Reviewerly/exHarmony huggingface dataset:
O$$^2$$-Bert: Two-Stage Target-Based Sentiment Analysis

Abstract Target-based sentiment analysis (TBSA) is one of the most important NLP research topics for widespread applications. However, the task is challenging, especially when the targets contain multiple words or do not exist in the sequences. Conventional approaches cannot accurately extract the (target, sentiment) pairs due to the limitations of the fixed end-to-end architecture design. In this paper, we propose a framework named O $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> -Bert , which consists of O pinion target extraction (OTE-Bert) and O pinion sentiment classification (OSC-Bert) to complete the task in two stages. More specifically, we divide the OTE-Bert into three modules. First, an entity number prediction module predicts the number of entities in a sequence, even in an extreme situation where no entities are contained. Afterwards, with predicted number of entities, an entity starting annotation module is responsible for predicting their starting positions. Finally, an entity length prediction module predicts the lengths of these entities, and thus, accomplishes target extraction. In OSC-Bert, the sentiment polarities of extracted targets from OTE-Bert. According to the characteristics of BERT encoders, our framework can be adapted to short English sequences without domain limitations. For other languages, our approach might work through altering the tokenization. Experimental results on the SemEval 2014-16 benchmarks show that the proposed model achieves competitive performances on both domains (restaurants and laptops) and both tasks (target extraction and sentiment classification), with F1-score as evaluated metrics. Specifically, OTE-Bert achieves 84.63%, 89.20%, 83.16%, and 86.88% F1 scores for target extraction, while OSC-Bert achieves 82.90%, 80.73%, 76.94%, and 83.58% F1 scores for sentiment classification, on the chosen benchmarks. The statistics validate the effectiveness and robustness of our approach and the new “two-stage paradigm”. In future work, we will explore more possibilities of the new paradigm on other NLP tasks.
Deep Learning Approach for Seamless Navigation in Multi-View Streaming Applications

Quality of Experience (QoE) in multi-view streaming systems is known to be severely affected by the latency associated with view-switching procedures. Anticipating the navigation intentions of the viewer on the multi-view scene could provide the means to greatly reduce such latency. The research work presented in this article builds on this premise, by proposing a new predictive view-selection mechanism. A VGG16-inspired Convolutional Neural Network (CNN) is used to identify the viewer’s focus of attention and determine which views would be most suited to be presented in the brief term, i.e., the near term viewing intentions. This way, those views can be locally buffered before they are actually needed. To this aim, two datasets were used to evaluate the prediction performance and impact on latency, in particular when compared to the solution implemented in the previous version of our multi-view streaming system. Results obtained with this work translate into a generalized improvement of the perceived QoE. A significant reduction of latency during view-switching procedures was effectively achieved. Moreover, results also demonstrated that the prediction of the user’s visual interest was achieved with a high level of accuracy. An experimental platform was also established, on which future predictive models can be integrated and compared with previously implemented models.
Efficient Human Vision Inspired Action Recognition Using Adaptive Spatiotemporal Sampling

Adaptive sampling that exploits the spatiotemporal redundancy in videos is critical for always-on action recognition on wearable devices with limited computing and battery resources. The commonly used fixed sampling strategy is not context-aware and may under-sample the visual content, and thus adversely impacts both computation efficiency and accuracy. Inspired by the concepts of foveal vision and pre-attentive processing from the human visual perception mechanism, we introduce a novel adaptive spatiotemporal sampling scheme for efficient action recognition. Our system pre-scans the global scene context at low-resolution and decides to skip or request high-resolution features at salient regions for further processing. We validate the system on EPIC-KITCHENS and UCF-101 (split-1) datasets for action recognition, and show that our proposed approach can greatly speed up inference with a tolerable loss of accuracy compared with those from state-of-the-art baselines. Source code is available in https://github.com/knmac/adaptive_spatiotemporal.
Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic Tweets

