Reviewerly/exHarmony huggingface dataset:
Autonomous Vehicles and Aerospace Engineering as a STEAM development platform

This paper presents the work in progress of using nanosatellites and autonomous vehicles as education platforms for pre-college preparation and career development at Tec de Monterrey in Mexico. Students are recruited in their junior high school year. They undergo a set of tests, until a selected set is recruited to participate in two MIT programming competitions. They are encouraged to continue their participation until the end of their senior year. When they begin their undergraduate studies, they work as mentors and conduct research on autonomous navigation. Participants develop not only high technical skills, but social and leadership skills as well. This program has been running since September 2016 in the Mexico City campus and due to its success, it is being implemented in other campuses. The alumni and current members of these projects have demonstrated a significant growth as compared to their class.
Towards the Integration of Reverse Converters into the RNS Channels

The conversion from a Residue Number System (RNS) to a weighted representation is a costly inter-modulo operation that introduces delay and area overhead to RNS processors, while also increasing power consumption. This paper proposes a new approach to decompose the reverse conversion into operations that can be processed by the arithmetic units already present in the RNS independent channels. This leads to a more effective reuse of the processor circuitry while enhancing parallelism. Experimental results show that, when the proposed techniques are applied to architectures based on ripple-carry adders for the traditional 3-moduli set, the delay is improved in average by 16 percent, the circuit area by 36 percent and the power consumption by 47 percent. When carry-lookahead adder topologies are considered, these improvements are in average of 45 percent for the circuit area and 58 percent for the power consumption while the delay is only slightly reduced. The proposed techniques are applied to a use case in digital filtering, showing an increase in throughput/area of up to 1.25 times, and average reductions in energy consumption of 15.6 percent. This work is a step forward to the usage of RNS in practice, since reverse conversion underpins other hard inter-modulo operations, like comparison, scaling and division.
Documenting Physical Objects with Live Video and Object Detection

Responding to requests for information from an application, a remote person, or an organization that involve documenting the presence and/or state of physical objects can lead to incomplete or inaccurate documentation. We propose a system that couples information requests with a live object recognition tool to semi-automatically catalog requested items and collect evidence of their current state.
Adaptive Robust Blind Watermarking Scheme Improved by Entropy-Based SVM and Optimized Quantum Genetic Algorithm

With the intensive study of machine learning in digital watermarking, its ability to balance the robustness and transparency of watermarking technology has attracted researchers’ attention. Therefore, quantum genetic algorithm, which serves as an intelligent optimized scheme combined with biological genetic mechanism and quantum computing, is widely used in various fields. In this study, an adaptive robust blind watermarking algorithm by means of optimized quantum genetics (OQGA) and entropy classification-based SVM (support vector machine) is proposed. The host image was divided into two parts according to the odd and even rows of the host image. One part was transformed by DCT (discrete cosine transform), and then the embedding intensity and position were separately trained by entropy-based SVM and OQGA; the other part was by DWT (discrete wavelet transform), in which the key fusion was achieved by an ergodic matrix to embed the watermark. Simulation results indicate the proposed algorithm ensures the watermark scheme transparency as well as having better resistance to common attacks such as lossy JPEG compression, image darken, Gaussian low-pass filtering, contrast decreasing, salt-pepper noise, and geometric attacks such as rotation and cropping.
Survey of Cognitive Assisted Living Ambient System Using Ambient intelligence as a Companion

The population change towards an ageing people is introducing major impact and drastic challenge to our society. We therefore need to identify ways to assist people to stay independently especially kids and elders. The Ambient Assist Living (AAL) with the advantage of Ambient intelligence becomes a field to examine new expertise to provide assistance and also in healthcare and entertainment and rehabilitation to people with impairment AmI vision in Educating the kids. The paper provides a review of research related and skills of AAL using AmI in the caretaking of the kids.
Study of skin rigidity variations due to UV radiation using digital holographic interferometry

The skin elasticity is a relevant biomechanical property that can be related to different physiological processes and diseases: hence, it is important to understand and characterize the mechanisms that affect the skin pathologies. In this work, an analysis of the rigidity variations of porcine skin caused by exposure to ultraviolet radiation is presented. For this purpose digital holographic interferometry (dhi) is used as a non-invasive optical technique to study these skin affectations. A new mathematical scheme for the skin rigidity variations was developed and the experimental data was used to study and determine the rigidity coefficients for the samples before and after ultraviolet exposure of the skin. The results after high doses of ultraviolet radiation at 8 mW/cm2 UVA, 8 mW/cm2 UVB and 10 mW/cm2 UVC for periods of 3 min showed that these rigidity coefficients increased above 100% (more than twice) their value after the ultraviolet exposure. The latter suggests a considerable reduction of the porcine skin elasticity when exposed to high doses of ultraviolet radiation, a feature that has been proved to lead to skin melanoma.
A Unifying Reference Framework and Model for Adaptive Distributed Hybrid User Interfaces

