Reviewerly/exHarmony huggingface dataset:
Nigerian Press Coverage of Hate Speeches in the <i>Daily Trust, The Nation</i> and <i>The Guardian</i> Newspapers

In Nigeria, as well as in modern democratic nations, the press has always functioned as a tool for disseminating information on public affairs, interpreting government policies and programs, and providing a good platform to engage the citizens for discussion on issues affecting society. The media play a powerful role as intermediaries between political leaders and the public. The variables of frequency, location, direction, and journalistic genre were used in the study. Within this context, the study adopts content analysis. The study employs Lazarsfeld and Katz’s Two-Step Flow and Castells’ Theory of Network Society as theoretical framework. The study uses stratified sampling by days of the week and coding sheet as a method of data collection. The study found that the manifestation of hate speech was frequent in the 2015 general election. The study also found that the manifestation of hate speech had an overbearing on political news by the selected newspapers in the 2015 general election in Nigeria. The study concludes that such publications (hate speech) tend to make electorates have a different connotation to a candidate.
Quantum wavelength-division-multiplexing and multiple-access communication systems and networks: Global and unified approach

The march towards successful global quantum internet requires introducing all-quantum networks and signal processing techniques. In this paper, we develop and discuss methods for various wavelength-division-multiplexing and multiple-access (WDM) communication systems and networks in fully quantum mechanical terms to obtain all-quantum WDM (QWDM) systems and networks. We begin the paper with a detailed discussion on various possible narrow-band and wideband sources of light signals used in typical WDM systems, such as coherent, number, and Poissonian mixed states. After introducing a generic and fully quantum mechanical WDM network, we develop methodologies for obtaining the necessary mathematical evolutions through wavelength distributors such as arrayed-waveguide-grating multiplexers and demultiplexers, and wavelength-sensitive and -insensitive broadcasting star couplers. In particular, using the methodologies introduced, one can use the results to obtain a complete and exact expression for any evolved pure quantum light signal through a typical WDM network using the aforementioned optical components. To test the validity and robustness of our mathematical expression for the evolved QWDM light signals, we use two opposing and extreme signals, namely, coherent state and number state, as inputs to various WDM communication and network systems and architecture. The methodologies and the required mathematical QWDM models introduced here can be extended to any fully quantum network architecture deemed necessary in a future global quantum internet, e.g., fiber to the home, Lambdanet-based broadcast WDM networks, and quantum routers based on a waveguide grating router.
Industrial Data Homogenization and Monitoring Scheme with Blockchain Oracles

Research efforts on Distributed Ledger Technologies (DLTs) for industrial applications have constantly been increasing over the last years. The use of DLTs in the Industry 4.0 paradigm provides traceability, integrity, and immutability of the generated industrial data. However, Industry 4.0 ecosystems are typically composed of multiple smart factory clusters belonging to several companies, which are immersed in constant interaction with other business partners, clients, or suppliers. In such complex ecosystems, multiple DLTs are necessarily employed to maintain the integrity of the data throughout the whole process, from when the data is generated until it is processed at higher levels. Moreover, industrial data is commonly heterogeneous, which causes compatibility issues, along with security and efficiency issues in the homogenization process. Thus, the data needs to be pre-processed and homogenized in a secure manner before being exploited. Consequently, in this work, we address the issues mentioned above by providing an industrial raw data pre-processing and homogenization process according to a standard data model. We employ decentralized blockchain oracles to guarantee the integrity of the external data during the homogenization process. Hereafter, we design an interoperable plant blockchain for trustworthy storage and processing of the resulting homogenized data across several industrial plants. We also present a prototype implementation of the aforementioned scheme and discuss its effectiveness. Finally, we design a monitoring scheme to overview the usage the performance of the architecture processes and identify possible performance and security issues.
Side-by-Side vs Face-to-Face: Evaluating Colocated Collaboration via a Transparent Wall-sized Display

Traditional wall-sized displays mostly only support side-by-side co-located collaboration, while transparent displays naturally support face-to-face interaction. Many previous works assume transparent displays support collaboration. Yet it is unknown how exactly its afforded face-to-face interaction can support loose or close collaboration, especially compared to the side-by-side configuration offered by traditional large displays. In this paper, we used an established experimental task that operationalizes different collaboration coupling and layout locality, to compare pairs of participants collaborating side-by-side versus face-to-face in each collaborative situation. We compared quantitative measures and collected interview and observation data to further illustrate and explain our observed user behavior patterns. The results showed that the unique face-to-face collaboration brought by transparent display can result in more efficient task performance, different territorial behavior, and both positive and negative collaborative factors. Our findings provided empirical understanding about the collaborative experience supported by wall-sized transparent displays and shed light on its future design.
Multipath transmission aware ABR algorithm for SVC HAS

