Reviewerly/exHarmony huggingface dataset:
Interest Level Estimation Based on Feature Integration Considering Distribution of Partially Paired User’s Behavior, Videos and Posters

This paper presents a method for interest level estimation based on feature integration considering distribution of partially paired user's behavior, videos and posters. The proposed method collaboratively uses videos, their corresponding poster images, and user's behavior to estimate the interest levels. For dealing with the multi-view data, the proposed method newly derives semi-supervised divergence-aware multi-set canonical correlation analysis (SDMCCA). SDMCCA has two contributions. First, since the number of viewable videos is limited, consideration of unviewed videos is necessary, and SDMCCA adopts the semi-supervised approach. Second, information which data have is different due to the difference between similarities to other data. SDMCCA can simultaneously realize the above points and realize successful interest level estimation. Experimental results show the effectiveness of the proposed method.
Bepari: A Cost-aware Comprehensive Agent Architecture for Opaque Cloud Services

Cloud computing has become popular in various application domains based on infrastructure, platform, and software as a service model. Rapid deployment, high scalability, on-demand, and (theoretically) infinite resources have driven the industry towards the wide adoption of cloud computing services. However, the difficulty of cross-provider resource allocation and seamless resource transition is a major concern for such services. Therefore, the segregated cloud market forces its clients to use provider-specific and pre-configured options for their required resources and services. Thus, the overall market, even with the presence of multiple cloud service providers, operates as a direct service from the providers to the clients, and with non-negotiable pricing strategies for the cloud services. In this article, we propose Bepari, a cost-driven model for opaque service platforms for cloud computing. Bepari acts as a negotiation-based approach to deliver composite cross-provider cloud-based services to the end-users. Bepari provides a detailed service-oriented architecture for multiple cloud service providers to provide cross-platform and composite services. Furthermore, Bepari delivers a detailed cost model and comparison between establishing a cloud service vs. an opaque cloud service. Our empirical framework allows a Bepari service provider to analyze the profit model and create a market niche accordingly. Simulation results are provided, which validate the efficiency of a negotiated pricing strategy in terms of maximized resource utilization and profits for cloud service providers and cost reduction for the cloud users.
Poetic Discourse Processing Applying Graph Theoretic Rules

In this article, we present a way of graph theory application to analyze the structure and content of poetic discourse. The study has been carried out in terms of applied linguistics and illustrates the integration of information systems technologies. The main contribution is an attempt to implement the open-source text analytics software to analyze the poetic discourse of the late 16th ‒ early 17th centuries and provide its infographics visualization. The topicality of graph theory and data visualization applications in linguistic studies are connected with the ability to structure the retrieved information, identify the contextual relations between the key phrases and language units, and make predictions regarding the dynamics of discourse organization. Overall, the research findings indicate the most frequent connections between the lingual cultural type of woman and the lexical variables used to depict physical, social, or spiritual objects of the reality in English culture.
Nondeterministic Secure LSB Steganography for Digital Images

The practice of obscuring information on a `need to know' basis, but still sharing it with others, is called `secrecy.' The information that requires fortification is known as the secret. However, the pace at which data gets generated, gathered, disseminated, and analyzed in today's Internet of Things (IoT) has inhibited the individuals from keeping their secrets `secret.' Cloud computing further hardened the situation by providing a pay-as-you-go method for data processing and information retention (persistent storage). Although the cloud service providers are bound to maintain data integrity, confidentiality, and the privacy of users, yet the gray areas of proxy points still await a resolution. The judicial endorsement on gaining and having access to the encrypted data and the pre-shared keys to the law-enforcing authorities by the Australian parliament - the first amongst the five-eye countries inspecting the global communication has worsened the situation further. Hence, the current state of affairs not just demand for information hiding but the evolution of `secure' information hiding solutions to maintain secrecy both at the personal and national level. Least Significant Bit embedding technique for hiding data has brought a stir in the information security arena, and since its inception has remained a topic of research for the said purpose. This research endeavor proposes an enhancement to the existing LSB embedding mechanism to deceive attackers from detecting hidden information but without compromising image perceptibility. The results confer on the effectiveness of the proposed algorithm with regard to Peak Signal to Noise Ratio and known-cover attack scenario.
8-bit Convolutional Neural Network Accelerator for Face Recognition

