Reviewerly/exHarmony huggingface dataset:
Experimental Study on Image Filtering Algorithm

With the continuous development of intelligent assembly industry, the research on robotic assembly systems has became a hot and cutting-edge issue concerned by powers. During the development of the system, robot vision is mainly used for tracking and positioning. In the establishment of robot vision system, image preprocessing technology is one of the key steps. This paper is aiming at the robot system assembled on the ground of the satellite, which involves machine vision part, the median filtering method and mean filtering method are analyzed through PSNR of peak noise ratio and fuzzy coefficient based on Matlab platform. The noise reduction ability of the five filtering algorithms, including gaussian filtering, guided filtering and bilateral filtering. Then, comparing the best algorithm with the operation time, bilateral filtering has the best effect on image noise reduction and the longest time.
Novel Approach to New Domain Aspect Identification Using Deep Learning and Word Replacement

Customers of businesses enter a vast amount of data online. The data are analyzed by businesses to measure customer sentiment, allowing businesses to gain insights into what customers feel about their products and services. Sentiment analysis is therefore of prime importance to businesses. Sentiment predictions in sentences can be improved by identifying aspects, also known as Aspect Term Extraction (ATE), and then measuring the polarity of the sentiments associated with the aspects. This detailed measurement of aspect sentiment polarity helps in the overall sentiment classification of sentences. In the area of deep learning using neural networks, embeddings are created across a large corpus of data. These embeddings provide similarity measures that can be used for predicting aspects in unseen domains. In the proposed approach, unseen words of a domain are replaced with words from the training domain. The replacement words are identified using similarity scores between unseen words of a domain and existing words of the training domain. The modified sentences afterword replacement are given to the various domain-specific models for unseen domain aspect prediction. Extensive experiments are carried out across ten datasets to validate our approach. In a majority of the unseen domains, and across the different algorithms used, our approach performs well and gives a higher overall prediction accuracy.
Outsourced Privacy-Preserving Reduced SVM Among Multiple Institutions

Executing data mining algorithms locally is usually computationally intensive. A promising solution is to outsource the heavy data mining tasks and datasets. On the other hand, combining data from multiple institutions for a big and varied training set helps enhance the performance of data mining. Due to privacy concerns, different institutions should encrypt their datasets with different keys. Support Vector Machine (SVM) is a popular classifier. It is challenging to train SVM on encrypted datasets in the cloud. Existing schemes use either the multikey fully homomorphic encryption on one server, or partially homomorphic encryption on two non-colluding servers. The former is inefficient and the institutions have to remain online, while the latter relies too heavily on the assumption of two non-colluding servers. To remove these limitations, we demonstrate how to train SVM for both horizontally and vertically partitioned datasets. To reduce training complexity and enhance security, we focus on reduced SVM with a secure kernel matrix. We proved the security of our scheme and the experimental results validated its efficiency.
Secure and Robust Watermarking Scheme based on Motion Features for Video Object

The rapid development of worthy internet bandwidth transfers the digital multimedia objects to remote computer system instantaneously. The advent of digital technologies makes possible to create multiple identical copies of the original video. These two revolutionary changes in technologies exploited by malicious user also to make false claim for ownership protection, copyright protection, copy control and many more issues. The proposed technique suggested an innovative method by embedding the different segments of scrambled watermark in the extracted motion features of the video. The encryption key used to encrypt the watermark is depend upon the features of video and it always different for each different video. The scheme is robust and imperceptible enough to secure the watermark embedded into the video object. The outcome reveals that the scheme is robust to intentional and unintentional image and video specific attacks considered to occur frequently in nature. The superiority of the algorithm is that it found better robustness against compression attack.
Syntax-Informed Interactive Neural Machine Translation

In interactive machine translation (MT), human translators correct errors in automatic translations in collaboration with the MT systems, and this is an effective way to improve productivity gain in translation. Phrase-based statistical MT (PB-SMT) has been the mainstream approach to MT for the past 30 years, both in academia and industry. Neural MT (NMT), an end-to-end learning approach to MT, represents the current state-of-the-art in MT research. The recent studies on interactive MT have indicated that NMT can significantly outperform PB-SMT. In this work, first we investigate the possibility of integrating lexical syntactic descriptions in the form of supertags into the state-of-the-art NMT model, Transformer. Then, we explore whether integration of supertags into Transformer could indeed reduce human efforts in translation in an interactive-predictive platform. From our investigation we found that our syntax-aware interactive NMT (INMT) framework significantly reduces simulated human efforts in the French-to-English and Hindi- to-English translation tasks, achieving a 2.65 point absolute corresponding to 5.65% relative improvement and a 6.55 point absolute corresponding to 19.1% relative improvement, respectively, in terms of word prediction accuracy (WPA) over the respective baselines.
Not All Synonyms Are Created Equal: Incorporating Similarity of Synonyms to Enhance Word Embeddings

