Reviewerly/exHarmony huggingface dataset:
An Effective Approach for Bengali Handwritten Punctuation Recognition by Using a Low-cost Convolutional Neural Network

Bengali handwritten character recognition is considered difficult due to the complex curvatures and diverse variations of Bengali characters. Being the seventh most-spoken language of the world, Bengali has received some worth-mentioning contributions in this domain with the creation of different datasets such as NumtaDB, BanglaLekha, CMATERdb, and so on. However, none of the previous studies solely focused on recognizing punctuation marks in Bengali literature. Punctuations are important in Bengali language as without them the meaning of the whole sentence can be changed drastically. Another challenge in handwritten character recognition is the lack of low-cost convolutional neural networks (CNN). Recent studies suggested remarkable performance utilizing transfer learning approaches. However, due to the high computation of such methods, they are not suitable for recognizing in real-time via mobile devices. To solve this dilemma, in this study, we have proposed a low-cost CNN for the recognition of handwritten punctuations. Experimental results showed that our proposed architecture achieved an overall accuracy of 96.25% and outperformed popular transfer learning approaches such as DenseNet-201, InceptionV3, Xception, and EfficientNet-B6 by a significant margin with the least recognition time and the least number of parameters. Moreover, to solve the lack of dataset problem in this domain, we have collected 50 handwritten samples for each of the 28 Bengali punctuation marks in this study and formed a new dataset called ‘BiramDB’ (In Bengali, ‘Biram’ signs means punctuations). This research will be beneficial for diverse applications such as converting handwritten poems, stories, historical documents, and other writings into machine-encoded texts.
Exploiting Deep Learning Based Classification Model for Detecting Fraudulent Schemes over Ethereum Blockchain

The extensive usage of the Blockchain technology as one of the most popular forms of decentralized platform has been spread across a numerous field over the recent years. From financial sectors like banking industry to the supply chain management of multiple large corporate farms, blockchain technology has been proven its productivity across different communities. However, the reliability of the blockchain system has often been compromised with the introduction of various scams and fraudulent activity within the system. Due to the absence of a comprehensive and definitive dataset, the challenges of building an effective fraud detection model becomes even more sever in this particular field. Thus, in our work, we propose a deep learning based blockchain fraud detection model based on the Ethereum blockchain transaction data. With the association of a reliable dataset in this field, we build a deep learning-based detection model to classify the fraudulent activities within the system. The proposed classification model is comprised of a Long Short-Term Memory (LSTM) unit and a dense unit to detect the fraudulent transactions. For the sake of reducing down the complexity and avoiding the unnecessary transactional features, Information Gain has been utilized as the feature selection unit of the model. When compared to the corresponding values of different models on the same dataset, experimental results show a significant improved results in different aspects using the proposed approach.
A Fusion of Three Custom-Tailored Deep Learning Architectures for Waste Classification

Population growth is exploding exponentially in this current world. Due to this escalating population and urbanization, the overall amount of waste is experiencing a rapid growth worldwide. Consequently, plenty of waste contributes to climate change, affecting our ecosystems and species. Fortunately, trash management would help alleviate some of these effects since a large quantity of trash is highly biodegradable and recyclable. However, classifying waste manually based on its contents is highly expensive and time-consuming. This is why the classification of wastes based on their contents is a critical criterion for ensuring cost-effective performance throughout recycling procedures. This paper proposes a hybrid deep-learning framework to classify waste into four categories: paper, glass, plastic, and organic. To address the lack of sufficient data, we used the albumentation function to augment the data. Later, in order to remove any duplicates that might have existed in the updated dataset, we also applied an image hashing technique that tackles the problem of overfitting. After preprocessing, we integrate three different models (2 EfficientNet models with noisy-student and imagenet and a custom convolutional neural network model) and provide prediction, and a heatmap of the eXplainable Artificial Intelligence (X-AI) generated images based on the test dataset to improve the trustworthiness of the inference. In comparison to various earlier state-of-the-art studies in the area of waste management, our technique performed substantially better, scoring at around 97% accuracy.
Assessment of Sentiments: A Performance Evaluation on Bangla Noisy Text

