Reviewerly/exHarmony huggingface dataset:
Eye Blinks Describing the State of the Learner Under Uncertainty

Adaptive systems are able to support the human-machine interaction in a great manner. However, the question arises which parameter are useful to gain insights into the user and can be easily implemented in the adaptive system. Eye blinks are frequent and most of the time automatic actions that reflect attentional and cognitive processes. They have not gained much attention in the context of adaptive systems until now. Thus, the current experiment investigated the number of blinks as an indicator of the state of the user while interacting with a technical system. Participants had to perform a dynamic visual spatial search task while their eye blinks were tracked. The task is to predict the appearance of target objects and thereby to learn a probability concept in order to improve the prediction. Results showed that eye blinks could distinguish between good and poor learner and increased parallel to the increasing task performance. Further, eye blinks reflected the information processing during the performance of a trial and the completion of the task. Thus, eye blinks might inform about the needs of the user with regard to the amount and detailedness of new information as well as additional help. However, the individual variability necessitates a separate baseline to be determined for each user. Further research is needed to foster the results also in more applied settings.
Dynamic Password Protocol for User Authentication

The Internet security is no more a choice, but a priority. Even with the availability of best security tools and protocols, it is inevitable to avoid unauthorized access and cyber threats. The existing user authentication which is based on username and password fails to work against Cyber-attacks because of its static nature. A novel approach for user authentication called “Dynamic Password Protocol” is proposed, where passwords used for authentication consist of two parts one is a static password which remains static and dynamic password which changes regularly based on the user-defined parameters. These parameters refer to distinct objects which are unique to every user and are accessible by users only at the time of login, parameters defined may be a clock-based mechanism or based on the geo-location of the machine from which the user is logging in. The parameters set by one user are unknown to the other users. Hence the proposed protocol strongly protects the Internet against replay attacks, phishing attacks and other cyber-related threats by means of generating dynamic passwords that are valid for a particular period of time.
Homologous Mesh Extraction via Monocular Systems

Pose estimation of humanoid objects in monocular systems is a non-trivial problem that has been at the forefront of the human-computer interaction field. The ability for a computer to not only to detect the presence of a humanoid shape within an image but also to infer relative location and configuration has particular use for many applications. We explore a novel approach to solving this task by introducing a multi-stage preprocessing algorithm and a constrained pose estimator.
DupNet: Towards Very Tiny Quantized CNN With Improved Accuracy for Face Detection

Deploying deep learning based face detectors on edge devices is a challenging task due to the limited computation resources. Even though binarizing the weights of a very tiny network gives impressive compactness on model size (e.g. 240.9 KB for IFQ-Tinier-YOLO), it is not tiny enough to fit in the embedded devices with strict memory constraints. In this paper, we propose DupNet which consists of two parts. Firstly, we employ weights with duplicated channels for the weight-intensive layers to reduce the model size. Secondly, for the quantization-sensitive layers whose quantization causes notable accuracy drop, we duplicate its input feature maps. It allows us to use more weights channels for convolving more representative outputs. Based on that, we propose a very tiny face detector, DupNet-Tinier-YOLO, which is 6.5X times smaller on model size and 42.0% less complex on computation and meanwhile achieves 2.4% higher detection than IFQ-Tinier-YOLO. Comparing with the full precision Tiny-YOLO, our DupNet-Tinier-YOLO gives 1,694.2X and 389.9X times savings on model size and computation complexity respectively with only 4.0% drop on detection rate (0.880 vs. 0.920). Moreover, our DupNet-Tinier-YOLO is only 36.9 KB, which is the tiniest deep face detector to our best knowledge.
AgentSpeak(ER): Enhanced Encapsulation in Agent Plans

In this chapter, we introduce AgentSpeak(ER), an extension of the AgentSpeak(L) language tailored to support encapsulation. The AgentSpeak(ER) extension allows for significantly improving the style of BDI agent programming along relevant aspects, including program modularity and readability, failure handling, and reactive as well as goal-based reasoning. The chapter introduces the novel language based on AgentSpeak, illustrates the features of the language through examples, and discuss results of a case study based on the implementation of the proposed language.
Design of Mobile Integrated Development Platform for Business System Based on B/S Architecture

Abstract With the popularity of intelligent devices, mobile application APP has become the most widely used application software at present. This paper analyzes the current situation of mobile development and data integration of business systems, and presents a platform design for mobile application development of business systems oriented to B/S architecture, which realizes independent and fast migration of mobile application functions of the original business systems under the specifications of user rights and business processes of the original business systems.
Understanding Deep Learning Techniques for Image Segmentation

