Reviewerly/exHarmony huggingface dataset:
Automated Word Sense Disambiguation Using WordNet Ontology

Automatic word sense disambiguation is a major challenge in natural language processing domain. In recent years, many of supervised and knowledge-based approaches were developed to solve this problem. The use of a sense inventory as a knowledge background to disambiguate words is a very useful technique rather than supervised approaches, with its need of a large pre-trained text corpus. This paper proposes a new approach to disambiguate words in text based on WordNet ontology, as sense inventory. The authors introduce a new technique called Gloss+ for word-sense disambiguation (WSD), which is based on using of the glosses of WordNet synonyms of the target word and the local context in which this word is used. This technique exploits a special behavior of the polysemous synsets. This behavior is detected during the disambiguation process and is used to improve the results obtained. In the experiment part, the authors compare the proposed approach to the methodologies which use synonyms or glosses only.
Towards Real-Time Temporal Graph Learning

In recent years, graph representation learning has gained significant popularity, which aims to generate node embeddings that capture features of graphs. One of the methods to achieve this is employing a technique called random walks that captures node sequences in a graph and then learns embeddings for each node using a natural language processing technique called Word2Vec. These embeddings are then used for deep learning on graph data for classification tasks, such as link prediction or node classification. Prior work operates on pre-collected temporal graph data and is not designed to handle updates on a graph in real-time. Real world graphs change dynamically and their entire temporal updates are not available upfront. In this paper, we propose an end-to-end graph learning pipeline that performs temporal graph construction, creates low-dimensional node embeddings, and trains multi-layer neural network models in an online setting. The training of the neural network models is identified as the main performance bottleneck as it performs repeated matrix operations on many sequentially connected low-dimensional kernels. We propose to unlock finegrain parallelism in these low-dimensional kernels to boost performance of model training.
RRSR:Reciprocal Reference-Based Image Super-Resolution with Progressive Feature Alignment and Selection

Reference-based image super-resolution (RefSR) is a promising SR branch and has shown great potential in overcoming the limitations of single image super-resolution. While previous state-of-the-art RefSR methods mainly focus on improving the efficacy and robustness of reference feature transfer, it is generally overlooked that a well reconstructed SR image should enable better SR reconstruction for its similar LR images when it is referred to as. Therefore, in this work, we propose a reciprocal learning framework that can appropriately leverage such a fact to reinforce the learning of a RefSR network. Besides, we deliberately design a progressive feature alignment and selection module for further improving the RefSR task. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection in a progressive manner, thus more precise reference features can be transferred into the input features and the network capability is enhanced. Our reciprocal learning paradigm is model-agnostic and it can be applied to arbitrary RefSR models. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm. Furthermore, our proposed model together with the reciprocal learning strategy sets new state-of-the-art performances on multiple benchmarks.
Virtual and Hybrid Classes as The Challenge Assumed During The SARS-CoV-2 Pandemic: An Interdisciplinary Qualitative Approach

The SARS-CoV-2 pandemic forced educational institutions worldwide to deploy virtual and hybrid classes as a unique option to continue with the teaching-learning process. However, these modalities imply important efforts on the part of teachers, students, and administrative staff to ensure the teaching-learning process quality. Considering the above, this research work carries out a qualitative interdisciplinary analysis using the focus group technique intending to determine the perceptions, challenges, trends, and trade-offs of the virtual and hybrid modalities during and after the SARS-CoV-2 pandemic. Our analysis involves various disciplines (e.g., pedagogical and andragogical, social-economic, ergonomic, educational technology, and product & service innovation perspectives). As a result, some particular findings of each perspective are shown. The results of this research work can help the body of technical knowledge related to the curricula design at different educational levels to increase the efficiency of hybrid and virtual classes. Finally, this research work has some limitations, therefore the findings presented are suggestive rather than definitive.
Sentiment Analysis of Covid-19 on Indonesian Twitter by Implementing the Naïve Bayes Method

