Reviewerly/exHarmony huggingface dataset:
Transformer vision-language tracking via proxy token guided cross-modal fusion

Tracking by vision-language is an emergent topic. Previous researchers mainly adopt CNN and sequential models for video and language encoding, however, their methods are limited by poor generalization performance. To address this problem, this paper presents a novel vision-language tracking framework based on Transformer. Specifically, our proposed framework contains the image encoder, language encoder, cross-modal fusion module, and task-specific heads. We adopt the residual network and BERT for image and language embedding, respectively. More importantly, we propose a proxy token guided cross-modal fusion module based on the transformer network, which can link the vision and language features effectively and efficiently. The proxy token acts as a proxy for word embeddings and interacts with the visual feature. By absorbing vision information, the proxy token is used to modulate word embeddings and make them attend to the visual feature. Finally, we get the organically fused features via a dynamic modal aggregation method and feed them into the task-specific heads for tracking. Extensive experiments demonstrate that our method set new state-of-the-art on multiple language-assisted tracking datasets, including OTB-LANG, LaSOT, TNL2K, and a newly proposed Ref-LTB50 annotated with dense language specifications. Source code of this paper will be publicly available.
VIRTUAL INTERACTION FOR VISUALLY IMPAIRED AND SIGHTED PEOPLE

Virtual Reality is an emerging area and its core technologies advance rapidly. It is used mostly for training tools in many domains such as medicine, military, space, education and entertainment. The current paper is a state-of-the-art paper with the main goal of bringing people that are not visually impaired closer, from a social point of view, to the visually impaired community. The virtual environments are to be used as a point of interaction between these two categories of people. The strategy consists of developing highly interactive and immersive virtual environments, where multiple users need to work together in order to achieve that goal. The virtual environments are based on gamification principles and, in order to be consumer ready, the project needs to use consumer ready electronics and technology that is already used in the gaming industry or cost effective solutions. Virtual Reality is an emerging area and its core technologies advance rapidly. It is used mostly for training tools in many domains such as medicine, military, space, education and entertainment. The current paper is a state-of-the-art paper with the main goal of bringing people that are not visually impaired closer, from a social point of view, to the visually impaired community. The virtual environments are to be used as a point of interaction between these two categories of people. The strategy consists of developing highly interactive and immersive virtual environments, where multiple users need to work together in order to achieve that goal. The virtual environments are based on gamification principles and, in order to be consumer ready, the project needs to use consumer ready electronics and technology that is already used in the gaming industry or cost effective solutions.
Multiparameter estimation of continuous-time quantum walk Hamiltonians through machine learning

The characterization of the Hamiltonian parameters defining a quantum walk is of paramount importance when performing a variety of tasks, from quantum communication to computation. When dealing with physical implementations of quantum walks, the parameters themselves may not be directly accessible, and, thus, it is necessary to find alternative estimation strategies exploiting other observables. Here, we perform the multiparameter estimation of the Hamiltonian parameters characterizing a continuous-time quantum walk over a line graph with n-neighbor interactions using a deep neural network model fed with experimental probabilities at a given evolution time. We compare our results with the bounds derived from estimation theory and find that the neural network acts as a nearly optimal estimator both when the estimation of two or three parameters is performed.
Basic and Depression Specific Emotions Identification in Tweets: Multi-label Classification Experiments

We present an empirical analysis of basic and depression specific multi-emotion mining in Tweets, using state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion model consisting of the commonly identified emotions from four highly regarded psychological models. Moreover, we augment that emotion model with new emotion categories arising from their importance in the analysis of depression. Most of these additional emotions have not been used in previous emotion mining research. Our experimental analyses show that a cost sensitive RankSVM algorithm and a Deep Learning model are both robust, measured by both Micro F-Measures and Macro F-Measures. This suggests that these algorithms are superior in addressing the widely known data imbalance problem in multi-label learning. Moreover, our application of Deep Learning performs the best, giving it an edge in modeling deep semantic features of our extended emotional categories.
Initiating Cultural Change in the German Earth System Sciences Community with a Commitment Statement

