Reviewerly/exHarmony huggingface dataset:
Computing feature vectors of students for face recognition using Apache Spark

With a massive increase of images and videos in the internet, processing these data for face detection and recognition is a bottleneck in current situation. At least there is a lack of frameworks and tools to process lots of data efficiently and not time consuming. System proposed in this paper offers face recognition framework based on Apache Spark to analyze lots images and videos from Instagram. As a people need to be searched and recognized are university students, their feature vectors used for recognition initially computed and can be enlarged at any moment. Spark's Resilient Distributed Databases gives ability to compute all the intermediate data directly in memory making computations faster meanwhile keeping resilience property.
Indonesian Language Sign System (SIBI) Recognition Using Threshold Conditional Random Fields

The Sign System for Indonesian Language or 'Sistem Isyarat Bahasa Indonesia' (SIBI) is a sign language system that is used to represent Indonesian language. The referred sign language is a systematic movement of fingers and hands to represent a vocabulary. This paper utilizes the Threshold Conditional Random Field (TCRF) model to identify gesture and non-gesture automatically. The generated model is an early model to establish a SIBI translation system automatically. Data that were utilized in this research are Skeleton, Image, and Skeleton-Image Combination. Data were processed by implementing TCRF algorithm to provide gesture and non-gesture labels automatically. Several experiments had pointed to the highest accuracy up to 81, 5% by using skeletal data as an input in TCRF.
True Random Number Generator using Superconducting Qubits

We evaluated the quality of conventional quantum True Random Number Generator (TRNG) using superconducting qubits from IBM. Our analysis indicated that the 1/0 ratio is severely affected by the noise. We proposed swapping of readout qubit and parameter optimization to fix readout and gate errors. We validate our proposals by running experiments of IBM's su-perconducitng qubit based quantum computer. Experiments show deviation of ≈ 35% from ideal 1/0 ratio for baseline implementation. After the application of our proposed techniques the ratio improves and reaches close to ideal value of 1. The random bits generated through our techniques passes NIST statistical tests as well.
Improvement of Pose Recognition by Sparse Regularized Convolutional Neural Network

The paper proposes a method of using deep model (CNN) with sparse regularization term to improve the performance of pose recognition. Convolutional neural network shows its limitations for pose recognition because of its bad convergence. It is believed that the activation function could be simplified by adding the sparseness term. Therefore, we present an algorithm applying the sparse regularization for the deep model with ReLU. Experimental results confirm that the proposed method can accelerate the convergence speed while a high recognition rate maintained.
Synergy of Trust, Blockchain and Smart Contracts for Optimization of Decentralized IoT Service Platforms

There is a high potential of creating new and market-ready services for the Internet of Things (IoT) community by enabling end-users to make their resources inside the end-user environment available to others. Thus, end-user friendly service platforms facilitate the process of designing and configuring new local and cooperative services. However, decentralized networks of end-users acting as service providers enable the risk of uncontrollable behavior and trustless activities. Blockchain technology coupled with smart contracts now gain popular attention by providing benefits of data immutability and process automation in open or closed communities. This research summarizes several blockchain-based IoT approaches, concluding with their benefits and limitations. Besides them it proposes an optimized and decentralized IoT marketplace consisting of end-user friendly IoT service platforms, forced by a synergy of trust, blockchain and smart contract elements. To highlight the benefits of this synergy, two smart contract use cases (based on Ethereum) in the IoT marketplace are presented.
Extraction and Recognition of Numerals from Machine-Printed Urdu Documents

The work presented in this paper proposes extraction and recognition of Urdu Numerals from Machine-Printed Urdu Documents. Feature extraction approaches for the features of a numeral that have been considered include Zernike, Discrete Cosine Transform (DCT), Zoning, Gabor filter, Directional Distance Distribution and Gradient. In addition, performance evaluation has also been done by varying the feature vector length of Zernike and DCT features. For classification, k-Nearest Neighbor (k-NN) with different values of k and Support Vector Machine (SVM) with Linear, Polynomial and Radial Basis Function (RBF) kernels functions have been employed by changing the parameters, like degree of the polynomial kernel, and γ for RBF kernels. 1470 samples for training and 734 samples for testing the classifiers for Urdu numerals have been used. Maximum recognition accuracy of 99.1826% has been achieved.
A Reparameterized Construction in L2-Norm Weight Decay for Rectifier Neural Networks

