Reviewerly/exHarmony huggingface dataset:
Interaction spaces and socioenactive dimensions

Perception and action on physical or virtual objects are related to the perturbations (light, sound, etc.) caused by them. For the design of collective spaces, with computational technologies embedded in things, it is necessary to understand the environment as a system in the Social, Physical and Digital dimensions. They exchange information and perceive perturbations, acting on feedback. This vision has contributed to the conception of collective spaces in which the dimensions articulate dynamically in a Socioenative way, with a focus on human relations. The practices of Socially Aware Design (SAwD) is a way of thinking and designing Socionative systems, integrating collective actions and perceptions, between people and computing in spaces where things are organized to create an environment. In this article we present the conguration of a Physical space developed in a hospital to provide a Socioenative environment that has contributed to the construction and theoretical-practical evolution in the studies of socioenation. The design of artifacts to compose environments in the hospital helped to understand the dimensions for application in IoHT (Internet of Human Things) systems as Socioenative Systems from the perspective of SAwD, and to map the dimensions in relation to their composition, characteristics, dynamics and interdependencies, allowing the visualization of their connections.
Testimony and Follow-Up Responses, United States Senate Committee on the Judiciary, Subcommittee on Intellectual Property Hearing on the Digital Millennium Copyright Act at 22: How Other Countries Are Handling Online Piracy

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An integrative multi-dimensional evaluation of Service Ecosystem

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. To meet the customized user demands, service ecosystems begins to emerge with the formation of various IT services collaboration network. However, service ecosystem is a complex social-technology system with the characteristics of natural ecosystems, economic systems and complex networks. Hence, how to realize the multi-dimensional evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this paper proposes a value entropy model to analyze the performance of service ecosystem, which is conducive to integrate evaluation indicators of different dimensions. In addition, a computational experiment system is constructed to verify the effectiveness of value entropy model. The result shows that our model can provide new means and ideas for the analysis of service ecosystem.
Policy Transfer from Simulation to Real World for Autonomous Control of an Omni Wheel Robot

We aim to develop an autonomous mobile robot which supports workers in warehouse to reduce their burden. The robot acquire state-action policy to avoid obstacles and reach a destination by reinforcement learning using LiDAR sensor. In case of real-world application of reinforcement learning, the policy learned previously under simulation environment are generally diverted to real robots because of uncertainties that is unexpected under simulation environment, for example, friction, sensor noise and so on. In this paper, we proposed a method to refine action control of an omni wheel robot by transfer learning on real environment to deal with this problem. We conduct the experiment of searching the route for reaching a goal on real environment using transfer learning's results and verify the effectiveness of the policy acquired.
Non-malleable Codes, Extractors and Secret Sharing for Interleaved Tampering and Composition of Tampering

Non-malleable codes were introduced by Dziembowski, Pietrzak, and Wichs (JACM 2018) as a generalization of standard error correcting codes to handle severe forms of tampering on codewords. This notion has attracted a lot of recent research, resulting in various explicit constructions, which have found applications in tamper-resilient cryptography and connections to other pseudorandom objects in theoretical computer science. We continue the line of investigation on explicit constructions of non-malleable codes in the information theoretic setting, and give explicit constructions for several new classes of tampering functions. These classes strictly generalize several previously studied classes of tampering functions, and in particular extend the well studied split-state model which is a “compartmentalized” model in the sense that the codeword is partitioned a prior into disjoint intervals for tampering. Specifically, we give explicit non-malleable codes for the following classes of tampering functions. Our results are the first explicit constructions of non-malleable codes in any of these tampering models. As applications, they also directly give non-malleable secret-sharing schemes with binary shares in the split-state joint tampering model and the stronger model of affine tampering composed with split-state joint tampering. We derive all these results from explicit constructions of seedless non-malleable extractors, which we believe are of independent interest. Using our techniques, we also give an improved seedless extractor for an unknown interleaving of two independent sources.
Iris: Amortized, Resource Efficient Visualizations of Voluminous Spatiotemporal Datasets

The growth in observational data volumes over the past decade has occurred alongside a need to make sense of the phenomena that underpin them. Visualization is a key component of the data wrangling process that precedes the analyses that informs these insights. The crux of this study is interactive visualizations of spatiotemporal phenomena from voluminous datasets. Spatiotemporal visualizations of voluminous datasets introduce challenges relating to interactivity, overlaying multiple datasets and dynamic feature selection, resource capacity constraints, and scaling. In this study we describe our methodology to address these challenges. We rely on a novel mix of algorithms and systems innovations working in concert to ensure effective apportioning and amortization of workloads and enable interactivity during visualizations. In particular our research prototype, Iris, leverages sketching algorithms, effective query predicate generation and evaluation, avoids performance hotspots, harnesses coprocessors for hardware acceleration, and convolutional neural network based encoders to render visualizations while preserving responsiveness and interactivity. We also report on several empirical benchmarks that demonstrate the suitability of our methodology to preserve interactivity while utilizing resources effectively to scale.
Cross-level Feature Aggregation and Fusion Network for Light Field Salient Object Detection

