Reviewerly/exHarmony huggingface dataset:
Computational Stylistics

Computational Stylistics (CS) is a field of enquiry that examines the forms, social embedding, and the aesthetic potential of literary texts by means of computational and statistical methods. Operating on larger data sets with more transparent methodologies, CS offers literary studies new scales of observation and new methods of interpretation, to test existing theories and form new ones. As in many data-driven fields, methods range across exploratory, explanatory, and predictive modeling, with important debates addressing the affordances and limitations of each. From its multiple heritages in authorship attribution, stylistics, and natural language processing, CS has evolved to tackle ever more ambitious theoretical questions, including style, genre, and epoch; literary topoi, plot, and character networks; narrative perspective, figure characterization, and emotion; gender, race, and social status; canonicity, literariness, and textual quality; and cognitive representations of word beauty, metaphor, and rhyme. Situated within the data sciences, CS comprises distinct knowledge domains, in which the affordances of the digital (method, medium) and the statistical interact with the epistemic to produce new knowledge at the analytic levels of “text,” “context,” “author,” and “reader.”
A study of the internet financial interest rate risk evaluation index system in cloud computing

Cloud computing is a product of computer technologies combined with network technologies and it has been widely applied in China. Experts and scholars in all fields begin to make many studies of cloud computing infrastructure construction and effective resource utilisation. With ITFIN, people can enjoy financial services in dealing with various problems. However, one person can play many identities in the network. This phenomenon posed a severe challenge to ITFIN network security and has largely intensified the risks, including the operational risk, market selection risk and network and information security risk. ITFIN resolves the risks by establishing a reliable, reasonable and effective risk assessment model. We conducted theoretical and empirical analysis, then constructed an assessment model against China's ITFIN risk. The model integrates rough set and particle swarm optimisation support vector machine (PSO-SVM). Finally, the model was used to assess the ITFIN risk in China. The empirical research results indicate that the model can effectively reduce redundant data information with rough set theory. The theory also guarantees a reliable, reasonable and scientific model, enhance the classification effect of the model. The parameters of SVM model obtained by optimising with PSO can effectively avoid local optimum, improve the effect of the classification model. Overall, the model has good generalisation ability and learning ability.
Research on cutting characteristics of fiber bundle with high-speed photography

Cutting is an essential and complicated process in many fields. Efficient and low-consumption cutting operations are of great significance for environmental protection and energy conservation. The development of high performance cutting parts relies on a deep understanding of the cutting process and cutting mechanism. In this research, a new type of cutting test bench with high-speed photography was developed, and the cutting tests were conducted on the jute fiber bundle from quasi-static cutting at 10 mm/s to dynamic cutting in the speed range of 0.6-2.4 m/s. The cutting process was captured by a high-speed camera. Analysis shows that compression exists before quasi-static cutting, and the compression force curve with respect to the compression ratio follows an exponential function. The cutting speed has a significant effect on cutting energy. The cutting energy consumption is not a monotonous function of cutting speed owing to the combined effect of elastic deformation and friction of fibers. The cutting energy increases with increasing cutting speed in the range of 0.6-1.2 m/s due to the increase of the friction within fibers and the friction between the blade and fibers. The cutting energy decreases with increasing cutting speed in the range of 1.2-1.8 m/s, and tends to be a fixed value when the cutting speed exceeds 1.8 m/s due to the stabilized elastic deformation and friction coefficient. From the perspective of energy saving, it is meaningless to increase the blade speed excessively when cutting fiber bundles. Keywords: jute fiber, cutting, high-speed photography, energy consumption, friction DOI: 10.25165/j.ijabe.20201303.5677 Citation: He Z T, Ding H L, Du S M, Li Z, Ji J T, Li J, et al. Research on cutting characteristics of fiber bundle with high-speed photography. Int J Agric & Biol Eng, 2020; 13(3): 94–99.
Automated identification of network anomalies and their causes with interpretable machine learning: The CIAN methodology and TTrees implementation

