Reviewerly/exHarmony huggingface dataset:
Augmented Reality in Training Processes with VISCOPIC

It is an ongoing challenge for companies in all industries to train their employees. Augmented Reality (AR) is a promising approach to make such trainings more effective and more efficient. In this chapter, we provide insights into how AR can improve corporate trainings. Therefore, we present insights into the implementation of AR training tools at Deutsche Bahn (the largest German railway company) and the Audi Quality Management Centre (automotive). Both companies applied the VISCOPIC Pins solution on a Microsoft HoloLens Device.
Edge Camera System Using Dee p Learning Method with Model Compression on Embedded Applications

This paper proposes an edge camera system using a compressed deep learning model for enhanced Video surveillance Management System of Smart City that analyzes existing recorded video. The proposed edge camera at the end terminal of the Video Surveillance Management System send the analyzed image, information and warning to the central system according to the situation analysis based on the information obtained by compressed deep learning for low memory and real-time operation in embedded system. We tested with edge camera installed in street lamp and confirmed stable operating performance and high recognition rate compared to existing system.
Stairs Descent Identification for Smart Wheelchair by Using GLCM and Learning Vector Quantization

The smart wheelchair helps the activities of someone who has a physical disability. The smart wheelchair has several capabilities, one of these capabilities is detecting obstacles in the form of stairs descent. Where if they are not aware of the stairs descent, they can fall, it will be an effect injuring. Therefore this study aims to create a system that is able to detect stairs descent based on digital image and provide notifications. The system was built using the Gray Level Co-occurrence Matrix method as feature extraction and Learning Vector Quantization to classify the stairs descent based on the digital image. From the results of the tests that have been carried out using 200 training data and 40 test data obtained an accuracy rate of 92.5 The faster average computation time is 0.02779 (s) for detecting the stairs descent.
Design and Evaluation of WriteBetter: A Corpus-Based Writing Assistant

Corpus-based writing assistants are aimed to show how words are used in real context: they provide word use examples from which users can (1) draw inspiration for their writing and (2) understand how words are used together to improve their own writing. Although the idea of integrating a corpus-based writing assistant (concordancer) into word processors is not new, their integration is designed to be not as straightforward as writing in a word processor. In this paper, we present WriteBetter, a corpus-based writing assistant designed to be integrated into Microsoft Word, Google Docs, and Overleaf. This integration makes its use straightforward and easy as users can see corpus-based examples (1) in real-time while writing in the word processor or (2) just selecting a piece of text in their document. This facilitates user-corpus interaction as the required user's interaction is minimal. After contextualising the state of the art regarding the benefits of corpus-consultation, we discuss the design features of WriteBetter that make it novel in relation to other tools. Next, we present a user evaluation of the first version of WriteBetter, which was carried out on 11 undergraduate students of a Chilean university, who were asked to trial the software while writing in English. Based on this evaluation, we designed a new version of WriteBetter, which was further evaluated online on 36 users. WriteBetter is now available for everyone as SaaS.
Anchoring the Value of Cryptocurrency

A decade long thrive of cryptocurrency has shown its potential as a source of alternative-finance and the security and the robustness of the underpinning blockchain technology. However, most cryptocurrencies fail to show inimitability and their meanings in the real world. As a result, they usually start off as favourites but quickly become the outcasts of the digital asset market. The blockchain society attempts to anchor the value of cryp-tocurrency with real values by employing smart contracts and link it with computation resources and the digital-productivity that have value and demands in the real world. But their attempts have some undesirable effects due to a limited number of practical applications. This limitation is caused by the dilemma between high performance and decentralisation (universal join-ability). The emerging of blockchain sharding models, however, has offered a possible solution to address this dilemma. In this paper, we explore a financial model for blockchain sharding that will build an active link between the value of cryptocurrency and computation resources as well as the market and labour behaviours. Our model can adjust the price of resources and the compensation for maintaining a system based on those behaviours. We anchor the value of cryptocurrency by the amount of computation resources participated in and give the cryptocurrency a meaning as the exchange between computation resources globally. Finally, we present a working example which, through financial regularities, regulates the behaviour of anonymous participants, also incents/discourages participation dynamically.
Logic Programming, Argumentation and Human Reasoning

