Reviewerly/exHarmony huggingface dataset:
LAWA: Loss-Aware Workload Assignment in Data Centers

Information technology (IT) equipment consumes a significant amount of power in data centers. As part of this consumption, there is significant wastage due to the power loss of servers. To date, workload assignment algorithms have not taken server power losses into account. In this paper, we find that focusing on server power losses in constructing a workload assignment algorithm may have a significant impact in reducing the power consumption of data centers. In particular, this savings may be significant as compared to the commonly employed approach of server consolidation.
Review of visible watermark’s influence on private information-related machine learning techniques

Under the increasing needs of sharing datasets and risks of leaking privacy during data sharing, there is growing attention of the owners of datasets to claim ownership and protect their privacy from being misused. To achieve this secure purpose, the visible watermark may be an effective way. However, due to the loss of visual information, it is still unclear the influence of these visible watermark methods on related machine learning techniques. To investigate these, this paper provides a review of visible watermark's influence on private information-related machine learning techniques. This paper compared the performance of different CNN methods with or without visible watermarking. According to the results of the experiments, the accuracy of watermarked images' identification decreased significantly. By average, the accuracy would decrease by 4.75%. And the performance changed when using different CNN structures, watermarked methods and datasets. After that, there are some experiments to explore the feasibility of using none-watermarked images trained network to identify the watermarked images. The results show that the accuracy of identification at least decreased by 25% when only one of the train set and test set are watermarked.
ELASTIC CLOUD COMPUTING APPROACH FOR BIG DATA ANALYTICS

The latest developments in the communication and the information technologies have turned out to be the foundation for the emergence of the industrial developments and progress in the business causing digital transformation in the industrial and the business operations. The clubbing of the internet and the information technology along with the tangible things that are the responsible for the industrial operations, generate a huge set of data that requires enormous network bandwidth, high processing power and accessible resolutions. So the paper presents the elastic cloud computing approach to bring down the complexities in deploying, cost of the groundwork and maintenance and provide an automated resource provisioning according to the demands. This method increases the amount of the computing power available, network properties and the storage. The experimental results for the novel elastic approach for the big data analytics is obtained by the simulation through MATLAB in terms response time, processing power and cost.
Cloudlet-Cloud Network Communication Based on Blockchain Technology

The Cloudlet technology was invented and first released in 2009 by Mahadev Satyanarayanan in order to bring Cloud services closer to the final user and to reduce the dependence on the Cloud server. As a middleware between the user and the Cloud provider, a user has access to his data even in the presence of an accessing problem to the Cloud platform.Blockchain technology has recently gained significant attention, especially in providing robust secure solutions to distributed applications. Based on this innovative technology, in this paper we propose to integrate Blockchain based security solution to ensure a number of security services such as transactions traceability and the secure exchanges between the user Cloudlets and the Cloud servers. Further, due to the lack of security and data synchronization problems in Cloud systems, caused by some deadlock situations, a super-agent component has been integrated to control and then resolve crushes of concerned transactions between the Cloud and the Cloudlets and among the Cloudlets themselves. We implemented the proposed Blockchain based solution and experimented with in an industrial environment.
An Efficient Real Time Implementation of a Fast IMM for Tracking a Manoeuvring Target

In this paper we present a real time implementation of the fast interacting multiple models (IMM) algorithm proposed in [1], and compare it with that of the standard IMM [5], in terms of speed of execution. The implementation is performed on the TMS320C6713 floating-point digital signal processor (DSP), from Texas Instruments. In order to reduce the execution time, the optimization techniques developed by Texas Instruments (TI) as well as some algorithm dependent optimization solutions, proposed by us, are applied.
ORIOT: A Source Location Privacy System for Resource Constrained IoT Devices

Privacy and Security are one of the major research topics regarding the Internet of Things (IoT). Due to the vast amount of devices collecting and processing sensitive data, anonymity and privacy mechanism are needed. Source Location Privacy (SLP) plays a key role in prohibiting adversaries from tracing back this kind of data to its origin. In this paper we propose a SLP preserving system that leverages techniques from the well established Onion Routing paradigm. The system is specifically designed for resource constrained IoT devices, i.e., devices lacking computing power. It features combined encryption schemes and symmetric key exchanges via Elliptic-Curve Diffie- Hellman (ECDH). Our performance measurements, conducted on typical resource constrained IoT devices, show the feasibility of ORIOT and facilitate the integration into existing or planned IoT systems, depending on SLP features.
Dialogue Acts Classification via RNNs with One- Versus-All Layers Considering Rare Utterances

