Reviewerly/exHarmony huggingface dataset:
Accuracy Improvement of Object Selection in Gaze Gesture Application using Deep Learning

Gaze-based interaction is a crucial research area. Gaze gesture provides faster interaction between a user and a computer application because people naturally look at the object of interest before taking any other actions. Spontaneous gaze-gesture-based application uses gaze-gesture as an input modality without performing any calibration. The conventional eye tracking systems have a problem with low accuracy. In general, data captured by eye tracker contains errors and noise within gaze position signal. The errors and noise affect the performance of object selection in gaze gesture based application that controls digital contents on the display using smooth-pursuit eye movement. The conventional object selection method suffers from low accuracy (<; 80%). In this paper, we addressed this accuracy problem with a novel approach using deep learning. We exploited deep learning power to recognize the pattern of eye-gaze data. Long Short Term Memory (LSTM) is a deep learning architecture based on recurrent neural network (RNN). We used LSTM to perform object selection task. The dataset consisted of 34 participants taken from previous study of object selection technique of gaze gesture-based application. Our experimental results show that the proposed method achieved 96.17% of accuracy. In future, our result may be used as a guidance for developing gaze gesture application.
Dataset of User Reactions When Filling Out Web Questionnaires

This paper presents the dataset and the results of the analysis of user reactions when filling out questionnaires. Based on the analysis of 1980 results of users’ responses to simple questionnaire questions, patterns in user reactions were revealed. Data analysis shows that a user is characterized by reactions when answering a variety of questions, reflecting the individual skills of the interface, reading speed, speed of choosing an answer, which can be used to supplement personal verification in information systems. The built-in reaction time does not significantly load the data volumes for logging and transferring and does not contain confidential information. The data would be of interest for further research by specialists in the field of psychology, information security, and information systems design.
Face Detection Using Colour and Haar Features for Indoor Surveillance

For the past few decades, the evolution of human-computer interaction has significantly impacted face detection methods. In this paper, an experiment was conducted to detect frontal and side-view faces from indoor surveillance videos. The proposed method comprises skin colour segmentation, Haar feature extraction and classification. Skin colour segmentation involves the conversion of RGB images to the YCbCr colour space. Then, histogram analysis is performed to extract skin pixels in images. Afterward, Haar features are used. Finally, the cascaded AdaBoost classifier is used to classify faces into frontal and sideview faces while removing non-face regions. The proposed method successfully detected an average of 70.96% of frontal faces. The detection for side-view faces, however, have low performance, with average of 32.67%.
E-Wallet Sentiment Analysis Using Naïve Bayes and Support Vector Machine Algorithm

Abstract Nowadays most of consumers in urban areas are accustomed to using digital wallets. The habit of transaction in cashless has been widely applied to the transportation system, restaurants and shops in the mall or supermarket. Apart of the ease of conducting transactions, various promotions in the form of points and cashback offered from various digital wallet application developers or e-wallets have become very attractive to users. One of the most widely used e-wallets by the public is OVO and DANA. This phenomena encourages researchers to do a research and make it as an object of study due to both are widely discussed by various groups, especially in the capital of Jakarta lately. As it is used, many customers write product and service reviews based on their experience on the Google Play store. Sentiment analysis is a technique that can find the right solution in creating a system that can automatically analyse these reviews and extract information that is most relevant to users. Researchers collected OVO and DANA review data on the Google Play store with a total of 2000 datasets. In this study, researchers compared the two algorithms namely Naïve Bayes and Support Vector Machine (SVM). The stages carried out in this study are data collection, initial data processing, modelling with the chosen method, experimental &amp; model testing as well as evaluation and validation of result. Evaluation is carried out using 10 Fold Cross Validation. The result showed that OVO is the most popular e-wallet application by the public with an accuracy measurement using the Confusion Matrix reaching 91.00% for the SVM algorithm. The ROC curve showed the best AUC result of 0.986 (Excellent Classification).
An Ontology-based Information Model for Multi-Domain Semantic Modeling and Analysis of Smart City Data

Smart city services are typically defined according to domains (e.g., health, education, safety) and supported by different systems. Consequently, the analysis of smart city data is often domain-specific, thus limiting the capabilities of the offered services and hampering decision-making that relies on isolated domain information. To support a suitable analysis across multiple domains, it is necessary having a unified data model able to handle the inherent heterogeneity of smart city data and take into account both geographic and citizen information. This paper presents an ontology-based information model to support multi-domain analysis in smart cities to foster interoperability and powerful automated reasoning upon unambiguous information. The proposed information model follows Linked Data principles and takes advantage of ontologies to define information semantically. The semantic relationships and properties defined in the model also allow inferring new pieces of information that improve accuracy when analyzing multiple city domains. This paper reports an evaluation of the information model through ontological metrics and competence questions.
A Rabin-Karp Implementation for Handling Multiple Pattern-Matching on the GPU

