Reviewerly/exHarmony huggingface dataset:
A Code-Diverse Tulu-English Dataset For NLP Based Sentiment Analysis Applications

Due to expanded praxis of social media, there is an elevated interest in the Natural Language Processing (NLP) of textual substance. Code swapping is a ubiquitous paradox in multilingual nation and the social communication shows mixing of a low resourced language with a highly resourced language mostly written in non-native script in the same text. It is essential to refine the code swapped text to support distinctive NLP tasks such as Machine Translation, Automated Conversational Systems and Sentiment Analysis (SA). The preeminent objective of SA is to identify and analyze the attitude, opinion, emotion or the sentiment in the dataset. Though there are multiple systems skilled on monodialectal dataset, all of them break down when it comes for code-diverse data because of the heightened intricacy of blending at various standards of text. Nonetheless, there exist a smaller number of assets for modelling such definitive code-mixed data and the Machine Learning or the Deep Learning algorithms enforcing supervised learning approach yield the better results compared to the unsupervised learning. Such datasets are available for Hindi-English, Tamil-English, Malayalam-English, Bengali-English, German-English, Spanish-English, Japanese-English, Arabic-English etc. Though our research is concentrated towards NLP for emotion and sentiment detection of Tulu, a vibrant south Indian language, to start with, we build the first ever platinum standard corpus for NLP applications of code-diverse text in Tulu-English, as there is no such resource in our native language. The performance analysis of our dataset through Krippendorff&#x2019;s Alpha value of 0.9 indicates that it is a benchmark in development of Automatic Sentiment Analysis system for Tulu.
EDU_INSIGHTS: The Analytic Tools to Assess Students' Satisfaction Level in Open and Distance Learning (ODL)

Open and distance learning (ODL) has become a new norm in higher institutions due to the Covid-19 outbreak. The implementation of ODL is grounded in an internet-based teaching system that enables university students to learn through a virtual platform. However, to date, the tool to assess the ODL performance from the students' perspectives is insufficient to improve the service quality. In this regard, the objective of this study is to develop an analytic tool using sentiment analysis techniques in evaluating the ODL performance from the students' perspective. To fulfill that, a new innovative sentiment tool namely EDU_INSIGHTS was developed and tested among the students from Universiti Teknologi MARA (UiTM) Kedah, Malaysia. A sample of questions was distributed to capture, analyze and visualize the real-time data on students' perspectives towards ODL. Subsequently, the sentiments were visualized in terms of the chart and word clouds. Consequently, this beneficial tool assists the top management of the university in assessing the performance of ODL for a better learning experience in the future.
Semiotics of Transaction in Digital Age

Semiotics of transaction is presented here as a new approach, oriented towards the semioticSemiotics mechanisms of the transaction of economic valueEconomic value. After analyzing the nature of the main semioticSemiotics device for value transactions—the moneyMoney sign—we have identified in its core the trustTrust in the future of the economic system of exchangeExchange and the scarcityScarcity of its availability as a condition for its value and meaning. Such characteristics brought us to search for theoretical support in the existential analytics of temporalityTemporality devised by Heidegger, where we found homology between economic scarcityScarcity and the thesis of the primordial finitudeFinitude of temporalityTemporality, which is a scarcity of timeScarcity of time. Thus we have distinguished two levels for the semioticSemiotics analysis of transactions—one deep/authentic level of the primordial formation of the value and one superficial level of the commercialCommercial practices of market exchangeExchange. From this point of view, the new digital economyDigital economy seems to bring closer the two levels, transforming the availability of timeTime for each person into the major economical resourceResources.
Why is Your Trojan NOT Responding? A Quantitative Analysis of Failures in Backdoor Attacks of Neural Networks

Backdoor has offered a new attack vector to degrade or even subvert deep learning systems and thus has been extensively studied in the past few years. In reality, however, it is not as robust as expected and oftentimes fails due to many factors, such as data transformations on backdoor triggers and defensive measures of the target model. Different backdoor algorithms vary from resilience to these factors. To evaluate the robustness of backdoor attacks, we conduct a quantitative analysis of backdoor failures and further provide an interpretable way to unveil why these transformations can counteract backdoors. First, we build a uniform evaluation framework in which five backdoor algorithms and three types of transformations are implemented. We randomly select a number of samples from each test dataset, and then these samples are poisoned by triggers. These distorted variants of samples are passed to the trojan models after various data transformations. We measure the differences of predicated results between input samples as influences of transformations for backdoor attacks. Moreover, we present a simple approach to interpret the caused degradation. The results as well as conclusions in this study shed light on the difficulties of backdoor attacks in the real world, and can facilitate the future research on robust backdoor attacks.
Design and implementation of knowledge graph of listed companies based on Neo4j

