Reviewerly/exHarmony huggingface dataset:
Generative Text Convolutional Neural Network for Hierarchical Document Representation Learning

For document analysis, existing methods often resort to the document representation that either discards the word order information or projects each word into a low-dimensional dense embedding vector. However, confined by the data's sparsity and high-dimensionality, limited effort has been made to explore the semantic structures underlying the document representation that formulates each document as a sequence of one-hot vectors, especially in the probabilistic modeling literature. To construct a probabilistic generative model for this type of document representation, we first develop convolutional Poisson factor analysis (CPFA) that not only utilizes the sparse property of data but also enables model parallelism. Through interleaving probabilistic Dirichlet-gamma pooling layers with learnable parameters, we extend the shallow CPFA into a generative text convolutional neural network (GTCNN), which captures richer semantic information with multiple probabilistic convolutional layers and can be coupled with existing deep topic models to alleviate their loss of word order. For efficient and scalable model inference, we not only develop both a parallel upward-downward Gibbs sampler and SG-MCMC based algorithm for training GTCNN, but also construct a hierarchical Weibull convolutional inference network for fast out-of-sample prediction. Experimental results on document representation learning tasks demonstrate the effectiveness of the proposed methods.
Empowering Graduate Students’ Service Delivery by Using an Agile Chatbot: A Conceptual Framework

Today advances in information and communication technology are providing information services to answer students’ questions. Innovative ways to support teaching and learning in higher education institutions are welcome in today’s competitive environment. They will enable students to stay engaged in their studies and finish their studies on time. In this paper, I have outlined how chatbots are developed by applying Agile principles to enable chatbots that fully meet the needs of service providers and service users. For instance, there is chatbot development based on the software development life cycle. Let's start with the collection of user requirements from all concerned stakeholders. This is done in order to draw up a chatbot model and assess its suitability. Then the development of the first version of the chatbot with Agile principles was undertaken. After testing and receiving user feedback, a new version of the chatbot was designed. This study covers the development of chatbots, including monitoring the needs of both service providers and service users. This process enables chatbots to provide information services efficiently.
Multi-loop Adaptive Differential Evolution for Large-Scale Expensive Optimization

Data-driven evolutionary algorithms (DDEAs) have shown promising performance in solving small- and medium-scale expensive optimization problems (EOPs) with low and medium dimensions. However, the performance of existing DDEAs is still not good enough for large-scale EOPs. To efficiently solve the large-scale EOPs, this paper proposes a new offline DDEA based on adaptive differential evolution (DE), called ADE-DDEA. To obtain a sufficiently accurate surrogate, ADE-DDEA introduces a Latin hypercube sampling strategy with standardization for collecting data and a grid searching based approach for training the surrogate. Moreover, ADE-DDEA employs a state-of-the-art adaptive DE (i.e., the jSO) as the optimizer and a multi-loop strategy to avoid trapping into local optima. In the experiment, this paper compares ADE-DDEA with the traditional algorithms on five commonly used test functions with different dimension scales. ADE-DDEA shows significant advantages on large-scale EOPs with only 10% computational budgets of traditional methods. Even if compared with the state-of-the-art DDEAs for large-scale EOPs, ADE-DDEA shows sufficient competitiveness. Furthermore, the experimental results also show the advantage of the algorithm in terms of running time consumption.
Cultural Entrepreneurship and Disruptive Blockchain Applications in Vietnam: The Status Quo and the Transition Towards a Digital Economy

Although blockchain technology is broadly known as the technological underpinning on which cryptocurrencies are developed, enhancing trust, security, transparency and the traceability of data shared across a business network, blockchain real business applications are mainly limited to financial services. Recently, many innovative ideas in alignment with cultural entrepreneurship in startups have attempted to amplify the areas of blockchain utilisation. This chapter aims to investigate the status quo and the latest development of blockchain technology applications in the Vietnamese business context. Based on archival documents and interview data from several blockchain startups, our study highlights the disruptive effects and benefits that blockchain startups could bring to Vietnamese enterprises and highlights the contributions that have been made by these digital startups to the transition towards the digital economy proposed by the government of Vietnam.
Blockchain in Enterprise Applications: An Introduction

