Reviewerly/exHarmony huggingface dataset:
Museum Mobile Guide Preferences of Different Visitor Personas

Personalising museum mobile guides is widely acknowledged as being important for enhancing the visitor experience. Due to the lack of information about an individual visitor and the relatively limited time of his or her visit, adapting the user interface based on a museum visitor's type is a promising approach to personalisation. This approach first requires a mechanism to identify the visitor type (‘persona’) and, second, knowledge of the preferences and needs of different types to apply personalisation. In this article, we report a face-to-face questionnaire study carried out with 105 visitors to Scitech, a science and technology visitor centre. The study aims to investigate the main facts required to identify a visitor persona and to explore the preferences of different visitor personas for particular mobile guide features. We limited our concern to the user interface features of the guide (e.g., whether it provides recommendations for related items to view) rather than what content and services the guide provides (e.g., what related items are recommended). We found that we can reliably identify the visitor persona using two multiple choice questions about visit motivation and perceived success criteria. In addition, we found that visitors have significant preferences for particular features such as presentation media, venue navigation tool, object suggestions, details level, accessing external links, exhibit information retrieval method and social interaction features such as voice communication, instant messaging, group games and challenges. Some features were found to be preferred differently by different personas such as the challenges feature, some were found to be preferred by personas differently to the overall preference such as in presentation media, and some were found to be preferred by some personas with no particular preference for others such as a venue navigation tool. Instant messaging was found to be significantly not preferred by all personas. The results provide a basis for personalisation of museum guides and services using a personas approach, which is a solution where data about individual users may be limited and where the individual configuration of a user interface may not be practical or warranted.
CSCNet: A Shallow Single Column Network for Crowd Counting

Crowd counting in complex scene is an important but challenge task. The scale variation of crowd makes the shallow network hard to extract effective features. In this paper, we propose a shallow single column network named CSCNet for crowd counting. The key component is complementary scale context block (CSCB). It is designed to capture complementary scale context and obtains a high accuracy with limited depth of the network. As far as we know, CSCNet is the shallowest single column network in existing works. We demonstrate our methods on three challenge benchmarks. Compared to state-of-the-art methods, CSCNet achieves comparable accuracy with much less complexity. CSCNet provides an alternative to achieve comparable or even better performance with about 30% of depth and 50% of width decrease. Besides, CSCNet performs more stably on both sparse and congested crowd scenes.
The Design and Implementation of A Delivery System

Nowadays, robots have been applied to a variety of applications and industries. The rapid growth promotes the development of various robots for our life. For example, the huge labor work may be replaced with a robot in the catering industry. During the process, we observe if some routine jobs can be performed by a robot instead of people, then the performance can be improved and the cost can be saved. Especially, the meal delivery is a typical example that can be replaced by a robot. As a result, we address this issue by designing and implementing a meal delivery system. In contrast to existing meal delivery systems, our system allows consumers to take their own meals without waiters. In order to make our system user friendly, AGV (Automatic Guided Vehicle) and a robotic arm assembled by us are integrated to develop this system.In order to achieve the goal mentioned above, we aim at proposing a novel approach for the proposed system for the following stages. First, we try to construct a map of an environment for a robot because most food environments are indoors. Furthermore, we construct scenes for surrounding objects with laser sensors. Second, this map is applied to indoor navigation along the shortest path computed by the global planning. Third, the regional planning is applied to plan paths to avoid obstacles. Finally, we present a method to let a robot put a meal on the table by detecting its height first. In order to perform this action, we use an icon of the edge of a table.The proposed work can really provide a service while delivering a meal in a restaurant. Customers in a restaurant are unnecessary to taking a meal by themselves to replace waiters. As a result,, the sharing of labor saving time is achieved. The experiment shows that our work is really suitable for catering applications.
IDLA: An Instruction-based Adaptive CNN Accelerator

In this paper, we propose an instruction-based adaptive CNN accelerator named IDLA for fast and efficient deployments of CNN models on FPGA. The hardware engine of IDLA accelerates the computation of CNN models by adaptively using different functional modules. Following a modular design fashion, the hardware engine is attentively designed to enable all these modules to work concurrently and to improve the usage efficiency of on-chip resources. Besides, layer fusion and weight reuse strategies are applied to reduce data access to DDR. Coordinating with this hardware engine, a network parser is developed to automatically analyze different CNN models to generate an optimal scheduling scheme for each CNN model. Moreover, a customized instruction set with moderate-granularity is designed to further enhance the flexibility in joint-optimization between software and hardware. We build the IDLA on a Xilinx VU9P FPGA. The experimental results show that our proposed IDLA accelerator has reached an overwhelming performance of 168.76 (ResNet18) and 277.63 (VGG16-SVD) GOPS with an DSP efficiency of 1.62 Ops/DSP/cycle (VGG16-SVD), much better than existing advanced works.
Underneath the veneer of techno-positivity – exploring teachers’ perspectives on technology use in Further Education and Training