Over the last decade, research on adaptive and distributed user interfaces (DUIs) has increased.We also witness a growing number of Internet of Things (IoT) devices, allowing digital user interfaces (UIs) to communicate with physical objects and vice versa through so-called hybrid user interfaces.There exist various solutions to manage adaptive, distributed or hybrid UIs.However, none of them covers all three aspects and users have to deal with multiple applications and configurations when developing adaptive distributed hybrid user interfaces.We introduce the eSPACE reference framework and conceptual model unifying the domains of adaptive, distributed and hybrid interfaces.While our reference framework has been inspired by the CAMELEON reference framework, the conceptual model is based on the Resource-Selector-Link (RSL) hypermedia metamodel.We propose an approach for adaptive distributed hybrid user interfaces where users can author their user interfaces based on the different levels of abstraction introduced by our reference framework.We further present a use case illustrating the extensibility, flexibility and reusability offered by our unified approach and discuss potential future work.
Expanding Demand Driven Acquisition: Complete Just-In-Time Approval Plan

A DDA (Demand Driven Acquisitions) preferred Approval Plan allows libraries to acquire DDA eligible books triggered by users. Non-DDA books are acquired automatically, because there is no mechanism that allows these books to be requested by users. This study explores the procedure called Purchase by Request (PBR) that allows non-DDA books to be discoverable and requested by users. After incorporating both DDA and PBR into an Approval Plan, all books in the Approval Plan are exposed to users for just-in-time purchases, and in the process saving the library a substantial amount of money.
Modeling and Simulation as a Service for joint military space operations simulation

Current space operations simulation capabilities are limited and do not suffice to fulfill the requirements for military space mission areas. Therefore, a Modeling and Simulation as a Service architecture for joint military space operations simulation is introduced. In this architecture, every military space operations mission area is addressed by at least one or more simulation services, which can be run independently or as composed with others. Our layered service-oriented modeling and simulation environment, namely the HAVELSAN Training and Experimentation Cloud, is the implementation of this architecture.
Hardware implementation of Piccolo Encryption Algorithm for constrained RFID application

The deployment of smart devices in IoT applications are increasing with tremendous pace causing severe security concerns, as it trade most of private information. To counter that security issues in low resource applications, lightweight cryptographic algorithms have been introduced in recent past. In this paper we propose efficient hardware architecture of piccolo lightweight algorithm uses 64 bits block size with variable key size of length 80 and 128 bits. This paper introduces novel hardware architecture of piccolo-80, to supports high speed RFID security applications. Different design strategies are there to optimize the hardware metrics trade-off for particular application. The algorithm is implemented on different family of FPGAs with different devices to analyze the performance of design in 4 input LUTs and 6 input LUTs implementations. In addition, the results of hardware design are evaluated and compared with the most relevant lightweight block ciphers, shows the proposed architecture finds its utilization in terms of speed and area optimization from the hardware resources. The increment in throughput with optimized area of this architecture suggests that piccolo can applicable to implement for ultra-lightweight applications also.
Large-Scale Analysis of Visualization Options in a Citizen Science Game

Visualization is a valuable tool in problem solving, especially for citizen science games. In this study, we analyze data from 36,351 unique players of the citizen science game Foldit over a period of 5 years to understand how their choice of visualization options are affected by expertise and problem type. We identified clusters of visualization options, and found differences in how experts and novices view puzzles and that experts differentially change their views based on puzzle type. These results can inform new design approaches to help both novice and expert players visualize novel problems, develop expertise, and problem solve.
Image quality assessment based on adaptive multiple Skyline query

Non-reference image quality assessment has attracted great emphasis in recent years. Traditional image quality assessment algorithms based on structural similarity cannot make full use of the image gradient features, and the contrast similarity features often ignore the consistency of continuous color blocks within the image, which leads to large discrepancy between the evaluation result and the subjective judgment of human vision system. In this paper, we propose a deep model for image quality assessment where the spatial and visual features of image are both considered. For better feature fusion, we design an adaptive multiple Skyline query algorithm named MSFF, which takes as input multiple features of images, and learns the feature weights through end-to-end training. Extensive experiments on image quality assessment tasks prove that the proposed model exhibits superior performance compared with existing solutions.
Multidrug-resistant tuberculosis risk factors assessment with intuitionistic fuzzy cognitive maps

