Reviewerly/exHarmony huggingface dataset:
A Novel Neurosurgery Registration Pipeline Based on Heat Maps and Anatomic Facial Feature Points

Current neurosurgery registration methods can be divided into two categories: marker-based and markerless. Maker-based methods need fiducials on patients' heads, which may cause additional harm to them. Traditional markerless registration methods always require interaction, expensive equipment and specific posture of patients. In this paper, we explore how deep neutral network can be used in neurosurgery registration problem. And the method of this paper mainly includes three parts: 3D reconstruction and face mesh model feature point extraction, binocular face feature point cloud generation, point cloud registration. To this end, we make the following contributions: (a) we propose an automatic registration pipeline based on heat maps generated from Convolutional Neural Network (CNN), which is totally dependent on the anatomic facial feature points of patients and free of extra fiducials. (b) we use comparatively cheap binocular cameras instead of surface scanner to collect patients' facial information. (c) we don't use 3D CNNs to extract facial feature points of the medical images like CT, instead we use 2D CNNs on the projection images and get 3D facial feature points from their corresponding 2D facial feature points. As a result, it can relieve the lack of 3D training data and save the run-time memory. We show that the registration precision is dominant to the accuracy of facial key point extraction, and the network we use exhibits powerful performance.
Attention-Based Bidirectional Hierarchical LSTM Networks for Text Semantic Classification

Recently, in the semantic classification task at the document level, the traditional semantic classification method still has challenges in coding the hierarchical relationship between word level and sentence level. Aiming at this problem, we construct a bidirectional hierarchical LSTM network model (HBLSTM-ATT) based on the attention mechanism, and calculate the correlation before and after the sentence. At the same time, this paper focuses on document-level classification, using hierarchical structures to build document-level vectors from word vectors. Experimental results on Yelp, IMDB, Yahoo Answer and Amazon review datasets show that the proposed method is efficient in text semantic classification tasks compared to previous machine learning methods, and the semantic classification methods for gated neural networks. The accuracy rate for classification and stability have robust superiority over competitors and achieve the state-of-the-art results.
An Improved Algorithm for Building Suffix Array in External Memory

A suffix array (SA) is a data structure that has been widely used in many string processing applications, and it can be built on external memory model using the induced sorting (IS) method when the size of input and output exceeds the capacity of internal memory. In this paper, we make our first attempt to improve the performance of DSA-IS using new substring sorting and naming methods in the reduction phase. The experimental results indicate that, our program for the adapted algorithm DSA-IS+ runs as fast as eSAIS and consumes only half as much disk space as the latter on various real-world datasets.
A Survey on Ant Colony Optimization for Solving Some of the Selected NP-Hard Problem

This paper analyses various ant colony optimization (ACO) based techniques for solving some of the selected intractable problems. ACO is one of the popularly used techniques in the field of meta-heuristic techniques that gave acceptable solutions to intractable problems like Travelling Salesperson (TS), Subset Selection (SS), Minimum Vertex Cover (MVC), and 0/1 Knapsack in tolerable amount of time. We have reviewed literature on the usage of aforesaid meta-heuristic algorithms for solving the intractable problems like TS, SS, MVC, and 0/1-Knapsack. A review of several ACO for NP-Hard problems with different instances shows that ACO algorithm demonstrates significant effectiveness and robustness in solving intractable problems.
Classification of Streetsigns Using Gaussian Process Latent Variable Models

Since the rise of deep artificial neuronal nets, object detection and classification became an autonomous procedure, where both, feature extraction and feature processing (e.g.: classification) is done using an architecture based on artificial neurons. The shortcomings of deep neuronal nets are mainly based the black box models and the architecture of the networks, which cannot be estimated. Unknown behavior and over-fitting is still an unsolved problem. Thus, human-made parameters like the number of neurons or the definition of activation functions must be set. This work presents a non-parametric and non-linear approach for image processing using latent variable models. We used Gaussian process latent variable models for street sign feature extraction, where a latent representation is estimated without prior knowledge such as class label. Based on the latent representation, we visualizes the features and use state-of-the-art classifier for street sign classification. Our results proves, that our approach extracts useful features for classification. Our approach has still shortcomings, such as computational time, which are current areas of research.
Shopping Website Accessibility Study Based on Users’ Mental Models