This qualitative study explores the views of nine teachers working in the Further Education and Training in Ireland regarding the use of technology. Drawing on in-depth interviews, thematic analysis exposed a techno-positive perspective and a hierarchy of technologies with newer technologies being valued more. A sort of ‘intangible’ quality is ascribed to technology, which we labelled ‘techno-magic’. The study uncovered a phenomenon of ‘techno-guilt’ where teachers blamed themselves for not using new technologies. The study concludes that there needs to be a new dialogue that moves beyond pro/anti-technology and empowers teachers to approach technology with a more critical perspective.
Comparing Dynamic Hand Rehabilitation Gestures in Leap Motion Using Multi-Dimensional Dynamic Time Warping

We propose and evaluate the use of Multi-dimensional Dynamic Time Warping (MDTW) for comparing dynamic hand rehabilitation gestures that would be performed by a patient (query) relative to hand gestures prepared by a physiotherapist (reference). MDTW enables us to determine how similar or different a query dynamic hand gesture is to a reference one whilst filtering out unwanted sources of error resulting from positional, rotational or speed differences between the query and the reference actions. It produces a minimum-distance value of a warp path after aligning a query dynamic hand gesture with a reference one. A low minimum-distance value implies the two gestures being compared are similar and high minimum-distance value implies the two gestures vary to a greater extent. When we deliberately compare a specific hand gesture with itself, we obtain a minimum-distance value of 0° indicating the similarity is 100%. Furthermore, when we compare two closely similar hand gestures i.e. gesture 1 and gesture 4, a minimum-distance value of 35.9° is obtained. However, when we compare two quite different gestures i.e. gesture 2 and gesture 3, a minimum-distance value of 248.5° is obtained. Therefore, a physiotherapist can establish whether a patient performs hand rehabilitation gestures satisfactorily or an adjustment is required based on the minimum-distance values of the warp paths.
Transfer Learning Methods for Using Textural Features in Histopathological Image Classification

As the technological advances in computer hardware and machine learning have increased significantly, deep learning models have also been used in many different areas. Examples of these areas are image recognition, face detection, natural language processing, toxicology, suggestion systems, anomaly detection and disease diagnosis in the health sector. This study focuses on studies on disease prediction and diagnosis through histopathological images. The main purpose of the study is to apply deep learning models that can classify cancerous tissues with high accuracy. Besides that, implementation of deep models are done with a low computational cost so that models can be trained in a fast manner. Within the scope of this subject, the convolutional neural network models, which are very popular in image classification in the deep learning world, have been realized by applying transfer learning technique. In addition to these models, a deep learning model called CAT-Net is used to compare and evaluate the success of the transfer learning method. The results of the study are compared with overall accuracy, precision, recall, and F1 score metrics for each model.
FLY: Venue Recommendation using Limited Context

Recommendation of publication venues is very much the need of the hour. There is a sea of options for research conferences and journals. As a result, it is often found that researchers are not even aware of many publication venues in their research area. Existing work uses information, such as all the references in a given paper, past publication venues of the author(s), co-author relationships, and the paper content to do venue recommendation. However, in this paper, we propose a system that uses very limited context, namely the paper title, abstract, and a venue network, which is constructed using only a small subset of authors' publication history, to do venue recommendation. Our venue recommendation system, FLY, gives 30% higher accuracy compared to the current state-of-the-art limited context venue recommendation system.
Recognition of GNSS Jamming Patterns in ADS-B Data

With the increasing dependency on the Global Navigation Satellite System (GNSS) for navigation and surveillance, aviation is focusing on mitigation of threats caused by a possible system unavailability due to jamming or spoofing. This research deals with pattern recognition of the time evolution of quality indicators in transmitted Automatic Dependent Surveillance - Broadcast (ADS-B) messages during GNSS jamming in non jammed data over Central Europe. An evaluation of the quality indicators from all messages within a one month interval is presented. A practical jamming experiment was done to determine how the quality indicators are changing during a jamming presence. Afterwards, the results of the experiment are used as a foundation for the proposed jamming detection technique using ADS-B data based on pattern recognition and cluster analysis. A natural presence of a quality indicators variability is proven in the data using these methods, similar to the indicators variability during GNSS jamming.
Smart Complaint Redressal System Using Ethereum Blockchain

