Reviewerly/exHarmony huggingface dataset:
Conversations Sentiment and Intent Categorization Using Context RNN for Emotion Recognition

Nowadays, a chatbot is often found in daily life, and its role in business becomes more important. We develop Dinus Intelligent Assistant (DINA) chatbot for assisting student administration services. While having a text-based conversation, recognizing emotions in textual-based content is a challenging task. This study uses dataset which contains conversation dialogues. In this study, we preprocess conversation using sentiment analysis then collect the data by its conversations and sentiment analysis results. Then we apply RNN to categorize the emotions based on current conversations. The result of this approach is promising, with a precision accuracy of 0.76. From the result, this algorithm can indeed help recognize emotions from text-based conversations.
Deep Attentive Multimodal Network Representation Learning for Social Media Images

The analysis for social networks, such as the socially connected Internet of Things, has shown a deep influence of intelligent information processing technology on industrial systems for Smart Cities. The goal of social media representation learning is to learn dense, low-dimensional, and continuous representations for multimodal data within social networks, facilitating many real-world applications. Since social media images are usually accompanied by rich metadata (e.g., textual descriptions, tags, groups, and submitted users), simply modeling the image is not effective to learn the comprehensive information from social media images. In this work, we treat the image and its textual description as multimodal content, and transform other metainformation into the links between contents (such as two images marked by the same tag or submitted by the same user). Based on the multimodal content and social links, we propose a Deep Attentive Multimodal Graph Embedding model named DAMGE for more effective social image representation learning. We introduce both small- and large-scale datasets to conduct extensive experiments, of which the results confirm the superiority of the proposal on the tasks of social image classification and link prediction.
Fast Rotation Kernel Density Estimation over Data Streams

Kernel density estimation method is a powerful tool and is widely used in many important real-world applications such as anomaly detection and statistical learning. Unfortunately, current kernel methods suffer from high computational or space costs when dealing with large-scale, high-dimensional datasets, especially when the datasets of interest are given in a stream fashion. Although there are sketch methods designed for kernel density estimation over data streams, they still suffer from high computational costs. To address this problem, in this paper, we propose a novel Rotation Kernel. The Rotation Kernel is based on a Rotation Hash method and is much faster to compute. To achieve memory-efficient kernel density estimation over data streams, we design a method, RKD-Sketch, which compresses high dimensional data streams into a small array of integer counters. We conduct extensive experiments on both synthetic and real-world datasets, and experimental results demonstrate that our RKD-Sketch saves up to 216 times computational resources and up to 104 times space resources than state-of-the-arts. Furthermore, we apply our Rotation Kernel in active learning. Results show that our method achieves up to 256 times speedup and saves up to 13 times space to achieve the same accuracy as the baseline methods.
FutureText: A Blockchain-based Contract Signing Prototype with Security and Convenience

This paper proposes a safe, convenient and Blockchain-based way to sign Privately Offered Fund (POF) contracts. Our contributions are as follows: First, in order to reduce the CO2 emission from printing, delivery and mostly for the sake of security, we set up an electronic procedure for signing POF contracts. Second, we design and implement FutureText prototype using Hyperchain, which helps to share privacy information among stakeholders while keeping forged signature, counterfeited seal and twin-contracts away. Finally, to follow the legitimacy and compliance, we put forward methods for both authentication and signature of tripartite contracts. Besides, we put encrypted contracts on Blockchain while keeping original contracts on local database using heterogeneous storage, and that helps alleviate the prototype's storage pressure.
Exploring Explicit And Implicit Visual Relationships For Image Captioning

Image captioning is one of the most challenging tasks in AI, which aims to automatically generate textual sentences for an image. Recent methods for image captioning follow encoder-decoder framework that transforms the sequence of salient regions in an image into natural language descriptions. However, these models usually lack the comprehensive undemanding of the contextual interactions reflected on various visual relationships between objects. In this paper, we explore explicit and implicit visual relationships to enrich region-level representations for image captioning. Explicitly, we build semantic graph over object pairs and exploit gated graph convolutional networks (Gated GCN) to selectively aggregate local neighbors’ information. Implicitly, we draw global interactions among the detected objects through region-based bidirectional encoder representations from transformers (Region BERT) without extra relational annotations. To evaluate the effectiveness and superiority of our proposed method, we conduct extensive experiments on Microsoft COCO bench-mark and achieve remarkable improvements compared with strong baselines.
A Tag-based Recommender System for Regression Test Case Prioritization

