Reviewerly/exHarmony huggingface dataset:
Exploiting Virtual Reality for Enhancing the Shopping Experience in the Fashion Industry: Between Interaction and Perception

Nowadays, buying a product online is no longer about the product itself but the experience it offers.The planned thesis work aims to understand how to improve the user's shopping experience in the context of online shopping for the fashion industry.To enhance the shopping experience, retailers need to sell new services by leveraging technologies such as Virtual Reality (VR).The first part of the research investigates which shopping experience, between one developed for a desktop computer -Desktop Virtual Reality (DVR) -and one developed in Virtual Reality (VR), generates better results in terms of hedonic and utilitarian values, cognitive load, and user experience.Also, the lack of touch in online shopping is a crucial issue.The second part of the research concerns the implementation of pseudo-haptics feedback within the online shopping experience with VR.Pseudo-haptics can induce haptic sensations without requiring actual touch through the influence of other sensory modalities, such as vision.To this end, we aim to explore the feasibility of recreating the sensation of people's actual touch with fashion products and fabrics through a "visualized touch" on an interface.
A Blockchain and ML-Based Framework for Fast and Cost-Effective Health Insurance Industry Operations

Health insurance is crucial for each person, bearing in mind the increasing medical costs. COVID-19 has been an eye-opener as to how important it is to have health insurance. Medical emergencies can have a severe emotional and financial impact. Thus, a health insurance policy can help mitigate financial risks in unpredictable circumstances. However, the current insurance system is very expensive, as thousands of people pay the premiums, and very few take the claims. Furthermore, the claim settlement process is excruciatingly long and tiresome. In this article, we focus on establishing a rapid and cost-effective framework for the health insurance market, based on machine learning and blockchain technology. By developing a smart contract, blockchain may eliminate any third-party organizations and make the complete process safer, easier, and more efficient. The contract pays the claim based on the claimant’s documentation. We optimized the premiums using a regression model based on the net amount claimed during the current policy tenure and various other criteria. For anticipating risk, a random forest classifier is used, which aids in the risk-rated premium rebate computation for policyholders for their next term of insurance.
DiscOrDance: Visualizing Software Developers Communities on Discord

New communication platforms have emerged to support developers in finding and creating the knowledge they need for program comprehension, maintenance, and evolution. Instant messaging applications are supplanting developer mailing lists in collaborative development toolchains. These applications provide a new medium, supporting faster and richer communication (e.g., embedded previews, images, files, videos). Research so far focused on extracting information from these platforms, but there is a lack of tools to visually and interactively explore them.We present DiscOrDance, a tool for the interactive visual exploration of the complete message history of a Discord server. We show how three categories of views elicit insights on aspects of the structure, members, and software related content of a Discord server. We demonstrate use cases of DiscOrDance to support software maintenance and evolution activities on an active software developer community, the Pharo Discord server.Demo video: https://youtu.be/eYCLGWwM9HYTool homepage: https://DiscOrDance.si.usi.ch
An Efficient Takagi–Sugeno Fuzzy Zeroing Neural Network for Solving Time-Varying Sylvester Equation

In this article, we propose an efficient Takagi–Sugeno fuzzy zeroing neural network (TS-FZNN) activated by a new activation function for solving the time-varying Sylvester equation. The self-adaptive convergence parameter is designed by the Takagi–Sugeno fuzzy logic system. Convergence and robustness of the proposed model are analyzed. Theoretical analysis shows that the proposed model not only has less fixed convergence time than the recently suggested ZNN models, but also is noise-tolerant. Numerical experiments are performed to illustrate its efficiency and effectiveness as well as the superior performance over the existing ZNN models for solving the time-varying Sylvester equation, including the applicability of the proposed model to robot manipulator. The effects of model parameters on the dynamic response of the robotic manipulator system are also illustrated by the convergence rate of position errors of the robotic manipulator trajectory.
Task-Aware Weakly Supervised Object Localization With Transformer

Weakly supervised object localization (WSOL) aims to predict both object locations and categories with only image-level class labels. However, most existing methods rely on class-specific image regions for localization, resulting in incomplete object localization. To alleviate this problem, we propose a novel end-to-end task-aware framework with a transformer encoder-decoder architecture (TAFormer) to learn class-agnostic foreground maps, including a representation encoder, a localization decoder, and a classification decoder. The proposed TAFormer enjoys several merits. First, the designed three modules can effectively perform class-agnostic localization and classification in a task-aware manner, achieving remarkable performance for both tasks. Second, an optimal transport algorithm is proposed to provide pixel-level pseudo labels to online refine foreground maps. To the best of our knowledge, this is the first work by exploring a task-aware framework with a transformer architecture and an optimal transport algorithm to achieve accurate object localization for WSOL. Extensive experiments with four backbones on two standard benchmarks demonstrate that our TAFormer achieves favorable performance against state-of-the-art methods. Furthermore, we show that the proposed TAFormer provides higher robustness against adversarial attacks and noisy labels.
Design of Question-and-Answer Interface using Moodle DATABASE Function

