Reviewerly/exHarmony huggingface dataset:
Exploring the Augmented Intelligence and Augmented Reality

In the technology-driven world, the continuous evolution in technology has an immense impact on every domain of human civilization. Intuitive, assistive technological advancement enables the use of innovative applications. In recent years, among such rapidly developing technologies are augmented intelligence and augmented reality. Augmented intelligence is a technology that combines artificial intelligence with human intelligence to achieve operation. On the other hand, augmented reality is a technology where users interact through the lens of a camera and feel as if they work in a real-world environment. In this milieu, it is imperative for libraries to use these technologies and offer a strategic medium through which libraries can deliver the information to the patrons. The subject of this article is to explore the concept of augmented intelligence and augmented reality and their application in libraries.
Research and Design in Controversial Spaces

Research within public safety and law enforcement in America highlights important issues and considerations for designers and researchers. Within this industry exists controversial points of views and high stakes consequences. How do we as researchers balance empathy in spaces where points of views don't just differ, but actively clash? Who should we consider to be our true users within a product life cycle, and how do we ensure we are designing for the future rather than the present state of the world? This PechaKucha surfaces some of the strategies employed by the research and design teams at Motorola Solutions in a holistic effort to navigate these challenges.
Prognostication of Crime Using Bagging Regression Model

Crime is a social and economic problem that affects a country's quality of day-to-day life and economic growth. However, analyzing and forecasting crime is not a straightforward job for a law enforcement investigator to manually unravel the underlying nuances of crime data. To make this process easier and more automated, the authors present a machine-learning model for crime analysis and predictions. The authors used a London crime dataset and enhanced the data set by incorporating population density, percentage of economically inactive working age, and average monthly temperature. The pre-process step prepares the raw data and makes it suitable for the machine-learning model. Bagging and boosting ensemble techniques were used to find a better- machine-learning model. GridSearchCV was used to tune hyperparameters to find the best-performed model. Parameters were tuned as an iterative processes. Eventually, the researchers compared all the algorithms and selected the Random Forest bagging regression model as the best-performed algorithm.
A Systematic Literature Review on the Application of Blockchain Technology in Biometric Analysis Focusing on DNA

In the present world, we need a secured human identification system to identify humans. These kinds of systems will help forensics and the healthcare sector. But with the rapid development of technology there might have a huge security problem with these kinds of security systems. So, we also need more highly secured technology to implement this secured system. Because of this concern the authors move with the blockchain as the main technology area for this application. Also, the authors chose to use DNA or genome to identify humans as the unique key. Applying DNA binary sequence to the blockchain will provide multiple problems, which are finding and suitable algorithms. So, in this review, the authors are providing a systematic literature review (SLR) by focusing on multiple factors which are blockchain and the DNA using standard eight databases. From these reviews, the authors found the 73 total research publications, and after reading the title and the abstraction the authors selected the most relevant 10 research papers for this research domain. This paper is discusses the most suitable technology combinations and future works.
Theory Instruments as Tangible Ways of Knowing

While ethnographers and the data they produce already play a role in affecting industry practices, there is potential to integrate anthropological ways of seeing and knowing into a shared transdisciplinary design praxis. In a series of design research experiments, we have taken a pragmatic and playful approach to physicalizing theory. The result is a set of ‘Theory Instruments’ that transform theory into tangible interaction. Theory Instruments scaffold knowledge production by encouraging new ways of seeing organizations, products, users, and the relations between them. We present two of these instruments, Actor‐Network Rings and Reciprocity Balance, through a case study with a design team at a health product company that wished to generate new design concepts from field material. Theory Instruments helped bridge the gap between the epistemic modes of knowing employed by ethnography practitioners and the technical and tacit modes of knowing familiar to design practitioners. This new mode of collaboration helped them to cross worlds, cultivating a more resilient, transdisciplinary praxis.
Student Experience on Distance Learning in Mathematics in Times of Pandemic

The educational changes brought about by the global pandemic caused by the SARS-CoV-2 virus have generated new scenarios for students around the world. The vast majority of teachers in both non-university and university education have adapted their traditional classes to continue the teaching and learning process. This text presents the learning experience of students of compulsory secondary education in Spain in the subject of mathematics. The research carried out analyzes the students' perceptions in different dimensions such as their experience with the educational materials and the tasks or the technology used to achieve the learning objectives. The collection of information was carried out during the third quarter of the 2019/2020 academic year. The most outstanding results indicate that students mostly perceived the distance learning experience as positive.
Proposal of a Unit Design for Online English Lessons Using a Technology-Based Plan

