Reviewerly/exHarmony huggingface dataset:
CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Texts

The task of automatically identifying a language used in a given text is called Language Identification (LI).India is a multilingual country and many Indians especially youths are comfortable with Hindi and English, in addition to their local languages.Hence, they often use more than one language to post their comments on social media.Texts containing more than one language are called "code-mixed texts" and are a good source of input for LI.Languages in these texts may be mixed at sentence level, word level or even at sub-word level.LI at word level is a sequence labeling problem where each and every word in a sentence is tagged with one of the languages in the predefined set of languages.For many NLP applications, using code-mixed texts, the first but very crucial preprocessing step will be identifying the languages in a given text.In order to address word level LI in code-mixed Kannada-English (Kn-En) texts, this work presents i) the construction of code-mixed Kn-En dataset called CoLI-Kenglish dataset, ii) code-mixed Kn-En embedding and iii) learning models using Machine Learning (ML), Deep Learning (DL) and Transfer Learning (TL) approaches.Code-mixed Kn-En texts are extracted from Kannada YouTube video comments to construct CoLI-Kenglish dataset and code-mixed Kn-En embedding.The words in CoLI-Kenglish dataset are grouped into six major categories, namely, "Kannada", "English", "Mixed-language", "Name", "Location" and "Other".Code-mixed embeddings are used as features by the learning models and are created for each word, by merging the word vectors with sub-words vectors of all the sub-words in each word and character vectors of all the characters in each word.The learning models, namely, CoLI-vectors and CoLI-ngrams based on ML, CoLI-BiLSTM based on DL and CoLI-ULMFiT based on TL approaches are built and evaluated using CoLI-Kenglish dataset.The performances of the learning models illustrated, the superiority of CoLIngrams model, compared to other models with a macro average F1-score of 0.64.However, the results of all the learning models were quite competitive with each other.
MechSense: A Design and Fabrication Pipeline for Integrating Rotary Encoders into 3D Printed Mechanisms

We introduce MechSense, 3D-printed rotary encoders that can be fabricated in one pass alongside rotational mechanisms, and report on their angular position, direction of rotation, and speed. MechSense encoders utilize capacitive sensing by integrating a floating capacitor into the rotating element and three capacitive sensor patches in the stationary part of the mechanism. Unlike existing rotary encoders, MechSense does not require manual assembly but can be seamlessly integrated during design and fabrication. Our MechSense editor allows users to integrate the encoder with a rotating mechanism and exports files for 3D-printing. We contribute a sensor topology and a computational model that can compensate for print deviations. Our technical evaluation shows that MechSense can detect the angular position (mean error: 1.4°) across multiple prints and rotations, different spacing between sensor patches, and different sizes of sensors. We demonstrate MechSense through three application examples on 3D-printed tools, tangible UIs, and gearboxes.
LEARNING THE STRUCTURE OF BAYESIAN NETWORKS FROM INCOMPLETE DATA USING A MIXTURE MODEL

In this paper, we provide an approach to learning optimal Bayesian network (BN) structures from incomplete data based on the BIC score function using a mixture model to handle miss- ing values. We have compared the proposed approach with other methods. Our experiments have been conducted on different models, some of them Belief Noisy-Or (BNO) ones. We have performed experiments using datasets with values missing completely at random having differ- ent missingness rates and data sizes. We have analyzed the significance of differences between the algorithm performance levels using the Wilcoxon test. The new approach typically learns additional edges in the case of Belief Noisy-or models. We have analyzed this issue using the Chi-square test of independence between the variables in the true models; this approach reveals that additional edges can be explained by strong dependence in generated data. An important property of our new method for learning BNs from incomplete data is that it can learn not only optimal general BNs but also specific Belief Noisy-Or models which is using in many applica- tions such as medical application.
Exploring Augmented Reality Waste Data Representations for Eco Feedback.

In this demo, we show how Augmented Reality can be used to visualize waste accumulation data in an engaging and visceral way. The negative impact humans have on the environment is partly caused by thoughtless consumption leading to unnecessary waste. A likely contributing factor is the relative invisibility of waste: waste produced by individuals is either out of their sight or quickly taken away. Nevertheless, waste disposal systems sometimes break down, creating natural information displays of waste production that can have educational value. We take inspiration from such natural displays and introduce a class of situated visualizations we call augmented-reality waste accumulation visualizations or ARwavs, which are literal representations of waste data embedded in users’ familiar environment. We implemented examples of ARwavs and will present them at the venue.
Secure and efficient online data sharing scheme using broadcast encryption with personalised message system