The weak completion semantics, a computational logic approach, has been shown to adequately model various episodes of human reasoning. Since the inception of abstract argumentation in the 1990s, connections between argumentation semantics and logic programming semantics have been studied, but existing work on this connection has not yet covered the weak completion semantics. In this paper we define a novel translation from logic programs to abstract argumentation frameworks and show that under this translation the weak completion semantics corresponds to the grounded semantics of abstract argumentation. Combining this translation with argumentation semantics other than grounded semantics gives rise to novel logic programming semantics. We discuss the potential relevance of these novel semantics to modeling human reasoning and give an outlook on possible future research on this topic.
Four Categories Vehicle Detection in Hsuehshan Tunnel via Single Shot Multibox Detector

Taiwan has many vehicles and as a result many traffic problems. In particular, during the Spring Festival and the holidays, Hsuehshan Tunnel (HST) between Yilan and Taipei is always a traffic jam. To solve this problem, intelligent transportation system (ITS) is necessary, and accurate vehicle detection (VD) is the first stage for ITS. In order to detect vehicles in HST, three training methods based on single shot multibox detector (SSD) are presented to detect four categories of vehicle in the Tunnel. The experimental results demonstrated that the presented three training methods, which only used 1000 training frames, can detect and categorize more vehicles than the pre-trained SSD model which used a large training dataset. Specifically, the SSD trained by our collected data set and data augmentation has the highest detection rates for sedan, van, bus, and truck - 93.6%, 90.9%, 100%, and 100%, respectively.
A face recognition based system to assist seniors in managing access to their home

This paper describes the design of a system for supporting seniors, which may have limited physical and/or cognitive capabilities or sudden illnesses, in managing the access to their home, automating the process of opening the door in the case of trusted visitors, forbidding the entrance to untrusted visitors and notifying parents or social assistants about abnormal situations, such as repeated attempts of entering by strangers. The prototype is characterized by the simple interface, the low-cost hardware and the modularity of the approach, for simplifying the possible expansion of the system with additional functionalities. The proposal is also part of an educational project which involved students with different backgrounds. The project started with an initial conceptual design, based also on an initial survey with a group of seniors, which was iteratively refined and followed by a prototypical implementation, based on the face recognition services of the Microsoft Viso platform.
Knowledge Recommendation Based on Item Response Theory

In Knowledge Management System (KMS), the users have to spend much time searching for knowledge items because of the overabundance of information. For improving users' satisfaction, several approaches have been proposed to recommend knowledge by using user feedback, especially collaborative filtering algorithm. But the user feedback easily suffers from noise which affects the accuracy of recommendation. In this paper, we propose a new knowledge recommendation method that aims at extracting latent trait from user feedback and substituting latent trait to user feedback in recommendation model. First, motivated by the Item Response Theory (IRT), we view each feedback as a user's detailed response to an item, and assume the response is jointly determined by the ability of users and the difficulty of items. Second, we suppose that the user feedback obeys Gaussian distribution, in which the latent trait represents the users' ability of comprehending the knowledge items and is jointly determined by user feedback and parameters of model. The parameter mean of Gaussian distribution denotes the difficulty of items, and its parameter variance denotes the ability of users. Last, we recommend knowledge item by Matrix Factorization (MF) method, in which we minimize the squared error between the latent trait and its predictive value. The predictive value is the inner product value of the two latent matrix factorized from the known matrix. Extensive experiments on call center dataset show the effectiveness of the proposed solution by comparing the state-of-the-art methods of MF.
Tagging Part of Speech in Hausa Sentences

Despite the recent exponential growth of Hausa online content on websites like Aminiya.dailytrust.com.ng, Freedomradio.com, Hausa.Leadership.ng, Arewa24.com, and BBC.com/hausa, Sentiment Analysis on such Hausa web content have not been explored in the research community. Thus, Sentiment Analysis of Hausa based web content is a virgin researchable area. However, there are some pre-requisite activities prior to conducting sentiment analysis such as stemming/Lemmatization of words, and Part of Speech (POS) tagging. Unfortunately, for Hausa language, researches towards tools and techniques for these pre-requisite activities are still in the infancy stage, with little work done on stemming of words, but no work so far on Lemmatization and POS tagging. Consequently, this research work proposes a technique/model for POS tagging of Hausa sentences towards the realization of sentiment analysis of Hausa web content. The work is in progress. Preliminary results are encouraging.
Automatic Evaluation of Text Summarization Based on Semantic Link Network

This paper proposes an approach for automatically evaluating summaries based on Semantic Link Network (SLN). Three factors about the quality of summary are taken into account: 1) Fidelity, inspecting whether a summary conveys the core themes of the source text; 2) Conciseness, inspecting the non-redundancy between the sentences of a summary; and, 3) Coherence, inspecting the relevance among all the themes contained in a summary. A summary gets a quality rating based on its performance on the three factors. Experimental results show that the quality ratings of summaries given by our approach are close to the results of manual evaluation.
An Feature Image Generation Based on Adversarial Generation Network

