Reviewerly/exHarmony huggingface dataset:
Density-Guided Label Smoothing for Temporal Localization of Driving Actions

Temporal localization of driving actions plays a crucial role in advanced driver-assistance systems and naturalistic driving studies. However, this is a challenging task due to strict requirements for robustness, reliability and accurate localization. In this work, we focus on improving the overall performance by efficiently utilizing video action recognition networks and adapting these to the problem of action localization. To this end, we first develop a density-guided label smoothing technique based on label probability distributions to facilitate better learning from boundary video-segments that typically include multiple labels. Second, we design a post-processing step to efficiently fuse information from video-segments and multiple camera views into scene-level predictions, which facilitates elimination of false positives. Our methodology yields a competitive performance on the A2 test set of the naturalistic driving action recognition track of the 2022 NVIDIA AI City Challenge with an F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of 0.271.
Efficacy of the Diagnostic Assessment and Achievement of College Students on Multiple Success Indicators

The purpose of this study was to examine the effects and predictive power of the Diagnostic Assessment and Achievement of College Skills (DAACS) on student success.DAACS is a nostakes, open-source, diagnostic assessment tool designed to measure newly enrolled college students' reading, writing, mathematics, and self-regulated learning (SRL) skills, and provide them with feedback and resources to enhance their functioning and success.A randomized control trial was performed at two online colleges (n = 21,381) to measure the effects of DAACS on academic achievement and credit acquisition.Predictive power of DAACS on student success was also examined.Results highlight the importance of using DAACS as a suite of assessments and supports, and not as isolated components.
What Prevents the Adoption of Regenerative Agriculture and What Can We Do about It? Lessons and Narratives from a Participatory Modelling Exercise in Australia

Regenerative agriculture (RegenAg) can help landholders attune their agricultural practices to the natural design of the earth’s cycles and support systems. The adoption of RegenAg, however, hinges not only on a good understanding of biophysical processes but perhaps more importantly on deep-seated values and beliefs which can become an obstacle for triggering widespread transitions towards synergistic relationships with the land. We designed and facilitated a Participatory Modelling exercise with RegenAg stakeholders in Australia—the aim was to provide a blueprint of how challenges and opportunities could be collaboratively explored in alignment with landholders’ personal views and perspectives. Fuzzy Cognitive Maps (FCM) were used to unpack and formalise landholder perspectives into a semi-quantitative shared ‘mental model’ of the barriers and enablers for adoption of RegenAg practices and to subsequently identify actions that might close the gap between the two. Five dominant narratives which encode the key drivers and pain points in the system were identified and extracted from the FCM as a way to promote the internalisation of outcomes and lessons from the engagement. The Participatory Modelling exercise revealed some of the key drivers of RegenAg in Australia, highlighting the complex forces at work and the need for coordinated actions at the institutional, social, and individual levels, across long timescales (decades). Such actions are necessary for RegenAg to play a greater role in local and regional economies and to embed balancing relationships within systems currently reliant on conventional agriculture with few internal incentives to change. Our methods and findings are relevant not only for those seeking to promote the adoption of RegenAg in Australia but also for governments and agriculturalists seeking to take a behaviorally attuned stance to engage with landholders on issues of sustainable and resilient agriculture. More broadly, the participatory process reported here demonstrates the use of bespoke virtual elicitation methods that were designed to collaborate with stakeholders under COVID-19 lockdown restrictions.
Proposal of SVM Utility Kernel for Breast Cancer Survival Estimation

The advancement of medical research in the field of cancer prognosis and diagnosis using various modalities has put oncologists under tremendous stress. The complexity and heterogeneity involved in multiple modalities and their significantly varied clinical outcomes make it difficult to analyze the disease and provide the correct treatment. Breast cancer is the major concern among all cancers worldwide, specifically for females. To help oncologists and cancer patients, research for breast cancer survival estimation has been proposed. It ranges from complex deep neural networks to simple and interpretable architectures. We propose a utility kernel for a support vector machine (SVM) in this article. It is a simple yet powerful function, which performs better than other popular machine learning algorithms and deep neural networks in the task of breast cancer survival prediction using the TCGA-BRCA dataset. This study validates the proposed utility kernel using four different modalities (gene expression, copy number variation, clinical, and histopathological tissue images) and their multi-modal combinations. The SVM based on our utility kernel empirically proves its efficacy by achieving the highest value on various performance measures, whereas advanced deep neural networks fail to train on small and highly imbalanced breast cancer data.
Augmentation Adversarial Training for Self-Supervised Speaker Representation Learning

