Reviewerly/exHarmony huggingface dataset:
Background of Semantic Intelligence Research and the Principle of Technical Framework

Through semantic analysis of the words “sweet” and “good”, this thesis aims to understand the cognitive methods and limitations of human beings, trying to reveal the research background, technical framework and resonance principle of semantic intelligence. The method is as follows: Firstly, based on the application perspective, the artificial intelligence technology is divided into three categories: motion, perception and semantics. Furthermore, from the perspective of technical framework, it is based on semantic concepts and cosmic energy material system, human self-perception and functional system, computer artificial intelligence system, energy material logic function system, human language grammar system, computer artificial intelligence function logic software programming system, etc. the internal logic of the five aspects advances the principles of semantic intelligence systems, engineering implementation techniques, and systematic research of product systems. The result is: from its research background, it highlights the logic relationship between Chinese classical religion, philosophy and culture, people and people, consciousness and the nature of the universe, and assists with the modern scientific and technological methods to propose a new framework of semantic intelligence technology content and ideas. The significance is that it can construct a system intelligence theory and principle that integrates the universe’s natural, material world, philosophy, science, graphic and symbolic systems, human consciousness and semantic virtual world, and realizes the breakthrough of semantic intelligence research.
Evaluation of exact quantum query complexities by semidefinite programming

One of the difficult tasks in quantum computation is inventing efficient exact quantum algorithms, which are the quantum algorithms that output the correct answer with certainty on any input. We improve and generalize the semidefinite programming (SDP) method of Montanaro et al. (Algorithmica 71:775–796, 2015) in order to evaluate exact quantum query complexities of partial functions. We present a more systematical approach to achieve the “inspired” result by Montanaro et al. for the function $$\text {EXACT}_{2}^{4}$$ , which is the Boolean function of 4 bits that output only when 2 of the input bits are equal to 1. The same approach also allows us to reduce the size of the ancilla space used by the algorithms that evaluate symmetric functions like $$\text {EXACT}_{3}^{6}$$ . We employ the generalized SDP to verify the complexities of the earliest and best known quantum algorithms in the literature, namely, Deutsch–Jozsa and Grover algorithms for a small number of input bits. We utilized the method to solve the weight decision problem of bit strings with lengths up to 10 bits and observed that the generalized SDP gives better exact quantum query complexities than the known methods. Finally, we test the method on some selected functions and demonstrate that they all exhibit quantum speedup.
Simulation and Modeling of Modbus Protocol Based on Petri Net

Petri net is a widely applied tool, and is very efficient in the area of network protocol modeling. A method for modeling and simulating Modbus protocol is proposed in the paper based on Petri net. According to the Modbus protocol data frame state machine, the corresponding Petri net structure model is presented. The Petri model is described with Petri net graphics and formal language. Meanwhile, and the Petri net reachability graph analysis method is applied to analyze the Petri net model of Modbus protocol, the properties of Modbus protocol model is verified. This modeling method has both good intuitiveness and reliability analysis for Modbus protocol.
TOWARDS ANALYZING QUALITATIVE DATA

Managing a software project at distributed sites which is called as Global Software Engineering (GSE) are very challenging compared to managing a project at collocated site. There are many contributing factors which makes the coordination as a big challenge in GSE because of the software project is developed across the countries and the team members are segregated all over the world (da Silva, Costa, Franca, &amp; Prikladinicki, 2010). Therefore, projects that are developed in GSE needs more extensive coordination. Many models have been developed to overcome the coordination challenges in GSE. Despite of having numerous models in GSE, to the best of researcherâ€™s knowledge there is no model to assess the coordination procedures in GSE projects. Thus, at the end of the research, the researchers proposed an Evaluation Model to assess the coordination procedures effectively in GSE projects. So to formulate this model, one of the research objective is to identify the coordination approaches and related indicators in GSE from the real GSE project practitioners, this is done through qualitative analysis using Nvivo.
3D Reconstruction of Tubular Structure Using Radially Deployed Projections

Acquiring volumetric data plays a crucial role in the field of medical imaging. 3D reconstruction is mostly performed using multislice image datasets. The objective of this research is to introduce a magnetic resonance technique for imaging tubular structures and their 3D reconstructions using multiple radially deployed projections. The oblique projection sequence was evaluated on a phantom, and multislice dataset is collected using the same phantom for the reference. To compute the correctness of the 3D reconstruction process, the resulting meshes were compared using the Hausdorff Distance Calculation and Point Cloud Comparison methods.
Fixing digital elections

