Reviewerly/exHarmony huggingface dataset:
Perfect quantum state transfer on Cayley graphs over semi-dihedral groups

Perfect quantum state transfer plays a crucial role in quantum information processing and quantum computation. There has been extensive study of perfect quantum state transfer on Cayley graphs over abelian groups. In this paper, we consider the existence of perfect quantum state transfer on Cayley graphs over semi-dihedral groups which are non-abelian groups. Using the representations of semi-dihedral groups, we provide some necessary and sufficient conditions for Cayley graphs over semi-dihedral groups admitting perfect quantum state transfer. By those conditions, we present examples of perfect quantum state transfer on Cayley graphs over semi-dihedral groups. In addition, we propose results about whether some new Cayley graphs over non-abelian groups admit perfect quantum state transfer.
CAMBI: Contrast-aware Multiscale Banding Index

Banding artifacts are artificially-introduced contours arising from the quantization of a smooth region in a video. Despite the advent of recent higher quality video systems with more efficient codecs, these artifacts remain conspicuous, especially on larger displays. In this work, a comprehensive subjective study is performed to understand the dependence of the banding visibility on encoding parameters and dithering. We subsequently develop a simple and intuitive no-reference banding index called CAMBI (Contrast-aware Multiscale Banding Index) which uses insights from Contrast Sensitivity Function in the Human Visual System to predict banding visibility. CAMBI correlates well with subjective perception of banding while using only a few visually-motivated hyperparameters.
Adaptive cross-fusion learning for multi-modal gesture recognition

Gesture recognition has attracted significant attention because of its wide range of potential applications. Although multi-modal gesture recognition has made significant progress in recent years, a popular method still is simply fusing prediction scores at the end of each branch, which often ignores complementary features among different modalities in the early stage and does not fuse the complementary features into a more discriminative feature. This paper proposes an Adaptive Cross-modal Weighting (ACmW) scheme to exploit complementarity features from RGB-D data in this study. The scheme learns relations among different modalities by combining the features of different data streams. The proposed ACmW module contains two key functions: (1) fusing complementary features from multiple streams through an adaptive one-dimensional convolution; and (2) modeling the correlation of multi-stream complementary features in the time dimension. Through the effective combination of these two functional modules, the proposed ACmW can automatically analyze the relationship between the complementary features from different streams, and can fuse them in the spatial and temporal dimensions. Extensive experiments validate the effectiveness of the proposed method, and show that our method outperforms state-of-the-art methods on IsoGD and NVGesture.
Quantum Computing for Military Applications

Quantum computers have the potential to outshine classical alternatives in solving specific problems, under the assumption of mature enough hardware. A specific subset of these problems relate to military applications. In this paper we consider the state-of-the-art of quantum technologies and different applications of this technology. Additionally, four use-cases of quantum computing specific for military applications are presented. These use-cases are directly in line with the 2021 AI strategic agenda of the Netherlands Ministry of Defense.
Adversarial strategy for transductive zero-shot learning

Zero-shot learning (ZSL) realizes unseen object recognition by transferring knowledge from seen classes to unseen classes under common semantic space assumption, such as attribute space and semantic word vector space. Previous works used seen image feature and semantic representation but ignored unseen test image features to learn a projection from visual space to semantic space, which may lead to the domain shift problem, i.e., due to disjoint seen and unseen classes, the projection learnt from auxiliary dataset is biased when applied directly to the target dataset. In this paper, adversarial strategy is proposed with an instantiation to deal with domain shift problem. It is described as a two-player game in which player 1 is projector while player 2 is classifier. Projector expects to learn a projection from visual space to semantic space with good semantic preservation property while classifier expects to achieve high accuracy. The adversarial meaning comes from the design of parallel structure between loss function on training samples and that on test samples, semantic compatibility of these two loss functions and loss function in classifier, and distribution alignment. A theoretical analysis is provided and experiments are performed on benchmark datasets to ascertain the effectiveness of adversarial strategy.
As Accurate as Needed, as Efficient as Possible: Approximations in DD-based Quantum Circuit Simulation

Quantum computers promise to solve important problems faster than conventional computers.However, unleashing this power has been challenging.In particular, design automation runs into (1) the probabilistic nature of quantum computation and (2) exponential requirements for computational resources on non-quantum hardware.In quantum circuit simulation, Decision Diagrams (DDs) have previously shown to reduce the required memory in many important cases by exploiting redundancies in the quantum state.In this paper, we show that this reduction can be amplified by exploiting the probabilistic nature of quantum computers to achieve even more compact representations.Specifically, we propose two new DD-based simulation strategies that approximate the quantum states to attain more compact representations, while, at the same time, allowing the user to control the resulting degradation in accuracy.We also analytically prove the effect of multiple approximations on the attained accuracy and empirically show that the resulting simulation scheme enables speed-ups up to several orders of magnitudes.
Enhancing Internet Search Abilities for People with Intellectual Disabilities in Sri Lanka

