Reviewerly/exHarmony huggingface dataset:
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search

Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art~(SOTA) semantic segmentation and object detection approaches reuse neural network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, performance gains can be achieved by designing network architectures specifically for detection and segmentation, as shown by recent neural architecture search (NAS) research for detection and segmentation. One major challenge though, is that ImageNet pre-training of the search space representation (a.k.a. super network) or the searched networks incurs huge computational cost. In this paper, we propose a Fast Neural Network Adaptation (FNA) method, which can adapt both the architecture and parameters of a seed network (e.g. a high performing manually designed backbone) to become a network with different depth, width, or kernels via a Parameter Remapping technique, making it possible to utilize NAS for detection/segmentation tasks a lot more efficiently. In our experiments, we conduct FNA on MobileNetV2 to obtain new networks for both segmentation and detection that clearly out-perform existing networks designed both manually and by NAS. The total computation cost of FNA is significantly less than SOTA segmentation/detection NAS approaches: 1737$\times$ less than DPC, 6.8$\times$ less than Auto-DeepLab and 7.4$\times$ less than DetNAS. The code is available at https://github.com/JaminFong/FNA .
Efficient and High-Quality Monocular Depth Estimation via Gated Multi-Scale Network

The key issue in monocular depth estimation is how to construct the depth image better and improve the quality of the depth map. At present, most of the monocular depth estimation methods based on deep learning manipulate images at low resolution that leads to loss of detail and blurring of boundaries. Nevertheless, deep learning with a large number of parameters needs highly computational complexity, which makes it difficult to apply high-resolution (HR) images to the depth estimate. In this work, model accuracy and runtime are two important factors to be considered. To improve the depth map quality and reduce the running time of the network, we introduce super-resolution techniques as methods of up-sampling to generate high-quality depth images at a faster rate for the depth estimation network. A novel approach is proposed for collecting high-level features that are captured under different receptive fields. The gated multi-scale decoder allows us to effectively filter information by the gated module. By combining the gated module to aid the super resolution of depth images, our method reduces memory consumption while improves reconstruction quality. Experiment results on the challenging NYU Depth v2 dataset demonstrate that both contributions provide significant performance gains over the state-of-the-art in self-supervised depth estimation.
The self-generated full body illusion is accompanied by impaired detection of somatosensory stimuli

Research has found that body illusions may be accompanied by consequences for the real body whereby various somatosensory and homeostatic bodily functions may be impaired. These findings stem from research where an experimenter induced the body illusions. In line with advances in the domains of videogames and virtual reality where the real body is used as a controller we investigate if these consequences also accompany self-generated body illusions. In two preregistered experiments we made use of a head-mounted display set-up to induce the full body illusion (FBI) whereby touch is felt to originate from a 3PP body, and examined effects in the simple detection of supra-threshold vibrotactile stimuli presented to the participants' back and head. Results of both experiments indicate that it is possible to induce a FBI through self-stroking of the neck and that the FBI is accompanied by reduced accuracy and delayed reaction times in detection of somatosensory stimuli. In an additional preregistered control experiment the alternative explanation that a difference in motion presented in the conditions was responsible for these findings was ruled out. Our findings corroborate previous studies that have found body illusions to be accompanied by bodily consequences and further extend these findings to the domain of self-induced body illusions. These results are relevant for video games and VR setups that are geared towards virtual embodiment as they advance our understanding of the conditions and mechanisms in which bodily consequences may express themselves.
Efficient and Expressive Access Control With Revocation for Privacy of PHR Based on OBDD Access Structure

With the advancement of information and communication technology (ICT), the medical sector is undergoing a massive transformation.Health records are being digitized, stored remotely in the cloud and shared with different stakeholders.However, the use of the cloud for personal health record (PHR) storage presents data security and privacy challenges.Ciphertext-policy attribute-based encryption (CP-ABE) is being widely studied for fine-grained access control of PHRs in the cloud.Expressiveness, efficiency and attribute revocation, among others, are some key requirements of a cloud based health systems.But, many of the proposed CP-ABE schemes rely on access structures that are either restrictive or cumbersome and thus result in less expressive and efficient schemes.Many of the schemes also lack mechanisms for efficient and immediate attribute/user revocation.In this work, we propose an expressive and efficient access control scheme with attribute/user revocation based on ordered binary decision diagram (OBDD) access structure.We use the attribute group approach to achieve the attribute/user revocation in our work.Additionally, the ciphertexts and private keys are assigned version numbers to prevent the revoked group members from colluding with non-revoked members.Security and efficiency analysis show that our proposed scheme is secure, expressive and efficient.
KNOWLEDGE MANAGEMENT AND JOB PERFORMANCE: THE CASE OF LEBANESE BANKING SECTOR

