Reviewerly/exHarmony huggingface dataset:
Intelligent User Profiling Based on Sensors and Location Data to Detect Intrusions on Mobile Devices

Continuous authentication systems are considered as a promising solution to secure access to mobile devices. Their main benefit is the improvement of the users' experience when they use the services or applications of their mobile device. Specifically, continuous authentication avoids having to remember or possess any key to access an application or service that requires authentication. In this sense, having the user authenticated permanently increases the security of the device. It also allows the user interaction with applications to be much more fluid, simple, and satisfactory. This chapter proposes a new continuous authentication system for mobile devices. The system acquires data from the device sensors and the GPS location to create a dataset that represents the user's profile or normal behaviour. Then, the proposed system uses Machine Learning algorithms based on anomaly detection to perform user identification in real time. Several experiments have been carried out to demonstrate the performance and usefulness of the proposed solution.
A Semi-automated Approach to Generate an Adaptive Quality Attribute Relationship Matrix

[Context and Motivation] A critical success factor in Requirements Engineering (RE) involves recognizing conflicts in Quality Requirements (QRs). Nowadays, Quality Attributes Relationship Matrix (QARM) is utilized to identify the conflicts in QRs. The static QARM represents how one Quality Attribute (QA) undermines or supports to achieve other QAs. [Question/Problem] However, emerging technology discovers new QAs. Requirements analysts need to invest significant time and non-trivial human effort to acquire knowledge for the newly discovered QAs and influence among them. This process involves searching and analyzing a large set of quality documents from literature and industries. In addition, the use of static QARMs, without knowing the purpose of the QRs in the system may lead to false conflict identification. Rather than taking all QAs, domain-specific QAs are of great concern for the system being developed. [Principal ideas/results] In this paper, we propose an approach which is aimed to build an adaptive QARM semi-automatically. We empirically evaluate the approach and report an analysis of the generated QARM. We achieve 85.67% recall, 59.07% precision and 69.14% F-measure to acquire knowledge for QAs. [Contributions] We provide an algorithm to acquire knowledge for domain-specific QAs and construct an adaptive QARM from available unconstrained natural language documents and web search engines.
Secure Method for De-Identifying and Anonymizing Large Panel Datasets

Government agencies, as well as private companies, may need to share private information with third party organizations for various reasons. There exist legitimate concerns about disclosing the information of individuals, sensitive details of agencies and organizations, and other private information. Consequently, information shared with external parties may be redacted to hide confidential information about individuals and companies while providing essential data required by third parties in order to perform their duties. This paper presents a method to de-identify and anonymize large-scale panel data from an organization. The method can handle a variety of data types, and it is scalable to datasets of any size. The challenge of de-identification and anonymization a large-scale and diverse dataset is to protect individual identities and retain useful data in the presence of unstructured field data and unpredictable frequency distributions. This is addressed by analyzing the dataset and applying a filtering and aggregation method. This is accompanied by a streamlined implementation and post-validation process, which ensures the security of the organization&#x0027;s data, and the computational efficiency of the approach when handling large-scale panel data sets.
Non-intrusive Runtime Monitoring for Power System Intelligent Terminal Based on Improved Deep Belief Networks (I-DBN)

Power system intelligent terminal equipment is widely used in real-time monitoring, data acquisition, power management, power distribution and other tasks of smart grid. The power system intelligent terminal can obtain various information of users and power companies in the power grid, but there is still a lack of protection means for the connection and communication process of the terminal components. In this paper, a novel method based on improved deep belief network(IDBN) is proposed to accomplish the business-level security monitoring and attack detection of power system terminal. A non-intrusive business-level monitoring platform for power system terminals is established, which uses energy metering intelligent terminals as an example for non-intrusive data collection. Based on this platform, the I-DBN extracts the spatial and temporal attack characteristics of the external monitoring data of the system. Some fault conditions and cyber attacks of the model have been simulated to demonstrate the effectiveness of the proposed detection method and the results show excellent performance. The method and platform proposed in this paper can be extended to other services in the power industry, providing a theoretical basis and implementation method for realizing the security monitoring of power system intelligent terminals from the business level.
Decentralized Framework for Record-Keeping System in Government Using Hyperledger Fabric

Blockchains are spreading their popularity in a wide range of applications due to their capability to ensure decentralization, immutability, transparency, verifiability, resilience and consensus-based record-keeping system. Hyperledger Fabric is a general-purpose permissioned blockchain featuring pluggable consensus mechanism and chain code that can be written in any general-purpose programming language, providing a consistent distributed ledger that is shared by a set of peers in the network. In this paper, we propose a novel decentralized framework for record-keeping system in government, which brings immutability, resilience and verifiability in the system that is based on permissioned blockchain Hyperledger Fabric.
The Liability system of Art. 17 DSMD and national implementation – contravening prohibition of general monitoring duties?

