Reviewerly/exHarmony huggingface dataset:
Human Activity Detection Employing Full-Type 2D Blazepose Estimation with LSTM

Detection of human activity with computer vision has become a concern lately. So many visionary approaches and algorithms. In vision approaches such as Raw Footage, TVL-1 Optical Flow Image, Rectangle and Ellipsoid Fitting, Area Distribution Around Centroid, and Pose Estimation. And algorithmic approaches such as CNN (Convolutional Neural Network), Random decision forest (RDF), 3D-CNN with LSTM, R-CNN, and Multi stream-deep CNN. The algorithm approach with pose estimation is considered to have resistance to sudden changes in the environment and movement. Pose estimation consists of 2D and 3D. 3D estimation is heavier than 2D computationally, but 2D has the disadvantage of stray movements that can cause errors. The 2D pose estimation algorithm in humans consists of OpenPose, PoseNet, AlphaPose, and BlazePose. BlazePose is considered appropriate for use in real-time applications and feature extraction. BlazePose has three types, namely Lite, Full, and Heavy. Implementation of BlazePose with LSTM on activity detection has been carried out and has a good performance. But the BlazePose implementation adds preprocessing to improve performance. In this paper, we try to build a complex human activity detection system consisting of walking, falling, sleeping position facing up, sleeping position facing the right side, and sleeping position facing the left side. This real-time system is built with full-type 2D BlazePose pose estimation with an LSTM prediction model without additional preprocessing. The resulting performance for activity detection is above 80% on average with a frame rate of 25+ FPS.
DeePhy: On Deepfake Phylogeny

Deepfake refers to tailored and synthetically generated videos which are now prevalent and spreading on a large scale, threatening the trustworthiness of the information available online. While existing datasets contain different kinds of deepfakes which vary in their generation technique, they do not consider progression of deepfakes in a “phylogenetic” manner. It is possible that an existing deepfake face is swapped with another face. This process of face swapping can be performed multiple times and the resultant deepfake can be evolved to confuse the deepfake detection algorithms. Further, many databases do not provide the employed generative model as target labels. Model attribution helps in enhancing the explainability of the detection results by providing information on the generative model employed. In order to enable the research community to address these questions, this paper proposes DeePhy, a novel Deepfake Phylogeny dataset which consists of 5040 deep-fake videos generated using three different generation techniques. There are 840 videos of one-time swapped deep-fakes, 2520 videos of two-times swapped deepfakes and 1680 videos of three-times swapped deepfakes. With over 30 GBs in size, the database is prepared in over 1100 hours using 18 GPUs of 1,352 GB cumulative memory. We also present the benchmark on DeePhy dataset using six deep-fake detection algorithms. The results highlight the need to evolve the research of model attribution of deepfakes and generalize the process over a variety of deepfake generation techniques. The database is available at: http://iab-rubric.org/deephy-database
Robust Ensemble Morph Detection with Domain Generalization

Although a substantial amount of studies is dedicated to morph detection, most of them fail to generalize for morph faces outside of their training paradigm. Moreover, recent morph detection methods are highly vulnerable to adversarial attacks. In this paper, we intend to learn a morph detection model with high generalization to a wide range of morphing attacks and high robustness against different adversarial attacks. To this aim, we develop an ensemble of convolutional neural networks (CNNs) and Transformer models to benefit from their capabilities simultaneously. To improve the robust accuracy of the ensemble model, we employ multi-perturbation adversarial training and generate adversarial examples with high transferability for several single models. Our exhaustive evaluations demonstrate that the proposed robust ensemble model generalizes to several morphing attacks and face datasets. In addition, we validate that our robust ensemble model gain better robustness against several adversarial attacks while outperforming the state-of-the-art studies.
Efficient motional-mode characterization for high-fidelity trapped-ion quantum computing

To achieve high-fidelity operations on a large-scale quantum computer, the parameters of the physical system must be efficiently characterized with high accuracy. For trapped ions, the entanglement between qubits are mediated by the motional modes of the ion chain, and thus characterizing the motional-mode parameters becomes essential. In this paper, we develop and explore physical models that accurately predict both magnitude and sign of the Lamb-Dicke parameters when the modes are probed {\it in parallel}. We further devise an advanced characterization protocol that shortens the characterization time by more than an order of magnitude, when compared to that of the conventional method that only uses mode spectroscopy. We discuss potential ramifications of our results to the development of a scalable trapped-ion quantum computer, viewed through the lens of system-level resource trade offs.
The future of standardised assessment: Validity and trust in algorithms for assessment and scoring

