Reviewerly/exHarmony huggingface dataset:
GPro3D: Deriving 3D BBox from ground plane in monocular 3D object detection

Considering the inherent ill-posed nature, monocular 3D object detection (M3OD) is extremely challenging. The ground plane prior is a highly informative geometry clue in M3OD. However, it has been neglected by most mainstream methods. This paper introduces an original M3OD framework that leverages the ground plane to directly derive the object’s 3D Bounding Box (BBox) and 3D attributes geometrically. We identify and tackle three key factors that limit the applicability of the ground plane: the projection point localization issue, the ground plane tilt issue, and the lack of ground plane annotation issue. For the projection point localization issue, we propose leveraging the car’s explicit and salient wheel pixels, which are easier for the neural network to detect compared to the bottom vertices or the bottom center of the 3D BBox. To tackle the ground plane tilt problem, we propose a vertical-edge-enhanced horizon line detection algorithm to precisely deduce the ground plane equation. Moreover, using only M3OD labels, wheel pixel and horizon line pseudo-labels can be easily generated to train the network without extra data or annotation cost. Extensive experiments demonstrate the effectiveness and superiority of our framework over previous methods.
Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach

The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets.
What roles might automation play in the future of public administration journal peer review processes?

Abstract Publishing in journals is crucial in the creation of knowledge within the public administration field and the career advancements of individuals. Each year, more articles enter journal publishing systems, and it is becoming more difficult and time consuming for editors to secure reviewers. A range of technological applications have been developed that apply automation techniques to various aspects of the peer review process and these are explored in this paper. We find some potentially promising applications in terms of assessing aspects of quality and in identifying potential reviewers. The replacement of humans in review processes is less clear cut and there are dangers in exacerbating inequities within the field. The value of peer review is particularly important for early career researchers and building a community of scholarship in the public administration field. It is crucial we recognise this and do not lose positives of peer review processes to address some of the issues currently experienced. Points for practitioners Academic journals help create knowledge within the field of public administration and play a role in career advancement of individual academics. Yet, there are a number of well‐established challenges with peer review processes. Recent advancements in technologies such as Artificial Intelligence offer the potential to automate some processes associated with peer review. Several automation processes are already available and in use around some areas of peer review processes and these are explored in this paper. But it is unlikely that automation will replace humans in peer review processes, and it is important that the public administration community revisits the importance of these processes and considers how these are valued and rewarded.
Investigation of Object Detection and Identification at Different Lighting Conditions for Autonomous Vehicle Application

Ensuring the safety of autonomous vehicles requires effective detection and tracking of surrounding objects. This paper proposes the design and development of a driverless transportation system module focused on identifying obstacles around vehicles. By integrating computer vision with deep learning, the system presents a reliable and cost-effective solution for autonomous driving. Utilizing Raspberry Pi 4B and a USB webcam, a compact hardware setup is created for seamless implementation in autonomous vehicles. The algorithm presented in this study enables the detection, classification, and tracking of both moving and stationary objects, including cars, buses, trucks, people, and motorcycles. TensorFlow Lite, a deep-learning network, is employed for efficient object detection and classification. Leveraging Python as the primary programming language, known for its high-level object-oriented features and integrated semantics, the algorithm is tailored for web and application development. Experimental results demonstrate the system’s capability to concurrently detect and identify multiple local objects with an accuracy ranging from 50% to 80% in day and night conditions. These findings underscore the potential of deep learning in advancing autonomous vehicle technology.
TG-CSR: A human-labeled dataset grounded in nine formal commonsense categories

Machine Common Sense Reasoning is the subfield of Artificial Intelligence that aims to enable machines to behave or make decisions similarly to humans in everyday and ordinary situations. To measure progress, benchmarks in the form of question-answering datasets have been developed and published in the community to evaluate machine commonsense models, including large language models. We describe the individual label data produced by six human annotators originally used in computing ground truth for the Theoretically-Grounded Commonsense Reasoning (TG-CSR) benchmark's composing datasets. According to a set of instructions, annotators were provided with spreadsheets containing the original TG-CSR prompts and asked to insert labels in specific spreadsheet cells during annotation sessions. TG-CSR data is organized in JSON files, individual raw label data in a spreadsheet file, and individual normalized label data in JSONL files. The release of individual labels can enable the analysis of the labeling process itself, including studies of noise and consistency across annotators.
Facing Up to Virtual Reality: A Case Report