With the development of convolutional neural network (CNN), the accuracy of face recognition has been greatly improved. But the huge amount of weights and calculations hinders its implementation in portable devices. Designing hardware accelerator is an effective solution to the problem. In this paper, a face recognition algorithm is designed based on deep separable convolution. The weights and activations are quantified to 8 bits, reducing the requirement of data access and bandwidth. In addition, a generic CNN accelerator based on systolic array is designed and validated on Xilinx Zynq-XC7Z035 FPGA. The face recognition algorithm achieved an accuracy of 94.4% in the LFW dataset. The performance and power efficiency of the accelerator are 52.9 GOPS and 9.71GOPS/W at 100MHz, respectively. And the accelerator can process 160×160 face image at 25FPS.
De-identification and restoration methods for protecting privacy in off-line documents

The necessity of protecting personal information contained in off-line documents (e.g., identification cards, driver's licenses, passports, and delivery invoices) is increasing as the subject of privacy is expanding to various objects in the physical world from the existing security perspective, which was limited only to on-line services. In this paper, we propose off-line document protection technology that can safely protect ID cards and important confidential documents released with personal information exposed on the website, and prevent cases where personal information (name, address, phone number, etc.) on delivery and mail is leaked and abused in the crime. The proposed method consists of masking and unmasking process using two-dimensional bar codes and a user's secret keys. Through the experimental results using the proposed method, we confirmed that personal information in off-line documents can be changed as an unreadable form and that only authorized user can restore the unrecognizable area to prevent the leakage of personal information.
Data Mining Algorithms Parallelization in the Framework of Agent-Oriented Programming

This article describes an approach to parallelization of data mining algorithms in the framework of agent-oriented programming for distributed data processing. The conversion of Naive Bayes algorithm into agent-oriented form for distributed execution and the algorithm implementation in the framework of agent-oriented programming are described as an example.
Design Integrated Staff Welcoming and Administration System Based on Facial Recognition for Smart University

In recent years, facial recognition technology is not only applied for security, healthcare, business but it is also exploited creatively in education and for smart university development. In this study, an Automated Employee Attendance Management System has been designed and implemented in a university's building for welcoming staff and supporting university administration. The system starts with a facial detection module which is based on the pre-trained MultiTask Cascaded Convolutional Network model. Then the feature vectors are created by using the ResNet34 network which results in the 128-dimension embedding vector. Face recognition is carried out by using various techniques such as K-Nearest Neighbors, Support Vector Machine. Besides the welcoming front-end module, a web-based application for querying information to manage people entering the building is also built as a back-end module. The proposed face recognition models have been trained and tested on a collected face database and a self-built face database of employees who work at the university building. The evaluation results show high recognition rates in terms of Precision, Recall, Accuracy, Fl-score and reasonable processing time. The proposed system has been piloted at the university for further development and research on face recognition technologies and smart building management.
Conditional Density Estimation, Latent Variable Discovery, and Optimal Transport

Abstract A framework is proposed that addresses both conditional density estimation and latent variable discovery. The objective function maximizes explanation of variability in the data, achieved through the optimal transport barycenter generalized to a collection of conditional distributions indexed by a covariate—either given or latent—in any suitable space. Theoretical results establish the existence of barycenters, a minimax formulation of optimal transport maps, and a general characterization of variability via the optimal transport cost. This framework leads to a family of nonparametric neural network‐based algorithms, the BaryNet, with a supervised version that estimates conditional distributions and an unsupervised version that assigns latent variables. The efficacy of BaryNets is demonstrated by tests on both artificial and real‐world data sets. A parallel drawn between autoencoders and the barycenter framework leads to the Barycentric autoencoder algorithm (BAE). © 2020 Wiley Periodicals LLC.
A conversation on AI activism

opinion Share on A conversation on AI activism Authors: Sarayu Natarajan Aapti Institute Aapti InstituteView Profile , Smriti Parsheera National Institute of Public Finance and Policy National Institute of Public Finance and PolicyView Profile Authors Info & Claims InteractionsVolume 28Issue 1January - February 2021 pp 28–33https://doi.org/10.1145/3436946Online:23 December 2020Publication History 0citation3,653DownloadsMetricsTotal Citations0Total Downloads3,653Last 12 Months199Last 6 weeks15 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Limitations of CNNs for Approximating the Ideal Observer Despite Quantity of Training Data or Depth of Network

