Reviewerly/exHarmony huggingface dataset:
CLGNet: A New Network for Human Pose Estimation using Commodity Millimeter Wave Radar

This paper introduces a new network (CLGNet: Combined Local and Global information encoding Network) for human pose estimation based on commodity millimeter wave (mmWave) radar. Based on the benchmark model of ResNet, a global spatial information encoding module is introduced at the early stage of the network. This new module is expected to help learning the relationship between sparsely distributed human pose keypoints with internal relations. Our experimental results show that the addition of this structural module improves the prediction accuracy of human pose keypoints, especially on minor body parts, such as hands and feet.
Artistic Text Style Transfer based on Generative Adversarial Networks

Artistic text style transfer refers to transferring the art style of a certain artistic text image to another text image. However, the generation of an artistic word requires professional designers to spend a lot of time to design by hand. The previous style transfer method is to transfer the overall style, which is not suitable for highly structured text images. In this paper, we propose a kind of artistic text style transfer based on Generative Adversarial Networks. By disassociating the font features of the text image and the style features of the artistic text image, and then reorganizing the font features and style features, a new artistic text image is generated. Experimental results show that the method in this paper can generate better style transfer effect than other methods.
Improving Visualization Design for Effective Multi-Objective Decision Making

Decision-makers across many professions are often required to make multi-objective decisions over increasingly larger volumes of data with several competing criteria. Data visualization is a powerful tool for exploring these complex 'solution spaces', but there is limited research on its ability to support multi-objective decisions. In this article, we explore the effects of chart complexity and data volume on decision quality in multi-objective scenarios with complex trade-offs. We look at the impact of four common multidimensional chart types (scatter plot matrices, parallel coordinates plots, heat maps, radar charts), the number of options and dimensions and participant chart usage experience on decision time and accuracy when selecting the 'optimal option'. As objectively evaluating the quality of multi-objective decisions and the trade-offs involved is challenging, we employ rank- and score-based accuracy metrics. While heat maps demonstrate a time advantage, our findings show no strong performance benefit for one chart type over another for accuracy. We find mixed evidence for the impact of chart complexity on performance, with our results suggesting the existence of a 'ceiling' in the number of dimensions considered by participants. This points to a potential limit to data complexity that is useful for decision making. Lastly, participants who use charts frequently performed better, suggesting that users can potentially be trained to effectively use complex visualizations in their decision-making.
WITHDRAWN: Deceptive reviewer detection by analyzing web data using HMM and similarity measures

Deceptive review on online platforms is the bitter reality in the present day. These reviews are specially written to encourage or downgrade specific services, brands, and products. There are lots of work has already been done on detection of these type of reviews. In all this work, either review text features or review metadata features to identify the review. The former provides detailed information as many features can be extracted from it, such as duplicate text writing techniques, sentiments, and common words. However, with growing technologies such as article rewriter and article spinner, these features can be easily tricked by deceptive reviewers. Moreover, there are no labeled datasets available for such classification problems. This research article solved the issue referred to above by firstly creating a labeled dataset. After developing a synonyms-based n-grams approach to extract features of review text and finally effectiveness, these features are tested against some state of art feature extracting techniques for different classifiers such as SVM and Naive Bayes classifier. The evaluation results show that the state-of-art technique significantly fails to detect deceptive reviews rewritten by software. The proposed method performs better for software rewritten deceptive review detection and reduces the length of feature vectors.
4.7 A 91mW 90fps Super-Resolution Processor for Full HD Images