Arabic is one of the world’s most widely spoken languages, and there is a huge amount of digital content available in Arabic. By the categorization of Arabic documents, it becomes easier to search and access specific content of interest. With the increasing quantity of user-generated content on social media platforms and online forums, text classification becomes important for content filtering and moderation. Text classification on an Arabic corpus has broad applications, ranging from information retrieval and content moderation to sentiment analysis and machine translation. It enables efficient organization, analysis, and utilization of Arabic text data, contributing to various industries and domains. Therefore, this study develops a Modified Seagull Optimization with Deep Learning based Affect Classification on Arabic Tweets (MSGODL-ACAT) technique. The goal of the MSGODL-ACAT approach lies in the recognition and categorization of effects or emotions that exist in Arabic tweets. At the preliminary level, the MSGODL-ACAT technique preprocesses the input data to make the Arabic tweets into a meaningful format. Next, the Glove technique is used for the word embedding process. Moreover, the MSGODL-ACAT technique makes use of the deep belief network (DBN) method for affect categorization. At last, the MSGO algorithm is used for the optimal hyperparameter tuning of the DBN method which in turn enhances the classification results. The experimental evaluation of the MSGODL-ACAT approach is evaluated using Arabic tweets databases. The experimental outcomes signify the effectual performance of the MSGODL-ACAT algorithm over other current approaches.
Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition

Cross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities. Cross-modal representation learning from different data types – such as images and time-series data (e.g., audio or text data) – requires a deep metric learning loss that minimizes the distance between the modality embeddings. In this paper, we propose to use the contrastive or triplet loss, which uses positive and negative identities to create sample pairs with different labels, for cross-modal representation learning between image and time-series modalities (CMR-IS). By adapting the triplet loss for cross-modal representation learning, higher accuracy in the main (time-series classification) task can be achieved by exploiting additional information of the auxiliary (image classification) task. We present a triplet loss with a dynamic margin for single label and sequence-to-sequence classification tasks. We perform extensive evaluations on synthetic image and time-series data, and on data for offline handwriting recognition (HWR) and on online HWR from sensor-enhanced pens for classifying written words. Our experiments show an improved classification accuracy, faster convergence, and better generalizability due to an improved cross-modal representation. Furthermore, the more suitable generalizability leads to a better adaptability between writers for online HWR.
An Interactive Threshold-Setting Procedure for Improved Multivariate Anomaly Detection in Time Series

Anomaly detection in multivariate time series is valuable for many applications. In this context, unsupervised and semi-supervised deep learning methods that estimate how normal a new observation is have shown promising results on benchmark datasets. These methods are dependent on a threshold that determines which points should be regarded as anomalous and not be anomalous. However, finding the optimal threshold is not easy since no information about the ground truth is known in advance, which implies that there are limitations to automatic threshold-setting methods available today. An alternative is to utilize the expertise of users that can interact in a threshold-setting procedure, but for this to be practically feasible, the method needs to be both accurate and efficient in relation to the state-of-the-art automatic methods. Therefore, this study develops an interactive threshold-setting schema and examines to what extent it can outperform the current state-of-the-art automatic threshold-setting methods. The result of the study strongly indicates that the suggested method with little effort can provide higher accuracy than the automatic threshold-setting methods on a general basis.
A Comparative Study on R Packages for Text Mining

The term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Text Mining</i> , which is given to the set of techniques used for the extraction, cleaning and processing of the information in texts, has become useful to provide valuable information to other algorithms and widely used with statistical and machine learning methods. By enabling the extraction of useful insights from textual data, Text Mining has become a potent tool in decision-making and knowledge discovery across many areas, including health care, government, education and industry. R is a mature open-source programming environment that has overstepped its initial scope of application for statistical computing and graphics to be used in pretty all the Data Science knowledge Area Groups. The objective of this paper is to present review and benchmarking analysis of packages for text mining techniques with R in computational systems. The paper reviews thirteen different packages comparing them on their execution time and memory used, for which new tests have been specifically designed. The results of this approach have been intended to be used over the most common tasks carried out when analyzing texts, and comparisons included allow R users to know which packages are best for each task and to improve their performance. Text mining package (tm) stands out particularly in Tokenization and Stemming techniques, while fastTextR is the best choice for Topic Modeling and Normalization. Also in the case of the Term Frequency–Inverse Document Frequency (TF-IDF) technique, the textir package is a clear choice. The other packages will depend on whether the technique is applied to a document-term matrix (DTM) or to plain text. In addition, there are packages that perform better in runtime than in memory usage and vice versa, making the choice more difficult. Packages such as udpipe can achieve better results working in parallel. Future works will include the same analysis for parallel computing, hybrid approaches, and novel algorithms.
Associative Memory With Spatio-Temporal Enhancement for Video Anomaly Detection