Academia faces enormous challenges to realign publication and evaluation practices towards a sustainable, open, transparent, diverse, equitable, and inclusive way of conducting research. However, values, attitudes, behaviours, and habits are difficult to change. Furthermore, individuals and organisations do not act isolated but in different contexts - from local to international, from labs to academic societies, from early career researchers to university leadership. This makes cultural change very demanding. The Earth System Sciences (ESS) consortium of the German Research Data Infrastructure programme (NFDI4Earth, https://nfdi4earth.de/) takes deliberate steps towards introducing such cultural change in the form of the NFDI4Earth FAIRness and Openness Commitment (https://nfdi4earth.de/2coordinate/cultural-change). Intentionally going beyond FAIR data, the commitment is set up as a means to introduce a cultural change towards openness and to initiate a discourse on the full circle of related values and practices as well as connected social, cultural, and economic topics.&amp;#160; The NFDI4Earth Commitment can be endorsed by the institutions and organisations involved and related to NFDI4Earth, such as research institutions, publishers, or funders, as well as signed by individual researchers. The commitment is designed as a continuous activity and is iterative in its nature: signatories are invited annually to sign the latest version, which over time becomes more extensive or focused, based on the community discussions and the general developments, e.g., in&amp;#160;academia&amp;#160;as a whole.&amp;#160; The NFDI4Earth Commitment will create a sense of identity for all actors and becomes an instrument to demonstrate values. It provides commonly accepted and manifested documentation that the signatories strive to adhere to, e.g., best practices in ESS research data management and can be held accountable by members or partners. In its current incarnation, the NFDI4Earth commitment puts a particular focus on the need for individuals and organisations to question and reflect their own personal, institutional and their fellows behaviours and attitudes, on the broadness of scientific contributions, scientific evaluation and incentives. Furthermore, it stresses the value of sharing and collaborating and presents a positive picture of change. To emphasize the iterative nature, the NFDI4Earth commitment includes&amp;#160;a first&amp;#160;level or stage of commitment, with more levels to be expected in future incarnations.&amp;#160; In this work, we present the current version of the NFDI4Earth Commitment, the process for its creation, the steps taken and planned for community engagement, and considerations for the future development, in particular the transferability to international communities or other disciplines within the context of the NFDI. Where already clear, we present lessons learned on the creation and introduction of the commitment.
A study on next-generation digital tool for health data management: the e-Pulse portal

Purpose This study aims to investigate the working way of the e-Pulse portal in Türkiye as a sample of a next-generation digital tool for health data management. Accordingly, this study focuses on explaining the structure and key services of the e-Pulse portal in the context of health data management. Design/methodology/approach This study is a technical paper that will explain how the e-Pulse portal works in Türkiye. Accordingly, the data are based on secondary sources and mostly the official website of the e-Pulse portal. As a sample case, this study investigates the e-Pulse portal from Türkiye. The data are categorized by tables, and some key factors are classified based on review results. Findings As a result of the review of the e-Pulse portal's sample account, it is seen that the e-Pulse portal provides comprehensive data for personal health data for both individuals and healthcare professionals. By permitting healthcare professionals, users or patients can share their personal health data on specific dates and numbers whenever they need and want. When sharing recorded personal health data, citizens or patients can get more efficient healthcare service on the time. Research limitations/implications By giving descriptive evidence and review through the e-Pulse portal, countries with high-populated can see the key e-services and elements to manage health data through digital tools. On the other side, this study has some limitations. This study investigated the e-Pulse portal and its e-services for Türkiye and gave some findings mostly based on subjective deduction. Another digital portal can give different findings for the literature. Practical implications Based on the e-Pulse portal case, it is determined that by creating a digital portal with recorded personal up-to-date health data, healthcare services can be ensured more efficiently among high-populated countries in the long term. While population growth and pandemic possibilities such as COVID-19 increase throughout the world, serving more patients with these portals will increase efficiency and service quality, provided that patient information is well protected. Originality/value This study reveals key e-services and segments to provide personal health data management by a next-generation digital tool based on the e-Pulse portal. The main contribution of this study is expected to guide other countries when adapting next-generation technology or systems to manage health data in the future.
LSTM Method for Human Activity Recognition of Video Using PSO Algorithm

