Reviewerly/exHarmony huggingface dataset:
Towards Joint Loss and Bitrate Adaptation in Realtime Video Streaming

Recent years have seen booming development of realtime streaming services, highly improving user experience in remote work, online education, and entertainment. Unlike video-on-demand (VoD) or live services, realtime streaming service has extremely stringent delay requirements, rendering the TCP-based transmission no longer applicable. Existing works based on UDP (or its variants) either suffer from the packet loss problem or only focus on improving several QoS metrics, which cannot achieve satisfactory user QoE. Our insight is to slightly sacrifice the bitrate and video quality to trade for the most significant delay to maximize the overall QoE. We propose Oppugno <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‡</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‡</sup> Oppugno is a spell in Harry Potter that makes magical creatures attack the caster. It is a metaphor that we use an additional mechanism to mitigate the influence of packet loss., an integrated framework that achieves joint loss adaptation and bitrate adaption towards maximized QoE in realtime streaming services. Oppugno leverages existing UDP mechanisms and employs an advanced deep reinforcement learning algorithm Proximal Policy Optimization (PPO), to adaptively select optimal actions based on network conditions. Trace-driven experiments demonstrate the superiority of our framework, which outperforms the SOTA work by 3.9% ∼ 11.6%.
Narrative-based learning using mobile devices

Purpose The aim of this paper is to explore whether the use of an active learning methodology implemented through a mobile phone can help future teachers to develop more effective reading promotion activities than those based on traditional learning methodologies. Design/methodology/approach A study was conducted based on the comparison of perceptions of two groups of teacher training students. The experimental group was trained in an active methodology to promote reading on mobile phones, whilst the control group was trained in a classical methodology also using the same devices. Variables were observed using a self-administered questionnaire, and the scores obtained were analysed from their descriptive statistics of the comparison of means of Kruskal–Wallis H test. Findings The results showed that students perceived significant improvements associated with active learning methodology. The variables with the most remarkable results were those related to better use of the class, participation and satisfaction. However, the ubiquitous variable obtained the fewest differences, maybe because both learning methodologies were applied using mobile devices. Originality/value The conclusions of this study clearly suggest that combining active learning methodologies and the use of mobile phones to promote reading could lead to better results than applying traditional learning methodologies. The value of this study paves the way for future research to move forward in the discovery of effective teaching strategies based on active methods and mobile devices.
A crisis that changed the banking scenario in India: exploring the role of ethics in business

Digital business has marked an era of transformation, but also an unprecedented growth of cyber threats. While digital explosion witnessed by the banking sector since the COVID-19 pandemic has been significant, the level and frequency of cybercrimes have gone up as well. Cybercrime officials attribute it to remote working—people using home computers or laptops with vulnerable online security than office systems; malicious actors relentlessly developing their tactics to find new ways to break into enterprise networks and grasping defence evasion; persons unemployed during the pandemic getting into hacking; cloud and data corruption; digital fatigue causing negligence; etc. This study adopts a case-based approach to explore the importance of business ethics, information sharing and transparency to build an information-driven society by scouting the case of Punjab and Maharashtra Co-operative (PMC) Bank, India. PMC defaulted on payments to its depositors and was placed under Reserve Bank of India’s directions due to financial irregularities and a massive fraud perpetrated by bank officials by orchestrating the bank’s IT systems. The crisis worsened when panic-stricken investors advanced their narrative through fake news peddled via social media channels, resulting in alarm that caused deaths of numerous depositors. It exposed several loopholes in information management in India’s deposit insurance system and steered the policy makers to restructure the same, thus driving the country consistent with its emerging market peers. The study further identifies best practices for aligning employees towards ethical behaviour in a virtual workplace and the pedagogical approaches for information management in the new normal.
Cybersecurity Awareness Through Serious Games: A Systematic Literature Review

Technological innovations represent a challenge for security professionals and ordinary individuals in many industries, including business, e-learning, and health. This increases the need for more understanding and knowledge of cybersecurity. The worldwide digital system, as well as people’s privacy, are under constant threat in relevant areas such as health, where patients’ data is among the most confidential information. The serious game domain is rapidly expanding, and it focuses on using game elements in non-game situations to reassure learning and motivation among users while maintaining a fun element throughout the procedure. The purpose of this study is to outline existing literature, and investigate the numerous serious games used as a tool to increase cybersecurity skills among employees in different domains.
Broad learning system based on driving amount and optimization solution