Adaptive video streaming over Hypertext Transfer Protocol (HTTP) is one of the most popular applications that have been used on the Internet in the last decade. In such applications, clients change the quality of the received video throughout the streaming session with respect to current network conditions. In these systems, the use of scalable coded video is one of the alternatives that can obtain video files encoded at different qualities. In this paper, we propose an adaptive bitrate (ABR) algorithm that aims to improve the quality of not only future segments but also the segments in the buffer. The quality of the already buffered segments is improved by downloading additional layers of the SVC-coded video, hence, preventing network resources from being wasted while increasing Quality of Experience (QoE). We use multiple paths between the server and the clients for transferring the video packets. In such systems, the ABR algorithm should determine which segment and layer should be requested from which path and when. The proposed ABR algorithm selects a group of segments to increase the quality of the buffered segments and for future segments, and selects paths for each segment in this group. Simulation results show that clients achieve higher QoE by using our approach when compared with conventional ABR algorithms. Furthermore, we show that using multiple paths cannot guarantee that QoE is maximized unless the end-system is aware of multipath transmission.
Multi-Stage Spatial and Frequency Feature Fusion using Transformer in CNN-Based In-Loop Filter for VVC

Versatile Video Coding (VVC)/H.266 is a video coding successor to High Efficiency Video Coding (HEVC)/H.255 and Advanced Video Coding (AVC)/H.264 with significant technical and coding improvement. Nonetheless, it follows the conventional block-based hybrid video coding scheme similar to its predecessors. The consequence is, that the reconstructed picture contains compression artifacts. VVC, by default, has in-loop filters to correct the deformities but these handcrafted filters offer suboptimal performance. In this work, we designed a novel convolutional neural network (CNN) to replace the inbuilt in-loop filter of VVC. The proposed CNN-based in-loop filter utilizes a modified Spectral-wise Multi-Head Self-Attention (S-MSA) layer of Multistage Spectral-wise Transformer (MST++) at multiple stages to fuse spatial and frequency-decomposed features extracted from pixel and its discrete-cosine-transform (DCT) applied input respectively. We named the proposed network MSTFNet where the first three letters represent MST++ and F stands for fusion. Because of the multi-stage feature fusion operation, the proposed CNN acts as a powerful learned in-loop filter that significantly outperforms previous methods. Our experimental results show that the proposed method can achieve coding improvements up to 10.31% on average Bjøntegaard Delta (BD)-Bitrate savings under all-intra (AI) configurations for the luma (Y) component.
Optimal Tile-Based Encoding for 360-Degree Video Streaming

360-degree videos pose a challenge to their transmission due to the ultra-high resolution. To achieve efficient streaming, current tile-based schemes divide the 2D projection of spherical frames into small rectangular tiles, which are treated as individual sources for encoding and then delivered according to user's actual viewport. However, the trade-off between transmission and encoding efficiency, as controlled by encoding parameters such as tile sizes and quantization steps, has not been fully studied yet. In this paper, we propose a tile-based encoding scheme to strike this trade-off, by selecting the optimal encoding parameters that minimize the distortion of video displayed for user's viewport under a given transmission capacity. Specifically, we quantify the transmission efficiency by estimating the average number of pixels required for transmission to cover the user's viewport, while a CNN-based predictor is trained to predict the encoding efficiency based on video content characteristics, expressed as number of bits needed for encoding one pixel. We then formulate a rate-distortion optimization problem for tile-based encoding, which is solved efficiently by decreasing the feasible set of encoding parameters. Simulation results demonstrate that the proposed optimal tile-based encoding outperforms both fixed tiling and state-of-the-art adaptive tiling schemes in terms of rate-distortion behavior.
Accelerated Deep Lossless Image Coding with Unified Paralleleized GPU Coding Architecture

We propose Deep Lossless Image Coding (DLIC), a full resolution learned lossless image compression algorithm. Our algorithm is based on a neural network combined with an entropy encoder. The neural network performs a density estimation on each pixel of the source image. The density estimation is then used to code the target pixel, beating FLIF in terms of compression rate. Similar approaches have been attempted. However, long run times make them unfeasible for real world applications. We introduce a parallelized GPU based implementation, allowing for encoding and decoding of grayscale, 8-bit images in less than one second. Because DLIC uses a neural network to estimate the probabilities used for the entropy coder, DLIC can be trained on domain specific image data. We demonstrate this capability by adapting and training DLIC with Magnet Resonance Imaging (MRI) images.
Flexible luma-chroma bit allocation in learned image compression for high-fidelity sharper images

