Reviewerly/exHarmony huggingface dataset:
Data Anonymous Method Based on Fuzzy Clustering

Aiming at the unnecessary loss of information caused by the over-generalization of data by traditional anonymization methods, this paper proposes the fuzzy anonymization method FCMGD (Fuzzy C-Means Clustering with Generalization Data). Based on the similarity of data record attributes, redefine the distance between records, and update the membership matrix in conjunction with constraints. Set the privacy disclosure threshold to divide the data set into clusters, and use flexible generalization strategies to generalize attributes to form equivalent classes. In this paper, the Adult standard data set is selected for experiments. The results show that the FCMGD algorithm effectively reduces the information loss caused by generalization and satisfies the anonymity protection requirements required for data publishing.
An Analysis of The Use of Presentation and Debate Techniques by The Mentors in Muhadatsah Activity at Pondok Pesantren Modern Islam Assalaam Surakarta

One of the very effective way to improve students' speaking skills is to use the debate and presentation method. Debate can motivate students thinking, meanwhile, presentations is a great way to have students practise all language systems areas (vocabulary, grammar, discourse and phonology) and skills (speaking, reading, writing and listening). This presentations will also build confidence, and presenting a skill that most people will need in the world of work. Thus, this research has purpose to describe the technique of presentation and critical debate used by the mentors of Muhadatsah activity at Modern Islamic Boarding School (PPMI) Assalam Surakarta for practicing speaking and the difficulties faced by the mentors in conducting presentation and debate technique in Muhadatsah activity at Modern Islamic Boarding School (PPMI) Assalam Surakarta for practicing speaking. The data collection technique applied in this research were observation, interview, documentation and visual materials, and deciding the protocol of information record. The data analysis consists of three main stages, they are data reduction, data display or presentation, and drawing conclusion as stated in theory of Miles and Huberman. Triangulation technique used in this research was by using triangulation method. The findings of the research showed that first, the English speaking program at Muhadatsah activity are debate and speech or presentation. In the activity, the mentor used some strategies there are opinion exchange, discussion and making a game. Second, there are some problem faced by the mentor generally in the English speaking program at Muhadatsah activity are students’ lack confidence when speaking, correcting the student mistakes well, dividing time between speech and debate activity, and the lack of student consistency in the attendance. The third point is about the problem solving in English conversation club. Problem solving did by the mentor to make activities in the English speaking program at Muhadatsah activity effective and more pleasant for all the member of the English speaking program at Muhadatsah activity. The problem solving are giving motivation, giving the correction and consultation, and the last creating the work plan. The fourth point is about the students’ motivation to join the the English speaking program at Muhadatsah activity. The students’ motivation to join the English speaking program at Muhadatsah activity are increasing their knowledge and experience, mastering speaking, and finding more friends. In conclusion, the English speaking program at Muhadatsah activity such as speech or presentation and debate is a communicative interaction that involves different perspective and build critical strong arguments. Presentation and debate facilitates the students to enrich their vocabularies through comprehending the issue and how the way they defend their perspectives. Also, students have to speak up their ideas fluently and grammatical correct.
A Collaborative Multi-modality Selection Method Based on Data Utility Assessment

Multimodal fusion is more and more widely used in the field of machine learning, but it faces a prominent problem in practical application: data utility is not stable. The data of different modalities may be missing and noisy randomly, which will interfere the machine learning model of multi-modal fusion. Most of the existing multi-modal fusion methods neglect data utility problems or only adopt simple data denoising methods to improve data utility. To solve the problem of unstable data utility, we propose a data selection method based on the evaluation of data utility. By training a special machine learning model, the optimal modal combination is predicted according to the quality evaluation of multi-modal data samples to accomplish the dynamic selection of data modalities. The experimental results show that the proposed method can effectively improve the accuracy of multi-modal recognition under low data utility.
Face Tagging and Recognition Using Inception Network and Triplet Loss Generator Function

Tagging friends and acquaintances is quite popular in modern scenarios. The paper models the concept of triplet loss generator function based on anchor, positive, and negative images for face detection, recognition and tagging on the color FERET dataset. The designed neural network will output 128 face encodings for a given person’s image and then these encodings are compared against each other to achieve face recognition. Once recognized, name tags appear over the faces present in the picture so as to utilize this name functionality to tag them. The experimental results show that the proposed method performs better in terms of tagging as well as recognition in small duration of time.
Proposal of an IVR Solution and Granting of Credit With Asterisk's AGI and a Flask RESTFul Framework in an IMS Network: Case of an Advertisement