Abstract In the healthcare industry, online data sharing has become increasingly important, but it also raises significant security concerns. To address these concerns, we propose a secure and efficient online data sharing scheme that uses a cryptographic primitive called broadcast encryption with personalised message system. This notion is applicable to many realistic broadcast services that supports IoT and cloud technology. Specifically, in our system, the data owner creates personalized data for each legitimate user in a group and then transmits a common encrypted data for the entire group in a way that completely hides the subscribed user set from outsiders. Only authorized users can recover personalised data, while all authorized consumers can decrypt the common data. Proposed scheme employs shamir secret sharing scheme to fairly distribute and reconstruct secret among various set up authority to prevent the insider attack from malicious setup authority.We highlight the security consideration in such a scenario and capture the security requirements in the security model. Subsequently, we present a concrete scheme, which is proven CPA secure under the decisional bilinear Diffie-Hellman exponent assumption. In addition, it uses asymmetric pairings whereas rest of the schemes used symmetric pairings, which in turn leads to the smaller and constant compact size ciphertext, constant decryption keys, reduced channel requirement, prevent fault attacks on symmetric bilinear map, and, enhanced performance.
Method comparison of Naïve Bayes, Logistic Regression, and SVM for Analyzing Movie Reviews

A film can be categorized as a successful film based on the reviews given by the critics. The reviews can range from professional critics to public reviews from the general audience. Due to a large number of reviews and opinions on a film, this study aims to create a sentiment analysis model and compare the methods used to analyze datasets from a movie review. Sentiment Analysis is a method for studying and analyzing opinions, then classifying these opinions into several classes. This research will use the Naïve Bayes method, Logistic Regression, and Support Vector Machine (SVM) to analyze film review data. The film review dataset used is a collection of film reviews taken from the Rotten Tomatoes website and will be pre-processed before implementing the Naïve Bayes, Logistic Regression, and SVM methods. The SVM classifier with 80:20 data splitting has the best performance, with a result of 99.4% accuracy score and 93.5% F1 score.
The EmpathiCH Workshop: Unraveling Empathy-Centric Design

EmpathiCH aims to bring together and blend a diverse set of expertise to develop a new research agenda in the context of "Empathy-Centric Design". Building on the discussions that emerged in the previous edition, the main research objective is to form a comprehensive and coherent framework that utilizes empathy as a new dimension of human-factors research and practice. We aim to consolidate the existing theoretical and conceptual constructs of empathy from diverse domains to reflect on its temporality, materiality, and the risks related to its instrumentalization. With a mix of author panels, expert discussion, and interactive activities, we aim to make this workshop the ideal venue to foster collaboration, expand the community, and shape the future direction of "Empathy-Centric Design".
Foundations of artificial intelligence and machine learning

Artificial Intelligence (AI) is an interdisciplinary field of study that focuses on building machines that are able to think and, in particular, act in an intelligent manner. In this context, it is preferable to characterise intelligence as the ability to accomplish complex tasks, as opposed to anchoring this concept on the notion of human intelligence or thought. Machine Learning (ML) is a subfield of AI that encompasses software that improves with experience. A variety of teaching methods can be used to train ML algorithms, including supervised learning, unsupervised learning and reinforcement learning. In turn, Deep Learning (DL) is a subfield of ML that uses deep neural networks (DNN) to identify useful patterns in the input data. On the technical side, this chapter draws a distinction between artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial superintelligence (ASI). All current instances of AI fall within the realm of ANI, but the increasing power of learning algorithms is gradually shifting the balance towards the AGI paradigm. We also explored the distinction between weak AI and strong AI based on consciousness, though concerns have been raised about the relevance and falsifiability of these concepts. AI is already playing an important role in the financial industry, including in the banking, investing and insurance sectors. However, the journey towards widespread adoption has been bumpy and halted by several roadblocks. An appreciation of key historical milestones helps to put the technology’s achievements into perspective and understand some of the future directions that AI research may take.
Assessing Virtual Reality Spaces for Elders Using Image-Based Sentiment Analysis and Stress Level Detection

Seniors, in order to be able to fight loneliness, need to communicate with other people and be engaged in activities to keep their minds active to increase their social capital. There is an intensified interest in the development of social virtual reality environments, either by commerce or by academia, to address the problem of social isolation of older people. Due to the vulnerability of the social group involved in this field of research, the need for the application of evaluation methods regarding the proposed VR environments becomes even more important. The range of techniques that can be exploited in this field is constantly expanding, with visual sentiment analysis being a characteristic example. In this study, we introduce the use of image-based sentiment analysis and behavioural analysis as a technique to assess a social VR space for elders and present some promising preliminary results.
Text Classification Method Based on PEGCN

