Reviewerly/exHarmony huggingface dataset:
Community Internet of Things as Mobile Infrastructure: Methodological Challenges and Opportunities

From smart devices to homes to cities, Internet of Things (IoT) technologies have become embedded within everyday objects on a global scale. We understand IoT technologies as a form of infrastructure that bridges the gaps between offline spaces and online networks as they track, transmit, and construct digital data from and of the physical world. We examine the social construction of IoT network technologies through their technological design and corporate discourses. In this article, we explore the methodological challenges and opportunities of studying IoT as an emerging network technology. We draw on a case study of a low-power wide-area network (LPWAN), a cost-effective radio frequency network that is designed to connect sensors across long distances. Reflecting on our semi-structured interviews with LPWAN users and advocates, participant observation at conferences about LPWAN, as well as a community-based LPWAN project, we examine the intersections of methods and practices as related to space, data, and infrastructures. We identify three key methodological obstacles involved in studying the social construction of networked technologies that straddle physical and digital environments. These include (a) transcending the invisibility and abstraction of network infrastructures, (b) managing practical and conceptual boundaries to sample key cases and participants, and (c) negotiating competing technospatial imaginaries between participants and researchers. Through our reflection, we demonstrate that these challenges also serve as generative methodological opportunities, extending existing tools to study the ways data connects online and offline spaces.
Pupil trajectory tracing from video-oculography with a new definition of pupil location

Pupil localization is the critical technology of eye tracking, which plays a significant role in many eye movement analysis applications, such as pupil trajectory tracing from video-oculography(VOG). However, when the pupil is occluded, the existing representative pupil center based localization methods are imprecise or even unable to find the pupil. Unfortunately, in actual VOG, the pupil is inevitably covered by the upper eyelid, resulting in a poor effect of these approaches in practice. In order to alleviate this drawback, we initiate a new way to determine the pupil position by locating the lower pole of pupil (LLPP), which can accurately locate the pupil with about 5% of the exposed area. As a result, the proposed method can reduce locating mistakes, increase data utilization, and maintain pupil trajectories continuity. The experimental results in real VOG of the benign paroxysmal positional vertigo (BPPV) patients demonstrate the effectiveness and superiority of the proposed LLPP.
Network statistics‐based routing and path orient data encryption scheme for efficient healthcare monitoring with IoT in WSN

Summary Wireless communication has been adapted for a variety of difficulties as a result of recent developments in sensor networks. The sensors are meant to communicate with network nodes located at a distance and to adapt to various conditions. In addition, Internet of Things (IoT) devices are becoming a part of WSN and are being tailored to various healthcare systems. Nonetheless, the presence of massive nodes and rogue nodes makes transmitting data packets difficult. The procedure, however, confronts a number of obstacles, including data security, continuous monitoring, and routing issues. A variety of ideas are explored to help with the situation, but they all have flaws. A network statistics‐based routing and path orient data encryption system has been developed to address this problem (NSR‐PDE). The strategy focuses on keeping track on patients and providing strong, decisive support systems. The model is created to access data stored on cloud servers and allows for the monitoring of multiple patients in different locations. The model begins by monitoring the whole bodily states of patients in real time using dedicated equipment. Such monitored characteristics are provided over a variety of channels, with route selection based on the patient's class and the type of information being transmitted. The approach calculates the network constraint secure route measure (NCSRM), which is calculated using route information, node behavior, and physical attributes. Similarly, data transmission is protected by path orient data encryption (PDE), which ensures data security according on the route's type and conditions.
Demonstration of a quantum advantage by a joint detection receiver for optical communication using quantum belief propagation on a trapped-ion device

Demonstrations of quantum advantage have largely focused on computational speedups and on quantum simulation of many-body physics, limited by fidelity and capability of current devices. Discriminating laser-pulse-modulated classical-communication codewords at the minimum allowable probability of error using universal-quantum processing presents a promising parallel direction, one that is of both fundamental importance in quantum state discrimination, as well as of technological relevance in deep-space laser communications. Here we present an experimental realization of a quantum joint detection receiver for binary phase shift keying modulated codewords of a 3-bit linear tree code using a recently-proposed quantum algorithm: belief propagation with quantum messages. The receiver, translated to a quantum circuit, was experimentally implemented on a trapped-ion device -- the recently released Honeywell LT-1.0 system using ${}^{171}Yb+ $ ions, which possesses all-to-all connectivity and mid-circuit measurement capabilities that are essential to this demonstration. We conclusively realize a previously postulated but hitherto not-demonstrated joint quantum detection scheme, and provide an experimental framework that surpasses the quantum limit on the minimum average decoding error probability associated with pulse-by-pulse detection in the low mean photon number limit. The full joint-detection scheme bridges across photonic and trapped-ion based quantum information science, mapping the photonic coherent states of the modulation alphabet onto inner product-preserving states of single-ion qubits. Looking ahead, our work opens new avenues in hybrid realizations of quantum-enhanced receivers with applications in astronomy and emerging space-based platforms.
Forwarding the ACBS Task Force recommendations: The case for the functional-cognitive framework and out-of-sample prediction