Virtual reality (VR) is a computer-generated three-dimensional simulation, often used with a wireless headset and controllers. It provides the user with visual, auditory and tactile stimuli for an immersive experience. Popularity of VR devices has snowballed, with use in domestic, education, healthcare and business sectors. We present a patient who jumped off a plank into water in a virtual reality world, while in reality, the patient had jumped into their TV unit, striking their chin. This resulted in a displaced and mobile fracture of the mandibular parasymphysis that required reduction and fixation under a general anaesthetic. This novel technology is altering our world's interface in multiple fields, but there are potential risks associated with fully immersing ourselves in another world. CPD/Clinical Relevance: It is important to be aware of trauma that can result from injuries during virtual reality headset use.
A privacy‐sensitive data identification model in online social networks

Abstract Privacy protection in online social networks (OSNs) has received a great deal of attention in recent years. One way of circumventing conventional privacy protection is privacy inference based on data that can be easily obtained in OSNs. Previous work on privacy inference has studied the issue mostly from the viewpoint of the attackers and methods thus designed were mostly aimed at pursuing the accuracy of the inference results with little regard on the causes of privacy breaches. To develop more effective privacy protection mechanisms that takes privacy inference into consideration, it is necessary to identify the information that plays a more important role in privacy breaches. In this paper, we propose a privacy‐sensitive data identification model in OSNs, which can identify key pieces of data that are most sensitive as far the privacy of the user is concerned. Firstly, a privacy inference method is proposed based on conditional random fields to infer the privacy of the target users. Then, a privacy‐sensitive data identification method is proposed by using the intermediate data of the proposed privacy inference method based on the targeted influence maximization algorithm. Thus, the key pieces of data in the form of user attributes and relationships on which the privacy of the target user depends can be determined to facilitate the implementation of privacy protection mechanisms. The effectiveness as well as the advantages of the proposed model is verified and demonstrated through experiment using real datasets. The impact of the key factors on privacy inference is also analyzed to guide the design of effective privacy protection strategies.
GFRNet: Rethinking the global contexts extraction in medical images segmentation through matrix factorization and self‐attention

Abstract Due to the large fluctuations of the boundaries and internal variations of the lesion regions in medical image segmentation, current methods may have difficulty capturing sufficient global contexts effectively to deal with these inherent challenges, which may lead to a problem of segmented discrete masks undermining the performance of segmentation. Although self‐attention can be implemented to capture long‐distance dependencies between pixels, it has the disadvantage of computational complexity and the global contexts extracted by self‐attention are still insufficient. To this end, the authors propose the GFRNet, which resorts to the idea of low‐rank matrix factorization by forming global contexts locally to obtain global contexts that are totally different from contexts extracted by self‐attention. The authors effectively integrate the different global contexts extract by self‐attention and low‐rank matrix factorization to extract versatile global contexts. Also, to recover the spatial contexts lost during the matrix factorization process and enhance boundary contexts, the authors propose the Modified Matrix Decomposition module which employ depth‐wise separable convolution and spatial augmentation in the low‐rank matrix factorization process. Comprehensive experiments are performed on four benchmark datasets showing that GFRNet performs better than the relevant CNN and transformer‐based recipes.
Characterization and Coherent Control of Spin Qubits with Modulated Electron Beam and Resonator

Abstract The coherent dynamics and control of spin qubits are fundamental requirements for the development and implementation of quantum technology. However, controlling a single spin qubit within a set of qubits is challenging due to the spatial extent of the applied magnetic field, which can negatively impact the coherent dynamics of neighboring qubits. In this study, a scheme is proposed for characterizing and controlling the coherent dynamics of spin qubits. The proposed scheme comprises a resonator that encloses the desired quantum system and a modulated electron beam that passes through the resonator in close proximity to the qubit of interest. The scheme involves solving the quantum master equation with Lindblad terms to obtain the system's dynamics. Reliability of the proposed model is validated by testing it on a potassium atom, 41 K, and an NV ‐ center in diamond. The results demonstrate that by controlling the parameters of the resonator and electron beam, the coherence and decoherence rates of these quantum systems can be enhanced. The proposed scheme has the potential to characterize various types of spin‐based quantum systems and implement quantum logic gates for quantum computation .
Dynamically Synthetic Images for Federated Learning of medical images