The goal of this work is to train robust speaker recognition models using self-supervised representation learning. Recent works on self-supervised speaker representations are based on contrastive learning in which they encourage within-utterance embeddings to be similar and across-utterance embeddings to be dissimilar. However, since the within-utterance segments share the same acoustic characteristics, it is difficult to separate the speaker information from the channel information. To this end, we propose an augmentation adversarial training strategy that trains the network to be discriminative for the speaker information, while invariant to the augmentation applied. Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general. Extensive experiments on the VoxCeleb and VOiCES datasets show significant improvements over previous works using self-supervision, and the performance of our self-supervised models far exceeds that of humans. We also conduct semi-supervised learning experiments to show that augmentation adversarial training benefits performance in presence of speaker labels.
Proposal of a Method for the Analysis of Sentiments in Social Networks with the Use of R

Decision making is vital for the management of all organizations. For this reason, data analysis has become one of the fastest-growing technologies when it comes to generating information and knowledge about data generated by organizations. However, data generation is not limited to traditional sources. On the contrary, emerging technologies and social networks have become non-traditional sources that provide large volumes of data that can be exploited using different data analysis methods. Here, the objective is to determine the feelings of the population toward a brand, a product, or a service and to even identify the reactions of people to events and trends generated in their environment. Sentiment analysis, for organizations and social groups, has become a necessity that must be covered to identify the acceptance of an idea or its management. Therefore, this work proposes a method for the analysis of sentiment in social networks in such a way that it adapts to the needs of organizations or sectors, and the acceptance or rejection of the population can be efficiently identified from what is exposed in a social network.
Resolution Enhancement of Placenta Histological Images Using Deep Learning

In this study, a method has been developed to improve the resolution of histological human placenta images.For this purpose, a paired series of high-and low-resolution images have been collected to train a deep neural network model that can predict image residuals required to improve the resolution of the input images.A modified version of the U-net neural network model has been tailored to find the relationship between the low resolution and residual images.After training for 900 epochs on an augmented dataset of 1000 images, the relative mean squared error of 0.003 is achieved for the prediction of 320 test images.The proposed method has not only improved the contrast of the low-resolution images at the edges of cells but added critical details and textures that mimic high-resolution images of placenta villous space.
THE ANALYSIS OF GOOGLE TRANSLATE ACCURACY IN TRANSLATING PROCEDURAL AND NARRATIVE TEXT

Translation is an activity to interpret a text from one language to another language without changing the content of the translated text. The objectives this research is: (1) to find out the accuracy of Google Translate in translating Procedural Text and Narrative Text and (2) to investigate which of the two text is translated more accurately comparing to the other text. This research uses descriptive qualitative method. There was one text for each text type, that is procedure text entitled “How to Make Special Fried Sandwich” wad divided into 31 sentences, and narrative text entitled “Surabaya” was divided into 20 sentences. The data using the accuracy rating assessment which consist of three categories that is accurate, less accurate, and not accurate. The researcher took three raters in evaluating the output of Google Translate. The result shows that the accuracy of Google translate in translating procedure text is accurate with average value of 79.57%, less accurate is 24.19%, and not accurate is 12.9%. And narrative text is accurate with average value of 86%, less accurate is 12.5%, and not accurate is 1.5%. It can be concluded that Google translate is more accurate in translating narrative text with 86%, whereas the procedure text is 79.57%.
Communicative skills as the basis of successful partnership of the future teacher

