Reviewerly/exHarmony huggingface dataset:
Predicting Potential Propensity of Adolescents to Drugs via New Semi-supervised Deep Ordinal Regression Model

Addiction to drugs between young people is one of the most severe problems in the real world, and it imposes a huge financial and emotional burden on their families and societies. Therefore, predicting potential inclination to drugs at earlier ages can prevent lots of detriments. In this paper, we propose a new semi-supervised deep ordinal regression model to predict the possible propensity of adolescents to marijuana using the diffusion MRI-derived mean diffusivity (MD) from 148 Regions of Interest (ROIs). The traditional deep ordinal regression models cannot be directly applied to our biomedical problem which only has a small number of labeled data, not enough to train the deep learning models. Thus, we design a semi-supervised learning mechanism for deep ordinal regression, such that both labeled and unlabeled data can be used to enhance the model training. In our experiments, we use the ABCD dataset, which contains MRI images of the adolescents under study and their answers in the Likert scale to a questionnaire containing questions about Marijuana. Experimental results on the ABCD dataset validate the superior performance of our new method. Our study provides an inexpensive way to predict the drug tendency using brain MRI data.
MufiNet: Multiscale Fusion Residual Networks for Medical Image Segmentation

U-Net has been considered as an outstanding deep learning neural network in medical image segmentation problems. The segmentation results of the U-Net based model, however, are always too conservative and smooth. MufiNet, a segmentation model using multiple U-Net chains (with multiple encoder-decoder branches), is proposed in this paper. It can fuse the receptive fields obtained from different scales. The convolution layer of 1 × 1 is introduced to add the residual connection to enhance the adaptability to the depth of the network. The multi-scale fusion module with residuals is combined with the U-Net chain architecture to retain more information flow paths, and the multi-scale context information is used to improve the performance and robustness of the segmented network. MufiNet model is extensively evaluated on three datasets in this paper, including two benchmark datasets (lung segmentation and skin cancer lesion segmentation) and cervical cancer dataset jointly constructed with a hospital. The experimental results show that MufiNet could yield better performance in medical image segmentation tasks than U-Net and LadderNet models.
Towards a Deep-Pipelined Architecture for Accelerating Deep GCN on a Multi-FPGA Platform

CNN (convolutional neural networks) have achieved great success in learning features from Euclidean-structured data. While lots of learning tasks require dealing with graph data. In these application scenarios where CNN cannot operate, GCN (graph neural networks) have shown appealing performance and increasing attention in recent years. However, according to our research, the computational complexity and storage overhead of the network also increase, making it a challenge to accelerate on a single FPGA. Accordingly, in this work, we focus on accelerating a deep GCN (DAGCN) on a CPU-multi FPGA platform by proposing a deep-pipelined acceleration scheme. To fully explore the parallelism that exists in DAGCN, we propose a graph convolutional neural accelerator (GCNAR) characterized by integration of a multiple 1-D systolic array. In addition, we also adopt an existing CSR algorithm-based partitioning scheme for large-scale matrix-vector multiplication in the design of our GCNAR, which effectively improves the computational efficiency of GCNAR. Moreover, we develop performance and resource evaluation models to help us determine the optimal design parameters for maximizing the accelerator throughput. Evaluation on real-world graph datasets demonstrates that our FPGA-based solution can achieve comparable performance to state-of-the-art GCN accelerations. In addition, compared to CPU and GPU solutions, our accelerator can achieve 196 times and 115 times the improvement for graph classification respectively in terms of processing latency.
Selection Of Web-Based Framework for System Development using AHP (Analytic Hierarchy Process) Method in PT. XYZ

PT. XYZ is a distributor company with more than 20 branches in Indonesia that has been using the Windows platform to run its daily operations. Where is PT. XYZ wants to develop its system using web-based applications, where applications can run on all devices. System development is carried out using PHP based web applications. The PHP programming language has many frameworks. Most available frameworks are open source and have their advantages and disadvantages. Among the PHP frameworks that are widely used there are two very popular and frequently used frameworks, namely Laravel and CodeIgniter. Because both of these frameworks have an easier, neat system development process and their security is guaranteed. Based on these problems the researcher wants to choose what framework is most appropriate for PT. XYZ with AHP Method, Where the goal of researchers using the AHP method can decide which framework is most suitable for use. And the conclusion PT. XYZ is to use the Laravel framework.
AI and Blockchain Integration

AI and Blockchain are two disruptive technologies that have the potential to change business models and impact the society. Their integration can lead to Decentralized AI, which enables analysis, decisions and self-learning on trusted and shared data stored on the Blockchain. Autonomous agents in a multi agent environment can collaborate, act and take decisions. Decentralized AI can help improve system performance by processing relevant data, as well as perform parallel processing across nodes based on different objectives. This paper reviews concepts of Blockchain, AI, power of combining these two technologies, and different platforms providing these capabilities.
SaeCAS: Secure Authenticated Execution Using CAM-Based Vector Storage

