Reviewerly/exHarmony huggingface dataset:
Descriptive Literature Review and Classification of Community Cloud Computing Research

Cloud computing has been devoted to the development of recent advanced technology, aiding in improving a government’s functioning and the services it offers to its people and organizations. It has achieved widespread popularity due to the rising number of benefits, including scalability, versatility, reliability, safety, confidentiality, pay-per-use, and dynamicity for the distribution of IT services. This paper explores a descriptive literature review and aims to classify the scattered communities involved in cloud computing research in information technology. This involves 51 referred published articles between 2010 and 2020 relating to the inception of community cloud computing by various organizations. Based on a systematic structured of reviewed literature and grounded theory approach, articles are categorized with respect to the eleven key influencing variables: cost, security, performance, QoS, trust, accuracy, ease of use, usefulness, architecture, framework, and model. Following that, those variables are comprehended into final one in order to envisage the community cloud computing adoption factors and concepts. This will consent for a strong examination of the community cloud computing phenomenon. The study is a novel attempt to demonstrate the differences in key factors for cloud computing in varying community settings and their causal relationships among the considered variables. The findings from the long-term systematic review contribute by assisting a collection of determinants for the penetration of community clouds services and potential adopters. It could serve as a basis for future directions and the development of theories in the exploring the community cloud computing in the field of information systems. This study contributes to identify various existing research gaps by providing holistic insights and future exploratory approaches that are anticipated to result in a robust unified structure for the adoption of community cloud computing services in the higher education institution (HEIs).
Separable Reversible Data Hiding in Encrypted VQ-Encoded Images

In this paper, a reversible data-hiding scheme in encrypted, vector quantization (VQ) encoded images is proposed. During image encryption, VQ-encoded image, including codebook and index table, is encrypted by content owner with stream-cipher and permutation to protect the privacy of image contents. As for additional-data embedding, a baseline method is first proposed and its corresponding optimized method is then given. By grouping one high-occurrence index with one or multiple low-occurrence indices, a series of index groups are constructed. Thus, by modifying the high-occurrence index to the corresponding index within the same group according to the current to-be-embedded bits, data embedding can be realized. The optimal hiding capacity is obtained by optimizing the coefficient vector for different types of index groups. Separable operations of data extraction, image decryption, and recovery can be achieved on the receiver side based on the availability of the encryption and data-hiding keys. Experimental results show that our scheme can achieve high hiding capacity and satisfactory directly decrypted image quality and guarantee security and reversibility simultaneously.
Digital data donations: A quest for best practices

This preview article discusses PORT-a data donation software newly developed by Boeschoten et al.-toward the background of three core data donation principles: privacy protection, meaningful data extraction, and securing user agency.
Memory-Based Message Passing: Decoupling the Message for Propagation from Discrimination

Message passing is a fundamental procedure for graph neural networks in the field of graph representation learning. Based on the homophily assumption, the current message passing always aggregates features of connected nodes, such as the graph Laplacian smoothing process. However, real-world graphs tend to be noisy and/or non-smooth. The homophily assumption does not always hold, leading to sub-optimal results. A revised message passing method needs to maintain each node’s discriminative ability when aggregating the message from neighbors. To this end, we propose a Memory-based Message Passing (MMP) method to decouple the message of each node into a self-embedding part for discrimination and a memory part for propagation. Furthermore, we develop a control mechanism and a decoupling regularization to control the ratio of absorbing and excluding the message in the memory for each node. More importantly, our MMP is a general skill that can work as an additional layer to help improve traditional GNNs performance. Extensive experiments on various datasets with different homophily ratios demonstrate the effectiveness and robustness of the proposed method.
Phase noise estimation based white light scanning interferometry for high-accuracy surface profiling

White light scanning interferometry (WLSI) has been an extremely powerful technique in precision measurements. In this work, a phase noise estimation based surface recovery algorithm is proposed, which can significantly improve the measurement accuracy by decreasing the noise level in phase map coming from the systemic and environmental disturbances. The noise existed in phase map is firstly researched in spectrum domain and defined as the linear combination of complex terms at each angular wavenumber. Afterwards, based on the theoretical linearity of the phase distribution, the surface features can be redefined through establishing the function with respect to phase noise. By applying least square estimation (LSE), a spectral coefficient is defined to determine the optimal estimation of phase noise that represents the best statistical consistency with the actual case, from which a more accurate surface after removing most phase noise will then be generated. In order to testify the noise elimination ability of the proposed method, a nano-scale step height standard (9.5nm±1.0nm) is scanned, and the measurement result 9.49nm with repeatability 0.17nm is successfully achieved. Moreover, a leading edge of an aero-engine blade is also tested to investigate the potential of this method in industrial inspections. The measurement comparison with AFM is also displayed.
Research Ethics in HCI: A SIGCHI Community Discussion