The performance of a convolutional neural network (CNN) on an image texture detection task as a function of linear image processing and the number of training images is investigated. Performance is quantified by the area under (AUC) the receiver operating characteristic (ROC) curve. The Ideal Observer (IO) maximizes AUC but depends on high-dimensional image likelihoods. In many cases, the CNN performance can approximate the IO performance. This work demonstrates counterexamples where a full-rank linear transform degrades the CNN performance below the IO in the limit of large quantities of training data and network layers. A subsequent linear transform changes the images&#x2019; correlation structure, improves the AUC, and again demonstrates the CNN dependence on linear processing. Compression strictly decreases or maintains the IO detection performance while compression can increase the CNN performance especially for small quantities of training data. Results indicate an optimal compression ratio for the CNN based on task difficulty, compression method, and number of training images.
Single Photon Quantum Key Distribution Using Phase-Squeezed State

Generation and application of squeezed light source has been the interest in the field of quantum optical experiments, especially with regards to the field of quantum key distribution. In this paper, a method using phase-squeezed state as the input state of the single photon quantum key distribution protocol is introduced, and the rate of the gained secure key of the proposed method after the post-processing techniques is analyzed. Compared to the conventional quantum key distribution protocol using weak coherent light source as the single photon source, it is shown that in the proposed method there are significant improvements in both the transmittable distance and the gained secure key rate.
Software Quality is Multidimensional

In the face of the dynamic nature of modern software systems, the quality of software products must be monitored and assured from different perspectives at the same time and throughout the development cycle. Although different characteristics and subcharacteristics can represent a software product quality, they have been historically studied and observed in a unidimensional way, which restricts the ability to observe the multiple relationships among quality characteristics. There is empirical evidence indicating the multidimensionality of this phenomenon. Therefore, an n-dimensional software quality base model is presented. The fundamentals of tensor algebra provide its basis, which explores the combination of different dimensions and relations of quality (internal, external, and in use) to suggest the level of quality of a software product. Preliminary results of its operationalization are presented to support this proposal.
IoT based climate prediction using ANN for green networking

Abstract Weather forecasting plays a major role in various fields such as agriculture, transportation etc., It is also essential in prediction of natural disasters like Flood, storm etc., So in order to prevent the damages from such natural disorders, accurate weather prediction is essential. IoT topology is a combination of both hardware and wireless communication network utilized for real-time data analysis. Hence, this proposed topology formulated an IoT based weather monitoring using artificial neural network (ANN) for prediction. The main objective of this designed topology system is monitoring weather parameters such as temperature/humidity/pressure/rainfall etc., IoT is organized to collect data from sensors and is communicated through Wi-Fi network. ANN is tailored for weather prediction. The analysis proves that the proposed architecture shows better efficiency in weather prediction than the conventional methods.
Phoneme Set Extension Based on Jensen-Shannon Divergence for Broadcast News Transcription

This paper investigates a method for obtaining a discriminative phoneme set extended by assigning a new label to acoustically ambiguous speech segments. The proposed method first extracts phoneme-level segments from a large amount of Korean broadcast data and then converts the variable-length segments into fixed-length vectors using the LSTM structure. Then, the fixed-length vectors belonging to each unit are used to generate a multivariate Gaussian model. Finally, we assign a new label to the samples located between the most overlapping Gaussian model pair, which is found by using the Jensen- Shannon divergence with the Monte-Carlo sampling. The above extension process is repeatedly performed until the desired number of units is reached. The newly assigned units constitute a new phoneme set used as the pronunciation dictionary. In broadcast news transcription experiments, we confirmed that the proposed method has the modest effect on discrimination by assigning a new label to the ambiguous speech samples.
Making of nusantara decorative motif creations necklace based on hdpe on type plastic waste

Abstract The problem of HDPE plastic waste from thick inorganic household waste that is difficult to destroy can damage the environment, the need for the utilization of HDPE plastic waste into products that are beneficial to humans. The purpose of this study was to determine the results of ornamental motif necklaces in the archipelago in accordance with the provisions of the aesthetic theory derived from HDPE plastic waste material that produces good quality so that it can attract consumers as users. The method used is a qualitative method based on the collection of literature data and information from the opinions of experts in their field of accessories to provide input or to the archipelago creation creations based on product theory and aesthetics. The results obtained are necklace products that have design elements derived from decorative motifs of the archipelago (Kawung and Tumpal) made from HDPE plastic waste that have benefits in fashion. The conclusion from the analysis based on theory and accessory expert opinion on the archipelago ornamental motif necklace especially kawung motif made from HDPE plastic waste can produce necklace creations that have good aesthetics and quality so that they can attract attention. consumers as users.
Selective Adaptation of End-to-End Speech Recognition using Hybrid CTC/Attention Architecture for Noise Robustness