Broad learning system (BLS) was proposed by C. L. Philip Chen to overcome the time-consuming problem of traditional deep learning. However, the prediction precision of BLS is mainly dependent on its regularized parameter λ. Usually, λ is calculated by the trial and error method, which often suffers from the problem of too much calculation. To alleviate this issue, we propose an improved BLS with the driving amount and optimization solution (i.e., DA-BLS) in the study. The contributions of this study include: First, we use the iterative least square method to replace the ridge regression calculation of BLS, which avoids the selection of λ. Second, we provide the formulas of the driving amount and optimization solution under specific conditions. Third, the universal approximation property of DA-BLS is given. Last but not the least, extensive experimental results on the 1-D nonlinear function, UCI data-sets, and fault diagnosis of TEP show that DA-BLS outperforms the relevant methods such as BLS and the stochastic configuration network.
Proof of Concept and Horizons on Deployment of FAIR Data Points in the COVID-19 Pandemic

Abstract Rapid and effective data sharing is necessary to control disease outbreaks, such as the current coronavirus pandemic. Despite the existence of data sharing agreements, data silos, lack of interoperable data infrastructures, and different institutional jurisdictions hinder data sharing and accessibility. To overcome these challenges, the Virus Outbreak Data Network (VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated, but, instead, algorithms can visit the data and query multiple datasets in an automated way. To make this possible, FAIR Data Points—distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines (that data should be Findable, Accessible, Interoperable and Reusable)—have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box (ViB). ViB is a set of multiple FAIR-enabling and open-source services with a single goal: to support the gathering of World Health Organization (WHO) electronic case report forms (eCRFs) as FAIR data in a machine-actionable way, but without exposing or transferring the data outside the facility. Following the execution of a proof of concept, ViB was deployed in Uganda and Leiden University. The proof of concept generated a first query which was implemented across two continents. A SWOT (strengths, weaknesses, opportunities and threats) analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution.
Multi‐scale pedestrian detection with global–local attention and multi‐scale receptive field context

Abstract As a basic component in the field of computer vision, the pedestrian detection plays an essential role in several real-world applications such as video surveillance. The promising performance has been achieved in pedestrian detection relying on deep learning, but large-scale variance and small-scale pedestrian detection remain inherently hard as before. In order to deal with the aforementioned problems, this paper proposes a multi-scale pedestrian detection method with global–local attention and multi-scale receptive field context (MRFC). To make the network focus on small-scale pedestrians, we add a high-resolution detection branch on the original detector. To better integrate the incongruous semantic feature, the global–local attention module is embedded to highlight the feature representation of pedestrians so as to implement the feature fusion effectively. In order to adapt the receptive field of the network to achieve scale-variance detection, the MRFC is applied. Based on integrating the above structures, the proposed method achieves competitive results on Caltech and CityPersons datasets. The source code is released in https://github.com/xiaopan999/yolov5-pedestrian_detection.
Malaria parasites detection and identification using object detectors based on deep neural networks: a wide comparative analysis

Malaria is an infectious disease transmitted by the bite of the female Anopheles mosquito, infected by Plasmodium spp. Early diagnosis and prompt and effective treatment are needed to avoid anaemia, organ failure, and death. Manual microscopy is the primary technique used for diagnosing malaria. However, this exam is labour-intensive and requires qualified personnel. In order to alleviate these difficulties, researchers are using computer vision concepts to detect and classify cells infected by the Plasmodium spp. This paper presents a comparative study of seven object detectors based on deep neural networks. We evaluated four versions of the You Only Look Once (YOLO), Single Shot MultiBox Detector, EfficientDet, and Faster R-CNN convolutional neural networks to detect and classify the malaria parasite in six different scenarios. We used four public image datasets, and the YOLOv5 model outperformed the other evaluated models. Their results had similar performance to the state-of-the-art works demonstrating the feasibility and effectiveness of YOLOv5 to detect and classify malaria parasites in thin blood smear images with high precision and sensitivity.
A Motion Planning Method for Robots Based on DMPs and Modified Obstacle-Avoiding Algorithm