The need to communicate has become a necessity in Africa today, particularly in Senegalese families whose income level is low. The implementation of Interactive Voice Mailboxes (IVR) in an Internet Multimedia Sybsystem (IMS) environment increases communication services for 4G or even 5G. In this article, we propose an innovative Interactive Voice Response (IVR) solution in an IMS network. The goal is to have a network user, the ad or an audio ad listen to the end, in order to benefit from a phone credit rate without hanging up the phone. IVRs are implemented with Asterisk's Asterisk Gateway Interface (AGI). Once the call is launched, a check of its balance is carried out, if the person has credit allowing him to make a call, the announcement will not be played for him, but as soon as one notes a lack of credit, we propose to listen to the announcement until the end to grant him a sum allowing him to continue his call. Automatic credit reloading at the end of the ad is ensured by a Flask RESTFul Framework which is responsible for connecting to the MySQL database on the account of the user in question to credit his balance with a fixed sum. This solution will have a major impact on the current population and also on the level of research in general. It will make it possible to communicate even if one does not have money, the essential thing is to listen to an advertisement or a publicity until its end. As part of the research, it will allow researchers who are in areas where access to credit becomes very difficult and the need to want to exchange feels.
Shape From Bandwidth: Central Projection Case

Consider an unknown surface painted with a band-limited texture. We show that only the knowledge of the bandwidth of the texture is enough to estimate the shape of the surface from a single image taken by a camera. We model the problem as a central projection operation in 2D and propose a two step approach for finding the surface from the observed image. Our proposed algorithm first estimates the arc-length of the surface using the texture bandwidth and then reconstructs the surface from the estimated arc-length. We evaluate our algorithm with a simulation as well as real experiments.
Research on the Cost Control Model of Power Gird Maintenance Based on Fuzzy Pattern Recognition Theory

Abstract The power grid lines and equipment maintenance of power enterprises is a complicated construction process, the cost of which is affected by meteorological and geographical factors, and the influence mode is uncertain. Using fuzzy clustering method and the threshold intervals of the objective function in clusters, this paper builds a predictive control model to control the project cost. This model uses relative fuzzy operator to build fuzzy matrix, construct correlation between factors, and describe the factors’ effect. Extracting the cluster’s eigenfunction, and defining the boundaries of various clusters, we determined the type of the predicted points and the range of the objective function. When the actual cost of the maintenance project is within the range calculated by the cost model, then it is normal. If the actual cost exceeds this range, then further analysis of all the aspects of the cost is needed to find out the reason.
Analysing and Evaluating Syntactic Privacy Games via a Recommender Systems Case Study

The probabilistic notion of differential privacy has emerged as a popular measure for systems that support interactive data release. Consequently, there exists a portfolio of mechanisms to achieve differential privacy. A complementary approach is to represent syntactic privacy notions such as anonymity and unlinkability in the form of games. Via these games, the understanding of, and relationship between, privacy notions is clarified; further, an unambiguous understanding of adversarial actions is given. Representing privacy notions as games is aimed to invoke a 'different' perspective of visualising and abstracting privacy notions. Yet, without any practical context, these notions can be incomprehensible to system designers and software developers. We provide means to combine, enhance and extend these theoretical contributions by showing that the game-based definitions have the potential to interconnect privacy implications and to reason about privacy properties. We motivate our analysis through a case study based on recommender systems.
An approach to analyse and protect data on Untrusted Cloud Network

The outsourcing of data has gradually increased with the development of cloud computing environment to provide features like pay as you go and auto scaling. This helps small and medium size vendors to expand their business by migrating to cloud. This expansion needs to address important security concern for its customers including data privacy and vulnerability towards the cyber-attacks. One such attack: insider attack continues to worsen the situation of security affecting the confidentiality, integrity and availability of data. This paper reviews different security frameworks designed to detect and prevent abnormalities in the cloud infrastructure. It analyzes two important security hypervisors: TrustVisor and Flicker. Both hypervisors are responsible for providing the information isolation and integrity using piece of logic (PAL) mechanism on the devices where operating system, BIOS and dynamic memory allocation are not recommended reliable. The analysis was done for the execution environment using TPM Remote Attestation, Secure VM launch, Memory Seal Storage based on Storage Root Key (SRK). In the end this paper presents a comparative performance evaluation metrics, for TrustVisor and Flicker hypervisors using eucalyptus cloud software for private cloud. Through this research it was observed that TrustVisor provides high information isolation and integrity as compared to Flicker hypervisor.
A Stoic Cyber Meta-Ontology - Applying Stoicism to Modern Ontology Design