Twitter is one of the social media used in Indonesia to express opinions/opinions. One of them is the opinion about Covid-19 which is taking the world by storm. The government’s provisions regarding Covid-19 itself reap many pros and cons on social media, one of which is Twitter. In this study, “Covid-19” will be used as a keyword to conduct sentiment analysis. Sentiment analysis is the process of understanding, extracting and processing textual data automatically to obtain information contained in an opinion sentence. The Naive Bayes Classifier method is used to classify and calculate the total accuracy of the class that has been obtained. Based on the results from the kaggle dataset, there are a total of 2269 tweet documents with the keyword “Covid-19” on March 23-May 14, 2020 which can be trusted because the data has been labeled by experts. The Naive Bayes Classifier method has 2269 data sets, then divides it into 1815 training data, and 453 data testing data and produces an accuracy of 0.674.
Perancangan Media Video Company Profile Bank Sampah NTB Mandiri

Garbage is one of the problems in Indonesia including in West Nusa Tenggara which has become a global problem, garbage that has accumulated and is not properly processed or handled can cause environmental pollution. One way that can be done to overcome the problem of waste is separating waste according to its type (organic and inorganic), by doing 3R (Reduce, Reuse, Recycle) and also by recycling waste. Currently the handling of waste recycling by the community has been carried out at the Waste Bank. The NTB Mandiri Garbage Bank is a container or place used to save waste, organic waste can be processed into compost while inorganic waste can be recycled into several handicraft items such as bags, tablecloths tables, wallets, key chains, and several other items made of plastic and used tires or other inorganic waste. This waste will later be saved in the form of money and rewarded according to its type. Therefore, a company profile video of the NTB MANDIRI Waste Bank will be designed to help introduce it to the community.&#x0D; &#x0D; Keywords - Video, Company Profile, Waste Bank&#x0D;
Beyond Transformers: fault type detection in maintenance tickets with Kernel Methods, Boost Decision Trees and Neural Networks

The proper handling of customer tickets and main-tenance requests is pivotal for enterprises as it directly impacts customer satisfaction. The ability to rapidly and efficiently react and solve reported issues is in fact a key factor from the customers' perspective, resulting in positive feedback for the company, leading to higher economic and brand-image revenues. The automatic detection of failures from maintenance tickets and support requests can grant faster and more efficient reactions to customers' equipment failures as well as reduced maintenance costs. The analysis of support and maintenance requests is a well-known problem in Natural Language Processing (NLP). State-of-the-art solutions in this field rely on Transformers models, pre-trained on large text corpora, and then fine-tuned on the specific downstream task. However, due to their intrinsic nature, support requests are highly domain-specific and usually similar to short telegraph messages, where the focus is typically encapsulated in short sequences rather than in long dependencies. Hence, ad-hoc methods for pattern recognition might provide comparable performances with respect to Transformers. In this work, two alternative approaches are proposed, based on: Kernel Meth-ods in conjunction with Boost Decision Trees (SpectrumBoost), and Neural Networks for Multiple Representation Learning (DeepMRL). These models have been tested and compared against state-of-the-art models on a real-world set of 131305 maintenance tickets in the Italian language, suggesting that the proposed models outperform Transformers both in the prediction accuracy and in the time and computational resources required for their training.
Training Efficient Siamese Network with Probability Label for Object Tracking

Most object tracking algorithms based on siamese network are trained end-to-end with binary labels. However, the binary labels with only “1” and “0” states may not be a perfect way to represent the similarity between exemplar image and candidate in search image. In this paper, we analyze the weakness of training algorithm with binary labels and explore the scheme of utilizing probability labels to train a more efficient siamese network. The distribution of probability labels is yielded by an exemplary siamese network with state-of-art performance. The probability label indicates the probability of a candidate being the target, which provides more supervised information than binary label. With the probability labels, we attempt to design a more efficient siamese network. We conduct experiments on OTB-2013/2015 and VOT-2016/2017/2019, which demonstrate that training same siamese network with probability labels can achieve better performance than that with binary labels and other trackers.
Colour Space Defence: Simple, Intuitive, but Effective

Deep neural networks (DNNs) are widely applied in autonomous intelligent systems. However, DNNs are vulnerable to adversarial attacks from exclusively crafted input images, leading to performance degradation such as wrong classifications. A wrong classification made by an AIS could result in severe and possibly lethal consequences. While several existing works proposed applying classic computer vision techniques to adversarial defense, these methods generally deteriorate the input information to a considerable extent. To re-store model performances while minimising such deterioration, we propose a novel method for adversarial defence named Colour Space Defence. We first demonstrated the weak transferability of adversarial information across different colour spaces. We then proposed to defend against adversarial examples by ensembling models trained in multiple colour spaces. Experiments have verified the validity of Colour Space Defence in maintaining performances on clean images. In most cases of defence, this method outperformed several of its comparators.
Microtextural, spectroscopic, and chemical characterization of amazonite from the Serra Branca Pegmatite, Northeastern Brazil

