Reviewerly/exHarmony huggingface dataset:
Improved artificial bee colony algorithm-based path planning of unmanned autonomous helicopter using multi-strategy evolutionary learning

Aiming at producing a high-quality flight path for the unmanned autonomous helicopter with multi-constraints, a path planning method is proposed based on the multi-strategy evolutionary learning artificial bee colony algorithm in this paper. Firstly, an evolutionary learning framework is established for the artificial bee colony algorithm based on brain-like cognition. By integrating the swarm intelligence and human cognitive mechanism, this framework gives more autonomy and intelligence to the bee colony. In addition, a multi-strategy evolutionary database is built based on the evolutionary learning framework to replace the traditional evolutionary approach of the artificial bee colony algorithm. Different nectar sources adopt different evolutionary strategies according to the integrated feedback mechanism, and evolutionary behavioral selection probability is updated through the accumulation of experience and the exploration of new knowledge. The simulation results show that the trajectories produced by the multi-strategy evolutionary learning artificial bee colony algorithm have better fuel economy and higher safety than other comparison algorithms, and the number of optimization iterations can be reduced by at least 12%.
Passing the Turing Test? AI Generated Poetry and Posthuman Creativity

“Robots would be starving artists if they tried to write literature or poetry,” John Cramer wrote about a Turing test for poetry. In my paper I will examine forms and functions of AI-generated poetry and discuss some of the aesthetic, formal, theoretical, and philosophical implications of this cultural form. Can AI compete with human poets (and should they)? What makes the poetic genre specifically suited to experiments in artificial intelligence, and where does it reveal the weaknesses of its theoretical assumptions? What are the pleasures and politics of reading AI-generated poetry? What happens if the reader of such poetry is no longer human but a machine? In my essay I aim to look at AI-generated poetry in order to pose larger questions about human-machine interaction in the arts, about posthumanist creativity, and about the future of writing and reading in an increasingly computational environment.
Social media disclosure of intellectual capital and firm value

This paper aims to examine the relationship between firm value and the level of social media disclosure of intellectual capital by Kuwaiti and Omani listed firms. The level of social media disclosure of intellectual capital was measured by adapting a checklist of 78 items. Also, the financial reports were used to determine Tobin's Q. The results showed that the level of social media disclosure of intellectual capital was 71% by Kuwaiti and Omani firms. Moreover, the findings indicate that there is a significant positive relationship between the firm value and the level of social media disclosure of intellectual capital. The study recommends that the Kuwaiti and Omani bourses must develop a formal guideline for social media disclosure of intellectual capital to create harmony in disclosing information and to enhance the firms' value through taking the advantages of social media in attracting the more talented employees and to maximise the customers and investors benefits.
Mobile Robot Path Planning Using a QAPF Learning Algorithm for Known and Unknown Environments

This paper presents the computation of feasible paths for mobile robots in known and unknown environments using a QAPF learning algorithm.Q-learning is a reinforcement learning algorithm that has increased in popularity in mobile robot path planning in recent times, due to its self-learning capability without requiring a priori model of the environment.However, Q-learning shows slow convergence to the optimal solution, notwithstanding such an advantage.To address this limitation, the concept of partially guided Q-learning is employed wherein, the artificial potential field (APF) method is utilized to improve the classical Q-learning approach.Therefore, the proposed QAPF learning algorithm for path planning can enhance learning speed and improve final performance using the combination of Q-learning and the APF method.Criteria used to measure planning effectiveness include path length, path smoothness, and learning time.Experiments demonstrate that the QAPF algorithm successfully achieves better learning values that outperform the classical Q-learning approach in all the test environments presented in terms of the criteria mentioned above in offline and online path planning modes.The QAPF learning algorithm reached an improvement of 18.83% in path length for the online mode, an improvement of 169.75% in path smoothness for the offline mode, and an improvement of 74.84% in training time over the classical approach.
Cx22: A new publicly available dataset for deep learning-based segmentation of cervical cytology images