How to check the voting machines being used in India's election is contentious. Better statistics could be the answer, says Edd Gent
Intrinsic Currying for C++ Template Metaprograms

C++ template metaprogramming is a form of strict functional programming, with a notable absence of intrinsic support for elementary higher-order operations. We describe a variadic template metaprogramming library which offers a model of implicitly curried, left-associative metafunction application through juxtaposition; inspired by languages such as Haskell, OCaml and F $$^\sharp $$ . New and existing traits and metafunctions, constructed according to conventional idioms, seemlessly take advantage of the framework’s features. Furthermore, a distinctive versatility is exposed, allowing a user to define higher-order metafunction classes using an equational definition syntax; without recourse to elaborate nested metafunctions. The primary type expression evaluator of the library is derived from a single application of an elementary folding combinator for type lists. The definition of the fold’s binary operator argument is therefore a focal point; and constructed mindful that substitution failure of a template parameter’s deduced type produces no compilation error. Two distinctive features of C++ metafunctions require particular consideration: zero argument metafunctions; and variadic metafunctions. We conclude by demonstrating characteristics of the library’s main evaluation metafunction in conjunction with the universal property of an updated right-fold combinator, to compose a range of metafunctions including map, reverse, left-fold, and the Ackermann function.
Iceberg Detection by CNN Based on Incidence-Angle Confusion

In this paper, an image object identification problem for the Kaggle Iceberg Classifier Challenge was tackled by deep neural network. Basic convolutional neural network (CNN) was implemented and tested firstly. Then, deeper networks including VGG16 and ResNet50 are adopted to improve the accuracy. The deep learning-based methods are also compared with the conventional machine learning method i.e. SVM (support Vector Machine). Three feature augmentation approaches are utilized and compared, i.e. incidence angle confusion of satellite radar signals, multi-band composition and data augmentation of the original image data. Tentative results by GAN (Generative Adversarial Network) and Capsule Network are also presented. Results demonstrate the applicability and superiority of CNN over the conventional method (SVM) on the given dataset.
Privacy Enhancement of Telecom Processes Interacting with Charging Data Records

Telecommunication is the foundation stone of the current economic context which allows an opening in all areas and facilitates the interaction using different platforms and technologies (from 2G to 5G). In such context, personal data is massively collected, stored and processed for different aims which is considered as big privacy concerns. Personal data should not be known by no matter whom. It is thus important to raise the question of what can be communicated and to which. The protection of personal data needs a deep attention from companies that process with this data such telecommunication operator. The dilemma of privacy in telecommunication operators is that telecommunication employees, suppliers and subcontractors need personal data access but the privacy requirements consist on knowing less personal data and protects such data. The goal of this article is to deal with this dilemma by providing a privacy approach for protecting personal data in telecommunication operators processes interacting with Charging Data Records CDR in such way to ensure that the illegible employees whom access to personal data cannot use them for purpose other than authorized one.
Gesture based terrain mapping and recognition-overview

Up until a few years ago, the only way to get an aerial overview of a designated area was to fly over it with a manned aircraft and physically inspecting the areas or taking pictures of it. Unmanned Aerial Vehicles, have been a blessing to all the industries which relied on aerial images or drawings for their work. Unmanned aerial vehicles also knows as drones, in more colloquial terms, is a blessing to all industries in the world. The cost of manufacturing and assembling drones have gone down significantly as majority of people adapted to using it. The film industries are using it to get excellent footage with accurate direction and lighting. Civilians can use it as toys. Students can use it to learn avionics and flight mechanisms. Youtubers use it for professional high resolution footage. Rescue teams use it for surveying the area. Armies use it to get a tactical advantage in a firefight and recently, Amazon is using drones for superfast delivery service. The possibilities are endless if properly invested on. Machine Learning and Artificial Intelligence are next best thing in the field of Computer Science and that's why I plan to merge these two fields and present something which fundamentally adds more features and a hive mind facility to even work without a commanding signal.
Modeling Item Categories for Effective Recommendation

One-class collaborative filtering and item cold-start are two of the most important and challenging problems in recommender systems. In this paper, we focus on addressing these two issues by taking item category information into account. Item categories embody rich information about product attributes, which are available in most E-commerce websites. However, existing methods usually ignore such information or utilize them at a shallow level. For example, the category information is used to regularize model parameters or to extract hand-crafted features. As a response, we propose to model users’ different preference spaces over different item domains. Specifically, we design a unified method called CatRec in order to model the complex interactions among a user, an item and the item’s category information. Empirically, our method consistently outperforms the state-of-the-art methods on two real-world datasets.
How to Understand the Basic Structural Unit