The fact that people have sentiments is perhaps the most significant distinction between robots and humans. Researchers have been working on ways to imitate sentimentality in computers for decades. The majority of recent Sentiment Analysis research in Natural Language Processing (NLP) has concentrated on the English language. Because of the rich grammatical structure of the text, a few notable studies have been conducted in the Bangla language sector. It should also be highlighted that Bangla lacks a comprehensive dataset. As a consequence, current research projects including Bangla have failed to yield findings that are similar to those produced by researchers in other languages and reusable for future study. In this work three categorical machine learning models namely classical, neural network, and transformers that are prevalent in sentiment analysis tasks have been evaluated on a recently introduced noisy Bangla dataset. The experimental outcome showed that the classical machine learning model Support Vector Machine trained with n-gram feature extractors from the category of classical methods performed preferably in contrast to the models in the same category and other categories of approaches implemented. The results acquired in this work can be subsidiary in terms of understanding the impact of the content and human perception from comments that include distorted words or regional dialects associated with different media domains.
A 320 FPS Pixel-Level Pipelined Stereo Vision Accelerator with Regional Optimization and Multi-direction Hole Filling

Semi-global matching (SGM) is a canonical depth estimation algorithm widely used in the fields of autonomous driving, 3D reconstruction, and SLAM. However, high complexity withholds its application in high-speed and low-power depth extraction scenarios, notably in IoT and edge devices. To tackle these problems, we propose a region-optimized SGM algorithm, which can alleviate memory consumption bottleneck and strike a balance among power dissipation, processing speed, and resource consumption. First, we design a fully parallel initial matching costs calculation architecture, which ensures synchronization of the left and right pixel stream. Then, a two-layer parallel two-stage pipeline structure (TPTP) calculates the cost aggregation in two directions (0° and 135°) to mitigate the high computational complexity. Finally, we adopt a LUT-based cosine sub-pixel interpolation architecture and a multi-directional parallel hole filling architecture (MPHF) to improve accuracy further in the disparity refinement process. The experimental results show that the proposed pixel-level pipeline architecture achieves a processing speed of 320 frames per second (fps) at 98MHz on the Stratix-IV FPGA device.
Retrieving and Ranking Relevant Products from Boolean Natural Language Queries

E-commerce is a massive sector in the US economy, generating $767.7 billion in revenue in 2021. E-commerce sites maximize their revenue by helping customers find, examine, and purchase products. To help users easily find the most relevant products in the database for their individual needs, e-commerce sites are equipped with a product retrieval system. Many of these systems parse user-specified constraints or keywords embedded in a simple natural language query, which is generally easier and faster for the customer to specify their needs than navigating a product specification form, and does not require the seller to design or develop such form. These natural language retrieval systems, however, suffer from low relevance in retrieved products, especially for complex constraints specified on products. The reduced accuracy is in part due to under-utilizing the rich semantics of natural language, specifically queries that include Boolean operators, and lacking of the ranking on partially- matched relevant results that could be of interested to the customers. This undesirable effect costs e-commerce vendors to lose sells on their merchandise. In solving this problem, we propose a product retrieval system, called QuePR. The advantages of QuePR are its ability to process explicit and implicit Boolean operators in queries, handle natural language queries using similarity measures on partially-matched records, and perform best guess or match on ambiguous or incomplete queries. QuePR is unique, easy to use, and scalable to all product categories. We have conducted different performance analysis to verify the effectiveness of QuePR and compared QuePR with other ranking and retrieval systems. The empirical results show that QuePR outperforms others and is efficient.
Mining and Understanding Arabic Tweets Related to Cognitive Disabilities

Social media has become an important part of everyday life for all segments of a society. Twitter is one of the leading platforms, where people can freely express their opinions and tweet about different topics to a large audience. Due to the core nature of tweets exchange on Twitter, text mining is essential to understand how a topic is discussed and perceived within society. We utilized text mining approaches, including sentiment and temporal analyses, to examine and understand how Arab users engage with Twitter to discuss cognitive disabilities. Content volume, temporal evolution, users, and sentiment were analyzed. We applied Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment analysis to identify the overall opinions and attitudes toward the researched neurological conditions. The results provide new insights into public perspectives, which may assist interested entities to form and distribute appropriate resources and information. We discussed our findings, provided recommendations to interested stakeholders, and introduced potential opportunities and future directions.
Handling out of vocabulary words at the semantical level using recurrent neural networks

Text recognition through natural language processing (NLP) faces challenges when it encounters a word that is not categorized. These types of words are called out-of-vocabulary words (OOV). They are often the subject of representation, local slang, or typing mistakes. These types of content have grown exponentially as the Internet has popularized, making people interact more assiduously through texting. Given the importance of this subject, we present three OOV classification models based on deep learning using a corpus with words in Portuguese as a case study. These models are bidirectional simple recurrent neural networks (RNN), short-term long memory (LSTM), and gated recurrent units (GRU). The purpose is to enable the system to recognize the embedding of OOV and place them in a vector space. In addition, the meaning of the words was verified using cosine similarity. The results of LSTM are promising for identifying OOV and generating semantically similar words. The model can be used in pre-processing pipelines for user-generated content analysis, adding more value to social media studies.
A Context-Aware BERT Retrieval Framework Utilizing Abstractive Summarization