The need for curriculum design, material development, the creation of units, and lesson plans is incontrovertible in that they all provide teachers with the opportunity of enhancing self-directed learning, enthusiasm, and learner confidence besides building language awareness, particularly in virtual classes. To that end, a unit design entitled ‘World without Crime' has been shaped to strengthen the intake of young adults or teenagers at the pre-intermediate level. This design was composed of four language skills with pre-, while-, and post-activities to be implemented in some e-learning platforms. Likewise, grammar and vocabulary exercises were incorporated based on a process-oriented approach and integrated syllabus. Communicative practices were adopted considering the issues of visual literacy and digital citizenship to promote the learning of the connected online community. Finally, some recommendations have been presented for future directions and foreign language teachers.
Remote Teaching and Learning

Since the COVID-19 pandemic situation affected the entire world, several studies were conducted to analyse, evaluate, and inform the education community of the best practices to adopt regarding a remote teaching and learning context, bridging the necessity to understand how online learning, mostly remote teaching and learning, can effectively accomplish and fulfill the professional and academic goals of curricula. Which are the practices implemented? And how did teachers who were used to face-to-face teaching deal with it? This book chapter aims to understand the experience of teachers in Portugal of different grades of education (n=466), during the COVID-19 Pandemic, in the year of 2021, prompting to identify the educational approaches, techniques, and strategies that teachers adopted to teach their students and maintain or increase their level of motivation to learn in a remote teaching environment. Additionally, the chapter proposes and sets recommendations based on the data collected, and on literature reviewed about this theme.
Move It Like Picasso: A Flexible Platform to Combine Movement, Drawing, and Communicative Play

Tangible and embodied explorations have delivered many designs to rejuvenate many traditional leisure activities such as parlor games, and dancing, and offered ideas on how to bridge inter-generational divides through play, and allow players of different skill levels to compete relatively equally. However there seem to be few examples of designs for non-competitive play which allow participants to take very different roles. Bringing together elements of playing with dance, communication, and drawing, we present the Move It Like Picasso platform. This facilitates a collaboration between a player trying to physically trace flickering lines from an animation which are unpredictably triggered by the second player’s bodily movements. We offer this as an example of how combining simple digital technologies and everyday physical materials may offer a route to rich social play experiences that might simultaneously appeal to a wide range of tastes.
Addressing children and caregivers’ concerns in the design of child-targeted locative systems

Children’s independent mobility has proved to have a tremendous positive impact on their well-being, social and cognitive development, and spatial understanding. However, children’s independent mobility is declining in several countries although the increasing number of investments in urban mobility and the incentives for sustainable adoption of public transport and shared mobility. Concurrently, ownership of mobile devices is growing, opening up compelling opportunities for location- and map-based applications and services designed for and with children. This article investigates the needs and concerns in the design of child-targeted locative systems addressing children’s independent mobility while considering caregivers’ approval and fears. We present a study with seven families (nine children, 8-13-year-old, and seven adults) by taking an interdisciplinary approach. Inspired by scenario-based design, we invited the participants to imagine themselves in independent mobility scenarios (e.g., daughter and father negotiating a school path) and tell what design choices they would make. Our results indicate that the design of a child-targeted locative system should enhance physical safety on the road and enhance online safety.
On the Experience of a Comprehensive Study of Handwritten Books of Old Russia: Parchment Illuminated “Synodic” of the Main Collection of Handwritten Books, F.p.IV.1 in the Manuscripts Department of the National Library of Russia

The parchment “Synodic” (Commemoration Book: “CB”) from the Main Collection of Handwritten Books, F.p.IV.1 in the Manuscripts Department of the National Library of Russia, which dates back to the period between 1576 and 1581, is studied. One of the basic research methods of the Synodic (Commemoration Book) is visualization in the near-infrared (IR) region of the spectrum, which is carried out using a high-sensitivity QHY 163m camera and a 1060 nm narrow-band light filter. In addition to spectral band imaging, X-ray fluorescence analysis (XRF) is also carried using the Olympus InnovX Delta portable spectrometer and X-Rite eXaсt spectrodensitometer. In many cases, the results of XRF studies allow us to speak about the presence of sulfur, and it is more pronounced, the more clearly iron manifests itself in the measurement, which may indicate the use of ferrous sulfate. An interesting technological feature of some parts of the Synodic (Commemoration Book: “CB”) is the absence of permeability in the IR region of the spectrum, which clearly indicates the presence of a carbon component in the ink. The pronounced unevenness in the properties of the ink of the Synodic suggests that this is the result of the practice of using an “ink nest.”
Yolo-Based Improvements in Remote Sensing Image Applications

