Reviewerly/exHarmony huggingface dataset:
Automatic Localization and Discrete Volume Measurements of Hippocampi From MRI Data Using a Convolutional Neural Network

Automatic hippocampal volume measurement from brain magnetic resonance imaging (MRI) is a crucial task and an important research area, especially in the study of neurodegenerative diseases; hippocampal volume atrophy is known to be connected with Alzheimer's disease. In this research work, we propose a deep learning-based method to automatically measure the discrete hippocampal volume without prior segmentation of the volumetric MRI scans. We constructed a 2-D convolutional neural network (CNN) model that uses 3-channel 2-D patches to predict the number of voxels attributed to the hippocampus; the number of estimated hippocampal voxels is multiplied by the voxel volume to measure the discrete volume of the hippocampus. In addition, we demonstrate a preprocessing scheme to prepare the data using a relatively small number of MRI scans. The average errors in the measured volumes of the proposed approach and the compared atlas-based system were 4.3173 ± 3.5436 (avg. error% ± STD) and 4.1562 ±3.5262 (avg. error % ± STD) for the left and right hippocampi, respectively. The correlation coefficients of the proposed approach with atlas-based volume measurement were statistically significant (p-value <; 0.01, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.834 (left hippocampus), and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.848 (right hippocampus) based on 0.05 significance level), which suggests that the proposed approach can be used as a proxy method for the atlas-based system. Furthermore, the proposed approach is computationally efficient and requires less than 2 seconds to calculate the number of voxels for an MRI scan. Moreover, our method outperforms the state-of-the-art deep learning approach, such as 2-D U-Net and SegNet in the context of voxel/volume estimation errors% for the left and right hippocampi.
Detection of probe flow anomalies using information entropy and random forest method

Aiming at the problems of excessive dependence on manual work, low detection accuracy and poor real-time performance of current probe flow anomaly detection in power system network security detection, a detection method for calculating information entropy of probe flow and random forest classificat ion is proposed. Firstly, the network probe stream data are captured and aggregated in real-time to extract network stream metadata. Secondly, by calculating Pearson correlation coefficient and maximum mutual information coefficient, feature selection of network metadata is carried out. Finally, the information entropy and stochastic forest algorithm are combined to detect the anomaly of probe traffic based on the selected key feature groups, and the probe traffic is accurately classified by multiple incremental learning. The results show that the proposed method can quickly locate the abnormal position of probe traffic and analyze the abnormal points, which greatly reduces the workload of application platform for power system security monitoring, and has high detection accuracy. It effectively improves the reliability and early warning ability of power system network security.
Likelihood Ratios for Deep Neural Networks in Face Comparison

Abstract In this study, we aim to compare the performance of systems and forensic facial comparison experts in terms of likelihood ratio computation to assess the potential of the machine to support the human expert in the courtroom. In forensics, transparency in the methods is essential. Consequently, state‐of‐the‐art free software was preferred over commercial software. Three different open‐source automated systems chosen for their availability and clarity were as follows: OpenFace, SeetaFace, and FaceNet; all three based on convolutional neural networks that return a distance (OpenFace, FaceNet) or similarity (SeetaFace). The returned distance or similarity is converted to a likelihood ratio using three different distribution fits: parametric fit Weibull distribution, nonparametric fit kernel density estimation, and isotonic regression with pool adjacent violators algorithm. The results show that with low‐quality frontal images, automated systems have better performance to detect nonmatches than investigators: 100% of precision and specificity in confusion matrix against 89% and 86% obtained by investigators, but with good quality images forensic experts have better results. The rank correlation between investigators and software is around 80%. We conclude that the software can assist in reporting officers as it can do faster and more reliable comparisons with full‐frontal images, which can help the forensic expert in casework.
Development of measurement instrument for visual qualities of graphical user interface elements (VISQUAL): a test in the context of mobile game icons

