Reviewerly/exHarmony huggingface dataset:
Person Re-Identification With Multi-Features Based on Evolutionary Algorithm

Person re-identification can identify a particular pedestrian automatically by computer in surveillance video, who has occurred in the monitoring network. It is one of the hot topics in the field of intelligent video surveillance. However, it faces many challenges. For example, robust feature representation model and ideal similarity measure method are both lacking, which will seriously affect the identification accuracy. These problems can be solved to some extent through applying appropriate optimization algorithm, which could optimize feature selection process, and guide similarity measure method design. Evolutionary algorithm is a good optimization method, but two obvious drawbacks have been found in practical application, which are slow convergence speed and easy convergence to local optimal solution respectively. Therefore, traditional evolutionary algorithm is improved in this paper firstly. The existing problem will be solved by improving search efficiency and maintaining population diversity, after that, improved evolutionary algorithm is applied to optimize the process of person re-identification. Feature representation model with high robustness will be designed, and reasonable and effective similarity measure method will be obtained through learning. Thus, the identification accuracy and system performance will be improved. The algorithm proposed in this paper has been compared with some classical algorithms and the case without using evolutionary algorithm to optimize. Experimental results show that method in this paper is an effective person re-identification scheme.
CAE: A Framework for Computer-Aided Talent Evaluation

This paper proposes a framework for computer-aided talent evaluation. Intelligent evaluation is added on the basic questionnaire evaluation, in which questionnaire evaluation is used as explicit evaluation, and the results are quantitatively scored based on industry development questionnaires, scales and explanations. Intelligent evaluation is mainly based on data mining algorithm. As an implicit evaluation, it aims at processing talents’ characteristic engineering, forming users profile and recommending jobs. The final evaluation report is obtained by integrating different evaluation methods. This paper explains the composition of the evaluation framework in detail, and studies its application cases in evaluating talents’ digitalization capability.
Study on Virtual Reality Performance from the User’s Individual Characteristics

In this study, we aimed at revealing the difference and diversity of users working in a virtual environment and the relation between individual characteristics, performance, and sense of embodiment (SoE). Therefore, we conducted two experiments including multiple design factors in the virtual environment and carefully observed the participants’ task performances. The participants were 20 students in the first experiment and 8 students in the second experiment. The task was to quickly hit the button on the desk in a virtual environment with a hand avatar. Thus, the participants hit the button setting on the real desk; therefore, they had to recognise the sense of depth and size in the virtual environment while performing the task. Task performance was measured by time, performance level, and SoE score proposed in our previous study. As a result, the relationship between the user’s physical or psychological characteristics and the task performance and the SoE are revealed. Although the number of participants and the relation among the design factors, user characteristics, and SoE is not sufficient, some clues for designing tasks and virtual environments are obtained.
Mobile Use of the Internet Among the Poor in the Global South: Preferences, Theories, and Policies

Arora (The next billion users: digital life beyond the West. Harvard University Press, 2016) has claimed that, contrary to what one may have expected, poor Internet users in the Global South spend more time on leisure and entertainment than users with higher incomes. She argues furthermore that this anomalous finding is not whimsical or irrational, but is rather an expression of something we all desire, rich or poor. The problem, as Arora sees it, is that the motivations of the poor have been misconceptualized, especially by development agencies, keen to show that the mobile Internet can yield powerful developmental gains to this group.This chapter is concerned with challenging her hypothesis. It first reviews the evidence in support of it and then criticizes the analytical foundations. Crucially, while Arora refers to fleeting bouts of pleasure from entertainment, she ignores the fact that in some poor countries, up to four hours a day are spent on social media. At this level, all sorts of questions arise about excessive use and its implications, for both mental and physical reasons, not to speak of the possible deprivations suffered by young children in the large family size that is typical of sub-Saharan Africa. Note here that psychologists believe that a maximum of thirty minutes a day would lead to significant improvements in well-being.I also contend that Arora does not consider alternative explanations outside her own discipline, anthropology. The main one being, in my view, the comparative lack of digital skills in Africa and the Global South. Drawing on case study and country-comparative evidence, it appears that first-time users of the Internet do not avail themselves of reliable evidence on how the Internet works and are thus driven to use shallow leisure type uses of the Internet, rather than those that are more development oriented (which are more demanding in terms of digital skills).Policy-wise, I am led to reject Arora’s implicit assumptions of consumer sovereignty and well-informed preferences, which would lead to inactivity on the part of those who make policy (see also Chap. 3 above).On the contrary, my view is that there is considerable scope for such persons to deal with problems of excessive Internet use on entertainment; to alleviate problems of information that are rife for users in the Global South; and to deal urgently with the scarcity of digital skills in that region. Some scattered attempts have been made along these lines in sub-Saharan Africa.
Automatic dataset collection for speech-driven gesture generation