Leveraging machine learning (ML) for the detection of network problems dates back to handling call-dropping issues in telephony. However, troubleshooting cellular networks is still a manual task, assigned to experts who monitor the network around the clock. To help in this task we present CIAN (from Causality Inference of Anomalies in Networks), a practical and interpretable ML methodology, which we implement in the form of a software tool named TTrees (from Troubleshooting Trees). We have designed CIAN to automate the identification of the causes of performance anomalies in cellular networks. Our methodology is unsupervised and combines multiple ML algorithms (e.g., decision trees and clustering) and Kolmogorov complexity-inspired data analysis tools that we have developed for this work. CIAN can be used with small volumes of data and is quick at training. Our experiments use diverse data sets obtained from measurements in operational commercial mobile networks. They show that the TTrees implementation of CIAN can automatically identify and accurately classify network anomalies – e.g., cases for which a network low performance is not apparently justified by operational conditions – training with just a few hundreds of data samples. The resulting information hence enables precise troubleshooting actions. In particular, we showcase how TTrees can be flexibly used to monitor the performance of TCP and QUIC protocols when they are adopted to serve mobile users.
Progress report on computing the disconnected QCD and the QCD plus QED hadronic contributions to the muon’s anomalous magnetic moment.

Fermilab Lattice, HPQCD, and MILC CollaborationsWe report progress on calculating the contribution to the anomalous magnetic moment of the muon from the disconnected hadronic diagrams with light and strange quarks and the valence QED contribution to the connected diagrams.The lattice QCD calculations use the highlyimproved staggered quark (HISQ) formulation.The gauge configurations were generated by the MILC Collaboration with four flavors of HISQ sea quarks with physical sea-quark masses.
A study of growth based morphological development in neural network controlled walkers

In nature, the physical development of the body that takes place in parallel to the cognitive development of the individual has been shown to facilitate learning. This opens up the question of whether the same principles could be applied to robots in order to accelerate the learning of controllers and, if so, how to apply them effectively. In this line, several authors have run experiments, usually quite complex and heterogeneous, with different levels of success. In some cases, morphological development seemed to provide an advantage and in others it was clearly irrelevant or even detrimental. Basically, morphological development seems to provide an advantage only under some specific conditions, which cannot be identified before running an experiment. This is due the fact that there is still no agreement on the underlying mechanisms that lead to success or on how to design morphological development processes for specific problems. In this paper, we address this issue through the execution of different experiments over a simple, replicable, and straightforward experimental setup that makes use of different neural network controlled walkers together with a morphological development strategy based on growth. The morphological development processes in these experiments are analyzed both in terms of the results obtained by the different walkers and in terms of how their fitness landscapes change as the morphologies develop. By comparing experiments where morphological development improves learning and where it does not, a series of initial insights have been extracted on how to design morphological development processes.
Joint Encoder-Decoder Self-Supervised Pre-training for ASR

Self-supervised learning (SSL) has shown tremendous success in various speech-related downstream tasks, including Automatic Speech Recognition (ASR). The output embeddings of the SSL model are treated as powerful short-time representations of the speech signal. However, in the ASR task, the main objective is to get the correct sequence of acoustic units, characters, or byte-pair encodings (BPEs). Usually, encoder-decoder architecture works exceptionally well for a sequence-to-sequence task like ASR. Therefore, in this paper, we propose a new paradigm that exploits the power of a decoder during self-supervised learning. We use Hidden Unit BERT (HuBERT) SSL framework to compute the conventional masked prediction loss for the encoder. In addition, we have introduced a decoder in the SSL framework and proposed a target preparation strategy for the decoder. Finally, we use a multitask SSL setup wherein we jointly optimize both the encoder and decoder losses. We hypothesize that the presence of a decoder in the SSL model helps it learn an acoustic unit-based language model, which might improve the performance of an ASR downstream task. We compare our proposed SSL model with HuBERT and show up to 25% relative improvement in performance on ASR by finetuning on various LibriSpeech subsets.
Reliable data communication using post‐quantum encryption in Internet of Everything

Summary Internet of Everything (IoE) is one of the emerging technologies in the advancement of digital life and innovation. But the major issue that is to be addressed is the security concerns over such an environment, especially in end‐to‐end device communication. The proposed approach primarily focuses on reliable end‐to‐end device communication using post‐quantum location‐aware encryption, aiming to achieve confidentiality and integrity in an Internet of Everything environment. Even though the post‐quantum techniques have shown to be one of the evolving solutions for reliable data communication, but its robustness against the Man‐In‐The‐Middle and Sybil attacks is still a wide‐open subject and undiscovered. Numerous traditional encryption algorithms are now in use, while post‐quantum encryption is being studied and considered as a quantum‐safe substitute. The proposed approach also demonstrates a robust attack model which focuses on communication network threats like namely, Man‐In‐The‐Middle and Sybil attacks intending to evaluate the overall performance of the network by analyzing the performance parameters like, number of nodes in the network, message size, execution time, and memory consumption by the nodes and aiming to achieve an appreciable accuracy in an IoE environment.
Survey on deep learning-based 3D object detection in autonomous driving