To develop deep learning models for medical diagnosis, it is important to collect more medical data from several medical institutions. Due to the regulations for privacy concerns, it is infeasible to collect data from various medical institutions to one institution for centralized learning. Federated Learning (FL) provides a feasible approach to jointly train the deep learning model with data stored in various medical institutions instead of collected together. However, the resulting FL models could be biased towards institutions with larger training datasets.In this study, we propose the applicable method of Dynamically Synthetic Images for Federated Learning (DSIFL) that aims to integrate the information of local institutions with heterogeneous types of data. The main technique of DSIFL is to develop a synthetic method that can dynamically adjust the number of synthetic images similar to local data that are misclassified by the current model. The resulting global model can handle the diversity in heterogeneous types of data collected in local medical institutions by including the training of synthetic images similar to misclassified cases in local collections.In model performance evaluation metrics, we focus on the accuracy of each client's dataset. Finally, the accuracy of the model of DSIFL in the experiments can achieve the higher accuracy of the FL approach.In this study, we propose the framework of DSIFL that achieves improvements over the conventional FL approach. We conduct empirical studies with two kinds of medical images. We compare the performance by variants of FL vs. DSIFL approaches. The performance by individual training is used as the baseline, whereas the performance by centralized learning is used as the target for the comparison studies. The empirical findings suggest that the DSIFL has improved performance over the FL via the technique of dynamically synthetic images in training.
Activity classification using unsupervised domain transfer from body worn sensors

Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, labeled data corresponding to the new locations are generally required, but this is expensive to acquire. In this work, we present an innovative method to leverage an existing activity classifier, trained on Inertial Measurement Unit (IMU) data from a reference body location (the source domain), in order to perform activity classification on a new body location (the target domain) in an unsupervised way, i.e. without the need for classification labels at the new location. Specifically, given an IMU embedding model trained to perform activity classification at the source domain, we train an embedding model to perform activity classification at the target domain by replicating the embeddings at the source domain. This is achieved using simultaneous IMU measurements at the source and target domains. The replicated embeddings at the target domain are used by a classification model that has previously been trained on the source domain to perform activity classification at the target domain. We have evaluated the proposed methods on three activity classification datasets PAMAP2, MHealth, and Opportunity, yielding high F1 scores of 67.19%, 70.40% and 68.34%, respectively when the source domain is the wrist and the target domain is the torso.
From undecidability of non-triviality and finiteness to undecidability of learnability

Machine learning researchers and practitioners steadily enlarge the multitude of successful learning models. They achieve this through in-depth theoretical analyses and experiential heuristics. However, there is no known general-purpose procedure for rigorously evaluating whether newly proposed models indeed successfully learn from data. We show that such a procedure cannot exist. For PAC binary classification, uniform and universal online learning, and exact learning through teacher-learner interactions, learnability is in general undecidable, both in the sense of independence of the axioms in a formal system and in the sense of uncomputability. Our proofs proceed via computable constructions that encode the consistency problem for formal systems and the halting problem for Turing machines into whether certain function classes are trivial/finite or highly complex, which we then relate to whether these classes are learnable via established characterizations of learnability through complexity measures. Our work shows that undecidability appears in the theoretical foundations of artificial intelligence: There is no one-size-fits-all algorithm for deciding whether a machine learning model can be successful. We cannot in general automatize the process of assessing new learning models.
A novel color image encryption scheme using elliptic curve cryptography and hyperchaotic system

Abstract This paper develops an asymmetric color image encryption algorithm based on elliptic curve cryptography(ECC), five dimensions(5D) hyperchaotic system, and DNA dynamic coding. To embed the characteristics of original image in the image encryption algorithm, this algorithm builds a mathematical model to strengthen the connection between the original image, elliptic curve Diffie-Hellman(ECDH) algorithm and hyperchaotic system. The red, green and blue(RGB) channels of encrypted image is reshaped into a three dimensions(3D) matrix. Grouping and scrambling of 3D matrix is accomplished at pixel level, bit level and DNA level based on a 5D hyperchaotic system, which effectively enhances the cross-layer variation of images. Then, improved ECC is performed on the scrambled image where multiple elliptic curves and dynamic shared private keys can guarantee the forward secrecy of the image encryption algorithm. At last, the image is performed diffusion to obtain the final encrypted image. Simulation results and security analysis both indicate the image encryption algorithm has better performances in terms of key space, Shannon entropy, clipping attack resistance, etc.
An Open Source Eye Gaze Tracker System to Perform Remote User Testing Evaluations