Generative antagonistic network (GAN) was proposed in 2014 to assist in generating realistic visual images, which has become one of the most popular research objects in deep learning in recent years. In the field of image generation, GAN is more effective than the traditional method, but it is difficult to train, unstable network and difficult to convergence. In this paper, GAN is applied in the field of feature image generation, and a new framework is proposed based on the C-SEGAN. By adding additional condition features to generator and discriminator, the similarity of distributed error is learned, and the discriminator is self-encoder, the mean square error loss is added to discriminator, and the generated model generates the specified sample. The model can generate the specified clear image according to the feature conditions. The experimental results show that the method can achieve faster convergence rate and generate better quality and diversity images with a simpler network than other supervised class generation models.
Self-Layer and Cross-Layer Bilinear Aggregation for Fine-Grained Recognition in Cyber-Physical-Social Systems

Cyber-Physical-Social Systems (CPSS) integrates cyber, physical and social spaces together, which makes our lives more convenient and intelligent by providing personalized service. In this paper, we will provide CPSS service for fine-grained recognition. Fine-grained visual recognition is a hot but challenging research in computer vision that aims to recognize object subcategories. The reason why it is challenging is that it extremely depends on the subtle discriminative features of local parts. Recently, some bilinear feature based methods were proposed, and the experimental results show state-of-the-art performance. However, most of them neglect the spatial relationships of part-region feature among multiple layers. In this paper, a novel approach of Self-layer and Cross-layer Bilinear Aggregation(SCBA) is proposed for fine-grained recognition. Firstly, a self-layer bilinear feature fusion module is proposed to model the spatial relationship of feature at the same layer. Secondly, we propose a cross-layer bilinear feature fusion module to capture the inter-layer interreaction of information to boost the ability of feature representation. In summary, the method we proposed not only can learn the correlations among different layers but the same layer, which makes it efficient and the experimental results show that it achieves state-of-the-art accuracy on three common fine-grained image datasets.
Review Rating Prediction with Gaussian Process Classification

Many researchers pay attention to training a nonlinear function according to training data because machine learning can represent a more flexible function. One of the approaches is extending a linear function to a nonlinear function with a kernel method. A typical method is Support Vector Machine (SVM), which is a discriminative model. In SVM, a kernel function is defined previously and you need much knowledge on tasks to choose a good kernel function. Hence, the kernel function selection affects the final performance of SVM are many kernel functions are developed according to each task. Another approach is Gaussian process, which is a generative model. Gaussian process estimate describes a distribution over functions and directly search the optimal function in function space. In SVM, a cost function is defined previously and the optimal parameters are searched with respect to training data. Gaussian process and SVM can construct a nonlinear function but their approaches are different. How are Gaussian process and SVM different in practice? In this paper, we evaluate Gaussian process and SVM with respect to review rating prediction, which is one of natural language tasks. Sentiment analysis research denotes some words contribute to sentiment polarity of a sentence strongly. However, deep learning improves the sentiment analysis adding nonlinear feature construction and it is clear that we have to deal with nonlinearity in sentiment analysis. Moreover, review rating prediction is one of multi-class classifications and the prediction needs more flexibility. Finally, when a review is transformed into a numerical vector with a Bag-of-Words model, the review vector has a very high dimension. Hence, the task has to avoid the curse of dimensionality. Kernel trick is the main solution in SVM and a flexible regression function is an additional solution in Gaussian process regression.
Multiscale Entropy for Physical Activity Recognition

This paper presents the evaluation of multiscale entropy (MSE) as a feature in physical activity recognition compared to the mostly used traditional features. Walking, jogging, and running were chosen as the physical activities for the comparison considering their similarities. Selection of similar activities can give a better evaluation of which features are useful in detecting slight differences. The acceleration data from x-, y-, and z-axes were collected using wearable accelerometers and then evaluated using Matlab and Weka. The k-Nearest neighbors (KNN), J48, and random forest (RF) were chosen as the classifiers. From the comparative evaluation, the MSE performed better compared to the traditional features. Further, the addition of the MSE significantly increased the performance of the traditional features.
Tensor Layout Optimization of Convolution for Inference on Digital Signal Processor

