Reviewerly/exHarmony huggingface dataset:
HetTreeSum: A Heterogeneous Tree Structure-based Extractive Summarization Model for Scientific Papers

Scientific paper summarization aims at generating a short and concise digest while preserving important information of the original document. Currently, scientific paper summarization faces two main challenges. First, inter-sentence relations are hard to learn, especially in the case of long-form scientific papers. Second, structural information of the well-structured scientific papers has not been fully exploited. To overcome the above two challenges, we propose a novel Heterogeneous Tree structure-based extractive Summarization (HetTreSum) model, where each document is modeled as a tree structure to learn inter-sentence relations and structural information of the original document is incorporated, enabling the tree structure to have a global perspective of the whole document. Then an iterative updating strategy is presented to interactively refine nodes of the tree structure for better contextualized representations, which can further enhance summarization performance. Experimental results on PubMed and arXiv datasets show that our proposed HetTreeSum model achieves significantly advanced performance compared with various scientific paper summarization models.
A color channel multiplexing approach for robust discrete wavelet transform based image watermarking

Summary Copyright protection of digital images has become a consequential issue in recent years. To prevent a breach of copyright for a color image, this article presents a novel discrete wavelet transform (DWT) based robust image watermarking method with a color channel multiplexing approach. In this proposed method, the original color image is separated into red, green, and blue color channels. One of these color channels is copied and a set of four channels is obtained. Then these channels are combined to obtain a new gray‐level cover image. 2‐level DWT is performed on the cover image. Arnold scrambled binary watermark image i embedded into the low‐frequency sub‐band of the cover image. 2‐level inverse DWT is performed to produce a gray‐level watermarked image. Then a demultiplexing process is performed by separating the gray‐level watermarked image into four color channels. The average values of the two copied color channels are calculated and then converted into a single‐color channel. Finally, the watermarked color image is get by merging three different color channels. To approve the performance of the presented method, various experimental tests have been performed and a benchmarking analysis has been conducted. The results show that the proposed method is robust against non‐linear and linear attacks while protecting watermarking imperceptibility.
Primitive elements with prescribed traces

Given a prime power q and a positive integer n, let Fqn denote the finite field with qn elements. Also let a,b be arbitrary members of the ground field Fq. We investigate the existence of a non-zero element ξ∈Fqn such that ξ+ξ−1 is primitive and T(ξ)=a, T(ξ−1)=b, where T(ξ) denotes the trace of ξ in Fq. This was a question intended to be addressed by Cao and Wang (2014). Their work dealt instead with another problem already in the literature. Our solution deals with all values of n≥5. A related study involves the cubic extension Fq3 of Fq. We show that if q≥8⋅1012 then, for any a∈Fq, we can find a primitive element ξ∈Fq3 such that ξ+ξ−1 is also a primitive element of Fq3, and for which the trace of ξ is equal to a. This improves a result of Cohen and Gupta (2021). Along the way we prove a hybridised lower bound on prime divisors in various residue classes, which may be of interest to related existence questions.
Practical issues to consider when working with big data

Abstract Increasing access to alternative or “big data” sources has given rise to an explosion in the use of these data in economics-based research. However, in our enthusiasm to use the newest and greatest data, we as researchers may jump to use big data sources before thoroughly considering the costs and benefits of a particular dataset. This article highlights four practical issues that researchers should consider before working with a given source of big data. First, big data may not be conceptually different from traditional data. Second, big data may only be available for a limited sample of individuals, especially when aggregated to the unit of interest. Third, the sheer volume of data coupled with high levels of noise can make big data costly to process while still producing measures with low construct validity. Last, papers using big data may focus on the novelty of the data at the expense of the research question. I urge researchers, in particular PhD students, to carefully consider these issues before investing time and resources into acquiring and using big data.
Programming physical quantum systems with pulse-level control

Quantum information processing holds great potential for pushing beyond the current frontiers in computing. Specifically, quantum computation promises to accelerate the solving of certain problems, and there are many opportunities for innovation based on proposed applications in chemistry, engineering, finance, and more. To harness the full power of quantum computing, however, we must not only place emphasis on manufacturing better qubits, advancing our algorithms, and developing quantum software. We must also refine device-level quantum control to scale to the fault tolerant quantum regime. On May 17–18, 2021, the Chicago Quantum Exchange (CQE) partnered with IBM Quantum and Super.tech to host the Pulse-level Quantum Control Workshop. At the workshop, representatives from academia, national labs, and industry addressed the importance of fine-tuning quantum processing at the physical layer. This work summarizes the key topics of the Pulse-level Quantum Control Workshop for the quantum community at large.
Classical Computer Assisted Analysis of Small Multiqudit Systems

