Reviewerly/exHarmony huggingface dataset:
Investigating the user experience of customer service chatbot interaction: a framework for qualitative analysis of chatbot dialogues

Abstract The uptake of chatbots for customer service depends on the user experience. For such chatbots, user experience in particular concerns whether the user is provided relevant answers to their queries and the chatbot interaction brings them closer to resolving their problem. Dialogue data from interactions between users and chatbots represents a potentially valuable source of insight into user experience. However, there is a need for knowledge of how to make use of these data. Motivated by this, we present a framework for qualitative analysis of chatbot dialogues in the customer service domain. The framework has been developed across several studies involving two chatbots for customer service, in collaboration with the chatbot hosts. We present the framework and illustrate its application with insights from three case examples. Through the case findings, we show how the framework may provide insight into key drivers of user experience, including response relevance and dialogue helpfulness (Case 1), insight to drive chatbot improvement in practice (Case 2), and insight of theoretical and practical relevance for understanding chatbot user types and interaction patterns (Case 3). On the basis of the findings, we discuss the strengths and limitations of the framework, its theoretical and practical implications, and directions for future work.
Exposing numerical bugs in deep learning via gradient back-propagation

Numerical computation is dominant in deep learning (DL) programs. Consequently, numerical bugs are one of the most prominent kinds of defects in DL programs. Numerical bugs can lead to exceptional values such as NaN (Not-a-Number) and INF (Infinite), which can be propagated and eventually cause crashes or invalid outputs. They occur when special inputs cause invalid parameter values at internal mathematical operations such as log(). In this paper, we propose the first dynamic technique, called GRIST, which automatically generates a small input that can expose numerical bugs in DL programs. GRIST piggy-backs on the built-in gradient computation functionalities of DL infrastructures. Our evaluation on 63 real-world DL programs shows that GRIST detects 78 bugs including 56 unknown bugs. By submitting them to the corresponding issue repositories, eight bugs have been confirmed and three bugs have been fixed. Moreover, GRIST can save 8.79X execution time to expose numerical bugs compared to running original programs with its provided inputs. Compared to the state-of-the-art technique DEBAR (which is a static technique), DEBAR produces 12 false positives and misses 31 true bugs (of which 30 bugs can be found by GRIST), while GRIST only misses one known bug in those programs and no false positive. The results demonstrate the effectiveness of GRIST.
MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching

Recent methods in stereo matching have continuously improved the accuracy using deep models. This gain, however, is attained with a high increase in computation cost, such that the network may not fit even on a moderate GPU. This issue raises problems when the model needs to be deployed on resource-limited devices. For this, we propose two light models for stereo vision with reduced complexity and without sacrificing accuracy. Depending on the dimension of cost volume, we design a 2D and a 3D model with encoder-decoders built from 2D and 3D convolutions, respectively. To this end, we leverage 2D MobileNet blocks and extend them to 3D for stereo vision application. Besides, a new cost volume is proposed to boost the accuracy of the 2D model, making it performing close to 3D networks. Experiments show that the proposed 2D/3D networks effectively reduce the computational expense (27%/95% and 72%/38% fewer parameters/operations in 2D and 3D models, respectively) while upholding the accuracy. Code: https://github.com/cogsys-tuebingen/mobilestereonet.
Learning Attribute-driven Disentangled Representations for Interactive Fashion Retrieval

Interactive retrieval for online fashion shopping provides the ability to change image retrieval results according to the user feedback. One common problem in interactive retrieval is that a specific user interaction (e.g., changing the color of a T-shirt) causes other aspects to change inadvertently (e.g., the retrieved item has a sleeve type different than the query). This is a consequence of existing methods learning visual representations that are semantically entangled in the embedding space, which limits the controllability of the retrieved results. We propose to leverage on the semantics of visual attributes to train convolutional networks that learn attribute-specific subspaces for each attribute to obtain disentangled representations. Thus operations, such as swapping out a particular attribute value for another, impact the attribute at hand and leave others untouched. We show that our model can be tailored to deal with different retrieval tasks while maintaining its disentanglement property. We obtain state-of-the-art performance on three interactive fashion retrieval tasks: attribute manipulation retrieval, conditional similarity retrieval, and outfit complementary item retrieval. Code and models are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks

