Reviewerly/exHarmony huggingface dataset:
Continuous Sign Language Recognition based on Multi-Part Skeleton Data

Most existing sign language recognition works are based on videos, which have the characteristics of weak robustness and high computational complexity. In this paper, we propose a new continuous sign language recognition (CSLR) framework to translate sign language represented by multi-part skeleton data into text. In the framework, the Multi-Stream Spatial-Temporal Graph Convolutional Networks (Multi-Stream ST-GCNs) is designed to model dynamic multi-part skeletons with graph structure. Meanwhile, we utilize adaptive fusion to weighted fuse the features of different parts, which can highlight the critical parts and weaken the auxiliary parts. The Attention-based Encoder-Decoder Network is applied to learn the mapping between the fused feature sequence and target sentence. In addition, we build the first skeleton-based CSLR dataset using Leap Motion, named LMSLR. Experimental results demonstrate that our approach achieves advanced performance in word error rate (WER).
Nonlocal quantum computing theory and Poincare cycle in spherical states

Four new fundamental nonlocal quantum computing diagonal operator-state relations are derived which model the interaction between two adjacent atoms of an entangled atomic chain. Each atom possesses four eigen-states. These relations lead to four momentum-space cyclic transformations and are used as the computation states in one-dimensional cellular automaton. Four interacting half-observable periodic planar states appear with the same Poincare cycle. Due to the space-time rotational symmetry of these operator-state relations, a new type of periodic spherical state can be constructed consisting of eight finite space-time quadrants as the special quantum computing result.
Multimodal Blog Sentiment Classification Based on MD-HFCE

In recent years, the rapid growth of multimodal information has become an important factor affecting the results of sentiment analysis. However, a few state-of-the-art works take into account the multimodal features and sentiment fuzziness. To this end, a fuzzy method is proposed for assessing sentiment intensity in this paper. Firstly, based on the visual-text conversion network (CNN-LSTM), as well as sentiment optimization through SentiBank and SentiBridge, the visual features are normalized to the text features. At the same time, the emotional features of the extracted audio will be predicted by the random forest algorithm. Subsequently, the sentiment characteristics are processed by dual hesitant fuzzification to form positive and negative sentiment intensity factors. Finally, a classification method, that is, MD-HFCE (multilayer dual hesitant fuzzy comprehensive evaluation), fuzzy comprehensive evaluation method improved by Mamdani fuzzy reasoning, is proposed to realize the multifeature fuzzy sentiment classification based on the comprehensive sentiment dictionary. The classification results are applicable to the topics of sentiment monitoring. The experimental results show that the proposed algorithm can effectively realize feature integration and improve the average sentiment classification accuracy of multimodal blogs to 82.2%.
Intelligent Strategy by Deep Learning for Thyroid Case Images Classification

The sparse network structure of traditional convolutional neural network cannot keep the high efficiency of dense computation of fully connected network and the inaccuracy of classification results or slow convergence rate caused by low utilization rate of convolution features in the experimental process. This paper proposes an effective feature extraction scheme by increasing the number of convolution kernels. The strategy can reasonably expand the data set to make the model converge faster. The influence of different activation functions on the experiment is compared, and the images are classified by Softmax method. Finally, the improved model is used to classify the normal and pathological thyroid tissue images. The classification result of the model is better, and the average classification accuracy is 96.6%.
Cost-Effective Knowledge Graph Reasoning for Complex Factoid Questions

The task of reasoning over knowledge graph for factoid questions has received significant interest from the research community of natural language processing. Performing this task inevitably faces the issues of question complexity and reasoning efficiency. In this paper, we investigate modern reasoning approaches over knowledge graph to tackle complex factoid questions of diverse reasoning schemas with attractive speedup in computational efficiency. To this end, we propose two evidence retrieval strategies to generate concise and informative evidence graph of high semantic-relevance and factual coverage to the question. Then, we adopt DELFT, a graph neural networks based framework that takes the linguistic structure representation of a question and the evidence graph as input, to predict the answer by reasoning over the evidence graph. We evaluate the performance across several baselines in terms of effectiveness and efficiency on two real-world datasets, MOOCQA and MetaQA. The results show the superiority of message passing paradigm in delivering a robust reasoner with better answer quality and significantly improved computational efficiency.
Non-divergent Imitation for Verification of Complex Learned Controllers