In today's world, more importance is given on the availability of the applications and various websites available in the digital market. People will manage their daily work on time, precisely, very fast, and with satisfaction. So various technologies are used to fulfil daily work. In India, there is no direct and efficient way of communication between the government and the public, for solving a problem i.e for getting a problem solved at any place, people may have to wait for three months, but it can probably be solved sooner. Nowadays, the scenario has changed. Many applications are available, which allow users to register their complaints. But there are some problems related to its transparency. This paper proposes an Ethereum blockchain application that will help people to register their complaints and get updates about the complaint. Adoption of blockchain technology makes the application more secure, transparent and immutable.
Exploring the Capabilities and Limits of 3D Monocular Object Detection - A Study on Simulation and Real World Data

3D object detection based on monocular camera data is a key enabler for autonomous driving. The task however, is ill-posed due to lack of depth information in 2D images. Recent deep learning methods show promising results to recover depth information from single images by learning priors about the environment. Several competing strategies tackle this problem. In addition to the network design, the major difference of these competing approaches lies in using a supervised or self-supervised optimization loss function, which require different data and ground truth information. In this paper, we evaluate the performance of a 3D object detection pipeline which is parameterizable with different depth estimation configurations. We implement a simple distance calculation approach based on camera intrinsics and 2D bounding box size, a self-supervised, and a supervised learning approach for depth estimation. Ground truth depth information cannot be recorded reliable in real world scenarios. This shifts our training focus to simulation data. In simulation, labeling and ground truth generation can be automatized. We evaluate the detection pipeline on simulator data and a real world sequence from an autonomous vehicle on a race track. The benefit of training on simulation data for the application of the network on real world data is investigated. Advantages and drawbacks of the different depth estimation strategies are discussed.
REINDEAR : REINforcement learning agent for Dynamic system control in Edge-Assisted Augmented Reality service

Nowadays the industry of the Augmented/Virtual Reality is rapidly growing in various domains such as AI assistance or gaming. While the main goal of the AR service itself is to provide the user the visualized computing experience in their life, the systematic requirements include the real-time performance since the service should be reacting to the user's state which changes dynamically over time. This kind of real-time response is often very hard to achieve on typical edge devices such as mobile phone or AR goggles due to lack of computational power. To overcome this issue, many of the researches suggest server offloading technique which can provide sufficient amount of computational power in exchange of the transmission overhead. The tradeoff relationship between the computational power and transmission overhead makes the control of the offloading procedure important for the overall service quality. In this paper we propose an RL based server-client controlling scheme REINDEAR. REINDEAR is a system that conducts class-wise characteristic analysis from the experience, so that it could control the AR service tradeoff quality adaptively. From the result of the experiments, we showed that our REINDEAR system learns the underlying behavioural patterns of video objects and provides controls that suits each pattern.
An Empirical Study on Configuration-Related Code Weaknesses

Developers often use the C preprocessor to handle variability and portability. However, many researchers and practitioners criticize the use of preprocessor directives because of their negative effect on code understanding, maintainability, and error proneness. This negative effect may lead to configuration-related code weaknesses, which appear only when we enable or disable certain configuration options. A weakness is a type of mistake in software that, in proper conditions, could contribute to the introduction of vulnerabilities within that software. Configuration-related code weaknesses may be harder to detect and fix than weaknesses that appear in all configurations, because variability increases complexity. To address this problem, we propose a sampling-based white-box technique to detect configuration-related weaknesses in configurable systems. To evaluate our technique, we performed an empirical study with 24 popular highly configurable systems that make heavy use of the C preprocessor, such as Apache Httpd and Libssh. Using our technique, we detected 57 configuration-related weaknesses in 16 systems. In total, we found occurrences of the following five kinds of weaknesses: 30 memory leaks, 10 uninitialized variables, 9 null pointer dereferences, 6 resource leaks, and 2 buffer overflows. The corpus of these weaknesses is a valuable source to better support further research on configuration-related code weaknesses.
Important Scene Prediction of Baseball Videos Using Twitter and Video Analysis Based on LSTM