JIPITEC 10 (2020) 3 - The article deals with the complex liability system of Art. 17 DSM Directive, the famous “upload filter provision” in copyright law concerning content sharing platforms. The conflict between the prohibition of general monitoring obligations for platform providers derived from European fundamental rights by the CJEU and the obligations of providers are scrutinized. Moreover, possible options for national legislators in order to safeguard fundamental rights of users concerning freedom of speech (and copyright limitations) are discussed.
Analysis of Ways to Create Augmented Reality

Augmented reality is an actively developing technology that allows increasing the amount of information received by a person about objects that he perceives with his senses. There are several ways to create augmented reality images. One of them is the selection of a simple geometric shape (marker). Another method is based on finding the features of the image and their numerical description. In this paper, both methods are described and a comparative analysis of the results is made, on the basis of which it can be concluded that it is advisable to use one or another method in a particular AR-system.
Legal smart contracts for derivative trading in mining

Abstract This research demonstrates financial derivative trade of unprocessed materials, for the mining industry through legal smart contracts. Within the mining supply chain, a stock of mined resources can reside in a mineral stockpile for over twenty years without gaining financial interest and without undergoing the mineral extraction process to derive value from the asset. This research elaborates on a blockchain solution implemented to increase miners’ short-term cash flow for business operations through the issuance of derivative assets on mineral stockpiles which can be traded through legally binding smart contracts. The system is the first to enable mining companies’ access to the underlying asset’s value earlier in the production lifecycle through smart contract technology whilst providing hedge funds with access to new financial products for investment portfolios.
Architecture and language in the <i>seven String Quartets</i> by Iulia Cibişescu-Duran

Abstract Written between 1990 and 2009, my seven works for string quartet: Poems for string Quartet and 6 numbered Quartets approach different modal languages, from prepentatonic or pentatonic structures to heptachordic or dodecaphonic configurations, sometimes overlapping musical languages, generating those polymorphous languages, with moments of overlapping or juxtaposing of syntaxes, with a motivic cycling determinant of a conceptual unit of works linked to synthetic, elaborate thinking. Pluripartite, seen as suites of miniatures ( String Quartet No. 3, Poems for string quartet ), tripartite ( Quartets No. 2, 4, 5, 6 ) or monopartite ( Quartet No. 1), the 7 String Quartets are written in the sphere of formal patterns caused by construction based on dramaturgy inspired by literary works (see Quartet No. 5 inspired by Winter at Lisbon by Antonio Munoz Molina, see Poems for string quartet and Quartet no. 3 inspired by my own poems from the volumes Hiding places of Masks and Egyptian Mystery ), from the contemplation of the chordal sonorities of some tonal-functional relations or of some jazz sonorities ( Quartet No. 4), of a Byzantine song or children’s songs ( Quartet No. 3 ), of philosophical meditations (see Quartet No. 1), of sonorities belonging to the Romanian song and dance ( Quartet No. 6 ) or of some concision and refinement as reflections of Webern’s music, overlaying on small temporal spaces different musical languages belonging to different tuning systems ( Poems for string quartet ). The first audition of String quartets was at the International Festivals of the Musical Autumn of Cluj and Cluj Modern Festival (1990, 1993, 1999, 2001, 2003, performers: Concordia Quartet : Albert Markos, Grigore Botar, Olimpiu Moldovan, Adalbert Torok), as well as at the International Meridian Festival , Bucharest (2018, Quartet No. 6 played by the Ad Hoc Quartet : Vlad Răceu, Diana Man, Ovidiu Costea, Vlad Rațiu, musical management: Matei Pop).
Selectively Oversampling Difficult Positive Samples from Imbalanced Data for Preprocessing

Oversampling is a procedure traditionally has been applied to train machine learning classifiers for a better performance in presence of class imbalance. This work suggests a new insight for oversampling imbalanced data. In literature Borderline samples are mainly focused for oversampling. How-ever, because of low number of samples within the positive class a huge percentage of samples can be labeled as Rare and Outliers. These samples are often overlooked by the traditional oversampling methods or the nearest negative samples are often removed to increase positive prediction rate- while sacrificing the negative prediction rate. This work demonstrates that by only oversampling the Borderline, Rare and Outlier samples at different rate, better performance can be achieved than all other pre-processing methods. The proposed method is applied on four datasets- Abalone, CMC, Solar Flare and Seismic Bump, collected from the UCL digital library and compared with four traditional pre-processing methods ADYSYN, SMOTE, Border-line SMOTE 1 and 2 from imbalanced learn toolkit python. The result analysis shows that with fine tuning better performance can be achieved for all known performance measurements: Accuracy, True Positive Rate, True Negative Rate, Geometric Mean, Area Under the Curve measure and F-measure .
ANN-based Performance Analysis on Human Activity Recognition