Gestures complement the utterance contents to help the human understand. In the field of gesture generation, the task of generating gestures from utterances have attracted attention. The main method for generating gestures from utterances is to associate utterances with gestures using deep neural networks. To associate utterances with gestures using deep neural networks, a co-speech gesture dataset is required. However, building such datasets is costly and time-consuming because it requires a reliable pose estimation system (such as motion captures) and manual adjustments. We proposed an automatic method to collect a co-speech gesture dataset from online speech videos. The method extracts various utterance and gesture pairs from online speech videos. In addition, we use the collected dataset to train a deep neural network and confirm that our automatically-collected dataset can be a supervisory signal for speech-driven gesture generation.
Current Status of User Experience of the Keyboard on Smartphones: An Overall Questionnaire Analysis

The present status of the user experience of the keyboard on smartphones (UXKS) remains ambiguous because of the fast-changing developments of smartphones. This study adopted an online questionnaire (N = 866) to explore users’ current experience, habits, and preferences towards specific objects, including large smartphones and the dual-task input when interacting with their smartphones and the keyboards. The results concluded the user profile of UXKS, and found that users were suffering from long-time use of large smartphones. Additionally, the dual-task operation has become common, with increasing numbers using type or swipe keyboards while driving and walking in particular. The study could provide research objects and basis for organizations and corporations to enlighten future studies on the optimization of the smartphone keyboard design under various scenarios.
DVAR: Data Visualization using Augmented Reality

In this paper, we have discussed the use of augmented reality for data visualization. As we know that data is one of the most valued resources of today’s world, analysis of this data is a task of great importance. Multiple technologies have come up with data visualization for enabling accurate data analysis. Clearer the visualization the easier it is to perform accurate data analysis. Another technology that has grown vastly in various domains is Augmented Reality. It aims to bridge the gap between the physical and the digital world by introducing virtual elements into our environment. Our Implementation is a use case of AR technology in data visualization and analytics. Our proposed system uses mobile AR to perform data visualization giving more engagement and immersion to the users which can be used to gain more human insights over data such as trends and patterns that would otherwise get missed out in a depthless 2D visualization. We have developed an application as a prototype of our proposed system demonstrating different visualization techniques.
Prediction of future paths of mobile objects using path library

Abstract In situational awareness, the ability to make predictions about the near future situation in the area under surveillance is often as essential as being aware of the current situation. We introduce a privacy-preserving instance-based prediction method, where a path library is collected by learning earlier paths of mobile objects in the area of surveillance. The input to the prediction is the most recent coordinates of the objects in the scene. Based on similarity to short segments of currently tracked paths, a relative weight is associated with each path in the library. Future paths are predicted by computing the weighted average of the library paths. We demonstrate the operation of a situational awareness system where privacy-preserving data are extracted from an inexpensive computer vision which consists of a camera-equipped Raspberry PI-based edge device. The system runs a deep neural network-based object detection algorithm on the camera feed and stores the coordinates, object class labels, and timestamps of the detected objects. We used probabilistic reasoning based on joint probabilistic data association, Hungarian algorithm, and Kalman filter to infer which detections from different time instances came from the same object.
Autonomous Wheelchair for Physically Challenged

An area of bodily challenged people makes it very hard to use traditional wheelchairs. To move patients or physically disabled people from one place to another either in hospital or in day to day life, wheelchairs are used. Wheel chairs are designed such that they could be used either with the help of attendee or it can be self-propelling. To lessen the complexities for people who do not have power to move the wheelchair, it is automated. Accelerometer is used to control the wheelchair using head movements. Ultrasonic sensors are used to discover obstacles, joystick is used to provide input through hand, and Bluetooth module is used to interface smartphone to give the voice command. The special modes can be chosen in keeping with the requirement the use of a switch. It is a low cost machine which has unique methods incorporated to manipulate the wheelchair.
Motion Primitive Segmentation Based on Cognitive Model in VR-IADL