For understanding the contents of a user's utterances, dialogue acts classification is introduced to chat systems. Because the number of utterances differs greatly in each dialogue act, classifiers tend to confuse dialogue acts that rarely appear in dialogue with frequent ones. This paper proposes Enhanced One-versus-All RNN (ENOVA RNN), which is a method to capture some features of dialogue acts that rarely appear in dialogues and discusses the effectiveness. In this study, it was confirmed that ENOVA RNN can improve rare dialogue acts classification performance keeping the overall quality of the performance.
Effectiveness of Scrum and Kanban on Agile-Based Software Maintenance Projects

Software maintenance is an important phase in the lifespan of a software system. This chapter examines the effectiveness of Scrum and Kanban methods in terms of their impacts on project management factors for a software maintenance project. The six-point star model defines project management factors like schedule, scope, budget, risk, resources, quality, and study, and uses two other non-quantitative techniques such as team commitment and work organization. A quantitative survey was carried out together with the questionnaire distributed to IT industry professionals in Sri Lanka. Each question in the questionnaire was related to one of the above factors. The Pearson correlation coefficient was used to find the correlation between the coefficients. A strong correlation was identified. Based on the results of multiple linear regression modeling, a model has been proposed. Scrum-based software maintenance projects can improve project quality by managing work organization and resources whereas Kanban-based can improve project quality by controlling the risk and scope of the project.
Design and Analysis of Neural Networks Based on Linearly Translated Features

In this paper, neural networks based on linearly translated features (LTFs) are presented. LTFs including uniform, non-uniform, and multiple translation vectors are embedded into feedforward neural networks. Learning algorithms are presented for the neural networks. Learning capabilities of the neural networks are analyzed. Experimental results on approximation' identification, and evaluation problems are reported to substantiate the efficacy of the neural networks and learning algorithms.
Comparative Analysis of Selected Geotagging Methods

Geotagging is a rapidly growing technology in digital photography and searching for specific landmarks, and helps everyone in our daily lives. Navigation applications and travel guides put a number of geotagged photos on the maps, providing a good overview of the destination. Recently, the development of photo geotagging methods has become a popular issue. Implementations of the SIFT and SURF algorithms and training of convolutional neural networks to obtain image classification for landmark images are presented in this paper. Based on the results of the classification of images and geotags of other similar images, geotags have been assigned to the target images. In addition, the results of image classification obtained using feature detection algorithms and neural networks were compared and analyzed.
Qubit Network Barriers to Deep Learning

The popularity of artificial neural networks (ANNs) of great depth and Quantum computing have led to many speculations as to their convergence. We enumerate barriers to utilizing qubit networks for deep learning architectures. We also describe the criteria for an effective usage of qubit networks and then assert that the bottleneck in their implementation is a lack of quantum algorithms for utilizing the topology of a deep neural network.
Application Research of Transmission Technology in Information Communication Engineering

Abstract In the planning, design and concrete construction of information communication engineering, transmission technology has an irreplaceable role in the transmission system because it has very important influence and effect. The overall application effect of the transmission technology will directly affect the basic situation of the entire information communication project during construction and utilization.Therefore, this paper analyzes the specific application of transmission technology in information communication engineering, and lays a good foundation for improving the efficiency of information communication engineering.
Four slot dynamic binary search anti-collision Algorithm

To improve the performance of the Radio Frequency Identification (RFID) system and the multi-tag collision problem, a new anti-collision building algorithm is proposed based on the dynamic frame time slot Aloha algorithm and the analysis of binary search and four-slot binary search. In the new algorithm, the reader stack is used to form a further search command; the response tag is divided into four subsets, and the data information is sent in four-time slots, and up to eight tags can be recognized in one search. Simulation results show that the new algorithm greatly reduces the search times and recognition time.
Generative Dialogue System using Neural Network

A conversation between human beings and computers is considered one of the most long standing problems in computer science; involving interdisciplinary techniques from the field of information retrieval, machine learning, natural language processing and artificial intelligence. It is an Interactive Entity that automatically makes conversations to exchange seamless information between people with little knowledge of the computer. The challenges lie in how we respond to people and to maintain a continuous conversation. This research implements a generative model-based method for speech production. This research is for the development of a dialogue system that produces speeches using the recurrent neural network and its associated memory unit.
Legal Aspects of Blockchain Technology