This paper addresses the motion planning of the manipulator in task space. To improve the overall trajectory performance, a special motion planning method based on DMPs (Dynamic Movement Primitives) and the modified obstacle-avoiding algorithm is proposed. The proposed method solves the problems of trajectory jitter and inability to avoid obstacles in some scenarios, which are faced by the scheme of steering angle. Besides, it improves the retention of teaching intentions, reduces the loss of free space, and helps the system adapt to the dynamic environment. At the theoretical level, the convergence of the target state has been proved using Lyapunov stability theory-based analysis. The availability of the proposed method is validated and analyzed by performing a series of numerical simulations and Baxter robot experiments. The results indicate that the proposed method can provide reliable solutions for motion planning. Note to Practitioners—From the perspective of demonstration learning, this paper aims to elevate the motion planning of the manipulator in task space, especially in improving the obstacle avoidance performance. The existing DMPs-based motion planning algorithm uses the scheme of steering angle to achieve obstacle avoidance, and the obstacle is regarded as a mesh of points on the boundary. The obstacle-avoiding performance is limited. How to combine the obstacle-avoiding algorithm with the DMPs-based motion planning algorithm more effectively, so as to simultaneously achieve retaining teaching intentions as much as possible, still faces challenges. This paper designs a modified obstacle-avoiding algorithm, which solves the problems of trajectory jitter and inability to avoid obstacles in some circumstances, and improves the retention of teaching intentions. The modified obstacle-avoiding algorithm is also applied to the dynamic environment. The proposed method satisfies Lyapunov stability theory-based analysis, and the experiments on Baxter robot verify the feasibility.
Design and Validation of an IMU Based Full Hand Kinematic Measurement System

Measurement of hand kinematics is commonly done using data gloves and optical trackers and finds application in biomechanics, motor control research, clinical assessment, virtual reality etc.While data gloves are expensive and restrict the dexterity of the hand, optical trackers are susceptible to the line-of-sight problem and can be used only in a laboratory setting.Over the last decade, the use of Inertial Measurement Units (IMUs) to measure kinematics has gained traction due to their affordability and good accuracy.This paper presents the design and validation of a BNO055 IMU-based full Hand Kinematics Measurement System (HKMS).The best features from existing IMU-based devices were identified from the literature and incorporated.The HKMS streams orientation information of 16 BNO055 IMUs in real-time at 100 Hz via Wi-Fi or USB.A rigorous static and dynamic validation of the BNO055 IMU was done against 3D printed models and the highly accurate Electro Magnetic Tracking System (EMTS).The RMSE errors were found to be acceptable for the measurement of hand kinematics.Two experiments were conducted to collect hand kinematic data for various postures and object grasps using the HKMS and EMTS, respectively.Synergy analysis was done and range of motion of the joints was calculated using the two datasets.The results were compared to get an idea of the quality of the dataset collected using the HKMS.The results as well as the overall validation of the sensors indicate that the HKMS may be suitable for usage in a laboratory or clinical setup.
Smooth Three-Dimensional Route Planning for Fixed-Wing Unmanned Aerial Vehicles With Double Continuous Curvature

This paper presents a smooth flight path planner for maneuvering in a 3D Euclidean space, which is based on two new space curves. The first one is called “Elementary Clothoid-based 3D Curve (ECb3D)”, which is built by concatenating two symmetric Clothoid-based 3D Curves (Cb3D). The combination of these curves allows to reach an arbitrary orientation in 3D Euclidean space. This new curve allows to generate continuous curvature and torsion profiles that start and finish with a null value, which means that they can be concatenated with other curves, such as straight segments, without generating discontinuities on those variables. The second curve is called “Double Continuous Curvature 3D Curve (DCC3D)” which is built as a concatenation of three straight line segments and two ECb3D curves, allowing to reach an arbitrary configuration in position and orientation in the 3D Euclidean space without discontinuities in curvature and torsion. This trajectory is applied for autonomous path planning and navigation of unmanned aerial vehicles (UAVs) such as fixed-wing aircrafts. Finally, the results are validated on the FlightGear 2018 flight simulator with the UAV kadett 2400 platform.
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction

Although Machine Learning can be seen as a promising tool to improve clinical decision-making, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos, making traditional Machine Learning challenging. Federated Learning can counteract those limitations by training Machine Learning models over data silos while keeping the sensitive data localized. This study proposes a Federated Learning workflow for Intensive Care Unit mortality prediction. Hereby, the applicability of Federated Learning as an alternative to Centralized Machine Learning and Local Machine Learning is investigated by introducing Federated Learning to the binary classification problem of predicting Intensive Care Unit mortality. We extract multivariate time series data from the MIMIC-III database (lab values and vital signs), and benchmark the predictive performance of four deep sequential classifiers (FRNN, LSTM, GRU, and 1DCNN) varying the patient history window lengths (8h, 16h, 24h, and 48h) and the number of Federated Learning clients (2, 4, and 8). The experiments demonstrate that both Centralized Machine Learning and Federated Learning are comparable in terms of AUPRC and F1-score. Furthermore, the federated approach shows superior performance over Local Machine Learning. Thus, Federated Learning can be seen as a valid and privacy-preserving alternative to Centralized Machine Learning for classifying Intensive Care Unit mortality when the sharing of sensitive patient data between hospitals is not possible.
Construction and Analysis of College English Teaching Path under the Multiple Network Teaching Environment and Credit System