The concept of concrete and abstract entities relates to an ontological specification problem, described by Plato’s Sophist concept of the Gods and Giants and their interpretation of metaphysical reality. Developing an ontology without considering the full metaphysical implications leaves conceptual gaps in the creation of events, change in states and management of thoughts, plans and deceptions. This paper seeks to resolve conceptual gaps in the field of cyberspace representation, enabling both ontological interoperability and the development of more complex artificial intelligence technologies. The beginnings of a new meta-ontology for cyberspace are proposed, based on Stoic metaphysics. A meta-ontology provides high-level construction of meaning, upon which more specific ontologies can be built. Coherence between ontologies is important because cyberspace is a contextually dependent knowledge space within the broader infosphere, with many interdependent concepts. This paper enhances the potential for ontological interoperability and transparency between users, human or machine.
A Proposal of Prediction Method Using Word Polarity Information for Future Event Prediction Support System

In recent years, there has been an increased demand for future prediction in relation to social affairs, progress in science and technology, and economic circumstances. Moreover, there are large amounts of text data on the web, and there has been research into methods of future prediction support using natural language processing technology aimed at this. In previous research, we confirmed the effectiveness of future prediction supporting sentences, using an answer model generated through the use of pattern combination-based machine learning that considers language processing in word order for sentences referring to future events (FRS) using a newspaper corpus as learning data. However, there is other effective information on the Web in addition to just newspaper corpus, and this can also be used for sentence supporting prediction. Additionally, a method of improving prediction accuracy is to prepare an FRS classifier for each domain considering the sentence characteristics of each news domain, and acquire sentences supporting prediction. In this research, we obtained prediction support sentences related to future events from the news corpus on the Web, proposed a future event prediction support method using word polarity information, and showed that the prediction results exceeded the results of previous experiment.
Developing Root Problem Aims to Create a Secure Digital Signature Scheme in Data Transfer

This paper presents the proposed method of building a digital signature algorithm which is based on the difficulty of solving root problem and some expanded root problems on Zp. The expanded root problem is a new form of difficult problem without the solution, also originally proposed and applied to build digital signature algorithms. This proposed method enable to build a high-security digital signature platform for practical applications.
View-Angle Invariant Object Monitoring Without Image Registration

Object monitoring can be performed by change detection algorithms. However, for the image pair with a large perspective difference, the change detection performance is usually impacted by inaccurate image registration. To address the above difficulties, a novel object-specific change detection approach is proposed for object monitoring in this paper. In contrast to traditional approaches, the proposed approach is robust to view angle variation and does not require explicit image registration. Experiments demonstrate the effectiveness and advantages of the proposed approach.
Motion Tracking for Volumetric Motion Capture Data

Motion capture is often used to study the biomechanics of human motion. The first step in this type of research is to process and retrieve biomechanical data from the motion capture data. In this paper, we introduce a new method to dynamically track joint locations in volumetrically captured human motion sequences. The challenge is to identify corresponding components in a sequence of 3D meshes that not only transform and deform but may also have different vertex structures. To solve this problem, we have developed a method that uses user-selected vertices from the surface data, derives joint locations, and attempts to track their positions frame by frame.
Resilient to Byzantine Attacks Finite-Sum Optimization Over Networks

This contribution deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced gradient noise makes it challenging to distinguish malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the friendly workers. This motivates gradient noise reduction as a means of robustifying SGD in the presence of Byzantine attacks. To this end, the present work puts forth a Byzantine attack resilient distributed (Byrd-) SAGA approach for learning tasks involving finite-sum optimization over networks. Rather than the mean employed by distributed SAGA, the novel Byrd-SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, the robustness of geometric median to outliers enables Byrd-SAGA to achieve provable linear convergence to a neighborhood of the optimal solution, where the size of neighborhood is determined by the number of Byzantine workers. Numerical tests demonstrate the robustness of Byrd-SAGA to various Byzantine attacks, as well as the merits of Byrd-SAGA over Byzantine-resilient SGD.
A Deep Learning Neural Network for Classifying Good and Bad Photos