Recently, blockchain has rapidly been developed and applied in various fields, not only in cryptocurrencies, including but not limited to digital exchanges, the internet of things, supply chains. Blockchain primitively provides privacy, security, and transparency that are needed to be consolidated in centralized applications. Most enterprise applications have been developed and deployed using a centralized approach which makes them inherent weaknesses of centralized applications. In this work, we propose an approach to apply blockchain in enterprise applications with multi-party smart contracts integration. This integration helps enterprise applications improve privacy, security, and transparency. We also propose a procedure to evaluate the suitability of blockchain for enterprise applications to avoid abuse and irrationality. The analysis demonstrates that choosing the feasible modules to deploy on the blockchain improves the reliability of enterprise applications.
Incorporating Explanations to Balance the Exploration and Exploitation of Deep Reinforcement Learning

Discovering efficient exploration strategies is a central challenge in reinforcement learning (RL). Deep reinforcement learning (DRL) methods proposed in recent years have mainly focused on improving the generalization of models while ignoring models’ explanation. In this study, an embedding explanation for the advantage actor-critic algorithm (EEA2C) is proposed to balance the relationship between exploration and exploitation for DRL models. Specifically, the proposed algorithm explains agent’s actions before employing explanation to guide exploration. A fusion strategy is then designed to retain information that is helpful for exploration from experience. Based on the results of the fusion strategy, a variational autoencoder (VAE) is designed to encode the task-related explanation into a probabilistic latent representation. The latent representation of the VAE is finally incorporated into the agent’s policy as prior knowledge. Experimental results for six Atari environments show that the proposed method improves the agent’s exploratory capabilities with explainable knowledge.
Data Association with Graph Network for Multi-Object Tracking

Multi-Object Tracking (MOT) methods within Tracking-by-Detection paradigm are usually modeled as graph problem. It is challenging to associate objects in dense scenes with frequent occlusion. To further model object interactions and repair detection errors, we use graph network to extract embeddings for data association. Graph neural network makes it possible for embeddings aggregate and update between vertices (detections and trajectories). We both introduce priori confidence to detection attention and trajectory attention, which consider the interaction between occluded objects in the same frame. Based on MHT framework, we train two graph networks for clustering in adjacent frame and association between long spaced tracklets. Experiments on MOT17/20 benchmarks demonstrate the significant improving in tracking accuracy of proposed method and show state-of-the-art performance for MOT with public detections.
Semantic Annotation of Videos Based on Mask RCNN for a Study of Animal Behavior

Detection and tracking of object video are of great interest in various fields like security, traffic, and public places... In this article, we are interested in agriculture and animal husbandry. The majority of existing video object tracking applications aim to extract the object's trajectory based on the detection results and then correlate the obtained coordinates in most frames. But to build knowledge about animal behavior in open pasture, and how long it takes for each behavior, we need the coordinates of each object in all the video's frames. Therefore, in our paper, we aim in the first step to extract all the information about each animal (sheep) from the video. Our research is not limited to knowing behaviors only, but we also seek in the second step to collect the information extracted using specific rules to build knowledge about the effects of animal behavior on themselves, on the pastures, and to know the status of the pastures. This knowledge subsequently contributes to making the best decisions to preserve the vegetation cover and the quality of animal production. We use the Mask R-CNN detector and MS COCO dataset to detect and extract the location of the animal in each frame. We use the Hungarian algorithm to associate similar objects in all video frames. Then we correct the detection and association mistakes. Our approach was able to achieve 100% tracking of all sheep if the sheep not moving very fast. Finally, we use ontologies OWL to represent and extract Knowledge and we express in SWRL the semantic rules which help us to study animals behavior.
NNDF: A New Neural Detection Network for Aspect-Category Sentiment Analysis

Aspect-category sentiment analysis (ACSA) is crucial for capturing and understanding sentiment polarities of aspect categories hidden behind in sentences or documents automatically. Nevertheless, existing methods have not modeled semantic dependencies of aspect terms and specified entity’s aspect category in sentences. In this paper, we propose a New Neural Detection Network, named NNDF in short, to enhance the ACSA performance. Specifically, representations of input sentences and aspect categories contained in our method are generated by a CNN-pooling-BiLSTM structure respectively, where sentences are represented based on their contextual words and aspect categories are represented based on word embeddings of entities category-specific. Then, a Transformer-based encoder is used to model implicit dependency of sentence contexts and aspect categories of entities in sentences. Finally, the embedding of aspect-category is learned by the novel bidirectional attention mechanism for the sentiment classification. Besides, experiments conducted on Restaurant and MAMS benchmark datasets for the task demonstrate that NNDF achieves more accurate prediction results as compared to several state-of-the-art baselines.
Effect of Different Color Spaces on Deep Image Segmentation