With the wide spread of the Internet, information technology begins to enter all aspects of people's lives, such as teaching. The trend of economic globalization and export-oriented economic development have put forward urgent requirements for the level and quality of English teaching in colleges and universities. However, the current diversified information technology has brought many opportunities and challenges to college English teaching, and the traditional teaching mode can no longer meet the requirements of the new era. Both teachers and students need to communicate and interact through modern network media. Teaching reform has become an inevitable trend. The important premise of deepening college English teaching reform under the credit system is to grasp the needs of students. This paper analyzes the characteristics of the interactive teaching mode, points out the problems and challenges of the traditional English teaching, reveals the achievements and the problems of the reform under the credit system, and concludes that the English teaching mode, which is a practical and effective teaching mode.
Object Class Aware Video Anomaly Detection through Image Translation

Semi-supervised video anomaly detection (VAD) methods formulate the task of anomaly detection as detection of deviations from the learned normal patterns. Previous works in the field (reconstruction or prediction-based methods) suffer from two drawbacks: 1) They focus on low-level features, and they (especially holistic approaches) do not effectively consider the object classes. 2) Object-centric approaches neglect some of the context information (such as location). To tackle these challenges, this paper proposes a novel two-stream object-aware VAD method that learns the normal appearance and motion patterns through image translation tasks. The appearance branch translates the input image to the target semantic segmentation map produced by Mask-RCNN, and the motion branch associates each frame with its expected optical flow magnitude. Any deviation from the expected appearance or motion in the inference stage shows the degree of potential abnormality. We evaluated our proposed method on the ShanghaiTech, UCSD-Pedl, and UCSD-Ped2 datasets and the results show competitive performance compared with state-of-the-art works. Most importantly, the results show that, as significant improvements to previous methods, detections by our method are completely explainable and anomalies are localized accurately in the frames.
Enhancing Deep Training of Image Landmarking with Image CAPTCHA

Image landmark classification is the ability to define the geolocation of an image using a landmark inside it. It is needed in many applications such as storing social images and image sharing. The landmark classification has exceptionally low accuracy in literature. Without proper dataset augmentation, classification comes with about 20% accuracy. When using proper dataset augmentation and weight initialization, the accuracy is increased to about 44%. This low accuracy occurs because of the nature of the problem. In this paper we will introduce a new methodology to overcome the accuracy problem. Image CAPTCHA will be used to add more correctly classified images to the training dataset. A few experiments using the proposed system show the ability of the new model to overcome overfitting. The accuracy is increased to about 70%. In addition, the performance of the model during training and validation phases is greatly improved.
Effect of Color Normalization on Nuclei Segmentation Problem in H&amp;E Stained Histopathology Images

Cancer is one of the most common causes of death today. Early diagnosis is of great importance for treatment. Early diagnosis is made by detecting and examining cancerous nuclei. For this reason, studies that automatically perform nuclei segmentation on histopathology images are given wide coverage in the literature to assist pathologists. There may be differences in histopathology images due to staining or external factors. In order to eliminate these differences, it becomes necessary to perform color normalization on the image before starting the segmentation process, both to standardize the images and to improve the results obtained. In this study, the effects of three different color normalization processes on the image were compared. The comparison process was carried out according to the nuclei segmentation results obtained by applying two different deep learning models, U-Net and Residual U-Net. As a result of the study, it has been observed that the training with the images with the color normalization process gives more successful results than the training with the original image.
Cybersecurity Maturity Assessment Design Using NISTCSF, CIS CONTROLS v8 and ISO/IEC 27002

Cyberspace was created by the development of Information and Communication Technology (ICT). This makes it easier to access, manage information faster and more accurately, and improve the efficiency of performing activities and achieving business goals. On the other hand, the higher the usage of information technology, the higher the potential for organizational security incident gaps and cybercrime. Addressing this issue requires security standards that are appropriate and meet the requirements for organizations to know the maturity of cybersecurity. XYZ Organization is one of the government instances managing Indonesia's critical infrastructures. Although some international security standards have been implemented, the results of preparing for information security management are not yet optimal. Analysis of the NIST, CIS Controls v8, and ISO27002 standards was performed in this research. In addition, the analysis results are used as resources to create a cybersecurity maturity framework through the three standard approaches that underlie ICT management. And for the result, the proposed concepts of the 21 integrated cybersecurity categories are expected to become an asset in terms of XYZ organization's ICT management performance.