In continuous integration development environments (CI), the software undergoes frequent changes due to bug fixes or new feature requests. Some of these changes may accidentally cause regression issues to the newly released software version. To ensure the correctness of the newly released software, it is important to perform enough testing prior to code submission to avoid breaking builds. Regression testing is one of the important maintenance activities that can control the quality and reliability of modified software, but it can also be very expensive. Test case prioritization can reduce the costs of regression testing by reordering test cases to meet testing objectives better. To date, various test prioritization techniques have been developed, however, the majority of the proposed approaches utilize static or dynamic analyses to decide which test cases should be selected. These analyses often have significant cost overhead and are time consuming. This paper introduces a new method for automatic test case prioritization in a CI environment intending to minimize the testing cost. Our proposed approach uses information retrieval to automatically select test cases based on their textual similarity to the portion of the code that has been changed. Our technique not only helps developers to organize and manage the software repository but also helps them to find the relevant resources quickly. To evaluate our approach, we performed an empirical study using 37 versions of 6 open source applications. The results of our empirical study indicate that our proposed method can improve the effectiveness and efficiency of test case prioritization technique.
Extracting Keywords from Texts based on Word Frequency and Association Features

With the development of information technology such as mobile Internet and social media applications, network information is growing rapidly and leads to the problem of information overload. Keywords help to filter and find interesting information for users from massive text. Automatic extraction of keywords from text as tags of text help to improve recommendation and keyword-based information retrieval. This paper proposes a novel keyword extraction approach from text that combines features such as word frequency and association. Experiment results show that the precision rate, recall rate and F-measure are all better than those of TextRank and TF-IDF.
Spatial Content Alignment for Pose Transfer

Due to unreliable geometric matching and content misalignment, most conventional pose transfer algorithms fail to generate fine-trained person images. In this paper, we propose a novel framework – Spatial Content Alignment GAN (SCA-GAN) which aims to enhance the content consistency of garment textures and the details of human characteristics. We first alleviate the spatial misalignment by transferring the edge content to the target pose in advance. Secondly, we introduce a new Content-Style DeBlk which can progressively synthesize photo-realistic person images based on the appearance features of the source image, the target pose heatmap and the prior transferred content in edge domain. We compare the proposed framework with several state-of-the-art methods to show its superiority in quantitative and qualitative analysis. Moreover, detailed ablation study results demonstrate the efficacy of our contributions. Codes are publicly available at github.com/rocketappslab/SCA-GAN.
Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling

Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models.One added benefit when using representations from multilingual models is that they facilitate zero-shot polylingual topic modeling.However, while it has been widely observed that pre-trained embeddings should be fine-tuned to a given task, it is not immediately clear what supervision should look like for an unsupervised task such as topic modeling.Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling.We consider fine-tuning on auxiliary tasks, constructing a new topic classification task, integrating the topic classification objective directly into topic model training, and continued pre-training.We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality, and that fine-tuning encoder representations on any task is the most important factor for facilitating cross-lingual transfer.
Heterogeneous academic network embedding based multivariate random-walk model for predicting scientific impact

The prediction of current scientific impact of papers and authors has been extensively studied to help researchers find valuable papers and recent research directions, also help policymakers make recruitment decisions or funding allocation. However, how to accurately evaluate the future impact of them, especially for new papers and young researchers, is the focus of scientific impact prediction research, and is less explored. Existing graph-based methods heavily depend on the global structure information of heterogeneous academic network and ignore the local structure information and text information, which may provide important clues to identify influential papers and authors with novel perspective. In this paper, we propose a hybrid model called ESMR to predict the future influence of papers and authors by mainly exploiting these information mentioned above. Specifically, we first put forward a novel network embedding-based model, which can capture not only the local structure information, but also the text information of papers into a unified embedding representation. Then, the future impact of papers and authors is mutually ranked by integrating the learned embedding representations into a multivariate random-walk model. Empirical results on two real datasets demonstrate that the proposed method significantly outperforms the existing state-of-the-art ranking methods.
Restoration of HDR Images for SVE-Based HDRI via a Novel DCNN