This study examines the impact of knowledge management on the job performance of Lebanese banking sector during 2019.The research employed knowledge acquisition, knowledge sharing, knowledge creation, knowledge codification and knowledge retention as proxies to analyze the knowledge management variable.The job performance is measured by considering quantitative and qualitative variables.The research reveals that knowledge acquisition, knowledge sharing, knowledge creation and knowledge retention have a positive impact on the job performance of the Lebanese banking sector.The result also reveals a non significant impact of knowledge codification on the job performance.The knowledge management should be as a mandatory condition in banking sector for making high level of job performance.
Software engineering for machine learning applications

Both Software Engineering and Machine Learning have become recognized disciplines. In this article I analyse the combination of the two: engineering of machine learning applications. I believe the systematic way of working for machine learning applications is at certain points different from traditional (rule-based) software engineering. The question I set out to investigate is “How does software engineering change when we develop machine learning applications”?. This question is not an easy to answer and turns out to be a rather new, with few publications. This article collects what I have found until now.
Data Extraction and Forensic Analysis for Smartphone Paired Wearables and IoT Devices

Wearable devices and Internet of Things (IoT) devices have marked the beginning of a new era in forensic science.Data from smart home gadgets and wearable devices can serve as an important "witness" in civil as well as criminal cases.Thus data extracted from these devices has started to impact and transform litigation.Data collected from wearable devices can help determine truths in witness testimony since these devices document several types of activities of an individual at all times.Increased use of smart home devices also opens a new window for investigators.The collective data extracted from wearables and smart home devices can help investigators view the detailed events that have happened in an environment in a larger context, and give them better perspectives in the case under investigation.Our work aims to provide a solution to the challenges faced by the investigators in both extracting and analyzing the sheer volume of extracted data, and illustrates techniques to automatically highlight anomalies and correlations in the time series data collected from these devices.
Towards Intelligent Chatbots for Customer Care - Practice-Based Requirements for a Research Agenda

Chatbots bear a great potential to save efforts and costs in customer care through service automation.Current results are however still at an early stage in functionality and not widely attainable.Here, developing a new form of intelligent chatbots is a current challenge still under review.While there have been numerous proposals for future work, virtually all agenda-setting contributions are solely based on scientific literature.This is unsatisfactory from both an academic and practical perspective, as the industrial view on the future of chatbots seems to be neglected.Therefore, this work explores how professional experts see the future of intelligent chatbots for customer care and suggests how practice can guide research based on an expert panel with 17 industrial partners.Our work identifies research opportunities based on the demands and views of key practitioners by pin-pointing expected trends.Furthermore, based on the expert opinions, we derive guidelines for organizations which state key factors that should be considered in the development or adoption of chatbots in customer care.
A generative adversarial network–based method for generating negative financial samples

In financial anti-fraud field, negative samples are small and sparse with serious sample imbalanced problem. Generating negative samples consistent with original data to naturally solve imbalanced problem is a serious problem. This article proposes a new method to solve this problem. We introduce a new generation model, combined Generative Adversarial Network with Long Short-Term Memory network for one-dimensional negative financial samples. The characteristic association between transaction sequences can be learned by long short-term memory layer, and the generator covers real data distribution by the adversarial discriminator with time-sequence. Mapping data distribution to feature space is a common evaluation method of synthetic data; however, relationships between data attributes have been ignored in online transactions. We define a comprehensive evaluation method to evaluate the validity of generated samples from data distribution and attribute characteristics. Experimental results on real bank B2B transaction data show that the proposed model has higher overall ratings, which is 10% higher than traditional generation models. Finally, well-trained model is used to generate negative samples and form new dataset. The classification results on new datasets show that precision and recall are all higher than baseline models. Our work has a certain practical value and provides a new idea to solve imbalanced problem in whatever fields.
Annals of Precambrian Lithospheric Evolution and Metallogeny in the Dharwar Craton, India: Recent Paradigms and Perspectives