High-fidelity learned image/video compression solutions are typically optimized with respect to l1 or l2 loss in RGB 444 format and evaluated by RGB PSNR. It is well-known that optimization of a fidelity criterion results in blurry images, which is typically alleviated by adding a content-based and/or adversarial loss terms. However, such conditional generative models result in loss of fidelity. In this paper, we propose a simple solution to obtain sharper images without losing fidelity based on learned flexible-rate coding using gained variational auto-encoder (gained-VAE) in the luma-chroma (YCrCb 444) domain. This allows us to implement image-adaptive luma-chroma bit allocation during inference, i.e., to increase Y PSNR at the expense of slightly lower chroma PSNR to obtain sharper images without introducing color artifacts based on the observation that Y PSNR correlates with image sharpness better than RGB PSNR. We note that the proposed inference-time image-adaptive luma-chroma bit allocation strategy can be incorporated into any VAE-based image compression model. Experimental results show that sharper images with better VMAF and Y PSNR can be obtained by optimizing models for YCrCb MSE with the proposed image-adaptive luma-chroma bit/quality allocation compared to state-of-the-art models optimizing RGB MSE at the same bpp.
A Model of Discovering Customer Insights in Tourism Sector Approach to Vietnamese Reviews Analytics

Today, customer-centric business models are more focused. In addition, businesses increasingly own more data about customers and user behavior, especially from social networking platforms. However, the effective exploitation and use of this amount of data is still very limited. Based on those objective motivations, this research proposes a model as a foundation for analyzing and understanding customers to help businesses transform and transform this data into forms of knowledge to support the business, make intelligent business decisions. A data set of 90,513 reviews in Vietnamese from Agoda e-commerce websites in the tourism sector is collected and experimented. In experimental phase, raw corpus was cleaned by text preprocessing, then, the research will use the Net Promoter Score (NPS) to separate the data into three data sets according to three groups of Detractors, Passives and Promoters. From that, Latent Dirichlet Allocation (LDA) was built to extract the hidden topics in the input dataset. The experimental results show that the extracted topics and their features from corpus of customer reviews are reliable. The novelty in this research is to identify the topics that customers are interested in and complaining about.
Implementation of PRESENT Algorithm on Contactless Access Control using Raspberry Pi

This research develops a contactless and secure access control system based on face recognition and body temperature measurement. This research aims to establish a security system that also fulfills health protocols for COVID-19 spreading, in this case, the limitation of physical contact. The PRESENT algorithm, a lightweight block cipher encryption-decryption algorithm, is implemented to keep the transmitted data safe. The face recognition method consists of the Viola-Jones face detection algorithm and LBPH face recognition algorithm. The body temperature is measured using a contactless sensor. The performance tests show the accuracies of recognizing faces are 68% under 198 Lux lighting and 52% under 105 Lux lighting. The precision of measuring body temperature using the sensor reaches 98,85%. Based on the sniffing attack test of the system, the encrypted data transmitted from the system to the web-based database is safe from attackers. Besides the face spoofing attack tests, the system will not authenticate attackers with face photos or face videos.
Use of Blockchain for Designing Digital Documents in Public Services

The use of information technology (IT) in a government services can increase work effectiveness and efficiency and help the government to communicate and interact with the community and citizen. One of the uses of IT in Government is to automate all government processes and operations or what is called electronic government. Although it has adopted and implemented information technology to automate services to the community, the impact of using IT is not optimal. This can be seen from the large number of uses of paper or hardcopy documens that are popularized by the government or OPD to get services from them. In addition, the issue in the implementation of information systems in government is the integration of data between applications in one OPD and other OPD applications in Ogan Ilir district. This issue causes each OPD to have its own policy of issuing documents to the community if they want to get services. To solve the issue, this study aims to design the use of blockchain technology for the integration of e-government services. The method used is the Rapid Application Development (RAD) method to develop an information system based on blockchain smart contracts. It is expected that the results of this study can benefit the government digitize all the documents required in the e-government services process.
An automatic tool for yoga pose grading using skeleton representation