An ongoing challenge within the HCI community is coming to a shared understanding of research ethics in the face of our diverse disciplinary traditions, evolving technologies and methods, and multiple geographic and cultural settings. Building upon previous panels and town halls organized by the SIGCHI Research Ethics Committee at conferences including CSCW, CHI, GROUP, and IDC, this special interest group convening is intended to provide space for the broader HCI community to highlight, debate, and discuss current ethical challenges within our domain, and work toward the development of guidelines and processes for supporting–and evaluating– ethical research. For 2022, we are proposing a SIG rather than a more formal panel, to encourage more people who are not committee members to speak, share, and discuss their perspectives and challenges when engaging with research ethics in HCI. This conversation will benefit from a diversity of voices and perspectives to help us all learn from each other and think deeply and the values and ethical commitments of our research community.
A Wide Field-of-View Light-Field Camera with Adjustable Multiplicity for Practical Applications

The long-fascinated idea of creating 3D images that depict depth information along with color and brightness has been realized with the advent of a light-field camera (LFC). Recently advanced LFCs mainly utilize micro-lens arrays (MLAs) as a key component to acquire rich 3D information, including depth, encoded color, reflectivity, refraction, occlusion, and transparency. The wide field-of-view (FOV) capability of LFCs, which is expected to be of great benefit for extended applications, is obstructed by the fundamental limitations of LFCs. Here, we present a practical strategy for the wide FOV-LFC by adjusting the spacing factor. Multiplicity (M) is the inverse magnification of the MLA located between the image plane and the sensor, which was introduced as the overlap ratio between the micro-images. M was adopted as a design parameter in several factors of the LFC, and a commercial lens with adjustable FOV was used as the main lens for practicality. The light-field (LF) information was evaluated by considering the pixel resolution and overlapping area in narrow and wide FOV. The M was optimized for narrow and wide FOV, respectively, by the trade-off relationship between pixel resolution and geometric resolution. Customized wide FOV-LFCs with different M were compared by spatial resolution test and depth information test, and the wide FOV-LFC with optimized M provides LF images with high accuracy.
Searching for Computer Vision North Stars

Abstract Computer vision is one of the most fundamental areas of artificial intelligence research. It has contributed to the tremendous progress in the recent deep learning revolution in AI. In this essay, we provide a perspective of the recent evolution of object recognition in computer vision, a flagship research topic that led to the breakthrough data set of ImageNet and its ensuing algorithm developments. We argue that much of this progress is rooted in the pursuit of research “north stars,” wherein researchers focus on critical problems of a scientific discipline that can galvanize major efforts and groundbreaking progress. Following the success of ImageNet and object recognition, we observe a number of exciting areas of research and a growing list of north star problems to tackle. This essay recounts the brief history of ImageNet, its related work, and the follow-up progress. The goal is to inspire more north star work to advance the field, and AI at large.
Generalized Maximum Correntropy Kalman Filter for Target Tracking in TianGong-2 Space Laboratory

Target tracking plays an important role in the construction, operation, and maintenance of the space station by the robot, which puts forward high requirements on the accuracy of target tracking. However, the special space environment may cause complex non-Gaussian noise in target tracking data. And the performance of traditional Kalman Filter will deteriorate seriously when the error signals are non-Gaussian, which may lead to mission failure. In the paper, a novel Kalman Filter algorithm with Generalized Maximum Correntropy Criterion (GMCKF) is proposed to improve the tracking accuracy with non-Gaussian noise. The GMCKF algorithm, which replaces the default Gaussian kernel with the generalized Gaussian density function as kernel, can adapt to multi-type non-Gaussian noises and evaluate the noise accurately. A parameter automatic selection algorithm is proposed to determine the shape parameter of GMCKF algorithm, which helps the GMCKF algorithm achieve better performance for complex non-Gaussian noise. The performance of the proposed algorithm has been evaluated by simulations and the ground experiments. Then, the algorithm has been applied in the maintenance experiments in TianGong-2 space laboratory of China. The results validated the feasibility of the proposed method with the target tracking precision improved significantly in complex non-Gaussian environment.
A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition

In this article, to reduce the complexity and improve the generalization ability of current gesture recognition systems, we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition.The proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then utilizes it to recalibrate the weights of the feature outputs from the convolutional layer.By enhancing important features while suppressing useless ones, the model realizes gesture recognition efficiently.The last procedure of the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of sEMG signals to perform multi-channel sEMG-based gesture recognition tasks.To evaluate the effectiveness and accuracy of the proposed algorithm, we conduct experiments involving multi-gesture datasets Ninapro DB4 and Ninapro DB5 for both inter-session validation and subject-wise cross-validation.After a series of comparisons with the previous models, the proposed algorithm effectively increases the robustness with improved gesture recognition performance and generalization ability.
Cost-Effective ML-Powered Polarization-Encoded Quantum Key Distribution

Secure communications have become a requirement for virtually all kind of applications. Currently, two distant parties can generate shared random secret keys by using public key cryptography. However, quantum computing represents one of the greatest threats for the finite complexity of the mathematics behind public key cryptography. In contrast, Quantum Key Distribution (QKD) relies on properties of quantum mechanics, which enables eavesdropping detection and guarantees the security of the key. Among QKD systems, polarization encoded QKD has been successfully tested in laboratory experiments and recently demonstrated in closed environments. The main drawback of QKD is its high cost, which comes, among others, from: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) the requirements for the quantum transmitters and receivers; and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) the need of carefully selecting the fibers supporting the quantum channel to minimize the environmental effects that could dramatically change the polarization state of photons. In this paper, we propose a Machine Learning (ML) -based polarization tracking and compensation that is able to keep shared secret key exchange to high rates even under large fiber stressing events. Exhaustive results using both synthetic and experimental data show remarkable performance, which can simplify the design of both quantum transmitter and receiver, as well as enable the use of aerial optical cables, thus reducing total QKD system cost.
Image Translation for Oracle Bone Character Interpretation

The Oracle Bone Characters are the earliest known ancient Chinese characters and are an important record of the civilization of ancient China.The interpretation of the Oracle Bone Characters is challenging and requires professional knowledge from ancient Chinese language experts. Although some works have utilized deep learning to perform image detection and recognition using the Oracle Bone Characters, these methods have proven difficult to use for the interpretation of uninterpreted Oracle Bone Character images. Inspired by the prior knowledge that there exists a relation between glyphs from Oracle Bone Character images and images of modern Chinese characters, we proposed a method of image translation from Oracle Bone Characters to modern Chinese characters based on the use of a generative adversarial network to capture the implicit relationship between glyphs from Oracle Bone Characters and modern Chinese characters. The image translation process between Oracle Bone Characters and the modern Chinese characters forms a symmetrical structure, comprising an encoder and decoder. To our knowledge, our symmetrical image translation method is the first of its kind used for the task of interpreting Oracle Bone Characters. Our experiments indicated that our image translation method can provide glyph information to aid in the interpretation of Oracle Bone Characters.
Differential object indexing in Maltese – a corpus based pilot study

This paper presents the first corpus-based study of DOI in Maltese. In this pilot study, the potential triggering factors were tested as predictors in a descriptive model. The results show that the strongest predictor for object indexing in Maltese is word order, but when taking only semantic referential features into account, the analyses reveal that DOI seems to be strongly predictable by definiteness, as well as by the part of speech of the head of the NP. Our study therefore supports observations from previous investigations, both on Maltese and typological; furthermore, the analysis gives insight into the combined effects of the relevant factors.
Architecture of the Common Digital Space of Scientific Knowledge

The architecture of the Common Digital Space of Scientific Knowledge (CDSSK) is determined by its functions and objectives. CDSSK includes a set of subspaces related to various scientific fields. The unity of subspaces is provided by unified principles for constructing subspaces and ontological connections between their objects. Each subspace includes digital objects, metadata containing facts related to objects, and subject ontologies that provide advanced searches and navigation through space. All information is reflected in the CDSSK according to the rules of the "semantic WEB". The content of each subspace includes a core (time-tested reliable scientific results) and a superstructure - new scientific results that have passed preliminary examination.
Employee commitment to strategy implementation and strategic performance: organisational policy as moderator