Authenticated execution (AE) is a security mechanism that cryptographically validates an application's code as it executes, as well as verifies its control flow. AE provides fully local guarantees which can deliver protection for control flow, instruction flow, and software intellectual property which makes it ideal for devices with little to no connectivity. However, we find that previous AE approaches make concessions in their implementation that severely hinder their security guarantees. In this article, we examine the weaknesses in previous AE approaches and why they occur. We also introduce SAECAS as a mechanism to reliably perform AE in an embedded device. We formally prove the security aspects of SAECAS, demonstrating its security capabilities. Moreover, we implement SAECAS on a RISC-V core and test it on a Terasic DE2-115 FPGA board to demonstrate its capabilities, showing that a reliable system can be made with a hardware overhead of ≈ 2× when including extra SoC components and no performance impact.
Automatic Detection of Hidden Communities in the Texts of Russian Social Network Corpus

This paper proposes a linguistically-rich approach to hidden community detection which was tested in experiments with the Russian corpus of VKontakte posts. Modern algorithms for hidden community detection are based on graph theory, these procedures leaving out of account the linguistic features of analyzed texts. The authors have developed a new hybrid approach to the detection of hidden communities, combining author-topic modeling and automatic topic labeling. Specific linguistic parameters of Russian posts were revealed for correct language processing. The results justify the use of the algorithm that can be further integrated with already developed graph methods.
How Augmented Reality Influences Student Workload in Engineering Education

The primary objective of this study is to explore how augmented reality (AR) influences the student workload in engineering education. To investigate the impact of augmented reality on student workload, we used Microsoft HoloLens and developed AR modules for manual material handling (MMH) as engineering education learning contents. In this study, we conducted a between-subject study with forty-five students (AR vs. in-class environment). Their workload was measured by using NASA-TLX. The results showed that there was no overall workload difference between the AR and in-class environment. However, there were significant differences in mental, performance, effort, and frustration dimensions between AR and in-class conditions. The findings from this study could advance our understanding related to the correlations between the workload and the AR learning environment.
D-net: Siamese Based Network for Arbitrarily Oriented Volume Alignment

Alignment of contrast and non contrast-enhanced imaging is essential for quantification of changes in several biomedical applications. In particular, the extraction of cartilage shape from contrast-enhanced Computed Tomography (CT) of tibiae requires accurate alignment of the bone, currently performed manually. Existing deep learning-based methods for alignment require a common template or are limited in rotation range. Therefore, we present a novel network, D-net, to estimate arbitrary rotation and translation between 3D CT scans that additionally does not require a prior template. D-net is an extension to the branched Siamese encoder-decoder structure connected by new mutual, non-local links, which efficiently capture long-range connections of similar features between two branches. The 3D supervised network is trained and validated using preclinical CT scans of mouse tibiae with and without contrast enhancement in cartilage. The presented results show a significant improvement in the estimation of CT alignment, outperforming the current comparable methods.
Development Model of Warteg Online Applications based on Web and Mobile

The development of software engineering as well as mobile and web-apps has enabled the modernization of businesses such as warung tegal (warteg). The purpose of this research is to design an online warteg application. Application development uses the method of developing system development life cycle with the waterfall model. This application was built using the Native React Framework and with the PostgreSQL Database. The expected result is the design of online warteg applications that can be accessed on websites and mobile phones that can help warteg entrepreneurs to facilitate the promotion and sale of merchandise they sell, as well as to facilitate the public to buy food without having to come to the warteg.
Cryptanalysis and improvement of mutual authentication protocol for real-time data access in industrial wireless sensor networks

Wireless Sensor Networks (WSNs) have a very large number of applications in different domains of the industry. One of those applications is the industrial use of WSN. Industrial Wireless Sensor Networks (IWSNs) are generally used in places which are isolated from human involvement. Thus their security and privacy are a major concern. Recently, Gope et al. have discussed a lightweight mutual user authentication protocol for IWSN. In this paper, we perform the security analysis of their scheme and find that their scheme is vulnerable to the following attacks: user device loss attack, stolen verifier attack, and denial of service attack. We also propose an improved scheme that overcomes these vulnerabilities. The proposed scheme uses the Physically Unclonable Function (PUF), Exclusive OR (XOR), and one-way cryptographic hash function operations, which are all lightweight cryptographic functions. The analysis of formal security in our scheme uses random oracle model to prove the safety of the user identity, password, and session key. Through the informal security, we show that our scheme resists many known attacks. The formal verification of security is done using ProVerif tool. In the performance analysis, we present that our scheme provides better security features than the other related schemes.
An Input Residual Connection for Simplifying Gated Recurrent Neural Networks