Ghosting artifacts are believed to be the Achilles’ heel for high dynamic range (HDR) imaging (HDRI) via differently exposed images sequentially captured by a digital device. Spatially varying exposure (SVE)-based HDRI is an efficient solution to prevent the ghosting artifacts from appearing in a HDR image. However, it is challenging to restore a high-quality HDR image with the full resolution from a single raw Bayer image for the SVE-based HDRI. In this paper, a novel deep convolution neural network (DCNN) is proposed to address such a challenging problem. The proposed DCNN includes two distinctive components, a spatially varying convolution and an exposedness-aware compensation branch. The evaluations indicate that the quality of our results significantly surpasses several related algorithms. Related materials will be provided at https://github.com/yilun-xu/SVEHDRI/.
A novel distance-based algorithm for multi-user classification in keystroke dynamics

In this paper, we propose a novel distance-based localization algorithm for multi-user classification in keystroke biometrics. While this method can be applied across identification scenarios, here, we address a use-case to mitigate problems such as Facebook access misuse in shared settings. Our approach combines distance-based metric evaluation, dimensionality reduction, and localization, and is effective in dealing with challenges associated with keystroke dynamics, such as feature interaction, scale variations, and outliers. Our algorithm is evaluated with the CMU keystroke dynamics benchmark dataset and is shown to outperform classical approaches such as PCA and Kernel PCA combined with nearest neighbor classification.
Automated Breast Cancer Detection and Classification Techniques – A survey

Breast cancer (BC) is the world's second leading cause of death for women. Because the cause of the disease is unknown, early detection and diagnosis are critical for BC control, as they can improve treatment success, save lives, and lower costs. Gene mutation, changes in size, and breast skin texture are indicators of BC. Symptoms must be carefully investigated to offer appropriate care to patients and an automatic prediction system that can classify tumors as benign or malignant is required. The majority of data generated in today's internet world is collected on social media or healthcare websites. Using data mining (DM) techniques, symptoms can be extracted from this massive amount of data, which will be helpful in the identification and classification of BC. The main contributions in this study are to investigate major problems that faces BC diagnosis and classification methods, as well as to present an overview of recent research studies that used to identify and classify BC.
Research of the level of digital competencies of students of the University “Adriatic” Bar

Latest EU guidelines for the implementation of Digital Competence Framework and adopted Action Plan for Digital Education (2021-2027) clearly identifies two basic strategic priorities - Encouraging the development of a successful digital education ecosystem and Development of digital skills and competencies for digital transformation, clearly indicate the importance of high quality, inclusive and affordable digital education in Europe. Today, European higher education main problem is the management of the digital transition. As Montenegro is one of the candidate countries for EU, we believe that this is very important and attractive research area. The paper deals with basic and advanced digital competencies development levels of students of University “Adriatic” Bar, seen as Montenegro’s and the EU’s future labour market staff. The results of empirical research shows the real level of students digital competencies in relation to the EU required level. The results shows whether the teaching practice of digital competencies development is sufficiently present at the University, certain limitations, reasons, and possibilities for further improvement. Today, digitalization directly affects the modern labour market. As a result, young people in Montenegro must have highly developed digital competencies, in order to remain competitive in the EU labour market.
A Result Based Portable Framework for Spoken Language Understanding

Spoken language understanding (SLU), which is a core component of the task-oriented dialogue system, has made substantial progress in the research of single-turn dialogue. However, the performance in multi-turn dialogue is still not satisfactory in the sense that the existing multi-turn SLU methods have low portability and compatibility for other single-turn SLU models. Further, existing multi-turn SLU methods do not exploit the historical predicted results when predicting the current utterance, which wastes helpful information. To gap those shortcomings, in this paper, we propose a novel Result-based Portable Framework for SLU (RPFSLU). RPFSLU allows most existing single-turn SLU models to obtain the contextual information from multi-turn dialogues and takes full advantage of predicted results in the dialogue history during the current prediction. Experimental results on the public dataset KVRET have shown that all SLU models in baselines acquire enhancement by RPFSLU on multi-turn SLU tasks.
DGD-net: Local Descriptor Guided Keypoint Detection Network

In recent years, learning-based feature detection network has greatly improved the performance of keypoints matching. However, existing approaches have not fully utilized the representational ability of learned descriptors for feature detection. We propose a novel keypoint detection and description approach to make more use of the reliability of descriptors in matching on keypoint detection. We utilize the descriptor training loss to construct a guided score, which depicts the matching reliability of the descriptors, to supervise the detectors learning. We also propose a descriptor training loss to adapt to our detector training. In order to improve localization accuracy from the low-resolution feature map, we propose a method based on the idea of backtracking. Extensive experiments show the effectiveness of the proposed approach.
Implicit Partiality of Signature Morphisms in Institution Theory