Blockchain has been heralded by some as a potentially revolutionary technology, while others see it as a solution looking for a problem. In the field of law, particularly, novel technologies often collide with existing regulations, which may be too slow or rigid to adapt to the innovative functionalities explored, and blockchain is no exception to this. In this chapter, we take a look at blockchain’s promised benefits from the legal perspective in order to provide a general understanding on how the technology is seen to interact with three major fields of law. Firstly, with respect to contract law, we analyse, in particular, the implications blockchain technology may bring for smart contracts. Secondly, we deal with intellectual property law aspect, covering the potential benefits brought about by blockchain-based systems to holders – and users – of copyright, registered and unregistered trademarks and designs, as well as trade secrets. Thirdly, concerning personal data protection law, we focus on the contradictions between blockchain and the European General Data Protection Regulation – some merely apparent, while others unavoidable – and explore the ways in which the use of blockchain allows for, and even potentially enhances, the practical implementation of the principles of data protection by design and, more specifically, fairness by design.
I-MOVE: Independent Moving Objects for Velocity Estimation

We introduce I-MOVE, the first publicly available RGBD/stereo dataset for estimating velocities of independently moving objects. Velocity estimation given RGB-D data is an unsolved problem. The I-MOVE dataset provides an opportunity for generalizable velocity estimation models to be created and have their performance be accurately and fairly measured. The dataset features various outdoor and indoor scenes of single and multiple moving objects. Compared to other datasets, I-MOVE is unique because the RGB-D data and speed for each object are supplied for a variety of different settings/environments, objects, and motions. The dataset includes training and test sequences captured from four different depth camera views and three 4Kstereo setups. The data are also time-synchronized with three Doppler radars to provide the magnitude of velocity ground truth. The I-MOVE dataset includes complex scenes from moving pedestrians via walking and biking to multiple rolling objects, all captured with the seven cameras, providing over 500 Depth/Stereo videos. To access the dataset please visit www.vast.uccs.edu/imove.
Intelligent Personal Assistant – An Android Assistant Requisite

In the Modern Technical Era of updating technology, everything keeps on updating, but the humans spend more time with these devices to make the work easier for them, such as calling and texting a person, ordering food, setting alarms, taking a selfie and etc. These actions can be done automatically by the mobile using the voice input to make things even easier, but what if a device can be smarter to understand the emotions of the user and act accordingly to make the human emotionally stable. This paper mainly focuses on the existing systems of virtual assistants and what new features can be added to the existing system. In order to make the virtual assistant more interactive by making the on-time recommendations and reducing the glancing time at the screen, another enhancement that can be done by, adding offline access to as many commands as possible to make the user interaction with the device even faster.
Compact Prediction Tree (CPT) Application Research on the Causes of Faults in the Use of Multimedia Classrooms in Colleges and Universities

Abstract In addition to maintaining equipment, the management of multimedia classrooms in colleges and universities is mainly to provide timely assistance to teachers. When a failure occurs, if the cause can be automatically predicted and online real-time assistance is provided in time, this will greatly enhance the teacher teaching experience. Series prediction is one of the hotspots of deep learning research, for it has been widely used in many fields from recommendation systems to speech recognition to natural language processing. The Compact Prediction Tree (CPT) is faster and more accurate than other sequence prediction models. Based on this, this paper studies how to generate prediction trees for multimedia fault text events in colleges and universities, and uses CPT to predict the causes of multimedia classroom failures to improve management efficiency and user experience.
Editorial for Special Issue “Fluid Inclusion Characteristic of the Gold Deposit and Its Implication for Ore Genesis”

Fluid inclusions provide valuable information on the composition and physical and chemical parameters of mineral-forming hydrothermal fluids [...]
Study of wave energy harvesting technology based on pendulum and spring vibration pick-up mechanism

Wave energy is distributed widely in the ocean, and it can be collected and converted to supply electrical power for the devices in the buoy. A wave energy harvester based on pendulum and spring vibration pick-up mechanism is designed and tested in this paper. The motion model of the device is built based on Hamilton's energy law. The motion law of the single pendulum is calculated by the finite element method(FEM), and the output electromotive force(EMF) model is built on the basis of the angular displacement of the pendulum and the electromagnetic induction law. The prototype was fabricated and placed in a water tank for simulating the wave, and a 4F super-capacitors or 1W bulb were tested and analyzed as loads. The test results show that the output voltage and average power are about 5V and 0.58W, which can meet the voltage and power requirements of sensors and wireless communication modules.
On Influencing Factors in Human Activity Recognition Using Wireless Networks