In this paper, we investigate regularization method to construct better rectifier neural networks for the classification tasks from two aspects. First we propose a novel regularization method for effective training of rectifier neural networks by reparameterizing each connection weight in convolution layers into a product of two vectors and using the L2-norm penalty to constrain those two vectors rather than the connection weight. Second, we derive a robust initialization for our reparameterization method by taking the rectifier nonlinearities into account to help learn well regularized networks. This new method has been evaluated on the CIFAR-10 and CIFAR-100 datasets and demonstrated improved performance relative to the traditional L2-norm weight decay approach.
Eye Tracking Technology in Personal Authentication Protocols

Eye tracking technologies play important role in many VR and AI applications. In this paper will be described some possible solutions based on these technologies, which are oriented for security areas, and user authentication tasks. In particular new visual codes will be defined, which allow to authenticate persons, based on theirs cognitive skills, and possibilities of performing fast recognition and semantic evaluation of observed patterns.
A Scalable Computer Vision Framework For Mobile Device Auto-Typing

In this paper, we present a computer vision framework that controls robots to auto type on a mobile device such as an Android phone or an iPad. The framework consists of three parts: (i) an image undistortion and segmentation algorithm that supports images captured by a top mounted camera or a side mounted camera, (ii) a deep neural network (DNN) algorithm that detects the keyboard region and recognizes isolated characters from an input image, and (iii) a grid click algorithm to construct data points to compute the image to robot coordinate translation matrix that is scalable to any touch device of different sizes. We have demonstrated in this paper the accuracy and scalability of the proposed system.
Spatio-Temporal LSTM with Aggregated Feature Learning for Human Action Recognition

Human action recognition has received much research attention in recent years. Discriminative features learning is important for modeling the spatial and temporal evolutions of different actions. Recent works attempted to use RNN/LSTM to learn spatio-temporal features of joints. However, the features of joints are not fully explored in these conventional LSTMs, where each joint only receives context information from itself from previous frames. In this paper, we propose a LSTM network model by using aggregated feature learning strategy to extend feature representation in both spatial and temporal domains. In this model, the features of a joint are aggregated not only from all joints at previous moments, but also at current moment. Therefore, action-related features within the human skeleton sequences can be better represented and analyzed. To further optimize the network, we present a reparameterization that involves batch normalization and sparsemax classifier for the proposed LSTM network. We evaluate our approach on NTU RGB+D benchmark dataset. Experiment results demonstrate the effectiveness of the proposed approaches.
Face Image Retrieval Based on Fireworks Algorithm

Content-Based Image Retrieval (CBIR) is a technique used in search engine like google, yahoo, etc. for retrieving most relevant images depending on feeding information that may be text or image. The classical methods of image indexing in the database used keywords or numbers with description record, but these methods have become outmoded. This paper deals with retrieving face images from the datacenter that related to the input image. The proposed method used object detection to specify the object in the image (human face) using the viola-jones algorithm. Local Binary Pattern (LBP) is used for feature extraction and store the features in the database. The used method for image features clustering is K-means method. The proposed system optimizes the total features of each cluster using Firework algorithm (FWA) that work on reducing the search space and constructing a temporal table utilized as intermediate indexing of related image. The services provided by this system that are loading the related databases only in memory for query image and retrieve the most relevant in sorted order depend on distance measurement. The obtained results indicated that the proposed system provides an efficient detection for a person in the whole data center if there is one person available in the image or more than one person related to searching image (query image).
RDB-FSU: Residual Dense Network with Feature Selection Unit for Image Super Resolution

After the great success of deep convolution neural network (DCNN). In practical, residual and dense networks. Most deep methods do not make full use of features from the original low-resolution (LR) images. This paper proposed a novel residual in dense network with feature selection unit (RDB-FSU) that tackle this problem in both image and video SR. RDB-FSU fully use the features from all the convolution layers. Specifically, the residual dense block (RDB) presented to extract features via dense connected convolution layers. Then feature selection unit (FSU) is adaptively learn more effective features from extracted features to be up scaled using pixel-shuffle module. Extensive experiments with various models show that the presented RDB-FSU fulfills appropriate performance versus state-of-the-art techniques on both image and video benchmark dataset.
A Vectorization Approach for Graph-Structured Data to Pattern Recognition

Graphs are popular data structures that intuitively represent structured information. With the expansion of the graph scale and the complexity of graph attribute information, it is necessary to generalize graphs to apply graph-based learning techniques accurately and effectively. Based on the previous methods of generating fixed-size vectors for attributed graphs as the input to convolutional neural networks, we propose a novel vector structure to represent the complex attribute and structure information for graphs. In this paper, we define a general method of attribute classification and aggregation functions for node and edge attributes on the graphs, which are applied to weaken the performance bottlenecks of the previous methods in the multi-graphs scenario. Experiments show that our works can greatly improve the computation efficiency across multiple graphs while ensuring the accuracy of graph-based learning results.
Automatic Extraction of Frequently Confused Words in English Based on String Similarity Algorithm