Deep learning and convolutional neural networks allow achieving impressive performance in computer vision tasks, such as object detection and semantic segmentation (SS). However, recent studies have shown evident weaknesses of such models against adversarial perturbations. In a real-world scenario instead, like autonomous driving, more attention should be devoted to real-world adversarial examples (RWAEs), which are physical objects (e.g., billboards and printable patches) optimized to be adversarial to the entire perception pipeline. This paper presents an in-depth evaluation of the robustness of popular SS models by testing the effects of both digital and real-world adversarial patches. These patches are crafted with powerful attacks enriched with a novel loss function. Firstly, an investigation on the Cityscapes dataset is conducted by extending the Expectation Over Transformation (EOT) paradigm to cope with SS. Then, a novel attack optimization, called scene-specific attack, is proposed. Such an attack leverages the CARLA driving simulator to improve the transferability of the proposed EOT-based attack to a real 3D environment. Finally, a printed physical billboard containing an adversarial patch was tested in an outdoor driving scenario to assess the feasibility of the studied attacks in the real world. Exhaustive experiments revealed that the proposed attack formulations outperform previous work to craft both digital and real-world adversarial patches for SS. At the same time, the experimental results showed how these attacks are notably less effective in the real world, hence questioning the practical relevance of adversarial attacks to SS models for autonomous/assisted driving.
Handwritten character recognition using convolutional neural network

Abstract Handwritten character recognition (HCR) is the detection of characters from images, documents and other sources and changes them in machine-readable shape for further processing. The accurate recognition of intricate-shaped compound handwritten characters is still a great challenge. Recent advances in convolutional neural network (CNN) have made great progress in HCR by learning discriminatory characteristics from large amounts of raw data. In this paper, CNN is implemented to recognize the characters from a test dataset. The main focus of this work is to investigate CNN capability to recognize the characters from the image dataset and the accuracy of recognition with training and testing. CNN recognizes the characters by considering the forms and contrasting the features that differentiate among characters. Our CNN implementation is experimented with the dataset NIST to obtain the accuracy of handwritten characters. Test result provides that an accuracy of 92.91% accuracy is obtained on 200 images with a training set of 1000 images from NIST.
Multi-reservoir echo state networks with sequence resampling for nonlinear time-series prediction

In this paper, we consider various schemes of sequence resampling in reservoir computing models for nonlinear time series prediction. These schemes can enrich the features used for training the readout part with batch learning and lead to better prediction performance. To implement these schemes, first, we introduce a modular approach for constructing multi-reservoir ESN models by assembling encoding and decoding modules. The encoding module is composed of a resampling unit, a group-wise reservoir unit, and a collection unit, for extracting various features from a sequence. The decoding module is a linear regressor which is trainable to produce desired outputs. Then, we propose three novel multi-reservoir ESN models, DeepESN with Every-layer Sequence Resampling (DeepESN-ESR), DeepESN with Last-layer Sequence Resampling (DeepESN-LSR), and GroupedESN with Input-layer Sequence Resampling (GroupedESN-ISR). These three models provide demonstrations of sequence resampling on multi-reservoir ESN models. Numerical results on five challenging nonlinear time-series prediction tasks show that the proposed models outperform some state-of-the-art multi-reservoir ESN models. An evaluation of computational time shows that our proposed three models require less computational cost for learning than many existing multi-reservoir ESN models in practice. Moreover, a comprehensive comparative analysis reveals that our proposed models are able to memorize longer temporal information and generate richer dynamics from the reservoir states than some existing models. The proposed schemes for extracting various features hidden in a sequence of reservoir states can be widely leveraged in other reservoir computing systems for improving their performance in nonlinear time series prediction.
Transformer for Handwritten Text Recognition Using Bidirectional Post-decoding

Most recently, Transformers – which are recurrent-free neural network architectures – achieved tremendous performances on various Natural Language Processing (NLP) tasks. Since Transformers represent a traditional Sequence-To-Sequence (S2S)-approach they can be used for several different tasks such as Handwritten Text Recognition (HTR). In this paper, we propose a bidirectional Transformer architecture for line-based HTR that is composed of a Convolutional Neural Network (CNN) for feature extraction and a Transformer-based encoder/decoder, whereby the decoding is performed in reading-order direction and reversed. A voter combines the two predicted sequences to obtain a single result. Our network performed worse compared to a traditional Connectionist Temporal Classification (CTC) approach on the IAM-dataset but reduced the state-of-the-art of Transformers-based approaches by about 25% without using additional data. On a significantly larger dataset, the proposed Transformer significantly outperformed our reference model by about 26%. In an error analysis, we show that the Transformer is able to learn a strong language model which explains why a larger training dataset is required to outperform traditional approaches and discuss why Transformers should be used with caution for HTR due to several shortcomings such as repetitions in the text.
Deep image prior based defense against adversarial examples