Driven by the development of machine learning and the development of wireless techniques, lots of research efforts have been spent on the human activity recognition (HAR). Although various deep learning algorithms can achieve high accuracy for recognizing human activities, existing works lack of a theoretical performance upper bound which is the best accuracy that is only limited by the influencing factors in wireless networks such as indoor physical environments and settings of wireless sensing devices regardless of any HAR algorithm. Without the understanding of performance upper bound, mistakenly configuring the influencing factors can reduce the HAR accuracy drastically no matter what deep learning algorithms are utilized. In this paper, we propose the HAR performance upper bound which is the minimum classification error probability that doesn't depend on any HAR algorithms and can be considered as a function of influencing factors in wireless sensing networks for CSI based human activity recognition. Since the performance upper bound can capture the impacts of influencing factors on HAR accuracy, we further analyze the influences of those factors with varying situations such as through the wall HAR and different human activities by MATLAB simulations.
Semantically Enriched Multi-level Sequential Pattern Mining for Exploring Heterogeneous Event Log Data

Photovoltaic (PV) event log data are typically underexploited mainly because of the heterogeneity of the events. To unlock these data, we propose an explorative methodology that overcomes two main constraints: (1) the rampant variability in event labelling, and (2) the unavailability of a clear methodology to traverse the amount of generated event sequences. With respect to the latter constraint, we propose to integrate heterogeneous event logs from PV plants with a semantic model of the events. However, since different manufacturers report events at different levels of granularity and since the finest granularity may sometimes not be the right level of detail for exploitable insights, we propose to explore PV event logs with Multi-level Sequential Pattern Mining. On the basis of patterns that are retrieved across taxonomic levels, several event-related processes can be optimized, e.g. by predicting PV inverter failures. The methodology is validated on real-life data from two PV plants.
Relation Extraction and Visualization Using Natural Language Processing

This paper discusses relationship extraction among actors/nodes in the text provided. Given a text data, relationships are extracted using natural language processing and shown in a graph. Proper identification of “Nouns” and “Pronouns” can help to identify the actors/characters in the given text. Any relation usually would be between/among the nouns, so this paper first aims to extract “Nouns” and “Pronouns” in the given text and discusses how relation is extracted between/among them. Paper discusses step by step procedure of finding relation among actors/characters in the given plot/text. There are two prominent phases; one is, extracting “Nouns” and “Pronouns,” and the other is the relation among the characters/actors. This methodology is applied on movie story plot and tested. The above said methodology is applied on movie “Baahubali,” as it contains many characters and relations.
An Improvement Method Based on Similarity Theory for Equilibrium Manifold Expansion Model

Data-driven model has been employed as a powerful tool for identification of complex industrial systems with nonlinear dynamics, such as gas turbines (GT). In this study, a methodology based on data and prior knowledge fusion was developed to improve the equilibrium manifold expansion model to be a multi-input-multi-output (MIMO) model for offline system identification of a two-spool gas turbine. The methodology is evaluated through a case study involving data that are generated by general gas turbine simulation. Simulations show good precision of the proposed model in capturing the nonlinear behavior of the gas turbine.
Anomaly Detection Models Based on Context-Aware Sequential Long Short-Term Memory Learning

For a large and complex system that provides services to users, an exception can cause cascading failures if it is not detected and handled in time. System monitoring and anomaly detection can be used to identify system malfunctioning. However, as the size and the complexity of the online service system increases, anomaly detection becomes a challenging problem. This is because the size, complexity and correlation among the data bring great difficulties to anomaly detection process. To address the above challenges, we propose three context-aware sequential Long Short-Term Memory (LSTM) learning models for multi-dimensional anomaly detection, namely, LastLSTM model, AvgLSTM model and CirclLSTM model. In particular, the CirclLSTM model is a period-related LSTM model that can integrate cyclical system historical information into anomaly learning. We evaluated our methods based on three real-world datasets. Our experimental results show that our method can achieve a higher accuracy than other baseline methods such as the Gaussian Naive Bayes (GaussianNB) model, k-nearest neighbors (KNN) algorithm and Logistic Regression (LR) model.
Basketball Web Community System Based on Java Web

The basketball fan community system is mainly divided into front-end and back-end. The front-end page is designed with JSP technology. The web application developed by JSP has the advantage of cross-platform and high browser compatibility. At the same time, it can also be used very lightweight. jQuery handles DOM operations, and the front-end development framework uses a bootstrap to quickly build a responsible website. Maven's tools are used to build and manage the structure of the same project and to maintain a maintenance jar package. The mainstream SSM framework (Spring + Spring MVC + MyBatis) is used for the backend framework. Maria DB is another version of the MySQL database and is free to use. The entire project was developed under the Eclipse platform.