In the era of big data, the topic, how to use modern technology to build a suitable and perfect database for a specific demand, has become heated-discussed. The expectation given to databases is that database can organize the data, correlate the different data representations and mine data value. These requirements are in line with the original design intent of Neo4j graph database. This paper constructs the knowledge graph of listed companies based on Neo4j. The result shows that the knowledge graph, as a powerful form of graphical database, can sort out and mine the relationship between data. At the same time, it also provides a reliable system for the public to learn and understand all kinds of information of listed companies.
The Diagnosis Model of Line Loss Abnormity in Low-voltage Distribution Network Based on The Visualization Technology of Situation Awareness

This paper proposes a diagnosis model based on situation awareness for the diagnosis and analysis of the type of line loss anomalies in low-voltage distribution network (LVDN). First, sort out all the abnormal line loss causes, and divide them into 12 categories and 46 sub-categories; secondly, use situation awareness technology to generate situation base maps based on distribution network data, and use three-phase line loss calculations and situation base map rendering. After that, cluster analysis and mining of the abnormal situation map of the distribution network line loss, and obtain the abnormal situation map of the typical distribution network line loss, which is convenient for the subsequent rapid detection of the abnormal type of line loss; finally, build the abnormal line loss display of the distribution network based on the compass layout. The model is more conducive to system operators to clearly grasp the abnormal situation of the low-voltage distribution network. The effectiveness of the diagnostic model is verified by an example.
Android Application for Effective Timing Management of Classes

Since mobile phones are popular among us and used in our day-to-day life very frequently, we have attempted to create a timetable management Android application that the students can easily access through their smart phones. Although there are many mobile platforms available in the market these days, Android OS is the most user friendly and programmerfriendly platform. This application will make more comfortable students' study life. This application that we are discussing here is the Semester Scheduler. Using this app, student will be able to maintain their class timetable and make changes in their plan according to that. Using this Android app, students as well as faculty members will be able to know their scheduled class time whenever they are in need. To make this table more informative, all the information’s has been represented in an understandable manner.
Automatic Generation of Metaheuristic Algorithms

Designing a heuristic algorithm to solve an optimization problem can also be seen as an optimization problem. Such a problem seeks to determine the best algorithm contained in the search space. The objective function corresponds to the computational performance of the algorithm measured in terms of computational time, complexity, number of instructions or number of elementary operations. The automatic design of algorithms has been explored for several combinatorial optimization problems. In this work, we extend this exploration towards the automatic design of metaheuristics to find solutions for the traveling salesman problem. The process is carried out by genetic programming. The resulting algorithms are combinations of well-known metaheuristics and, in some cases, present better computational performance than the existing algorithms for the set of selected test instances.
Active learning with logistic models featuring simultaneous variable and subject selection

Modern computing and communication technologies can make data collection procedures very efficient, while our ability to analyze and extract information from large data sets is hard-pressed to keep up with our capacity for data collection. If we are interested in learning a classification/prediction rule that was not considered in the original data collection procedure, then there is usually a lack of label information in the original data. Thus, how to start with minimum number of labeled data and aggressively selected most informative samples for being labeled becomes an important issue. The thoughts of active learning, with subjects to be selected sequentially without using label information, is a possible outlet for this situation. In addition, if we can identify variables, from the lengthy variable list of the data set, for constructing an effective classification rule with good interpretation ability will be better. Here, we propose an active learning procedure targeted the maximum area under the receiver operating characteristic curve via a commonly use logistic model, which can sequentially select the effective informative subjects and variables, simultaneously. We discuss the asymptotic properties of the proposed procedure and illustrate it with some synthesized and real data sets.
Computational Models of Readers' Apperceptive Mass