2D saliency detection algorithms make nothing of 3D visual information of scenes, this leads to their poor performance in challenging scene images. Moreover, light field data contains rich 3D visual information, but existing CNN-based algorithms are specifically designed for processing 2D RGB images rather than light field images. To overcome these issues, in this paper, a cross-level feature aggregation and fusion network is proposed for Light Field Salient Object Detection. To make full use of 3D visual information, two stream sub-network are designed in parallel to handle all-focus images and depth maps separately. Then some feature aggregation modules are built to aggregate cross-level visual features to identify the salient objects in scene. In addition, many feature fusion modules are designed to fuse cross-modal features from all-focus images, focal stack and depth maps, which can highlight salient object consistently by utilizing of 3D visual information. Comprehensive experiments conducted on three benchmark datasets indicate that our algorithm outperforms state-of-the-art methods both quantitatively and qualitatively on five evaluation metrics.
Clustering Preserving Projections for High-Dimensional Data

Abstract In this paper, a novel clustering preserving projection method is presented to retain the cluster (or group) structures hidden in the original data. This method involves two steps: (1) the famous clustering method Fuzzy c-means is utilized to discover the cluster distribution in data; (2) such distribution is preserved by finding a linear embedding that minimizes the intra-cluster compactness in low-dimensional space. The feasibility and effectiveness of the proposed method are demonstrated on UCI dataset and USPS digital handwritten dataset.
On Japanese’s quasi-characters and their representation on computer systems

Japanese’s quasi-characters iejirushi, literally ‘house sign’, are traditional marks or logos for companies in Japan. In most cases, they involve one or a few Japanese characters, combined with other, various, non-character symbols. For different reasons as explained in this article, quasi-characters have been ignored by character encodings such as unifying ones like Unicode or local standards like JIS X 0213, and thus pose an information representation issue for computer systems. In this paper, building on previous works, we first conduct a detailed ontological analysis of Japanese’s quasi-characters in order to identify their characteristics, especially their contents and structures. This is a major part of this work. Second, we derive from this ontological discussion possible representation methods for computer systems and conduct some experiments. The experimental results show significant improvements with respect to quality, practicability and flexibility over conventional approaches.
Synergetic Scheme for Digital Image Watermarking

The proposed scheme uses blind and non-blind digital color image watermarking with hybrid transformation. An image which is binary in nature is embedded in to color image with the use of the technique blind watermarking. The blind watermarking technique makes use of “Discrete Wavelet Transformation” (DWT — 2-level Haar Wavelets). To locate correct place to embed the watermark HL bands are considered. The conversion of spatial domain to frequency is done with Fast Fourier Transformation (2-D FFT). Then with the help of pseudo random sequence, block wise the scanning of host image is done and watermark is also embedded at the same time into predefined binary sequence components. Then non-blind technique embeds first watermarked image into second host image. DMeyer DWT is applied to have approximation coefficients for critical components. The combination of “Discrete Fourier Transformation” (DFT) and “Singular Value Decomposition” (SVD) are applied to get sustainable components of the transformed image. From this combined approach we achieved good fidelity and robustness against – “Rotation”, “Cropping”, “JPEG compression” and for noises “Salt & pepper”, “Gaussian”, “Speckle” and multiple noises.
Iterative Prototyping and Co-design

Within the disciplinary contexts of engineering and human-computer interaction, participatory, user-centered, and co-design methods have long been used to facilitate collaboration between makers and users of technological systems. While approaches such as these are a positive move in the direction of engaged research, our experience in developing and carrying out user-centered design-based projects leads us to believe that one-off engagements among researchers, designers, and stakeholders are insufficient, especially when issues of equality and social justice are at stake. This chapter describes our experiences attempting to facilitate ongoing and interactive relations between makers and users. Specifically, we highlight how the steps of rapid sketching and prototyping in a design process are ideal sites to foster persistent collaborative engagement among researchers, designers, and stakeholders. Our primary goal in encouraging ongoing multifaceted collaboration is to support critical reflection on the social implications of real-world solutions at various steps in order to support more meaningful, just, and equitable outcomes. The key ideas outlined in this chapter are:
Image coding based on Selective Super-Resolution Network

