Reviewerly/exHarmony huggingface dataset:
SwinF: Swin Transformer with feature fusion in target detection

Abstract In recent years, transformer has made great achievements in the field of NLP and is gradually applied to Computer Vision. However, due to the particularity of images, the computational complexity of transformer is quite high. The windowing operation proposed by Swin transformer effectively solves this problem. We find that Swin transformer has the same hierarchical structure as CNN, so we propose SwinF network with feature fusion based on Swin transformer. On the coco type dataset, Swin transformer achieves 40.3mAP, while SwinF achieves 42.5mAP in the field of target detection.
Summarization assessment methodology for multiple corpora using queries and classification for functional evaluation

Extractive summarization is an important natural language processing approach used for document compression, improved reading comprehension, key phrase extraction, indexing, query set generation, and other analytics approaches. Extractive summarization has specific advantages over abstractive summarization in that it preserves style, specific text elements, and compound phrases that might be more directly associated with the text. In this article, the relative effectiveness of extractive summarization is considered on two widely different corpora: (1) a set of works of fiction (100 total, mainly novels) available from Project Gutenberg, and (2) a large set of news articles (3000) for which a ground truthed summarization (gold standard) is provided by the authors of the news articles. Both sets were evaluated using 5 different Python Sumy algorithms and compared to randomly-generated summarizations quantitatively. Two functional approaches to assessing the efficacy of summarization using a query set on both the original documents and their summaries, and using document classification on a 12-class set to compare among different summarization approaches, are introduced. The results, unsurprisingly, show considerable differences consistent with the different nature of these two data sets. The LSA and Luhn summarization approaches were most effective on the database of fiction, while all five summarization approaches were similarly effective on the database of articles. Overall, the Luhn approach was deemed the most generally relevant among those tested.
“It’s Like the Value System in the Loop”: Domain Experts’ Values Expectations for NLP Automation

The rise of automated text processing systems has led to the development of tools designed for a wide variety of application domains. These technologies are often developed to support non-technical users such as domain experts and are often developed in isolation of the tools primary user. While such developments are exciting, less attention has been paid to domain experts' expectations about the values embedded in these automated systems. As a step toward addressing that gap, we examined values expectations of journalists and legal experts. Both these domains involve extensive text processing and place high importance on values in professional practice. We engaged participants from two non-profit organizations in two separate co-speculation design workshops centered around several speculative automated text processing systems. This study makes three interrelated contributions. First, we provide a detailed investigation of domain experts' values expectations around future NLP systems. Second, the speculative design fiction concepts, which we specifically crafted for these investigative journalists and legal experts, illuminated a series of tensions around the technical implementation details of automation. Third, our findings highlight the utility of design fiction in eliciting not-to-design implications, not only about automated NLP but also about technology more broadly. Overall, our study findings provide groundwork for the inclusion of domain experts values whose expertise lies outside of the field of computing into the design of automated NLP systems.
Image Enhancement Method Based on Deep Learning

Image enhancement and reconstruction are the basic processing steps of many real vision systems. Their purpose is to improve the visual quality of images and provide reliable information for subsequent visual decision-making. In this paper, convolution neural network, residual neural network, and generative countermeasure network are studied. A rain fog image enhancement generative countermeasure network model structure including a scalable auxiliary generation network is proposed. The objective loss function is defined, and the periodic consistency loss and periodic perceptual consistency loss analysis are introduced. The core problem of image layering is discussed, and a layering solution framework with a deep expansion structure is proposed. This method realizes multitasking through adaptive feature learning, which has a good theoretical guarantee. This paper can not only bring a pleasant visual experience to viewers but also help to improve the performance of computer vision applications. Through image enhancement technology, the quality of low illumination image can be effectively improved, so that the image has better definition, richer texture details, and lower image noise.
Eliciting mental models of science and risk for disaster communication: A scoping review of methodologies