It is important that scientists reflect on their scientific aims and on how to achieve those aims. The report of the ACBS Task Force on the strategies and tactics of CBS research is an important document in that it provides explicit recommendations on what is needed to realize the aims of CBS. In this invited commentary on the report, we reflect on two ways in which several of the recommendations in the report can be fulfilled. More specifically, we specify the ways that the Functional-Cognitive framework can be used to foster communication and collaboration at different levels of analysis, and the ways in which modern data scientific approaches (specifically out-of-sample prediction) can be used in the context of existing statistical methods to provide ideographic prediction.
Navigation Path Based Universal Mobile Manipulator Integrated Controller (NUMMIC)

As the demand for service robots increases, a mobile manipulator robot which can perform various tasks in a dynamic environment attracts great attention. There are some controllers that control mobile platform and manipulator arm simultaneously for efficient performance, but most of them are difficult to apply universally since they are based on only one mobile manipulator model. This lack of versatility can be a big problem because most mobile manipulator robots are made by connecting a mobile platform and manipulator from different companies. To overcome this problem, this paper proposes a simultaneous controller which can be applied not only to one model but also to various types of mobile manipulator robots. The proposed controller has three main characteristics, which are as follows: (1) establishing a pose that motion planning can be carried out in any position, avoiding obstacles and stopping in a stable manner at the target coordinates, (2) preventing the robot from collision with surrounding obstacles while driving, (3) defining a safety area where the manipulator does not hit the obstacles while driving and executing the manipulation accordingly. Our controller is fully compatible with Robot Operating System (ROS) and has been used successfully with three different types of mobile manipulator robots. In addition, we conduct motion planning experiments on five targets, each in two simulation worlds, and two motion planning scenarios using real robots in real-world environments. The result shows a significant improvement in time compared to existing control methods in various types of mobile manipulator and demonstrates that the controller works successfully in the real environment. The proposed controller is available on GitHub.
A New Sine-Ikeda Modulated Chaotic Key for Cybersecurity

In the recent past, the storage of images and data in the cloud has shown rapid growth due to the tremendous usage of multimedia applications.In this paper, a modulated version of the Ikeda map and key generation algorithm are proposed, which can be used as a chaotic key for securely storing images in the cloud.The distinctive feature of the proposed map is that it is hyperchaotic, highly sensitive to initial conditions, and depicts chaos over a wide range of control parameter variations.These properties prevent the attacker from detecting and extracting the keys easily.The key generation algorithm generates a set of sequences using a designed chaos map and uses the harmonic mean of the generated sequences as the seed key.Furthermore, the control parameters are modified after each iteration.This change in the control parameters after each iteration makes it difficult for an attacker to predict the key.The designed map was tested mathematically and through simulations.The performance evaluation of the map shows that it outperforms other chaotic maps in terms of its parameter space, Lyapunov exponent, bifurcation entropy.Comparing the designed chaotic map with existing chaotic maps in terms of average cycle length, maximum Lyapunov exponent, approximate entropy, and a number of iterations, it is found to be very effective.The existence of chaos is also proved mathematically using Schwartz's derivative theorem.The proposed key generation algorithm was tested using the National Institute of Standards and Technology (NIST) randomness test with excellent results.
A Construction of Object Detection Model for Acute Myeloid Leukemia

The evolution of bone marrow morphology is necessary in Acute Myeloid Leukemia (AML) prediction.It takes an enormous number of times to analyze with the standardization and inter-observer variability.Here, we proposed a novel AML detection model using a Deep Convolutional Neural Network (D-CNN).The proposed Faster R-CNN (Faster Region-Based CNN) models are trained with Morphological Dataset.The proposed Faster R-CNN model is trained using the augmented dataset.For overcoming the Imbalanced Data problem, data augmentation techniques are imposed.The Faster R-CNN performance was compared with existing transfer learning techniques.The results show that the Faster R-CNN performance was significant than other techniques.The number of images in each class is different.For example, the Neutrophil (segmented) class consists of 8,486 images, and Lymphocyte (atypical) class consists of eleven images.The dataset is used to train the CNN for single-cell morphology classification.The proposed work implies the high-class performance server called Nvidia Tesla V100 GPU (Graphics processing unit).
Estimation of Residential PV Power Generation Using Panel Azimuth Information