We develop an extension of institution theory that accommodates implicitly the partiality of the signature morphisms and its syntactic and semantic effects. This is driven primarily by applications to conceptual blending, but other application domains are possible (such as software evolution). The particularity of this extension is a reliance on ordered-enriched categorical structures. This work is dedicated to Hajnal Andréka and István Németi whose early work played an important role in shaping the abstract model theory dream of the author.
Effective and direct control of neural TTS prosody by removing interactions between different attributes

End-to-end TTS advancement has shown that synthesized speech prosody can be controlled by conditioning the decoder with speech prosody attribute labels. However, to annotate quantitatively the prosody patterns of a large set of training data is both time consuming and expensive. To use unannotated data, variational autoencoder (VAE) has been proposed to model individual prosody attribute as a random variable in the latent space. The VAE is an unsupervised approach and the corresponding latent variables are in general correlated with each other. For more effective and direct control of speech prosody along each attribute dimension, it is highly desirable to disentangle the correlated latent variables. Additionally, being able to interpret the disentangled attributes as speech perceptual cues is useful for designing more efficient prosody control of TTS. In this paper, we propose two attribute separation schemes: (1) using 3 separate VAEs to model the real-valued, different prosodic features, i.e., F0, energy and duration; (2) minimizing mutual information between different prosody attributes to remove their mutual correlations, for facilitating more direct prosody control. Experimental results confirm that the two proposed schemes can indeed make individual prosody attributes more interpretable and direct TTS prosody control more effective. The improvements are measured objectively by F0 Frame Error (FFE) and subjectively with MOS and A/B comparison listening tests, respectively. The scatter diagrams of t-SNE also demonstrate the correlations between prosody attributes, which are well disentangled by minimizing their mutual information. Synthesized TTS samples can be found at https://xiaochunan.github.io/prosody/index.html.
Review on Text and Speech Conversion Techniques based on Hand Gesture

Communication is the act of conveving thoughts from one group to another through the use of mutually acknowledged signs, symbols and semiotic rules. It is the only medium through which people can exchange their thoughts or convey messages. The deaf-dumb people have their own set of signs. Hand gestures are a form of non-verbal communication which are used by the deaf and dumb people to communicate among them and with the external world. But the problem with the existing world is most of the people are not knowledgeable enough to interpret the hand gestures or convert it into the language which can be understood by the normal people. So, to bridge the communication gap between the deaf-dumb people and normal people, it is important to develop a system which can assist them in converting gestures to text and/or speech. Creating a robust communication system for the deaf and dumb community will help them be more independent and confident. In this article, an extensive review is made on the various approaches available for gesture recognition.
A Policy-Based Approach for Managing Socially Appropriate Interaction Behaviours in IoT Collectives

In this paper, we investigate a policy based approach for managing socially appropriate interaction behaviours in Internet of Things (IoT) Collectives, i.e., a collection of IoT devices. In particular, we consider scenarios in an aged care home environment. We have designed a policy management framework with three different types of rules - Authorisation, Obligation and Prohibition rules for smart internet-connected devices in an aged care centre where smart robots follow consensus-seeking processes while monitoring the elderly people. We consider the management of socially appropriate interaction behaviours in IoT devices based on a policy management framework and demonstrate its applicability with the system we prototyped called KATE.
Ontology-Based Context Event Representation, Reasoning, and Enhancing in Academic Environments

An Ambient Intelligence responds to user requests based on several contexts. A relevant context is related to what has happened in the ambient; therefore, it focuses a primordial interest on events. These involve information about time, space, or people, which is significant for modeling the context. In this paper, we propose an event-driven approach for context representation based on an ontological model. This approach is extendable and adaptable for academic domains. Moreover, the ontological model to be proposed is used in reasoning and enrichment processes with the context event information. Our event-driven approach considers five contexts as a modular perspective in the model: Person, temporal (time), physical space (location), network (resources to acquire data from the ambient), and academic events. We carried out an evaluation process for the approach based on an ontological model focused on (a) the extensibility and adaptability of use case scenarios for events in an academic environment, (b) the level of reasoning by using competence questions related to events, (c) and the consistency and coherence in the proposed model. The evaluation process shows promising results for our event-driven approach for context representation based on the ontological model.