Eye gaze trackers are devices designed to identify an individual's gaze fixation in relation to a screen or another reference point. These tools are widely applied in usability testing as they provide various metrics for studying how people interact with applications. In the past, these tools were expensive and required a controlled environment, as well as trained personnel for proper operation. Although nowadays, new implementations do not require physical hardware to perform these tests, they often rely on license-based models instead of being open source. The objective of this work is to create a standalone system that enables any user to implement a low-cost eye gaze tracker using web technologies. The goal is to facilitate its use in remote and face-to-face studies in a simple way, requiring only a computer and a webcam. We evaluated the impact of three different calibration techniques on the performance of a regression-based prediction algorithm in eye-tracking. In our experiments, the best result of linear regression was obtained with a circular calibration system that uses 160 points. Furthermore, we integrated the system with a web interface and an API, enabling users to record their usability sessions and analyze fixation points through heatmaps.
Who Killed the Winograd Schema Challenge?

In which we investigate the technical issues surrounding the defeat, or perhaps the sudden assassination, of the Winograd Schema Challenge. We argue that, while the obvious suspect is the WinoGrande-based solution, the real cause of death was the masked language modeling technique for learning large language models. The Winograd Schema Challenge was, in the end, just a test for masked language closure, and as such it was killed by the use of this technique at scale.
Single Image Super-Resolution Based on Capsule Neural Networks

Single image super-resolution (SISR) consists of obtaining one high-resolution version of a low-resolution image by increasing the number of pixels per unit area. This method has been actively investigated by the research community, due to the wide variety of problems ranging from real-world surveillance to aerial and satellite imaging. Most of the improvements in SISR come from convolutional networks, in which approaches often focus on the deeper and wider architectural paradigm. In this work, we decided to step up from the traditional convolutions and adopt the concept of capsules. Since their overwhelming results in image classification and segmentation problems, we question how suitable they are for SISR. We also verify that different solutions share similar configurations, and argue that this trend leads to fewer explorations of network designs. Throughout our experiments, we check various strategies to improve results, ranging from new and different loss functions to changes in the capsule layers. Our network achieved positive and promising results with fewer convolutional-based layers, showing that capsules might be a concept worth applying to the image super-resolution problem. In particular, we observe that the proposed method recreates the connection between the different characters more precisely, thus demonstrating the potential of capsules in super-resolution problems.
Investigation of Deep Active Self-learning Algorithms Applied to Named Entity Recognition

Active Self-Learning algorithms reduce the labeled data required to train a Machine Learning model through supervised training. This paper explores various Active Self-Learning algorithms for named entity recognition tasks. Firstly, we investigate the impact of different self-training techniques on Active Self-Learning algorithms. Secondly, we propose a novel token-level Active Self-Learning algorithm that achieves near-peak performance using fewer hand-annotated tokens compared to existing works. Through numerous experiments, we found that the sentence-level Active Self-Learning algorithm did not consistently yield significant results compared to pure active learning. However, our proposed token-level Active Self-Learning algorithm showed promising performance, training a neural model to nearly peak accuracy with fewer human-annotated tokens compared to state-of-the-art active learning baseline algorithms. The experimental results are presented and discussed, demonstrating the superior performance of the token-level Active Self-Learning algorithm
Webcam eye tracking close to laboratory standards: Comparing a new webcam-based system and the EyeLink 1000

This paper aims to compare a new webcam-based eye-tracking system, integrated into the Labvanced platform for online experiments, to a "gold standard" lab-based eye tracker (EyeLink 1000 - SR Research). Specifically, we simultaneously recorded data with both eye trackers in five different tasks, analyzing their real-time performance. These tasks were a subset of a standardized test battery for eye trackers, including a Large Grid task, Smooth Pursuit eye movements, viewing natural images, and two Head Movements tasks (roll, yaw). The results show that the webcam-based system achieved an overall accuracy of 1.4°, and a precision of 1.1° (standard deviation (SD) across subjects), an error of about 0.5° larger than the EyeLink system. Interestingly, both accuracy (1.3°) and precision (0.9°) were slightly better for centrally presented targets, the region of interest in many psychophysical experiments. Remarkably, the correlation of raw gaze samples between the EyeLink and webcam-based was at about 90% for the Large Grid task and about 80% for Free View and Smooth Pursuit. Overall, these results put the performance of the webcam-based system roughly on par with mobile eye-tracking devices (Ehinger et al. PeerJ, 7, e7086, 2019; Tonsen et al., 2020) and demonstrate substantial improvement compared to existing webcam eye-tracking solutions (Papoutsaki et al., 2017).