We consider the problem of verifying complex learned controllers using distillation. In contrast to previous work, we require that the distilled model maintains behavioural fidelity with an oracle, defining the notion of non-divergent path length (NPL) as a metric. We demonstrate that current distillation approaches with proven accuracy bounds do not have high expected NPL and can be out-performed by naive behavioural cloning. We thus propose a distillation algorithm that typically gives greater expected NPL, improved sample efficiency, and more compact models. We prove properties of NPL maximization and demonstrate the performance of our algorithm on deep Q-network controllers for three standard learning environments that have been used in this context: Pong, CartPole and MountainCar.
Some effective tricks are used to improve Soft Actor Critic

Abstract The development of artificial intelligence is becoming more intelligent, and Reinforcement learning (RL) plays an important role. RL has been widely used in recommendation systems, smart cars, game AI, and investment transactions. Since the emergence of DQN, DRL improvement methods have been proposed continuously. Soft Actor-Critic (SAC) is the current state-of-the-art RL algorithm. In this paper, some tricks are used to improve SAC. Firstly, the baseline is added to the policy gradient algorithm to reduce variance and speed up training. This paper adopts a new method to implement it in SAC. Because the temperature coefficient is a super parameter, which is difficult to adjust, so I propose a novel method to set it. These tricks proved to be effective on the MUJOCO.
Access Control in Cyber-Physical Spaces Using Virtual Perimeters

In recent years, physical access control systems have not developed to the same extent as their cyber counterparts. Cyber access control prevents unwarranted access to sensitive information. When a cyber document is displayed on a monitor, it loses its cyber protection as a passer-by can view the document on the monitor. A disparity thus exists between the level of security provided to a document in the cyber compared to the physical realm. Physically rendered documents can be better protected by establishing dynamic security perimeters, called virtual perimeters, around them in the physical realm. Indoor positioning systems can be used to dynamically locate people to determine if they violate the virtual perimeter around an object. When an event such as someone crossing the virtual perimeter occurs, access control can be performed. This research proposes a dynamic, event-driven, access control framework to provide security and privacy in physical spaces using virtual perimeters and camera-based indoor positioning systems. The Virtual Perimeter Access Control framework aims to prevent physical access to objects by monitoring the locations of entities within the space and prompting access control to be performed once a virtual perimeter is violated.
BEI-TAB: Enabling Secure and Distributed Airport Baggage Tracking with Hybrid Blockchain-Edge System

Global air transport carries about 4.3 billion pieces of baggage each year, and up to 56 percent of travellers prefer obtaining real-time baggage tracking information throughout their trip. However, the traditional baggage tracking scheme is generally based on optical scanning and centralized storage systems, which suffers from low efficiency and information leakage. In this paper, a blockchain and edge computing-based Internet of Things (IoT) system for tracking of airport baggage (BEI-TAB) is proposed. Through the combination of radio frequency identification technology (RFID) and blockchain, real-time baggage processing information is automatically stored in blockchain. In addition, we deploy Interplanetary File System (IPFS) at edge nodes with ciphertext policy attribute-based encryption (CP-ABE) to store basic baggage information. Only hash values returned by the IPFS network are kept in blockchain, enhancing the scalability of the system. Furthermore, a multichannel scheme is designed to realize the physical isolation of data and to rapidly process multiple types of data and business requirements in parallel. To the best of our knowledge, it is the first architecture that integrates RFID, IPFS, and CP-ABE with blockchain technologies to facilitate secure, decentralized, and real-time characteristics for storing and sharing data for baggage tracking. We have deployed a testbed with both software and hardware to evaluate the proposed system, considering the performances of transaction processing time and speed. In addition, based on the characteristics of consortium blockchain, we improved the practical Byzantine fault tolerance (PBFT) consensus protocol, which introduced the node credit score mechanism and cooperated with the simplified consistency protocol. Experimental results show that the credit score-based PBFT consensus (CSPBFT) can shorten transaction delay and improve the long-term running efficiency of the system.
Attention-based Seq2seq Regularisation for Relation Extraction