This paper presents an image compression method using deep learning based super-resolution. Different from traditional image compression methods, we propose to select the super resolution network to enhance the perceptual quality. In the proposed method, the network selection map is generated by utilizing the semantic segmentation, image classification, and encoding distortion. Compared with traditional scalable image coding methods, more texture area are visually pleased even in the severe low bitrate encoding. For this reason, our method has the potential to result better compression ratio in terms of subjective image quality. Experimental results demonstrate the effectiveness of the proposed method. When compared with high efficiency video coding (HEVC), our method achieve the average of 43.42% bitrate saving at the same perceptual image quality.
Code-Mixed Sentiment Analysis Using Machine Learning Approach – A Systematic Literature Review

Code-mixed language is ubiquitous. Having been commonly practiced among bilingual communities, code-mixed language has emerged as a common language among social media users. Despite its popularity, the analysis of a code-mixed text is challenging as the text does not typically comply with the monolingual grammar. Therefore, the popularity of social media in the past ten years has raised wide attention to develop methods for analyzing code-mixed text such as extracting popularity sentiment from the text. Machine learning-based classifier such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression have been widely used to analyze the sentiment. This paper intends to further explore machine learning classifiers, their performances, variables, and most common classifiers for the code-mixed sentiment analysis. Prisma Methodology was used in this paper, extracting 12 from 230 papers that met predefined required criteria, including publication year within the last 5 years. Our findings suggested that the most common classifiers found in the papers were Support Vector Machine, Naïve Bayes, and Logistic Regression. By using the accuracy and F1 as the performance measures, the Support Vector Machine exhibited a better performance compared to Naïve Bayes and Logistic Regression. Thus, this study supported the use of Support Vector Machine, Naïve Bayes and Logistic Regression as the main classifiers for the code-mixed sentiment analysis.
Developers experience (DX) in ALM software tools

In recent years, the work in software engineering has demanded professionals with not only technical but also social skills. Besides, these professionals have struggled to handle the different tools which impact straight in their experience. Among the various tools, virtual kanban boards arise as a tool intensely used by developers. However, little has been investigated about the developers' experience (DX) with these tools. This paper aims to examine the DX of software professionals with virtual kanban boards. We conducted focus group sessions with 10 developers to understand the issues they faced when using these tools. In our analysis, we explored the data gathered from the perspectives of how the developers perceive the resources of the virtual kanban tools, feel about their work, and see the value of their contribution. Our results showed that the developers often struggled in understanding the project scope from the virtual kanban boards they used in their workplace.
A Service Selection Framework for Anomaly Detection in IoT Stream Data

Many anomaly detection algorithms have been provided as services for more convenient and efficient utilization in IoT era. Due to concept drift existed in dynamic IoT stream data, it is a changeling task to apply proper anomaly detection services at run time. For effective on-line anomalous data discovery, this paper proposes a service selection framework to dynamically select and configure anomaly detection services. A fast classification model based on XGBoost is trained to identify the pattern of various stream data, so that suitable service can be selected and configured according to the pattern of stream data. Extensive experiments on real and synthetic data sets show that our framework can select suitable service for different scenarios, and the accuracy of the chosen services outperforms state-of-the-art methods.
Fractal Geometry for Early Detection and Histopathological Analysis of Oral Cancer

Oral squamous cell carcinoma (OSCC) has complex molecular structure stimulated by certain chromatin architectural changes which are not easy to be detected by the naked eye. Prediction of OSCC can be done through various clinical and histological factors. As the OSCC treatment is dependent on histological grading the recent focus is to discover the morphological changes studied by computer-assisted image analysis. One of such more specific diagnostic and prognostic factor is the analysis of the fractal geometry. Fractal Dimension (FD) is the analysis and quantification of the degree of complexity of the fractal objects. FD provides a way to precisely analyse the architecture of natural objects. The purpose of this research is to estimate Minkowski fractal dimension of histopathological images for early detection of oral cancer. The proposed model is developed for analysis medical images to estimate Minkowski fractal dimension using a box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of oral cancerous growth.
Adam Karalama Safsatasına Verilen Yanıtların Sav Modelleri: Facebook Haber Yorumlarından Örnekler