Recent progress in machine-learning-based distributed semantic models (DSMs) offers new ways to simulate the apperceptive mass (AM; Kintsch, 1980) of reader groups or individual readers and to predict their performance in reading-related tasks. The AM integrates the mental lexicon with world knowledge, as for example, acquired via reading books. Following pioneering work by Denhière and Lemaire (2004), here, we computed DSMs based on a representative corpus of German children and youth literature (Jacobs et al., 2020) as null models of the part of the AM that represents distributional semantic input, for readers of different reading ages (grades 1-2, 3-4, and 5-6). After a series of DSM quality tests, we evaluated the performance of these models quantitatively in various tasks to simulate the different reader groups' hypothetical semantic and syntactic skills. In a final study, we compared the models' performance with that of human adult and children readers in two rating tasks. Overall, the results show that with increasing reading age performance in practically all tasks becomes better. The approach taken in these studies reveals the limits of DSMs for simulating human AM and their potential for applications in scientific studies of literature, research in education, or developmental science.
An Integrated approach for smart object recognition

Detecting the humans and their activities is always an interesting domain to work with, as not all humans react or respond the same way to a situation it varies from person to person. When detecting the humans and their activities in the normal scenario is being a challenging part, the challenge increases, even more, when there is a panic situation they are facing. The one such panic situation often faced is the disaster. There are many proposed technologies and the government is trying to get the help of modern technology to save the lives of humans who got stuck in the damages caused by the disaster as a timely rescue can save the lives of alive humans. Human detection ideology is becoming a major study in all technologies like image processing, IoT (Internet Of Things), Robotics and many more. The major aim of this paper is to discuss upon the pros and cons of the proposed system and provide a solution to the challenges being faced.
Reversible Data Hiding Based on Adaptive Ipvo and Two-Segment Pairwise Pee

Article historyImproved pixel value order (IPVO) and pairwise prediction error extension (PEE) strategies can effectively improve the hidden capacity and invisibility of reversible data hiding (RDH) scheme since they can increase the effective prediction error rate (EPER). To further improve EPER, this paper proposes an improved RDH scheme by combining adaptive IPVO (AIPVO) with two-segment pairwise PEE. Different from the fixed predicted value (such as the highest value) for all blocks in the existing IPVO, AIPVO adaptively determine the predicted value of each block according to its own complexity level. To obtain the complexity level of each block, the block complexity level division model is constructed and optimized with EPER as the objective function. AIPVO can effectively improve EPER due to the fact that the pixels with large fluctuate in a block are eliminated by adaptively selecting the predicted value. In the embedding stage, the two-segment pairwise PEE is designed according to the distribution characteristics of the prediction error sequence obtained by AIPVO. Specifically, the prediction error sequence is firstly divided into two segments, and the 2D mappings of the front and back parts use small-magnitude and slightly large-magnitude prediction errors to embed data, respectively.
Virtual Garden

short-paper Virtual Garden: Digital Memory Share on Author: Vasileios Bouzas Department of Fine and Applied Arts, School of Fine Arts, University of Western Macedonia, Greece Department of Fine and Applied Arts, School of Fine Arts, University of Western Macedonia, GreeceSearch about this author Authors Info & Claims ARTECH 2021: 10th International Conference on Digital and Interactive ArtsOctober 2021 Article No.: 92Pages 1–4https://doi.org/10.1145/3483529.3483739Online:20 February 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Roberta-based model for identifying non-substantive factual elements of the case

Legal documents contain rich information of case elements, and the automatic extraction of case elements can effectively promote the development of China's judicial intelligence field. In this paper, a pre-trained multi-label text classification model based on the RoBERTa model domain is designed to identify non-substantive factual elements in legal documents. The model uses weighted summation in the text encoding layer to get the RoBERTa encoding output and employs a multi-headed attention mechanism to capture factual descriptions and association information between elements. The critical fact elements are identified by Transformer secondary encoding and CNN extraction of critical features. The experimental results at CAIL2019 show that the model proposed in this paper has substantial performance improvement compared to other baseline models, with Macro-F1 and Micro-F1 values improving by 5.04% and 6.43%, respectively to the traditional Roberta model on the three datasets. It indicates that the model can capture richer labeling features and thus effectively identify the non-substantive core elements of legal documents.
Multi-Band Texture Modeling Using Mixture of Multivariate Generalized Gaussians and Applications

In this paper, we present a unified statistical model for multivariate and multi-modal sub-band distribution in multi-resolution transforms of color-texture images. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables accurate representation of joint statistics of different sub-bands. The MoMGG not only enables to express correlation between sub-bands at different scales and orientations, but also between adjacent locations within the same sub-bands to better describe the spatial texture layout. Moreover, it enables to combine different multi-scale transforms (e.g., contourlets,wavelets) to build a richer and more representative texture signature. We successfully applied our model to color-texture image retrieval and reconstruction where promising results have been obtained comparatively to state-of-art methods.
Analysis of Emoji Generation Based on DCGAN Model

