Reviewerly/exHarmony huggingface dataset:
Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark

Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains a few pixels. State-of-the-art object detectors do not provide satisfactory results on tiny objects due to the lack of supervision from discriminative features. Our key observation is that the Intersection over Union (IoU) metric and its extensions are very sensitive to the location deviation of the tiny objects, which drastically deteriorates the quality of label assignment when used in anchor-based detectors. To tackle this problem, we propose a new evaluation metric dubbed Normalized Wasserstein Distance (NWD) and a new RanKing-based Assigning (RKA) strategy for tiny object detection. The proposed NWD-RKA strategy can be easily embedded into all kinds of anchor-based detectors to replace the standard IoU threshold-based one, significantly improving label assignment and providing sufficient supervision information for network training. Tested on four datasets, NWD-RKA can consistently improve tiny object detection performance by a large margin. Besides, observing prominent noisy labels in the Tiny Object Detection in Aerial Images (AI-TOD) dataset, we are motivated to meticulously relabel it and release AI-TOD-v2 and its corresponding benchmark. In AI-TOD-v2, the missing annotation and location error problems are considerably mitigated, facilitating more reliable training and validation processes. Embedding NWD-RKA into DetectoRS, the detection performance achieves 4.3 AP points improvement over state-of-the-art competitors on AI-TOD-v2. Datasets, codes, and more visualizations are available at: https://chasel-tsui.github.io/AI-TOD-v2/.
Memories Fade: The Relationship Between Memory Vividness and Remembered Visual Salience

Past events, particularly emotional experiences, are often vividly recollected. However, it remains unclear how qualitative information, such as low-level visual salience, is reconstructed and how the precision and bias of this information relate to subjective memory vividness. Here, we tested whether remembered visual salience contributes to vivid recollection. In three experiments, participants studied emotionally negative and neutral images that varied in luminance and color saturation, and they reconstructed the visual salience of each image in a subsequent test. Results revealed, unexpectedly, that memories were recollected as less visually salient than they were encoded, demonstrating a novel memory-fading effect, whereas negative emotion increased subjective memory vividness and the precision with which visual features were encoded. Finally, memory vividness tracked both the precision and remembered salience (bias) of visual information. These findings provide evidence that low-level visual information fades in memory and contributes to the experience of vivid recollection.
LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning

Low Probability of Intercept (LPI) radar waveform recognition is not only an important branch of the electronic reconnaissance field, but also an important means to obtain non-cooperative radar information. To solve the problems of LPI radar waveform recognition rate, difficult feature extraction and large number of samples needed, an automatic classification and recognition system based on Choi-Williams distribution (CWD) and depth convolution neural network migration learning is proposed in this paper. First, the system performs CWD time-frequency transform on the LPI radar waveform to obtain a 2-D time-frequency image. Then the system preprocesses the original time-frequency image. In addition, then the system sends the pre-processed image to the pre-training model (Inception-v3 or ResNet-152) of the deep convolution network for feature extraction. Finally, the extracted features are sent to a Support Vector Machine (SVM) classifier to realize offline training and online recognition of radar waveforms. The simulation results show that the overall recognition rate of the eight LPI radar signals (LFM, BPSK, Costas, Frank, and T1–T4) of the ResNet-152-SVM system reaches 97.8%, and the overall recognition rate of the Inception-v3-SVM system reaches 96.2% when the SNR is −2 dB.
Blockchain: How shipping industry is dealing with the ultimate technological leap

Despite its historical resilience to innovation, several novel technologies (e.g. IoT, AIS data, and automation) have recently been introduced in the maritime sector: the majority of them represents incremental changes in respect to current practice. Nevertheless, a few of these novel solutions could represent radical game changers for the whole shipping business. Among the latter category, blockchain registers a particular role, given the possibility for smoothening administrative problems, providing new ways to achieve secure and frictionless transactions at a global level. Despite the importance of such innovation, most of the published studies have just focused on technical aspects, while the adoption and implementation processes are far to be clarified since there is not a clear understanding on the potential effects of blockchain on shipping and logistics actors, especially at local level. The proposed study aims at filling this gap, providing insights that might help to better understand: i) which are the actors that impact the most on the blockchain implementation (and related potential frictions), and ii) the difference among alternative initiatives currently present on the market. In order to achieve these twofold aim, the study uses a triangulation approach, mixing literature and media reports research with both web-based research on main initiative characteristics and expert opinions. The proposed study defines that regulators and public authorities might potentially represent the main barrier to the full implementation of the block chain technology, especially due to a missing market standard.
A Benchmark of DIBR Synthesized View Quality Assessment Metrics on a New Database for Immersive Media Applications