Relation extraction is an important task for information extraction aiming to detect and extract semantic relationships between entity pairs in sentences. A lot of recently proposed methods for relation extraction have gained remarkable results using deep neural networks. However, a deep network usually requires expensive computational power and is time-consuming when training. To address this problem, we introduce a novel neural relation extraction model that jointly trains a seq2seq regulariser with a classification module. As the regulariser and the classification module share the encoder, the whole model is less complicated compared to novel approaches like BERT. We show that our model achieves the same or better performance than the pre-trained BERT model using sufficient labelled data in much less training time.
Learning How to Zoom In: Weakly Supervised ROI-Based-DAM for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) is challenging due to the difficulty of finding discriminative features and insufficient labeled training data. How to efficiently localize the subtle but discriminative features with limited data is not straightforward. In this paper, we propose a simple yet efficient region of interest based data augmentation method (ROI-based-DAM) to handle the circumstance. The proposed ROI-based-DAM can first localize the most discriminative regions without the need of bounding box or part annotations. Based on these regions, ROI-based-DAM then carries out selective sampling and multi-scale cropping for constructing a series of high-quality ROI-based images. Thanks to its simplicity, our method can be easily implemented in the standard training and inference phases to boost the fined-grained classification accuracy. Our experimental results on extensive FGVC benchmark datasets show that the baseline model such as ResNeXt-50 can achieve competitive state-of-the-art performance by utilizing the proposed ROI-based-DAM, which demonstrate its effectiveness.
Function Load Balancing Over Networks

Using networks as a means of computing can reduce the communication flow over networks. We propose to distribute the computation load in stationary networks and formulate a flow-based delay minimization problem that jointly captures the costs of communications and computation. We exploit the distributed compression scheme of Slepian-Wolf that is applicable under any protocol information. We introduce the notion of entropic surjectivity as a measure of function’s sparsity and to understand the limits of functional compression for computation. We leverage Little’s law for stationary systems to provide a connection between surjectivity and the computation processing factor that reflects the proportion of flow that requires communications. This connection gives us an understanding of how much a node (in isolation) should compute to communicate the desired function within the network. Our results suggest that to effectively compute different function classes with different surjectivities, the networks can be restructured with the transition probabilities being tailored for functions, i.e., task-based link reservations, which can enable mixing versus separately processing of a diverse function class. We numerically evaluate our technique for search, MapReduce, and classification functions, and infer how sensitive the processing factor to the surjectivity of each computation task is.
Shape Based Continuous Real Time Hand Gesture Recognition System of American Sign Language Using KNN Classifier

The sign language is a language of communication for the speech and hearing impaired people used for their daily conversations and needs. This research paper give away, a creative framework, aims to accomplish the transfiguration of 24 continuous real time alphabet gestures of American Sign Language into human and / or device recognizable English text. The real time alphabets gestures take into consideration for testing are absolutely invariant to location, background, illumination, skin color, gender, and distance in nature. The real time hand gesture recognition process is conceded using the regular web camera. Histogram of Oriented Gradients (HOG) is used for hand feature extraction after the preprocessing operations. As a result of this research work, experimentation is carried out using the K-Nearest Neighbor (KNN) classifier for classification and recognition of gestures with k=3, 5, and 7. Finally, yields an overall average recognition rate of continuous real time alphabet gestures is 98.44%using KNN Classifier (k=3) with the least recognition time of 0.38 seconds per gesture, which is excellent comparing with the proposed technique k=5 (accuracy 93.75%, time elapsed 0.42 seconds), and k=7 (accuracy 90.10%, time elapsed 0.45 seconds), and also with the state of art techniques in real time environment.
Multi-variable Discretization Based on Extended Maximum Information Coefficient

Supervised data discretization is partitioning continuous variables based on a label of classification. The label usually defaults to a categorical variable. But in some applying scenario, the label may be a continuous variable. The usual way to handle this problem is transferring the label to a categorical variable, then discretizing target variable. However, the error generated in the first step may be magnified in the second step. Therefore, we propose a data discretization method based on an extended maximum information coefficient which can deal with multiple variables to combine two steps in one. We analyze the mathematical properties and calculation strategy of the method, and give examples of data set from the world health organization.
Personalized Recommendation of Tourist Attractions Based on Collaborative Filtering

Scenic spot recommendation system is one of the main ways for tourist portals to recommend scenic spots to people. As the traditional collaborative filtering recommendation algorithm is a single overall score, it lacks multi-faceted understanding of user interest preferences, which leads to inaccurate recommendation. Therefore, this paper uses questionnaire survey to collect travel preferences of different user attributes, mine user interests based on hierarchical sampling statistical model, and uses AHP to set user attribute weights. Finally, the improved collaborative filtering algorithm is used to establish a personalized tourism recommendation model. The experimental results show that the calculation error of the system is small, and the recommendation effect is good, which has a broad development prospect.
Depth Perception with a Newly Developed Microscope Stereotest