In the Big Data era, where various devices can connect to each other through network and cloud services, a smartphone has numerous sensors that can detect data about everything around it. This makes the identifying-process activity (AR) applications and behavior aware of the context. In this paper, we used an algorithm to predict a person's activity based on the collected sensor data. Also, Principal Component Analysis(PCA) is applied to the 561 features of the dataset. PCA reduced the dimensions of the dataset from 561 to 50, decreasing the complexity of the data. Therefore, a number of important features are identified out of the 561 features. Consequently, the neural network outperforms the HF-SVM. The HF-SVM was chosen because it requires less memory, computational power, and battery consumption. This study suggests minimizing the resources used by the neural network or exploring another classification algorithm to achieve comparable results with fewer resources.
A Long-time Multi-object Tracking Method for Football Game Analysis

Multi-object tracking (MOT) is crucial in many applications, such as video analysis, intelligence surveillance, and robot navigation. Occlusion, complicated motion, and similar appearance pose challenges to achieving reliable MOT. Here we propose a long-time multi-object tracking method to overcome these challenges. In our framework, a number recognition method based on tracklet is proposed to provide reliable number labels, which facilitates long-time tracking in football video. Extensive experiments are taken to demonstrate that our method achieves excellent performance and provides reliable trajectories for football game analysis.
TRIZ Innovative Method for Eliminating Fissures in 3D Panorama Image Stitching

Abstract roaming splices several photographs taken by the camera into a panoramic image, and then uses computer graphics and image technology to construct panoramic space for the panoramic image. Image stitching will directly affect the user’s roaming experience, but some panoramic images will have obvious color seams after stitching. TRIZ is a knowledge-based, human-oriented inventive problem solving systematic method. Based on TRIZ innovation method, this paper describes, analyses, resolves and evaluates the final problem plan. By using TRIZ innovation method, we can break through the thinking barrier, break the thinking stereotype, and analyze the problem with new vision, and carry out logical and non-logical systematic thinking. The problem of stitching cracks in the process of 3D panorama stitching is solved thoroughly and effectively. Finally, according to the law of technological evolution, the future development trend is predicted, and competitive and innovative products are developed.
A PARADIGM SHIFT IN PRE SERVICE TEACHER EDUCATION IN INDIA

Professional preparation and professional development of teachers is a continuous process. It begins with the selection of an aspirant teacher and includes his initial preparation induction into the profession and his continuous development throughout his teaching carrier. The system of teacher preparation has come under considerable pressure as a result of the expansion and growth of school education, through efforts to universalize elementary education. Presently, the education of teacher educators takes place mainly through the well-entrenched two-year teacher trainee programme. It is a promising development that the education of teacher educators has been acknowledged as a major issue in teacher education and academic bodies have addressed themselves to the task of curriculum renewal. This is evident in the different models of teacher trainee programme that have been proposed by individual universities and innovative programmes of teacher preparation launched by some institutions. The concern to make the teacher trainee programme a focussed learning encounter rather than a general study of education is clearly visible. The need for professional rigour, relevance of content, emphasis on research related skills are also appreciated. These concerns demanded that out of the box to think in addressing the issue of new programme designs for the education and training of teacher educators. Keeping these vital ingredients in teacher education programme, the National Council of Teacher Education (NCTE) has prepared the National Curriculum Framework of Teacher Education. Reform of teacher education to move forward on a sound footing demands dedicated research in the area of foundations of education in the Indian context by universities, preferably in independently established departments. The research in such departments would help develop the teacher education programmes on a sound theoretical basis.
Batch Normalization based Unsupervised Speaker Adaptation for Acoustic Models

This paper proposes a simple yet effective unsupervised speaker adaptation approach for batch normalization based deep neural network acoustic models. The basic idea of this approach is to recompute means and variances in all batch normalization layers over the test data for every speaker. Thus the distribution of the test data can be close to the training data. This approach doesn't need to adjust any trainable parameters of the acoustic model. Experiments are conducted on CHiME-3 datasets. The results show that the proposed adaptation obtains improvement on the real test set by 2.17 % relative average word error rate (WER) reduction when compared with the scaling and shifting factors (SSF) adaptation. Combining our proposed MV adaptation with the SSF adaptation obtains further improvement.
Multilevel deep representation fusion for person re-identification