The segmentation of cervical cytology images plays an important role in the automatic analysis of cervical cytology screening. Although deep learning-based segmentation methods are well-developed in other image segmentation areas, their application in the segmentation of cervical cytology images is still in the early stage. The most important reason for the slow progress is the lack of publicly available and high-quality datasets, and the study on the deep learning-based segmentation methods may be hampered by the present datasets which are either artificial or plagued by the issue of false-negative objects. In this paper, we develop a new dataset of cervical cytology images named Cx22, which consists of the completely annotated labels of the cellular instances based on the open-source images released by our institute previously. Firstly, we meticulously delineate the contours of 14,946 cellular instances in1320 images that are generated by our proposed ROI-based label cropping algorithm. Then, we propose the baseline methods for the deep learning-based semantic and instance segmentation tasks based on Cx22. Finally, through the experiments, we validate the task suitability of Cx22, and the results reveal the impact of false-negative objects on the performance of the baseline methods. Based on our work, Cx22 can provide a foundation for fellow researchers to develop high-performance deep learning-based methods for the segmentation of cervical cytology images. Other detailed information and step-by-step guidance on accessing the dataset are made available to fellow researchers at https://github.com/LGQ330/Cx22.
Overview of the sixth dialog system technology challenge: DSTC6

This paper describes the experimental setups and the evaluation results of the sixth Dialog System Technology Challenges (DSTC6) aiming to develop end-to-end dialogue systems. Neural network models have become a recent focus of investigation in dialogue technologies. Previous models required training data to be manually annotated with word meanings and dialogue states, but end-to-end neural network dialogue systems learn to directly output natural-language system responses without needing training data to be manually annotated. Thus, this approach allows us to scale up the size of training data and cover more dialog domains. In addition, dialogue systems require a meta-function to avoid deploying inappropriate responses generated by themselves. To challenge such issues, the DSTC6 consists of three tracks, (1). End-to-End Goal Oriented dialogue Learning to select system responses, (2). End-to-End Conversation Modeling to generate system responses using Natural Language Generation (NLG) and (3). Dialogue Breakdown Detection. Since each domain has different issues to be addressed to develop dialogue systems, we targeted restaurant retrieval dialogues to fill slot-value in Track 1, customer services on Twitter by combining goal-oriented dialogues and ChitChat in Track 2 and human-machine dialogue data for ChitChat in Track 3. DSTC6 had 141 people declaring their interests and 23 teams submitted their final results. 18 scientific papers were presented in the wrap-up workshop. We find the blending end-to-end trainable models associated to meaningful prior knowledge performs the best for the restaurant retrieval for Track 1. Indeed, Hybrid Code Network and Memory Network have been the best models for this task. In Track 2, 78.5% of the system responses automatically generated by the best system were rated better than acceptable by humans and this achieves 89% of the number of the human responses rated in the same class. In Track3, the dialogue breakdown detection technologies performed as well as human agreements, in both data-sets of English and Japanese.
Facial expression recognition from image based on hybrid features understanding

Facial expression recognition (FER) plays an important role in the applications of human computer interaction. Given the wide use of convolutional neural networks (CNNs) in automatic video and image classification systems, higher-level features can be automatically learned from hierarchical neural networks with big data. However, learning CNNs require large amount of training data for adequate generalization, while the Scale-invariant feature transform (SIFT) does not need large training samples to generate useful feature. In this paper, we propose a new hybrid feature representation for the recognition of facial expressions from a single image frame that uses a combination of SIFT and deep-learning feature of different level extracted from the CNN model, then adopt the combined features and classify the expression by support vector machines (SVM). The performance of the proposed method has been validated on public CK+ databases. To evaluate the generalization ability of our method, we also performed an experiment on a cross-database environment. Experimental results show that the proposed approach can achieve better classification rates compared with state-of-art CNN methods, which indicate the considerable potential of combining shallow feature with deep-learning feature.
Computational image speckle suppression using block matching and machine learning

We develop an image despeckling method that combines nonlocal self-similarity filters with machine learning, which makes use of convolutional neural network (CNN) denoisers. It consists of three major steps: block matching, CNN despeckling, and group shrinkage. Through the use of block matching, we can take advantage of the similarity across image patches as a regularizer to augment the performance of data-driven denoising using a pre-trained network. The outputs from the CNN denoiser and the group coordinates from block matching are further used to form 3D groups of similar patches, which are then filtered through a wavelet-domain shrinkage. The experimental results show that the proposed method achieves noticeable improvement compared with state-of-the-art speckle suppression techniques in both visual inspection and objective assessments.
Making Big Money from Small Sensors: Trading Time-Series Data under Pufferfish Privacy