Gated Recurrent Neural Networks (GRNNs) are important models that continue to push the state-of-the-art solutions across different machine learning problems. However, they are composed of intricate components that are generally not well understood. We increase GRNN interpretability by linking the canonical Gated Recurrent Unit (GRU) design to the well-studied Hopfield network. This connection allowed us to identify network redundancies, which we simplified with an Input Residual Connection (IRC). We tested GRNNs against their IRC counterparts on language modelling. In addition, we proposed an Input Highway Connection (IHC) as an advance application of the IRC and then evaluated the most widely applied GRNN of the Long Short-Term Memory (LSTM) and IHC-LSTM on tasks of i) image generation and ii) learning to learn to update another learner-network. Despite parameter reductions, all IRC-GRNNs showed either comparative or superior generalisation than their baseline models. Furthermore, compared to LSTM, the IHC-LSTM removed 85.4% parameters on image generation. In conclusion, the IRC is applicable, but not limited, to the GRNN designs of GRUs and LSTMs but also to FastGRNNs, Simple Recurrent Units (SRUs), and Strongly-Typed Recurrent Neural Networks (T-RNNs).
Deep Markov models of multidimensional random fields

The paper discusses the use of a doubly stochastic random field model based on autoregressions with multiple roots of characteristic equations for solving the problem of image generation and segmentation. It was proposed to form an image taking into account hidden Markov parameter fields. In particular, the correlation coefficients of the model, as well as the average values and brightness variances, are realizations of random fields, the assessment of which in explicit form is not possible. In addition, a modification of a variational autoencoder for a doubly stochastic model is considered. Finally, based on the use of a modified variational auto encoder, classification and segmentation of satellite images is performed. The combined use of the developed segmentation algorithm with the Unet neural network provides an increase in segmentation accuracy of 3%.
Efficient Evolution for Neural Architecture Search

The intensive consumption of resources by evolutionary algorithms makes it very time-consuming to search for network architectures. In this paper, We proposed a efficient evolution method for neural architecture search. Our method adopts the weight sharing strategy, in which a supernet is built to subsume all architectures, to speed up architecture evaluation. A universal choice strategy is designed to deal with the inaccurate evaluation caused by the methods that speeding up evaluation. Instead of searching for the best architecture, we search for the set of excellent architectures and derive the final architecture from derive the target architecture according to commonalities of these architectures. The proposed method achieved better results(2.40% test error rate on CIFAR-10 with 3.66M parameters) compared to other the-state-of-art method using less than 0.4 GPU days.
Multi-view Human Action Recognition Based on TSN Architecture Integrated with GRU

Human action recognition is one of the most important problems in computer vision. In this paper, we propose a novel architecture for multi-view human action recognition. The proposal exploits the temporal features and fuses the information from different camera views. Based on the idea of TSN (Temporal Segment Networks) which is working with segments of videos, we recommend aggregating scores from segments by an RNN (Recurrent Neural Network) module to enhance the quality of dynamics features. Furthermore, the proposed architecture is designed to form a multi-branch network with each branch taking responsibility for extracting a view-specific information, and the final feature is formed by combining results from branches lastly. Experiments on two datasets NUMA and MicaHandGesture have proved that the proposed architecture works effectively in different scenarios. Our model has achieved promising results with the accuracy slightly surpass other previous works.
Depth Map Inpainting And Super-Resolution With Arbitrary Scale Factors

Depth information is useful for many applications in the real world such as view synthesis, and 3D reconstruction.However, the resolution of the depth map is much lower than the resolution of the color image.Therefore, super-resolution on depth maps has drawn a lot of attention and achieved great success with machine learning-based methods in recent years.However, the prior work is still limited to training a specific model for each integer scale factor and only works with a set of few integer scale factors.Moreover, the depth map collected by the depth sensor can also have a lot of depth missing values and depth error along the edge and corners of observed objects.In this work, we propose a novel method to perform both super-resolution by an arbitrary scale factor and inpainting to fill in the missing depth values on the depth map.The experimental results on Middlebury RGBD datasets show the effectiveness of our proposed approach.
Analysis of Univariate and Multivariate Filters Towards the Early Detection of Dementia