This paper aims to apply the understanding model to understand the basic structural unit of the material world. The method steps are as follows: A to understand the old terms, such as: Wave-Particle Duality, “material and space”, “physical and field” and “particles and waves”; B, to understand the new terms, such as: “π-type triple-wave particle duality” and “Tai Chi particle wave”; C, to understand the statement of new and old, such as: A brief review of the process of exploring the “basic structural unit of the material world” in human history, focusing on the basic theory of material structure – “Tai Chi Particle Wave” theory on the basis of “classical electromagnetic field according to photon corresponding decomposition found classic Correspondence between the basic unit between electromagnetic field and quantum electromagnetic field. The result is to break the relationship between physical and field (particles and waves) and predict the existence of the basic structural unit of the material world - Tai Chi Particle Wave. Its significance is to make material and space, physical and field, particles and waves effectively unified. This is an interesting question that clarifies the most basic structural unit of the material world. Revealed the third wave-particle duality; and unified the three wave-particle duality revealed by Einstein 1905, De Broglie 1923 and Gan 1995 respectively, revealing the microscopic object π-type triple wave particle duality.
Constant-Round Maliciously Secure Two-Party Computation in the RAM Model

The random-access memory model of computation allows program constant-time memory lookup and is more applicable in practice today, covering many important algorithms. This is in contrast to the classic setting of secure 2-party computation (2PC) that mostly follows the approach for which the desired functionality must be represented as a Boolean circuit. In this work, we design the first constant-round maliciously secure two-party protocol in the RAM model. Our starting point is the garbled RAM construction of Gentry et al. (EUROCRYPT, pp 405–422, 2014) that readily induces a constant round semi-honest two-party protocol for any RAM program assuming identity-based encryption schemes. We show how to enhance the security of their construction into the malicious setting while facing several challenges that stem due to handling the data memory. Next, we show how to apply our techniques to a more recent garbled RAM construction by Garg et al. (STOC, pp 449–458, 2015) that is based on one-way functions.
Increasing the Production Productivity with Artificial Bee Colony Optimisation Method

In this paper, the tool path length minimisation or reduction of tool path total time was considered. The main goal is optimisation of tool path length on the selected technological task, where is need to drill a large number of holes and Artificial Bee Colony (ABC) optimisation method was used. The results, (achieved by criteria of minimum tool path) leads to saving of the technological time and reducing the total costs of production. Proposed algorithm gives the sustainable results, and it is reliable for the use. The solution achieved by the ABC algorithm was implemented in the MATLAB program and for validation of its performance it was compared with Ant Colony Optimisation (ACO) algorithm, CAMConcept software and with the result achieved by manual programming. The drilling simulation was performed using the EMCO WinNC educational program for the Sinumerik 840D Mill control unit.
Prim’s algorithm to model the pipe network at the water supply company

It is the network in the water distribution already optimal, or does it form circuit instead? The circuit on the network will increase production costs which will have a direct impact on the price of the service. In this paper, the optimal model was further analyzed to detect the circuit — the minimum spanning tree as a mathematical model, with vertex end pipe and edge length pipe. Modelling with the Prim's Algorithm produces a length of pipe distribution which more optimal than a pipe installed in PDAM Tirtanadi Medan. The result shows the required optimally of the distribution network by applying Prim's Algorithm.
Hyot: Leveraging Hyperledger for Constructing an Event-Based Traceability System in IoT

In this work it is introduced Hyot, a blockchain solution for activity registration in the Internet of Things. Specifically, a permissioned blockchain is used to record anomalous occurrences associated with sensors located on a Raspberry Pi 3. Likewise, a web system is provided to consume the information collected in real time.
A Similarity-Based Object Classification Using Deep Neural Networks

This chapter proposes a hybrid method for classification of the objects based on deep neural network and a similarity-based search algorithm. The objects are pre-processed with external conditions. After pre-processing and training different deep learning networks with the object dataset, the authors compare the results to find the best model to improve the accuracy of the results based on the features of object images extracted from the feature vector layer of a neural network. RPFOREST (random projection forest) model is used to predict the approximate nearest images. ResNet50, InceptionV3, InceptionV4, and DenseNet169 models are trained with this dataset. A proposal for adaptive finetuning of the deep learning models by determining the number of layers required for finetuning with the help of the RPForest model is given, and this experiment is conducted using the Xception model.
A Hardware-Software Co-Design for Object Detection Using High-Level Synthesis Tools