Recently, deep neural networks (DNNs) have shown serious vulnerability to adversarial examples with imperceptible perturbation to clean images. To counter this issue, many powerful defensive methods (e.g., ComDefend) focus on rectifying the adversarial examples with well-trained models from a large training dataset (e.g., clean-adversarial image pairs). However, such methods rely heavily on the learned external priors from an external large training dataset, while neglecting the rich image internal priors of the input itself, thus limiting the generalization of the defense models against the adversarial examples with biased image statistics from the external training dataset. Motivated by deep image prior that can capture rich image statistics from a single image, we propose an effective Deep Image Prior Driven Defense (DIPDefend) method against adversarial examples. With a DIP generator to fit the target/adversarial input, we find that our image reconstruction exhibits quite interesting learning preference from a feature learning perspectives, i.e., the early stage primarily learns the robust features resistant to adversarial perturbation, followed by learning non-robust features that are sensitive to adversarial perturbation. Besides, we develop an adaptive stopping strategy that adapts our method to diverse images. In this way, the proposed model obtains a unique defender for each individual adversarial input, thus being robust to various attackers. Experimental results demonstrate the superiority of our method over the state-of-the-art defense methods against white-box and black-box adversarial attacks.
Designing 1D Chaotic Maps for Fast Chaotic Image Encryption

Chaotic maps that can provide highly secure key sequences and ease of structure implementation are predominant requirements in image encryption systems. One Dimensional (1D) chaotic maps have the advantage of a simple structure and can be easily implemented by software and hardware. However, key sequences produced by 1D chaotic maps are not adequately secure. Therefore, to improve the 1D chaotic maps sequence security, we propose two chaotic maps: 1D Improved Logistic Map (1D-ILM) and 1D Improved Quadratic Map (1D-IQM). The proposed maps have shown higher efficiency than existing maps in terms of Lyapunov exponent, complexity, wider chaotic range, and higher sensitivity. Additionally, we present an efficient and fast encryption method based on 1D-ILM and 1D-IQM to enhance image encryption system performance. This paper also introduces a key expansion method to reduce the number of chaotic map iteration needs, thereby decreasing encryption time. The security analyses and experimental results are confirmed that 2D Correlation Coefficient (CC) Information Entropy (IE), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Mean Absolute Error (MAE), and decryption quality are able to meet the encryption security demands (CC = −0.00139, IE = 7.9990, NPCR = 99.6114%, UACI = 33.46952% and MAE = 85.3473). Furthermore, the proposed keyspace reaches 10240, and the encryption time is 0.025s for an image with a size of 256 × 256. The proposed system can yield efficacious security results compared to obtained results from other encryption systems.
How effective is China's cryptocurrency trading ban?

Cryptocurrency trading is subject to strict government-imposed restrictions in China since September 2017, when trading platforms were shut down. Using a VAR-GARCH-BEKK model, we investigate the effectiveness of this trading ban by examining its consequences on the relationship between Chinese investors’ attention and Bitcoin. Our results demonstrate that Chinese investors played a dominant role in the Bitcoin price formation before the ban, and that their influence has not significantly decreased after the shutdown of Chinese trading platforms. Aiming to explain our findings, we provide strong evidence that the crackdown on Bitcoin trading has not been effective as Chinese investors continue to purchase Bitcoin using the stablecoin Tether instead of Chinese yuan.
Multi-label sampling based on local label imbalance

Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation. To address this issue, we propose a novel measure to assess the local label imbalance of multi-label datasets, as well as two multi-label sampling approaches, namely Multi-Label Synthetic Oversampling based on Local label imbalance (MLSOL) and Multi-Label Undersampling based on Local label imbalance (MLUL). By considering all informative labels, MLSOL creates more diverse and better labeled synthetic instances for difficult examples, while MLUL eliminates instances that are harmful to their local region. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.
Universal Quantum Computing with Twist-Free and Temporally Encoded Lattice Surgery