The volume of digital information is growing at an extremely fast pace which, in turn, exacerbates the need of efficient mechanisms to find the presence of a pattern in an input text or a set of input strings. Combining the processing power of Graphics Processing Unit (GPU) with matching algorithms seems a natural alternative to speedup the string-matching process. This work proposes a Parallel Rabin-Karp implementation (PRK) that encompasses a fast-parallel prefix-sums algorithm to maximize parallelization and accelerate the matching verification. Given an input text T of length n and p patterns of length m, the proposed implementation finds all occurrences of p in T in O(m+q+n/τ+nm/q) time, where q is a sufficiently large prime number and τ is the available number of threads. Sequential and parallel versions of the PRK have been implemented. Experiments have been executed on p≥1 patterns of length m comprising of m=10, 20, 30 characters which are compared against a text string of length n=227. The results show that the parallel implementation of the PRK algorithm on NVIDIA V100 GPU provides speedup surpassing 372 times when compared to the sequential implementation and speedup of 12.59 times against an OpenMP implementation running on a multi-core server with 128 threads. Compared to another prominent GPU implementation, the PRK implementation attained speedup surpassing 37 times.
Detecting Malicious URL

With the ubiquitous use of Internet technology and the rapid development, many of essential life activities (banking, medicine, social networks, etc.) have shifted into Web-based services. As Web-based services became increasingly ubiquitous, they attract cybercriminals through malicious Uniform Resource Locator (URLs) and other ways to perform illegal actions. In recent years, malicious URLs have become an important security issue and an increasingly serious threat to the Web security, it is crucial to detect such threats. In this paper, therefore we applied four machine learning algorithms to detect malicious URLs. The experimental results show that the best performance is achieved by Random Forest algorithm with an accuracy of 96% and 95% during two test phases.
An Enhanced Hybrid Genetic Algorithm And Particle Swarm Optimization Based on Small Position Values for Tasks Scheduling in Cloud

Cloud computing is becoming irresistible considering its benefits, such as low maintenance, up-front costs and ease of scaling. On the other hand, the proliferation of cloud users is causing the expansion of more data centres that require a lot of power. As such, it triggered the problem of energy consumption in data centre storage systems and emission of carbon footprints in the cloud environments. To mitigate the problems, many approaches based on hybrid GA-PSO were proposed in the literature for tasks scheduling in the cloud infrastructure to efficiently manage cloud resources, enhance energy efficiency and quality of service (QoS). The already discussed GA-PSO typically applied randomization in generating initial population. However, in solving tasks scheduling problems, randomness slows convergence speed of the algorithm. In this paper, we propose an enhanced hybrid of Genetic algorithm (GA) and Particle Swarm Optimization (PSO) (GA-PSO) by applying small position values (SPV) to generate initial population to deviate from the limitation of the randomness and improve convergence speed. The proposed enhance GA-PSO with SPV is applied to efficiently schedule tasks to cloud computing resources. The result indicated that the propose GA-PSO perform better than the classical hybrid GA-PSO algorithm in terms of makespan and resource utilization.
Copper Content Inversion of Copper Ore Based on Reflectance Spectra and the VTELM Algorithm

Copper is an important national resource, which is widely used in various sectors of the national economy. The traditional detection of copper content in copper ore has the disadvantages of being time-consuming and high cost. Due to the many drawbacks of traditional detection methods, this paper proposes a new method for detecting copper content in copper ore, that is, through the spectral information of copper ore content detection method. First of all, we use chemical methods to analyze the copper content in a batch of copper ores, and accurately obtain the copper content in those ores. Then we do spectrometric tests on this batch of copper ore, and get accurate spectral data of copper ore. Based on the data obtained, we propose a new two hidden layer extreme learning machine algorithm with variable hidden layer nodes and use the regularization standard to constrain the extreme learning machine. Finally, the prediction model of copper content in copper ore is established by using the algorithm. Experiments show that this method of detecting copper ore content using spectral information is completely feasible, and the algorithm proposed in this paper can detect the copper content in copper ores faster and more accurately.
Canary: Decentralized Distributed Deep Learning Via Gradient Sketch and Partition in Multi-Interface Networks