This paper investigates supervised adaptation of end-to-end speech recognition, which uses hybrid connectionist temporal classification (CTC)/Attention architecture, for noise robustness. The components of the architecture, namely the shared encoder, the attention decoder's long short-term memory (LSTM) layers, and the soft-max layers of the CTC part and attention part, are adapted separately or together using limited amount of adaptation data. When adapting the shared encoder, we propose to adapt only the connections of the memory cells in the memory blocks of bidirectional LSTM (BLSTM) layers to improve performance and reduce the time for adapting the models. In within-domain and cross-domain adaptation scenarios, experimental results show that adaptation of end-to-end speech recognition using the hybrid CTC/Attention architecture is effective even when the amount of adaptation data is limited. In cross-domain adaptation, substantial performance improvement can be achieved with only 2.4 minutes of adaptation data. In both adaptation scenarios, adapting only the memory cells of the BLSTM layers in the shared encoder yields comparable or slightly better performance while yielding smaller adaptation time than the adaptation of other components or the whole architecture, especially when the amount of adaptation data is less than or equal to 10 minutes.
Reducing Energy Consumption by Behavioural Change

On global as well as national levels, top-down approaches to reduce climate-related energy consumption have not been successful and effective at all until now. Thus, a bottom-up approach is recommended: citizens role should change from consumers to prosumers, to (co-)decision-makers regarding energy-related behaviour, and to co-investors in the new energy infrastructure. However behavioural change is difficult to realize in face of the ‘knowledge-behaviour gap’, ‘value-action gap’ or ‘attitude-behaviour gap’. Thus, the CODALoop project collected data with its platform Energanz and developed the socio-cognitive model for energy-related behaviour. In the new planned IF4E project that model becomes an integrated module of a process model for energy-related behavioural change with real-time feedback loops, real-time energy consumption data, and a user’s Personal Energy Profile (PEP) which is updated continuously. The challenges and risks in investigating and implementing the proposed process model in real settings for changing climate-related energy consumption behaviour are discussed. This position paper aims receiving feedback of experts regarding the planned project before its realization.
Learning Progress Modeling for Monitoring Student

Monitoring the progress of students is part of the teacher's job which is very important and very time-consuming. Especially if there are many students with various subjects. This is the experience of most primary school teachers in Indonesia. One way to solve this problem is to predict student progress. In this study, the students' progress was predicted using Random Forest. The Random Forest algorithm is used because it can classify data that has incomplete attributes, which are usually found in student assessment data. The prediction model was built based on assessment data from 2 classes with 46 elementary school students in subjects: Indonesian, mathematics, SBdP (Cultural Arts and Crafts), PPKN (Pancasila and Citizenship Education), and Computers. The dataset comes from the formative and summative assessment results from 3 aspects (cognitive, psychomotor, and affective). The resulting model performance will be measured using accuracy and recall. The results showed that using a dataset of 5 subjects from 46 students, the Random Forest algorithm produced a learning progress model with 100% accuracy for training data and 94% for testing data. Meanwhile, the learning progress prediction model for each subject has 100% accuracy on training data and more than 96% on test data.
Edge Detection based on Local-Friis-Radiation-Magnitude-Ratio (LFRMR)

The advent of computer-vision based systems has given rise to the need for efficient edge detection algorithms. This paper presents a novel approach called the Local-Friis-Radiation-Magnitude-Ratio (LFRMR) for edge detection. LFRMR incorporates the renowned Friis Equation of antenna radiation and extends it to the grid of image pixels to establish a relation among the pixels residing in a local neighbourhood, to extract accurate illumination-invariant and noise resistant edge maps. Quantitative and qualitative experimental results on BSDS500 dataset depicts that the proposed scheme can extract true edges with utmost precision and recall. Furthermore, the proposed scheme is quite robust against Gaussian channel noise and Salt & Pepper noise. A detailed mathematical investigation has also been carried out to prove that the proposed framework is illumination-invariant and robust in noisy environments. Optimum algorithm parameters are decided via experimental analysis. A comparison with the latest state-of-the-art methods is also presented.