The machine learning community has been overwhelmed by a plethora of deep learning based approaches. Many challenging computer vision tasks such as detection, localization, recognition and segmentation of objects in unconstrained environment are being efficiently addressed by various types of deep neural networks like convolutional neural networks, recurrent networks, adversarial networks, autoencoders and so on. While there have been plenty of analytical studies regarding the object detection or recognition domain, many new deep learning techniques have surfaced with respect to image segmentation techniques. This paper approaches these various deep learning techniques of image segmentation from an analytical perspective. The main goal of this work is to provide an intuitive understanding of the major techniques that has made significant contribution to the image segmentation domain. Starting from some of the traditional image segmentation approaches, the paper progresses describing the effect deep learning had on the image segmentation domain. Thereafter, most of the major segmentation algorithms have been logically categorized with paragraphs dedicated to their unique contribution. With an ample amount of intuitive explanations, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.
Seamless Collaborative Learning Method to Learn Business Japanese with eBook and Chat System

The breakthrough of information technology has accelerated the evolutionary change in teaching and learning methodologies. In particular, pervasion of high-efficiency smartphones has raised the potential to generate a new learning environment called seamless learning, which has been drawing much attentions from researchers in pedagogy of any domains. The Japanese government, in 2016, declared “Japan Revitalization Strategy 2016”. It includes the increasing of the number of foreign workers who gain employment in Japan after graduation. They set the goal from 30% to 50% of the international job hunting students find jobs in Japan. Therefore, career education for international students has become an urgent issue to tackle with in Japan. In fact, many universities in Japan have started providing international students with career education such as business Japanese, business communication and career design. Since Japanese job hunting process is very unique, most foreign students have anxieties about it. The objective of this study is to propose an effective business Japanese learning support system. Our ultimate goal is to contribute to the enhancement of their employment rate in Japan.
Modelling and Linking Company Data in the euBusinessGraph Platform

In the business environment, knowledge of company data is essential for a variety of tasks. The European funded project euBusinessGraph enables the establishment of a company data platform where data providers and consumers can publish and access company data. The core of the platform is the semantic data model that is the conceptual representation of company data in a common way so that it is easier to share and interlink company data. In this paper we show how the unified model and Grafterizer, a tool for manipulating and transforming raw data into Linked Data, support the linking challenge proposed in FEIII 2019. Results show that geographical enrichment of RDF data supports the interlinking process between company entities in different datasets.
A system for home appliance operation by hand waving in a user-definable command space

In this paper, a novel system that operates home appliances at arbitrary positions in a room is proposed based on a “command space” associated with the operation of the home appliance. In the proposed system, a hand waving gesture is used to operate the home appliances. First, three-dimensional (3D) positions are extracted from the hand waving gestures at two different positions, and a command space is set up based on the extracted 3D positions. Thus, it is possible to operate the home appliances freely in an arbitrary place chosen by the user by installing the user-definable command space. Next, detailed operations are performed by hand waving in the command space. Experiments were conducted to confirm that detailed operations, such as TV channel switching, can be executed from different places using the proposed system.
Application Research of Clustering Algorithm in Earthquake Disaster Prediction System of Sanhe

In order to better provide decision-making technical support for the government after the earthquake, this paper takes all buildings in Sanhe as the research target and simulates the earthquake damage based on the circular intensity attenuation algorithm. According to the earthquake resistance level of the building and the type of building, the damage of the building can be judged, and the rescue efficiency and effect can be improved more efficiently. This project uses artificial intelligence clustering algorithm to cluster analysis of all buildings according to the damage level, and combines the GIS platform to display the analysis results in the form of graphs. Finally, the system used PHPWord technology to provide a detailed output of a detailed earthquake damage prediction analysis document, in order to efficiently arrange and schedule emergency rescue work.
Weather Forecasting Using Artificial Neural Network

Weather forecasting is a blessing of modern technology. It enables us to understand the nature of the atmosphere. Precise weather forecasting is one of the greatest challenges in the modern world. Unlike traditional methods, modern weather forecasting involves a combination of computer models, observation (by use of balloons and satellites) and patterns recognition along with various trends. Forecasting can be made accurately and precisely by the proper application of these methods. For forecasting various kinds of computer methods are used and these methods are related to various complex formulas. Researchers have done many things to establish a relationship of recent (input) data and target data which is linear. But practically the relationship is nonlinear. After establishing the nonlinearity, many models have been made to get future weather data. As the weather data is nonlinear, Artificial Neural Network (ANN) has become an effective way of predicting weather data precisely and accurately. Neural Network is a system that can be trained with certain input and output. It creates its own structure based upon how it is trained. In this paper we predicted weather data for a particular month of a season and compared the result for different functions and training method of ANN.
Breast Density Quantification Using Weakly Annotated Dataset