The multi-interface networks are efficient infrastructures to deploy distributed Deep Learning (DL) tasks as the model gradients generated by each worker can be exchanged to others via different links in parallel. Although this decentralized parameter synchronization mechanism can reduce the time of gradient exchange, building a high-performance distributed DL architecture still requires the balance of communication efficiency and computational utilization, i.e., addressing the issues of traffic burst, data consistency, and programming convenience. To achieve this goal, we intend to asynchronously exchange gradient pieces without the central control in multi-interface networks. We propose the Piece-level Gradient Exchange and Multi-interface Collective Communication to handle parameter synchronization and traffic transmission, respectively. Specifically, we design the gradient sketch approach based on 8-bit uniform quantization to compress gradient tensors and introduce the colayerabstraction to better handle gradient partition, exchange and pipelining. Also, we provide general programming interfaces to capture the synchronization semantics and build the Gradient Exchange Index (GEI) data structures to make our approach online applicable. We implement our algorithms into a prototype system called Canary by using PyTorch-1.4.0. Experiments conducted in Alibaba Cloud demonstrate that Canary reduces 56.28 percent traffic on average and completes the training by up to 1.61x, 2.28x, and 2.84x faster than BML, Ako on PyTorch, and PS on TensorFlow, respectively.
Bidirectional Pyramid Networks for Semantic Segmentation

Semantic segmentation is a fundamental problem in computer vision that has attracted a lot of attention. Recent efforts have been devoted to network architecture innovations for efficient semantic segmentation that can run in real-time for autonomous driving and other applications. Information flow between scales is crucial because accurate segmentation needs both large context and fine detail. However, most existing approaches still rely on pretrained backbone models (e.g. ResNet on ImageNet). In this work, we propose to open up the backbone and design a simple yet effective multiscale network architecture, Bidirectional Pyramid Network (BPNet). BPNet takes the shape of a pyramid: information flows from bottom (high-resolution, small receptive field) to top (low-resolution, large receptive field), and from top to bottom, in a systematic manner, at every step of the processing. More importantly, fusion needs to be efficient; this is done through an add-and-multiply module with learned weights. We also apply a unary-pairwise attention mechanism to balance position sensitivity and context aggregation. Auxiliary loss is applied at multiple steps of the pyramid bottom. The resulting network achieves high accuracy with efficiency, without the need of pretraining. On the standard Cityscapes dataset, we achieve test mIoU 76.3 with 5.1M parameters and 36 fps (on Nvidia 2080 Ti), competitive with the state of the time real-time models. Meanwhile, our design is general and can be used to build heavier networks: a ResNet-101 equivalent version of BPNet achieves mIoU 81.9 on Cityscapes, competitive with the best published results. We further demonstrate the flexibility of BPNet on a prostate MRI segmentation task, achieving the state of the art with a 45\(\times \) speed-up.
3DRTE: 3D Rotation Embedding in Temporal Knowledge Graph

Temporal knowledge graph (TKG) embedding has received increasing attention in the academia. However, most existing methods are extensions of traditional translation models. Due to their intrinsic limitations, it is often difficult for such methods to effectively model essential characteristics of TKG, namely three basic relation patterns including symmetry/antisymmetry, inversion, and composition. In this paper, a new 3-Dimensional Rotation Temporal Embedding (3DRTE) method is proposed. Firstly, we selectively fuse temporal and relational features of fact triples by taking advantages of self-attention mechanism in processing sequential information. Then, entities are modelled as points in three-dimensional space, and the relations are interpreted as two isoclinic rotations between entities with Quaternion. Experimental results on several public datasets show that our method obtains state-of-the-art results.
Augmenting cryptocurrency in smart supply chain

Blockchain has been successfully used in many applications for international asset transfers and domestic payments. In these cases, payment processes are more liquidated and automated leading to the paradigm shift in the payment industry. The advancement of Blockchain technology (BCT) tends to replace conventional payment methods in supply chain that may involve payment terms. However, when BCT is applied, some payment conditions can be met by adopting smart contracts; while other scenarios such as a payment in arrears have yet been addressed. For example, no payment solution is available for anticipated funds associated with contractual dues. To improve the flexibility, we propose a new architecture based on smart contracts to lubricate the payments, and we evaluate the effectiveness of the architecture. The system has its great theoretic and practical significance since it creates the new technical thread that enterprises would be able to fully utilize future resources in current businesses.
Sentiment analysis of facebook comments on indonesian presidential candidates using the naïve bayes method