Super resolution is the process of reconstructing a high-resolution (HR) image from a low-resolution (LR) one. Super-resolution technology enables high-resolution video streaming, image zoom-in, and far object recognition. Fig. 4.7.1 shows such an application scenario. The details of the videos/images can be reconstructed and projected to a higher-resolution screen, thereby providing a better visual experience. A hardware accelerator is needed to speed up the super-resolution process to support real-time high-resolution video streaming. Conventionally, dictionary-based approaches, such as ANR/GR and A+, convert the LR image into the HR one from learned mapping functions. Neural network (NN)-based algorithms generate better-quality super-resolution images by extracting features from training. However, the complexity of the dictionary-based and the NN-based algorithms is excessively high, making them unsuitable for high-speed applications. A rapid and accurate image super resolution (RAISR) algorithm is proposed to achieve comparable quality with a much faster processing speed when compared to the previous solutions. It employs pre-learned filters to enhance the image quality based on bicubic interpolation. A pre-learned filter (also known as kernel) is selected by a hash function to address the structure-related details.
TKFIM: Top-K frequent itemset mining technique based on equivalence classes

Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset’s characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.
Colorful 3d reconstruction from a single image based on deep learning

Simultaneously recovering the 3D shape and its surface color from a single image has been a very challenging. In this paper, we substantially improve Soft Rasterizer that is a state-of-the art method for 3D color object reconstruction. The model adopts the structure of the encoder and decoder with a single image as input. Firstly, the features are extracted by the encoder, and then they are simultaneously sent to the shape generator and the color generator to obtain the shape estimate and the corresponding surface color, and finally the final colorful 3D model is rendered by the differentiable renderer. In order to ensure the details of the reconstructed 3D model, this paper introduces an attention mechanism into the encoder to further improve the reconstruction effect. For surface color reconstruction, we propose a combination loss. The experimental results show that compared with the 3D reconstruction network models 3D-R2N2 and OccNet, the intersection-over-union (IOU) increases by 10% and 3% in our model. Compared to the open source project SoftRas_O, the model increases by 3.8% on structural similarity (SSIM) and decreases by 1.2% on mean square error (MSE).
Assessing Cyber Security Education through NICE Cybersecurity Workforce Framework

This paper presents the results of research and assessment of cyber security education in higher education in Europe and the United States of America. The quantitative research data of the education curricula was gathered and mapped to NICE Cybersecurity Workforce Framework (NCWF) categories to provide a common background for data comparison and analysis.
Edge AI for Industry 4.0: An Internet of Things Approach

In this paper, we study the edge artificial intelligence (AI) techniques for industry 4.0. More specifically, we assume fog computing takes place on the edge of Industrial Internet of Things (IIoT) networks. We provide details about the three main edge AI techniques that can contribute to the future industrial applications. In particular, we deal with the active learning (AL), transfer learning (TL) and federated learning (FL), where AL is used to deal with the problem of unlabeled data, the TL is used to start training with a pre-trained model and the FL is a distributed solution to provide privacy. Finally, their combination is developed too that we name it federated active transfer learning (FATL). Simulation results are carried out that reveal the gain of each solution and their FATL combination. The deployment of FATL in IIoT networking standards such as IEEE P2805 is described too that can be extended as our future work.
Reversible Fade Gate as Decoder, Encoder and Full Adder

The technique of reversible logic can be applied on any digital, VLSI and quantum computing applications for optimization of power. A decoder and an encoder are the basic building blocks that can be used to realize any logic network. This pair can be used in communication system applications as transceivers. A new 4 × 4 reversible gate named FADE is proposed in this paper which is novel, unique and first of its kind as it performs the operation of three combinational logic circuits simultaneously, i.e., a full adder, a 2:4 decoder and a 4:2 encoder simultaneously. The design of the proposed reversible gate is coded using Verilog, synthesized and simulated using Xilinx software. It is implemented and tested on FPGA XC3S400-5PQ208. This gate would act as a threshold in designing complex arithmetic logic circuits and thus enhance the performance of computing systems.
Construction of Carbon Trading Platform using Sovereignty Blockchain

Carbon trading is a property right granted by governments or international organizations to the right to emit greenhouse gases including carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). Through administrative and legal measures, the implementation of carbon quotas to curb carbon emissions, thereby promoting the transformation of the production process from a high energy consumption model to a low carbon model. Therefore, there has also been a carbon trading market for the purpose of carbon trading. Due to the immaturity of emerging markets and the severe impact of policies, there is great instability and uncertainty. Therefore, the use of the sovereign blockchain model to build a carbon trading system model provides some references for improving the carbon trading market. By discussing the feasibility and necessity of establishing a carbon trading system based on the sovereign blockchain model, and carrying out corresponding design, it will be of reference value for the further realization of carbon trading and provide certain innovative ideas for subsequent research.
Consuming E-Learning Twitter Data by Live Streaming