Traditional word embedding approaches learn semantic information from the associated contexts of words on large unlabeled corpora, which ignores a fact that synonymy between words happens often within different contexts in a corpus, so this relationship will not be well embedded into vectors. Furthermore, existing synonymy-based models directly incorporate synonyms to train word embeddings, but still neglect the similarity between words and corresponding synonyms. In this paper, we explore a novel approach that employs the similarity between words and corresponding synonyms to train and enhance word embeddings. To this purpose, we build two Synonymy Similarity Models (SSMs), named SSM-W and SSM-M respectively, which adopt different strategies to incorporate the similarity between words and corresponding synonyms during the training process. We evaluated our models for both Chinese and English. The results demonstrate that our models outperform the baselines on seven word similarity datasets. For the analogical reasoning and text classification tasks, our models also surpass all the baselines including a synonymy-based model.
Generating Motion of Cooperating Robots—The Dual Arm Case

Modern production lines rely on the extended use of cooperating robots as a basis of multiple tasks such as assembling, welding, painting, etc. Thus, there is a high need of automated motion generation of the involved industrial robots. This chapter presents an intelligent search algorithm for the motion planning of 13 DOF Dual arm industrial robots. The main purpose of the algorithm is the definition of all the intermediate Dual Arm robot’s configurations which lead the robot’s end effectors from the initial positions to the desired positions and orientations. The search algorithm is based on the gradually approach of all the alternatives configurations and evaluates them. The gradually approach of the configurations is limited from a grid which is based on a set of parameters and aims to reduce the computational time. Grid resolution as well as the values of the parameters are changeable and depends on the estimation level. Automatic motion planning is used in order to automatically generate free collision paths of the robot. In this way the programmer has to define only the end position of the robot and not the intermediate points for ensuring free collision paths. All the selected configurations constitute the desired path of each arm of the dual arm robot from the initial configurations to the final positions. In this way, an opportunity for automatic generation of the desired paths is given to unexperienced programmers.
A Frame-Aggregation-Based Approach for Link Congestion Prediction in WiFi Video Streaming

Video streaming using WiFi networks poses the challenge of variable network performance when multiple clients are present. Hence, it is important to continuously monitor and predict the network changes in order to ensure a higher user quality of experience (QoE) for video streaming. Existing approaches that aim to detect such network changes have several disadvantages. For example, active probing approaches are expensive so that generate more additional traffic flow during the testing. To overcome its shortcomings, we propose a passive, lightweight approach, CP-DASH, whereby queuing effects present in frame aggregation are leveraged to predict link congestion in the WiFi network. This approach allows the early detection which can be used to adapt our video appropriately. We conduct experiments simulating a WiFi network with multiple clients and compare CP-DASH with five contemporary rate selection mechanisms. We found that our proposed method significantly reduces the switch rates and stall rates from 22% to 5% and from 38% to 25% compared with an existing throughput-based algorithm, respectively.
RCapsNet: A Recurrent Capsule Network for Text Classification

In this paper, we propose RCapsNet, a recurrent capsule network for text classification. Although a variety of neural networks have been proposed recently, existing models are mainly based either on RNN or on CNN, which are rather limited in encoding temporal features in these network structures. In addition, most of these models require to integrate prior linguistic knowledge into them, which is not practical for a non-linguistician to handcraft such knowledge. To address these issues on temporal relational variabilities in text classification, the RCapsNet is presented by employing a hierarchy of recurrent structure-based capsules. It consists of two components: the recurrent module considered as the backbone of the RCapsNet and the reconstruction module designed to enhance the generalization capability of the model. Empirical evaluations on four benchmark datasets demonstrate the competitiveness of the RCapsNet. In particular, it is shown that prior linguistic knowledge is dispensable for the training of our model.
M3LA: A Novel Approach Based on Encoder-Decoder with Attention Framework for Multi-modal Multi-label Learning

