Reviewerly/exHarmony huggingface dataset:
Research on value perception of commercial service of Science and Technology Museum based on Roberta model: taking text analysis as an example

Sentiment analysis has gradually become an important content of natural language processing (NLP), and plays an increasingly important role in the fields of system recommendation, user emotion information acquisition, and public opinion reference for governments and enterprises. In the period of comprehensive well-off, the leisure consciousness of Chinese residents has been significantly improved, the income growth has brought about the release of leisure consumption potential, and the time guarantee for leisure has been further enhanced. As the urban public cultural space, the modern Science and Technology Museum bears the diversified spatial functions of knowledge production, cultural empowerment and public welfare. An appropriate range of commercial service supply is an important part of the public policy supply of the Science and Technology Museum. It is very important to understand the emotional tendency of the public for the commercial service of the Science and Technology Museum. Roberta adds a dynamic mask mechanism on the basis of the model Bert, taking a larger amount of pre training data and a larger batch size. This paper introduces a multi-channel mask mechanism on the basis of the Roberta model, and increases the mask ratio, so that the model can learn more levels of emotional information, and the effect on text sentiment analysis is better. Therefore, taking Shanghai Science and Technology Museum as an example, the Roberta model is used to extract and interpret the public perception data of the public comment network, and study the value perception and emotional tendency of the public to the commercial services of the Science and Technology Museum, so as to better guide the Science and Technology Museum to improve the service quality level.
Spatial parquet

Modern data analytics applications prefer to use column-storage formats due to their improved storage efficiency through encoding and compression. Parquet is the most popular file format for column data storage that provides several of these benefits out of the box. However, geospatial data is not readily supported by Parquet. This paper introduces Spatial Parquet, a Parquet extension that efficiently supports geospatial data. Spatial Parquet inherits all the advantages of Parquet for non-spatial data, such as rich data types, compression, and column/row filtering. Additionally, it adds three new features to accommodate geospatial data. First, it introduces a geospatial data type that can encode all standard spatial data types in a column format compatible with Parquet. Second, it adds a new lossless and efficient encoding method, termed FP-delta, that is customized to efficiently store geospatial coordinates stored in floating-point format. Third, it adds a light-weight spatial index that allows the reader to skip non-relevant parts of the file for increased read efficiency. Experiments on large-scale real data showed that SpatialParquet can reduce the data size by a factor of three, even without compression. Compression can further reduce the storage size. Additionally, Spatial Parquet can reduce the reading time by two orders of magnitude when the light-weight index is applied. This initial prototype can open new research directions to further improve geospatial data storage in column format.
Compression strategy of structured text based on prior dictionary for data distribution system

Data distribution service (DDS) is a middleware API standard from Object Management Group (OMG), which transfers data using a publisher-subscriber model. The number of distributed nodes deployed in today's DDS communication system can reach tens of thousands, thus improving the efficiency of the communication system is important. In this work we present a structured text encoding strategy based on prior dictionary. This compression method has considerable compression effect on structured text such as HTML. In our evaluation, the average data compression rate is reduced by 7.07%, and the average system latency is reduced by 8.61% comparing to Zlib.
Validating Four Hmong Word Recognition Tests With Normal-Hearing Bilingual Hmong Individuals

The purpose of this study was to validate four digitally recorded and phonetically balanced 50-word recognition lists in the White Hmong dialect with normal-hearing bilingual Hmong adults.Using a randomized, incomplete-block design, each participant listened to and repeated four unique Hmong lists delivered by a female and a male talker. Participants were also tested with an English word list-List 1A of the Northwestern University Auditory Test No. 6. Participants' correct pronunciation of each word was scored. A nonparametric Mann-Whitney U Location Difference Test for Equivalence using two one-sided tests equivalence hypothesis: -0.02 < [(List_1) - (List_2)] < 0.02 was conducted to assess equivalence among all four Hmong and the English lists.Seventy Hmong speakers participated in this study (35 women, 35 men; Mage = 29.5 years, SD = 7.1). In all four Hmong lists, 93.5% (187/200) words met the validation criteria for ≥ 92% correct pronunciation. The 13 difficult words were deemed adequate by a Hmong panel and, therefore, were included to maintain four unique, balanced word lists. The test revealed that the Hmong and English word lists were considered equivalent at the 2% bound.The four Hmong word lists were validated to ensure an equal range of word difficulty across the lists.
A Sensitivity-based Pruning Method for Convolutional Neural Networks