In the previous decade, e-learning has been increasingly hot and is still heading for another summit, evidently due to its straightforward entry and the speed the e-learner controls. A participant in e-learning does not need to be a qualified computer operator and they are not relied upon to be limited by age or background. Unquestionably, people's views on the vast voluminous e-learning data need to be investigated. Twitter is one of the famous social networks that receives a large number of tweets every day. Data streaming is a revolutionary concept in big data where data size extends a lot from internet-based life, inclining pages, and smartphone devices. Data streaming today eagerly tends to gather live streaming data to perform research and produce results for data prediction while complication factor emerges that talented expert is necessary to deal with this situation of acquiring live stream data using complex coding and queries. The proposed approach uses technique that uses massive pieces of information such as Flume, Hadoop Distributed File System to configure twitter's framework for collecting live e-Iearning tweets from twitter. For sentimental research, the data is inserted into the hive table.
Collaboration in research: weaving Kaupapa Māori and computer science

We describe a collaboration between Māori (Indigenous people of Aotearoa/New Zealand) and Tauiwi (non-Māori) researchers on a software engineering project. Te Tiriti o Waitangi (The Treaty of Waitangi) provides the basis for Māori to lead research that involves Māori as participants or intends to impact Māori outcomes. Through collaboration, an extension of the traditional four-step software design process was created, culminating in a nine-step integrated process that included Kaupapa Māori (Māori ideology) principles. The collaboration experience for both Māori and Tauiwi highlighted areas of misunderstanding within the research context based on differing worldviews and our ability to navigate and work through this. This article provides context, guiding principles, and recommended research processes where Māori and Tauiwi aim to collaborate.
Automatic Speaker Recognition using Transfer Learning Approach of Deep Learning Models

Speaker Recognition has been one of the most interesting yet challenging problems in the field of machine learning and artificial intelligence. It is used in the areas of human voice authentication for security purpose and identifying a person from a group of speakers. It has been a arduous task to teach a machine the differences in human voices, especially when people belong to different background like gender, language, accent, etc. This paper has used the deep learning approach to build and train two models, Artificial Neural Network (“ANN”) and Convolutional Neural Network (“CNN”), and compared their results. In former, the neural networks are fed on diverse extracted features from audio collection, whereas the latter is trained on spectrograms. Finally, transfer learning approach is deployed on both to get a viable output using limited data.
An Instrument to Assess the Organizational Climate of Agile Teams - A Preliminary Study

Background: Organizational climate surveys allow the access to the opinions, preferences, or dissatisfactions of the team members of their working conditions. Understanding how different factors influence the organizational climate of agile software development teams can be challenging for organizations. TACT is an instrument to assess the organization climate of agile teams. Its initial version comprises the Communication, Collaboration, and Leadership dimensions. Objective: We present the preliminary evaluation of TACT. Method: We planned and executed a case study considering two development teams. We evaluated TACT using open-ended questions, quantitative methods, and TAM dimensions of Intention to Use, Perceived Usefulness, and Output Quality. Results: TACT allowed to classify the organizational climate of both teams as positive for the Collaboration, Communication, and Leadership dimensions. However, some items were assessed negatively or neutrally which represent attention points. TACT captured the lack of agile ceremonies, the difficulty of the product owner in planning iterations, and the distance in leadership. In addition, TACT presented high levels of reliability. Conclusions: TACT captured the organizational climate of the teams correctly. The team leaders reported intention of future use. The items that compose TACT can be used by researchers investigating the influence of human factors in agile teams, and practitioners that need to design organizational climate assessments of agile teams.
Poetry Generation Model via Deep learning incorporating Extended Phonetic and Semantic Embeddings