Depth-image-based rendering (DIBR) is a fundamental technology in several 3-D-related applications, such as free viewpoint video, virtual reality, and augmented reality. However, new challenges have also been brought in assessing the quality of DIBR-synthesized views since this process induces some new types of distortions, which are inherently different from the distortion caused by video coding. In this paper, we present a new DIBR-synthesized image database with the associated subjective scores. We also test the performances of the state-of-the-art objective quality metrics on this database. This paper focuses on the distortions only induced by different DIBR synthesis methods. Seven state-of-the-art DIBR algorithms, including inter-view synthesis and single-view-based synthesis methods, are considered in this database. The quality of synthesized views was assessed subjectively by 41 observers and objectively using 14 state-of-the-art objective metrics. Subjective test results show that the interview synthesis methods, having more input information, significantly outperform the single-view-based ones. Correlation results between the tested objective metrics and the subjective scores on this database reveal that further studies are still needed for a better objective quality metric dedicated to the DIBR-synthesized views.
Residual Invertible Spatio-Temporal Network for Video Super-Resolution

Video super-resolution is a challenging task, which has attracted great attention in research and industry communities. In this paper, we propose a novel end-to-end architecture, called Residual Invertible Spatio-Temporal Network (RISTN) for video super-resolution. The RISTN can sufficiently exploit the spatial information from low-resolution to high-resolution, and effectively models the temporal consistency from consecutive video frames. Compared with existing recurrent convolutional network based approaches, RISTN is much deeper but more efficient. It consists of three major components: In the spatial component, a lightweight residual invertible block is designed to reduce information loss during feature transformation and provide robust feature representations. In the temporal component, a novel recurrent convolutional model with residual dense connections is proposed to construct deeper network and avoid feature degradation. In the reconstruction component, a new fusion method based on the sparse strategy is proposed to integrate the spatial and temporal features. Experiments on public benchmark datasets demonstrate that RISTN outperforms the state-ofthe-art methods.
Manifest Deadlock-Freedom for Shared Session Types

Shared session types generalize the Curry-Howard correspondence between intuitionistic linear logic and the session-typed $$\pi $$ -calculus with adjoint modalities that mediate between linear and shared session types, giving rise to a programming model where shared channels must be used according to a locking discipline of acquire-release. While this generalization greatly increases the range of programs that can be written, the gain in expressiveness comes at the cost of deadlock-freedom, a property which holds for many linear session type systems. In this paper, we develop a type system for logically-shared sessions in which types capture not only the interactive behavior of processes but also constrain the order of resources (i.e., shared processes) they may acquire. This type-level information is then used to rule out cyclic dependencies among acquires and synchronization points, resulting in a system that ensures deadlock-free communication for well-typed processes in the presence of shared sessions, higher-order channel passing, and recursive processes. We illustrate our approach on a series of examples, showing that it rules out deadlocks in circular networks of both shared and linear recursive processes, while still being permissive enough to type concurrent implementations of shared imperative data structures as processes.
Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation

The increasing popularity of the internet suggests that digital multimedia has become easier to transmit and acquire more rapidly. This also means that this multimedia has become more susceptible to tampering through forgery. One type of forgery, known as copy-move duplication, is a specified type that usually involves image tampering. In this study, a keypoint-based image forensics approach based on a superpixel segmentation algorithm and Helmert transformation has been proposed. The purpose of this approach is to detect copy-move forgery images and to obtain forensic information. The procedure of the proposed approach consists of the following phases. First, we extract the keypoints and their descriptors by using a scale-invariant feature transform (SIFT) algorithm. Then, based on the descriptor, matching pairs will be obtained by calculating the similarity between keypoints. Next, we will group these matching pairs based on spatial distance and geometric constraints via Helmert transformation to obtain the coarse forgery regions. Then, we refine these coarse forgery regions and remove mistakes or isolated areas. Finally, the forgery regions can be localized more precisely. Our proposed approach is a more robust solution for scaling, rotation, and compression forgeries. The experimental results obtained from testing different datasets demonstrate that the proposed method can obtain impressive precision/recall rates in comparison to state-of-the-art methods.
metaphactory: A platform for knowledge graph management