Recently, many studies have been conducted using virtual reality (VR) technology in the medical field. Our research group has been developing a virtual kitchen challenge (VKC), which is a system to evaluate instrumental activities of daily living (IADL) using VR technology. In the previous study, we focused on motion primitives, which are the smallest unit of motion in VKC. In this study, we focused on the motion of the VKC task when there is no contact with the screen and developed a model that can be segmented by motion primitives. Furthermore, using a two-step process based on time-series data of the subject's fingertip velocity during the VKC task, we developed a model that can be segmented in terms of motion primitives. Therefore, the segmentation accuracy was 83.4%, and the percentage of false positives was as high as 28%.In the future, we plan to revise the feature set.
Sequence Analysis of Game Data

Abstract This chapter is devoted to sequence game data analysis. It will first define what sequence data is and how it is represented, and then delve more deeply into how to develop models from such data. Sequence analysis has a lot of utility and is important as it conserves the sequence of player actions and can shed light on how players solved different problems within the different game levels. Further, sequence analysis can also be a great way to develop a more robust and accurate player model. The chapter will discuss such advantages in light of showcasing the use of sequential analysis for DOTA 2. Further, the chapter will also be a practical guide on how to develop models from sequence data using practical step-by-step labs. Please note that this chapter was written with Erica Kleinman (a PhD student at University of California at Santa Cruz).
Working as a Health Cybersecurity Specialist

The need for cybersecurity expertise in the health workforce is rapidly growing as security compromises and attacks on hospitals and government departments accelerate at an alarming rate. Healthcare presents a rich and unique environment to protect, requiring an informed perspective on how to protect it. The challenges of applying cybersecurity protections in healthcare, the pace of technical change, the complexity of regulation and the potential for patient safety impact means healthcare needs specialised skills in cybersecurity. The characterisation of this specialty, together with five case studies, describes its vital importance and the variety of careers in this area.
Customized Human Mask-Face Recognition using Computer Vision

looking at the present problem of coronavirus disease, it has been advised by World Health Organization to take preventive measures to safeguard ourselves. One of the main preventive measures taken by the government and WHO is to wear a face mask while traveling outside along with social distancing. On the other hand, identifying a person with a facemask is also challenging. To overcome such situations there has been an urgent requirement for the technology to step forward. The proposed work has been implemented to overcome the situation and able to recognize a person behind the mask as well. In this work, haar cascade is used for locating the coordinates of pixels of face & mouth and abstracting the pixels of face & mouth from an image. The process of identifying starts by taking an input of the image from the default camera in the RGB format and is converted to a greyscale for fast processing of the image. The proposed work with still images and also with live video streaming with an accuracy of 90%.
AR Peephole Interface: Extending the workspace of a mobile device using real-space information

In our previous work, we have proposed a user interface that enables six degrees-of-freedom (6-DOF) peephole interaction with a mobile device using augmented reality technology. This interface allows users to change the display area quickly by moving or tilting the device, and to obtain a lot of information easily. In addition, by fixing a virtual screen on a flat surface in real space, the interface allows users to easily grasp the distance to the virtual screen and to move the device quickly to the desired position. It is also expected that users can easily remember the positions of objects on the virtual screen by associating them with real-space information. In the present study, we conducted a new experiment using a task that requires users to scroll and zoom the screen frequently to evaluate the effectiveness of our proposed interface. The results showed that 6-DOF peephole interaction made it easier for users to change display areas and enabled more comfortable operation than a touchscreen interface. It was also shown that the users’ workload was reduced by fixing a virtual screen on a flat surface in real space.
On coloring different objects of the same class

Every object created in GeoGebra has a color property that can be easily changed by the user. This is useful for identifying different objects of the same class. However, if we create lists of objects of the same class (e. g. a list of circles) and try to change the color of this list, then we notice that all the objects change color. How can we create a set of objects of the same class, such that each element has a different color? In this article, I will show an efficient method to color different objects of the same class.
A Novel Approach to Identify Age and Gender using Deep Learning