Quantum computation model is regarded as a model which can overcome barriers in calculations efficiency of problems which appear in modern science. In spite of hardware development, in particular a recent emergence of several different physical installations of the pioneering quantum machines, the contemporary and numerical analysis of problems concerning quantum computing is very important. In the first part of this article, some useful computing techniques for quantum registers processed by a quantum circuits are presented. Applied classical parallel computational techniques are utilised to shorten the whole computational time. New methods of processing state vectors for qudits and density matrices are presented, indicating which operations may be performed in parallel in the context of the implementation of local unitary operations. There is also shown, how to use the reduction operation in parallel implementation of the von Neumann measurement by performing local measurements on a system of qudits. In addition to the purely technical results as described above, the paper includes also a bunch of purely theoretical results which substitute a solid mathematical ground for the computations performed with the help of the computational routines as described in Section III. In particular, a discussion concerning general multi-qudit quantum states through the prism of Entropy and Negativity measures of entanglement included in has been presented. Additionally, the notion of the total entanglement has been introduced. For certain classes of popular multi-qudit states, the introduced deficits of entanglement defined with the use of von Neumann Entropy and Negativity have been discussed. In particular, by the use of Gram matrix technique, the corresponding deficits of entanglement in the analysed states have been computed in an explicite way. Additionally, some new results on AME states for some multiqudit systems are also included in Section V. The last part of the article presents some numerical experiments on multi-qudit entanglement and determination of total entanglement values for convex combinations of GHZ andWstates. Some details concerning technical nature of results are included in the attached Appendix A.
STDP-Net: Improved Pedestrian Attribute Recognition Using Swin Transformer and Semantic Self-Attention

An image location requiring focus to recognize a specific pedestrian attribute often depends on the state of the pedestrian within the image. In addition, various pedestrian attributes are closely related to each other. For example, the &#x201C;Boots&#x201D; and &#x201C;ShortSkirt&#x201D; attributes are related to the &#x201C;Female&#x201D; attribute. For these reasons, we propose a novel encoder-decoder network for pedestrian attribute recognition, called Swin Transformer and Decoder for Pedestrian attribute recognition Network (STDP-Net). First, we utilize a Swin Transformer that uses self-attention as the encoder. This allows the proposed method to understand the relative relationship between the spatial regions of the images, unlike conventional convolution-based methods. This enables an accurate recognition of the attributes, even in misaligned pedestrian image inputs. Second, we add a transformer decoder with learnable attribute queries to the encoder to understand the semantic relationships among the attributes. Using the decoder, the proposed method captures such relationships based on the self-attention of the attribute queries. Extensive experimental results demonstrate that the proposed method achieves a state-of-the-art performance on six pedestrian attribute recognition datasets. In addition, misalignment experiments show the superiority of the encoder-decoder structure in comparison with other state-of-the-art methods.
A Machine Speech Chain Approach for Dynamically Adaptive Lombard TTS in Static and Dynamic Noise Environments

Recent end-to-end text-to-speech synthesis (TTS) systems have successfully synthesized high-quality speech. However, TTS speech intelligibility degrades in noisy environments because most of these systems were not designed to handle noisy environments. Several works attempted to address this problem by using offline fine-tuning to adapt their TTS to noisy conditions. Unlike machines, humans never perform offline fine-tuning. Instead, they speak with the Lombard effect in noisy places, where they dynamically adjust their vocal effort to improve the audibility of their speech. This ability is supported by the speech chain mechanism, which involves auditory feedback passing from speech perception to speech production. This paper proposes an alternative approach to TTS in noisy environments that is closer to the human Lombard effect. Specifically, we implement Lombard TTS in a machine speech chain framework to synthesize speech with dynamic adaptation. Our TTS performs adaptation by generating speech utterances based on the auditory feedback that consists of the automatic speech recognition (ASR) loss as the speech intelligibility measure and the speech-to-noise ratio (SNR) prediction as power measurement. Two versions of TTS are investigated: non-incremental TTS with utterance-level feedback and incremental TTS (ITTS) with short-term feedback to reduce the delay without significant performance loss. Furthermore, we evaluate the TTS systems in both static and dynamic noise conditions. Our experimental results show that auditory feedback enhanced the TTS speech intelligibility in noise.
Toward the InterPlanetary Health Layer for the Internet of Medical Things With Distributed Ledgers and Storages