Lattice surgery protocols allow for the efficient implementation of universal gate sets with two-dimensional topological codes where qubits are constrained to interact with one another locally. In this work, we first introduce a decoder capable of correcting spacelike and timelike errors during lattice surgery protocols. Afterwards, we compute logical failure rates of a lattice surgery protocol for a biased circuit-level noise model. We then provide a new protocol for performing twist-free lattice surgery, where we avoid twist defects in the bulk of the lattice. Our twist-free protocol eliminates the extra circuit components and gate scheduling complexities associated with the measurement of higher weight stabilizers when using twist defects. We also provide a protocol for temporally encoded lattice surgery that can be used to reduce both runtimes and the total space-time costs of quantum algorithms. Lastly, we propose a layout for a quantum processor that is more efficient for rectangular surface codes exploiting noise bias, and which is compatible with the other techniques mentioned above.
Total Reflection-Induced Efficiency Enhancement of the Spin Hall Effect of Light

The spin Hall effect of light (SHEL) refers to the spin-dependent and transverse splitting of oblique incidence that occurs in both refraction and reflection. Enhancement of the SHEL is generally accompanied by a degradation in the efficiency. Recently, an anisotropic metamaterial has been proposed to attain a large SHEL with near-unity efficiency, but is limited to a horizontally polarized incidence. Here, a new approach to achieve a large SHEL and high efficiency simultaneously for arbitrarily polarized incidence is proposed by exploiting total external and internal reflection. The total reflection at the interface of a dense-to-sparse medium yields a theoretical maximum of the shift that is allowed for a unity efficiency. The SHEL can be further enhanced by increasing the refractive index contrast. Furthermore, we suggest a three-dimensional isotropic metamaterial that is designed to have an index below unity as a platform to experimentally demonstrate the SHEL with high efficiency. Our work will find wide applications in spin-dependent photonic devices.
A Framework for Personalizing Atypical Web Search Sessions with Concept-Based User Profiles Using Selective Machine Learning Techniques

Human beings have been using all kinds of devices to perform diverse things since their evolution. The human brain's imagination contributed to the creation of numerous devices. This devises human existence simply by empowering individuals to fulfill diverse needs for existence, including transport, industries, houses, and computers. Current custom online search mechanisms do not take into consideration specific sites that are unvisited by the customer and could be direct responses to the information needs of the consumer. Furthermore, pages in the outcome package, while not explicitly applicable to the need for user knowledge, may include a connection to relevant pages. Only through doing semantic analysis will certain connections be established. This paper aims to classify certain related sites by semantic review of the quest route and offers an efficient customized site search. This facilitates online search by offering content and the relationship between the search question and the related web pages dependent on individuals.
Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences

Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers. Importantly, data from human teachers can be collected either passively or actively in a variety of forms: passive data sources include demonstrations (e.g., kinesthetic guidance), whereas preferences (e.g., comparative rankings) are actively elicited. Prior research has independently applied reward learning to these different data sources. However, there exist many domains where multiple sources are complementary and expressive. Motivated by this general problem, we present a framework to integrate multiple sources of information, which are either passively or actively collected from human users. In particular, we present an algorithm that first utilizes user demonstrations to initialize a belief about the reward function, and then actively probes the user with preference queries to zero-in on their true reward. This algorithm not only enables us combine multiple data sources, but it also informs the robot when it should leverage each type of information. Further, our approach accounts for the human’s ability to provide data: yielding user-friendly preference queries which are also theoretically optimal. Our extensive simulated experiments and user studies on a Fetch mobile manipulator demonstrate the superiority and the usability of our integrated framework.
Dual-Camera Super-Resolution with Aligned Attention Modules

We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and high-fidelity results. Our proposed method generalizes the standard patch-based feature matching with spatial alignment operations. We further explore the dual-camera super-resolution that is one promising application of RefSR, and build a dataset that consists of 146 image pairs from the main and telephoto cameras in a smartphone. To bridge the domain gaps between real-world images and the training images, we propose a self-supervised domain adaptation strategy for real-world images. Extensive experiments on our dataset and a public benchmark demonstrate clear improvement achieved by our method over state of the art in both quantitative evaluation and visual comparisons. Our code and data are available at https://tengfei-wang.github.io/Dual-Camera-SR/index.html.