With the commoditization of personal data, pricing privacy has become an intriguing topic. In this paper, we study time-series data trading from the perspective of a data broker in data markets. We thus propose HORAE, which is a PufferfisH privacy based framewOrk for tRAding timE-series data. HORAE first employs Pufferfish privacy to quantity privacy losses under temporal correlations, and compensates data owners with distinct privacy strategies in a satisfying way. Besides, HORAE not only guarantees good profitability at the data broker, but also ensures arbitrage freeness against cunning data consumers. We further apply HORAE to physical activity monitoring, and extensively evaluate its performance on the real-world Activity Recognition with Ambient Sensing (ARAS) dataset. Our analysis and evaluation results reveal that HORAE compensates data owners in a more fine-grained manner than entry/group differential privacy based approaches, well controls the profit ratio of the data broker, and thwarts arbitrage attacks launched by data consumers.
A Neural Model for Method Name Generation from Functional Description

The names of software artifacts, e.g., method names, are important for software understanding and maintenance, as good names can help developers easily understand others’ code. However, the existing naming guidelines are difficult for developers, especially novices, to come up with meaningful, concise and compact names for the variables, methods, classes and files. With the popularity of open source, an enormous amount of project source code can be accessed, and the exhaustiveness and instability of manually naming methods could now be relieved by automatically learning a naming model from a large code repository. Nevertheless, building a comprehensive naming system is still challenging, due to the gap between natural language functional descriptions and method names. Specifically, there are three challenges: how to model the relationship between the functional descriptions and formal method names, how to handle the explosion of vocabulary when dealing with large repositories, and how to leverage the knowledge learned from large repositories to a specific project. To answer these questions, we propose a neural network to directly generate readable method names from natural language description. The proposed method is built upon the encoder-decoder framework with the attention and copying mechanisms. Our experiments show that our method can generate meaningful and accurate method names and achieve significant improvement over the state-of-the-art baseline models. We also address the cold-start problem using a training trick to utilize big data in Github for specific projects.
Domain Generalization by Solving Jigsaw Puzzles.

Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly effective, because supervised learning can never be exhaustive and thus learning autonomously allows to discover invariances and regularities that help to generalize. In this paper we propose to apply a similar approach to the task of object recognition across domains: our model learns the semantic labels in a supervised fashion, and broadens its understanding of the data by learning from self-supervised signals how to solve a jigsaw puzzle on the same images. This secondary task helps the network to learn the concepts of spatial correlation while acting as a regularizer for the classification task. Multiple experiments on the PACS, VLCS, Office-Home and digits datasets confirm our intuition and show that this simple method outperforms previous domain generalization and adaptation solutions. An ablation study further illustrates the inner workings of our approach.
Neural Text Normalization with Subword Units

Courtney Mansfield, Ming Sun, Yuzong Liu, Ankur Gandhe, Björn Hoffmeister. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
How to design innovative information services at the library?

Purpose The subject of the paper is innovative information services. The purpose of this paper is to identify the potential sources of innovation in library information services and point out how they can be used to improve the overall service quality. Design/methodology/approach In the first stage, literature analysis and critique was used to establish the state of research in the field of innovative solutions and select the main sources of innovation in services. Next, a systematic search of the subject literature and network resources was carried out according to the selected criteria to find the examples of innovative commercial services, particularly in the information sector. Findings Libraries, like all customer-oriented service organizations, must innovate and continually evolve to better meet the needs of their audience. It seems that in libraries, the basis of innovation and building a competitive advantage over other institutions may primarily be the thoughtful design of services in the spirit of the assumptions of the experience economy. Innovative ideas based on in-depth knowledge of user needs are less dependent on financial conditions and more on the creativity and enthusiasm of library staff. Research limitations/implications This paper is a conceptual work presenting the issue of innovation from the perspective adopted by the author. The paper does not aim to fulfill the subject but to show some interesting aspects of this issue and help initiate discussions on innovation in libraries from the perspective of phenomena present on the commercial information services market. This approach, which has not yet been adapted in library science, may show some new aspects and lead to new conclusions. This is a literature review type of paper that is not based on empirical research and it has not yet been tested in practice; however, the author believes that it can provide a valuable framework for designing and implementing innovative services in libraries. The presented process is a preliminary proposal that can and should be modified in the future based on further scientific reflection and examples of implementations in libraries. Practical implications The results can be widely used in practice as a framework for designing innovative services in libraries. The paper, based on subject literature, proposes a process of designing innovative information services that can and should be tested in practice. Social implications The paper can help initiate the debate on the need to implement innovative solutions in library services. Originality/value The issue of innovation in library services has not yet been widely discussed in library and information science journals from the perspective proposed by the author. This paper presents a specific approach to library innovation based on the observation of the certain phenomena on the commercial market, which opens the door to new reflections.
The Automatic Statistician