Autonomous driving technology has entered into the fast lane of development in recent years. An essential component of autonomous driving technology is scene perception, especially 3D object detection. This work gives a comprehensive survey on the up-to-date deep learning-based approaches for 3D object detection in autonomous driving, and categorizes the existing detection models into three classes in terms of their input data format, including LiDAR point cloud-based, Camera RGB image-based, and LiDAR point cloud-camera image fusion-based 3D object detection methods. This work also discusses and analyzes these models according to their characteristics, basic frameworks, advantages and disadvantages, and exhibits the benchmark datasets which are commonly used in the research community. At last, this work summarizes the review work and provides a discussion on the practical challenges and future trend of the research domain.
Perception of Society’s Trust in Care Robots by Public Opinion Leaders

The rapid demographic shift toward a greater percentage of the elderly population increases the need for welfare services. Welfare technology and especially care robots can be regarded as an important measure to counteract such demographic challenges. However, when implementing new technologies, structured information is of immense importance to develop societal trust. Frequently, research addresses trust solely at the level of the end-user. However, trust at the level of opinion leaders and political decision-makers is also relevant as they are catalysts for trust. This study aims to detect the perceived trust level of users from the viewpoint of opinion leaders (politicians, insurance organizations, and media) in the Swedish, Finnish, and German society. Furthermore, this study uses qualitative expert interviews and identifies four trust categories: trust in the health care system, trust in regulations, trust in technology, and interpersonal trust. The findings stress that targeting only the end-users is not sufficient for developing technology trust in society.
pHSCNN: CNN-based hyperspectral recovery from a pair of RGB images

To increase the fidelity of hyperspectral recovery from RGB images, we propose a pairwise-image-based hyperspectral convolutional neural network (pHSCNN) to recover hyperspectral images from a pair of RGB images, obtained by the same color sensor with and without an optical filter in front of the imaging lens. The proposed method avoids the pitfall of requiring multiple color sensors to obtain different RGB images and achieves higher accuracy than recovery from single RGB image. Besides, pHSCNN can also optimize the optical filter to further improve the performance. To experiment on real data, we built a dual-camera hyperspectral imaging system and created a real-captured hyperspectral-RGB dataset. Experimental results demonstrate the superiority of pHSCNN with the highest accuracy of the recovered hyperspectral signature perceptually and numerically.
Ultra-lightweight FPGA-based RC5 designs via data-dependent rotation block optimization

Lightweight cryptography provides security to resource-constrained applications, such as embedded systems and Internet of Things (IoT) devices. This work explores an ultra-lightweight implementation of RC5 block cipher, via two levels of serialization. We also exploit a resource-sharing optimization technique for the data-dependent rotation (DDR) module, since it is the most computationally intensive component of the RC5 block cipher. We present two RC5 hardware structures: (i) an 8-bit serial RC5 architecture using a dual-part DDR unit; this design occupies only 54 slices on a Spartan-6 FPGA device and is 69% superior in terms of Throughput-per-Slice (TPS) compared to the smallest 8-bit AES FPGA implementation, (ii) a novel bit-serial RC5 architecture based on shift-register LUT (SRL) primitives; this design consumes only 20 Spartan-6 slices and shows 81% improvement in throughput performance at the expense of only 5% additional area resources compared to the smallest bit-serial AES design reported. The proposed DDR-based optimizations and efficient time scheduling make our proposed designs outperform most of the state-of-the-art lightweight block cipher designs in terms of hardware efficiency (with regard to TPS).
DetIE: Multilingual Open Information Extraction Inspired by Object Detection