Compared to face-to-face lectures, where the teacher can respond immediately through conversation alone, the number of questions from students increases in asynchronous distance lectures, since they are sent by email or posted on a forum. In addition, if the information in a student's question is unclear, the teacher is burdened with extra interactions, such as asking the student questions. We propose the question database with an improved user interface that allows students to write their questions using the Moodle module. We analyse the number of questions and answers in asynchronous distance lectures to see if the extra communication between teacher and student could be reduced.
Extending the value of a CRIS with Research Data Management

In this paper we aim to analyse the adoption of Current Research Information Systems (CRIS) for Research Data Management (RDM). We show how CRISs hold a key role in facilitating the management and reporting of an institution's research activities and outputs – not only do they offer extensive functionality for researchers and research administrators to effectively manage all aspects of their research information, but are also integrating more and more with specialized RDM tools, Institutional Repositories (IR), and other external systems. This paper provides an overview of how CRISs have evolved and integrated to become a crucial part of the RDM chain, including the interoperability, registration, linking, and archiving of data.
A Gaussian Particle Swarm Optimization-Based Phase Unwrapping Algorithm

A Gaussian particle swarm optimization-based phase unwrapping (PU) technique is presented to recover unwrapped phases reflecting the deformation or height of the observed objects from measured interferograms composed of wrapped phases. First, the Gaussian particle swarm optimization strategy is exploited into PU for measured interferograms, and a robust PU program based on the Gaussian particle filter is constructed by combining a robust phase slope estimation technique demonstrated well previously. Second, an efficient path-following approach is exploited to route the paths of PU to improve the accuracy and efficiency in PU for interferograms. Finally, the performances of the proposed method are fully demonstrated with the experiments of PU for the simulated and measured interferograms, and the advantages of this method in the accuracy of PU for interferograms are also shown, with respect to some other traditional methods and representative methods.
A Reasoning Engine Architecture for Building Energy Metadata Management

During the Buildings’ lifecycle, massive amounts of data, that contain information related to their energy consumption, are generated. Towards the creation of smart building networks, this produced information must be intercepted and harmonized according to building ontologies and schemas. The pattern recognition from building metadata is based on inferencing and intelligent querying, that can be achieved with the utilization of graph and property databases that deploy and host building information. This paper presents a Reasoning Engine Architecture implemented in the context of the H2020 project called MATRYCS that persists building semantic information. It will be leveraged to support real life applications by improving the inference operations.
On-Device Training of Deep Learning Models on Edge Microcontrollers

Recent advancements in Artificial Intelligence (AI) together with the increase of device computational power catalyzed the widespread diffusion of Intelligent Cyber Physical Systems (ICPSs) as a novel solution to run smart applications where the inference is performed on the Micro Controller Unit (MCU). Unfortunately, the hardware constraints of these devices limit the tasks that can be accomplished, for this reason, most of the times hybrid Cloud/Edge approaches have been proposed to enable the offloading of those operations (e.g., the training of deep learning models) that would not fit the computation, memory, and energy requirements. However, when security, latency, and connection stability aspects are relevant, it is clear that the use of such an approach is no longer suitable. This becomes even more evident in those contexts where it is asked the detection of new data patterns that can emerge over time. Focusing our attention on the analysis of time series data, in this paper we propose a novel Echo State Network (ESN) model that enables the on-device training directly on the MCUs of the STM32 family. Starting from a custom layer implementation on Keras, we extended the functionalities of the X-CUBE-AI tool provided by STMicroelectronics to recognize this type of network and enable the automatic deployment on the smart boards. To test the proposed solution, we realized a testbed and conducted a set of experiments that demonstrate the feasibility of our MCU on-device training approach which reached a good level of precision, recall, and F1-score.
SOMALib: Library of Exact and Approximate Activation Functions for Hardware-efficient Neural Network Accelerators

