate the spread of malware. Optimally diversifying network resources can improve the resilience of a network against malware propagation. This work proposes a scalable method to compute such an optimal deployment, in the context of upgrading a legacy Industrial Control System with modern IT infrastructure. Our approach can tolerate various constraints when searching for optimal diversification, such as outdated products and strict configuration policies. We explicitly measure the vulnerability similarity of products based on the CVE/NVD, to estimate the infection rate of malware between products. A Stuxnet-inspired case demonstrates our optimal diversification in practice, particularly when constrained by various requirements. We then measure the improved resilience of the diversified network in terms of a well-defined diversity metric and Mean-time-to-compromise (MTTC), to verify the effectiveness of our approach. Finally, we show the competitive scalability of our approach in finding optimal solutions within a couple of seconds to minutes for networks of large scales (up to 10,000 hosts) and high densities (up to 240,000 edges).
Implementation White Box Cryptography in Substitution-Permutation network

Advances in technology have led not only to increased security and privacy but also to new channels of information leakage. New leak channels have resulted in the emergence of increased relevance of various types of attacks. One such attacks are Side-Channel Attacks, i.e. attacks aimed to find vulnerabilities in the practical component of the algorithm. However, with the development of these types of attacks, methods of protection against them have also appeared. One of such methods is White-Box Cryptography.
Virtual Tourism in a Game Environment: Untangling Judged Affordances and Sense of Place

The present study enriches and deepens understanding of the concepts of telepresence and sense of place, by examining their relevance to capture a place experience evoked in 3D environments through a research design that includes the affordance concept in virtual tourism. Many previous presence studies concern the in-the-moment experience without modeling the relationship with response variables that can help explain behavior in the digital or the material space. In this paper we tested affordance as the response variable in two different research models. The participants in our experiment explored the city of Los Angeles in a virtual environment (VE). Often, though not always, the participants had a feeling of “being there.” The results indicated that both concepts – telepresence and sense of place – can be used to measure the user experience in a VE. Telepresence appears to be a more reliable predictor than the alternative concept sense of place, but its meaning aspect is generally less well known, particularly with regard to a tourist’s intended activities and relevant affordances during a visit to a place.
Search-Based Transformation Synthesis for 3-Valued Reversible Circuits

A novel bounded search transformation-based synthesis approach is presented that finds a reversible circuit implementation for a given reversible function. Methods for simplifying the circuit post-synthesis are presented. Quantum implementation constraints are also considered. Experimental results for all 2-input 3-valued functions show the effectiveness of the new approaches compared to earlier transformation-based synthesis approaches. Other examples are given to show both the effectiveness and limitations of the new approach which point to a number of key areas for further research.
Bayesian LPV-FIR Identification of Wheelchair Dynamics and Its Application to Feedforward Control

This paper constructs a mathematical model of wheelchair dynamics in a data-driven manner. In particular, we focus on the forward-backward movement of the wheelchair, for which we adopt a linear-parameter-varying finite-impulse-response model. To avoid overfitting behavior, we employ the Bayesian estimation method. We show by experimental results that the constructed model reproduces the observed data more precisely than linear models. We also show the identified model is effective for the feedforward input design.
Construction of an Inexpensive Eye Tracker for Social Inclusion and Education

In this paper we described how to build an inexpensive eye-tracker and to apply it to social inclusion and educational activities in schools. The building of the device includes both the construction of the eye-tracking headset as well as the implementation of the code for translating eye trajectories into output data which can afterwards be analyzed and modeled. The procedure is cheap and can be easily implemented in high-schools and first-years undergraduate courses, to teach specific matters in computer sciences, physical sciences and mathematics. Moreover, we also discuss up to which extension such a cheap device can substitute commercial solutions for promoting social inclusion, particularly to develop empathy in communities by showing the difficulties behind eye-movement languages used by non-verbal paralyzed individuals.
Application Architecture of Product Information Traceability Based on Blockchain Technology and a Lightweight Secure Collaborative Computing Scheme