Memory network has been extensively used to record prototypical normal patterns to prevent overgeneralization of the network to reconstruct anomalies for video anomaly detection. However, existing memory-based methods only record the lossy representation of normal item prototypes, without recording the rich relationships between them. In this work, we propose an Associative Memory with Spatio-Temporal Enhancement (AMSTE) which introduces the global context information constraint of motion to enhance the appearance features and learn the normal item prototypes and their relationship. Specifically, we utilize two encoders to extract spatio-temporal features with the Spatio-Temporal Enhancement Module (STEM) to enhance appearance features with global motion constraints. Then, the prototypical patterns of normal data and their relationships are recorded in the item memory and relational memory, respectively. Finally, we retrieve features from the memory pools and reconstruct the video frame through the decoder. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach. The code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HuYongting/AMSTE</uri> .
Reasoning in Different Directions: Triplet Learning for Scene Graph Generation

Scene graph generation aims to detect objects and their relations in images, providing structured representations for scene understanding. Currently, mainstream approaches first detect the objects and then solve a classification task to determine the relation between each object pair, ignoring the other combinations of the subject-predicate-object triplet. In this work we propose a triplet learning paradigm for scene graph generation, where given any two entities of the triplet we learn to predict the third. The multi-task learning scheme is adopted to equip a scene graph generation model with the triplet learning task, in which the prediction heads for the subject, object and predicate share the same backbone and are jointly trained. The proposed method does not require any additional annotation and is easy to embed in existing networks. It benefits scene graph generation models in gaining more generalizability and thus can be applied to both biased and unbiased methods. Moreover, we introduce a new Graph Structure-Aware Transformer (GSAT) model that incorporates the structural information of the scene graph via a modified self-attention mechanism. Extensive experiments show that the proposed triplet learning consistently improves the performance of several state-of-the-art models on the Visual Genome dataset.
A Complete One-Time Passwords (OTP) Solution Using Microservices: A Theoretical and Practical Approach

The objective of this paper is to share the knowledge required for developing a One-Time Password (OTP) system and the practical experience of developing a real one using microservices and different programming languages, remembering that the OTP is the most popular mechanism to carry out a two-factor authentication process. To achieve this purpose, an incremental iterative methodology was used that allowed the prototype to be implemented in different parts. The developed prototype was designed to work in mobile applications with iOS and Android operating systems. In this work, different types of OTP algorithms were used, such as HMAC-based One-Time Password, Time-based One-Time Password and True Random Number Generator. Additionally, for the development of the prototype, various tools and frameworks such as Flask, React Native and Flutter were used, which allowed the development of application components in an agile and efficient manner. The combination of these programming languages and tools resulted in a more efficient and effective implementation of the OTP generator prototype. Once the proposed system was developed, different types of tests were carried out to verify its optimal and efficient functionality.
AutoBookFinder: A Case Study of Automated Book Rack Identification in Library Through UGV

The misplacement of books in libraries often results in wasted time when searching for a specific book on the wrong shelf. To address this issue, we created a novel, low-cost prototype Unmanned Ground Vehicle (UGV) equipped with a camera and Wi-Fi modules. The UGV wirelessly sends data to a local server, which uses image processing to determine the location of the book. A web user interface was created to enable users to easily locate the exact shelf of the desired book. This solution has not been previously explored and was tested in our Institute’s library. The adoption of UGVs greatly improved the overall user experience, transforming the manual and time-consuming process of book-finding into an automated and time-saving operation.
Support for Fictional Story Writing and Copy Editing

Fictional writing is the art of creating stories that are not based on real events or people. In recent years, book projects for smaller communities have become popular, not least due to the success of e-book platforms. In the context of story wringing, copy editing is an important step in the writing process as it ensures the overall quality concerning grammar, spelling and style. Currently, this step usually is executed by costly specialists. This is a problem for writers who are starting their careers. This paper presents a tool platform that simplifies the process of copy editing. Authors can execute first check steps themselves. The amount of further iterations with a commercial lector is reduced or may even be omitted. Our tool is able to apply even complex style rules that are based on deeper grammar analyses of texts.
Predicting Unseen Process Behavior Based on Context Information from Compliance Constraints

Predictive process monitoring (PPM) offers multiple benefits for enterprises, e.g., the early planning of resources. The success of PPM-based actions depends on the prediction quality and the explainability of the prediction results. Both, prediction quality and explainability, can be influenced by unseen behavior, i.e., events that have not been observed in the training data so far. Unseen behavior can be caused by, for example, concept drift. Existing approaches are concerned with strategies on how to update the prediction model if unseen behavior occurs. What has not been investigated so far, is the question how unseen behavior itself can be predicted, comparable to approaches from machine learning such as zero-shot learning. Zero-shot learning predicts new classes in case of unavailable training data by exploiting context information. This work follows this idea and proposes an approach to predict unseen process behavior, i.e., unseen event labels, based on process event streams by exploiting compliance constraints as context information. This is reasonable as compliance constraints change frequently and are often the cause for concept drift. The approach employs state transition systems as prediction models in order to explain the effects of predicting unseen behavior. The approach also provides update strategies as the event stream evolves. All algorithms are prototypically implemented and tested on an artificial as well as real-world data set.
AutoNLP: A System for Automated Market Research Using Natural Language Processing and Flow-based Programming