Recently, the multi-stage reranking framework based on pre-trained language model BERT can significantly improve the ranking performance on information retrieval tasks. However, most of these BERT-based reranking frameworks independently process query-chunk pairs and ignore cross-passages interaction. The context information around each candidate passage is extremely important for relevance judgement. Existing relevance aggregation methods obtain context information through statistical method and lost part of semantic information. Therefore, to capture this cross-passages interaction, this paper proposes a context-aware BERT ranking framework that utilizing abstractive summarization to enhance text semantics. By utilizing PEGASUS to summarize both sides of candidate passage accurately and then concatenate them as the input sequence, BERT could acquire more semantic information under the limitation of the input sequence’s length. The experimental results of two TREC data sets reveal the effectiveness of our proposed method in aggregating contextual semantic relevance.
Morphologically-Aware Vocabulary Reduction of Word Embeddings

We propose SubText, a compression mechanism via vocabulary reduction. The crux is to judiciously select a subset of word embeddings which support the reconstruction of the remaining word embeddings based on their form alone. The proposed algorithm considers the preservation of the original embeddings, as well as a word’s relationship to other words that are morphologically or semantically similar. Comprehensive evaluation of the compressed vocabulary reveals SubText’s efficacy on diverse tasks over traditional vocabulary reduction techniques, as validated on English, as well as a collection of inflected languages.
Explainable Recommendation Enhancing Review Properties and PPLM

Explainable recommendation, which provides items and explanation why they are recommended, have attracted a lot of attention as it could improve transparency, persuasiveness, effectiveness, reliability, and user satisfaction of recommender systems. A lot of explainable recommender systems have been proposed so far. However, they do not take review properties into account, need to further improve generation of explanation, and also need to analyze specific cases in recommendation.Our work aims at developing an explainable recommender model that improves quality of generated explanations as well as accuracy of recommendation. To achieve this, we propose an end-to-end architecture that leverages properties of review sentences and Plug and Play Language Model (PPLM). Experimental results on publicly available datasets demonstrate that our proposed recommender model improves both accuracy of rating prediction and quality of generated explanations by employing multi-task learning framework.
What Really Matters in a Table? Insights from a User Study

Better understanding human visual attention during reading can provide valuable insights for developing user-centred computations models. A considerable amount of data, presented in a tabular form, is used in daily activities and is available on the Web nowadays. Several approaches have proposed an automated table summarisation method to improve the users’ experience and give them succinct summaries of tables. However, there has been little attention to considering user behaviour in the design of automated table summarisation. In this paper, we present the findings of an empirical study, where we investigate, with the help of standard User Experience tools (eye-tracking technology and surveys), how users approach the reading of a table. We focus on evaluating how the domain knowledge and interest of the users influence their comprehension, eventually identifying four possible user-profiles and their different information needs. In order to show the impact of our findings on the selection of the information to keep in summary, we present and release a tool that, in addition to supporting the development of similar experiments, allows checking the information presented in summary in the form of Resource Description Framework (RDF) triples, by exploiting the semantic annotation of the table.
EurOpi: Multilingual Aspect-Based Sentiment Analysis enabled by a Knowledge Base

Opinionated texts are widely available as the use of e-commerce platforms has become more popular around the world. Sentiment Analysis is the field of study which aims to understand users’ preferences and opinions, extracting useful knowledge from this kind of data. Despite advances in the last years, some topics remain unexplored. This is the case of multilingual aspect-based sentiment analysis (MABSA), which aims to process reviews written in multiple languages. In this work, we propose EurOpi, a MABSA technique that relies on a knowledge base, i.e., a multilingual product catalog, to map the aspects evaluated by users from opinionated texts into product attributes. We show that (i) EurOpi effectively maps aspects to catalog attributes, and (ii) a multilingual product catalog enriched with opinions makes it easy to compare opinions across different languages. Finally, we release a dataset, containing reviews in English, Spanish, and Italian annotated at the aspect level to help increment research on MABSA.
Active Learning Strategies Based on Text Informativeness