The purpose of this paper is to examine employee commitment to strategy implementation and the moderating role of organisational policy in the relationship between employee commitment to strategy implementation and strategic performance. The study adopts a survey method to collect data from 105 employees on managerial levels of the four multinational firms operating in the mobile telecommunication sector in Nigeria. This research suggests that employee commitment to strategy implementation and organisational policy have direct positive and significant relationship with strategic performance. Additionally, organisational policy moderates the relationship between employee commitment to strategy implementation and strategic performance. By paying attention to the importance of employee commitment to strategy implementation managers/executives can significantly increase their firm's strategic performance. Managers and executives have a better understanding of the importance of organisational policy and creating an environment that supports employee commitment to strategy implementation to achieve superior strategic performance.
An optimised pixel-based classification approach for automatic white blood cells segmentation

The pixel-based classification is an automatic approach for classifying all pixels in the image but does not take into account the spatial information for the region of interest. On the other hand, region-growing methods take into account the neighbourhood pixels information. However, in region-growing methods, a pixel-group called 'points of interest' are needed to initialise the growing process. In this paper, we proposed an optimised pixel-based classification by the cooperation of region growing strategy. This original segmentation scheme is performed in two phases for the automatic recognition of white blood cells (WBC): the first is a learning step with colour characteristics of each pixel in the image. The second is a region growing application by classifying neighbouring pixels from pixels of interest extracted by the ultimate erosion technique. This process has proved that the cooperation allows obtaining a nucleus and cytoplasm segmentation as closer to what as expected in the reference images.
Small UAV Camera Gimbal Stabilization Using Digital Filters and Enhanced Control Algorithms for Aerial Survey and Monitoring

Aerial photography, monitoring and survey using small Unmanned Aerial Vehicles (UAVs) is a modern, cheap, simple, helpful developing and improving area.For these purposes research is focused mainly on the cameras and image processing methods and software.However, as it was confirmed in the article, a stabilized camera gimbal is also very necessary to obtain quality and bright pictures or video records and to allow the operator or the tracking computer to track the camera's line of sight to the point of an interest.Because the camera stabilization is a k influencing the quality of the pictures or videos and considering the application on the UAVs performing the flights in and often also in the mountain terrain, the wind conditions, turbulences, wind shears, which can vary in the directions significantly, the convenient stabilization of the camera gimbal can have a significant influence on the obtained results, which are very important for the creation of the precise 2D or 3D models.Furthermore, to payload, it is important to use lightweight solutions.onboard electronics of small UAVs, regarding and computational performance, a small microcontroller convenient, simple, and still fast enough control algorithm needs to be designed and implemented.In order to stabilize it is needed to design a model of the actuators as well as the gearings, to propose an effective control algorithm and to implement the control algorithms into microcontrollers.This article deals with the modelling of the actuator, conventional commercial servomotor used for gimbal stabilization and with the design and verification of the improved control algorithm based on the inverse char the actuator model.Due to the requirement of the images, where the fast stabilization is needed, a dynamic correction feedback was implemented.And as the gyroscopes sensitive to the UAVs vibrations, the vibrations of gimbal were eliminated by the digital low pass filter.background was experimentally verified by the geological survey of the stone pits in Sedlice, Vechec and Klatov in the
CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks

Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that exploits the superposition property of the wireless medium and performs model aggregation for federated learning in the air. OTA-FL is naturally sensitive to wireless channel fading, which could significantly diminish its learning accuracy. To address this challenge, in this paper, we propose an OTA-FL algorithm called CHARLES (channel- quality-aware over-the-air local estimating and scaling). Our CHARLES algorithm performs channel state information (CSI) estimation and adaptive scaling to mitigate the impacts of wireless channel fading. We establish the theoretical convergence rate performance of CHARLES and analyze the impacts of CSI error on the convergence of CHARLES. We show that the adaptive channel inversion scaling scheme in CHARLES is robust under imperfect CSI scenarios. We also demonstrate through numerical results that CHARLES outperforms existing OTA-FL algorithms with heterogeneous data under imperfect CSI.
Are Prompt-based Models Clueless?

Finetuning large pre-trained language models with a task-specific head has advanced the stateof-the-art on many natural language understanding benchmarks.However, models with a task-specific head require a lot of training data, making them susceptible to learning and exploiting dataset-specific superficial cues that do not generalize to other datasets.Prompting has reduced the data requirement by reusing the language model head and formatting the task input to match the pre-training objective.Therefore, it is expected that few-shot promptbased models do not exploit superficial cues.This paper presents an empirical examination of whether few-shot prompt-based models also exploit superficial cues.Analyzing few-shot prompt-based models on MNLI, SNLI, HANS, and COPA has revealed that prompt-based models also exploit superficial cues.While the models perform well on instances with superficial cues, they often underperform or only marginally outperform random accuracy on instances without superficial cues.