With the dramatic increase of the Internet of Medical Things devices, self and remote health data monitoring is consistently receiving more attention.However, medical devices are usually challenging to deploy due to privacy regulations, and they generally leverage a centralized third party.Enabling data sharing would enhance new medical studies, formulate new treatments, and deliver new digital health technologies.Solving the issue will have a triple impact: we will handle sensitive information easily, contribute to international medical advancements, and enable personalized care.A possible solution is to exploit decentralization distributing privacy concerns directly to users.Solutions enabling this vision are closely linked to Distributed Ledger Technologies.Through its characteristics of immutability and transparency, this technology would allow privacy-compliant solutions in contexts where privacy is the first need.This paper envisions the InterPlanetary Health Layer and related real-world implementations in the Internet of Medical Things domain.The main idea of the proposed solution is to handle sensitive data by preserving privacy and guaranteeing data availability.Specifically, users can build their private network, collaboratively authorize operations among their data and manage their privacy conditions without relying on a third party.The results of several stress tests conducted on a real case study confirmed the feasibility of the proposed solution, which shows good scalability and a modest impact on the application performance measured during the decentralized data access.
Self-Adaptive Imbalanced Domain Adaptation With Deep Sparse Autoencoder

Domain adaptation aims to transfer knowledge between different domains to develop an effective hypothesis in the target domain with scarce labeled data, which is an effective method for remedying the problem of labeled data requirement in deep learning. In reality, it is unavoidable that the dataset has a large gap in the number of positive and negative instances across different categories in source and target domains, which is the imbalanced domain adaptation problem. However, since the imbalanced degree always varies greatly in different source- and target-domain datasets, most of the existing imbalanced domain adaptation models fix the imbalanced parameters, which cannot adapt to the change of the proportion between positive and negative instances in different domains. To address this problem, in this article, we propose a self-adaptive imbalanced domain adaptation method via a deep sparse autoencoder, which can adjust the model automatically according to the imbalanced extent for bridging the chasm of domains. More specifically, the self-adaptive imbalanced cross-entropy loss is designed for emphasizing more on minority categories and compensating the bias of training loss automatically. In addition, to alleviate the deficient problem of labeled data, we further propose the unlabeled information incorporating method by minimizing the distribution discrepancy of high-level representation space between the source and target domains. Experiments on several real-world datasets demonstrate the effectiveness of our method compared to other state-of-the-art methods.
Not all temporal shift modules are profitable

With the increasing coverage of video surveillance systems in modern society, demand for using artificial intelligence algorithm to replace humans in violent behavior recognition has also become stronger. By moving some channels in the temporal dimension, temporary shift module (TSM) can achieve the performance of three-dimensional convolution neural network (CNN) with the complexity of two-dimensional CNN, and extract the temporal and spatial information at the same time. Our intuition is that too many temporary shift modules may fuse too much action information in each frame, which weakens the capability of CNN on spatiotemporal information extraction. To verify the aforementioned conjecture, we adjusted the network structure based on TSM, proposed partial TSM, selected the optimal model through experiments, and verified the performance of the algorithm on multiple datasets and our expanded datasets. The proposed optimal model not only reduced the memory usage of hardware but also achieved higher accuracy on multiple datasets with 77.3% running time. Meanwhile, we achieved state-of-the-art performance of 91% on RWF-2000 dataset.
Histologic Screening of Malignant Melanoma, Spitz, Dermal and Junctional Melanocytic Nevi Using a Deep Learning Model

The integration of an artificial intelligence tool into pathologists' workflow may lead to a more accurate and timely diagnosis of melanocytic lesions, directly patient care. The objective of this study was to create and evaluate the performance of such a model in achieving clinical-grade diagnoses of Spitz nevi, dermal and junctional melanocytic nevi, and melanomas.We created a beginner-level training environment by teaching our algorithm to perform cytologic inferences on 136,216 manually annotated tiles of hematoxylin and eosin-stained slides consisting of unequivocal melanocytic nevi, Spitz nevi, and invasive melanoma cases. We sequentially trained and tested our network to provide a final diagnosis-classification on 39 cases in total. Positive predictive value (precision) and sensitivity (recall) were used to measure our performance.The tile-classification algorithm predicted the 136,216 irrelevant, melanoma, melanocytic nevi, and Spitz nevi tiles at sensitivities of 96%, 93%, 94% and 73%, respectively. The final trained model was able to correctly classify and predict the correct diagnosis in 85.7% of unseen cases (n = 28), reporting at or near screening-level performances for precision and recall of melanoma (76.2%, 100.0%), melanocytic nevi (100.0%, 75.0%), and Spitz nevi (100.0%, 75.0%).Our pilot study proves that convolutional networks trained on cellular morphology to classify melanocytic proliferations can be used as a powerful tool to assist pathologists in screening for melanoma versus other benign lesions.
Chance-constrained sneaking trajectory planning for reconnaissance robots