In the course of the rapid development of e-commerce in China, the problems of information asymmetry, unclear market responsibilities and traceability of product information in B2C sales have been restricting the long-term development of China's online retail market. In the special period when novel coronavirus is raging, e-commerce is very consequential for related medical industries such as surgical masks. Given the gradual development of blockchain technology, it can be explored to employ this technology as a basis to solve the problem of information traceability in the B2C sales process, and through the blockchain's distributed storage architecture, lightweight digital signature algorithm, consistency algorithm and other technologies to make up for product security vulnerabilities, to prevent information from being tampered with. Nevertheless, the authenticity, reliability and integrity of blockchain information transmission can also be solved by the EPC system network technology and Hash Encryption Algorithm. In this paper, we design a product information traceability application framework based on blockchain technology, aiming at the urgent need to solve the product traceability of mask enterprises during the current new crown epidemic. Also, for the corresponding scenarios in e-commerce, we propose a lightweight cooperation scheme based on blockchain technology and give security analysis. Based on lightweight cryptography, our scheme achieves identity-based authentication, peer-to-peer communication and secure collaborative computing.
Tractable inference in credal sentential decision diagrams

Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. They allow for a compact representation of joint probability mass functions defined over sets of Boolean variables, that are also consistent with the logical constraints defined by the circuit. The probabilities in such a model are usually "learned" from a set of observations. This leads to overconfident and prior-dependent inferences when data are scarce, unreliable or conflicting. In this work, we develop the credal sentential decision diagrams, a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with (so-called credal) sets of mass functions. These models induce a joint credal set over the set of Boolean variables, that sharply assigns probability zero to states inconsistent with the logical constraints. Three inference algorithms are derived for these models. These allow to compute: (i) the lower and upper probabilities of an observation for an arbitrary number of variables; (ii) the lower and upper conditional probabilities for the state of a single variable given an observation; (iii) whether or not all the probabilistic sentential decision diagrams compatible with the credal specification have the same most probable explanation of a given set of variables given an observation of the other variables. These inferences are tractable, as all the three algorithms, based on bottom-up traversal with local linear programming tasks on the disjunctive gates, can be solved in polynomial time with respect to the circuit size. The first algorithm is always exact, while the remaining two might induce a conservative (outer) approximation in the case of multiply connected circuits. A semantics for this approximation together with an auxiliary algorithm able to decide whether or not the result is exact is also provided together with a brute-force characterization of the exact inference in these cases. For a first empirical validation, we consider a simple application based on noisy seven-segment display images. The credal models are observed to properly distinguish between easy and hard-to-detect instances and outperform other generative models not able to cope with logical constraints.
Troubleshooting Electronic Resources from an Interlibrary Loan Perspective

As library functions and tasks increasingly take place in a digital environment, areas of overlap arise between traditionally separate units and departments, spurring opportunities to evaluate and reimagine how these functions and tasks are carried out. In 2018, Louisiana State University utilized ILLiad, an interlibrary loan (ILL) request management software, to report electronic resource access issues to the Electronic Resources Librarian. A year's worth of data reveals the most common types of access issues impacting LSU's electronic resources, training opportunities for ILL staff, and most significantly, that ILL staff and troubleshooters engage in identical procedures at the start of their work. Does the intersection of ILL request processing and troubleshooting present an opportunity for restructuring?
A Machine Learning Approach for the Classification of Disease Risks in Time Series

In this work, a new hybrid algorithm for disease risk classification is proposed. The proposed methodology is based on Dynamic Time Warping (DTW). This methodology can be applied to time series from various domains such as vital sign time series available in medical big data. To validate our methodology, we applied it to risk classification for sepsis, which is one of the most challenging problems within the area of medical data analysis. In the first step the algorithm uses different statistical properties of time series features. Furthermore, using differently labeled training data sets, we created a DTW Barycenter Averaging (DBA) on each feature. In the second step, validation data sets and DTW are used to validate the precision of classification and the final results are compared. The performance of our methodology is validated with real medical data and on six different criteria definitions for the sepsis diseases. Results show that our algorithm performed, in the best case, with precision and recall of 96,38% and 90,90%, respectively.
An Architecture for Integrating BDI Agents with a Simulation Environment

