Reviewerly/exHarmony huggingface dataset:
Coherent-State-Based Twin-Field Quantum Key Distribution

Abstract Large-scale quantum communication networks are still a huge challenge due to the rate-distance limit of quantum key distribution (QKD). Recently, twin-field (TF) QKD has been proposed to overcome this limit. Here, we prove that coherent-state-based TF-QKD is a time-reversed entanglement protocol, where the entanglement generation is realized with entanglement swapping operation via an entangled coherent state measurement. We propose a coherent-state-based TF-QKD with optimal secret key rate under symmetric and asymmetric channels by using coherent state and cat state coding. Furthermore, we show that our protocol can be converted to all recent coherent-state-based TF-QKD protocols by using our security proof. By using the entanglement purification with two-way classical communication, we improve the transmission distance of all coherent-state-based TF-QKD protocols.
Augmented Reality in Manufacturing and Logistics: Lessons Learnt from a Real-Life Industrial Application

One of the most prominent technologies in the Industry 4.0's arsenal, when it comes to manufacturing and logistics operations, is Augmented Reality (AR). AR takes the capabilities of computer-generated display, sound, text and effects to enhance the user's real-world experience and support workers in their every-day tasks such as assembly, order picking and maintenance. This paper, after a short review of AR in manufacturing and logistics, showcases the introduction of a real-life AR-Smart Glasses application in support of one of the most repetitive, prone to error, and expensive warehouse logistics processes, i.e. order picking. The implementation of the AR system takes place in actual production settings and is integrated in the picking process of a distribution centre operated by a large Telecommunications Service Provider. This paper describes the functionalities of the developed AR system and highlights functional details and lessons learnt from its pilot implementation.
Adversarial Network With Multiple Classifiers for Open Set Domain Adaptation

Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples to a domain with scarce labeled samples. Prior research has introduced various open set domain adaptation settings in the literature to extend the applications of domain adaptation methods in real-world scenarios. This paper focuses on the type of open set domain adaptation setting where the target domain has both private ('unknown classes') label space and the shared ('known classes') label space. However, the source domain only has the 'known classes' label space. Prevalent distribution-matching domain adaptation methods are inadequate in such a setting that demands adaptation from a smaller source domain to a larger and diverse target domain with more classes. For addressing this specific open set domain adaptation setting, prior research introduces a domain adversarial model that uses a fixed threshold for distinguishing known from unknown target samples and lacks at handling negative transfers. We extend their adversarial model and propose a novel adversarial domain adaptation model with multiple auxiliary classifiers. The proposed multi-classifier structure introduces a weighting module that evaluates distinctive domain characteristics for assigning the target samples with weights which are more representative to whether they are likely to belong to the known and unknown classes to encourage positive transfers during adversarial training and simultaneously reduces the domain gap between the shared classes of the source and target domains. A thorough experimental investigation shows that our proposed method outperforms existing domain adaptation methods on a number of domain adaptation datasets.
SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN training requires good training data: many pairs of speckle-free / speckle-corrupted images. This is an issue in SAR applications, given the inherent scarcity of speckle-free images. To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data. The first strategy applies a CNN model, trained to remove additive white Gaussian noise from natural images, to a recently proposed SAR speckle removal framework: MuLoG (MUlti-channel LOgarithm with Gaussian denoising). No training on SAR images is performed, the network is readily applied to speckle reduction tasks. The second strategy considers a novel approach to construct a reliable dataset of speckle-free SAR images necessary to train a CNN model. Finally, a hybrid approach is also analyzed: the CNN used to remove additive white Gaussian noise is trained on speckle-free SAR images. The proposed methods are compared to other state-of-the-art speckle removal filters, to evaluate the quality of denoising and to discuss the pros and cons of the different strategies. Along with the paper, we make available the weights of the trained network to allow its usage by other researchers.
Leveraging creativity in requirements elicitation within agile software development: A systematic literature review