Breast density is known to be an efficient biomarker for cancer risk, and of particular interest in early breast cancer detection, when masses are not yet visible. The quantification of the breast density is difficult due to limitations of mammography imaging, as well as to the ambiguities in defining the limits of the relevant regions. Though inherently a regression task, breast density quantification has been typically approached as a rough classification problem. In this paper, we model the problem of breast density evaluation as an image-wise regression task that seeks to quantify the percentage of fibroglandular tissue. We propose a deep learning method offering a clinically acceptable estimate with low requirements on expert annotations. We also discuss the use of the X-ray acquisition parameters as additional input to the neural network. Our best performing model yields an optimistic mean absolute error around 6.0% of breast density.
Teacher’s Strategies in Teaching English for Students of Hearing Disorder

Teaching English for the students of hearing disorder seems difficult to English teacher. The students need different kind of language treatment in learning and the teacher needs various strategies in teaching. This study aims to determine the application of strategies used by an English teacher for the students of hearing disorder and the challenges faced by the teacher in teaching at SMPLB YPAC Banda Aceh. The design of this study was descriptive qualitative research. It concerned on the strategies used by the teacher and the challenges faced by the teacher in implementing the strategies. The writer used observation sheet and guiding interview as the instruments. The data were presented in form of list and narration. Based on the results of research and discussion could be concluded the strategies used by the teacher were facilitative strategies, repair strategies and memory strategies. The difficulties faced by teacher in implementing the strategies were in communication, student’s limited ability to comprehend the material and applying grammar learning strategies. Facilitative and repair strategies are suggested to be applied in order to avoid and to fix communication breakdown. Memory strategies also could be used to help the students in memorizing and retrieving information.
A Novel Edge Detection Algorithm Based on Distance

Abstract Under the framework of Canny algorithm, a distance-based edge detection algorithm is proposed to improve the gradient magnitude of Canny. In our algorithm, the gradient magnitude can be acquired by taking the distance from the center of the mask as the weight factor. The operator not only can calculate the image gradient better, but also has good separability in the horizontal and vertical directions, and its gradient amplitude has a certain degree of rotation invariance. Finally, this operator is compared with various gradient operators for the Lena image simulation and the actual waterfront edge detection experiment. It shows that the operator has a better edge detection effect from the experimental results.
Augmented and Virtual Reality:Future of Marketing Trends

Cognitive technology, or artificial intelligence (AI), has always had a way of capturing the public's imagination. Artificial Intelligence is digital technology that is meant to reproduce human psychology and intelligence and prime concern in digital world. AI has grown in scope in terms of its use in marketing, Digital marketing and trends in virtual era. With the beginning of digital marketing and the internet revolution, mass media advertising took a backseat and things like consumer behaviour, preferences, search history tracking, SEO, content marketing, speech recognition, came to the front of the line and which provide more prominent way for the understanding for consumer behaviour. AREM (Augmented Reality Experiential Marketing) is beneficial to make customer satisfaction by using knowledge. It also gives the company a plus point in customer satisfaction and customer retention. The focus in VR activity is on knowledge and emotional engagement with various techniques. The goal of gamification is to “support the user's overall value creation by providing gameful experience” in various encounter techniques. AI, AR, VR and Gamification are mechanism of extraordinary relevance for the companies of the 21st century.
TradeMarker - Artificial Intelligence Based Trademarks Similarity Search Engine

A trademark is a mark used by a company or a private human for the purpose of marking products or services that they manufacture or trade in. A restriction on the use of the trademark is necessary to enable sellers and manufacturers to build a reputation for themselves, to differentiate themselves from their competitors and thereby promote their businesses. In addition, the restriction also serves consumers and prevents their misuse by a name similar to another product. This restriction is done through the formal examination and approval of the trademarks. This process entails trademark examination against other approved trademarks which is currently a long manual process performed by experienced examiners. Current state-of-the-art trademark similarity search systems attempt to provide a single metric to quantify trademark similarities to a given mark [6–11]. In this work we introduce a new way to carry out this process, by simultaneously conducting several independent searches on different similarity aspects - Automated content similarity, Image/pixel similarity, Text similarity, and Manual content similarity. This separation enables us to benefit from the advantages of each aspect, as opposed to combining them into one similarity aspect and diminishing the significance of each one of them.
TALS: A Framework for Text Analysis, Fine-Grained Annotation, Localisation and Semantic Segmentation