Abstract Graphical user interfaces are widely common and present in everyday human–computer interaction, dominantly in computers and smartphones. Today, various actions are performed via graphical user interface elements, e.g., windows, menus and icons. An attractive user interface that adapts to user needs and preferences is progressively important as it often allows personalized information processing that facilitates interaction. However, practitioners and scholars have lacked an instrument for measuring user perception of aesthetics within graphical user interface elements to aid in creating successful graphical assets. Therefore, we studied dimensionality of ratings of different perceived aesthetic qualities in GUI elements as the foundation for the measurement instrument. First, we devised a semantic differential scale of 22 adjective pairs by combining prior scattered measures. We then conducted a vignette experiment with random participant ( n = 569) assignment to evaluate 4 icons from a total of pre-selected 68 game app icons across 4 categories (concrete, abstract, character and text) using the semantic scales. This resulted in a total of 2276 individual icon evaluations. Through exploratory factor analyses, the observations converged into 5 dimensions of perceived visual quality: Excellence/Inferiority, Graciousness/Harshness, Idleness/Liveliness, Normalness/Bizarreness and Complexity/Simplicity. We then proceeded to conduct confirmatory factor analyses to test the model fit of the 5-factor model with all 22 adjective pairs as well as with an adjusted version of 15 adjective pairs. Overall, this study developed, validated, and consequently presents a measurement instrument for perceptions of visual qualities of graphical user interfaces and/or singular interface elements (VISQUAL) that can be used in multiple ways in several contexts related to visual human-computer interaction, interfaces and their adaption.
Fuzzy rule based intelligent system for user authentication based on user behaviour

Security of the data is one of the major concerns now a days. To secure data text–based authentication vulnerable to attacks like shoulder surfing, hidden cameras and hackers. To avoid these attacks, biometric schemes are useful by means of face recognition, voice recognition, thumb impression detection, fingerprint detection etc which is more costly as it requires some external equipments. In this paper, we proposed a security system based on fuzzy rule based intelligent system which can be used for authentication process based on the user’s behavior. The behavioral biometrics such as typing pattern, time and speed of a particular person can be recorded through keyboard even without the knowledge of the user, and need not to fill any form or to install any additional hardware, thus it is cost effective and robust in security aspect.
Towards activation function search for long short-term model network: A differential evolution based approach

In Deep Neural Networks (DNNs), several architectures had been proposed for the various complex tasks such as Machine Translation, Natural Language processing and time series forecasting. Long-Short Term Model (LSTM), a deep neural network became the popular architecture for solving sequential and time series problems and achieved markable results. On building the LSTM model, many hyper-parameters like activation function, loss function, and optimizer need to be set in advance. These hyper-parameters play a significant role in the performance of the DNNs. This work concentrates on finding a novel activation function that can replace the existing activation function such as sigmoid and tanh in the LSTM. The Differential Evolution Algorithm (DEA) based search methodology is proposed in our work to discover the novel activation function for the LSTM network. Our proposed methodology finds an optimal activation function that outperforms than the traditional activation functions like sigmoid (σ), hyperbolic tangent (tanh) and Rectified Linear Unit (ReLU). In this work, the newly explored activation function based on DEA methodology is sinh(x)+sinh-1(x) named as Combined Hyperbolic Sine (comb-H-sine) function. The proposed comb-H-sine activation function outperforms the traditional functions in LSTM with accuracy of 98.83%,93.49% and 78.38% with MNIST, IMDB and UCI HAR datasets respectively.
Modeling and Simulation of Sidescan Using Conditional Generative Adversarial Network

Sidescan sonar has been used for maritime surveys since the mid-20th century. Due to its wide swath coverage and the sharpness of the produced images, it is an invaluable tool still to this day. When simulating sidescan data, there is a tradeoff between the quality of the produced images, the fidelity of the environment simulation, and the complexity of the sidescan model. In this article, we propose data-driven models as a way of removing some of this tradeoff. Using recently proposed conditional generative adversarial nets, we create a generative model that takes the environment as input, and produces realistic sidescan measurements. We show that end-to-end learning of flexible models allows simulating more complex sidescan data than would otherwise be possible given only geometric bathymetry.
Raising Regional Academic Voices (Alongside Data) in Higher Education (HE) Debate

As agendas for data-driven measures of excellence dominate policy in UK Higher Education (HE), we argue that the generic structure of national policy frameworks virtually silences regional voices. This furthers a territorially agnostic discourse about universities, downplays institutional history and purpose, risks concealing innovative practices, and fails to tackle entrenched inequalities. In response, we point to the value of live, place-based debate in HE institutions to highlight distributional inequity, raise local voices and connect these with national policy. Yet even as we compiled this article about HE debate, the Covid-19 pandemic took hold globally, cancelling face-to-face meetings, by necessity. We therefore draw on a postdigital perspective, as we share our individual dialogues in support of debate, via collective writing, against this new backdrop of social distancing and widespread uncertainty. We may not currently be able to convene our Midlands HE Policy Network (MHEPN) debates in person, but we can voice the essential part that regional universities play in connecting global technological and biological change, with local social projects, citizens and industry. Postdigital theory offers one route to understanding that Covid-19 does not sit apart from other political economic challenges in HE and beyond, that we need to debate simultaneously.
Development of an Open Source Automated Library System with Book Recommedation System for Small Libraries