This paper presents a novel important scene prediction method using baseball videos and tweets on Twitter based on a Long Short-Term Memory (LSTM). For considering baseball videos and tweets, the proposed method utilizes textual, visual and audio features. Introducing these multi-modal features into the important scene prediction of baseball videos is the first work. In order to deal with multi-modal time-series features constructed from textual, visual and audio features, the proposed method adopts LSTM which is effective for training such multimodal time-series features by maintaining a long-term memory. The effectiveness of the proposed method is confirmed by experimental results.
Adaptations of Scrum roles in software projects

Scrum is one of the most popular agile methods due to its framework structure. This structure allows flexibility to adapt to project characteristics. Given this, expected adaptations in the Scrum elements is something natural. Roles are one of these elements. They are of great importance in the successful deployment of Scrum. Therefore, we expect to understand the extent of the adaptations practiced in the Scrum roles Scrum Master, Product Owner, and Development Team by conducting a survey with practitioners. To better comprehend such adaptations and their relationships, we decided to systematize, group, and represent them using a feature model. We, then, evaluated the created feature model inviting Scrum deployment experts. Results point out the feasibility of our feature model based on the perceived usefulness and ease of use. Such model makes it possible for practitioners to have a set of features they can instantiate and customize according to their needs and recommendations from the Scrum guide. Another benefit of such feature model is that it records Scrum users' background and compares similar situations where certain features have been adopted.
Energy-Efficient Smart Wearable IoT Device for the Application of Collapse Motion Detection and Alert

As we know if anyone falls from a narrow height it is very critical especially for the people who are under 10 years or above 60 years of age. The age group under 10 years is very sensitive, if any child who falls can face injuries which will affect them in terms of mental and physical disability or fatal injuries can also be caused in some cases. Similar for the age group of above 60 is also crucial if they got any kind of injury due to falling and by the time if they didn’t get any medical treatment so it will cause a decisive loss for that person. In the present era we are living in the age of 4G and 3D technology which means in this era everyone is connected with each other by means of smart phone and internet, also the smart wearable devices, which will be connected with human beings and send a kind of information signal through the medium of internet which is commonly known as Internet of Things (IoT). According to this kind of technology we are able to rectify the injury causing by collapsing on the ground. In this paper, we proposed an energy-efficient smart wearable IoT device which is able to detect the motion of human body and based on that it will detect the collapsing motion and immediately take action by sending data to the cloud server via internet which will immediately send an SMS to the concerned person. Apart from that we also introduce one more feature which is panic alert, so if anyone who need any emergency support, this wearable device will send an SMS to the concerned person. Here we used ESP8266-01 for the communication channel, for the calculation of body motion we use ADXL335 and for the reduction of power we used ATTINY85 which will reduce the ample of energy and utilize the power intelligently which results in the long life of 220 mAh battery. Its battery back-up is approximately up to 3000 h which is extremely far better than all the previous existing approaches.
Research Paper Recommendation Based on Content Similarity, Peer Reviews, Authority, and Popularity

According to the Canadian Science Publishing, there are approximately 2.5 million scientific papers published each year. The huge volume of publications can be contributed to a substantial increase in the total number of academic journals, including the increasing number of predatory or fake scientific journals, which yield high volumes of poor-quality research work. The effect of this scenario is that there is an obsolete jungle of journals to flip through in searching for high-quality and relevant references for researchers, ranging from the ones who simply look for citations to cite or latest development and knowledge in a specific scientific area of study. In solving this problem, we propose a unique, elegant research paper recommender. Besides considering the topics and contents of related publications, our recommender also examines the peer reviews, authority, and popularity of each publication to ensure its quality. Conducted empirical study shows that our recommender outperforms existing research paper recommenders and contributes to the design of searching relevant publications.
A Distilled BERT with Hidden State and Soft Label Learning for Sentiment Classification

Abstract BERT is a pre-trained language model. Although the model is proven to be highly performant in a variety of natural language understanding tasks, its large size makes it hard to implement in practical situation where computing resource is limited. In order to improve the model efficiency of BERT for sentiment analysis task, we propose a novel distilled version of BERT. It distills knowledge from the full-size BERT model, which serves as the teacher model. The distilled model efficiently learns the last hidden state and soft label of the teacher model, which are different from previous models. We use distillation learning objective that is able to effectively transfer knowledge from the original big model to the compact model. Our model reduces BERT model size by ∼40%, but retains ∼98.2% of performance in sentiment classification task. Our model achieves promising results in SST-2 sentiment analysis, and outperforms previous distilled model.