We present a scoping review of methods used to elicit individuals' mental models of science or risk. Developing a shared understanding of the science related to risk is crucial for diverse individuals to collaboratively manage disaster consequences. Mental models, or people's psychological representation of how the 'world works', present a valuable tool to achieve this. Potential applications range from developing effective risk communication for use in short-warning situations to community co-development of future communication protocols for the co-management of risk. A diverse range of tools, in diverse fields, have thus been developed to elicit these mental models. Forty-four articles were selected via inclusion criteria from 561 found through a systematic search. We identified a wide range of direct and indirect elicitation techniques (concept, cognitive, flow, information world, knowledge, mind, and fuzzy cognitive maps, and decision influence diagrams) and interview-based techniques. Many used multiple elicitation techniques such as free-drawing, interviews, free-listing, sorting tasks, attitudinal surveys, photograph elicitation, metaphor analysis, and mapping software. We identify several challenges when designing elicitation methods, including researcher influence, the importance of external visualization, a lack of evaluation, the role of 'experts', and ethical considerations due to the influence of the process itself. We present a preliminary typology for elicitation and analysis and suggest future research should explore methods to assess the evolution of mental models to understand how conceptualisations change through time, experience, or public education programs. These lessons have the potential to benefit both science and disaster risk communication activities, given best practice calls for mutually constructed understanding.
An Automatic Pronunciation Error Detection and Correction Mechanism in English Teaching Based on an Improved Random Forest Model

Teachers in traditional English classes focus more on writing and grammar instruction, while oral language instruction is neglected. In exam-oriented education, most Chinese students can master English written test skills, but only a few students can communicate effectively in English daily. People are progressively realizing that language is a tool for communication and communication in recent years, as the frequency of international exchanges has increased and that language learning should focus on oral language education. However, there are numerous issues with teaching oral English. When students perform individual oral practice after class, for example, they are unable to determine whether their pronunciation is correct. As a result, a computer-assisted study into automatic pronunciation of spoken English has become a viable solution to these issues. However, the present spoken English pronunciation mistake correction model’s accuracy and stability have not yet reached an optimal level. Based on this background, this work provides an enhanced random forest model and uses it to detect and correct automatic pronunciation errors in English classes. The improved random forest (RF) algorithm is used to classify and detect whether the learner’s pronunciation is correct. Mel cepstral coefficient (MFCC) is used for feature extraction, and principal component analysis (PCA) is used for dimensionality reduction of feature data. The experimental structure demonstrates that by using a combination classification framework based on MFCC, PCA, and RF, the learner’s pronunciation difficulty may be resolved. This allows for different error categories to receive feedback corrections.
Pythia: Unsupervised Generation of Ambiguous Textual Claims from Relational Data

Applications such as computational fact checking and data-to-text generation exploit the relationship between relational data and natural language text. Despite promising results in these areas, state of the art solutions simply fail in managing "data-ambiguity", i.e., the case when there are multiple interpretations of the relationship between the textual sentence and the relational data. To tackle this problem, we introduce Pythia, a system that, given a relational table D, generates textual sentences that contain factual ambiguities w.r.t. the data in D. Such sentences can then be used to train target applications in handling data-ambiguity. In this demonstration, we first show how our system generates data ambiguous sentences for a given table in an unsupervised fashion by data profiling and query generation. We then demonstrate how two existing applications benefit from Pythia's generated sentences, improving the state-of-the-art results. The audience will interact with Pythia by changing input parameters in an interactive fashion, including the upload of their own dataset to see what data ambiguous sentences are generated for it.
Vision-based estimation of the number of occupants using video cameras

Although occupancy information is critical to energy consumption of existing buildings, it still remains to be a major source of uncertainty. For reliable and accurate occupant modeling with minimal uncertainties, capturing precise occupant information on occupants is essential. This paper proposes a computer vision-based approach that utilizes deep learning architectures to estimate of the number of people in large, crowded spaces using multiple cameras. Various vision techniques (head detection, background elimination, head tracking) are implemented in three methods: (i) a method that instantaneously counts people in a scene, (ii) a method that incrementally counts people entering/exiting a room and (iii) a combination of the first two methods. These methods were applied in a classroom with heavy occlusions, and resulted in a high prediction capacity when compared to ground truth measurements. Future work in video-analytical approaches can address problems regarding lowering the computational cost of analysis, capturing occupancy data in complex room geometries and addressing concerns in privacy preservation.
Accurate Node Feature Estimation with Structured Variational Graph Autoencoder

Given a graph with partial observations of node features, how can we estimate the missing features accurately? Feature estimation is a crucial problem for analyzing real-world graphs whose features are commonly missing during the data collection process. Accurate estimation not only provides diverse information of nodes but also supports the inference of graph neural networks that require the full observation of node features. However, designing an effective approach for estimating high-dimensional features is challenging, since it requires an estimator to have large representation power, increasing the risk of overfitting. In this work, we propose SVGA (Structured Variational Graph Autoencoder), an accurate method for feature estimation. SVGA applies strong regularization to the distribution of latent variables by structured variational inference, which models the prior of variables as Gaussian Markov random field based on the graph structure. As a result, SVGA combines the advantages of probabilistic inference and graph neural networks, achieving state-of-the-art performance in real datasets.
Quantum limits to classically spoofing an electromagnetic signal

