Reviewerly/exHarmony huggingface dataset:
Quantum algorithm for visual tracking

Visual tracking (VT) is the process of locating a moving object of interest in a video. It is a fundamental problem in computer vision, with various applications in human-computer interaction, security and surveillance, robot perception, traffic control, etc. In this paper, we address this problem in the quantum setting, and present a quantum algorithm for VT based on the framework proposed by Henriques et al. [IEEE Trans. Pattern Anal. Mach. Intell. 7, 583 (2015)]. Our algorithm comprises two phases: training and detection. In the training phase, in order to discriminate the object and background, the algorithm trains a ridge regression classifier in the quantum state form where the optimal fitting parameters of ridge regression are encoded in the amplitudes. In the detection phase, the classifier is then employed to generate a quantum state whose amplitudes encode the responses of all the candidate image patches. The algorithm is shown to be polylogarithmic in scaling, when the image data matrices have low condition numbers, and therefore may achieve exponential speedup over the best classical counterpart. However, only quadratic speedup can be achieved when the algorithm is applied to implement the ultimate task of Henriques's framework, i.e., detecting the object position. We also discuss two other important applications related to VT: (1) object disappearance detection and (2) motion behavior matching, where much more significant speedup over the classical methods can be achieved. This work demonstrates the power of quantum computing in solving computer vision problems.
Open-quantum-system dynamics: Recovering positivity of the Redfield equation via the partial secular approximation

We show how to recover complete positivity (and hence positivity) of the Redfield equation via a coarse-grained averaging technique. We derive general bounds for the coarse graining timescale above which the positivity of the Redfield equation is guaranteed. It turns out that a coarse grain timescale has strong impact on the characteristics of the Lamb shift term and implies, in general, noncommutation between the dissipating and the Hamiltonian components of the generator of the dynamical semigroup. Finally, we specify the analysis to a two-level system or a quantum harmonic oscillator coupled to a fermionic or bosonic thermal environment via dipolelike interaction.
Distance Measures between the Interval-Valued Complex Fuzzy Sets

Complex fuzzy set (CFS) is a recent development in the field of fuzzy set (FS) theory. The significance of CFS lies in the fact that CFS assigned membership grades from a unit circle in the complex plane, i.e., in the form of a complex number whose amplitude term belongs to a [ 0 , 1 ] interval. The interval-valued complex fuzzy set (IVCFS) is one of the extensions of the CFS in which the amplitude term is extended from the real numbers to the interval-valued numbers. The novelty of IVCFS lies in its larger range comparative to CFS. We often use fuzzy distance measures to solve some problems in our daily life. Hence, this paper develops some series of distance measures between IVCFSs by using Hamming and Euclidean metrics. The boundaries of these distance measures for IVCFSs are obtained. Finally, we study two geometric properties include rotational invariance and reflectional invariance of these distance measures.
Do you understand the words that are comin outta my mouth? Voice assistant comprehension of medication names

Abstract This study investigated the speech recognition abilities of popular voice assistants when being verbally asked about commonly dispensed medications by a variety of participants. Voice recordings of 46 participants (12 of which had a foreign accent in English) were played back to Amazon’s Alexa, Google Assistant, and Apple’s Siri for the brand- and generic names of the top 50 most dispensed medications in the United States. A repeated measures ANOVA indicated that Google Assistant achieved the highest comprehension accuracy for both brand medication names ( M = 91.8%, SD = 4.2) and generic medication names ( M = 84.3%, SD = 11.2), followed by Siri (brand names M = 58.5%, SD = 11.2; generic names M = 51.2%, SD = 16.0), and the lowest accuracy by Alexa (brand names M = 54.6%, SD = 10.8; generic names M = 45.5%, SD = 15.4). An interaction between voice assistant and participant accent was also found, demonstrating lower comprehension performance overall for those with a foreign accent using Siri ( M = 48.8%, SD = 11.8) and Alexa ( M = 41.7%, SD = 12.7), compared to participants without a foreign accent (Siri M = 57.0%, SD = 11.7; Alexa M = 53.0%, SD = 10.9). No significant difference between participant accents were found for Google Assistant. These findings show a substantial performance lead for Google Assistant compared to its voice assistant competitors when comprehending medication names, but there is still room for improvement.
Exploring the implications of blockchain technology for brand–consumer relationships: a future research agenda