The arrival of the Internet information age promotes the development of electronic and information platform - website. A website is a provider of information and a platform for users to communicate and interact. Website interface design must consider human cognitive factors, emotional factors, so the website interface design and interaction design is particularly important. Through experimental demonstration and research, this paper takes shopping website as the research object to analyze the mental model of users in website interface design and the influence of different users’ occupations on the mental model. According to the conclusion of the analysis, firstly, the change process of user mental model in interface interaction is proposed. Secondly, based on the user mental model, this paper summarizes the design principles of the website. Finally, this paper provides a new way of thinking for the study of website accessibility.
Sustainable Member Motivation System Proposal for NGOs: NGO-TR

Non-governmental organizations (NGO) depend on their members to survive. They need to reach the potential volunteers. They also aspire to keep track of their members' effectiveness and to motivate those members to be more active. The volunteers need a transparent system, where their privacy is preserved. Our meetings with several NGOs show that there is a solid need for a transparent, effective and trustworthy system, where NGOs can track their members' effectiveness, create enthusiasm and even collaborate with the volunteers from other NGOs. A system, where gamification principles are used for member motivation and the reputation of any member is calculated by his/her activities, is proposed. The blockchain technology will be used for the gamification process and the decentralized digital identity management. The decentralized digital identity management approach will help in providing the volunteers to gain full control over their identities and ensure the privacy. A design approach for the proposed system, the expected requirements and some possible use case scenarios have been presented. The prototype is implemented via Hyperledger Fabric and Hyperledger Indy software platforms, as a proof of concept. Chaincodes are to be implemented for the system to work autonomously.
Cloud Container Placement Policies: A Study and Comparison

Virtualization is a core technique used for running multiple applications and services at the same time. In addition to effectively increasing the utilization rate of bare metal hardware, virtual machines are also used for replicating test environments. However, the emergence of containers has been seen as an enhancement to virtualization. This paper discusses the concept of containers, how they function, and provides a theoretical analysis of containerization with reference to virtualization. In the end, the performance of container migration policies provided as part of CloudSim framework, namely First Fit, Least Full, Most Full and Random is evaluated using allocated number of hosts, containers, and virtual machines.
Digital Document Security on Legalize Higher Education Diplomas with Digital Signature and SHA-1 Algorithm

Digital documents are objects that must be secured because they contain a variety of important information both about personal or organizational data. securing digital documents which means securing information, information security guarantees business continuity, reducing risk, and optimizing return on investment. In the face of the need for rapid validation for the process of legalizing copies of diplomas and grades. But in the legalization must have good security. Where it aims to overcome the threat of modification and falsification of digital documents in the form of diplomas. This study aims to reduce the risk of falsification of diploma digital documents by using the concept of Digital Signatures which are expressed on copies of documents, using the SHA-1 algorithm will be able to produce digital signatures based on data available on diplomas and diploma legalization dates. Digital marking method designed to be applied in a person's diploma validation system or student validity by applying the SHA-1 algorithm. Where the results of a digital signature will be applied to digital documents as proof of diploma legalization for the validity of digital documents.
Bug Finder Evaluation Guided Program Analysis Improvement

There are many static analysis tools implemented with diverse principles for finding program bugs. The theoretical principles they based are usually pattern matching, abstract interpretation, model checking and symbolic execution. To evaluate the bug detection capability of different tools, we selected five static analysis tools for C/C++ programs implemented with diverse theoretical principles and evaluated them in detail. We have found that one single tool cannot achieve great results for bug detection. Guided by the evaluation results, we propose a code property graph based graph traversal method to handle those defect types which are not well-supported by static analysis tools. Experimental results show that the code property graph based graph traversal method can detect many defects which are difficult for existing static analysis tools.
Evolving Gaussian Process Kernels for Translation Editing Effort Estimation

In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, a text that has been translated using a machine translation method. Recent developments in this area have shown that Gaussian Processes can be accurate for post-editing effort prediction. However, the Gaussian Process kernel has to be chosen in advance, and this choice influences the quality of the prediction. In this paper, we propose a Genetic Programming algorithm to evolve kernels for Gaussian Processes. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and achieves predictions that, in many cases, outperform those obtained with fixed kernels.
Consistent numerical methods for state and control constrained trajectory optimisation with parameter dependency

This paper describes and proves the consistency of a flexible numerical method for producing solutions to state and control constrained control problems with parameter dependencies. This method allows for the use of a variety of underlying discretisation schemes, which can be catered to differing numerical challenges of specific problems, such as rapid convergence or large parameter spaces. The paper first provides a broad formulation for optimal control problems with parameter dependencies which includes multiple types of state, control, and end time constraints to enable a wide scope of application. For this formulation, the consistency of these methods for state and control constrained problems is then proved. Finally, a numerical example of an optimal search problem with constraints is demonstrated.
An Unsupervised Sequence-to-Sequence Autoencoder Based Human Action Scoring Model