This article brings out a comprehensive overview of the scientific studies that have been executed in different parts of Dharwar Craton during last four years 2016-2020.Recent trends of research and scientific perspectives have emphasized on integrated approaches involving a wide spectrum of geochemical, isotopic and geochronological proxies to address fundamental questions in the Dharwar Craton on Precambrian evolution of the earth, episodes and mechanisms of crust generation and the mantle dynamics.The excellent preservation of Precambrian lithologies including basement gneisses, volcano-sedimentary sequences of greenstone belts, granitoid plutons, ultramafic-mafic complexes, ultrapotassic magmatism and dyke swarms in the Dharwar Craton have provided an opportunity for understanding the geodynamic transitions in the secular evolution of the Earth.A four year status report including the principal lithological associations of Dharwar Craton have been described in this communication detailing the scientific investigations and their implications are brought out under various sections.The salient aspects of the scientific contributions on granite-greenstone terranes of Dharwar Craton emphasising on geochronology, geochemistry, biogeochemical processes, geodynamics and mineralization.
A modal approach for shape defect measurement based on global stereocorrelation

The metrology of mechanical parts using non-contact systems often requires complex post-processing operations to evaluate shape defects of the studied surface. For large parts, the geometric complexity combined with the amount of acquired data make such treatments long and tedious. To overcome this challenge, a shape defect measurement system based on global stereocorrelation is developed. This approach integrates a self-calibration step using the CAD model of the studied part that allows the measurement results to be directly expressed in the numerically defined frame of the part. The defects will be measured in a predefined modal basis, thereby introducing registrations with a limited number of degrees of freedom and allowing for easier analyses of the measured defects.
The Challenges and Solutions of Teachers' Problems to Achieve Education Golden Era

This research aims to describe the problems of education in terms of the budget, accreditation, and teachers as well as the challenges of developing them, and solutions to the problems of education in Indonesia.This research is a literature review.Data collected through the Central Statistics Agency and Education data from the Indonesian Ministry of Education.The data is then explained and analyzed critically to provide conclusions in the context of designing and solving education problems in Indonesia to welcome the golden era.Education should soon adapt to era development if it is not willing to be left.A country's responsibilities in overseeing the education to achieve a golden education era are required.The planning, process and results of education will reflect the quality of the nation.The results showed that the problem of absorption of school budgets, equitable distribution of school accreditations, and teacher problems were important things that needed to be resolved immediately.This study provides a contribution to the new strategic plan on how to achieve the golden era of education, there not much research focuses discuss the budget, accreditation, vocational education, and character education as the basis for the determination of educational policy.The conclusion is the government should focus more on equity of Education budget absorption, enhancement of teachers from the aspect of skill and well-being, to make vocational education as an arrow civilization, and making character education as a human resource control to keep moral and intellectual integrity.
Research on Web Application Security Vulnerability Scanning Technology

With the rapid development of the Internet, web applications are becoming more and more popular. The client applications has gradually replaced by web applications in the power industry, but its security level is still relatively low, so web applications have become the main target of attackers. In order to ensure the security of the information system in the power industry, it is particularly important to find out web application security vulnerabilities quickly and accurately. The automated vulnerability scanning system can improve the efficiency of web application vulnerability scanning compared to traditional manual detection vulnerabilities that are time-consuming, labor-intensive, and inefficient. Based on the above problems, this paper studies the common web application security vulnerability scanning technology in the power industry. Firstly, the working principle of vulnerability scanning is elaborated. Secondly, the specific vulnerability scanning technology is analyzed, and the implementation process of vulnerability scanning technology is emphasized. Finally, the key functions of the vulnerability scanning system are realized, and the feasibility of the technology research is proved by the scanning results.
A Novel Method for Recognizing Vietnamese Voice Commands on Smartphones with Support Vector Machine and Convolutional Neural Networks

This paper will present a new method of identifying Vietnamese voice commands using Google speech recognition (GSR) service results. The problem is that the percentage of correct identifications of Vietnamese voice commands in the Google system is not high. We propose a supervised machine-learning approach to address cases in which Google incorrectly identifies voice commands. First, we build a voice command dataset that includes hypotheses of GSR for each corresponding voice command. Next, we propose a correction system using support vector machine (SVM) and convolutional neural network (CNN) models. The results show that the correction system reduces errors in recognizing Vietnamese voice commands from 35.06% to 7.08% using the SVM model and 5.15% using the CNN model.
Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data