This paper aims to develop an open source automated library system with book recommendation by using a combined method applied for small library systems. This approach employs the machine learning of support-vector machines consisting of multiple features such as title similarity, NDC category and bibliographic information of books based on loan record as learning data. The bibliographic data are collected in the library database center. This recommended method is based on the title similarity and bibliographic information of books such as author, category, number of views and year of publication. The various data used for the evaluation in Banpasao Chiangmai school was collected covering 2555 books in the database and 4612 books in the load record. This paper provides an effective solution for open source automated library systems suitable for small libraries where librarians can download and use our method using the OPAC (Online Public Access Catalog Online) represented in our framework.
Why Do Smart Contracts Self-Destruct? Investigating the Selfdestruct Function on Ethereum

The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts ( SCs ) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and summarize the key reasons. Their feedback shows that 66.67% of the developers will deploy an updated contract to the Ethereum after destructing the old contract. According to this information, we propose a method to find the self-destructed contracts (also called predecessor contracts) and their updated version (successor contracts) by computing the code similarity. By analyzing the difference between the predecessor contracts and their successor contracts, we found five reasons that led to the death of the contracts; two of them (i.e., Unmatched ERC20 Token and Limits of Permission ) might affect the life span of contracts. We developed a tool named LifeScope to detect these problems. LifeScope reports 0 false positives or negatives in detecting Unmatched ERC20 Token . In terms of Limits of Permission , LifeScope achieves 77.89% of F-measure and 0.8673 of AUC in average. According to the feedback of developers who exclude selfdestruct functions, we propose suggestions to help developers use selfdestruct functions in Ethereum smart contracts better.
Design and Evaluation of Interactive Small Multiples Data Visualisation in Immersive Spaces

We explore the adaptation of 2D small-multiples visualisation on flat screens to 3D immersive spaces. We use a "shelves" metaphor for layout of small multiples and consider a design space across a number of layout and interaction dimensions. We demonstrate the applicability of a prototype system informed by this design space to data sets from different domains. We perform two user studies comparing the effect of the shelf curvature dimension from our design space on users’ ability to perform comparison and trend analysis tasks. Our results suggest that, with fewer multiples, a flat layout is more performant despite the need for participants to walk further. With an increase in the number of multiples, this performance difference disappears due to the time participants had to spend walking. In the latter case, users prefer a semi-circular layout over either a fully surrounding or a flat arrangement.
A Novel Design of Gesture and Voice Controlled Solar-Powered Smart Wheel Chair with Obstacle Detection

Disable individuals are disregarded more often than not. Now they need human help 24/7 to proceed onward. They need a wheelchair to move from one place to another as per their need. It would be much easier if the wheelchair needed for their task be automated and controllable by the person himself rather another constant human engagement. In this paper, a design has been proposed considering these facts. The wheelchair will be controlled by voice and gesture. To make the system energy efficient solar power will be used. Along these lines, while moving around all around the battery can be revived effectively. This keen wheelchair is additionally fit for hindrance recognition.
Downside Up:Rethinking Parcel Position for Aerial Delivery

The conventional approach to parcel placement in most delivery drone designs today is to place the parcel centrally beneath the drone's rotor plane. However, if the parcel is too large, this will result in an obstruction of the propeller slipstream, incurring significant drag. As such, the parcel's location below the rotor plane limits the size of parcels that can be delivered, specifically super-sized parcels that protrude beyond the bounds of the four rotors. Delivering these large parcels requires bigger drone platforms, which consequently consume more energy, necessitate large storage spaces, and are less portable. In this letter, we propose an alternative approach, placing the parcel above the rotor plane. However, placing a parcel above a quadcopter encounters two main challenges. The first is the optimal position of the CG to maintain stability of the drone. Second is the aerodynamic influence on the lift of the drone with a parcel is placed above the propellers. To address these challenges, aerodynamic characterization experiments were performed and a new drone design was proposed. This letter presents a prototype quadcopter design solution that places the parcel above the propellers to enable delivery of super-sized parcels, as well as a compact rotor design configuration for lower storage cost and increased portability. The new approach of placing an over-sized parcel above propellers provides the 82% increase in thrust over putting the parcel below the propellers.
Research on New Adaptive Whale Algorithm