Image segmentation is an important application in computer vision, proposed to partition an image into meaningful regions on a specific criterion. In recent days, image segmentation tasks have achieved state of the art performance using deep neural and fully connected networks. The datasets used for the segmentation task mainly consist of image data in RGB color space and the deep segmentation architectures are trained without modifying the color space. In this study, the importance of color space is investigated and the obtained results show that the color space can affect the segmentation performance remarkably. Certain regions of interest in images belonging to a particular domain can be segmented better when represented in a certain form of color space. To explore on this two datasets from medical and satellite imagery are considered. The UNET model is modified to accept images as a combination of color spaces and is trained to segment the colonoscopy images for polyps and satellite images for roads under individual and combination of color spaces. Experiments show that the performance of polyp segmentation is better when a combination of HSV&#x002B;YCbCr color space is considered. Road segmentation in satellite imagery is better in LAB&#x002B;HSV color space.
Vision Based Human Action Classification Using CNN model with Mode Calculation

Video classification is a wide field of study in computer vision research. Many types of approaches have been used for video classification: sensor-based approaches, deep learning-based approaches, handcrafted approaches like hog and blob detection, optical flow-based, sliding window-based, a nd many more. Among all of the strategies, Deep learning has shown quite progressive outcomes. In this work, a CNN-based model has been used. The CNN-based approach for video classification is considered a ladder to the world of deep learning-based computer vision research. The proposed methodology is quite simple. Considering the whole video as a series of frames, it predicts each frame using CNN and then identifies t he c lass of the video using mode calculation of the predictions. To predict the frame, we have followed two types of models. The first approach was building a CNN model from scratch and training the model with our image data, and then using the model for prediction. The second approach used transfer learning for feature extraction and then used those features to train a modified C NN model. The proposed approaches have been tested on a compromised UCF101 sports dataset, and the obtained accuracy was 88.35&#x0025; for the first model and 93.15&#x0025; for the second one.
Post-quantum secure identity-based signature achieving forward secrecy

Identity-based cryptography ( IBC) has gained significant attention over the years by enabling authentication of public key of a party without relying on certificates. Since the discovery of IBC , we have witnessed several ID-based identification ( IBID ) and identity-based signature ( IBS ) schemes in the literature. However, most of them depend heavily on discrete logarithm problem, making them susceptible to quantum attacks. This necessitates the development of IBID and IBS schemes that are immune to quantum attacks. In this work, we reveal the shortcomings of the recently (Peng et al., 2020) proposed isogeny-based IBS by Peng et al. We have discovered severe vulnerabilities in the key component of their IBS scheme, which rendered their IBS scheme unable to meet their claimed security guarantees. More positively, we have adopted a potential framework to resolve the flaws in their scheme. This led us to develop the first isogeny-based IBID scheme that provides security against impersonation under passive attacks . The practical deployment of IBS in surveillance systems raises subtle security issues that should be addressed. To resolve these problems, we have also devised the first forward-secure identity-based signature ( FSIBS ) from isogenies. The comprehensive study of provable security reveals that our scheme provides correctness, forward security and is immune to forging attacks.
dualDup: A secure and reliable cloud storage framework to deduplicate the encrypted data and key

Cloud Storage Providers generally maintain a single copy of the identical data received from multiple sources to optimize the space. They cannot deduplicate the identical data when the clients upload the data in the encrypted form. To address this problem, recently, Duplicateless Encryption for Simple Storage (DupLESS) scheme is introduced in the literature. Besides, the data stored in the cloud is unreliable due to the possibility of data losses in remote storage environments. The DupLESS scheme, on the other hand, keeps both the key and the data on a single storage server, which is unreliable if that server goes down. In essence, the existing related works aim to handle either secure-deduplication or reliability limited to either key reliability or the data reliability. Hence, there is a need to develop a secure-deduplication mechanism that is not vulnerable to any malicious activity, semantically secures both data and key, and achieves the reliability. To address these problems, this paper proposes the dualDup framework that (a) optimizes the storage by eliminating the duplicate encrypted data from multiple users by extending DupLESS concept, and (b) securely distributes the data and key fragments to achieve the privacy and reliability using Erasure Coding scheme. The proposed approach is implemented in Python on the top of the Dropbox datacenter and corresponding results are reported. Experiments are conducted in a realistic environment. The results demonstrate that the proposed framework achieves reliability with an average storage overhead of 66.66% corresponding to the Reed–Solomon(3,2) codes. We validated through security analysis that the proposed framework is secure from insider and outsider adversaries. Moreover, dualDup framework provides all the aspects of deduplication, attack mitigation, key security and management, reliability, and QoS features as compared to other state-of-the-art deduplication techniques.
Automatic contrast enhancement with reversible data hiding using bi-histogram shifting