Daily living activities (DLAs) classification using data collected from wearable monitoring sensors is very challenging due to the imbalance characteristics of the monitored data. A major research challenge is to determine the best combination of features that returns the best accuracy results using minimal computational resources, when the data is heterogeneous and not fitted for classical algorithms that are designed for balanced low-dimensional datasets. This research article: (1) presents a modification of the classical version of the binary particle swarm optimization (BPSO) algorithm that introduces a particular type of particles called sensor particles, (2) describes the adaptation of this algorithm for data generated by sensors that monitor DLAs to determine the best positions and features of the monitoring sensors that lead to the best classification results, and (3) evaluates and validates the proposed approach using a machine learning methodology that integrates the modified version of the algorithm. The methodology is tested and validated on the Daily Life Activities (DaLiAc) dataset.
A Design and Verification Methodology for a TrustZone Trusted Execution Environment

Hardware support for isolated execution (e.g., ARM TrustZone) enables the development of a trusted execution environment (TEE) that ensures the security of the code and data while communicating with a compromised rich execution environment (REE). The ability to satisfy various security services is complicated and usually consists of trusted applications, a trusted kernel and a secure monitor. However, formally verifying the security of an entire TEE security remains challenging. We present a methodology for designing a TEE in a way that enables verification of its security properties. Our methodology consists of forcing a trusted application and kernel to communicate with an REE via a narrow interface and compile and link them with a small secure monitor that implements the interface and runs at the highest privilege level. We provide functional verification of the secure monitor to ensure that it correctly switches the TEE/REE, communicates with the REE at a pre-defined memory space and has no integer overflow vulnerability. We also perform a verification of the secure monitor's scheduler to ensure that it satisfies information flow noninterference. We present a modular verification framework that can prove an end-to-end security property for cross-language programmes (e.g., C and assembly languages). Our evaluation suggests that the methodology scales to real-world TEE applications.
Design of highly effective multilayer feedforward neural network by using genetic algorithm

Abstract This paper presents a highly effective and precise neural network method for choosing the activation functions (AFs) and tuning the learning parameters (LPs) of a multilayer feedforward neural network by using a genetic algorithm (GA). The performance of the neural network mainly depends on the learning algorithms and the network structure. The backpropagation learning algorithm is used for tuning the network connection weights, and the LPs are obtained by the GA to provide both fast and reliable learning. Also, the AFs of each neuron in the network are automatically chosen by a GA. The present study consists of 10 different functions to accomplish a better convergence of the desired input–output mapping. Test studies are performed to solve a set of two‐dimensional regression problems for the proposed genetic‐based neural network (GNN) and conventional neural network having sigmoid AFs and constant learning parameters. The proposed GNN has also been tested by applying it to three real problems in the fields of environment, medicine, and economics. Obtained results prove that the proposed GNN is more effective and reliable when compared with the classical neural network structure.
New Approach in Application of the AHP–Fuzzy TOPSIS Method in Mineral Potential Mapping of the Natural Bitumen (Gilsonite): A Case Study from the Gilan-e-Gharb Block, the Kermanshah, West of Iran

The Gilan-e-Gharb block is known as a prospective area for hydrocarbon resources in the form of oil, gas in deep potential and natural bitumen (Gilsonite) on the surface.Natural bitumen is not clearly detectable by geochemical or geophysical methods.Hereupon, identifying high potential areas for further exploration, attempted with the help of AHP-Fuzzy and TOPSIS methods.The comprehensive database of geological and geostructural records, satellite imagery analysis by remote sensing and mine indexes counted as the inputs for this method.First, the lithological unit as the main mineralization hosts determined with respect to the dominant geological structures as a factor of controlling natural bitumen placement (fold, fracture and faults) in the Gilan-e-Gharb block.The Gachsaran, Asmari, Pabdeh and Gurpi Formations identified as the most important lithological units for mineralization.Placement and distribution of natural bitumen mineralization in the form of mine indexes are added to the geology database.Finally, we assigned appropriate weights to applied information layers using Analytical Hierarchy Processing (AHP) based on knowledgeable information and field studies to synthesize exploratory data.Then, we used the FTOPSIS method to define the Positive Ideal Solution (FPIS, A+) that allows maximizing the beneficial characteristics and minimizing the impediment characteristics and the Negative Ideal Solution (FNIS, A-) that minimizes the beneficial characteristics and maximizes the impediment characteristics.This method, as a new approach in the exploration of minerals with a shortage of data, is applicable to other mineral deposits.