The Serra Branca pegmatite in northeastern Brazil is unique because of its megacrysts of bluish-green amazonite currently exploited for ornamental purposes and as high-quality gemstones.Here we analyze microtextural, spectroscopic, and geochemical data on specimens from two magmatic generations of amazonite from this pegmatite in order to characterize their mineralogy and propose a schematic evolution diagram for twinning during their development.The amazonite consists of intergrowths of the low microcline and low albite XRD varieties that evolved according to a twin coarsening process, in which the first generation (I-tg) of ± A/P twinning developed at the monoclinic-triclinic transformation, whereas the second generation (II-tg) gave rise mainly to ± A twins.The bluish-green color of the amazonite was preserved during the process of microcline twinning.In addition to the irrational II-tg twinning, which is typically found in microcline from anorogenic settings, the serra branca amazonite crystals also show elevated contents of Rb, Pb, Fe, Cs, and Tl in both generations, whereas albitic intergrowths present high concentrations of Fe, Pb, Sr, and Ga.Finally, our chemical and spectroscopic results are compatible with the hypothesis that Pb-water centers account for the bluish green color in amazonite.
Renewable energy in a dense urban fabric: solar gain estimation in the case of a turn of the century district

Abstract Buildings are responsible for significant amount of energy consumption. The new constructions already obliged to use decreased amount of energy, but the older buildings are ineffective compared to them. The new building energetic prescriptions in force since 2021 in Hungary require considerable share of renewable energy produced on-site. In case of a densely built urban fabric, the above renewable ratio can mostly be reached by using solar technologies. The author surveys the solar characteristics of a Budapest downtown area. Considerable ratio of the stock is residential, traditional apartment house type built around the turn of the 19th-20th century. The retrofit solutions and renewable power generation are restricted by the dense fabric and the heritage protection rules as well. In present paper the estimation of solar energy potential of the above characteristic building type is introduced. The European Union conform Hungarian calculations used system is used to define the values. The methodology contains 3D modelling and irradiation software simulation. As result, the connection between the footprint and irradiation data of the characteristic heritage buildings are introduced, as well as the solar power generation potential of the building type in question. Also, the heritage protection compatibility of the solar systems are studied.
Application and Research of Small Object Detection in Oil Field

With the construction of intelligent oilfield, in the current operation site of oilfield, the computer has been able to analyze some violations intelligently through the deep neural network, while the recognition of small objects (small objects refer to less than 32 × 32 pixel points) such as playing cell phone and smoking is still an unsolved problem. Small object detection has been a core task in computer vision, which is more difficult to recognize because it contains less semantic information and is not easy to distinguish from the background. In this paper, we analyze and experimentally apply the classical small target recognition methods and propose the Yolo-SO network, which is tested and verified in the dataset, and under the evaluation criteria of Ap75, the detection accuracy of Yolo-SO network is 2.6% higher than the existing classical network EFPN.
Distributed Cross-Domain Authentication Scheme Under the Smart City Management Platform

With the development of city management information construction, the data of different city management platforms are fragmented, and there is the problem of information islanding. For the circulation of data between different city management platforms, cross-domain authentication is needed, but the current cross-domain authentication is centralized, and once the certification authority is attacked, security cannot be guaranteed. To solve the problems above, smart city management is born. There are two cross-domain scenarios in the smart city management platform, which correspond to the cross-space domain problem and the cross-heterogeneous domain problem. In this paper, we proposed a distributed cross-spatial domain and cross-heterogeneous domain authentication scheme based on blockchain in the context of a smart city management system. We give the model of the cross-domain scheme, describe the cross-domain process, and analyze the cross-domain scheme in terms of security, efficiency, and practicality. The results show that the scheme has strong security, high efficiency, and the potential to be put into practical use.
Recognition Performance of Facial Expression for the Face’s Partial Regions