Abstract This article considers the challenges of using artificial intelligence (AI) and machine learning (ML) to assist high‐stakes standardised assessment. It focuses on the detrimental effect that even state‐of‐the‐art AI and ML systems could have on the validity of national exams of secondary education, and how lower validity would negatively affect trust in the system. To reach this conclusion, three unresolved issues in AI (unreliability, low explainability and bias) are addressed, to show how each of them would compromise the interpretations and uses of exam results (i.e., exam validity). Furthermore, the article relates validity to trust, and specifically to the ABI+ model of trust. Evidence gathered as part of exam validation supports each of the four trust‐enabling components of the ABI+ model (ability, benevolence, integrity and predictability). It is argued, therefore, that the three AI barriers to exam validity limit the extent to which an AI‐assisted exam system could be trusted. The article suggests that addressing the issues of AI unreliability, low explainability and bias should be sufficient to put AI‐assisted exams on par with traditional ones, but might not go as far as fully reassure the public. To achieve this, it is argued that changes to the quality assurance mechanisms of the exam system will be required. This may involve, for example, integrating principled AI frameworks in assessment policy and regulation.
Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection

Multispectral pedestrian detection is a technology designed to detect and locate pedestrians in Color and Thermal images, which has been widely used in automatic driving, video surveillance, etc. So far most available multispectral pedestrian detection algorithms only achieved limited success in pedestrian detection because of the lacking take into account the confusion of pedestrian information and background noise in Color and Thermal images. Here we propose a multispectral pedestrian detection algorithm, which mainly consists of a cascaded information enhancement module and a cross-modal attention feature fusion module. On the one hand, the cascaded information enhancement module adopts the channel and spatial attention mechanism to perform attention weighting on the features fused by the cascaded feature fusion block. Moreover, it multiplies the single-modal features with the attention weight element by element to enhance the pedestrian features in the single-modal and thus suppress the interference from the background. On the other hand, the cross-modal attention feature fusion module mines the features of both Color and Thermal modalities to complement each other, then the global features are constructed by adding the cross-modal complemented features element by element, which are attentionally weighted to achieve the effective fusion of the two modal features. Finally, the fused features are input into the detection head to detect and locate pedestrians. Extensive experiments have been performed on two improved versions of annotations (sanitized annotations and paired annotations) of the public dataset KAIST. The experimental results show that our method demonstrates a lower pedestrian miss rate and more accurate pedestrian detection boxes compared to the comparison method. Additionally, the ablation experiment also proved the effectiveness of each module designed in this paper.
Inverse-Covariance-Intersection-Based Distributed Estimation and Application in Wireless Sensor Network

In a sensor network, estimation performance may degrade when unknown correlations among local estimates are not addressed carefully. This article presents a novel distributed estimation algorithm based on inverse covariance intersection (ICI) for effectively solving the cross-correlation and dynamic state estimation problems in a wireless sensor network. The intermediate results of a set of consensus filters running in parallel are utilized to realize a global fusion of estimates, which involves all agents' local estimates and improves the estimation accuracy. Meanwhile, the global consistency of the proposed algorithm can be guaranteed theoretically since the fusion process considers all local estimates jointly and in a unified way. Furthermore, besides the asymptotic performance, the boundedness and consistency of the fused estimate can be achieved under finite iterations, which demonstrates its robustness and potential in practical applications. A cooperative target tracking problem illustrates the performance of the proposed algorithm.
FingerChain: Copyrighted Multi-Owner Media Sharing by Introducing Asymmetric Fingerprinting Into Blockchain

Nowadays, more and more people are engaged in media creation and sharing for income. A common way to earn income is to upload media to an intermediary platform and then let the platform distribute some profits. However, intermediary platforms generally not only extract most of the profits, but also lack transparency in their operation, where owners lose direct control over the media. Nevertheless, individual sharing is not feasible for owners because each owner holds too little media to attract enough users independently. Blockchain is a solution to the above problem by gathering media from multiple owners without intermediaries. Though a lot of works have studied the use of blockchain for decentralized management of media data, many of them either did not consider sharing needs or tracing the illegal redistribution by malicious users. As for other works, most of them adopted symmetric digital watermarking in their blockchain networks, and thus fail to protect the rights of users who may be framed by malicious owners. Although asymmetric watermarking has been used by two existing works, the owner-side embedding pattern results in low owner-side efficiency. In view of this, we design a media sharing blockchain network in which the asymmetric fingerprinting (i.e., watermarking) with user-side embedding is introduced. Besides superior owner-side efficiency, our scheme also outperforms the above two ones in terms of TTP-free. Moreover, our scheme is designed to offer a user-friendly experience and support record traceability. The performance of our scheme is verified by both theoretical and experimental evaluations.
A Universal Accelerated Coprocessor for Object Detection Based on RISC-V