Tuberculosis (TB) bacteria may develop resistance to the drugs, which are used in TB treatment. Multidrug-resistant TB (MDR-TB) is a type of TB that does not respond to at least rifampicin and isoniazid, the 2 most powerful anti-TB drugs. MDR-TB requires a more compelling treatment and it is more d ifficult to diagnose. The experience of physician is the key factor in the success of MDR-TB diagnose. The existence of TB bacteria in the body can be observed relatively faster with a standard sputum smear however, drug-susceptibility tests require nearly 45 days. To cope with this infectious disease, it is vital to estimate the resistance in a newly diagnosed TB patient to plan the initialization of the treatment in the testing period. Herein, the purpose of this study is to build a framework and establish a mathematical model that will help decision makers (physicians) while estimating the risk of multidrug resistance when a new tuberculosis patient arrives, using intuitionistic fuzzy cognitive maps (IFCM). Intuitionistic fuzzy sets are utilized to reflect the decision makers’ hesitancy degrees in the model.
A Novel Key-value based Real-time Data Management Framework for Ship Integrated Power Cyber-Physical System

The new generation ship integrated power system (IPS) realizes high level informatization for various physical equipments, and gradually develops to a cyber-physical system (CPS). The future trend is collecting ship big data to achieve data-driven intelligence for IPS. However, traditional relational data management framework becomes inefficient to handle the real-time data processing in ship integrated power cyber-physics system. In order to process the large-scale real-time data that collected from numerous sensors by field bus of IPS devices within acceptable latency, especially for handling the semi-structured and non-structured data. This paper proposes a novel key-value data model based real-time data management framework, which enables batch processing and distributed deployment to acquire time-efficiency as well as system scalable. We implement a real-time data management prototype system based on an open source in-memory key-value store. Finally, the evaluation results from the prototype verify the advantages of novel framework compared with traditional solution.
An efficient ElGamal cryptosystem scheme

ElGamal Cryptosystem (EC) is a non-deterministic scheme which produces different outputs for the same input, making the cryptosystem more secure. On the other hand, the efficiency of its cryptosystem is low as it produces a 2:1 expansion in size from plaintext to ciphertext, resulting in a delay in execution time. Therefore, this paper presents a Modified ElGamal Cryptosystem (MEC) to increase the efficiency by speeding up the execution time and reducing the expansion rate in the file size after the encryption process. A comparison between the proposed MEC and the traditional EC is carried out using the same programming environment, and the implementation is tested using text data of different sizes. The results show that the performance of the proposed MEC is better than the traditional EC in terms of execution time and expansion rate. Whereas, the security of the proposed MEC is analogous to the traditional EC, which is based on the difficulty of solving the discrete logarithm problem.
Quantum state transfer on a class of circulant graphs

We study the existence of quantum state transfer on non-integral circulant graphs. We find that continuous-time quantum walks on quantum networks based on certain circulant graphs with 2k k∈Z vertices exhibit pretty good state transfer when there is no external dynamic control over the system. We generalize a few previously known results on pretty good state transfer on circulant graphs, and this way we re-discover all integral circulant graphs on 2k vertices exhibiting perfect state transfer.
The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions

Telling cause from effect from observations of just two variables has attracted increasing interest since more than one decade. On the one hand, it defines a nice binary classification problem for which it is easy to define a success rate, in contrast to more general causal inference tasks where no straightforward performance criteria exist. On the other hand, it fascinates researchers because solving this elementary task implies statistical asymmetries between cause and effect that were previously unknown. Discussing some real-world and toy examples, I argue that humans seem to have some intuition about these asymmetries, but I also argue that some straightforward ideas to distinguish between cause and effect are flawed. The discussion on the origin of the true asymmetries relates machine learning, philosophy and physics. For instance, the postulate that P cause and P effect|cause contain no information about each other (while P effect and P cause|effect may satisfy some ‘non-generic’ relations), is relevant for semi-supervised learning on the one hand, but is also related to the thermodynamic arrow of time on the other hand.
Beauty Aware Network

Makeup product retrieval has gained more and more attention for its wide application prospects. However, the challenging problem is that the dataset crawled from Internet doesn't have annotated labels. Therefore, existing methods are unable to obtain well-trained networks. To solve this problem, this paper proposes a trainable network named Beauty Aware Network (BAN) for makeup product retrieval. The core of proposed method is using an unsupervised cluster method to train the beauty classification network. And then a covariance pooling layer is introduced to leverage the statistical information. Finally, a multi-layer fusion strategy is used to capture informative clues in images. The proposed method can get simpler but more efficient features for beauty product retrieval with less computation cost. The experiments conduct on Perfect-500k dataset which has more than half-million images. The results demonstrate the effectiveness of beauty aware network by competitive performance.
Path Planning in Dynamic Environment Using Particle Swarm Optimization Algorithm