Purpose The purpose of this conceptual paper is to delve into the implications of blockchain technology adoption for brands and consumers. Drawing on the existing branding literature and real-life applications of blockchain, the challenges, risks and opportunities from blockchain adoption for four important areas of the branding literature are canvassed (i.e. brand positioning and corporate brand image, consumer–brand relationships, online brand communication and consumers’ trust in the brand). Also, a future-oriented discussion is provided that highlights some important avenues for researchers in the field. Design/methodology/approach This conceptual paper sheds light on the potential implications of blockchain technology for brand–consumer relationships. To do so, an analytical review of the blockchain literature is conducted, the nature of blockchain technology is presented and its unique features and functions for brand–consumer interactions are discussed. Findings This paper ignites an exploratory discussion around how blockchain applications and platforms can affect consumer–brand relationships, drawing on a number of real-life examples of blockchain adoption. This discussion sheds light on how blockchain features can impact on various areas of interest for strategic brand management, such as the adoption of digital currencies, brand storytelling, use of blockchain-enabled loyalty programmes, role of intermediaries in online advertising, counterfeit consumption, brand transparency and trust for brands in online marketplaces, amongst others. Originality/value This is one of the first conceptual efforts in the branding literature that draws on the scarce existing knowledge around blockchain adoption and discusses the potential implications of blockchain technology for brands and consumers whilst also providing directions for future research.
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity

We provide a theoretical explanation for the effectiveness of gradient clipping in training deep neural networks. The key ingredient is a new smoothness condition derived from practical neural network training examples. We observe that gradient smoothness, a concept central to the analysis of first-order optimization algorithms that is often assumed to be a constant, demonstrates significant variability along the training trajectory of deep neural networks. Further, this smoothness positively correlates with the gradient norm, and contrary to standard assumptions in the literature, it can grow with the norm of the gradient. These empirical observations limit the applicability of existing theoretical analyses of algorithms that rely on a fixed bound on smoothness. These observations motivate us to introduce a novel relaxation of gradient smoothness that is weaker than the commonly used Lipschitz smoothness assumption. Under the new condition, we prove that two popular methods, namely, gradient clipping and normalized gradient, converge arbitrarily faster than gradient descent with fixed stepsize. We further explain why such adaptively scaled gradient methods can accelerate empirical convergence and verify our results empirically in popular neural network training settings.
Unsupervised Video Summarization via Attention-Driven Adversarial Learning

This paper presents a new video summarization approach that integrates an attention mechanism to identify the significant parts of the video, and is trained unsupervisingly via generative adversarial learning. Starting from the SUM-GAN model, we first develop an improved version of it (called SUM-GAN-sl) that has a significantly reduced number of learned parameters, performs incremental training of the model’s components, and applies a stepwise label-based strategy for updating the adversarial part. Subsequently, we introduce an attention mechanism to SUM-GAN-sl in two ways: (i) by integrating an attention layer within the variational auto-encoder (VAE) of the architecture (SUM-GAN-VAAE), and (ii) by replacing the VAE with a deterministic attention auto-encoder (SUM-GAN-AAE). Experimental evaluation on two datasets (SumMe and TVSum) documents the contribution of the attention auto-encoder to faster and more stable training of the model, resulting in a significant performance improvement with respect to the original model and demonstrating the competitiveness of the proposed SUM-GAN-AAE against the state of the art (Software publicly available at: https://github.com/e-apostolidis/SUM-GAN-AAE ).
Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation

End-to-end speech translation, a hot topic in recent years, aims to translate a segment of audio into a specific language with an end-to-end model. Conventional approaches employ multi-task learning and pre-training methods for this task, but they suffer from the huge gap between pre-training and fine-tuning. To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN) which bridges the gap by reusing all subnets in fine-tuning, keeping the roles of subnets consistent, and pre-training the attention module. Furthermore, we propose two simple but effective methods to guarantee the speech encoder outputs and the MT encoder inputs are consistent in terms of semantic representation and sequence length. Experimental results show that our model leads to significant improvements in En-De and En-Fr translation irrespective of the backbones.
Cooperative Aerial-Ground Multi-Robot System for Automated Construction Tasks

In this paper, we study a cooperative aerial-ground robotic team and its application to the task of automated construction. We propose a solution for planning and coordinating the mission of constructing a wall with a predefined structure for a heterogeneous system consisting of one mobile robot and up to three unmanned aerial vehicles. The wall consists of bricks of various weights and sizes, some of which need to be transported using multiple robots simultaneously. To that end, we use hierarchical task representation to specify interrelationships between mission subtasks and employ effective scheduling and coordination mechanism, inspired by Generalized Partial Global Planning. We evaluate the performance of the method under different optimization criteria and validate the solution in the realistic Gazebo simulation environment.
Deep Learning Based Channel Estimation Schemes for IEEE 802.11p Standard

IEEE 802.11p standard is specially developed to define vehicular communications requirements and support cooperative intelligent transport systems. In such environment, reliable channel estimation is considered as a major critical challenge for ensuring the system performance due to the extremely time-varying characteristic of vehicular channels. The channel estimation of IEEE 802.11p is preamble based, which becomes inaccurate in high mobility scenarios. The major challenge is to track the channel variations over the course of packet length while adhering to the standard specifications. The motivation behind this paper is to overcome this issue by proposing a novel deep learning based channel estimation scheme for IEEE 802.11p that optimizes the use of deep neural networks (DNN) to accurately learn the statistics of the spectral temporal averaging (STA) channel estimates and to track their changes over time. Simulation results demonstrate that the proposed channel estimation scheme STA-DNN significantly outperforms classical channel estimators in terms of bit error rate. The proposed STA-DNN architectures also achieve better estimation performance than the recently proposed auto-encoder DNN based channel estimation with at least 55.74% of computational complexity decrease.
TransCapsule Model for Sentiment Classification

Aspect-level sentiment classification is the aspect of determining the text in a given document and classifying it according to the sentiment polarity with respect to the objective. However, since annotation cost is very high, it might serve a big obstacle for this purpose. However, from a consumer point of view, this is highly effective in reading document-level labelled data such as reviews which are present online using neural network. The online reviews are packed with sentiment encoded text which can be analyzed using this proposed methodology. In this paper a Transfer Capsule Network model is used which has the ability to transfer the knowledge gained at document-level to the aspect-level to classify according to the sentiment detected in the text. As the first step, the sentence is broken down in semantic representations using aspect routing to form semantic capsule data of both document-level and aspect-level. This routing approach is extended to group the semantic capsules for transfer learning framework. The effectiveness of the proposed methodology are experimented and demonstrated to determine how superior they are to the other methodologies proposed.
Data collection and quality challenges for deep learning

Software 2.0 refers to the fundamental shift in software engineering where using machine learning becomes the new norm in software with the availability of big data and computing infrastructure. As a result, many software engineering practices need to be rethought from scratch where data becomes a first-class citizen, on par with code. It is well known that 80--90% of the time for machine learning development is spent on data preparation. Also, even the best machine learning algorithms cannot perform well without good data or at least handling biased and dirty data during model training. In this tutorial, we focus on data collection and quality challenges that frequently occur in deep learning applications. Compared to traditional machine learning, there is less need for feature engineering, but more need for significant amounts of data. We thus go through state-of-the-art data collection techniques for machine learning. Then, we cover data validation and cleaning techniques for improving data quality. Even if the data is still problematic, hope is not lost, and we cover fair and robust training techniques for handling data bias and errors. We believe that the data management community is well poised to lead the research in these directions. The presenters have extensive experience in developing machine learning platforms and publishing papers in top-tier database, data mining, and machine learning venues.
Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in natural language processing (NLP), information retrieval (IR), and beyond. In this survey, we provide a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. We cover a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage architectures and dense retrieval techniques that perform ranking directly. There are two themes that pervade our survey: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the field is heading.
The essential deployment metamodel: a systematic review of deployment automation technologies