Arabic poetry generation is a very challenging task since the linguistic structure of the Arabic language is considered a severe challenge for many researchers and developers in the Natural Language Processing field. In this paper, we propose a poetry generation model with extended phonetic and semantic embeddings (Phonetic CNN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">subword</sub> embeddings). The proposed approach consists of three stages: (1.) Word Embedding, (2.) Keywords Extraction and Expansion, and (3.) Poetry Generation stage. Our model is able to generate the first verse which explicitly incorporates the theme related phrase using the Backward and Forward Language Model (B/F-LM) with Gated Recurrent Unit (GRU) cell, and then generates other verses of the poem sequentially, where each verse is composed based on a keyword and all previous generated verses using our proposed Hierarchy-Attention Sequence-to-Sequence model (HASS). We show that Phonetic CNN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">subword</sub> embeddings have an effective contribution to the overall model performance. The Keywords Extraction and Expansion stage can ensure that the generated poem is coherent and semantically consistent with the input query intent. A comprehensive human evaluation confirms that the poems generated by our model outperform the base models in criteria including Meaning, Coherence, Fluency, and Poeticness. Extensive quantitative experiments using Bi-Lingual Evaluation Understudy (BLEU) scores also demonstrate significant improvements over strong baselines.
Functional Clustering on a Circle Using von Mises Mixtures

This paper addresses the question of clustering density curves around a unit circle by approximating each such curve by a mixture of an appropriate number of von Mises distributions. This is done first by defining a distance between any two such curves either via $$L^2$$ or a symmetrized Kullback–Leibler divergence. We show that both these measures yield similar results. After demonstrating via simulations that the proposed clustering methods work successfully, they are applied on an illustrative sample of Optical Coherence Tomography data.
On the application of sensor authentication with intrinsic physical features to vehicle security

Cybersecurity in the automotive sector is becoming increasingly important as modern vehicles can be exposed to cybersecurity threats and regulatory fraud. Sensor identification and authentication is an important function in the vehicle life cycle. This paper proposes an alternative or complementary identification and authentication approach to cryptographic techniques based on the physical properties of the Hall sensor, which is commonly used in odometers and motion sensors. The application of different signal processing algorithms to an experimental dataset of 12 Hall sensors was evaluated. In particular, a dimensionality reduction with decimation filters was applied to improve the time efficiency of the identification and authentication process while achieving high identification accuracy (accuracy=95%) and high authentication accuracy. In addition, the results show that different transformations with different hyperparameters are able to generate a wide range of challenges and responses to support the authentication process.
Summary and Prejudice

People with intellectual disability (ID) deserve appropriate access to information online. A vast amount of information on the Internet is written text in the form of articles, and it is often said that summarising these texts could enhance their accessibility. This qualitative research investigates how people with ID prefer to gather information from articles, either in their original form or automatically summarised. The researchers observed the choices and strategies of 10 participants with ID through the reading process, and conducted contextual interviews to understand their preferences, attitudes and the difficulties they faced. The study found that the length of the article is only secondary to the relevance of the article to the person's interests. Summarised articles were found easier to read and having familiar words and images to supplement the text can help people with intellectual disability understand what an article is about.
Depth Estimation Under Motion With Single Pair Rolling Shutter Stereo Images

Many methods have been proposed to process stereo matching for rolling shutter image pairs, they treat all pixels from an image pair in an identical way and require additional estimation approaches to estimate motion states of the camera. However, pixels from a rolling shutter image pair naturally have diverse baseline lengths, and motion estimation methods are unstable for one instantaneous image pair input. In this letter, we present a rolling shutter stereo depth estimation pipeline, which can robustly estimate motion states and depth maps by alternating the estimation of the depth maps and refining the motion states from coarse image levels to fine image levels. What is more, we design a novel cost volume building method for rolling shutter image pairs, which adapts depth candidates to the change of baseline lengths for all pixels. We further demonstrate the usability of the proposed method by constructing a new platform, building an outdoor evaluation dataset, and comparing it with baseline methods.