Semenii Nataliia.Communicative skills as the basis of successful partnership of the future teacher.One of the main tasks of future primary school teachers is the ability to effectively implement partnerships with all participants in the educational process.Student surveys show that most students are concerned about interacting with students' parents, staff, and administration.To reduce the psychological stress of students and adjust them to effective communication with all participants in the educational process, we have developed a series of tasks aimed at improving the level of communication skills with all participants in the educational process.This article presents a series of tasks, the main purpose of which is to improve students' communication skills.The nature of the tasks is aimed at students' awareness of their communication skills and the development of their own effective communication skills and more.To confirm the effectiveness of the proposed tasks, an experimental study was conducted with first-year students at Borys Grinchenko Kyiv University.The experiment involved 49 students majoring in «Primary Education».The control stage of the experiment showed an increase in the level of communicative skills of the students.If at the control stage the students of the experimental group had a slight low level of communication skills, then after classes he disappeared.A sufficient level of communicative skills of students increased by 13%, high level by 29%.The results of the repeated questionnaire showed the effectiveness of the developed tasks.
May I Remain Seated: A Pilot Study on the Impact of Reducing Room-Scale Trainings to Seated Conditions for Long Procedural Virtual Reality Trainings

Although modern consumer level head-mounteddisplays of today provide high-quality room scale tracking, and thus support a high level of immersion and presence, there are application contexts in which constraining oneself to seated set-ups is necessary.Classroom sized training groups are one highly relevant example.However, what is lost when constraining cybernauts to a stationary seated physical space?What is the impact on immersion, presence, cybersickness and what implications does this have on training success?Can a careful design for seated virtual reality (VR) amend some of these aspects?In this line of research, the study provides data on a comparison between standing and seated long (50-60 min) procedural VR training sessions of chemical operators in a realistic and lengthy chemical procedure (combination of digital and physical actions) inside a large 3-floor virtual chemical plant.Besides, a VR training framework based on Maslow's hierarchy of needs (MHN) is also proposed to systematically analyze the needs in VR environments.In the first of a series of studies, the physiological and safety needs of MHN are evaluated among seated and standing groups in the form of cybersickness, usability and user experience.The results (n=32, real personnel of a chemical plant) show no statistically significant differences among seated and standing groups.There were low levels of cybersickness along with good scores of usability and user experience for both conditions.From these results, it can be implied that the seated condition does not impose significant problems that might hinder its application in classroom training.A follow-up study with a larger sample will provide a more detailed analysis on differences in experienced presence and learning success.
E-Learning Technologies for Future Teachers: Introduction of Educational Innovations in Higher School Practice

Mass introduction of e-learning in the higher education system due to spread of the pandemic has led to a search for effective information technologies and their adaptation, taking into account peculiarities of professional training of specialists in a particular field of knowledge. Based on the theoretical analysis, the conditions for ensuring professional growth of higher education teachers in accordance with the requirements of innovative development of society (improvement of professional education, continuing professional development, introduction of innovative pedagogical technologies, etc.) are established. Peculiarities of e-learning of specialists of pedagogical specialties and its functions in the process of their professional training are determined. The content of the concept of “e-learning” and ways to ensure its effectiveness (scientifically sound selection of the content of training courses, creation of discussion groups, feedback and online counselling).Innovative technologies of e-learning in the practice of higher education (gamification of the training process, use of cloud-based systems, information systems of intellectual teaching, mobile learning technologies, as well as virtual and augmented reality) are characterized. Their advantages, disadvantages and possibilities of introduction into the system of professional training of future teachers are determined. Creation of a model of continuous distance learning for high school teachers and experimental verification of its effectiveness seem promising.
Domain-Specific Hybrid BERT based System for Automatic Short Answer Grading

Effective and efficient grading has been recognized as an important issue in any educational institution. In this study, a grading system involving BERT for Automatic Short Answer Grading (ASAG) is proposed. A BERT Regressor model is fine-tuned using a domain-specific ASAG dataset to achieve a baseline performance. In order to improve the final grading performance, an effective strategy is proposed involving careful integration of BERT Regressor model with Semantic Text Similarity. A set of experiments is conducted to test the performance of the proposed method. Two performance metrics namely: Pearson's Correlation Coefficient and Root Mean Squared Error are used for evaluation purposes. The results obtained highlights the usefulness of proposed system for domain specific ASAG tasks in real life.
Blockchain-Based Access Control in a Globalized Healthcare Provisioning Ecosystem