With the exponential growth of digital multimedia resources, in the real-world, most of the data are represented as a multi-modal form and usually with multiple semantic labels. Nowadays, Multi-modal Multi-label learning has become a very hot topic. However, previous methods either have not considered the relation between modalities and labels or the correlation among labels. In this paper, we considered the following three questions: (1) How to model the correlation among labels? (2) Is there a correlation between modality and label? (3) Whether the modal input order affects the prediction of individual instance, and how to find the most appropriate modal input sequence for each instance? To solve above problems, we proposed a novel method for Multi-modal Multi-label learning(MMML), which based on Encoder-Decoder with attention framwork named MMML-Attention(M3LA). The M3LA takes into account all of these issues. Specifically, benefit from the Encoder-Decoder with attention structure, on the one hand, M3LA can model the relation between modalities and labels. On the other hand, we introduce a correlation matrix to learn the correlation among labels, which can be obtained as parameter through the training process. It should be mentioned that label prediction occurs at every step of the decoder, and the prediction of the label is constantly corrected and then the most accurate prediction is obtained. To validate the effectiveness of the proposed method, we expermiented on widely used several benchmark datasets and compared with state-of-art approaches.
Distributed Environment of Decision Support Centers: An Interests Representation Model of Virtual Collaboration and Technological Basic

The possibilities of using the potential of cognitive maps in the practice of group interaction and solving managerial problems in a system of distributed decision support centers are considered. The principles of the formation of coordinated representations of participants in virtual cooperation using cognitive maps are proposed; an approach to the construction and justification of a cognitive map based on the integration of expert representations and information identified by methods of searching and analysing data in an open information space.
I’m already optimal: the Dunning-Kruger Effect, Sociogenesis, and Self-Integration

As novel connections between heterogenous systems becomes the norm, and these self-integrated systems aspire to being robust enough to incorporate previously-isolated entities with varying degrees of natural and artificial intelligence, we would do well to re-examine our foundational assumptions about the models of the human brain upon which our AIs are based. This position paper proposes that the human brain is actually a small, network of heterogenous processors. We further propose that the manner in which mature humans integrate these processors, both individually and in cooperation with others, may provide valuable insights into possible paths forward in the development of systems of systems that can learn how to improve themselves and the ways in which they integrate with others.
NodeDrop: A Method for Finding Sufficient Network Architecture Size

Determining an appropriate number of features for each layer in a neural network is an important and difficult task. This task is especially important in applications on systems with limited memory or processing power. Many current approaches to reduce network size either utilize iterative procedures, which can extend training time significantly, or require very careful tuning of algorithm parameters to achieve reasonable results. In this paper we propose NodeDrop, a new method for eliminating features in a network. With NodeDrop, we define a condition to identify and guarantee which nodes carry no information, and then use regularization to encourage nodes to meet this condition. We find that NodeDrop drastically reduces the number of features in a network while maintaining high performance. NodeDrop reduces the number of parameters by a factor of 114x for a VGG like network on CIFAR10 without a drop in accuracy.
A Spatio-Angular Binary Descriptor For Fast Light Field Inter View Matching

Light fields are able to capture light rays from a scene arriving at different angles, effectively creating multiple perspective views of the same scene. Thus, one of the flagship applications of light fields is to estimate the captured scene geometry, which can notably be achieved by establishing correspondences between the perspective views, usually in the form of a disparity map. Such correspondence estimation has been a long standing research topic in computer vision, with application to stereo vision or optical flow. Research in this area has shown the importance of well designed descriptors to enable fast and accurate matching. We propose in this paper a binary descriptor exploiting the light field gradient over both the spatial and the angular dimensions in order to improve inter view matching. We demonstrate in a disparity estimation application that it can achieve comparable accuracy compared to existing descriptors while being faster to compute.
A Hydraulic Condition Monitoring System Based on Convolutional BiLSTM Model

In this paper, to monitor the conditions of hydraulic system, a real-time monitoring method based on convergence of convolutional neural networks (CNN) and a bidirectional long short-term memory networks (BiLSTM) is proposed. This method uses CNN and BiLSTM. In the CNN, the feature is extracted from the time-series data entered as an input, and in the BiLSTM, information from the feature is learned. Then, the learned information is sent to the Sigmoid classifier and it classified whether the system is stable or unstable. The experimental results show that compared to other deep learning models, this model can more accurately predict the conditions of the hydraulic system with the data collected by the sensors.
Approaches to Avoid Overfitting in a Quantum Perceptron

In this paper, we analyze an existing Quantum Perceptron in terms of its generalization performance and introduce alternative strategies to avoid classification errors, the Circuit Threshold Operator (CTO) and Neuron Threshold Variation (NTV). The adjust of the CTO and NTV parameters increases the probability of the neuron to tolerate differences between input and stored weights allowing some noise level to be acceptable. Experiments were conducted in IBM quantum simulator, showing that our proposals are effective when compared to the original fixed parameter. A hybrid classical-quantum training was executed, using Genetic Algorithm to train the parameters of the model classically and running our model in a quantum simulator. Our solution achieves 78.57% and 76.43% of accuracy on a noisy dataset, while the original neuron approach does not exceed 53.57%.
Online Knowledge Acquisition with the Selective Inherited Model