The Automatic Statistician project aims to automate data science, producing predictions and human-readable reports from raw datasets with minimal human intervention. Alongside basic graphs and statistics, the generated reports contain a curation of high-level insights about the dataset that are obtained from (1) an automated construction of models for the dataset, (2) a comparison of these models, and (3) a software component that turns these results into natural language descriptions. This chapter describes the common architecture of such Automatic Statistician systems, and discusses some of the design decisions and technical challenges.
A Methodical Overview on Phishing Detection along with an Organized Way to Construct an Anti-Phishing Framework

Phishing is a security attack to acquire personal information like passwords, credit card details or other account details of a user by means of websites or emails. Phishing websites look similar to the legitimate ones which make it difficult for a layman to differentiate between them. As per the reports of Anti Phishing Working Group (APWG) published in December 2018, phishing against banking services and payment processor was high. Almost all the phishy URLs use HTTPS and use redirects to avoid getting detected. This paper presents a focused literature survey of methods available to detect phishing websites. A comparative study of the in-use anti-phishing tools was accomplished and their limitations were acknowledged. We analyzed the URL-based features used in the past to improve their definitions as per the current scenario which is our major contribution. Also, a step wise procedure of designing an anti-phishing model is discussed to construct an efficient framework which adds to our contribution. Observations made out of this study are stated along with recommendations on existing systems.
The effects of the introduction of Bitcoin futures on the volatility of Bitcoin returns

This paper investigates the effects of the launch of Bitcoin futures on the intraday volatility of Bitcoin. Based on one-minute price data collected from five cryptocurrency exchanges, we first examine the change in realized volatility after the introduction of Bitcoin futures to investigate their aggregate effects on the intraday volatility of Bitcoin. We then analyze the effects in more detail utilizing the discrete Fourier transform. We show that although the Bitcoin market became more volatile immediately after the introduction of Bitcoin futures, over time it has become more stable than it was before the introduction.
Noise Analysis of Photonic Modulator Neurons

Neuromorphic photonics relies on efficiently emulating analog neural networks at high speeds.Prior work showed that transducing signals from the optical to the electrical domain and back with transimpedance gain was an efficient approach to implementing analog photonic neurons and scalable networks.Here, we examine modulator-based photonic neuron circuits with passive and active transimpedance gains, with special attention to the sources of noise propagation.We find that a modulator nonlinear transfer function can suppress noise, which is necessary to avoid noise propagation in hardware neural networks.In addition, while efficient modulators can reduce power for an individual neuron, signal-to-noise ratios must be traded off with power consumption at a system level.Active transimpedance amplifiers may help relax this tradeoff for conventional p-n junction silicon photonic modulators, but a passive transimpedance circuit is sufficient when very efficient modulators (i.e.low C and low V-pi) are employed.
Analysis and Noise Modeling of the Intel RealSense D435 for Mobile Robots

Cameras that provide distance measurement along with RGB data have increasingly been appearing in the market as alternatives to the more expensive setup of LIDARs and webcams. While products such as the Kinect have existed in the past, its weight and form factor have been demanding constraints for mobile robots, specifically legged robots that are sensitive to payload. Recently Intel released a new lineup of Intel RealSense RGB-D cameras that have favorable characteristics for legged robots, specifically in terms of resolution, frames per second, form factor, weight, and price range. However, because these active stereo sensors are noisy for reasons such as non-overlapping image regions or lack of texture, it is beneficial to empirically model the noise. Systematic errors, specifically the distance inhomogeneity and depth bias, are observed to recognize and verify the limitations of the camera. We also analyze the non-systematic error by modeling both the axial and lateral noise as a function of distance and angle of incidence using a Gaussian distribution for its versatile applicability for mobile robots in mapping.
Intrinsic dimension of data representations in deep neural networks

Deep neural networks progressively transform their inputs across multiple processing layers. What are the geometrical properties of the representations learned by these networks? Here we study the intrinsic dimensionality (ID) of data representations, i.e. the minimal number of parameters needed to describe a representation. We find that, in a trained network, the ID is orders of magnitude smaller than the number of units in each layer. Across layers, the ID first increases and then progressively decreases in the final layers. Remarkably, the ID of the last hidden layer predicts classification accuracy on the test set. These results can neither be found by linear dimensionality estimates (e.g., with principal component analysis), nor in representations that had been artificially linearized. They are neither found in untrained networks, nor in networks that are trained on randomized labels. This suggests that neural networks that can generalize are those that transform the data into low-dimensional, but not necessarily flat manifolds.