A most alluring method is Human Activity Recognition (HAR) from video, cost-effective computer vision-based recognition research and it has wide range of applications such as Closed-Circuit Television (CCTV) surveillances, gaming and health care unit. The recognition of human activity labeled by the appearance and motions observed from the video. The rise of social informatization, the prominent information technology mentioned by machine vision is applied to many more scenes. The conventional methods of HAR facing problems such as slower recognition and precision rate are low. With the advancement of deep learning approach, the difficulties faced in HAR are addressed. For this analysis, various kinds of neural network have been recommended by researchers and they did HAR effectively. This study also proposes a neural network so called Long Term Short Memory (LSTM) combined with Particle Swarm Optimization (PSO) to predict human activities more accurately in a video. The dataset chosen for this study includes variety of actions including such as pushups, mixing, kayaking, playing basketball, drumming, etc. This approach improves the temporal and spatial dimensions to maximize the recognition rate. The accuracy obtained by processing UCF-50 dataset is relatively higher than the existing approach.
Teknologi Tepat Guna Sederhana Pengelolaan Air Jernih di Desa Wisata Sumbergedang Pasuruan

During the Covid-19 pandemic, the tourist village of Sumbergedang, Pandaan Pasuruan, experienced a drastic decrease in visitors, which greatly reduced their income. Meanwhile, in the village, water from the mountains is very abundant. Purpose and solution offered is to manage clear water in the village using Simple Appropriate Technology, namely making a tubing tube to fill the clear water which will later be processed into clean water. The clean water produced is ready to drink, and this will be a separate effort to increase the income of residents in thetourist village of Sumbergedang during the Covid-19 pandemic.
Blockchain Based Cloud Management Architecture for Maximum Availability

Contemporary cloud application and Edge computing orchestration systems rely on controller/worker design patterns to allocate, distribute, and manage resources.Standard solutions like Apache Mesos, Docker Swarm, and Kubernetes can span multiple zones at data centers, multiple global regions, and even consumer point of presence locations.Previous research has concluded that random network partitions cannot be avoided in these scenarios, leaving system designers to choose between consistency and availability, as defined by the CAP theorem.Controller/worker architectures guarantee configuration consistency via the employment of redundant storage systems, in most cases coordinated via consensus algorithms such as Paxos or Raft.These algorithms ensure information consistency against network failures while decreasing availability as network regions increase.Mainstream blockchain technology provides a solution to this compromise while decentralizing control via a fully distributed architecture coordinated through Byzantine-resistant consensus algorithms.This research proposes a blockchain-based decentralized architecture for cloud resource management systems.We analyze and compare the characteristics of the proposed architecture concerning the consistency, availability, and partition resistance of architectures that rely on Paxos/Raft distributed data stores.Our research demonstrates that the proposed blockchain-based decentralized architecture noticeably increases the system availability, including cases of network partitioning, without a significant impact on configuration consistency.
User Sentiment Analysis of COVID-19 via Adversarial Training Based on the BERT-FGM-BiGRU Model

With the rapid development of social network platforms, Sina Weibo has become the main carrier for modern netizens to express public views and emotions. How to obtain the tendency of public opinion and analyze the text’s emotion more accurately and reasonably has become one of the main challenges for the government to monitor public opinion in the future. Due to the sparseness of Weibo text data and the complex semantics of Chinese, this paper proposes an emotion analysis model based on the Bidirectional Encoder Representation from Transformers pre-training model (BERT), Fast Gradient Method (FGM) and the bidirectional Gated Recurrent Unit (BiGRU), namely BERT-FGM-BiGRU model. Aiming to solve the problem of text polysemy and improve the extraction effect and classification ability of text features, this paper adopts the BERT pre-training model for word vector representation and BiGRU for text feature extraction. In order to improve the generalization ability of the model, this paper uses the FGM adversarial training algorithm to perturb the data. Therefore, a BERT-FGM-BiGRU model is constructed with the goal of sentiment analysis. This paper takes the Chinese text data from the Sina Weibo platform during COVID-19 as the research object. By comparing the BERT-FGM-BiGRU model with the traditional model, and combining the temporal and spatial characteristics, it further studies the changing trend of user sentiment. Finally, the results show that the BERT-FGM-BiGRU model has the best classification effect and the highest accuracy compared with other models, which provides a scientific method for government departments to supervise public opinion. Based on the classification results of this model and combined with the temporal and spatial characteristics, it can be found that public sentiment is spatially closely related to the severity of the pandemic. Due to the imbalance of information sources, the public showed negative emotions of fear and worry in the early and middle stages, while in the later stage, the public sentiment gradually changed from negative to positive and hopeful with the improvement of the epidemic situation.
Wire grid polarizer based in-line laser interferometer for macroscopic and microscopic phase estimation of transmissive and reflective phase samples