This paper introduces a novel architecture for an automated market research system that utilizes the Flow-based Programming (FBP) paradigm. The described system allows users to select topics of interest, and then automatically collects website content related to these topics. The system also offers different search modes for searching the collected text corpus, including stem-based and semantic search. These search modes utilize Natural Language Processing (NLP) techniques. The design of the system was developed to meet specific requirements and underwent a thorough assessment. We analyzed the system’s runtime efficiency with and without the use of flow-based logic and found scalability issues in the Python library used. Finally, we conducted a user study that evaluated the system’s usability, showing that the system can be used without requiring extensive training.
Efficient Quantum Solution for the Constrained Tactical Capacity Problem for Distributed Electricity Generation

With the transition to sustainable energy sources and devices, the demand for and supply of energy increases ever more. With energy grids struggling to keep up with this increase, we need to ensure that supply and demand are correctly matched. The Tactical Capacity Problem tackles this issue by choosing the optimal location of sustainable power sources to minimise the total energy loss. We extend an existing quantum approach of solving this problem in two ways. Firstly, we extend the problem to include capacity constraints, resulting in the Constrained Tactical Capacity Problem. Secondly, we propose two ways of optimising the performance of the resulting model via variable reduction. These optimisations are supported by numerical results obtained on both classical and quantum solvers.
CarbonEdge: Collaborative Blockchain-Based Monitoring, Reporting, and Verification of Greenhouse Gas Emissions on the Edge

Decarbonization calls for efficient and trustworthy monitoring, reporting, and verification (MRV) of greenhouse gas (GHG) emissions. This article proposes a collaborative approach and exemplifies a concrete scenario: the biogas plant scenario. We propose a consortium blockchain as platform and aim to reduce costs and efforts by collaboration and automation in up-scaling GHG management. We present a demonstrator which employs an industry-grade radio frequency identification (RFID) security approach to manage certificate material, a low-code interface for interaction, and the operation of Hyperledger Fabric on industrial edge devices.
Using Clustering Algorithms to Automatically Identify Phishing Campaigns

Attackers attempt to create successful phishing campaigns by sending out trustworthy-looking emails with a range of variations, such as adding the recipient name in the subject line or changing URLs in email body. These tactics are used to bypass filters and make it difficult for the information system teams to block all emails even when they are aware of an ongoing attack. Little is done about grouping emails into campaigns with the goal of better supporting staff who mitigate phishing using reported phishing. This paper explores the feasibility of using clustering algorithms to group emails into campaigns that IT staff would interpret as being similar. First, we applied Meanshift and DBSCAN algorithms with seven feature sets. Then, we evaluated the solutions with the Silhouette coefficient and homogeneity score and find that Mean Shift outperforms DBSCAN with email origin and URLs based features. We then run a user study to validate our clustering solution and find that clustering is a promising approach for campaign identification.
The Contrastive Network With Convolution and Self-Attention Mechanisms for Unsupervised Cell Segmentation

Deep learning for cell instance segmentation is a significant research direction in biomedical image analysis. The traditional supervised learning methods rely on pixel-wise annotation of object images to train the models, which is often accompanied by time-consuming and labor-intensive. Various modified segmentation methods, based on weakly supervised or semi-supervised learning, have been proposed to recognize cell regions by only using rough annotations of cell positions. However, it is still hard to achieve the fully unsupervised in most approaches that the utilization of few annotations for training is still inevitable. In this article, we propose an end-to-end unsupervised model that can segment individual cell regions on hematoxylin and eosin (H&E) stained slides without any annotation. Compared with weakly or semi-supervised methods, the input of our model is in the form of raw data without any identifiers and there is no need to generate pseudo-labelling during training. We demonstrated that the performance of our model is satisfactory and also has a great generalization ability on various validation sets compared with supervised models. The ablation experiment shows that our backbone has superior performance in capturing object edge and context information than pure CNN or transformer under our unsupervised method.