Mobile robots have been widely used in various military applications, where an efficient and robust trajectory planner is of vital importance. In this paper, we focus on the sneaking trajectory planning of reconnaissance robots with consideration of uncertainty in the geometric parameters of the watchtower's searchlight. By analyzing the motion equations and the mission requirements, the trajectory planning problem is formulated as a chance-constrained optimal control problem. The convex bounding method, as a kind of sampling technique, is one of the most effective ways to address this optimization problem. However, it suffers from low computational efficiency since a great number of samples are required to chance constraints into determined ones. Calculating the constraint violation rate by numerical integration instead of sampling, a numerical integration assisted convex bounding method is developed. The improved method outperforms computational efficiency since the times of evaluating each constraint becomes less. The developed method is validated by two scenarios with different complexities. Numerical results demonstrate that, compared with the traditional method, the improved method can achieve satisfactory constraint violation rate of similar level but with higher computational efficiency. An animation of the simulation results is available at www.bilibili.com/video/BV1Bg41117xi/.
A Distance-Field-Based Pipe-Routing Method

Pipes are commonly used to transport fuels, air, water, gas, hydraulic power, and other fluid-like materials in engine rooms, houses, factories, airplanes, and ships. Thus, pipe routing is essential in many industrial applications, including ship construction, machinery manufacturing, house building, laying out engine rooms, etc. To be functional, a pipe system should be economical while satisfying spatial constraints and safety regulations. Numerous routing algorithms have been published to optimize the pipe length and the number of elbows. However, relatively few methods have been designed to lay out pipes which strictly meet the spatial constraints and safety regulations. This article proposes a distance-field-based piping algorithm to remedy this problem. The proposed method converts the workspace into a 3D image and computes a distance field upon the workspace first. It then creates a feasible space out of the workspace by peeling the distance field and segmenting the 3D image. The resultant feasible space is collision-free and satisfies the spatial constraints and safety regulations. In the following step, a path-finding process, subjected to a cost function, is triggered to arrange the pipe inside the feasible space. Consequently, the cost of the pipe is optimized, and the pipe path rigidly meets the spatial constraints and safety regulations. The proposed method works effectively even if the workspace is narrow and complicated. In three experiments, the proposed method is employed to lay out pipes inside an underwater vehicle, a machinery room, and a two-story house, respectively. Not only do the resultant pipes possess minimal costs, but they also meet the spatial constraints and safety regulations, as predicted. In addition to developing the routing procedure, we also design a visualization subsystem to reveal the progression of the piping process and the variation of the workspace in the run time. Based on the displayed images, users can therefore evaluate the quality of the pipes on the fly and tune the piping parameters if necessary.
Real world projects, real faults: evaluating spectrum based fault localization techniques on Python projects

Spectrum Based Fault Localization (SBFL) is a statistical approach to identify faulty code within a program given a program spectra (i.e., records of program elements executed by passing and failing test cases). Several SBFL techniques have been proposed over the years, but most evaluations of those techniques were done only on Java and C programs, and frequently involve artificial faults. Considering the current popularity of Python, indicated by the results of the Stack Overflow survey among developers in 2020, it becomes increasingly important to understand how SBFL techniques perform on Python projects. However, this remains an understudied topic. In this work, our objective is to analyze the effectiveness of popular SBFL techniques in real-world Python projects. We also aim to compare our observed performance on Python to previously-reported performance on Java. Using the recently-built bug benchmark BugsInPy as our fault dataset, we apply five popular SBFL techniques (Tarantula, Ochiai, OP, Barinel, and DStar) and analyze their performances. We subsequently compare our results with results from Java and C projects reported in earlier related works. We find that 1) the real faults in BugsInPy are harder to identify using SBFL techniques compared to the real faults in Defects4J, indicated by the lower performance of the evaluated SBFL techniques on BugsInPy; 2) older techniques such as Tarantula, Barinel, and Ochiai consistently outperform newer techniques (i.e., OP and DStar) in a variety of metrics and debugging scenarios; 3) claims in preceding studies done on artificial faults in C and Java (such as "OP outperforms Tarantula") do not hold on Python real faults; 4) lower-performing techniques can outperform higher-performing techniques in some cases, emphasizing the potential benefit of combining SBFL techniques. Our results yield insight into how popular SBFL techniques perform in real Python faults and emphasize the importance of conducting SBFL evaluations on real faults.