Agile approaches tend to focus solely on scoping and simplicity rather than on problem solving and discovery. This hampers the development of innovative solutions. Additionally, little has been said about how to capture and represent the real user needs. To fill this gap, some authors argue in favor of the application of “Creative thinking” for requirements elicitation within agile software development. This synergy between creativeness and agility has arisen as a new means of bringing innovation and flexibility to increasingly demanding software. The aim of the present study is therefore to employ a systematic review to investigate the state-of-the-art of those approaches that leverage creativity in requirements elicitation within Agile Software Development, as well as the benefits, limitations and strength of evidence of these approaches. The review was carried out by following the guidelines proposed by Dr. Kitchenham. The search strategy identified 1451 studies, 17 of which were eventually classified as primary studies. The selected studies contained 13 different and unique proposals. These approaches provide evidence that enhanced creativity in requirements elicitation can be successfully implemented in real software projects. We specifically observed that projects related to user interface development, such as those for mobile or web applications, are good candidates for the use of these approaches. We have also found that agile methodologies such as Scrum, Extreme Programming or methodologies based on rapid modelling are preferred when introducing creativity into requirements elicitation. Despite this being a new research field, there is a mixture of techniques, tools and processes that have already been and are currently being successfully tested in industry. Finally, we have found that, although creativity is an important ingredient with which to bring about innovation, it is not always sufficient to generate new requirements because this needs to be followed by user engagement and a specific context in which proper conditions, such as flexibility, time or resources, have to be met.
Mining Inter-Video Proposal Relations for Video Object Detection

Recent studies have shown that, context aggregating information from proposals in different frames can clearly enhance the performance of video object detection. However, these approaches mainly exploit the intra-proposal relation within single video, while ignoring the intra-proposal relation among different videos, which can provide important discriminative cues for recognizing confusing objects. To address the limitation, we propose a novel Inter-Video Proposal Relation module. Based on a concise multi-level triplet selection scheme, this module can learn effective object representations via modeling relations of hard proposals among different videos. Moreover, we design a Hierarchical Video Relation Network (HVR-Net), by integrating intra-video and inter-video proposal relations in a hierarchical fashion. This design can progressively exploit both intra and inter contexts to boost video object detection. We examine our method on the large-scale video object detection benchmark, i.e., ImageNet VID, where HVR-Net achieves the SOTA results. Codes and models are available at https://github.com/youthHan/HVRNet .
Securing IoT Devices Using Zero Trust and Blockchain

The benefits ushered through the use of IoT come with its share of security concerns. IoT networks are heterogeneous and existing network security tools and controls are inadequate for them. The IoT devices and the data transmitted to and from these devices remain exposed to a multitude of threats. In this paper, we review the security concerns involved in the implementation of IoT and propose a framework for security of IoT devices based on Zero Trust and blockchain. A risk-based segmentation of IoT network increases the homogeneity of IoT device attributes and communication protocols. Zero Trust extends the perimeter of trust beyond the IT/OT network. Blockchain improves the device identification and access control capabilities of the IoT network. We support the conceptual framework with a case study on the implementation of IoT security using Zero Trust and blockchain. We provide a risk-scoring method and a five-point recommendation for management of IoT security. Our proposed IoT security framework will help implementors of IoT to overcome existing security concerns and will benefit academic researchers and practitioners alike.
Correlations between deep neural network model coverage criteria and model quality

Inspired by the great success of using code coverage as guidance in software testing, a lot of neural network coverage criteria have been proposed to guide testing of neural network models (e.g., model accuracy under adversarial attacks). However, while the monotonic relation between code coverage and software quality has been supported by many seminal studies in software engineering, it remains largely unclear whether similar monotonicity exists between neural network model coverage and model quality. This paper sets out to answer this question. Specifically, this paper studies the correlation between DNN model quality and coverage criteria, effects of coverage guided adversarial example generation compared with gradient decent based methods, effectiveness of coverage based retraining compared with existing adversarial training, and the internal relationships among coverage criteria.
Compact fermion to qubit mappings

Mappings between fermions and qubits are valuable constructions in physics. To date only a handful exist. In addition to revealing dualities between fermionic and spin systems, such mappings are indispensable in any quantum simulation of fermionic physics on quantum computers. The number of qubits required per fermionic mode, and the locality of mapped fermionic operators strongly impact the cost of such simulations. We present a novel fermion to qubit mapping which outperforms all previous local mappings in both the qubit to mode ratio, and the locality of mapped operators.
Optimal design of a general type-2 fuzzy classifier for the pulse level and its hardware implementation