The development of artificial intelligence and 5G technology has driven Internet of Things into the era of Artificial Intelligence of Things (AIoT). As a part of AIoT, Digital Signal Processor (DSP) plays an important role in many AI chips. It is able to perform quickly in multiply-accumulate calculations. Unlike GPUs which usually apply the general matrix multiply for convolution calculations, DSP uses a direct convolution calculating way. Therefore, most optimization techniques on GPUs are not fit for DSP. In many AIoT scenes, the input and output of convolution layers are usually irregular, which affects the efficiency of data storage. In order to make full use of DSP, this work analyzes the performance effects of different tensor layouts. Based on our analysis, a hybrid tensor layout optimization approach is proposed. Furthermore, the test results check that the accuracy of the empirical criterion for the layout selection is not high. Hence, an automatic tuning approach is designed to accurately choose the suitable layouts for different convolution layers in a neural network. Numerical experiments show that for the convolution calculation of each layer in MobileNet, the hybrid tensor layout optimization approach could achieve 11x speed-up at most compared with the single layout schedules. Furthermore, the accuracy of the automatic approach can reach 99.27% for the ordinary convolution, and the convolution calculation of a whole network under the tensor layouts chosen by the automatic approach achieves a good performance which is 72.8% better than that based on the common layout way.
Explainable AI in industry

Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with the proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI have become far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability [2, 4]. Model explainability is considered a prerequisite for building trust and adoption of AI systems in high stakes domains such as lending and healthcare [1] requiring reliability, safety, and fairness. It is also critical to automated transportation, and other industrial applications with significant socio-economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
Efficient Selection of Visual Features in Automatic Image Retrieval

Feature selection is a process of finding an optimal subset of features from the original features set. It could solve the problem of the dimension disaster caused by high-dimensional features, which seriously affects the efficiency of the content-based image retrieval. This paper presents a method for generating an efficient feature reduction method of visual features with neighborhood rough set. By introducing the upper and lower approximation definition of neighborhood rough set, we calculate the approximation information to measure the relevance of the visual features. When the attributes of visual features are reduced and the rest of features can correctly describe the context of the images, the efficiency of the image retrieval could be improved. Furthermore, we use the selected the efficient visual features to perform the image retrieval. Experiment results show that the proposed algorithm is effective in comparison with the other mentioned methods.
A Supportive Environment for Knowledge Construction based on Semantic Web Technology: A Case Study in a Cultural Domain

It is indisputable that the process of knowledge construction and knowledge management require a lot of collaborations from many stakeholders such as domain experts and knowledge engineers. To effectively model the knowledge in ontology representation, which can serve business applications and organization's goals, we need tools to support and to facilitate the collaborative works and activities among many stakeholders. In this paper, we propose a CD-OAM framework that provides a supportive environment based on the collaborative development (COD) approach. In this framework, stakeholders can communicate and share their understandings and comments during the process of ontology design in order to improve the quality of the knowledge model. To demonstrate how our approach and framework can support the collaborations and knowledge integrations among multiple domain experts and knowledge engineers, we selected a case study in cultural domain because the cultural knowledge is complex and requires various experts to design the ontology. The key contributions of our framework are: (1) presenting an improvement of collaborative activities through a supportive environment based on the COD approach, and (2) demonstrating a collaborative situation to overcome limitations of a communication between domain exports and knowledge engineers.
Stroke Signs Detection System by SNS Agency Robot

This paper proposes a system which implements the Cincinnati Prehospital Stroke Scale (CPSS), the widely used screening method for the initial symptoms of a stroke, in a communication robot. AI on cloud analyses an acquired video through a conversation with the robot in real time and automatically determines the abnormalities. The judgement result is informed to his/her families by SNS. This study implemented two of the three CPSS scales such as “Arms” and “Speech”, we confirmed that the system enables to acquire, analyze and notify the information in real time.
Asymmetric Wide Tele Camera Fusion for High Fidelity Digital Zoom

Asymmetric multi-camera systems are growing popular among smartphone manufacturers due to their ability to enhance image quality in applications such as low light imaging and camera zoom. One such multi-camera system is a tele-wide configuration with single or multiple tele and wide cameras. The images from these multiple cameras can be fused to generate an output image with improved image quality as opposed to individual input images. Major challenges in this problem include handling of photometric and Field-of-View differences between frames from an asymmetric camera system. In this paper, novel techniques for both multi-camera image fusion as well as multi-camera transition addressing the aforementioned challenges to create a seamless user experience are presented. The proposed method is evaluated qualitatively using comparisons against the existing method in Galaxy Note 8 and quantitatively against similar methods published in literature, showing an improvement in PSNR of 20% for synthetic datasets and 3% improvement for real datasets. ...