Currently investigation has fixated on discovering data utilized from diverse biometric strategies, for example, fingerprints, iris, face, voice, palm, and so forth. This data contains highlights, for example, sex, age, whiskers, scars, moustache, stature, skin shading, hair, glasses, weight, facial imprints, tattoos and so on. The entirety of this data pays a great deal during ID. The identification of sex and age of person is biggest change that comes through facial recognition. The current study provides critical estimation of different gender and facial strategies of recognition. The effectiveness of the present strategies is discussed. This paper delivers instructions to precede the studies in different ways. Acquisition is the procedure by which several substances are recognized based on information and consistence with necessities. Researchers have been facing the challenge of gender and age previously. In this article about gender and age conclusions, many procedures have been established to investigate the characteristics of insertion of image and based on the photographic features the sex and the proximity of the ages are defined. The use of new procedure based on OpenCV and CNN is available for sex and age. The important features will be identified and input image will be scanned through the process. The simulations were acted in OpenCV and it was investigated that the planned methodology functions admirably in wording of the percentage of the details of the individuals and pictures obtained from camera bases, image planes, from satellites, and pictures apprehended in everyday life are known as processing of images. The pictures were processed in various ways and counts were completed based on the investigation and studies. There is a need to dissect and concentrate carefully produced pictures. There are binary significant and most normal advances tracked by CNN-based picture preparing that is a profound neural system (DNN). Enhanced picture superiority is further developed and can be utilized by different uses, called picture upgrade. Additional procedure is the much-looked for after method used to extricate data from a picture. There is a picture part into a particular number of parts or items with the goal that the issue can be illuminated. This procedure is known as division. The neural system covers numerous basic and similarly convincing materials. In the program all through it. There are various inward factors known as heaviness in the Artificial Neural system.
Insegnare (lessico del)le microlingue: specificità nell’insegnamento e nelle strategie didattiche

In the teaching of micro-languages, vocabulary teaching constitutes the main challenge for teachers. The ideal micro-language teacher should be a competent professional both in the discipline of specialization and in the corresponding micro-language. This type of teacher should be a graduate in languages with specialized training also in a different disciplinary sector, or a specialist with language and teacher training. In the Italian educational reality the micro-language teacher is (it is hoped) a graduate in languages - but not necessarily specialized in language teaching. The teacher-learner relationship is vitiated by a sense of imperfection due to the teacher's lack of training in the discipline concerned and the learner's lack of training in the language. This limit can be overcome in the teaching of scientific-professional micro-languages by means of a collaborative teaching model. The challenge becomes more difficult, when teaching disciplinary micro-languages, because learners do not have full mastery of the non-language discipline they are acquiring. This contribution aims to explore the specificities that arise when different didactic realities manifest critical aspects.
Evaluation of Image Classification Networks on Impulse Sound Classification Task

Impulse sound classification has several civil and security related applications. Convolutional Neural Networks (CNN s) have been proven very effective in image classification and show promise for audio applications as well. The main aim of this paper is to see if we can use pre-defined image classification networks for impulse sound classification task and evaluate their performance on said task. We use ballistic sound dataset containing gunshots of different weapons for impulse sound classification task. For using audio segments as images, we calculate a spectrogram representation of the data and treat them as images for the training. A convolutional 2D task specific network, SimpleNet 2D and a convolutional 1D task specific network, SimpleNet 1D with similar architecture are designed and their performance is compared to study the effect of data representation on training. From state-of-the-art of image classification networks, VG-GNet, Inception v3, Inception-ResNet-v2, NASNet, Res NeXt, EfficientNet are chosen as candidate architectures for evaluation. These networks along with SimpleNet 2D are evaluated on four different test datasets and we compare the performance of the networks when trained from scratch and trained on ImageNet pre-trained weights. From these evaluations, the effect of pre-trained weights on training is observed.
ET: A Workstation for Querying, Editing and Evaluating Annotated Corpora

In this paper we explore the functionalities of ET, a suite designed to support linguistic research and natural language processing tasks using corpora annotated in the CoNLL-U format.These goals are achieved by two integrated environments -Interrogatório, an environment for querying and editing annotated corpora, and Julgamento, an environment for assessing their quality.ET is opensource, built on different Python Web technologies and has Web demonstrations available on-line.ET has been intensively used in our research group for over two years, being the chosen framework for several linguistic and NLP-related studies conducted by its researchers.