The identification of some specific targets in remote sensing images is still quite challenging despite the adequate accuracy of deep learning-based target detection models. This work proposes a variant of YOLOv3 based on the residual structure as the backbone and the attention mechanism module, which improves the ability of YOLOv3 to extract features. SGE is a lightweight module that can fully extract features from images without bringing an increase in computation. Furthermore, the dilated encoder module used in YOLOF was introduced as a neck to enrich the perceptual field of the C5 feature layer by concatenating four layers of dilated convolution with different expansion coefficients. The C5 feature layer and the residual structure were further processed to contain sufficient scale information for further detection. In terms of the mean average precision (mAP), experimental results demonstrate that the proposed model outperforms the other models: YOLOv3, faster-RCNN-r50+GACL Net, and YOLOv4.
Semantic similarity and mutual information predicting sentence comprehension: the case of dangling topic construction in Chinese

This study uses semantic similarity and pointwise mutual information (PMI) to estimate and compute the relationship between topic and comment in dangling topic construction in Mandarin. It proposes three methods to calculate the semantic similarity between topic and comment. We also carry out experiments on human ratings of the acceptance degree for dangling topic constructions. The results show that PMI and three measures of semantic similarity can make good predictions for human-rated data. This is the first time that PMI and sentence-based semantic similarity are employed to predict how humans comprehend sentences as a whole. PMI and semantic similarity measures may further elucidate the concept of topic construction and to help in seeing how Chinese native speakers understand and process sentences. More importantly, this study creates a novel, effective and practical computational approach for predicting entire sentence comprehension/processing and syntactic analysis.
Three-Dimensional Task Allocation of Multiple-Agent Based on Graph Attention Pointer Network

The task allocation problem is a key problem in the study of multi-agent collaboration. The task allocation problem aims to assign tasks to appropriate agents under sequential and logic constraints, so that the quality and efficiency of task completion can be maximized. Several recent studies have shown that attention-based sequence generation models are promising for the task allocation. However, their results are restricted in a two-dimensional space. In this paper, a model based on Graph Attention Pointer Network is proposed for the task allocation problem in a three-dimensional space. The model fully extracts the task features in the three-dimensional space by the graph attention network, then combines the sequence generation model to achieve the task allocation. The attention mechanism in a graph attention network ensures the task allocation performance and the sequence generation method greatly improves the efficiency. Numerical simulations show that the proposed model is suitable for large-scale and dynamic task allocation problems in different three-dimensional scenarios.
Adversarial Example Attack and Defence of Object Recognition: A Survey

Deep learning-based intelligent object recognition algorithm has been widely applied in object detection, auto driving, ect. However, deep neural network is very vulnerable because of its high-dimensional linearization. The object recognition results can be easily mis-leaded through a tiny disturbance in the original image. This kind of sample that adds tiny disturbance to the original image is called adversarial example. The adversarial example attack is a factor that must be considered in the design of robust object recognition algorithm, that is, the need for adversarial defence. An in-depth understanding of adversarial attack can help the model defend successfully. In this survey, an introduction of adversarial attack and defense aiming to object recognition algorithm at the present stage is summarized, and the challenges to be faced and the development trend in the future are pointed out.
Design and Implementation of Task Description Language for UAV Swarms

The rapid development of technologies in the fields of computing, communication, control and sensors, supports researches about unmanned aerial vehicles (UAV) related applications. Multiple UAV cooperation is usually an important approach to improve application performance. In order to make the operation of UAV swarm more convenient, we proposed a formal model for describing and executing unmanned swarm tasks. It can support various types of application scenarios (search, surveillance, deliver and so on), while supports multi-UA V collaboration and processing of dynamic events. Based on this model, a domain-specific language Group Mission Language(GML) was designed and implemented to support the formal description of swarm missions. And designed a Single Mission Language(SML) for the description of individual UAV mission. The SML file is automatically generated based on the GML file and deployed to a single UAV. Moreover, GML is implemented based on the XML language, with simple and clear syntax, which can be mastered in a short time even by people without programming experience. GML and SML implementation have been validated through simulation experiments with six different UAVs.
FCVM: Foreground-based Classifier using Voting Mechanism for Few-Shot Learning