With around 2.77 billion users using online social media platforms nowadays, it is becoming more attractive for business retailers to reach and to connect to more potential clients through social media. However, providing more effective recommendations to grab clients' attention requires a deep understanding of users' interests. Given the enormous amounts of text and images that users share in social media, deep learning approaches play a major role in performing semantic analysis of text and images. Moreover, object localisation and pixel-bypixel semantic segmentation image analysis neural architectures provide an enhanced level of information. However, to train such architectures in an end-to-end manner, detailed datasets with specific meta-data are required. In our paper, we present a complete framework that can be used to tag images in a hierarchical fashion, and to perform object localisation and semantic segmentation. In addition to this, we show the value of using neural word embeddings in providing additional semantic details to annotators to guide them in annotating images in the system. Our framework is designed to be a fully functional solution capable of providing fine-grained annotations, essential localisation and segmentation services while keeping the core architecture simple and extensible. We also provide a fine-grained labelled fashion dataset that can be a rich source for research purposes.
Service Designers’ Information Seeking: Consulting Peers Versus Documenting Designs

Service design is an information intensive activity. This study aims to investigate service designers’ information behavior and understand the roles people and documents play as information sources for service designers. Ten designers were interviewed about their information seeking behavior in one service design project from its start to its completion. The interviewees were asked to describe and reflect upon their choice of information sources and their use of project documentation. Each interview lasted about 1.5 h. The interviews were transcribed in full and the transcripts were coded with respect to design activities, information sources used, and reflections on information behavior. People served five different roles as information sources and documents served four. Documents became increasingly important sources of information as projects progressed because still more information was recorded in writing. Consistent with previous research, people play an important role because of their easy accessibility and the good quality of the information they provide. In contrast, the forward-looking role of document creation restricts the backward-looking roles of the resulting documentation. We speculate that the consultancies suffer from poor integration across documents.
PENGARUH STRATEGI PEMBELAJARAN E-LEARNING DAN MINAT BELAJAR TERHADAP HASIL BELAJAR AKUNTANSI

The aim of this research is to know the influence of e-learning learning strategy and interest in learning to accounting learning result. This research was conducted at SMK Permata Bangsa Kelurahan Jakasetia, South Bekasi Subdistrict, Bekasi City involving 56 samples taken with random sampling technique to the equivalent class. Instrument used in this research is the accounting test and questionnaire interest in student learning; and the data analysis using two-way ANAVA and Tukey Test. The results of this study obtained: (1) there is a significant difference between the learning outcomes of students who are taught with e-learning learning strategies and expository strategies in which the results of student accounting learning taught by e-learning strategy is higher than the students taught by strategy expository learning. (2) There is an interaction between students who are taught using learning strategies with interest in learning on accounting learning outcomes. (3) This means that the result of group accounting learning which is taught using e-learning learning strategy is significantly higher than that taught using expository learning strategy in students who have high learning interest. (4) While the learning result of student group accounting that is taught using e-learning strategy is same as learning result which is taught using expository learning strategy to students who have low learning interest, influenced by student environment factor and learning design factor in research.
Training Genetic Neural Networks Algorithms for Autonomous Cars with the LAOP Platform

The challenge with self-driving cars is to create a model that converts sensors data (such as cameras or proximity sensors) into actions. This way the car can react to its changing environment and make the right decisions. In the literature, Neural Networks is the most promising technique used to parse these sensors data. A well trained and designed neural network can take the sensors values and output the right actions. In this paper, we introduce a Way to train Efficiently Neural Networks with Genetic principles, called WENNG. Moreover, we propose a comparative study between all the variations of WENNG to highlight the best-performing ones. To evaluate our WENNG training variation, we implement two well known neural network algorithms: the FullyConnected one and the NEAT algorithm. Through extensive simulations, we demonstrate that the Natural Selection WENNG outperforms the Greedy WENNG at training the genetic neural networks with a low mutation rate. Finally, we show that an IMproved version of NEAT called IMNEAT, minimizes twice the number of generations to reach the maximum fitness value compared to the traditional NEAT algorithm.