News-commenting in social media is a platform that offers an opportunity for online deliberation through argumentative discussion. Yet aggressive exchanges between commenters with clashing ideologies have become a prominent feature of online news-commenting. One example of the aggressive exchanges is the use of direct personal attacks among news-commenters, namely abusive ad hominem. In this paper, I aim to reveal the patterns of responses given to abusive ad hominem attacks by studying the comments to news items in Facebook. The reason is to shed light on how the discussion evolves after the ad hominem attack. The patterns that this paper illustrates are a summary of studying the responses to 20 ad hominem attacks that figure in the comments to news items topicalizing various social problems in Turkey. The examples were drawn from those that topicalise ‘violence against women in Turkey’. Three patterns were identified: (1) abusive ad hominem as a response to an abusive ad hominem attack; (2) refusing to carry on the discussion; and (3) critically evaluating the abusive ad hominem attack. These patterns show that the pragma-dialectical definition of the ad hominem fallacy proves to be functional in understanding its role in blocking the way to the resolution.
Digital Signal Modulation Classification using Data Conversion Method based on Convolutional Neural Network

Abstract Automated modulation classification (AMC) plays a very important part in cognitive electronic warfare. Deep learning (DL) is a new machine learning (ML) method, which has been successfully implemented in many application fields. This paper proposes a new data conversion algorithm in order to gain a better classification accuracy of target signal modulation. Then a Convolutional Neural Network (CNN) architecture is developed, and its performance is proved to be better than the traditional data conversion method. In addition, the impacts of representation on classification performance are also analysed. Experimental results demonstrate the significant performance advantage of the proposed data conversion method using DL-based approach for modulation classification.
Arm Hypervisor and Trustzone Alternatives

Many scenarios such as DRM, payments, and homeland security require a trusted and verified trusted execution environment (TEE) on ARM. In most cases such TEE should be available in source code mode. The vendor cannot conduct code review and ensure that the operating system is trustworthy unless source code is available. Android and other rich execution environments (REEs) support various TEE implementations. Each TEE implementation has its own unique way of deploying trusted applications and features. Most TEEs in ARM can be started at TrustZone™ or Hyp (Hypervisor) mode. Choosing a proper TEE operating system can be a problem for trusted application developers and hardware vendors. This article discuss the hypervisor vs. TrustZone™ implementation dilemma. Furthermore, the article surveys multiple ARM TrustZone™ TEE solutions and ARM virtualization solutions that are available today with source code. This article allows IoT vendors and SoC manufacturer to select a suitable TEE for their platform needs based on their criteria.
Visualizing Vietnam’s Scientific Research Projects Based on Pre-trained Language Models and UMAP

This paper presents a method for vector representations and dimensionality reduction of documents using pretrained language models and Uniform Manifold Approximation and Projection (UMAP). The method aims at visualizing Vietnam’s scientific research projects in order to help searching for, as well as exploring, similar projects given a new proposal or research topic. First, documents are vectorized using a pretrained language model. Then, the obtained document vectors are projected onto a two-dimensional space using UMAP. Given a query, it is also passed through two steps as a document. In the two-dimensional space, each document is represented as a circle and the nearest circles are, the more similar the corresponding documents are. We consider the abstract or title of a project as its representative and call each as a document. We conduct experiments in order to compare the representation power of multilingual BERT-base and PhoBERT by training classifiers using softmax, support vector machines, and multilayer perception; and visualizing the representations using PCA, t-SNE and UMAP, respectively. The experimental results show the representation power of PhoBERT is better than that of multilingual BERT-base and UMAP is superior to PCA and t-SNE. We also present a visualizing tool allowing human intervention in similarity search.
Non-parallel and many-to-many voice conversion using variational autoencoders integrating speech recognition and speaker verification

We propose non-parallel and many-to-many voice conversion (VC) using variational autoencoders (VAEs) that constructs VC models for converting arbitrary speakers' characteristics into those of other arbitrary speakers without parallel speech corpora for training the models. Although VAEs conditioned by one-hot coded speaker codes can achieve non-parallel VC, the phonetic contents of the converted speech tend to vanish, resulting in degraded speech quality. Another issue is that they cannot deal with unseen speakers not included in training corpora. To overcome these issues, we incorporate deep-neural-network-based automatic speech recognition (ASR) and automatic speaker verification (ASV) into the VAE-based VC. Since phonetic contents are given as phonetic posteriorgrams predicted from the ASR models, the proposed VC can overcome the quality degradation. Our VC utilizes d-vectors extracted from the ASV models as continuous speaker representations that can deal with unseen speakers. Experimental results demonstrate that our VC outperforms the conventional VAE-based VC in terms of mel-cepstral distortion and converted speech quality. We also investigate the effects of hyperparameters in our VC and reveal that 1) a large d-vector dimensionality that gives the better ASV performance does not necessarily improve converted speech quality, and 2) a large number of pre-stored speakers improves the quality.