Bionic algorithms have always played an important role in industrial, agricultural, and scientific research.The optimization of bionics algorithms has always been the focus of scholars in various countries.A whale algorithm based on optimization based on adaptive convergence and Levy features (IWOA) is proposed to overcome the disadvantages, such as low precision, slow convergence speed and tending to involve the local optimum of the whale algorithm.The improved Bernouilli Shift map is used to initialize the population to maintain diversity of the population.Optimizing the adaptive convergence factor is able to balance the local and global optimization ability.The Levy flight mechanism is introduced to optimize foraging behavior and improve global searching ability.In addition, the trigger rule is applied to screen individuals after each iteration to maintain individual vitality and enhance overall performance of the algorithm.In the simulation, IWOA, Ant Colony Optimization, Particle Swarm Optimization, Whale Optimization Algorithm and the optimized whale algorithms CWOA, LWOA are compared using the 20 classical test functions.The simulation results demonstrate that the IWOA algorithm possesses good global and local searching ability, especially in solving multi-peak and high-dimensional functions.
Fostering fashion retail experiences through virtual reality and voice assistants

This paper discusses the potential of using vocal commands in a virtual reality (VR) fashion store. In fact, online fashion shopping has bloomed in the last ten years, as technological developments are continuously fostering the evolution of fashion e-commerce and branding strategies. The advance in virtual reality devices, which are approaching the consumers’ sphere, cannot but further support such trend.However, such commercial opportunity turns into the challenge of making VR-based shopping experiences enjoyable and easy-to-use to the whole class of users. To this aim, in this paper we resort to one of the most desirable ways a non-expert would use to interact with a new environment: voice. For this reason, the current study explores the benefits of speaking and verbally interacting with a VR assistant, which embodies a salesman at the service of customers. Motivated by an increasing acceptance of voice assistants, we designed and implemented an immersive VR experience where the Amazon Alexa virtual assistant is integrated and exploited, to build a proof of concept. To evaluate our proposal, we selected a specific group of fashion-experts, i.e. demanding and non tech-savvy, which tested our application and appraised the perceived comfort and appreciation of the virtual experience. The preliminary results suggest how working on the VR interface and interaction modalities could open the door to a new wave of fashion e-commerce platforms, supporting a widespread adoption of VR into everyday routines.
Learning with privileged information for photo aesthetic assessment

Privileged information (PI), known as teacher providing students helpful comments, comparisons, and explanations to improve students’ performance, has been widely applied in various machine learning tasks, resulting in great success. Existing approaches utilizing attributes either fail to leveraging the attributes information thoroughly, or suffer from the complex network structures for automatically attributes learning. Therefore, we propose a new Deep Convolutional Neural Network with Privileged Information (PI-DCNN) for photo aesthetic assessment by utilizing the prior knowledge of photo and photographic elements as privileged information. This paper is the first to systematically summarize all the attributes (i.e., photo and photographic attributes) related to aesthetics assessment. Specifically, we first explore the privileged information of photo and photography attributes, which is available at the training stage but it is not available for the test set. After that, we transfer the probabilistic dependency relations as constraints, and formulate photo aesthetics assessment in a deep convolutional neural network. Lastly, we propose a new pair-wise ranking loss that can exploit the relationship of photo aesthetic quality within a pair of photos. Experimental results on two widely benchmark databases of aesthetic assessment, AADB and AVA, demonstrate the effectiveness of the proposed PI-DCNN method on photo aesthetic assessment task.
Application of Data Mining Technique using K-Medoids in the case of Export of Crude Petroleum Materials to the Destination Country

Abstract The purpose of this research is to analyze and implement the data mining technique in the export of crude petroleum materials to the destination country. This is because Indonesia is a member of OPEC (Organization of the Petroleum Exporting Countries) which is one of the largest petroleum exporters in the world. It aims to obtain profits in the form of foreign exchange income obtained by the State. The data source was obtained from the Central Statistics Agency (BPS) with the website https://www.bps.go.id for data for 2017-2018. The calculation process The technique used is clustering with the K-Medoids algorithm. The calculation process is carried out using the help of Rapid Miner tools. The clusters used in this study are 2 namely: high cluster (C1) for export of crude oil materials and low cluster (C2) for crude oil materials. The results of the study stated that the high cluster (C1) consisted of 3 countries (Japan, Thailand and the United States) and the low cluster (C2) consisted of 6 countries (South Korea, Taiwan, China, Singapore, Malaysia and Singapore). It is hoped that the research results will be input and information for the government to rearrange policies in order to increase competitiveness, ensure business certainty and the sustainability of domestic industrial raw materials.