The application of convolutional neural networks (CNNs) is sometimes limited by a large number of parameters and floating-point operations. Pruning methods have been proved to be effective to solve this problem. These methods improve the efficiency and storage occupancy of CNNs by removing weights connected with certain neurons/channels. The key issue is the selection of suitable neurons/channels to be pruned. Then, fine-tuning is usually applied to restore the performance of a pruned model to that before the pruning. However, existing neurons/channels selection methods do not explicitly consider the impact of the pruning on the model output. Moreover, the performance of a fine-tuned model may suffer from the information loss problem caused by the pruned neurons/channels. In this work, a stochastic sensitivity measure-based neurons/channels selection criterion is proposed to choose and prune insensitive neurons/channels, which effectively reduces the degradation of model performance. Moreover, a compensation operation followed by fine-tuning is proposed to relieve the information loss problem and restore model performance. Experimental results show that our method yields comparable compression and acceleration rates with less accuracy degradation compared with existing pruning methods for CNNs. For instance, the proposed method achieves more 6.8% FLOPs reduction and 0.25% accuracy improvement on VGG-16 compared with a recently proposed pruning method.
A Novel Block Storage Model for Consortium Blockchains

The storage overhead of blocks and storage load of nodes are increasing as consortium blockchains grow. The existing storage models consider either low-reliability issues or high storage overhead but not both. This work proposes a new storage model that combines group-based block storage with block splitting and unit encoding for the first time. The former is used to improve the reliability of a block, while the latter is adopted to reduce the storage overhead and increase the reliability of a block in a consortium blockchain. This work analyzes the storage overhead of blocks, storage load of nodes, and reliability of a consortium blockchain and designs a storage optimization method to reduce the storage overhead and improve the reliability of a blockchain system. Experimental results show that the proposed method outperforms Multi-layer Practical Byzantine Fault Tolerance, RapidChain, and Byzantine Fault Tolerance-Store in terms of storage overhead and reliability.
Readability Assessment of Academic Texts at Different Degree Levels

Developing machine learning tools to aid students in the process of writing a thesis document is of great interest to students, universities, supervisors and evaluation committees. This article presents the construction and evaluation of readability comparators based in Spanish-written thesis documents of four different academic levels: Advanced College Level Technician (ACT), Undergraduate, Master and Doctoral. Specifically, we provide comparators that can evaluate, between two academic texts which one is more readable than the other. In particular, we focus on three thesis sections: Problem Statement, Justification, and Results. The successful completion of these different comparators, as shown in results, allowed to test readability evaluators for new undergrad writings, determining whether correspond to its academic level. Some guidelines to determine comparators and academic level archetypes for readability assessment are proposed, based on our results.
Effects of tracker location on the accuracy and precision of the Gazepoint GP3 HD for spectacle wearers

Remote view eye-tracking systems are prone to errors when used on spectacle wearers due to reflections from the lenses and frame that result in inaccurate tracking. Traditionally, these trackers are situated below a computer monitor and the viewer's eye moments are recorded while they view the screen. Reflections may be influenced by the pantoscopic tilt of the spectacles, whereby the tilt angle causes incident light to be reflected to the camera. To overcome this problem, we propose mounting the tracker above the monitor to avoid these reflections and test the accuracy and precision of subjects with single vision spectacles, multifocals, and no correction, using both mounting positions. Experimental results showed that this alternate position had overall worse accuracy (4.06° ± 0.13) and precision (0.67° ± 0.05) compared to the standard configuration (2.15° ± 0.06 vs. 0.50° ± 0.03), with more invalid readings (5.91 vs. 19.19%) for single vision lens wearers. Multifocals performed better for the top-mounting position for the top portion of the monitor, suggesting higher-order aberrations from the bottom portion of the lens negatively impact data quality. Higher pantoscopic tilt angles displayed an improved accuracy for this alternate position (r(9) = - 0.69, p = 0.02), with superior accuracy for tilt angles greater than 14° compared to the standard configuration. This study quantifies the impact of spectacle wear on eye-tracking performance and suggests other alternate mounting positions may be viable in certain situations.
Retrieving Key Topical Sentences with Topic-Aware BERT When Conducting Automated Essay Scoring

Automated Essay Scoring (AES) automatically assign scores to essays at scale and may help to support teachers’ grading activities. Recently, AES methods based on deep neural networks (DNN) have significantly improved upon the state-of-the-art performance by learning relations between holistic essay scores and student essays. However, DNN-based AES methods function like black-box, negatively affecting the ability to provide automated writing evaluation (AWE). In this work, we proposed a new method, topic-aware BERT, based on fine-tuning the pre-trained language model to learn relations between essay scores and text representations of student essays as well as topical information in essay writing instructions. Moreover, we propose an approach to automatically retrieve key topical sentences in student essays by probing self-attention maps in intermediate layers of topic-aware BERT. We evaluate the performance of topic-aware BERT to (i) perform AES and (ii) retrieve key topical sentences using the open dataset Automated Student Assessment Prize and a manually annotated dataset, respectively. Our model achieves a strong AES performance compared with previous state-of-the-art DNN-based methods and shows effectiveness in identifying key topical sentences in argumentative essays.
Learning-Based Radio Fingerprinting for RFID Secure Authentication Scheme