We aimed to compare the depth perception under a microscope between binocular and monocular conditions using a newly developed microscopic stereotest (M-stereotest) and examine the effect of anisometropia on depth perception under a microscope.Thirty young and healthy subjects were examined. A manipulator attached with a stainless wire was placed under the objective lens of a stereomicroscope. The wire was moved up manually, and the subjects were instructed to stop the knob movement once the wire reached the same height as the fixed two wires. The deviation from the height of the fixed wires was measured under a best optically corrected binocular, a best optically corrected monocular, or an anisometropic binocular [±3 diopters (D) in the dominant or nondominant eye] condition.The deviation was significantly smaller in the binocular condition than in the monocular dominant eye condition (0.38 ± 0.26 mm vs 1.89 ± 1.15 mm, p < 0.001) and was also significantly smaller in the best optically corrected binocular than in the anisometropic binocular condition [1.07 ± 1.00 mm (-3.0 D), p = 0.003; 0.85 ± 0.67 mm (+3.0 D), p < 0.001].Depth perception under the microscope is more accurate under the binocular than in the monocular condition, indicating the potential role of stereopsis. Anisometropia deteriorates depth perception, suggesting that refractive error should be corrected during microscopic work, such as during ophthalmic surgery.
Energy and Quality Aware Multi-Objective Resource Allocation Algorithm in Cloud

Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massive resources for video representation and its streaming when several simultaneous players reach a particular point. Alternatively, various players may have separate necessities on Quality-of Experience, like low delay, high-video quality, etc. The challenging task is providing better service by the fixed cloud resource. Hence, there is a necessity for an energy-aware multi-resource allocation in the cloud. This paper devises a Fractional Rider-Harmony search algorithm (Fractional Rider-HSA) for resource allocation in the cloud. The Fractional Rider-HSA combines fractional calculus, Rider Optimization algorithm (ROA), and HSA. Moreover, the fitness function, like mean opinion score (MOS), gaming experience loss, fairness, energy consumption, and network parameters, is considered to determine the optimal resource allocation. The proposed model produces the maximal MOS of 0.8961, maximal gaming experience loss (QE) of 0.998, maximal fairness of 0.9991, the minimum energy consumption of 0.3109, and minimal delay 0.2266, respectively.
The Degree of Conflict Between Formulas in an Inconsistent Knowledge Base

Measuring inconsistency in a knowledge base has been increasingly recognized as a good starting point to better understand the inconsistency of that base. Most approaches to measuring inconsistency for knowledge bases focus on either the degree of inconsistency of a whole knowledge base or the responsibility of each formula of a knowledge base for the inconsistency in that base or both. In this paper, we propose an approach to measuring the degree of conflict between formulas, which allows us to have a more clear picture on the relation between formulas involved in inconsistency. Then we show that this measure can be explained well in the framework of Halpern-Pearl’s causal model.
The Importance of Token Granularity Matching of Pre-trained Word Vectors for Deep Learning-Based Spam Classification

Spam email detection is a research hotspot, and the most efficient detection method is based on deep learning. In the context of the extensive use of pre-trained word vectors in deep neural networks, this paper studies the impact of pre-trained word vector models on the Text-CNN-based spam classification model, and uses token granularity matching technology to optimize the word2vec pre-trained word vector model in the vector representation on the spam email. By comparing the accuracy and time complexity of the spam classification with or without token granularity matching, it can be concluded that the Word2Vec pre-trained word vectors combined with token granularity processing can improve the performance of the Text-CNN model on spam email classification.
Exploring the structure of BERT through Kernel Learning

Combining internal representations of a pre-trained Transformer model, such as the popular BERT, is an interesting and challenging task nowadays. Usually, internal representations are combined by simple heuristics, e.g. concatenation or average of a subset of layers, with a consequent need for calibrating multiple hyper-parameters during the fine-tuning phase. Inspired by the recent literature, we propose a principled approach to optimally combine internal representations of a Transformer model via Multiple Kernel Learning strategies. Broadly speaking, the proposed system consists of two elements. The former is a canonical Transformer model fine-tuned on the target task. The latter is a Multiple Kernel Learning algorithm that extracts and combines representations developed in the internal layers of the Transformer and performs predictions. Most important, we use the system as a powerful tool to inspect the information encoded into the Transformer network, emphasizing the limits of state-of-the-art models.