The high integration of residential Photovoltaics (PVs) to the low voltage distribution network, poses several challenges to the operation of the power systems today. The main reason is that roof-top solar PV sites are usually unobservable by the distribution system operators (DSOs) and consequently their generation is not measured. Therefore, the accurate knowledge regarding the real time generation of the residential PVs will become an imperative need. In this paper, a methodology for estimating the power generated by all the residential PVs of a certain area using the available information from some observable residential PVs is proposed. Considering the high impact of the solar panel orientation on the PV generation, the proposed method clusters together the residential PVs with the same orientation. The results show that there is a maximum reduction in the mean absolute error by 62% in the summer season for 25 degree panel azimuth in comparison to the case that the generation of the unobservable PVs are estimated without using the orientation of the solar panels. The methodology was validated using real data from residential PVs.
Simultaneous Obstacles Avoidance and Robust Autonomous Landing of a UAV on a Moving Vehicle

For unmanned aerial vehicles (UAVs), landing on a moving vehicle robustly is an open challenge, especially under cluttered surroundings with the presence of unknown obstacles. Those undesired environmental factors could induce collisions and thus affect flight safety significantly. Currently, there are few solutions to address such a challenge. In this paper, we propose a systematic autonomous landing scheme that enables the robust autonomous landing performance of a quadrotor UAV. The proposed scheme integrates target detection, state estimation, trajectory planning, and landing control. The position and attitude information of the target ground vehicle and the test quadrotor are estimated by the onboard vision system and GPS. In order to detect landing markers at different altitudes, a particular landing pad with an Apriltag bundle is implemented. As a typical aerial–terrestrial cooperation system, the trajectory planner of the quadrotor updates continuously to avoid obstacles via real-time sensing and re-planning. A finite state machine is used to label the current flight status and triggers the control laws correspondingly. The effectiveness of the proposed method has been validated in a high-fidelity simulator with environmental obstacles.
3D diffeomorphic image registration with Cauchy–Riemann constraint and lower bounded deformation divergence

In order to eliminate mesh folding in 3D image registration problem, we propose a 3D diffeomorphic image registration model with Cauchy–Riemann constraint and lower bounded deformation divergence. This model preserves the local shape and ensures no mesh folding. The existence of solution for the proposed model is proved. Furthermore, an alternating directional projection 3D image registration algorithm is presented to solve the proposed model. Moreover, numerical tests show that the proposed algorithm is competitive compared with the other four algorithms.
A High-Capacity Reversible Data-Hiding Scheme for Medical Image Transmission Using Modified Elias Gamma Encoding

Reversible data hiding (RDH) is a recently emerged research domain in the field of information security domain with broad applications in medical images and meta-data handling in the cloud. The amount of data required to handle the healthcare sector has exponentially increased due to the increase in the population. Medical images and various reports such as discharge summaries and diagnosis reports are the most common data in the healthcare sector. The RDH schemes are widely explored to embed the medical reports in the medical image instead of sending them as separate files. The receiver can extract the clinical reports and recover the original medical image for further diagnosis. This manuscript proposes an approach that uses a new lossless compression-based RDH scheme that creates vacant room for data hiding. The proposed scheme uses run-length encoding and a modified Elias gamma encoding scheme on higher-order bit planes for lossless compression. The conventional Elias gamma encoding process is modified in the proposed method to embed some additional data bits during the encoding process itself. The revised approach ensures a high embedding rate and lossless recovery of medical images at the receiver side. The experimental study is conducted on both natural images and medical images. The average embedding rate from the proposed scheme for the medical images is 0.75 bits per pixel. The scheme achieved a 0 bit error rate during image recovery and data extraction. The experimental study shows that the newly introduced scheme performs better when compared with the existing RDH schemes.
Information Technology in Education: An Educational Offshoot and a Monumental Add-on in Return