Developing a model for the task of assessing quality of human action is a key research area in computer vision. The quality assessment task has been posed as a supervised regression problem, where models have been trained to predict score, given action representation features. However, human proficiency levels can widely vary and so do their scores. Providing all such performance variations and their respective scores is an expensive solution as it requires a domain expert to annotate many videos. The question arises - Can we exploit the variations of the performances from that of expert and map the variations to their respective scores? To this end, we introduce a novel sequence-to-sequence autoencoder-based scoring model which learns the representation from only expert performances and judges an unknown performance based on how well it can be regenerated from the learned model. We evaluated our model in predicting scores of a complex Sun- Salutation action sequence, and demonstrate that our model gives remarkable prediction accuracy compared to the baselines.
Validation of Early Testing Method for E-Government Projects by Requirement Engineering

e-Government System and Software projects are costly. The success of projects has high impact to the level of effectiveness of service delivered by government to citizens. However, poorly managed system and software requirements by project team could lead to project failures. Therefore, validation of requirements at early stages is important. The objective of this paper is to validate the Early Requirement Testing Method for e-Government projects. A `Wilcoxon Matched Pairs Signed-Rank Test' was used, whereby test engineers were subjected to performing Early Requirement Testing Method across six(6) e-Government projects before and after an intervention has been introduced to the e-Government employees and the project team (Business Analyst/Requirement Engineer). Results revealed that the null hypothesis of “no statistically significant differences between employing the method in e-Government projects” can be rejected and therefore conclude that method reported was useful in reducing the requirement related defects, as the difference is negative (reduction of defects) Z= -2.892, p=0.004.
Speaker and Speech Recognition Using Hierarchy Support Vector Machine and Backpropagation

Voice signal processing has been proposed to improve effectiveness and facilitate the public, such as Smart Home. This study aims a smart home simulation model to move doors, TVs, and lights from voice instructions. Sound signals are processed using Mel-frequency Cepstrum Coefficients (MFCC) to perform feature extraction. Then, the voice is recognized by the speaker using a hierarchy Support Vector Machine (SVM). So that unregistered speakers are not processed or are declared not having access rights. For the process of recognizing spoken words such as “Open the Door”,“Close the Door”,“Turn on the TV”,“Turn off the TV”,“Turn on the Lights” and “Turn Off the Lights” are done using Backpropagation. The results showed that hierarchy SVM provided an accuracy of 71% compared to the single SVM of 45%.
Simultaneous Optimisation of Image Quality Improvement and Text Content Extraction from Scanned Documents

Convolutional neural networks are shown to achieve breakthrough performance for the task of single image super resolution (SISR) for natural images. These state-of-the-art (SOA) networks have been adapted to the task of single text image super resolution and have been shown to boost the optical character recognition (OCR) performance. However, these approaches depend on variations of the standard mean squared error (MSE) loss in order to train the SR network for improving the text image quality which does not guarantee optimal OCR performance. In this paper, we propose to combine the OCR performance into the loss function during network training. This results in the generation of high resolution text images that achieve high OCR performance that is comparable to the ground truth high-resolution text images and surpassing those of the SOA baseline results. We define novel intuitive metrics to capture the improvement in the OCR performance and provide extensive experiments to qualitatively and quantitatively assess improvement in the results of our proposed approach against the SOA baselines on the standard UNLV dataset.
Control and Efficiency Analysis of Multi-Motion of Four Wheel Drive Omni-Directional Robot

To control an Omni-directional robot in multimotion, a detail analysis is done which includes study of the Design and Kinematics of the robot leading to the motion analysis done mathematically and then experimentally. This research presented extends the pre-present research on the control and motion of the Omni-directional robot by presenting a unique motion methodology for higher efficiency. The major focus of this paper lies in the control and efficiency analysis and the results of the multi-motion analysis are compared with other literatures of the same workspace conditions. The motion of the robot is examined in two ways: Linear and Circular. The core concept of the motion analysis lies in the idea that the robot does not change its orientation during motion, thus challenging the motion efficiency of conventional and Omni-directional robots with higher maneuverability through experimentally compiled results. The results of these multi-motions are also compared with each other to get a better efficiency of Omni-directional robot. The research also addresses the issue of the Jacobian being nonsquare for inverse kinematics. The inverse kinematics is solved through Moore-Penrose pseudo inverse method, yielding to accurate results experimentally.
Pipelined Architectures of LILLIPUT Block Cipher for RFID Logistic Applications