Spoofing an electromagnetic signal involves measuring its properties and preparing a spoof signal that is a close enough copy to fool a receiver. A classic application of spoofing is in radar where an airborne target attempts to avoid being tracked by a ground-based radar by emitting pulses indicating a false range or velocity. In certain scenarios it has been shown that a sensor can exploit quantum mechanics to detect spoofing at the single-photon level. Here we analyze an idealized spoofing scenario where a transmitter-receiver pair, seeking to detect spoofing, utilizes a signal chosen randomly from a set of nonorthogonal, coherent states. We show that a spoofer optimally employing classical information on the state of the transmitted signal (i.e., the best measure-and-prepare strategy allowed by quantum mechanics) inevitably emits imperfect spoofs that can be exploited by the receiver to reveal the presence of the spoofer, or to discriminate between true reflections and spoofs. Importantly, we show that the quantum limitations on classical spoofing remain significant even in the large mean-photon-number regime.
Toward Effective and Efficient DevOps using Blockchain

Recently, blockchain and DevOps have received attention from the software industry as both offer number of benefits individually. Automation, measurement, and sharing are the core pillars of DevOps, and to securely manage these; blockchain can play an important role. This study aims to understand the role of blockchain technology in the DevOps paradigm. This study presents factors that influence the adoption of blockchain in DevOps. Furthermore, this study suggests a framework that assists in merging the characteristics of blockchain in the DevOps paradigm. The findings of this study give a knowledge base for industry experts and the research community to develop the roadmap and guidelines for the adoption of blockchain in the DevOps paradigm.
A revocable multi-authority fine-grained access control architecture against ciphertext rollback attack for mobile edge computing

Mobile edge computing is an emerging decentralized cloud paradigm that can provide real-time mobile services for users. But there are some limitations in the existing access control architectures for mobile edge computing, such as low efficiency, poor expansibility and high algorithm complexity. Therefore in this paper, we propose a decentralized ciphertext-policy attribute-based encryption scheme which with mutually verifiable outsourced decryption and non-delegated computing mode attribute revocation as the fine-grained access control model for the mobile edge cloud. Compared with the state-of-art access control models with revocation function, first, our scheme is based on Type-3 pairing, which has higher efficiency and stronger security. Second, our outsourcing decryption algorithm can realize mutual verification, that is, not only the server can verify the validity of the user’s outsourcing key, but also the user can verify the correctness of the decrypted plaintext. Thirdly, our revocation algorithm can not only resist ciphertext rollback attacks to achieve the genuine backward and forward security, but also does not need the assistance of any intermediate proxy server, which greatly simplifies the network topology.
Hashtagged Trolling and Emojified Hate against Muslims on Social Media

This empirical exploratory study examines a number of insulting hashtags used against Islam and Christianity on Twitter and Instagram. Using a mixed method, the findings of the study show that Islam is more aggressively attacked than Christianity by three major communities, unlike Christianity, which is targeted much less by two main online groups. The online discussion around the two religions is politically polarized, and the negative language especially used against Islam includes the strategic use of hashtags and emojis, which have been weaponized to communicate violent messages and threats. The study is situated within the discussion of trolling and hateful content on social media. Aside from the empirical examination, the study refers to the differences in Twitter’s and Instagram’s policies, for the latter does not allow using hashtags such as #f***Christians and #f***Muslims, unlike Twitter, which accepts all types of hashtags to be used.
Quantum Computing Applications in Future Colliders

High-energy physics is facing a daunting computing challenge with the large amount of data expected from the HL-LHC and other future colliders. In addition, the landscape of computation has been expanding dramatically with technologies beyond the standard x86 CPU architecture becoming increasingly available. Both of these factors necessitate an extensive and broad-ranging research and development campaign. As quantum computation has been evolving rapidly over the past few years, it is important to evaluate how quantum computation could be one potential avenue for development for future collider experiments. A wide variety of applications have been considered by different authors. We review here selected applications of quantum computing to high-energy physics, including topics in simulation, reconstruction, and the use of machine learning, and their challenges. In addition, recent advances in quantum computing technology to enhance such applications are briefly highlighted. Finally, we will discuss how such applications might transform the workflows of future collider experiments and highlight other potential applications.
Quantum lower and upper speed limits using reference evolutions