State of the art neural methods for open information extraction (OpenIE) usually extract triplets (or tuples) iteratively in an autoregressive or predicate-based manner in order not to produce duplicates. In this work, we propose a different approach to the problem that can be equally or more successful. Namely, we present a novel single-pass method for OpenIE inspired by object detection algorithms from computer vision. We use an order-agnostic loss based on bipartite matching that forces unique predictions and a Transformer-based encoder-only architecture for sequence labeling. The proposed approach is faster and shows superior or similar performance in comparison with state of the art models on standard benchmarks in terms of both quality metrics and inference time. Our model sets the new state of the art performance of 67.7% F1 on CaRB evaluated as OIE2016 while being 3.35x faster at inference than previous state of the art. We also evaluate the multilingual version of our model in the zero-shot setting for two languages and introduce a strategy for generating synthetic multilingual data to fine-tune the model for each specific language. In this setting, we show performance improvement of 15% on multilingual Re-OIE2016, reaching 75% F1 for both Portuguese and Spanish languages. Code and models are available at https://github.com/sberbank-ai/DetIE.
Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network

Human communication is the vocal and non verbal signal to communicate with others. Human expression is a significant biometric object in picture and record databases of surveillance systems. Face appreciation has a serious role in biometric methods and is good-looking for plentiful applications, including visual scrutiny and security. Facial expressions are a form of nonverbal communication; recognizing them helps improve the human machine interaction. This paper proposes an idea for face and enlightenment invariant credit of facial expressions by the images. In order on, the person's face can be computed. Face expression is used in CNN classifier to categorize the acquired picture into different emotion categories. It is a deep, feed-forward artificial neural network. Outcome surpasses human presentation and shows poses alternate performance. Varying lighting conditions can influence the fitting process and reduce recognition precision. Results illustrate that dependable facial appearance credited with changing lighting conditions for separating reasonable facial terminology display emotions is an efficient representation of clean and assorted moving expressions. This process can also manage the proportions of dissimilar basic affecting expressions of those mixed jointly to produce sensible emotional facial expressions. Our system contains a pre-defined data set, which was residential by a statistics scientist and includes all pure and varied expressions. On average, a data set has achieved 92.4% exact validation of the expressions synthesized by our technique. These facial expressions are compared through the pre-defined data-position inside our system. If it recognizes the person in an abnormal condition, an alert will be passed to the nearby hospital/doctor seeing that a message.
Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations

Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment costs compared with conventional bilingual systems.The MNMT training benefit, however, is often limited to many-to-one directions.The model suffers from poor performance in one-to-many and many-to-many with zero-shot setup.To address this issue, this paper discusses how to practically build MNMT systems that serve arbitrary X-Y translation directions while leveraging multilinguality with a two-stage training strategy of pretraining and finetuning.Experimenting with the WMT'21 multilingual translation task, we demonstrate that our systems outperform the conventional baselines of direct bilingual models and pivot translation models for most directions, averagely giving +6.0 and +4.1 BLEU, without the need for architecture change or extra data collection.Moreover, we also examine our proposed approach in an extremely large-scale data setting to accommodate practical deployment scenarios.
Generalized Equivariance and Preferential Labeling for GNN Node Classification

Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications (e.g., anonymized social networks). Previous GNNs either assign random labels to nodes (which introduces artefacts to the GNN) or assign one embedding to all nodes (which fails to explicitly distinguish one node from another). Further, when these GNNs are applied to unattributed node classification problems, they have an undesired equivariance property, which are fundamentally unable to address the data with multiple possible outputs. In this paper, we analyze the limitation of existing approaches to node classification problems. Inspired by our analysis, we propose a generalized equivariance property and a Preferential Labeling technique that satisfies the desired property asymptotically. Experimental results show that we achieve high performance in several unattributed node classification tasks.
A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes

This paper presents a model-free reinforcement learning (RL) algorithm for infinite-horizon average-reward Constrained Markov Decision Processes (CMDPs). Considering a learning horizon K, which is sufficiently large, the proposed algorithm achieves sublinear regret and zero constraint violation. The bounds depend on the number of states S, the number of actions A, and two constants which are independent of the learning horizon K.
Research on Radar Wave Avoidance for UAV Swarm Based on Improved Artificial Potential Field

This paper proposes an improved artificial potential field method on route planing for UAV swarm to evade radar detection during flight and finally reach the target point. We solve the problem of excessive gravitational force is solved by modifying the gravitational function to a segmental function compared to the shortcomings of the traditional artificial potential field method. And solve the problem of target unreachability by concentrating on the relative distance between the UAV swarm and the target. Finally, we solve the problem of local minima by adjusting the step size and direction. The feasibility of our improved artificial potential field method in obstacle avoidance is verified by Matlab simulation in experiment.