Path planning is an important guidance task that allows autonomous mobile robots and vehicles to achieve their goal or fulfill their mission. In this paper, a path planning algorithm based on Particle Swarm Optimization (PSO) is proposed. This algorithm is able to perform natural and smooth maneuvers from source to destination while ensuring the safety of the autonomous robot and its surroundings. The optimization criteria consist of collision-free path length, travel time and energy consumption. Various test scenarios are used to test the success of the proposed method to generate feasible and optimal solutions. Simulation in Unity3D is used to illustrate and validate the proposed approach. Simulation results show that the proposed approach is efficient and optimal in terms of relevant objective functions for various test scenarios.
Construction of Liver Fibrosis Diagnosis Ontology From Fuzzy Extended ER Modeling

Liver fibrosis diagnoses is a critical and core research study field due to its importance to the patient's life. Moreover, electronic health records (EHR) contain wealthy semantics connected to liver diseases yet ontological implementation is still a challenge. Ontology however, can play critical roles in E-health as a formalization of medical terminologies and decision support system knowledge base. But since clinical data contains a lot of data that is imprecise and vague, classical approaches of ontology construction would not be fruitful. However, Fuzzy ontology, an extension of the crisp ontology that requires different development methodology, can be implemented in this field due to its previous success in modeling semantic knowledge in various domains. In this article, the authors construct a fuzzy ontology by using a fuzzy extended entity relationship (EER) data model for liver fibrosis diagnosis. The resulting ontology is complete and consistent because it is based on a formal methodology of mapping the EER model into a fuzzy ontology.
Learning to Inpaint by Progressively Growing the Mask Regions

Image inpainting is one of the most challenging tasks in computer vision. Recently, generative-based image inpainting methods have been shown to produce visually plausible images. However, they still have difficulties to generate the correct structures and colors as the masked region grows large. This drawback is due to the training stability issue of the generative models. This work introduces a new curriculum-style training approach in the context of image inpainting. The proposed method increases the masked region size progressively in training time, during test time the user gives variable size and multiple holes at arbitrary locations. Incorporating such an approach in GANs may stabilize the training and provides better color consistencies and captures object continuities. We validate our approach on the MSCOCO and CelebA datasets. We report qualitative and quantitative comparisons of our training approach in different models.
Our transformational leader might be replaced, we are extremely stressed out!

Purpose This study aims to tackle employees’ stress and emotions in periods of changes in leadership positions, especially if the leader to be replaced is previously found to be transformational. Design/methodology/approach Qualitative data are collected from WhatsApp conversations between different subordinates, hours around the declaration of the appointment decision. Data presentation includes messages exchanged, as well as photos, emojis and voice notes. Findings The participants’ messages reflected their stress. They are drained, desperate and praying God so their transformational leader is reappointed. Fear and sadness are the two types of emotion experienced by employees during the anticipation and confirmation stages of the change process, respectively. The emotional contagion among subordinates is revealed in the way the messages and the replies are very close in content. Research limitations/implications The low number of participants makes the results obtained specific to the case itself. However, this study triggers future research addressing same research propositions under different leadership styles and in different cultures. Originality/value The value of this research lies in the topic addressed, as no literature is found discussing employees stress in periods of change in a transformational leadership position, specifically. Moreover, its originality lies in the method of data collection which depicts the participants’ live and real feelings and emotions on that moment.
Task Scheduling for cloud computing Based on Firefly Algorithm

Abstract In this paper a new method proposed, to solve the problem of scheduling resources in cloud computing, it is using a parallel scheduling model which can enhance parallel tasks scheduling while the relationship among serial tasks stay constant. The important feature of dynamic tasks is provided by users to sub-tasks in serial sequences and arranged as scheduling queue, these tasks have different and equal priorities according to running order. In this paper firefly algorithm (FA) have been introduced for scheduling the subtasks in cloud computer to prove a progress in short delay time. The most important features that can be considered are fairness and efficiency characteristics. Delay time Shortest and Fairness firefly algorithm (DSFFA) applied to solve task scheduling problem that realized the shortest delay time, which improving the user satisfaction. Experimental results show that DSFFA algorithm is better than the task scheduling –Firefly algorithm (TS-FA) algorithm in fairness, efficiency and task delay time, therefor it can be realized for the optimal scheduling in cloud computing.