In recent years, many low-cost identification systems have been proposed using RFID tags. Due to their limited compu-tational capability, passive RFID tags cannot provide secure links with cryptographic algorithms, and are vulnerable to cloning and replaying attacks. RF fingerprinting techniques based on distinctive hardware imperfections of devices have been explored for RFID systems. However, since RFID tags send their payloads by reflecting the carrier wave signal from readers, the received tag response signal will contain reader-specific fingerprints which can hinder the authentication accuracy using RF fingerprints. The rich dynamics of the wireless channel also present significant challenges for RF fingerprinting. To address these issues, we present FILES, a learning-based RF fingerprinting scheme for RFID systems. It is built upon the top of the EPC C1G2 protocol and works with any COTS tags. FILES analyzes the channel and reader information from the received carrier wave signal and decouples it from the tag response signal. It then extracts features from the preprocessed signal and classifies them with a neural network. We build a prototype of our system and conduct extensive experiments. The results show that our system can achieve an accuracy of up to 99% across readers, across tag locations, and across environments with 50 COTS RFID tags.
Eolo: IoT Proximity-based Authentication via Pressure Correlated Variations

Wireless channels are typically protected by crypto-graphic solutions. However, when those solutions cannot be applied (e.g. resource constrained such as Internet of Things (IoT)) or an additional layer of security is deemed necessary, it is possible to resort to auxiliary authenticatable out-of-band (OOB) channels. In this paper, we introduce Eolo, a novel, zero-interaction pairing approach to secure industrial IoT devices. Our solution pairs devices based on correlated ambient air pressure data. Eolo can serve, among others, as a co-location, key-based verification extension mechanism to support key agreement protocols such as Diffie-Hellman (DH). Indeed, the cited protocols are susceptible to man-in-the-middle (MITM) attacks. With our solution, the pairing devices first agree on a shared secret, then verify co-location by exchanging information related to the barometric pressure readings. Our proposed methodology is also experimentally verified by more than 115 hours of real data collected from an extensive measurement campaign in an open outdoor areas. To the best of our knowledge, Eolo represents the first solution leveraging barometric pressure to achieve context-pairing and key verification for devices in close proximity. Further, the applications of the proposed solution extends also well beyond the discussed scenario, being applicable for instance also to underwater context. As such, we believe that the proposed solution, other than being relevant on its own, also opens up further research avenues.
Formal Representation of Trusted Meta-requirements

Trusted requirements affect the trusted attributes of software, and have an important impact on whether trusted software can meet the trusted requirements. However, despite the continuous development of society and technology, the work of obtaining and analyzing trusted requirements has not become easy, has become more and more difficult. In the process of obtaining trusted requirements, a series of problems, such as low efficiency and inaccurate acquisition of requirements, serious software quality problems, budget overruns and delivery delays, have become increasingly prominent. In view of the above problems, this paper integrates the concept of meta-requirement into trusted requirement. Based on the respective characteristics of meta-requirement and trusted requirement, this paper puts forward the concept of trusted meta-requirement, and introduces the basic elements and characteristics. Then the trusted meta-requirements and some rules involved in it are formalized by the combination of first-order logic and set theory in order to improve the accuracy of the description and analysis of trusted requirements.
Motion Planning and Obstacle Avoidance for Robot Manipulators Using Model Predictive Control-based Reinforcement Learning

This paper presents a Nonlinear Model Predictive Control-based Reinforcement Learning (NMPC-based RL) framework for robot manipulators. The controller is developed to address the motion planning problem for robot manipulators in the presence of obstacles. The proposed control scheme includes a parametrized NMPC structure used as an approximator for the RL framework’s value function and action-value function. In the NMPC structure, the cost function, system constraints, and the manipulator’s model are parameterized. The Q-Learning algorithm based on the Temporal Difference method adjusts the parameters of the NMPC to increase the closed-loop performance of the whole control scheme. The controller has been applied to a 6-degrees-of-freedom (DoF) model of a robot manipulator, aimed at moving its end-effector to reach the desired pose when static obstacles are in the robot’s workspace. Numerical simulations demonstrate that the proposed controller can effectively control the end-effector’s pose in such a way as to avoid any collisions between the manipulator and the obstacles. It is shown that the learning capability of the proposed NMPC-based RL framework can enhance the efficiency of the control loop up to 21%.
An Adaptive Second-order Latent Factor Model via Particle Swarm Optimization