Current state-of-the-art solutions that automate the assessment of photo aesthetic quality use deep learning neural networks. Most of these networks are either binary classifiers or regression models that predict the aesthetic quality of photos. In this paper, we developed a deep learning neural network that predicts the opinion score rating distribution of a photo’s aesthetic quality. Our work focused on finding the best pre-processing method for improving the correlation between ground truth and predicted aesthetic rating distribution of photos in the AVA dataset. We investigated three ways of image resizing and two ways of extracting regions based on salience. We found that the best pre-processing method depended on the photos chosen for the training set.
Type-II Fuzzy Neural Networks for Image Stabilization of the Airborne Camera

Abstract The vibration rule of the airborne camera was studied to solve the image vibration in aerial photography of the Micro Aircraft Vehicle. A method based on the ability of function approximation of type 2 fuzzy neural networks with self-organizing recurrent intervals (SRIT2FNN) to simulate the vibration rule of airborne camera in the MAV and predict the vibration displacement vectors during image stabilization was proposed. The SRIT2FNN has no initial rules, which are generated from the simultaneous on-line parameter and structure learning. The results show that SRIT2FNN control system is more stable and the higher precision, and good real-time performance than combined BP neural networks.
Correlation Study between Photovoltaic Power Output and Environmental Variables Using an Embedded IoT System

The sunlight is one of the most important renewable sources. However, the systems designed for its usage are not a continuous power delivering option. A photovoltaic panel is the most common collecting device for solar energy, regardless of having low efficiency at high temperatures. The present paper shows the pre-processing stage for data analysis to study the relationship between the sunlight and weather variables, with the power output of a photovoltaic panel for further estimation models. In order to obtain representative and enough data, an embedded IoT system was proposed to collect and store measurements. Then, data were analyzed by using scatterplots matrix and a non-parametric correlation calculation. The correlation factors probe that data can be classified as fuzzy data with a high confidence level to be used for model developing.
Depth Map Fingerprinting and Splicing Detection

With the ubiquity of social networks, images have become crucial in todays exchange of information. Most of these images are taken by smartphones. For forensic approaches relying on fixed image formation pipelines, the capabilities of smartphones using computational photography pose new challenges. But these new capabilities also offer opportunities for forensic analysis. A growing amount of commodity devices are able to capture 3-D information using various technologies such as stereo imaging or structured light. Modern smartphones commonly save such 3-D information as depth maps alongside regular images.In this work, we propose to use characteristic artifacts of depth reconstruction algorithms as trace for forensic analysis. The proposed method is able to infer the source algorithm of stereo reconstructions with an accuracy of up to 97%. We further demonstrate the applicability of the method to collected smartphone data. It is able to discriminate patches from different sources with an AUC of up to 0.88 and can be used for splicing localization in depth maps.
Detection of Crossing Pedestrians and Control Support in Autonomous Vehicles using Edge-devices

The National Institute of Advanced Industrial Science and Technology aims to implement new means of transportation with autonomous vehicles. In autonomous vehicles, pedestrian recognition and control decisions are one of many important issues. In this study, a machine-learning-based-image analysis method is used to verify control judgment for pedestrian recognition, including their direction and their use of mobile phones. In addition, by using an edge computing system that utilizes multiple edge devices, we are aiming for recognition outside the vehicle that is not affected by communication failures. The results of the demonstration carried out for verification of the same have been reported in this paper.
Auto Resource Management to Enhance Reliability and Energy Consumption in Heterogeneous Cloud Computing

A classic information processing has been replaced by cloud computing in more studies where cloud computing becomes more popular and growing than other computing models.Cloud computing works for providing on-demand services for users.Reliability and energy consumption are two hot challenges and tradeoffs problem in the cloud computing environment that requires accurate attention and research.This paper proposes an Auto Resource Management (ARM) scheme to enhance reliability by reducing the Service Level Agreement (SLA) violation and reduce energy consumed by cloud computing servers.In this context, the ARM consists of three compounds, they are static/dynamic threshold, virtual machine selection policy, and short prediction resource utilization method.The Minimum Utilization Non-Negative (MUN) virtual machine selection policy and Rate of Change (RoC) dynamic threshold present in this paper.Also, a method of choosing a value as the static threshold is proposed.To improve ARM performance, the paper proposes a Short Prediction Resource Utilization (SPRU) that aims to improve the process of decision making by including the resources utilization of future time and the current time.The output results show that SPRU enhanced the decision-making process for managing cloud computing resources and reduced energy consumption and the SLA violation.The proposed scheme tested under real workload data over the CloudSim simulator.