Objective: Dementia is a progressive neurodegenerative brain disease emerging as a global health problem in adults aged 65 years or above, resulting in the death of nerve cells. The elimination of redundant and irrelevant features from the datasets is however necessary for accurate detection thus timely treatment of dementia. Methods: For this purpose, an ensemble approach of univariate and multivariate feature selection methods has been proposed in this study. A comparison of four univariate feature selection techniques (t-Test, Wilcoxon, Entropy and ROC) and six multivariate feature selection approaches (ReliefF, Bhattacharyya, CFSSubsetEval, ClassifierAttributeEval, CorrelationAttributeEval, OneRAttributeEval) has been performed. The ensemble of best univariate &amp; multivariate filter algorithms is proposed which helps in acquiring a subset of features that includes only relevant and non-redundant features. The classification is performed using Naïve Bayes, k-NN, and Random Forest algorithms. Results: Experimental results show that t-Test and ReliefF feature selection is capable of selecting 10 relevant features that give the same accuracy when all features are considered. In addition to it, the accuracy obtained using k-NN with an ensemble approach is 99.96%. The statistical significance of the method has been established using Friedman’s statistical test. Conclusion: The new ranking criteria computed by the ensemble method efficiently eliminate the insignificant features and reduces the computational cost of the algorithm. The ensemble method has been compared to the other approaches for ensuring the superiority of the proposed model. Discussion: The percentage gain in accuracy for all three classifiers, Naïve Bayes, k-NN, and Random Forest shows a remarkable difference noted down for the percentage gain in the accuracies after applying feature selection using Naïve Bayes and k-NN. Using univariate filter selection methods, the t-test is outshining among all the methods while selecting only 10 feature subsets.
ATT: Attention-based Timbre Transfer

In this paper, we tackle the issue of timbre transfer on a given monophonic music sample. The objective is to change the timbre of source audio from one instrument to another while preserving features such as loudness, pitch, and rhythm. Existing approaches use image-to-image translation techniques on the entire region of time-frequency representations of the raw audio wave, which may lead to the addition of unwanted elements in the final audio waveform. We propose Attention-based Timbre Transfer (ATT), an attention-based pipeline for transferring timbre. To the best of our knowledge, ATT is the first approach which leverages attention for achieving timbre transfer. Further, ATT uses MelGAN for spectrogram inversion, which provides a fast and parallel alternative to other autoregressive music generation approaches, without compromising on the quality. ATT shows promising results, thus efficaciously transferring timbre with minimal offset to other physical characteristics.
A Middleware Approach to Synchronize Transaction Data to Blockchain

Blockchain is a kind of distributed accounting system with the characteristics of decentralization, openness, transparency, non-tampering and de-trust. Recently, blockchain-based applications appear in various fields such as IoT, supply-chain, etc. A practical requirement of applying the Blockchain technology to the conventional supply-chain system is the trans-action synchronization between databases and the blockchain systems. This paper proposes a middleware that supports non-intrusive, efficient, fault-tolerant transaction data synchronization. The middleware is designed under three principles. The first is to divide the process of transaction synchronization into several independent phases. The second is to convert the representation of database transaction into an optimal form for blockchain, and transactions are packed and written into blockchain in a batch mode. The third is to guarantee the consistency between database and blockchain through failure recovery mechanism. Utilities like pipeline processing and transaction mapping built on this middleware achieve significant performance improvement. The peak TPS of Hyperledger Fabric reaches above 16000, which is more than 32 times of the direct submission method.
Hybrid Version of Apriori Using MapReduce

The era of technology is going to be changed so frequently, data size keeps on increasing exponentially, and as the data is increasing day by day, many new things are coming up in front that has to be considered in while getting information from the dataset. One of the most popular algorithms based on frequent itemsets is the Apriori algorithm. As the data size is going to be increased every day, number of items is also increasing, in such cases, the Apriori is not able to provide the best solutions, in such cases, the solution comes like the algorithm should run parallel, but it is not feasible solution. The solution can be feasible if the right method is hybrid with the Apriori. The MapReduce is one the best approaches that can provide solution in an efficient manner. The Apriori is also working on the frequency of the data points, and MapReduce can also work on the mapping of the key value with frequent data points. Therefore, the MapReduce can provide the parallel solution to Apriori in an efficient manner. In this paper, a hybrid version of Apriori and MapReduce for the fast and efficient execution is shown. The Apriori algorithm deployed on the MapReduce platform with suitable frequent key values. The hybrid approach is executed on the dataset and provides more accurate result. Experimental results show that the algorithm scales up linearly with respect to dataset sizes.
Hardware-Software Co-Design for Face Recognition on FPGA SoCs

With the development of deep learning, face recognition is attracting more and more attention in both industry and academia. Hardware implementation of face recognition systems on heterogeneous embedded devices, however has been rarely studies. In this paper, an embedded face recognition system is designed and implemented on FPGA SoC platforms. A hardware-software partition method is first introduced by analyzing the ratio between computation and memory access of critical tasks in the system. Several acceleration methods are then exploited to optimize the hardware implementation. The face recognition system is implemented on Xilinx FPGA MPSoC ZCU102 with 97.3% recognition accuracy and 203.7 ms latency. The neural network VIPLFace, as the most time consuming part of the system, has a 74 ms latency, 71× faster after hardware-software co-design.