In recent years, a plethora of deployment technologies evolved, many following a declarative approach to automate the delivery of software components. Even if such technologies share the same purpose, they differ in features and supported mechanisms. Thus, it is difficult to compare and select deployment automation technologies as well as to migrate from one technology to another. Hence, we present a systematic review of declarative deployment technologies and introduce the essential deployment metamodel (EDMM) by extracting the essential parts that are supported by all these technologies. Thereby, the EDMM enables a common understanding of declarative deployment models by facilitating the comparison, selection, and migration of technologies. Moreover, it provides a technology-independent baseline for further deployment automation research.
A review on speaker recognition: Technology and challenges

Voice is a behavioral biometric that conveys information related to a person's traits, such as the speaker's ethnicity, age, gender, and feeling. Speaker recognition deals with recognizing the identity of people based on their voice. Although researchers have been working on speaker recognition in the last eight decades, advancements in technology, such as the Internet of Things (IoT), smart devices, voice assistants, smart homes, and humanoids, have made its usage nowadays trendy. This paper provides a comprehensive review of the literature on speaker recognition. It discusses the advances made in the last decade, including the challenges in this area of research. This paper also highlights the system and structure of speaker recognition as well as its feature extraction and classifiers. The use of speaker recognition in applications is also presented. As recent studies showed the possibility of fooling machine learning into giving an incorrect prediction; thus, the adversarial attack is also discussed. The aim is to enhance researchers’ understanding in the area of speaker recognition.
Quantum relational Hoare logic

We present a logic for reasoning about pairs of interactive quantum programs – quantum relational Hoare logic (qRHL). This logic follows the spirit of probabilistic relational Hoare logic (Barthe et al. 2009) and allows us to formulate how the outputs of two quantum programs relate given the relationship of their inputs. Probabilistic RHL was used extensively for computer-verified security proofs of classical cryptographic protocols. Since pRHL is not suitable for analyzing quantum cryptography, we present qRHL as a replacement, suitable for the security analysis of post-quantum cryptography and quantum protocols. The design of qRHL poses some challenges unique to the quantum setting, e.g., the definition of equality on quantum registers. Finally, we implemented a tool for verifying proofs in qRHL and developed several example security proofs in it.
OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving

Surround View fisheye cameras are commonly deployed in automated driving for 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> near-field sensing around the vehicle. This work presents a multi-task visual perception network on unrectified fisheye images to enable the vehicle to sense its surrounding environment. It consists of six primary tasks necessary for an autonomous driving system: depth estimation, visual odometry, semantic segmentation, motion segmentation, object detection, and lens soiling detection. We demonstrate that the jointly trained model performs better than the respective single task versions. Our multi-task model has a shared encoder providing a significant computational advantage and has synergized decoders where tasks support each other. We propose a novel camera geometry based adaptation mechanism to encode the fisheye distortion model both at training and inference. This was crucial to enable training on the WoodScape dataset, comprised of data from different parts of the world collected by 12 different cameras mounted on three different cars with different intrinsics and viewpoints. Given that bounding boxes is not a good representation for distorted fisheye images, we also extend object detection to use a polygon with non-uniformly sampled vertices. We additionally evaluate our model on standard automotive datasets, namely KITTI and Cityscapes. We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks. We perform extensive ablation studies on various architecture choices and task weighting methodologies. A short video at https://youtu.be/xbSjZ5OfPes provides qualitative results.