Abstract The calculation method for the form similarity of English words is carried out. An algorithm where the upper limit of string similarity parameters can be set is used for automatic extraction of words with similar spellings from a specified vocabulary range. The frequently confused words with similar spellings screened can enrich the English lexical knowledge base after duplicate removal and classification. The frequently confused word knowledge base is of application value in the fields of textbook writing, vocabulary training design, dictionary compilation, and real word misspelling correction.
An Efficient Two Factor Authentication Scheme Providing Secure Communication in Mobile Cloud Computing

Integration of mobile networks with cloud computing platform led to development of mobile cloud computing. Since the communication between mobile devices and the cloud computing occur over wireless medium, securing the network becomes paramount. With the thorough literature survey, we found that many two factor authentication schemes proposed so far to preserve user anonymity are vulnerable to various security attacks, they also had shortcomes to achieve security goals. To overcome the issues related to the two factor authentication schemes in mobile cloud computing, a new scheme is proposed. Furthermore, we have simulated the proposed scheme using widely accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool. With the simulated result of the proposed scheme, we show that the proposed scheme is secure against active and passive security attacks. Additionally, the proposed scheme is compared with Gope and Hwang's scheme and other related schemes in terms of computational cost and communication overhead. The proposed scheme is efficient, robust and suitable for practical implementation.
Extracting architectural patterns of deep neural networks for disease detection

The importance of early detection of diseases with high-mortality is crucial to save lives. Deep Learning algorithms are recurrently used by many researchers that aim to model the progression and treatment of these conditions.
Write-protection enforcement

A rootkit is a piece of code that aims to manipulate the computer behaviour without being detected. Rootkits are mainly used to disable kernel self-protection, hide malware presence, provide a covert communication channel between malware and their Command and Control server (C&C), secretly maintain a breach viable. Depending on how rootkits are injected into the target, they can either reside in user or kernel space. Kernel space rootkits are the hardest to detect and prevent because they usually take control of the kernel once compromised.
Efficient Identity-Based Outsider Anonymous Public-Key Trace and Revoke with Constant Ciphertext-Size and Fast Decryption

In this paper, we present the first identity-based outsider anonymous public-key trace and revoke (OAnoPKTR) scheme achieving constant-size communication bandwidth and computation cost. Our construction is obtained by twitching Tardos fingerprinting code (TFC) over Waters identity-based encryption (IBE) framework (EUROCRYPT 2005) endowed with the most efficient asymmetric Type-3 variant of the bilinear maps. We efficiently couple two mutually orthogonal functionalities, namely receivers anonymity and public traceability, which is difficult to accomplish without losing cost-efficiency. Our scheme provably achieves indistinguishable chosen-plaintext attack (IND-CPA) security against adaptive adversary beneath the standard decisional bilinear Diffie-Hellman exponent (DBDHE) assumption. The security analysis is in the standard security model without applying random oracles.
Study of How to Obtain Control Flow Invariant

The traditional control flow integrity usually takes dynamic control transfer as the measurement object. And it means that the attacker can subvert software by modifying the obvious victim. To overcome it, a novel concept, called as control flow invariant, is presented in this paper. Control structure invariant is a certain law followed by software run. Not limited to dynamic control transfer, control structure invariant have more forms, thus supporting more types of control flow integrity measure. In order to find more control structure invariant, we proposes to use hardware to capture the trace information of software running, and analyze it to mine the control structure invariant. We give a case study that a system on Intel PMC is introduced to show the reasonableness of our solution.
Super-resolved Chromatic Mapping of Snapshot Mosaic Image Sensors via a Texture Sensitive Residual Network

This paper introduces a novel method to simultaneously super-resolve and colour-predict images acquired by snapshot mosaic sensors. These sensors allow for spectral images to be acquired using low-power, small form factor, solid-state CMOS sensors that can operate at video frame rates without the need for complex optical setups. Despite their desirable traits, their main drawback stems from the fact that the spatial resolution of the imagery acquired by these sensors is low. Moreover, chromatic mapping in snapshot mosaic sensors is not straightforward since the bands delivered by the sensor tend to be narrow and unevenly distributed across the range in which they operate. We tackle this drawback as applied to chromatic mapping by using a residual channel attention network equipped with a texture sensitive block. Our method significantly outperforms the traditional approach of interpolating the image and, afterwards, applying a colour matching function. This work establishes state-of-the-art in this domain while also making available to the research community a dataset containing 296 registered stereo multi-spectral/RGB images pairs.
Strengthening Water Irrigation Management to Increase Water Usage Efficiency