Abstract We derive generalized quantum speed limit inequalities that represent limitations on the time evolution of quantum states. They are extensions of the original inequality and are applied to the overlap between the time-evolved state and an arbitrary state. We can discuss the lower limit of the Bures angle, in addition to the upper limit as in the original inequality, which allows us to evaluate the lower and upper bounds of processing time for the evolution toward a target state. The inequalities are written by using an arbitrary reference state and are flexibly used to obtain a tight bound. We demonstrate these properties by using the twisted Landau–Zener model, the Grover Hamiltonian, and a periodically-oscillating Hamiltonian.
Towards Learning Generalizable Code Embeddings Using Task-agnostic Graph Convolutional Networks

Code embeddings have seen increasing applications in software engineering (SE) research and practice recently. Despite the advances in embedding techniques applied in SE research, one of the main challenges is their generalizability. A recent study finds that code embeddings may not be readily leveraged for the downstream tasks that the embeddings are not particularly trained for. Therefore, in this article, we propose GraphCodeVec , which represents the source code as graphs and leverages the Graph Convolutional Networks to learn more generalizable code embeddings in a task-agnostic manner. The edges in the graph representation are automatically constructed from the paths in the abstract syntax trees, and the nodes from the tokens in the source code. To evaluate the effectiveness of GraphCodeVec , we consider three downstream benchmark tasks (i.e., code comment generation, code authorship identification, and code clones detection) that are used in a prior benchmarking of code embeddings and add three new downstream tasks (i.e., source code classification, logging statements prediction, and software defect prediction), resulting in a total of six downstream tasks that are considered in our evaluation. For each downstream task, we apply the embeddings learned by GraphCodeVec and the embeddings learned from four baseline approaches and compare their respective performance. We find that GraphCodeVec outperforms all the baselines in five out of the six downstream tasks, and its performance is relatively stable across different tasks and datasets. In addition, we perform ablation experiments to understand the impacts of the training context (i.e., the graph context extracted from the abstract syntax trees) and the training model (i.e., the Graph Convolutional Networks) on the effectiveness of the generated embeddings. The results show that both the graph context and the Graph Convolutional Networks can benefit GraphCodeVec in producing high-quality embeddings for the downstream tasks, while the improvement by Graph Convolutional Networks is more robust across different downstream tasks and datasets. Our findings suggest that future research and practice may consider using graph-based deep learning methods to capture the structural information of the source code for SE tasks.
Healthcare 5.0: A Study on Improving Personalized Care

Medicine has been chiefly a reactionary profession throughout history. Even today, one has to wait until diseases have manifested before attempting to treat or cure them. And because the genetic and environmental components that contribute to major diseases like Cancer, Alzheimer’s and cardiovascular diseases are not fully understandable, our efforts to treat them are often haphazard, unpredictable, and inefficient. As a result, the drugs and therapies developed are evaluated on large groups of people and given based on statical averages. And because of the genetic variances, they function for some patients but not for others. Every prescription medicine now on the market only works for half of the people who take it. Based on each patient’s unique genetic composition and environment circumstances, personalized medicine is a critical milestone to be attained to address this issue. Artificial intelligence and blockchain have recently been innovations in the healthcare industry. Furthermore, blockchain can control patient data privacy among participants. It uses asymmetric cryptography to secure transactions between users without the need for any intermediary or a trusted third party, thanks to smart contracts. On the other hand, Artificial intelligence provides intellect and decision-making capabilities for robots similar to humans. This study also identifies and analyses open research problems associated with blockchain and artificial intelligence in personalized medicine.
Neural Sign Language Translation with SF-Transformer

The popular methods are based on the combination of CNNs and RNNs in the sign language translation. Recently, Transformer has also attracted the attention of researchers and achieved success in this subject. However, researchers usually only focus on the accuracy of their model, while ignoring the practical application value. In this paper, we propose the SF-Transformer, a lightweight model based on Encoder-Decoder architecture for sign language translation, which achieves new state-of-the-art performance on Chinese Sign Language (CSL) dataset. We used 2D/3D convolution blocks of SF-Net and Transformer's Decoders to build our network. Benefiting from fewer parameters and a high level of parallelization, the training and inference speed of our model is faster. We hope that our method can contribute to the practical application of sign language translation on low-computing devices such as mobile phones.