Lightweight cryptography is an exciting field which hits the perfect balance between safety, higher performance, low power consumption, and compactness. Many compact algorithms such as PRESENT, HIGHT, LILLIPUT, KLEIN, KATAN, SFN, and PICCOLO have made the mark in recent years that can be used as lightweight cryptosystems. The reprogrammable devices are highly attractive solutions for encryption algorithm in hardware implementation. A strong focus is placed on high-throughput implementations, which are required to support security for logistics and tracking applications. In this paper, two pipelined architectures are designed for achieving high throughput. Among them, sub-pipelined implementation achieves a high throughput of 684.06 Mbps and 654.20 Mbps on xc5vlx50t-3ff1136 and xc4vlx25-12ff668 devices, respectively. All results are simulated and verified for different devices of Xilinx in Spartan & Virtex families.
Unsupervised Teacher-Student Model for Large-Scale Video Retrieval

With the growth of video-sharing platforms and social media applications, video retrieval plays an import role in many aspects, such as copyright infringement detection, event classification, personalized recommendation, and etc. The content-based video retrieval presents the following two main challenges: (i) Distribution inconsistency for feature representation from the source domain to the target domain. (ii) Difficulty of video aggregation by sufficiently incorporating frame-based information. In this paper, we propose an unsupervised teacher-student model (UTS Net) to improve the performance of the content-based video retrieval tasks: (i) A teacher-student model maintaining the global consistency for feature representation from different domains and retaining the local inconsistency within the intra-batch data; (ii) A simple but effective video retrieval pipeline integrating the frame-level binarized feature. Our proposed framework experimentally outperforms the state-of-the-art approach on the DSVR, CSVR, and ISVR tasks in the FIVR datasets, and achieves a mean average precision of 76%, 72%, and 61%, respectively.
Precedency with Round Robin Technique for Loadbalancing in Cloud Computing

Load Balancing is a growing field that effort to distribute the dynamic workload as hubs in the cloud. Load Balancing is a crucial test of distributed computing. In Load balancing procedures, execution of the cloud is improved by using all assets ideally. The critical goal of Load balancing is to diminish asset utilization of vitality and to limit carbon emanation; this is the foreboding need of the time. The fundamental objective of this paper is to give a short diagram of different Load Balancing calculations and after that to provide a productive need-based round-robin and Precedency-based algorithm. Load balancing method which organizes different task to virtual machines are based on assets or processor required, the number of clients, time to run, work type, client type, programming utilized, cost and so on and after that passing on them to different accessible has in a round-robin and Precedency-based algorithm design. This methodology improves the capacity of the framework by upgrading different parameters, such as adaptation to non-critical failure, versatility and overhead, and so forth and by limiting asset use and reaction time. This methodology is recreated and tried over Cloud Analyst, which is generally utilized by the device to test cloud-based systems.
Adapting Market-Oriented Policies for Scheduling Divisible Loads on Clouds

Cloud computing has become an important alternative for solving big data processing. Nowadays, cloud service providers usually offer users a virtual machine with various combinations of prices. As each user has different circumstances, the problem of choosing the cost-minimized combination under a deadline constraint as well as user's preference is becoming more complex. This article is concerned with the investigation of adapting a user's preference policies for scheduling real-time divisible loads in a cloud computing environment. The workload allocation approach used in this research is using Divisible Load Theory. The proposed algorithm aggregates resources into groups and optimally distributes the fractions of load to the available resources according to user's preference. The proposed algorithm was evaluated by simulation experiments and compared with the baseline approach. The result obtained from the proposed algorithm reveals that a significant reduction in computation cost can be attained when the user's preferences are low priority.
A Source Authentication Mechanism for Multicast Communication System Using Adaptive Hash Tree

An internet is backbone of all kind of communication such as unicast, broadcast and multicast, as multicast communication is more efficient because it provide a better communication than unicast and broadcast in the context of bandwidth utilization. The multicast communication is popular in the area of audio-conferencing, video-conferencing and data distribution. There are several schemes such as hashing technique, tree based Hashing and Hash tree signing technique. These schemes have a lot of issues like communication cost, hash computation technique. Aim of this work is to compute the growth of proposed work and compare the performance with existing schemes. An adaptive technique has been proposed for source authentication in multicast system. The new technique is named elliptic curve cryptography source authentication (ECCSA). It has less communication cost as compared to SHS, EMSS, and HMSA.