Deep Convolution Generative Adversarial Networks consist of a generative neural network and a discriminative neural network. The generator's objective is to learn the features of existing images and output fake samples that imitate the training data. At the same time, the discriminator attempts to discriminate the generated images from the real training data. However, the training process of the model is unstable due to the simultaneous training of two adversarial networks: the modification of one network means changing the entire model. To further understand this issue, this paper constructed a DCGAN model, which can be utilized to generate fake cartoon emojis. This paper proposed a generative neural network which generates image samples from a random input. The output of the generator and the training data, in this case the emoji images, are then given to a discriminative neural network which classifies its input as fake or real. The loss of the discriminator is then calculated based on its precision, while the loss of the generator is calculated based on the output of the discriminator. If the discriminator correctly recognizes the generated images as fake, then the loss of the generator is high; otherwise, the loss of the generator is low. The process of backpropagation updates the parameters in the discriminator and the generator using gradient descent, improving their outputs and minimizing their loss functions. As for the results, the model successfully learned the crucial features of the training data and converted the random noise to emoji images.
Avoided Level Crossing (Entangled State) Engineering in the 4coupled Qubits System

This article studies the behavior of the avoided level crossing in the 4-coupled qubits and mainly focuses on how to engineer it. This phenomenon occurs due to the two transitions out of the ground state in a two-coupled qubits which is contributed to the entangled states. To engineer this unique behavior, a quantum circuit containing 4-qubits is designed and its quantum Hamiltonian and dynamic equation of the motion are theoretically derived. The results show that the avoided level crossing appears in the photonic mode entanglement which means that there are two or more transitions out of the ground state to the multiple excited states for each bias current. However, an interesting point is that when only a qubit is driven by an incident radio-frequency wave, the avoided level crossing just occurs for the qubits connected capacitively to the driven qubit, not for all.
Evaluation of different convolutional neural network encoder-decoder architectures for breast mass segmentation

In this work, we study convolutional neural network encoder-decoder architectures with pre-trained encoder weights for breast mass segmentation from digital screening mammograms. To automatically detect breast cancer, one fundamental task to achieve is the segmentation of the potential abnormal regions. Our objective was to find out whether encoder weights trained for breast cancer evaluation in comparison to those learned from natural images can yield a better model initialization, and furthermore improved segmentation results. We applied transfer learning and initialized the encoder, namely ResNet34 and ResNet22, with ImageNet weights and weights learned from breast cancer classification, respectively. A large clinically-realistic Finnish mammography screening dataset was utilized in model training and evaluation. Furthermore, an independent Portuguese INbreast dataset was utilized for further evaluation of the models. 5-fold cross-validation was applied for training. Soft Focal Tversky loss was used to calculate the model training time error. Dice score and Intersection over Union were used in quantifying the degree of similarity between the annotated and automatically produced segmentation masks. The best performing encoder-decoder with ResNet34 encoder tailed with U-Net decoder yielded Dice scores (mean±SD) of 0.7677±0.2134 for the Finnish dataset, and ResNet22 encoder tailed with U-Net decoder 0.8430±0.1091 for the INbreast dataset. No large differences in segmentation accuracy were found between the encoders initialized with weights pre-trained from breast cancer evaluation, and of those from natural image classification.
Fuzzy Logic Based self-switching multi-strategic pedagogical agent

The Self-Switching Multi-Strategic Pedagogical Agent is designed following the Autonomic Computing and the Component-Based Agents approaches. It is comprised of two sub-components: Manager (MrSS) and Managed (MdSS) Sub-Systems. These sub-systems are built independently, but they constantly interact with one another. The Multiple Pedagogical Strategies (MPSs) that are used by the system are implemented by MdSS. While the Switching Logic (SL), that is used to (re)conFigure the MdSS, is implemented by the MrSS as an Autonomic Control Loop (ACL). The MPSs and the SL permit to trigger the appropriate PS regarding the crisp values of the Student Knowledge Level (SKL). However, the SKL is characterized by a high degree of uncertainty. To overcome this deficiency, we integrate fuzzy control in the MrSS to handle the uncertainty and consequently improve the strategy selection process. Fuzzy MrSS links the different context parameters and SKL through fuzzy rules taking into account the system&#x2019;s fuzziness and uncertainties. We will present experiments carried with the Math sub-domain.