We present Simulated Autonomous Vehicle Infrastructure (SAVI), an open source architecture for integrating Belief-Desire-Intention (BDI) agents with a simulation platform. This allows for separation of concerns between the development of complex multi-agent behaviours and simulated environments to test them in. We identify and address the impedance mismatch between modelling and simulation, where time is explicitly modelled and differs from “wall clock” time, and BDI systems, where time is not explicitly managed. Our approach avoids linking the environment’s simulation time step to the agents’ reasoning cycles, relying instead on real time simulation where possible, and ensuring that the reasoning module does not get ahead of the simulation. This contributes to a realistic approximation of a real environment for the simulated BDI agents. This is accomplished by running the simulation cycles and the agent reasoning cycles each in their own threads of execution, and managing a single point of contact between these threads. Finally, we illustrate the use of our architecture with a case study involving the simulation of Unmanned Aerial Vehicles (UAVs) following birds.
Exploratory data analysis on large data sets: The example of salary variation in Spanish Social Security Data

New challenges arise in data visualization when the research involves a sizable database. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots, such as the boxplot that are of limited use in small samples, offer great potential to facilitate group comparison in the case of an extensive sample. This article presents exploratory data analysis methods useful for inspecting variation across groups in crucial variables and detecting heterogeneity. The exploratory data analysis methods (introduced by Tukey in his seminal book of 1977) encompass a set of statistical tools aimed to extract information from data using simple graphical tools. In this article, some of the exploratory data analysis methods like the boxplot and scatterplot are revisited and enhanced using modern graphical computational devices (as, for example, the heat-map) and their use illustrated with Spanish Social Security data. We explore how earnings vary across several factors like age, gender, type of occupation, and contract, and in particular, the gender gap in salaries is visualized in various dimensions relating to the type of occupation. The exploratory data analysis methods are also applied to assessing and refining competing regressions by plotting residuals-versus-fitted values. The methods discussed should be useful to researchers to assess heterogeneity in data, across-group variation, and classical diagnostic plots of residuals from alternative models fits. JEL CLASSIFICATION: C55; J01; J08; Y10; C80
Design for Manufacturing and Assembly (DFMA): Redesign of Joystick

Abstract Design is the first step in any manufacturing process where the most crucial decisions are made which affect the product’s ultimate cost. The researchers used DFMA method in this study to redesign and improve a joystick in order to achieve the goal to decrease the number of parts, handling and insertion time, and improve the design efficiency for a joystick. The main emphasis in this research has been on the design stage of a product development, with a view to accomplishing an optimal design approach for current product.DFMA principles have been used to develop new design ideas and the final design will be evaluate and compared to the current design. In result, the assembly time for redesign then increased by 21 % with a decrease in the assembly time from 294.2s to 232.44s and an improvement in design efficiency by 26.5 % from 20.4 % to 25.8 %.
Just a Natural Talk? The Rise of Intelligent Personal Assistants and the (Hidden) Legacy of Ubiquitous Computing

In this paper we trace the connection between the paradigm of disappearing computers and the design of today's Intelligent Personal Assistants (IPAs). We show how the concept of natural interaction stems from the emergence of a specific interpretation of the idea of disappearing computers (ubicomp). This interpretation has emerged as a technology centred perspective, losing the link with the complex philosophical principles which inspired the founders of UbiComp. As a result, the dominant understanding of natural interaction reflects this loss of complexity, influencing the way IPAs are currently developed. To better understand the implications of having adopted this paradigm, we carried out a literature review of recent academic studies on the use of IPAs. Using a three-lenses framework helped us identify six major assumptions behind the design of IPAs. The paper ends with a discussion of the emerging design challenges in the field of voice-activated devices and how these relate to the dominant paradigm of natural interaction.
A Racial Recognition Method Based on Facial Color and Texture for Improving Demographic Classification

Facial recognition is one of the important techniques in the security and authentication domain of the present time. Facial image recognition involves complex process which reduces the overall performance of the system for a large database, and consequently, it may incur inefficiency to the system in the commercial sector. In this paper, we split the image database into a set of smaller groups by classifying the face images in terms of race demography. First, facial components (i.e., eyes, nose and mouth) are captured using a segmentation technique and then race sensitive features: chromatic/skin tone and local features from face images are extracted using Color Coherence Vector and Gabor filter. K-Nearest Neighbors, Artificial Neural Network, and Support Vector Machines are then used to classify the face image according to race groups. We consider racial classification as Asian, African and European. It was found that the average classification accuracy with Gabor and CCV features for Artificial Neural Network is 91.74% and 84.18%, respectively, providing plausible results comparing to some other existing models.