The purpose of text classification is to label the text with known labels. In recent years, the method based on graph neural network (GNN) has achieved good results. However, the existing methods based on GNN only regard the text as the set of co-occurring words, without considering the position information of each word in the statement. Meanwhile, this method mainly extracts node features, but neglects the use of edge features between nodes. To solve these problems, a new text classification method, graph convolutional network using positions and edges (PEGCN), is proposed. In the word embedding section, a positional encoding input representation is employed to enable the neural network to learn the relative positional information among words. Meanwhile, the dimension of the adjacency matrix is increased to extract the multi-dimensional edge features. Through experiments on multiple text classification datasets, the proposed method is shown to be superior to the traditional text classification method, and has achieved a maximum improvement of more than 4%.
AI-enabled Decision Aid and Decision Support for Symbiotic Autonomous Systems

Symbiotic autonomous systems (SAS) are meant as advanced intelligent information systems which involve a synergistic cooperation and collaboration between a part of broadly perceived autonomous systems and a cognition based part, related to the human stakeholder, and the two parts should operate in a symbiosis. This combination should guarantee an added value in terms of the effectiveness and efficiency of the systems' operation and, above all, problem solving in which a considerable part is related to decision making, also profiting from some added values implied by, for instance, collective intelligence and behavior. A growing complexity of the world implies a growing complication of decision making problems, for instance, related to more and more stakeholders, much uncertain, imprecise and lacking information, and an explicit human centricity, that is, a crucial role of the human being in all decision processes. This all complicated these processes in particular in the context of the SASs. We are concerned with an effective and efficient setting, at least now and the near future, to run such decision processes in which the human being is still a decisive stakeholder but he/she should be aided or supported by some additional “units”, humans (e.g. advisors) or “machines” (e.g. algorithms and computer systems); the human being should not be replaced, at least for dealing with sophisticated tasks. We first consider the case when a human decision maker, who knows about the domain of his activities but not necessarily about solution tools, is aided by a domain expert who knows solution tools but not necessarily the domain. We advocate the use of the judge-advisor type approaches. They are augmented with some AI (artificial intelligence) tools and techniques, notably machine learning models which can help find patterns and relationships in bigger and bigger data sets. Moreover, we deal with the consistency of the AI models employed with some inherent human characteristics, the cognitive biases, and show that this is crucial for a synergistic collaborative partnership between the human being and AI models and systems, notably operating autonomously. We also show how such a synergistic cooperation and collaboration can be implemented by employing a decision support system (DSS) to combine the power of the computer and the power of the humans, notably using the data or document driven DSSs. This gives rise to a so-called AI-enabled DSSs which employ advanced tools and techniques of AI (artificial intelligence) for the gathering and analysis of data and evidence, identification and diagnosis of problems, proposing courses of actions with their analyses, etc. to better “mimic” human cognitive capabilities, observe some inherent human characteristic features, provide comprehensive and trustworthy procedures and forms of results, and - in general - help alleviate limitation of a sheer autonomy of the system. Some examples on supporting decision making in socio-economic planning will be presented.
Computational efficiency of interpolated band stop filters for odd spectral bands

Interpolated band stop FIR filters are excluded. A block diagram of an interpolated band stop FIR filter for odd spectral regions is proposed. The use of the proposed maximum allowable value of the filter coefficient in comparison with the reference filter calculated by the classical method, namely the maximum allowable value of the quantitative limitation to the computing resource. For the proposed structure, relations are obtained to determine the coefficient of computational efficiency and the coefficient of increase in the number of registers. In the proposed work, the following values were determined: the optimal interpolation coefficient, the maximum coefficient of computational efficiency and the coefficient of increase in the number of registers at the optimal interpolation coefficient. In the obtained relations for the synthesis of the interpolated band-stop FIR filter, the values of the stop band and the central frequency of the rejection are used. We also compared the interpolated band stop filter for odd spectral zones, synthesized according to the proposed structure, and the reference band stop filter calculated by the classical method. As a result of the study, it was found that the proposed nature is used under specific conditions. The obtained structure of the interpolated band stop FIR filter is optimal from a computational point of view, however, the calculated structure is not universal. The main limitation for the application of the resulting structure is the discreteness of switching between spectral zones and the need for fine tuning of the center rejection frequency.
ANALISIS PELAKSANAAN KURIKULUM 2013 DALAM PEMBELAJARAN DI KELAS I SDN 05 KECAPI JEPARA