Automatic grading of yoga poses may help yoga self-practitioners to rectify their poses to follow correctly the teacher. Some existing wearable or depth sensor based methods require the user to equip additional devices. This paper presents a framework for automatically grading yoga poses from images taken from a conventional RGB camera/phone camera. Our framework consists of three main phases. First, we estimate human joints from RGB images using BlazePose model. Second, we investigate various deep models and select VGG-16 as a model for yoga pose recognition. Finally, we define a score that takes the difference of angles at important joints and yoga pose label into account. Our solution is low-cost, easy, and light to implement on mobile devices. It gives a confident score on the YogaPose dataset and our self-collected dataset.
Robust hierarchical model for joint span detection and aspect-based sentiment analysis in Vietnamese

Recent successes of large pre-trained language models highlighted deep neural networks' potential in solving complicated natural language processing tasks, but some remain unsolved, especially for non-English languages. Two of them are span detection and aspect-based sentiment analysis. The former is a general sequence tagging, and the latter is a hierarchical classification task. Both are challenging in various ways, most notably due to the lack of available resources and effective methods for training. To overcome these drawbacks, this paper introduces a fashionable and robust hierarchical model that learns both tasks simultaneously using a multi-task learning framework for Vietnamese datasets. Our model is trained using a tunable custom task-dependent loss and easily adapted to a wide range of similar tasks. Experimental results showed that our approach is superior to the existing baseline systems and achieved state-of-the-art results. We also make the source code publicly available as a starter kit for further investigation and research <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/datnnt1997/ViSA.
Leveraging Semantic Representations Combined with Contextual Word Representations for Recognizing Textual Entailment in Vietnamese

RTE is a significant problem and is a reasonably active research community. The proposed research works on the approach to this problem are pretty diverse with many different directions. For Vietnamese, the RTE problem is moderately new, but this problem plays a vital role in natural language understanding systems. Currently, methods to solve this problem based on contextual word representation learning models have given outstanding results. However, Vietnamese is a semantically rich language. Therefore, in this paper, we want to present an experiment combining semantic word representation through the SRL task with context representation of BERT relative models for the RTE problem. The experimental results give conclusions about the influence and role of semantic representation on Vietnamese in understanding natural language. The experimental results show that the semantic-aware contextual representation model has about 1% higher performance than the model that does not incorporate semantic representation. In addition, the effects on the data domain in Vietnamese are also higher than those in English. This result also shows the positive influence of SRL on RTE problem in Vietnamese.
Association Discovery from Electronic Medical Records towards Personalized Treatment

In practice, personalized treatment is often used to help patients become well faster. It is mainly based on doctors' knowledge and experiences for each disease. Combining their knowledge and experiences for personalized treatment on a patient in a context of more than one disease is also frequent and more challenging. If documented, such a combination will be accumulated to be valuable knowledge and experiences for further treatment. Therefore, in this work, we focus on knowledge discovery from a large number of electronic medical records (EMRs) towards personalized treatment in the latter. In particular, associations between knowledge and experiences are discovered from existing composite treatments. Personalized treatment is then derived and examined from those associations. Compared to the existing works, our work is the first one that made association discovery from EMRs in the well-known MIMIC-III database for personalized treatment. The results can be used to support group diagnoses and future treatment on new patients with various diseases.
An Effective Contextual Language Ensemble Model for Vietnamese Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) allows finer-grained inferences to provide specific sentiment for each aspect of the same sentence. In this paper, we present an ensemble model combined with multi-task learning based on different pre-trained contextual language models on a compound task as Category-Sentiment Classification (CSC) for the Vietnamese language. Furthermore, we provide the performance of fine-tuning state-of-the-art pre-trained language BERTology models, which are available for the Vietnamese language. Experimental results demonstrate that our ensemble approach consistently achieves the best results in two out of three datasets benchmark datasets compared to previous results and individual models.
Implementation of Computer Vision in Low Fidelity Robots: Analysis, Challenges, and Design Implications

We observe the utilization of machine learning in a number of everyday aspects. Robotics is one of the major fields where we celebrate the prowess of machine learning. The application of machine learning and deep learning techniques to robotics has made them elegant, intelligent, and more competent. A subset of robotic gizmos consists of low-fidelity configurations that have abstract shapes and are generally constructed from low-end components. As a result, their capacities are oftentimes limited. Although several prior works sought to enforce machine learning for such contraptions, the lack of high-performance processing often bounds such applications. In this work, we fabricate a low-fidelity robot, named Tokai, implement deep learning-based object detection to perform waste bottle collection, and hence, investigate the feasibility, challenges, and design implications of implementing deep learning for low-fidelity robots.