Approximate computing along with quantized low-precision computing has gained significant interest in today’s neural network (NN) implementation. This paper proposes a library of VLSI implementations of different activation functions, aimed towards designing hardware-efficient NN accelerators. Cartesian genetic programming (CGP), an evolutionary algorithm was employed to generate gate-level designs of approximate and exact representations of activation functions. We open-source the hardware library of 9444 circuits containing a majority of the activation functions employed in NN architectures, including Sigmoid, Hyperbolic-Tangent, Gaussian, ReLU, GeLU, Softplus, and Binary-Step. The library also presents the error characteristics and hardware metrics of the designs which will aid in the usage of the library in future research. Additionally a hardware comparison of the proposed circuits against existing implementations including piecewise-linear (PWL), memory-based, hls4ml, DNNweaver implementations to realize activation functions on FPGA and ASIC flow is presented. The CGP evolved hardware library shows minimal silicon space requirement, least power consumption when investigated for ASIC flow, and the least LUT utilization’s in FPGA flow. Besides, SOMALib designs are purely combinatorial, allowing various synthesis stage optimizations towards the target Power-Performance-Area budget, which is not possible in standard memory block implementations.
Open-world Semantic Segmentation for LIDAR Point Clouds

Current methods for LIDARWang, Michael Yu semantic segmentation are not robust enough for real-world applications, e.g., autonomous driving, since it is closed-set and static. The closed-set assumption makes the network only able to output labels of trained classes, even for objects never seen before, while a static network cannot update its knowledge base according to what it has seen. Therefore, in this work, we propose the open-world semantic segmentation task for LIDAR point clouds, which aims to 1) identify both old and novel classes using open-set semantic segmentation, and 2) gradually incorporate novel objects into the existing knowledge base using incremental learning without forgetting old classes. For this purpose, we propose a REdundAncy cLassifier (REAL) framework to provide a general architecture for both the open-set semantic segmentation and incremental learning problems. The experimental results show that REAL can simultaneously achieves state-of-the-art performance in the open-set semantic segmentation task on the SemanticKITTI and nuScenes datasets, and alleviate the catastrophic forgetting problem with a large margin during incremental learning.
Feature-Transferable Pyramid Network for Dense Multi-Scale Object Detection in SAR Images

In synthetic aperture radar (SAR) images, there are a large number of dense multi-scale objects, especially dense multi-scale ships docked along the coast. Existing object detection methods are difficult to simultaneously detect dense multi-scale objects in complex background. A novel method for dense multi-scale object detection in SAR images based on Feature-Transferable Pyramid Network (FTPN) is proposed in this paper. In the stage of feature extraction, the feature maps of each layer are connected effectively and the feature maps of various scales are extracted. This method can extract the features of dense multi-scale objects more effectively, so as to realize simultaneous detection of dense multi-scale objects in SAR images. Experiments on SSDD dataset, AIR-SARShip-2.0 dataset and Gaofen-3 dataset show that the proposed method can achieve dense multi-scale object detection, and the overall performance is better than the state-of-the-art methods.
Exploring Topic Supervision with BERT for Text Matching

Text matching is a critical task in natural language processing to measure semantic similarity between two texts. A significant portion of online texts are labeled with a variety of coarse topic responses. These supervised topic indicators can provide prior structured and explicable semantics for textual similarity modeling. However, most existing state-of-the-art neural network methods cannot benefit from such complementary topic signals. Therefore, we propose a novel Topic Supervision BERT-based model (TSB) for text matching. TSB provides a reference multi-task joint training framework involving two types of topic supervision, including explicit and implicit topic supervision. To constrain consistent topic correspondences between texts, we introduce a supervised auxiliary learning task to incorporate explicit pre-defined topic supervision. Furthermore, to adapt to latent topic structures for mutual benefit between text representations and multiple tasks, we integrate a topic model into a contextual text representation model BERT to mine and incorporate implicit self-learnable topic supervision. Experimental results show that TSB supplements explicit and implicit topic information through a multi-task learning approach, which significantly improves the performance of text matching on two public datasets, especially on challenging short text matches.
Report Generation of Lungs Diseases From Chest X-ray using NLP

Pulmonary diseases are very severe health complications in the world that impose a massive worldwide health burden. These diseases comprise of pneumonia, asthma, tuberculosis, Covid-19, cancer, etc. The evidences show that around 65 million people undergo the chronic obstructive pulmonary disease and nearly 3 million people pass away from it each year that make it the third prominent reason of death worldwide. To decrease the burden of lungs diseases timely diagnosis is very essential. Computer-aided diagnostic, are systems that support doctors in the analysis of medical images. This study showcases that Report Generation System has automated the Chest X-Ray interpretation procedure and lessen human effort, consequently helped the people for timely diagnoses of chronic lungs diseases to decrease the death rate. This system provides great relief for people in rural areas where the doctor-to-patient ratio is only 1 doctor per 1300 people. As a result, after utilizing this application, the affected individual can seek further therapy for the ailment they have been diagnosed with. The proposed system is supposed to be used in the distinct architecture of deep learning (Deep Convolution Neural Network), this is fine tuned to CNN-RNN trainable end-to-end architecture. By using the patient-wise official split of the OpenI dataset we have trained a CNN-RNN model with attention. Our model achieved an accuracy of 94%, which is the highest performance.
Adaptive Activation Network for Low Resource Multilingual Speech Recognition