In the present world, ‘information technology has brought about a virtual revolution’ would be an absolute understatement. In fact, every field or sphere of life, considered generally, has been infused with a fresh life, via the channel of information technology. Moreover, the field of education is not any exception, to the good signs of information technology. The use of Information and Communication Technology (ICT) has the potential to significantly alter how teachers and students, teach and learn. ICT aids in increasing educational possibilities, enhancing the integrity of instruction and learning, prolonging lifelong learning, and enhancing managerial effectiveness and efficiency. In many ways, it can be said that the two are inseparable or work hand-in-glove. The present study aims to understand how Information and Technology are ruling the entire education system, as learning online as per the users’ flexibility is gaining popularity day by day and most importantly due to the implementation of new education system in India, many ICT based programs have been included in NEP-2020 like vocational courses, skill labs and coding programs. ICT also expands students’ and teachers’ educational opportunities and affordances, and this ultimately will also shape the future of the education system. This paper reviews various online learning platforms such as National Programme on Technology Enhanced Learning, which is the most subscribed educational channel, having more than 1.5 million subscribers.
Predictive Model for Human Activity Recognition Based on Machine Learning and Feature Selection Techniques

Research into assisted living environments -within the area of Ambient Assisted Living (ALL)-focuses on generating innovative technology, products, and services to provide medical treatment and rehabilitation to the elderly, with the purpose of increasing the time in which these people can live independently, whether they suffer from neurodegenerative diseases or disabilities. This key area is responsible for the development of activity recognition systems (ARS) which are a valuable tool to identify the types of activities carried out by the elderly, and to provide them with effective care that allows them to carry out daily activities normally. This article aims to review the literature to outline the evolution of the different data mining techniques applied to this health area, by showing the metrics used by researchers in this area of knowledge in recent experiments.
Bézier Coefficients Matrix for ElGamal Elliptic Curve Cryptosystem

It is well-known that cryptography is a branch of secrecy in science and mathematics, which usually preserves the confidentiality and authenticity of the information, where its growth is parallel with the rapid evolution of the internet and communication. As one of the prominent public key cryptosystems, the Elliptic Curve Cryptosystem (ECC) offers efficiency and complex mathematical operations with a smaller bit compared to other types of public key schemes. Throughout the evolution of cryptography, ElGamal Elliptic Curve Cryptosystem (ElGamal ECC) revolved from ElGamal public key scheme for user efficiency and privacy. In this study, an improved method will be introduced using ElGamal ECC as the foundation with the incorporation of the Bézier curve coefficient matrix, where the ElGamal ECC value is considered as the control point of the Bézier curve during the encryption and decryption processes. The proposed method is designed to develop a robust ciphertext system algorithm for better efficiency and to increase the level of protection in ElGamal ECC. In this paper, the performance of the proposed method is compared with the normal ElGamal ECC. The results of this study show that the proposed method offers no significant difference in terms of the implementation time during the encryption and decryption process. However, it does offer extra layers of protection when operated with complex mathematical operations.
Research hotspots and trends analysis of user experience: Knowledge maps visualization and theoretical framework construction

This study focuses on user experience from the perspective of big data to complete the aggregation, clustering, and visual presentation of knowledge. Using a combination of sample literature review, visualization technologies, knowledge map analysis, Carrot2 clustering, and other methodologies, this study intends to examine user experience from three perspectives: research state, hotspots, and trends. First, based on the double-map overlay, core institutions, core countries, core authors, core journals, and core references distribution research, the knowledge flow, research power, and research subjects of user experience are analyzed. Secondly, through keyword clustering analysis, this research intuitively presents the research topics of user experience and reveals the research hotspots and the evolution path of research methods. Finally, with the help of the subject clustering algorithm, the emerging trends of user experience research are predicted: the immersive experience upgrade of multi-scenario integration, the innovative design of multi-role collaboration, and the cross-disciplinary interactive exploration of multi-discipline. Following this, the user experience knowledge map is constructed, providing a global view and macro-cognition for subsequent research.
Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation

Targeted training-set attacks inject malicious instances into the training set to cause a trained model to mislabel one or more specific test instances. This work proposes the task of target identification, which determines whether a specific test instance is the target of a training-set attack. Target identification can be combined with adversarial-instance identification to find (and remove) the attack instances, mitigating the attack with minimal impact on other predictions. Rather than focusing on a single attack method or data modality, we build on influence estimation, which quantifies each training instance's contribution to a model's prediction. We show that existing influence estimators' poor practical performance often derives from their over-reliance on training instances and iterations with large losses. Our renormalized influence estimators fix this weakness; they far outperform the original estimators at identifying influential groups of training examples in both adversarial and non-adversarial settings, even finding up to 100% of adversarial training instances with no clean-data false positives. Target identification then simplifies to detecting test instances with anomalous influence values. We demonstrate our method's effectiveness on backdoor and poisoning attacks across various data domains, including text, vision, and speech, as well as against a gray-box, adaptive attacker that specifically optimizes the adversarial instances to evade our method. Our source code is available at https://github.com/ZaydH/target_identification.