Person re-identification (re-ID) aims at matching two pedestrian images across different cameras. Usually, the main scheme of re-ID based on deep learning includes two phases: feature extraction and metric calculation. We focus on how to extract more discriminative image features for re-ID. To address this problem, we propose a multilevel deep representation fusion (MDRF) model based on the convolutional neural network. Specifically, the MDRF model is designed to extract image features at different network levels through one forward pass. In order to produce the final image representation, these multilevel features are fused by a fusion layer. Then the final image representation is fed into a combined loss of the softmax and the triplet to optimize the model. The proposed method not only utilizes the abstract information of high-level features but also integrates the appearance information of low-level features. Extensive experiments on public datasets including Market-1501, DukeMTMC-reID, and CUHK03 demonstrate the effectiveness of the proposed method for person re-ID.
Enhanced ANDSF WiFi Discovery Mechanism Using Machine Learning for Mobile Data Offloading

Mobile data offloading using unlicensed WiFi spectrum represents one of the innovative approaches that can be adopted to cope with the huge amount of mobile traffic demand. However, the main challenge in such approach lies in the ability of the system to discover the WiFi coverage for users who may need to offload their data. For this, the access network discovery and selection function (ANDSF) has been introduced by 3GPP to provide the mobile user equipment (UE) with the discovery information and the list of available WiFi hotspots. However, the current ANDSF discovery mechanism depends on the geographical location of the UE, which need to be accurately determined and continuously transmitted to the ANDSF server. In this paper, an enhanced ANDSF WiFi discovery technique is proposed and validated. The proposed technique is designed to use the reference signal received power (RSRP) information measured at UEs from their surrounding cellular base stations to build fingerprints for various WiFi hotspot areas. Based on the constructed fingerprints, the UE's WiFi coverage state can be identified using the proposed decision tree (DT) machine learning approach. The conducted tests and generated results validate the operation and performance of the proposed machine learning-based discovery technique with accuracy reaches up to 95%.
Revisiting Dynamic Adjustment of Language Model Scaling Factor for Automatic Speech Recognition

Automatic speech recognition (ASR) systems use the language model scaling factor to weight the probability output by the language model and balance it against those from other models including acoustic models. Although the conventional approach is to set the language model scaling factor to a constant value to suit a given training dataset to maximize overall performance, it is known that the optimal scaling factors varies depending on individual utterances. In this work, we propose a way to dynamically adjust the language model scaling factor to a single utterance. The proposed method utilized a recurrent neural network (RNN) based model to predict optimum scaling factors given ASR results from a training dataset. Some studies have already tackled this utterance dependency in the 2000s, yet few have improved the quality of ASR due to the difficulty in directly modeling the relationship between a series of acoustic features and the optimal scaling factor; a recent breakthrough in RNN technology has now made this feasible. Experiments on a real-world dataset show that the dynamic optimization of the language model scaling factor can improve ASR quality and that the proposed method is effective.
Reconstructing 3D Human Avatars from Monocular Images

Creating convincing representations of humans is a fundamental problem in both traditional arts and modern media. In our digital world, virtual avatars allow us to simulate and render the human body for a variety of applications, including movie production, sports, human-computer interaction, and medical sciences. However, capturing digital representations of a person’s shape, appearance, and motion is an expensive and time-consuming process which usually requires a lot of manual adjustments. With the advances in consumer-grade virtual reality devices, personalized virtual avatars became an essential part of interactive and immersive applications like telepresence and virtual try-on for online fashion shopping, thereby increasing the need for versatile easy-to-use self-digitization. In this chapter, we discuss a selection of recent acquisition methods for personalized human avatar reconstruction. In contrast to conventional setups, these fully-automatic approaches only use low-cost monocular video cameras to effectively fuse information from multiple points in time and realistically complete reconstructions from sparse observations. We address both straight-forward and sophisticated reconstruction methods focused on accuracy, simplicity, and usability to compare and provide insights into their visual fidelity and robustness.
A Map-Reduce-Based Relation Inference Algorithm for Autonomous System

The business relationships between Autonomous systems (ASs) are crucial to understanding the internet structure, performance, evolution, and so on. However, the business relationships between ASs are often confidential and can only be captured by inference algorithms. This paper proposes a new algorithm to infer the ASs relationship based on the transmission capacity. A new metric for ASs node transmission capability based on path behavior is defined. Meanwhile, a big data processing technique based on Map-Reduce to deduce the ASs relationship. Besides, to analyze the accuracy of the algorithm, we compare its accuracy performance with those of existing algorithms. Through simulation experimental, the proposed scheme achieves substantial performance improvements in term of consistency and effectiveness. Also, the complexity of the proposed scheme is found to be reasonably low.