Abstract Sentiment analysis is a very interesting research object to study. Sentiment analysis itself is one branch of textmining whose research focuses on the opinion of a text document. In this study, the author examines the sentiment analysis of facebook commentary of 2 Indonesian presidential real candidates in the 2014. Henceforth in this study, the two candidates are referred to as Presidential Candidate 1 and Presidential Candidate 2, where the numbering sequence is adjusted to the numbering of real election data of president in Indonesia. Here the author chooses to use Facebook comment data on several statuses posted on the 2 official accounts of the Indonesian presidential candidates, because Facebook is a social media that is widely used by Indonesians, it is evident that Facebook’s social media ranks 3rd in Indonesia. In the process of classifying this text using the Naïve Bayes method because this method is very simple, has good performance in many domains and this method is very simple. But the Naïve Bayes method itself, has the disadvantage of being very sensitive to too many features, which can lead to low classification accuracy. To overcome the problems that exist in the Naïve Bayes method, this study uses a combination of feature selection methods, namely information gain and genetic algorithm, the two additional methods serve to improve the accuracy in Naïve Bayes classifier. In addition, in this study the author uses smile-forming character conversion, pre-processing of documents such as transform case, tokenization, Indonesian stopwords, Stemming, Token weighting, then classification and confusion matrix testing. This research produces a positive or negative text classification from Facebook comments. Then the measurement is based on the accuracy of Naïve Bayes before and after the addition of the feature selection method. The evaluation process uses 10 fold cross validation. From the results of the implementation and testing, the Naïve Bayes method with feature selection has an accuracy level of sentiment classification of 83.67% from the previous results of 60.00% here the researcher also displays the ROC curve.
The Cost to Break SIKE: A Comparative Hardware-Based Analysis with AES and SHA-3

This work presents a detailed study of the classical security of the post-quantum supersingular isogeny key encapsulation (SIKE) protocol using a realistic budget-based cost model that considers the actual computing and memory costs that are needed for cryptanalysis. In this effort, we design especially-tailored hardware accelerators for the time-critical multiplication and isogeny computations that we use to model an ASIC-powered instance of the van Oorschot-Wiener (vOW) parallel collision search algorithm. We then extend the analysis to AES and SHA-3 in the context of the NIST post-quantum cryptography standardization process to carry out a parameter analysis based on our cost model. This analysis, together with the state-of-the-art quantum security analysis of SIKE, indicates that the current SIKE parameters offer higher practical security than currently believed, closing an open issue on the suitability of the parameters to match NIST's security levels. In addition, we explore the possibility of using significantly smaller primes to enable more efficient and compact implementations with reduced bandwidth. Our improved cost model and analysis can be applied to other cryptographic settings and primitives, and can have implications for other post-quantum candidates in the NIST process.
Automatic Urinary Sediments Visible Component Detection Based on Improved YOLO Algorithm

In this paper, the end-to-end object detection algorithm based on deep learning is used to analyze the urinary sediment visible component. In order to further improve the detection accuracy of the YOLOv3 algorithm on urine sediment visible component dataset, this paper presented a new way of determined the training sample, which can enhance the quality of training samples. Then we change the convolution kernel receptive field size of the feature fusion layer to increase the detection precision. The experimental results on the dataset show that the improved YOLOv3 has higher detection accuracy, which 5 categories of urinary sediment visible component. The results shows that our method obtain the best mean average precision (mAP) of 90.1%, which is better than the original YOLOv3 model 0.6% higher. The average detection time of the model is 0.047s per frame at 800 × 600 resolution.
Research of Cartographer Graph Optimization Algorithm Based on Indoor Mobile Robot

Abstract In the recent years, mobile robot technology has received widespread attention from all over the world, and Simultaneous Localization and Mapping (SLAM) technology has always been a hot topic for researchers. In this paper, the Cartographer graph optimization SLAM algorithm is studied. Aiming at the problem that loopback detection may have wrong loopback points in the presence of noise, similar and uniform characteristics, a delay judgment module is added to filter the wrong loopback points. The Cartographer algorithm before and after optimization is deployed to the mobile robot platform based on ROS, and a 2D laser scanner is used for the map of indoor building. The experiment results prove that after the algorithm is improved, the overall relative error of the map is reduced by 1.148%, and the accuracy of mapping is improved.
Beware the evolving ‘intelligent’ web service! an integration architecture tactic to guard AI-first components

Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning. However, most of these intelligent services---such as computer vision---continually learn with time. When the internals within the abstracted 'black box' become hidden and evolve, pitfalls emerge in the robustness of applications that depend on these evolving services. Without adapting the way developers plan and construct projects reliant on intelligent services, significant gaps and risks result in both project planning and development. Therefore, how can software engineers best mitigate software evolution risk moving forward, thereby ensuring that their own applications maintain quality? Our proposal is an architectural tactic designed to improve intelligent service-dependent software robustness. The tactic involves creating an application-specific benchmark dataset baselined against an intelligent service, enabling evolutionary behaviour changes to be mitigated. A technical evaluation of our implementation of this architecture demonstrates how the tactic can identify 1,054 cases of substantial confidence evolution and 2,461 cases of substantial changes to response label sets using a dataset consisting of 331 images that evolve when sent to a service.