This study aims to discuss whether or not there are changes in the implementation of Curriculum 2013 and how to apply Curriculum 2013 in Class I learning at SDN 05 Kecapi Jepara. This research is a descriptive qualitative research with the research subjects of students and full professors of class I SDN 05 Kecapi. Research data obtained through interviews, observations and documentation. The data that has been collected will then be analyzed through data reduction, data presentation and conclusions. The results showed that the implementation of the 2013 curriculum in the I SDN 05 Kecapi class changed slightly, where learning was not carried out in an integrated thematic way, but separately between lessons and the use of the learning were also separated. However, the essence of K13 is still felt in the implementation of learning, where teaching materials always refer to K13. A student-centered scientific approach is also applied to classroom learning I at SDN 05 Kecapi, so that students remain active while learning. Student activity is highly visible when the teacher implements the learning model using digital cards, where students enthusiastically answer questions and practice in front of the class. The teacher also shows an assessment that includes cognitive (test), affective (observation) and psychomotor (practice) aspects.
Improved Face Recognition Rate Using Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have shown good performance in the domain of face recognition due to their capability of extracting discriminative features. In this paper, we present a face recognition system where a Multi-Task Convolutional Neural Network (MTCNN) is employed for face detection and preprocessing. Afterwards, we use the proposed model of CNN with optimization and a softmax function as a classifier for recognition. Experiments have been carried out on the ORL face database, which consists of 400 images for 40 classes. The results of the implementation illustrate that our model has achieved better performance compared to most of the state-of-the-art models, with an accuracy rate of 97.50%.
Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization

The K Nearest Neighbors (KNN) classifier is widely used in many fields such as fingerprint-based localization or medicine. It determines the class membership of unlabelled sample based on the class memberships of the K labelled samples, the so-called nearest neighbors, that are closest to the unlabelled sample. The choice of K has been the topic of various studies and proposed KNN-variants. Yet no variant has been proven to outperform all other variants. In this paper a KNN-variant is discussed which ensures that the K nearest neighbors are indeed close to the unlabelled sample and finds K along the way. The algorithm is tested and compared to the standard KNN in theoretical scenarios and for indoor localization based on ion-mobility spectrometry fingerprints. It achieves a higher classification accuracy than the KNN in the tests, while having the same computational demand.
Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis

The duality of content and style is inherent to the nature of art. For humans, these two elements are clearly different: content refers to the objects and concepts in the piece of art, and style to the way it is expressed. This duality poses an important challenge for computer vision. The visual appearance of objects and concepts is modulated by the style that may reflect the author's emotions, social trends, artistic movement, etc., and their deep comprehension undoubtfully requires to handle both. A promising step towards a general paradigm for art analysis is to disentangle content and style, whereas relying on human annotations to cull a single aspect of artworks has limitations in learning semantic concepts and the visual appearance of paintings. We thus present GOYA, a method that distills the artistic knowledge captured in a recent generative model to disentangle content and style. Experiments show that synthetically generated images sufficiently serve as a proxy of the real distribution of artworks, allowing GOYA to separately represent the two elements of art while keeping more information than existing methods.
GPT-NER: Named Entity Recognition via Large Language Models

Despite the fact that large-scale Language Models (LLM) have achieved SOTA performances on a variety of NLP tasks, its performance on NER is still significantly below supervised baselines. This is due to the gap between the two tasks the NER and LLMs: the former is a sequence labeling task in nature while the latter is a text-generation model. In this paper, we propose GPT-NER to resolve this issue. GPT-NER bridges the gap by transforming the sequence labeling task to a generation task that can be easily adapted by LLMs e.g., the task of finding location entities in the input text "Columbus is a city" is transformed to generate the text sequence "@@Columbus## is a city", where special tokens @@## marks the entity to extract. To efficiently address the "hallucination" issue of LLMs, where LLMs have a strong inclination to over-confidently label NULL inputs as entities, we propose a self-verification strategy by prompting LLMs to ask itself whether the extracted entities belong to a labeled entity tag. We conduct experiments on five widely adopted NER datasets, and GPT-NER achieves comparable performances to fully supervised baselines, which is the first time as far as we are concerned. More importantly, we find that GPT-NER exhibits a greater ability in the low-resource and few-shot setups, when the amount of training data is extremely scarce, GPT-NER performs significantly better than supervised models. This demonstrates the capabilities of GPT-NER in real-world NER applications where the number of labeled examples is limited.