Object detection is a vital component of modern video processing systems, and despite the availability of several efficient open-source feature-classifier frameworks and their corresponding implementation schemes, inclusion of this feature as a drop-in module in larger computer vision systems is still considered a daunting task. To this end, this work describes an open-source unified framework which can be used to train, test, and deploy an SVM-based object detector as a hardware-software co-design on FPGA using Simulink high-level synthesis tool. The proposed modular design can be seamlessly integrated within full systems developed using Simulink Computer Vision toolbox for rapid deployment. FPGA synthesis results show that the proposed hardware architecture utilizes fewer logic resources than the contemporary designs for similar operation. Moreover, experimental evidence has been provided to prove the generalization of the framework in efficiently detecting a variety of objects of interest including pedestrians, faces and traffic signs.
Research on driving force in dredging system

This paper presents a drag device which can provide driving force for sludge cleaning scraper in rainwater storage tank without any external energy. Based on its structure and principle, the design formula for the device is deduced. The working condition is analysed, the results of analysis show that the drag device can offer a large range of two-way pulling force for scraper device automatically and it has a great work environment adaptability.
Energy-Aware Virtual Machine Clustering for Consolidation in Multi-tenant IaaS Public Clouds

Cloud computing has gained a lot of interest from both small and big academic and commercial organizations because of its success in delivering service on a pay-as-you-go basis. Moreover, many users (organizations) can share server computing resources, which is made possible by virtualization. However, the amount of energy consumed by cloud data centres is a major concern. One of the major causes of energy wastage is the inefficient utilization of resources. For instance, in IaaS public clouds, users select Virtual Machine (VM) sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the kind of workloads to be executed in the VM. More often, the users overprovision the resources, which go to waste. Additionally, the CSPs do not have control over the types of applications that are executed and thus VM consolidation is performed blindly. There have been efforts to address the problem of energy consumption by efficient resource utilization through VM allocation and migration. However, these techniques lack collection and analysis of active real cloud traces from the IaaS cloud. This paper proposes an architecture for VM consolidation through VM profiling and analysis of VM resource usage and resource usage patterns, and a VM allocation policy. We have implemented our policy on CloudSim Plus cloud simulator and results show that it outperforms Worst Fit, Best Fit and First Fit VM allocation algorithms. Energy consumption is reduced through efficient consolidation that is informed by VM resource consumption.
An Unsupervised Joint Model for Claim Detection

Claim detection is one of the most important tasks in argument mining. Most existing work employs supervised methods that rely on not only good-quality and large-scale annotated corpora, but also highly engineered and sophisticated features. Unsupervised methods are a possible solution to the above problems but few work has been done from unsupervised perspective. In this paper, we propose an unsupervised joint model including position model, indicator model and TextRank model. Position information is important for argument components detection, and our position model not only considers the sentences at the beginning and the end of the whole text but also at the beginning and the end of each paragraph. Considering the discourse makers’ good indication of claims, we also introduce indicator model into our joint model. Experiments on three English argumentation corpora show that our model outperforms the state-of-the-art unsupervised methods for claim detection.
How to Understand: Three Types of Bilingual Information Processing?

Bilingual information processing has been being used widely in many tasks such as Natural Language Understanding (NLU), Cross-Domain, Machine Translation (MT) and Bilingual Cognition. There still remain some problematic phenomena for understanding bilingual information processing. In this work, we aim to fully understand both the principles and methods of bilingual information processing by taking advantage of appropriate comparisons. The major idea of our proposed approach is both human and the machine can cooperate or interact with each other according to their characteristics. Concretely, it is composed of the following three steps: First, find issues from the Chinese-English bilingual translation at phrase sentence level alignment. The challenge in Chinese-English translation lies in an inconsistency of the meanings between the Chinese characters and the English words. Then, analyze the problems from the view of interdisciplinary, cross-domain and cross-industry. Finally, address leveraging human-machine to divide the task and coordinate with each other. As a result, not only we discover both parallel dual formalized approach of big and small strings, but also find and validate both the superiority and followed scientific principles of generalized bilingual information processing method.