Continual learning, which updates machine learning models according to streaming data, is increasingly needed in the dynamic systems. Such a scenario requires both the preservation of previous knowledge, as well as the adaptation to new observations, with high computational and memory efficiency at the edge. Previous approaches attempt to learn the knowledge class by class from scratch, using either regularization based or memory replay-based methods. However, they still suffer from severe accuracy drop, a.k.a catastrophic forgetting, during this incremental process. Moreover, as the entire model is involved in each updating, their computation cost is too expensive for edge computing. In this work, we propose a novel brain- inspired paradigm named acquisitive learning (AL). Different from previous approaches that focus only on model adaptation, AL emphasizes the importance of both knowledge inheritance and acquisition: the model is first pre-trained and selected in the cloud (the selective inherited model) and then adapted to new knowledge (the acquisition). The quality of the inherited model is monitored by the landscape of the loss function, while the acquisition is realized by segmented training. The combination of both steps reduces accuracy drop by >10× on the CIFAR- 100 dataset. Furthermore, AL benefits edge computing with 5× reduction in latency per training image on FPGA prototype and 150× reduction in training FLOPs.
Controlling contents in data-to-document generation with human-designed topic labels

We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model. Conventional data-to-text model is useful when a reader seeks a global summary of data because it has only to describe an important part that has been extracted beforehand. However, since it differs from users to users what they are interested in, it is necessary to develop a method to generate various summaries according to users’ requests. We develop a model to generate various summaries and to control their contents by providing the explicit targets for a reference to the model as controllable factors. In the experiments, we used five-minute or one-hour charts of 9 indicators (e.g., Nikkei 225), as time-series data, and daily summaries of Nikkei Quick News as textual data. We conducted comparative experiments using two pieces of information: human-designed topic labels indicating the contents of a sentence and automatically extracted keywords as the referential information for generation. Experiments show that both models using additional information of target document achieved higher performance in terms of BLEU and human evaluation. We found that human-designed topic labels are superior to extracted keywords in terms of controllability.
Improving Expressivity of Graph Neural Networks

We propose a Graph Neural Network with greater expressive power than commonly used GNNs - not constrained to only differentiate between graphs that Weisfeiler-Lehman test recognizes to be non-isomorphic. We use a graph attention network with expanding attention window that aggregates information from nodes exponentially far away. We also use partially random initial embeddings, allowing differentiation between nodes that would otherwise look the same. This could cause problem with a traditional dropout mechanism, therefore we use a "head dropout", randomly ignoring some attention heads rather than some dimensions of the embedding.
Multilabel Classification With Multivariate Time Series Predictors

In many applications, multilabel classification involves time-series predictors, as in multilabel video classification. How to account for the temporal dependencies with respect to input variables remains an issue, especially in action learning from videos. Motivated by the problem of video categorization and captioning, we propose a nonlinear multilabel classifier based on a hidden Markov model and a weighted loss separating false positive and negative classification errors. This allows us to account for label dependence and temporal dependencies of input variables in classification. Computationally, we derive a decomposable algorithm based on block-wise coordinate descent for non-convex minimization, where it permits not only to block-wise updates but also label-wise updates, leading to scalable computation. Theoretically, we derive the Bayes rule and prove that the proposed method consistently recovers the optimal performance of the Bayes rule. In simulations, the proposed method compares favorably with its competitors ignoring either label dependence or time-dependence. Finally, the utility of the proposed method is demonstrated by an application to ActivityNet Captions dataset for understanding a video's contents.
Online learning for anomaly detection via subdivisible convex hulls

Due to the frequent use of anomaly detection systems in monitoring and the lack of methods capable of learning in real time, this research presents a new method that provides such online adaptability. The method developed is called OSHULL (Online and Subdivisible Distributed Scaled Convex Hull) and bases its operation on the properties of scaled convex hulls. It begins building a convex hull, using a minimum set of data, that is adapted and subdivided along time to accurately fit the boundary of the normal class data. The method has been evaluated and compared to several main algorithms of the field using some real and artificial data sets. As a consequence, an algorithm has been obtained with online learning ability and easily configurable, all without diminishing its effectiveness in relation to other batch state-of-the-art methods. Finally, its execution can be carried out in a distributed and parallel way, which is an interesting advantage in the treatment of big data sets.