The COVID-19 pandemic further outlined the importance of global healthcare services provisioning for diagnosing and treating patients who tend to travel and live for large periods away from home and can be anywhere at any given time. Advances in technology enable healthcare practitioners to access critical data regarding a person’s health status to provide better services. Medical data are sensitive in nature, and therefore, a reliable mechanism should ensure that only authorized entities can access data when needed. This paper, through a layered consideration of a Globalized Healthcare Provisioning Ecosystem (GHPE), reveals the interdependencies among its major components and suggests a necessary abstraction to identify requirements for the design of an access control suitable for the ecosystem. These requirements are imposed by the nature of the medical data as well as by the newly introduced potentials of Internet of Medical Things (IoMT) devices. As a result, an attribute-based access control framework is proposed aiming to provide prompt and secure access to medical data globally by utilizing state-of-the-art technologies and standards, including Next-Generation Access Control (NGAC), blockchain and smart contracts. Three types of smart contracts are proposed that enable access control to implement attribute and policy stores where policy classes and attributes are decentralized and immutable. In addition, the usage of blockchain-based distributed identities allows patients to be in control of access to their medical data and also enables healthcare service providers to access medical data promptly and reliably through the proposed access control framework. The qualitative characteristics of the proposed approach toward a decentralized and patient-centric access control in GHPE are demonstrated and discussed based on an application paradigm.
SERVICE QUALITY BY KNOWLEDGE MANAGEMENT CAPABILITY IN HIGHER EDUCATION INSTITUTIONS: MEDIATING EFFECT OF ORGANIZATIONAL COMMITMENT

In this aggressive competition taking place around the world, knowledge management is recognized as the main vehicle for organizations to achieve a competitive advantage: to compete and provide higher quality and better services that satisfy customers. Linking knowledge management to service quality could make a strong contribution in convincing managers of organizations about the need to adopt knowledge management strategies. Taking these issues into account, this study introduces the pathway to service quality (SQ) with the role of knowledge management capabilities (KMC) (knowledge infrastructure capability KIC and knowledge process capability KPC) and organizational commitment (OC). This paper aims at investigating the role of KMC in enhancing the quality of service in Higher Education Institutions (HEIs) through the mediating effect of OC. Based on the analysis conducted on 352 responses from academic middle managers and teaching staff selected from HEIs of Palestine- Gaza Strip. The results exhibit significant positive influence of knowledge management capabilities on enhancing service quality, and organizational commitment partially mediates the relationship between knowledge management capabilities and SQ. These results provide new avenues for managers to enhance service quality of Education institutions using KMC at prime level; with updated technology, sufficient structure, and supportive culture, in addition to ensuring effective implementation of knowledge processes. Additionally, these capabilities will help to increase the level of commitment in organizations, which will lead to quality of service, innovation, competitive advantage and enhanced overall performance.
A Preliminary Study on Personalized Spam E-mail Filtering Using Bidirectional Encoder Representations from Transformers (BERT) and TensorFlow 2.0

Email security has been a major concern in for a long time.One important aspect of e-mail security is effective and efficient detection of spam e-mails, which is an added overhead in the proper functioning of modern email communication systems.In this paper, a method based the Bidirectional Encoders Representations from Transformers (BERT) is proposed that stems from deep learning (DL).Similar to Word Embedding, BERT is a technique for text representation and a combination of various DL methods such as bidirectional encoder LSTM and Transformers.The pre-training phase takes significant computational effort, and to save computational time, we used a pre-trained BERT model.A preliminary analysis of the proposed algorithm is carried out using a standard test suite of Enron dataset.Initial results indicate that the proposed algorithm has a potential of generating promising results.
Packet-level and IEEE 802.11 MAC frame-level analysis for IoT device identification

In cyberspace, a large number of Internet of Things (IoT) devices from different manufacturers with heterogeneous functionalities are connected together.It is challenging to identify all these devices in an IoT ecosystem.The situation becomes even more complicated when the devices come from the same manufacturer and of similar types due to their analogous network communication behaviour.In this paper, a device fingerprinting (DFP) approach based on a set of combined features from packet-level and frame-level has been proposed.A large number of features has been studied, and consequently, a suitable subset of features has been selected according to gain-ratio and device-specific features for DFP.Furthermore, experiments with different types of IoT devices in a laboratory environment to collect network traffic traces have been conducted and used to evaluate the performance of the proposed approach using the J48 (C4.5) algorithm.It has been shown that the proposed model is able to identify individual device types with 99.0% precision and 98.9% recall with the approach capable of classifying IoT devices coming from the same manufacturer and of similar types, with higher accuracy.These results are significant as it can be used as a security reinforcement tool towards increasing the security and resilience of IoT networks.