Latent Factor (LF) models are highly effective in representing high-dimensional and incomplete (HDI) matrices. Hessian free (HF) optimization is an efficiency second-order algorithm to minimize the object function in LF models. An HF-based second-order LF model can achieve better accuracy representation results than first-order ones with affordable computational burden. However, its low rank representation ability relies on a more complex training process, multiple hyper-parameters work cooperatively to decide the results. Thus, these hyper-parameters should be turned with care since they are mutually influenced. The heavy hyper-parameters turning work reduces the practicability of a second-order LF model. To address this issue, this study incorporates the principle of particle swarm optimization into the second-order LF model to propose an adaptive second-order LF (ASLF) model. Experimental results on three HDI matrices reveal that ASLF model can be fine-tuned adaptively with acceptable computation burden.
Progressive Deep Subspace Clustering based on Sample Reliability

Deep subspace clustering methods have attracted extensive attention due to the great improvement in both representation ability and precision of non-linear data. However, the rich information which is contained in the self-expression matrix is unexplored since existing approaches use the self-expression matrix only as a tool for learning inter-sample relationships and clustering. In addition, such models treat outlier and noise points equally with other points, which inevitably degrades the clustering performance. To overcome these issues, we develop a progressive deep subspace clustering approach by extracting delayed fitting probabilities from the module and then use the probabilities to defer the fitting of unreliable points. Specifically, we calculate the probabilities that each sample lies in each subspace based on the results of the self-expression matrix and spectral clustering, and then estimate the reliability of the cluster assignment of each sample as delayed fitting probability to reweight the loss of each sample. Experiments on five benchmark datasets validate the effectiveness of the proposed method.
Reinforcement Learning to Efficiently Recover Control Performance of Robots Using Imitation Learning After Failure

Extreme environments, such as space and underwater, are difficult for humans to enter because they involve risks, hence it is necessary to employ autonomous robots instead of humans. When robots fail in extreme environments, it is essential for the robot to automatically recover control following the rules of failure because humans cannot repair the robot directly. Reinforcement learning is expected to automatically acquire the control rule; however, the retrieval of the control rule requires significant trial-and-error. Imitation learning cannot acquire the control rule if no suitable expert data exist. Methods combining imitation learning and reinforcement learning reduce the number of trial-and-errors; however, they are still not effective against robot failure because these methods cannot utilize expert data effectively. This paper proposes a reinforcement learning method that efficiently recovers control performance from failures by utilizing both the control rules prepared by the designer and multiple discriminators to calculate measures similar to expert data. Experimental results show that the proposed method recovers the control performance with fewer episodes than the conventional method. The main contribution of the proposed method is its efficiency against robot failure through utilizing expart data prepared for failure by the designers for imitation learning.
A Modified Deep Q-Learning Algorithm for Optimal and Robust Quantum Gate Design of a Single Qubit System

Precise and resilient quantum gate design is important for the building of quantum devices. In this paper, we consider the optimal and robust quantum gate design problem for three classes of two-level quantum systems. The aim is to construct quantum gates in a given fixed time with limited control resources. A modified dueling deep Q-learning (MDuDQL) is employed for the optimal and robust gate design problem. To improve the performance of the classical DuDQL method, we propose a unique semi-Markov DuDQL algorithm based on a modified action selection procedure, modified replay memory, and soft update procedure. The proposed algorithm outperforms ordinary DuDQL in terms of discovering global optimal or near-global optimal control protocols and faster convergence to a better policy. Moreover, the modified DuDQL agent shows improved performance in finding robust control protocols which achieve high-fidelity quantum gate design for varying uncertainties in a certain range. The effectiveness of the proposed algorithm for the optimal and robust gate design problems has been illustrated by numerical results.
2D Skeleton-based Action Recognition Using Action-Snippets and Sequential Deep Learning

Human action recognition (HAR) is an active and crucial field of computer vision due to its various applications, such as smart surveillance and human-computer interaction. Recently, the human skeleton, which is compact and intuitive for representing actions and body movements, has been widely used in numerous HAR frameworks. Despite the great success of the skeleton-based HAR, several challenges remain, such as intra-class variability and inter-class similarity. In this paper, we address this task by first proposing a discriminative representation of the action-snippet (i.e., the very short sequence) that captures meaningful characteristics of human pose and body transition. We then employ adequate deep sequential neural networks (DSNNs) to thoroughly learn the temporal relation of action-snippets in a whole sequence. In experiments, the results show that the proposed approach achieves high recognition rates on benchmark datasets while maintaining good computational efficiency (i.e., lightweight networks and high recognition speed).