. Nowadays, soft computing has been of great help in solving real-world problems and satisfying the needs in our everyday life. We require more than ever the development and implementation of models and techniques that assist in medical issues due to the recent pandemic. The aim of this work is the development and hardware implementation of a general type-2 fuzzy classifier for the pulse levels and the optimization of the general type-2 membership functions parameters using the Ant Lion Optimizer to compare results of type 1 and interval type-2 fuzzy classifiers with the patients of the Framingham database. This study additionally explains the implementation process of the general type-2 fuzzy classifier on the Jetson Nano hardware Development Board and the comparison of execution time with interval type-2, and type-1 fuzzy classifiers. This work offers a novel perspective in that the general type-2 fuzzy classifier can be implemented for embedded applications with excellent performance regarding hardware resources consumption.
Geometry Constrained Weakly Supervised Object Localization

We propose a geometry constrained network, termed GC-Net, for weakly supervised object localization (WSOL). GC-Net consists of three modules: a detector, a generator and a classifier. The detector predicts the object location defined by a set of coefficients describing a geometric shape (i.e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator. The classifier takes the resulting masked images as input and performs two complementary classification tasks for the object and background. To make the mask more compact and more complete, we propose a novel multi-task loss function that takes into account area of the geometric shape, the categorical cross-entropy and the negative entropy. In contrast to previous approaches, GC-Net is trained end-to-end and predict object location without any post-processing (e.g. thresholding) that may require additional tuning. Extensive experiments on the CUB-200-2011 and ILSVRC2012 datasets show that GC-Net outperforms state-of-the-art methods by a large margin. Our source code is available at https://github.com/lwzeng/GC-Net .
CONFIG: Controllable Neural Face Image Generation

Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind. If this new technology is to find practical uses, we need to achieve a level of control over generative networks which, without sacrificing realism, is on par with that seen in computer graphics and character animation. To this end we propose ConfigNet, a neural face model that allows for controlling individual aspects of output images in semantically meaningful ways and that is a significant step on the path towards finely-controllable neural rendering. ConfigNet is trained on real face images as well as synthetic face renders. Our novel method uses synthetic data to factorize the latent space into elements that correspond to the inputs of a traditional rendering pipeline, separating aspects such as head pose, facial expression, hair style, illumination, and many others which are very hard to annotate in real data. The real images, which are presented to the network without labels, extend the variety of the generated images and encourage realism. Finally, we propose an evaluation criterion using an attribute detection network combined with a user study and demonstrate state-of-the-art individual control over attributes in the output images.
From Shadow Segmentation to Shadow Removal

The requirement for paired shadow and shadow-free images limits the size and diversity of shadow removal datasets and hinders the possibility of training large-scale, robust shadow removal algorithms. We propose a shadow removal method that can be trained using only shadow and non-shadow patches cropped from the shadow images themselves. Our method is trained via an adversarial framework, following a physical model of shadow formation. Our central contribution is a set of physics-based constraints that enables this adversarial training. Our method achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. The advantages of our training regime are even more pronounced in shadow removal for videos. Our method can be fine-tuned on a testing video with only the shadow masks generated by a pre-trained shadow detector and outperforms state-of-the-art methods on this challenging test. We illustrate the advantages of our method on our proposed video shadow removal dataset.
Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation

Boyle et al. (TCC 2019) proposed a new approach for secure computation in the preprocessing model building on function secret sharing (FSS), where a gate g is evaluated using an FSS scheme for the related offset family $$g_r(x)=g(x+r)$$ . They further presented efficient FSS schemes based on any pseudorandom generator (PRG) for the offset families of several useful gates g that arise in “mixed-mode” secure computation. These include gates for zero test, integer comparison, ReLU, and spline functions. The FSS-based approach offers significant savings in online communication and round complexity compared to alternative techniques based on garbled circuits or secret sharing. In this work, we improve and extend the previous results of Boyle et al. by making the following three kinds of contributions:
Immersive virtual reality news: A study of user experience and media effects