Low resource automatic speech recognition (ASR) is a useful but thorny task, since deep learning ASR models usually need huge amounts of training data. The existing models mostly established a bottleneck (BN) layer by pre-training on a large source language, and transferring to the low resource target language. In this work, we introduced an adaptive activation network to the upper layers of ASR model, and applied different activation functions to different languages. We also proposed two approaches to train the model: (1) cross-lingual learning, replacing the activation function from source language to target language, (2) multilingual learning, jointly training the Connectionist Temporal Classification (CTC) loss of each language and the relevance of different languages. Our experiments on IARPA Babel datasets demonstrated that our approaches outperform the from-scratch training and traditional bottleneck feature based methods. In addition, combining the cross-lingual learning and multilingual learning together could further improve the performance of multilingual speech recognition.
Automatically finding Metamorphic Relations in Computational Material Science Parsers

Software testing is an important part of the software life-cycle. Unfortunately, some software systems have the inherent problem, that it is not clear a priori what the expected outcome is. This is known in the literature as the Oracle Problem. Scientific software suffers from this problem in particular. Metamorphic Testing is a testing approach that mitigates the Oracle Problem, as it is based on identified relations between a program's in- and output pairs. In this study, we investigate the feasibility of automatically finding such metamorphic relations on a software package known as the exciting-NOMAD parser which is widely used in computational material science. We are able to show that it is indeed possible to automatically find metamorphic relations within the NOMAD parser for the density functional theory code exciting. We analyse the metamorphic relations found through our tool in terms of both quantity and relation quality. Furthermore, we also publish our developed tool, as well as used data alongside this study through our replication package.
Developing Sentiment Analysis of Indonesian Social Media Based on Convolutional Neural Network for Smarter Society

The need for mining the public's opinion on specific national development issues can be attained by sentiment analysis. There has been much research on sentiment analysis in Indonesian, but there is room for improvement. In this study, we performed experimentation by comparing previously tested convolutional neural network (CNN) architecture with our proposal utilizing a pre-trained word-embedded model: Word2Vec and GloVe that proved to be having better accuracy. The experiment used the SmSA dataset from IndoBERTweet of 11000 sentences. Utilization of the pretrained Word2Vec and GloVe model that was created from 16.2 million BRIN monolingual text corpus could represent a text in a better way. The word-embedding model can capture the semantic and syntactic meaning of a word so that they can conceive knowledge that may be used to raise classification performances. The experimental results show a better F1 score value of 80.06%, which increases by approximately 2.24%.
Smartadapt: Multi-branch Object Detection Framework for Videos on Mobiles

Several recent works seek to create lightweight deep net-works for video object detection on mobiles. We observe that many existing detectors, previously deemed computationally costly for mobiles, intrinsically support adaptive inference, and offer a multi-branch object detection frame-work (MBODF). Here, an MBODF is referred to as a so-lution that has many execution branches and one can dy-namically choose from among them at inference time to sat-isfy varying latency requirements (e.g. by varying resolution of an input frame). In this paper, we ask, and answer, the wide-ranging question across all MBODFs: How to expose the right set of execution branches and then how to sched-ule the optimal one at inference time? In addition, we un-cover the importance of making a content-aware decision on which branch to run, as the optimal one is conditioned on the video content. Finally, we explore a content-aware scheduler, an Oracle one, and then a practical one, leveraging various lightweight feature extractors. Our evaluation shows that layered on Faster R-CNN-based MBODF, compared to 7 baselines, our Smartadapt achieves a higher Pareto optimal curve in the accuracy-vs-latency space for the ILSVRC VID dataset.
A Generalized and Robust Framework for Timestamp Supervision in Temporal Action Segmentation

In temporal action segmentation, Timestamp Supervision requires only a handful of labelled frames per video sequence. For unlabelled frames, previous works rely on assigning hard labels, and performance rapidly collapses under subtle violations of the annotation assumptions. We propose a novel Expectation-Maximization (EM) based approach that leverages the label uncertainty of unlabelled frames and is robust enough to accommodate possible annotation errors. With accurate timestamp annotations, our proposed method produces SOTA results and even exceeds the fully-supervised setup in several metrics and datasets. When applied to timestamp annotations with missing action segments, our method presents stable performance. To further test our formulation's robustness, we introduce the new challenging annotation setup of SkipTag Supervision. This setup relaxes constraints and requires annotations of any fixed number of random frames in a video, making it more flexible than Timestamp Supervision while remaining competitive.