With the application of object detection (OD) technology in various fields, common OD methods have been increasingly widely used, which poses a challenge to their calculative performance and accuracy. To improve the computational performance of OD algorithms, a general-purpose accelerated coprocessor was designed and implemented based on the extensibility of the RISC-V architecture. The accelerator was transformed into the form of a coprocessor and connected to its CPU core through the EAI coprocessor interface. Meanwhile, the corresponding instructions were designed, and the acceleration library functions were established. Finally, the performance and resource depletion of the coprocessor were analyzed on the FPGA platform. The experimental results suggest that only 10,091 LUTs were consumed by the accelerated coprocessor designed in this study, accounting for 52.2% of the entire SoC system. Moreover, the processing capacity of the YOLOv3 algorithm was 6.33 times that of a general-purpose CPU.
Another Side of Categorical Propositions: The Keynes–Johnson Octagon of Oppositions

The aim of this paper is to make sense of the Keynes–Johnson octagon of oppositions. We will discuss Keynes' logical theory, and examine how his view is reflected on this octagon. Then we will show how this structure is to be handled by means of a semantics of partition, thus computing logical relations between matching formulas with a semantic method that combines model theory and Boolean algebra.
News Recommendation Based on User Topic and Entity Preferences in Historical Behavior

A news-recommendation system is designed to deal with massive amounts of news and provide personalized recommendations for users. Accurately modeling of news and users is the key to news recommendation. Researchers usually use auxiliary information such as social networks or item attributes to learn about news and user representation. However, existing recommendation systems neglect to explore the rich topics in the news. This paper considered the knowledge graph as the source of side information. Meanwhile, we used user topic preferences to improve recommendation performance. We proposed a new framework called NRTEH that was based on topic and entity preferences in user historical behavior. The core of our approach was the news encoder and the user encoder. Two encoders in NRTEH handled news titles from two perspectives to obtain news and user representation embedding: (1) extracting explicit and latent topic features from news and mining user preferences for them; and (2) extracting entities and propagating users’ potential preferences in the knowledge graph. Experiments on a real-world dataset validated the effectiveness and efficiency of our approach.
Why and how to add direction to a quantum walk

We formalize the treatment of directed (or chiral) quantum walks using Hermitian adjacency matrices, bridging two developing fields of research in quantum information and spectral graph theory. We display results and simulations which highlight the conceptual differences between having directions encoded in the Hamiltonians or not. This leads to a construction of a new type of quantum phenomenon: zero transfer between pairs of sites in a connected coupled network, which is only possible in the directed model we study. Our main result is a description of several families of directed cycles that admit zero transfer.
Chaos game representation for authorship attribution

The authorship attribution task assumes the presence of several examples of documents written by various authors and it must be determined who wrote a given anonymous text. For each author, a specific writing style is hypothesized, with characteristics that the authors themselves are not aware of. The writing style acts as a fingerprint, as various features have been demonstrated to remain consistent for one author over the years. The Chaos Game Representation, a method for creating images from nucleotide sequences, is modified to make images from chunks of text documents. A text is transformed into a representation similar to a fingerprint that is used to check for similarities between the patterns existing in such marks from texts of distinct authors. Results indicate that this representation encodes sufficient particularities from an author writing style to make the methodology competitive for this field, with applications of both historic and current importance.
CCStokener: Fast yet accurate code clone detection with semantic token