In image classification task, the label of the image corresponds only to the foreground object. Some patches containing foreground of the image are more relevant to the label than other patches, and these patches are called the foreground patches. Using the foreground of an image in the classification stage is a way to improve the accuracy. In this paper, we propose a new classification algorithm based on foreground of support set for Few-Shot Learning image classification tasks, called Foreground-based Classifier using Voting Mechanism, abbreviated as FCVM. In the new algorithm, we use a voting mechanism to filter the more representative foreground patches and use these patches as the new support set. After that, we apply the voting mechanism to the query set, which gives pseudo-labels to expand the label information. By filtering the foreground patch more efficiently and introducing pseudo-labels, the category prototypes in the feature space are made more accurate. The method does not require additional training of the model, and its effectiveness has been demonstrated to be better than previous work in experiments.
UAV Swarm Task Assignment Method Based on Artificial Gorilla Troops Optimizer

When Unmanned Aerial Vehicle (UAV) swarm works together, how to reasonably assign multiple targets and tasks on the ground is a practical problem. To solve this problem, a task assignment method based on Artificial Gorilla Troops Optimizer (GTO) is proposed in this research. The proposed method establishes a Cooperative Multiple Task Assignment Problem (CMTAP) model taking the sum of distance cost and loss cost as the objective function. First, the initial task assignment scheme is obtained by adopting real number encoding method. Then, the objective function is substituted, and solved using GTO, where the scheme corresponding to the minimum value of the objective function is the final assignment solution. The experimental results show that the proposed method can generate an effective task assignment scheme. Moreover, compared with the assignment results by Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO) and Deth First Search (DFS), the proposed method has faster convergence speed, higher stability and less cost of task assignment.
Detection of Lymphoma from the Bone Marrow Microscopic Images using Convolutional Neural Networks

Cancer is defined by the growth of anomalous cell growth that divides rapidly and has the ability to infect and destroy normal bodily tissue. It is a fatal disease affecting people of all age groups. However, thanks to advancements in cancer detection, therapy, and prevention, survival rates for many forms of cancer are advancing. Lymphoma is one of the most common and fast progressive type of cancer that affects people of all ages. It starts in the immune system's infection-fighting cells, known as lymphocytes. These cells can be found in the lymph nodes, spleen, thymus, bone marrow, and other organs. Lymphocytes alter and develop uncontrollably in lymphoma patients. Follicular Lymphoma and Mantle Cell Lym phoma affects the older age group. Each year millions are diagnosed with this disease. One of the most important aspects for detecting the type of cancer is the blood count. Conventionally manual counting is done and yields a desirable result, but it consumes more time for processing. Deep learning approaches can address these issues by extracting beneficial properties from a large number of raw datasets in a short span of time. Since lymphoma requires early diagnosis, deep learning techniques can help save lives. The objective of the project is to detect if the patient has lymphoma using bone marrow microscopic images and to categorize the lymphoma into three classes namely- Follicular Lymphoma (FL) and Mantle Cell Lymphoma (MCL) and normal tissues using CNN architecture (Inception V3) and (ResNet-50) and to predict the accuracy and efficiency and compare with existing models. Image processing techniques such as segmentation, filters, have also been employed to locate the cancerous parts in the images.
The application of virtual reality in games

Virtual reality technology is a rapid development of technology in recent years, for the development of the field of video games has a vital role. Using image display, human-computer interaction, and other technologies to generate a three-dimensional environment. Enable participants to interact and manipulate virtual environments and objects in real-time. This paper analyzes the characteristics of virtual reality technology and expounds application of virtual reality technology in video games. Through the background, the characteristics of VR technology are analyzed. Furthermore, we discuss and analyze the interactive influence of virtual reality on games from the aspects of game sound production and somatosensory interaction. Besides, this article also analyzes the differences between traditional 2D games and virtual reality-based games (Horror games and Escape rooms, for example). Finally, it summarizes the electronic games on virtual reality equipment, shows the positive impact of virtual reality on economy and society, and looks into the future of virtual reality.
A Novel Cross Language Neural Retrieval Model

Cross-language information retrieval is one of the important tasks in the field of information retrieval. Existing cross-language neural retrieval methods usually use single-task learning, and the single feature capture model limits the performance of neural retrieval models. To this end, a cross-language retrieval method based on multi-task learning is proposed, using a text classification task as a secondary task, capturing feature information of both tasks simultaneously using a shared text feature extraction layer so that it learns the feature patterns of different tasks, and then inputting the feature vectors into the neural retrieval model and the text classification model to complete the two tasks, respectively. In addition, the external corpus introduced by the text classification task also plays a role in data augmentation to a certain extent, further increasing the level of feature information. Experiments conducted on four language pairs from the CLEF 2000–2003 dataset show that the present method significantly improves the text feature extraction and thus enhances the neural retrieval model performance, increasing the MAP values of the neural retrieval model by 0.014-0.203 and increasing the model convergence speed by 22.82% on average.