Aiming at resolving artificial distortions in marked images and increasing embedding capacity, this paper presents an automatic contrast enhancement (CE) method with reversible data hiding (RDH) using bi-histogram shifting (BHS). Current CE methods introduce distortions in marked images as embedding capacity increases. This paper focuses on resolving these artificial distortions by achieving better CE and increasing embedding capacity in marked images. The proposed method adaptively selects two peak bins and two lower bins based on four conditions depending on image brightness using the brightness preservation (BP) method. The two peak bins are used as splitting and embedding bins, whereas the lower bins are used as combining bins. The proposed method pairs each peak bin with a lower bin and performs BHS in the opposite direction to achieve histogram equalization (HE) while embedding secret data in marked images. The proposed method is compared with existing methods and evaluated with PSNR, SSIM, RCE, REE, RMBE, and VIF quality assessment metrics. Experimental results show that the proposed method's marked images are visually and qualitatively superior to existing works, with an average increase in embedding capacity of 37.6%. The proposed method is reversible and performs the extraction of embedded data to retrieve the original images losslessly.
Non-additive image steganographic framework based on variational inference in Markov Random Fields

The majority of image steganography methods lie within the additive scheme that assumes the pixel modifications are independent. Whereas, adapting the embedding modifications based on the mutual embedding impact of neighboring pixels reduce changes in the higher-order statistics of the cover imposed by embedding. However, non-additive schemes are more challenging due to the lack of practical embedding codes that minimize an arbitrary distortion function. In this paper, we propose a general non-additive image steganographic framework based on Markov Random Fields (MRF) which generalizes a few existing research in many aspects and offers an elegant way to model the mutual dependencies between neighborhood modifications in terms of pairwise cliques. The proposed model satisfies spatial coherence by penalizing the desynchronized embedding changes, which orients the changes towards the direction of the majority of changes in the neighbors. Mean Field (MF) inference is used to iteratively estimate the marginal probability at each pixel based on its neighbors so that the original interactions are information projected to the resulting marginals. MF replaces the values of variables with expectations so the result is compatible with practical embedding methods. The proposed framework can be applied to any additive scheme; the initial cost assignment is done by an additive method and MRF modeling defines a non-additive distortion related to statistical detectability that encourages the adjacent changes to synchronize. We study our framework in both symmetric and asymmetric schemes. In the symmetric scheme, a parallel MF is used as an adaptive filter to encourage synchronized embedding changes. The framework can upgrade to an asymmetric scheme that reduces the detectability by conditioning embedding based on the message using clamping exerted in a structural MF. Therefore, our framework is compatible with and generalizes existing non-additive methods, and as shown in experimental results, outperforms the state-of-the-art in both schemes.
Arbitrary Public Announcement Logic with Memory

Abstract We introduce Arbitrary Public Announcement Logic with Memory (APALM), obtained by adding to the models a ‘memory’ of the initial states, representing the information before any communication took place (“the prior”), and adding to the syntax operators that can access this memory. We show that APALM is recursively axiomatizable (in contrast to the original Arbitrary Public Announcement Logic, for which the corresponding question is still open). We present a complete recursive axiomatization, that includes a natural finitary rule, and study this logic’s expressivity and the appropriate notion of bisimulation. We then examine Group Announcement Logic with Memory (GALM), the extension of APALM obtained by adding to its syntax group announcement operators, and provide a complete finitary axiomatization (again in contrast to the original Group Announcement Logic, for which the only known axiomatization is infinitary). We also show that, in the memory-enhanced context, there is a natural reduction of the so-called coalition announcement modality to group announcements (in contrast to the memory-free case, where this natural translation was shown to be invalid).
INTRAPRENEURIAL PERSONALITY AND INDIVIDUAL INNOVATION BEHAVIOUR IN SERVICE ORGANISATIONS: NETWORK BUILDING ABILITY AS A MEDIATOR

The research investigates network building ability as a mediating mechanism of influence in the relationship between intrapreneurial personality and three stages of employees’ innovative behaviours: idea generation, promotion, and realisation. A quantitative research methodology was adopted with 410 questionnaires collected from employees in the UAE service sector and subsequently analysed using structural equation modelling. The research found a significant direct relationship between intrapreneurial personality and the idea realisation stage and no direct relationship between idea generation and idea promotion. In addition, network building ability was found to mediate these relationships, underlining the importance of building networks for intrapreneurial employees to innovate. These results across the three main stages of individuals’ innovative behaviours may enrich our teachable skills to improve individuals’ innovative behaviour.