Code clone detection refers to the discovery of identical or similar code fragments in the code repository. AST-based, PDG-based, and DL-based tools can achieve good results on detecting near-miss clones (i.e., clones with small differences or gaps) by using syntax and semantic information, but they are difficult to apply to large code repositories due to high time complexity. Traditional token-based tools can rapidly detect clones by the low-cost index (i.e., low frequency or k-lines tokens) on sequential source code, but most of them have the poor capability on detecting near-miss clones because of the lack of semantic information. In this study, we propose a fast yet accurate code clone detection tool with the semantic token, called CCStokener. The idea behind the semantic token is to enhance the detection capability of token-based tool via complementing the traditional token with semantic information such as the structural information around the token and its dependency with other tokens in form of n-gram. Specifically, we extract the type of relevant nodes in the AST path of every token and transform these types into a fixed-dimensional vector, then model its semantic information by applying n-gram on its related tokens. Meanwhile, our tool adopts and improves the location–filtration–verification process also used in CCAligner and LVMapper, during which process we build the low-cost k-tokens index to quickly locate the candidate code blocks and speed up detection efficiency. Our experiments show that CCStokener achieves excellent accuracy on detecting more near-miss clone pairs, which exhibits the best recall on Moderately Type-3 clones and detects more true positive clones on four java open-source projects. Moreover, CCStokener attains the best generalization and transferability compared with two DL-based tools (i.e., ASTNN, TBCCD).
Evolutionary Algorithm with Geometrical Heuristics for Solving the Close Enough Traveling Salesman Problem: Application to the Trajectory Planning of an Unmanned Aerial Vehicle

Evolutionary algorithms have been widely studied in the literature to find sub-optimal solutions to complex problems as the Traveling Salesman Problem (TSP). In such a problem, the target positions are usually static and punctually defined. The objective is to minimize a cost function as the minimal distance, time or energy. However, in some applications, as the one addressed in this paper—namely the data collection of buried sensor nodes by means of an Unmanned Aerial Vehicle— the targets are areas with varying sizes: they are defined with respect to the radio communication range of each node, ranging from a few meters to several hundred meters according to various parameters (e.g., soil moisture, burial depth, transmit power). The Unmanned Aerial Vehicle has to enter successively in these dynamic areas to collect the data, without the need to pass at the vertical of each node. Some areas can obviously intersect. That leads to solve the Close Enough TSP. To determine a sub-optimal trajectory for the Unmanned Aerial Vehicle, this paper presents an original and efficient strategy based on an evolutionary algorithm completed with geometrical heuristics. The performances of the algorithm are highlighted through scenarios with respectively 15 and 50 target locations. The results are analyzed with respect to the total route length. Finally, conclusions and future research directions are discussed.
Pauli String Partitioning Algorithm with the Ising Model for Simultaneous Measurements

We propose an efficient algorithm for partitioning Pauli strings into subgroups, which can be simultaneously measured in a single quantum circuit. Our partitioning algorithm drastically reduces the total number of measurements in a variational quantum eigensolver for a quantum chemistry, one of the most promising applications of quantum computing. The algorithm is based on the Ising model optimization problem, which can be quickly solved using an Ising machine. We develop an algorithm that is applicable to problems with sizes larger than the maximum number of variables that an Ising machine can handle (nbit) through its iterative use. The algorithm has much better time complexity and solution optimality than other existing algorithms. We investigate the performance of the algorithm using the second-generation Digital Annealer, a high-performance Ising hardware, for up to 65535 Pauli strings using Hamiltonians of molecules and the full tomography of quantum states. We demonstrate a time complexity of O(N) for N ≤ nbit and O(N2) for N > nbit for the worst case, where N denotes the number of candidate Pauli strings and nbit = 8,192 in this study. The reduction factor, which is the number of Pauli strings divided by the number of obtained partitions, can be 200 at maximum.
Improving anisotropy resolution of computed tomography and annotation using 3D super-resolution network

In clinical practice, abdomen computed tomography (CT) is obtained with a slice thickness of 5 mm which causes the anisotropy resolution. Especially in 3D automatic medical segmentation tasks, this anisotropy resolution in transverse plane and z-axial of CT image causes the unbalance of spatial feature for 3D convolution, which further limits the quality of segmentation. To recover context features between slices, super-resolution networks can better reconstruct detail information than interpolation methods. To reconstruct CT from different scanner models, a stronger generalization ability is indispensable. Moreover, to improve segmentation performance, the annotation of lesion area should be reconstructed at the same time. To address these issues, an Average Super-Resolution Generative Adversarial Network (ASRGAN) is proposed in this paper. We designed a multi-path average block to recover inter-slice information from CT with different image quality. Experimental results demonstrate that the proposed ASRGAN is superior to other methods on reconstruction with 2.42 db improvement on PSNR. And based on its reconstruction results, it further promotes improving 3D segmentation of the abdominal lesion liver tumor by 4.00% and the abdominal viscera pancreas by 2.25% on dice, to further reveal the effects of our reconstruction from view of this follow-up medical image analysis.