Recently, the concept of “immersive VR news” has been proposed in the news media field. However, unlike traditional video news, the user experience and media effects of immersive VR news have yet to be verified. To examine the influence of VR technology on the news media field, we designed an immersive VR news product to further discern differences in user experience and media effects among traditional video news, VR news without interaction, and VR news with interaction. The results show that traditional video news excelled in terms of empathy and nervousness, whereas VR news with interaction was superior in terms of immersion, interest, accuracy, and credibility. VR news without interaction performed the poorest in all categories. Based on the observed user behaviors during the experiment, we present a detailed discussion of the reasons for these differences and offer guidelines for the design and application of immersive VR news.
A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering

Chatbots are envisioned to dramatically change the future of Software Engineering, allowing practitioners to chat and inquire about their software projects and interact with different services using natural language. At the heart of every chatbot is a Natural Language Understanding (NLU) component that enables the chatbot to understand natural language input. Recently, many NLU platforms were provided to serve as an off-the-shelf NLU component for chatbots, however, selecting the best NLU for Software Engineering chatbots remains an open challenge. Therefore, in this paper, we evaluate four of the most commonly used NLUs, namely IBM Watson, Google Dialogflow, Rasa, and Microsoft LUIS to shed light on which NLU should be used in Software Engineering based chatbots. Specifically, we examine the NLUs’ performance in classifying intents, confidence scores stability, and extracting entities. To evaluate the NLUs, we use two datasets that reflect two common tasks performed by Software Engineering practitioners, 1) the task of chatting with the chatbot to ask questions about software repositories 2) the task of asking development questions on Q&A forums (e.g., Stack Overflow). According to our findings, IBM Watson is the best performing NLU when considering the three aspects (intents classification, confidence scores, and entity extraction). However, the results from each individual aspect show that, in intents classification, IBM Watson performs the best with an F1-measure <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 84%, but in confidence scores, Rasa comes on top with a median confidence score higher than 0.91. Our results also show that all NLUs, except for Dialogflow, generally provide trustable confidence scores. For entity extraction, Microsoft LUIS and IBM Watson outperform other NLUs in the two SE tasks. Our results provide guidance to software engineering practitioners when deciding which NLU to use in their chatbots.
A privacy-preserving and non-interactive federated learning scheme for regression training with gradient descent

In recent years, the extensive application of machine learning technologies has been witnessed in various fields. However, in many applications, massive data are distributively stored in multiple data owners. Meanwhile, due to the privacy concerns and communication constraints, it is difficult to bridge the data silos among data owners for training a global machine learning model. In this paper, we propose a privacy-preserving and non-interactive federated learning scheme for regression training with gradient descent, named VANE. With VANE, multiple data owners are able to train a global linear, ridge or logistic regression model with the assistance of cloud, while their private local training data can be well protected. Specifically, we first design a secure data aggregation algorithm, with which local training data from multiple data owners can be aggregated and trained to a global model without disclosing any private information. Meanwhile, benefit from our data pre-processing method, the whole training process is non-interactive, i.e., there is no interaction between data owners and the cloud. Detailed security analysis shows that VANE can well protect the local training data of data owners. The performance evaluation results demonstrate that the training performance of our VANE is around 103 times faster than existing schemes.
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations

Due to imperfections in transmitters' hardware, wireless signals can be used to verify their identity in an authorization system. While deep learning was proposed for transmitter identification, existing work has mainly focused on classification among a closed set of transmitters. Malicious transmitters outside this closed set will be misclassified, jeopardizing the authorization system. In this article, we formulate the problem of recognizing authorized transmitters and rejecting new transmitters as open set recognition and anomaly detection. We consider approaches based on one and several binary classifiers, multiclass classifiers, and signal reconstruction. We study how these approaches scale with the required number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The evaluation procedure takes into consideration that some transmitters might be more similar than others and nuances these effects. The authorization's robustness against temporal changes in fingerprints is also evaluated as a function of the approach and the dataset structure. When using 10 authorized and 50 known unauthorized WiFi transmitters from a publicly accessible testbed, we were able to achieve an outlier detection accuracy of 98% on the same day test set and 80% on the different day test set.