In this system paper we describe metaphactory, a platform for building knowledge graph applications. The metaphactory platform aims at supporting different categories of knowledge graph users within the organization by realizing relevant services for knowledge graph data management tasks, providing a rich and customizable user interface, and enabling rapid building of use case-specific applications. The paper discusses how the platform architecture design built on open standards enables its reusability in various application domains and use cases as well as facilitates integration of the knowledge graph with other parts of the organizational data and software infrastructure. We highlight the capabilities of the platform by describing its usage in four different knowledge graph application domains and share the lessons learnt from the practical experience of building knowledge graph applications in the enterprise context.
Deep Learning Based Representation for Face Recognition

Face Recognition is one of the challenging process due to huge amount of wild datasets. Deep learning has been provided good solution in terms of recognition performance, as day by day this have been dominating the field of biometric. In this paper our goal is to study deep learning based face representation under several different conditions like lower and upper face occlusions, misalignment, different angles of head poses, changing illuminations, flawed facial feature localization using deep learning approaches. For extraction of face representation two different popular models of Deep learning based called Lightened CNN and VGG-Face and have reflected in this paper. As both of this model show that deep learning model is robust to different types of misalignment and can tolerate localizations error of the intraocular distance.
Efficient transaction deleting approach of pre-large based high utility pattern mining in dynamic databases

Most traditional pattern mining is designed to process binary databases, so there is a limit to extracting meaningful information from real-world databases. To solve this problem, high utility pattern mining method for analyzing a non-binary database has been proposed and it is being actively studied recently. However, commonly encountered high utility pattern mining is not suitable for dynamic databases, which are subject to continuous changes, as they handle static databases. Transactions can be inserted, deleted, or modified from the database in a dynamic database environment. The pre-large method, which is one of the various techniques of processing a dynamic database, can efficiently operate by reducing the rescan of the original database using two thresholds. In this paper, we propose a method for mining high utility patterns applying a pre-large technique in an environment where transactions are continuously deleted. Also, we show on a basis of experimental results using real-world and synthetic datasets that the proposed algorithm exhibits better performance than the state-of-the-art methods.
Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence

Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis. In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.
MatryODShka: Real-time 6DoF Video View Synthesis Using Multi-sphere Images

We introduce a method to convert stereo 360 $$^\circ $$ (omnidirectional stereo) imagery into a layered, multi-sphere image representation for six degree-of-freedom (6DoF) rendering. Stereo 360 $$^\circ $$ imagery can be captured from multi-camera systems for virtual reality (VR), but lacks motion parallax and correct-in-all-directions disparity cues. Together, these can quickly lead to VR sickness when viewing content. One solution is to try and generate a format suitable for 6DoF rendering, such as by estimating depth. However, this raises questions as to how to handle disoccluded regions in dynamic scenes. Our approach is to simultaneously learn depth and disocclusions via a multi-sphere image representation, which can be rendered with correct 6DoF disparity and motion parallax in VR. This significantly improves comfort for the viewer, and can be inferred and rendered in real time on modern GPU hardware. Together, these move towards making VR video a more comfortable immersive medium.
Very low overhead fault-tolerant magic state preparation using redundant ancilla encoding and flag qubits

Abstract Fault-tolerant quantum computing promises significant computational speedup over classical computing for a variety of important problems. One of the biggest challenges for realizing fault-tolerant quantum computing is preparing magic states with sufficiently low error rates. Magic state distillation is one of the most efficient schemes for preparing high-quality magic states. However, since magic state distillation circuits are not fault-tolerant, all the operations in the distillation circuits must be encoded in a large distance error-correcting code, resulting in a significant resource overhead. Here, we propose a fault-tolerant scheme for directly preparing high-quality magic states, which makes magic state distillation unnecessary. In particular, we introduce a concept that we call redundant ancilla encoding. The latter combined with flag qubits allows for circuits to both measure stabilizer generators of some code, while also being able to measure global operators to fault-tolerantly prepare magic states, all using nearest neighbor interactions. We apply such schemes to a planar architecture of the triangular color code family and demonstrate that our scheme requires at least an order of magnitude fewer qubits and space–time overhead compared to the most competitive magic state distillation schemes. Since our scheme requires only nearest-neighbor interactions in a planar architecture, it is suitable for various quantum computing platforms currently under development.
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks

End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms.For EASA, compared to pipeline and multi-task approaches, joint aspect extraction and sentiment analysis provides a one-step solution to predict both aspect terms and their sentiment polarities through a single decoding process, which avoids the mismatches in between the results of aspect terms and sentiment polarities, as well as error propagation.Previous studies, especially recent ones, for this task focus on using powerful encoders (e.g., Bi-LSTM and BERT) to model contextual information from the input, with limited efforts paid to using advanced neural architectures (such as attentions and graph convolutional networks) or leveraging extra knowledge (such as syntactic information).To extend such efforts, in this paper, we propose directional graph convolutional networks (D-GCN) to jointly perform aspect extraction and sentiment analysis with encoding syntactic information, where dependency among words are integrated into our model to enhance its ability to represent input sentences and help EASA accordingly.Experimental results on three benchmark datasets demonstrate the effectiveness of our approach, where D-GCN achieves state-of-the-art performance on all datasets.
Correlation-Informed Permutation of Qubits for Reducing Ansatz Depth in the Variational Quantum Eigensolver

The Variational Quantum Eigensolver (VQE) is a method of choice to solve the electronic structure problem for molecules on near-term gate-based quantum computers. However, the circuit depth is expected to grow significantly with problem size. Increased depth can both degrade the accuracy of the results and reduce trainability. In this work, we propose a novel approach to reduce ansatz circuit depth. Our approach, called PermVQE, adds an additional optimization loop to VQE that permutes qubits in order to solve for the qubit Hamiltonian that minimizes long-range correlations in the ground state. The choice of permutations is based on mutual information, which is a measure of interaction between electrons in spin-orbitals. Encoding strongly interacting spin-orbitals into proximal qubits on a quantum chip naturally reduces the circuit depth needed to prepare the ground state. For representative molecular systems, LiH, H$_2$, (H$_2$)$_2$, H$_4$, and H$_3^+$, we demonstrate for linear qubit connectivity that placing entangled qubits in close proximity leads to shallower depth circuits required to reach a given eigenvalue-eigenvector accuracy. This approach can be extended to any qubit connectivity and can significantly reduce the depth required to reach a desired accuracy in VQE. Moreover, our approach can be applied to other variational quantum algorithms beyond VQE.
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-Shot Voice Conversion

One-shot voice conversion (VC), which performs conversion across arbitrary speakers with only a single target-speaker utterance for reference, can be effectively achieved by speech representation disentanglement. Existing work generally ignores the correlation between different speech representations during training, which causes leakage of content information into the speaker representation and thus degrades VC performance. To alleviate this issue, we employ vector quantization (VQ) for content encoding and introduce mutual information (MI) as the correlation metric during training, to achieve proper disentanglement of content, speaker and pitch representations, by reducing their inter-dependencies in an unsupervised manner. Experimental results reflect the superiority of the proposed method in learning effective disentangled speech representations for retaining source linguistic content and intonation variations, while capturing target speaker characteristics. In doing so, the proposed approach achieves higher speech naturalness and speaker similarity than current state-of-the-art one-shot VC systems. Our code, pre-trained models and demo are available at this https URL.
Use of Augmented Reality in the Instruction of Analytical Instrumentation Design

Instruction on the design of analytical instrumentation is a critical component of the analytical chemistry curriculum. To simplify this process and enable students to directly see how the instruments that are in their own laboratory setting work, the use of augmented reality technology can be implemented. In this report, the HP Reveal platform was used to create digital overlays that are triggered when students view an analytical instrument through their smartphone camera. From here, further information on the components and operation of the instrument can be presented to students. To demonstrate this technology, various overlays were created for four analytical instruments commonly taught in second-year undergraduate analytical chemistry courses: flame atomic absorption spectrometer, gas chromatograph–mass spectrometer, liquid chromatograph, and double-beam UV–vis spectrophotometer.