Abstract This work describes a full-field and near-common-path in-line laser interferometer and interferometric microscope utilizing a wire-grid polarizer (WGP) placed normally on the laser beam illuminating the sample. The WGP serves the dual purpose of a beam splitter and a polarization separator where the reference and sample beams reflected and transmitted from it respectively are orthogonally polarized so that, unlike other conventional interferometers, polarization phase shifting is inherent in its architecture. This arrangement presents experimental results showing quantitative phase analysis of transparent and reflecting phase samples.
A mixed reality system combining augmented reality, 3D bio-printed physical environments and inertial measurement unit sensors for task planning

Abstract Successful surgical operations are characterized by preplanning routines to be executed during actual surgical operations. To achieve this, surgeons rely on the experience acquired from the use of cadavers, enabling technologies like virtual reality (VR) and clinical years of practice. However, cadavers, having no dynamism and realism as they lack blood, can exhibit limited tissue degradation and shrinkage, while current VR systems do not provide amplified haptic feedback. This can impact surgical training increasing the likelihood of medical errors. This work proposes a novel Mixed Reality Combination System (MRCS) that pairs Augmented Reality (AR) technology and an inertial measurement unit (IMU) sensor with 3D printed, collagen-based specimens that can enhance task performance like planning and execution. To achieve this, the MRCS charts out a path prior to a user task execution based on a visual, physical, and dynamic environment on the state of a target object by utilizing surgeon-created virtual imagery that, when projected onto a 3D printed biospecimen as AR, reacts visually to user input on its actual physical state. This allows a real-time user reaction of the MRCS by displaying new multi-sensory virtual states of an object prior to performing on the actual physical state of that same object enabling effective task planning. Tracked user actions using an integrated 9-Degree of Freedom IMU demonstrate task execution This demonstrates that a user, with limited knowledge of specific anatomy, can, under guidance, execute a preplanned task. In addition, to surgical planning, this system can be generally applied in areas such as construction, maintenance, and education.
The Effects of Online Production-Oriented Approach (POA) on Chinese Undergraduates’ English Speaking Competence Development During COVID-19 Pandemic Era

The Covid-19 pandemic has expedited Online Teaching and Learning (OTL) following a sudden closure of academic institutions. Although within the past years, POA was fully developed and expanded in various projects that yielded fruitful college English learning results (Matsuda, 2017), recently, in the learning and practice of Oral English by Chinese undergraduates, there are distinct drawbacks and issues affecting language learning. This paper attempts to apply POA to Chinese undergraduates' oral English classes based on OTL during the covid-19 pandemic, specifically focusing on whether POA can increase the effectiveness of oral English learning for undergraduates. Data analysis of both the pre and post-tests revealed significant improvement in the experimental class and minimal improvement in the controlled class. Students' pronunciation, vocabulary, and fluency improved in the experimental group. It is implied that the POA application was effective in enhancing Chinese undergraduates' speaking skills. Some suggestions are put forth to enhance the application of online POA during the covid-19 pandemic era.
Modeling Topics in DFA-Based Lemmatized Gujarati Text

Topic modeling is a machine learning algorithm based on statistics that follows unsupervised machine learning techniques for mapping a high-dimensional corpus to a low-dimensional topical subspace, but it could be better. A topic model’s topic is expected to be interpretable as a concept, i.e., correspond to human understanding of a topic occurring in texts. While discovering corpus themes, inference constantly uses vocabulary that impacts topic quality due to its size. Inflectional forms are in the corpus. Since words frequently appear in the same sentence and are likely to have a latent topic, practically all topic models rely on co-occurrence signals between various terms in the corpus. The topics get weaker because of the abundance of distinct tokens in languages with extensive inflectional morphology. Lemmatization is often used to preempt this problem. Gujarati is one of the morphologically rich languages, as a word may have several inflectional forms. This paper proposes a deterministic finite automaton (DFA) based lemmatization technique for the Gujarati language to transform lemmas into their root words. The set of topics is then inferred from this lemmatized corpus of Gujarati text. We employ statistical divergence measurements to identify semantically less coherent (overly general) topics. The result shows that the lemmatized Gujarati corpus learns more interpretable and meaningful subjects than unlemmatized text. Finally, results show that lemmatization curtails the size of vocabulary decreases by 16% and the semantic coherence for all three measurements—Log Conditional Probability, Pointwise Mutual Information, and Normalized Pointwise Mutual Information—from −9.39 to −7.49, −6.79 to −5.18, and −0.23 to −0.17, respectively.