Abstract The land-use change from agricultural to the industrial city took place in Karawang. Besides land conversion, transfer in water usage also increased for industrial needs. This is caused by poor water management in terms of distribution and control of water usage, mainly in the industrial and agricultural sectors. The area of irrigated land was reduced from 97, 037 ha (2009) to 90, 062 ha (2017), while the amount of dry land increased from 22, 063 ha (2009) to 46, 299 ha (2017). It showed that the lack of irrigation water management has an impact on the reduction of fertile paddy fields. Therefore, this research aims to formulate an action plan of water irrigation management. The study was conducted in West Telukjambe District, Karawang Regency. The method used stakeholder analysis. Data obtained from observation and interview data, irrigated land area, and the amount of irrigation water availability. The results showed the overlapping and missing roles of several stakeholders in charge of irrigation water management in West Telukjambe. Model simulations showed the increasing of irrigation serving area for additional management performance of each stakeholder. Therefore, the integrated management of irrigation systems needed to control the distribution and water irrigation efficient.
Mobile Business Intelligence Assistant (m-BELA) for Higher Education Executives

In the era of Industrial Revolution 4.0 marked by the use of information technology, the Internet in particular, university executives, as well as executives in companies, are often in such a situation which forces them to conduct business intelligence activities ranging from simply accessing data and information to analyzing them to support their decision making whenever and wherever they are. Typically, university executives are able to access data and information not only from their university databases through their web applications but also from other data sources using internet browser installed on their laptops or mobile devices. However, information access model using web application tends to be rigid where the order of menu, layout, and user-interface provided are static which forces the user to memorize or search for the right place to find the data and information needed. Certainly, this model cannot keep up with the dynamic information needs. This study produces a mobile-based application named m-BELA which functions like a human personal assistant equipped with pre-defined data, information, and knowledge who is able to interpret user's voice command and then responds appropriately by following conversation or providing data and information relevant to the command. This new solution offers a competitive advantage since the user does not need to load a browser, open the web application, and then search for the menu and UI which provide information needed.
Integrating Traversal and Visual-based Techniques to Initiate UI Exploration Scripting

Initiating UI Test Automation project usually requires the testers who have related business knowledge, testing skill, and technical skill. Helping the testers start the project quickly and able to focus on other tasks in charge, this paper presents an approach to generate the initial UI test scripts by combining three main concepts of GUI automation including Scripting, Visual, and Traversal. The designed framework uses the data from the Accessibility API to extract target graphical elements on application under test (AUT). The traversal technique is applied for generating the sequences of interactions on all interactable objects based on Python GUI automated scripting (SikuliX) format. In case the API is not available on some OS/Applications, the computer vision technology will then be deployed on the Open Source Computer Vision Library (OpenCV) to extract the GUI elements. The implementation of the proposed framework is demonstrated via a couple of open-source applications.
Knowledge Representation: A Comparative Study

The intelligent computer system relies on the existence of knowledge. Knowledge refers to a set of facts and rules that describe the domain of interest. The encoded knowledge enables the computer system to interpret and analyze the received information. It also enables the computer system to infer new knowledge from the current facts. The encoded knowledge can include a set of rules that can be captured from the human experts to solve a complex problem such as the disease diagnosis. By this way, the intelligent computer system stimulates the human reasoning that is used to solve problems in an automated way. The process of the symbolization of knowledge in a formal manner in order to be understood by the intelligent systems called knowledge representation. The objective of this paper is the investigation of the major knowledge representation techniques. Furthermore, the paper presents a framework proposal for a comparative study to clarify the difference between the knowledge representation techniques.
THE EFFECT OF THE COLLABORATIVE TECHNOLOGY-ENHANCED ACTIVITIES ON STUDENTS’ MOTIVATION

The collaborative technology-enhanced activities developed for Algorithm Calculation course in Medical engineering subject-discipline and Computer Workshop courses in two Computer and IT engineering subject-disciplines in two consecutive semesters. Mixed-method research design was used in this study .Content of each course was used in order to develop the activities. Motivational Strategies for Leaning Questionnaire (MSLQ) was utilized for collecting quantitative data while qualitative data was collected using interview protocol for further investigation. The analyses showed that the “intrinsic value” was the category with the higher mean score for all students with three different subject-disciplines which is followed by cognitive strategy use. Moreover, the results showed that the students’ level of anxiety decreased after using the activities. Analysis of interview data showed that the students emphasized on the role of activities in terms of increasing their “collaboration” with their peers and instructor. Moreover, they perceived that the activities make the course more interesting for them. They also explained that using computerized devices especially mobile ones facilitated their communication and material-sharing. &nbsp