With the rapid development of artificial intelligence, automatic facial expression recognition has been intensively investigated. However, it cannot maintain high accuracy of facial expression recognition due to face’s partial occlusion because most of facial expression recognition methods are designed based on the assumption that the entire face is visible. Therefore, the purpose of this study is to develop a method that does not degrade the accuracy of facial expression recognition even if a part of the face is occluded. In this paper, we investigate the accuracy of the facial expression recognition for only the region around the eyes using the CK+ dataset. The 3-D CNN and 2-D CNN with synthetic or subtracted eye images as the input image were adopted in the experiment The experimental results showed that the accuracy of facial expression recognition using the 3-D CNN or 2-D CNN with subtracted eye images were improved. Therefore, the temporal variations of facial expression are effective for the facial expression recognition using only the region around the eyes.
OblivSend: Secure and Ephemeral File Sharing Services with Oblivious Expiration Control

Users have personal or business need to share most private and confidential documents; however, often at the expense of privacy and security. A sought after feature in the trending ephemeral context is to set download constraints of a particular file - a file can only be downloaded a limited number of times and/or for a limited period of time. Emerging end-to-end encrypted file sharing services with enhanced expiration control are attempts to meet the needs. Although such new services have drawn much attention, their server can still observe and control metadata of such download constraints, which could reveal partial data information. To address this challenge, we propose OblivSend, a privacy-preserving file sharing web service that 1) supports end-to-end encryption, 2) allows a limited period of time and a limited number of downloads at users’ control, and 3) protects expiration control metadata from the server efficiently by lightweight cryptographic primitives. We develop a proof of concept prototype implemented in Hyperledger Fabric on a Research Cloud and evaluations demonstrate that our prototype can function as intended to achieve privacy of metadata without sacrificing user experience.
Multi-objective Path Planning of Single Delivery Robot in Epidemic Control Campus

In order to improve the delivery efficiency of single delivery robot in the campus environment of epidemic closure and control, in this paper, an MMOEA/D algorithm that combines the multi-objective evolutionary algorithm MOEA/D based on decomposition strategy and local search strategy is proposed. Firstly, the campus environment model is established, and the A* algorithm is used to plan the path matrix between the target points. Then, with the shortest path length and the shortest average delay time as the goal, the MMOEA/D algorithm is used to determine the traversal sequence of each target point to get the best path. In this paper, MMOEA/D algorithm is compared with MOEA/D algorithm and NSGA-II algorithm. The experimental results show that the target value and path optimized by the MMOEA/D algorithm is better.
PyHENet: A Generic Framework for Privacy-Preserving DL Inference Based on Fully Homomorphic Encryption

Deep learning inference provides inference service by service provider with model for client with input of personal data. Due to the huge commercial value inside, on one hand, both client's original data and inference output should be kept secret from others, even including service provider. On the other hand, service provider's model should be kept secret, especially from his competitor. Current research on privacy-preserving deep learning inference focuses on building models with specific data. This paper proposes a generic framework PyHENet of privacy-preserving deep learning inference based on Pytorch and lattice-based FHE, such that crypto library can be flexibly embedded into network. Firstly, raw data is encrypted by lattice-based FHE and uploaded to service provider. Secondly, convolutional computation over float-point ciphertext data is proposed for service provider to execute low accuracy loss inference with aided parallel method SIMD. Thirdly, inference result in ciphertext format is sent back to client for decryption. To improve efficiency, inference procedure can be further divided into two phases. All the computations during the second phase are in plaintext format with GPU acceleration, while keeping the first phase unchanged. Using the same model and parameters, the relative accuracy of PyHENet is almost 100% compared to the plaintext inference. This paper is the first to propose a general framework of neural networks for fully homomorphic cryptographic inference, and is based on mainstream deep learning frameworks, which is both secure and more conducive to development.
Burn After Reading: Online Adaptation for Cross-domain Streaming Data

In the context of online privacy, many methods propose complex security preserving measures to protect sensitive data. In this paper we note that: not storing any sensitive data is the best form of security. We propose an online framework called “Burn After Reading”, i.e. each online sample is permanently deleted after it is processed. Our framework utilizes the labels from the public data and predicts on the unlabeled sensitive private data. To tackle the inevitable distribution shift from the public data to the private data, we propose a novel unsupervised domain adaptation algorithm that aims at the fundamental challenge of this online setting–the lack of diverse source-target data pairs. We design a Cross-Domain Bootstrapping approach, named CroDoBo, to increase the combined data diversity across domains. To fully exploit the valuable discrepancies among the diverse combinations, we employ the training strategy of multiple learners with co-supervision. CroDoBo achieves state-of-the-art online performance on four domain adaptation benchmarks. .