In this paper, we propose strategies for selecting the next item to label in active learning for text data. Text data have several text-specific features, such as TF-IDF vectors and document embeddings. These features have correlation with the informativeness of the text data, so our methods select the next item to label by using these text-specific features. We evaluate the performance of our strategies in two problem settings: the standard active learning setting, where we focus on the improvement of the model accuracy, and the learning-to-enumerate setting, where we focus on the efficiency in enumerating all instances of a given target class. We also combine our strategies with two existing strategies: uncertainty sampling, a well-known strategy for active learning, and the exploitation-only strategy, a strategy used in learning-to-enumerate problems. Our experiment on two publicly available English text datasets show that our method outperforms the baseline methods in both problem settings.
Developing a Dataset for Mining Reviews in Tweets focusing on Celebrities’ Aspects

The purpose of this paper is to make it easier for celebrity fans to search for information on critiques and interest trends about celebrities and matters related to them. As part of this process, we collect tweets on Twitter that mention aspects of celebrities and train and evaluate a token classification framework to judge whether or not tweets represent reviews on aspects of celebrities. Specifically, we develop a dataset by annotating the collected tweets whether or not aspect’s names of celebrities and adjectives/adjectival verbs have review relationships or not. We train and evaluate the token classification framework using BERT to determine whether the tweets represent reviews on aspects of celebrities. In the evaluation, we evaluate whether or not the TARGET and REVIEW labels annotated by the token classification framework using BERT are correct or not. The token classification framework using BERT achieved high enough performance, indicating that it is appropriate to apply the token classification framework using BERT to this task.
OCOsense Glasses for Facial Expressions Recognition and Contextual Affective Computing in Real World and Augmented Reality

The paper presents the novel OCOSenseTM smart glasses with integrated sensors, primarily non-contact optomyographic (OMG) OCOTM sensors, 9-axis inertial measurement unit (IMU), and an altimeter. The glasses connect with a smartphone application, which facilitates the continuous and real-time measurements of facial-muscles activation and head movement, thus allowing for the detection of facial expressions and the activities of the user in real-time. We will demonstrate how the system is used in practice, i.e., a participant will wear the OCOSenseTM glasses, which will stream the sensor data to a tablet, where the real-time visualization of the sensor data and the data interpretation will be presented such as facial expressions (smile, frown, surprise) and activities. We believe that the OCOSenseTM glasses are the next big thing in wearables, which will allow for better understanding of the user's context, activities, emotional state, and more, which can be easily coupled within Augmented and Extended Reality environments.
An Enhanced Neural Network Model Based on VGG-16 for Accurate Recognition of Oracle

Oracle bone inscriptions are ancient Chinese characters. The study of oracle bone inscriptions heavily relies on character identification. At present, the identification of oracle bone inscriptions on rubbings relies on manual annotation by experts. This is a complex and demanding task, requiring a great deal of labor and time. Therefore, automatic image recognition of oracle bone inscriptions is of great value. By fusing the traits of the VGG-16 model and the Inception structure, this paper suggests an enhanced multi-scale convolutional neural network model. Through the experiment on OBC306 data set, compared with the traditional VGG-16 and GoogleNet models, the accuracy of the suggested model in the training set and test set is better than other models, which demonstrates the correctness and superiority of the suggested model.
A CP-ABE method base on reused sub-policy

In recent years, with the widely used of cloud computing, the security of network for data is more and more important. Traditional symmetric encryption requires both parties to hold the same encryption and decryption keys, which is not suitable for cloud computing environment. Asymmetric encryption also requires that each user and the system have an unique public-private key pair, making the key generation and holding costs are too high. So Ciphertext Policy Attribute-Based Encryption scheme comes in handy. After the attribute mechanism generates a public key and a master private key, it only needs to assign a set of private keys according to the attributes for each user, reducing the cost of key pairs. However, with the increase of user data, it takes more and more time for the data owner to encrypt data. How to accelerate the encryption speed also becomes the key to optimize the system. Combined with relevant research, this paper proposes an encryption scheme based on the multiplexing sub-strategy. On the basis of ensuring the original security, it optimizes the speed of multiple encryption schemes with the same sub-strategy that need to be encrypted simultaneously for the data owner, which greatly improves the computing speed.
An intelligent recommendation model for small batch gradient descent method

In order to solve the problems of objective function oscillation and lack of accuracy caused by random gradient descent in intelligent recommendation system, a personalized recommendation model based on small batch gradient descent method is proposed. By establishing a personalized recommendation model based on small batch gradient descent method, the model can reduce the randomness of the optimal solution of the objective function, improve the accuracy and reduce the running time, so as to improve the quality of personalized recommendation. The model is verified with the public movielens data set. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of the model are better than the existing algorithms. It is verified that the personalized recommendation model based on small batch gradient descent method can get better recommendation effect.