This article presents how young adults with intellectual disability (ID) from Sri Lanka, who had not previously used the Internet, interacted with Google search while enhancing their web search abilities throughout three web search workshops. Considering the little attention paid to the learning needs of people with ID in the current offering of web search learning tools, we iteratively developed a suite of learning tools to support our participants when they need help in the web search workshops. We employed an iterative participatory approach, with observations and semi-structured interviews, to reflect on how to design eLearning tools that enhance the participants’ interactions with web search. The qualitative thematic analysis resulted in five distinct themes on strategies to support, build on, and develop the abilities of young adults with IDs as they engage with Google search in their native language: application of existing abilities, basic skills to match learning needs, conceptual understanding, animations to facilitate visual memory, and promoting active engagement. These themes will be a starting point for understanding participants’ learning needs and behavior on web search, which would be important for future research on learning support as well as on software design.
Cuckoo optimization algorithm for support vector regression potential analysis: An example from the Granites-Tanami Orogen, Australia

This study describes the supervised support vector regression method and cuckoo optimization algorithm (COA-SVR), a newly developed mineral potential modelling (MPM) technique, and a case study of its application to predicting gold potential in the Granites-Tanami Orogen (GTO), Australia. COA-SVR, which was borne out of a computer program coded in MATLAB®, incorporates a popular radial basis function (RBF) known as the Gaussian kernel function. The COA-SVR model was trained using a series of predictor maps previously generated and described by Roshanravan et al. (2020a), and corroborated by way of a 10-fold cross-validation. The modelling results indicate that the COA-SVR approach to MPM outperformed the previous data-driven (i.e., random forest) and continuous (i.e., fuzzy gamma, geometric average, and data-driven index overlay) models of Roshanravan et al. (2020a). As such, the newly proposed COA-SVR method presents a valid approach to MPM. To further constrain the results, the gold prospective domains identified via COA-SVR and the previous models of Roshanravan et al. (2020a) were delimited, using the student's t-value and concentration-area (C-A) fractal techniques. Following the above, indices of overall performance (Oe), accuracy (A), kappa (K) and normalized density (Nd) were applied to further assess the gold prospective domains as delimited by the student's t-value and C-A fractal techniques. The results are consistent with the COA-SVR and random forest models having outperformed the more traditional modelling approaches documented by Roshanravan et al. (2020a). Given the positive results obtained in this study and similarly positive results reported by others, we believe that the apparent superior nature of machine-learning algorithms strongly merits further consideration, research and more widespread application in MPM.
Detail Preserving Residual Feature Pyramid Modules for Optical Flow

Feature pyramids and iterative refinement have recently led to great progress in optical flow estimation. However, downsampling in feature pyramids can cause blending of foreground objects with the background, which will mislead subsequent decisions in the iterative processing. The results are missing details especially in the flow of thin and of small structures. We propose a novel Residual Feature Pyramid Module (RFPM) which retains important details in the feature map without changing the overall iterative refinement design of the optical flow estimation. RFPM incorporates a residual structure between multiple feature pyramids into a downsampling module that corrects the blending of objects across boundaries. We demonstrate how to integrate our module with two state-of-the-art iterative refinement architectures. Results show that our RFPM visibly reduces flow errors and improves state-of-art performance in the clean pass of Sintel, and is one of the top-performing methods in KITTI. According to the particular modular structure of RFPM, we introduce a special fine-tuning approach that can dramatically decrease the training time compared to a typical full optical flow training schedule on multiple datasets.
Mining Episode Rules from Event Sequences Under Non-overlapping Frequency

Frequent episode mining is a popular framework for retrieving useful information from an event sequence. Many algorithms have been proposed to mine frequent episodes and to derive episode rules from them with respect to a given frequency function and its properties such as the anti-monotony. However, the interpretation of these rules is often difficult as their occurrences are allowed to overlap. To address this issue, this paper studies the novel problem of mining episode rules using non-overlapping occurrences of frequent episodes. The proposed rules have the form $$\beta \Rightarrow \alpha $$ where $$\alpha $$ and $$\beta $$ are frequent episodes and $$\beta $$ is a prefix of $$\alpha $$ . This kind of rules is well adapted for prediction tasks where a phenomenon is predicted from some observed event(s). An efficient algorithm named NONEPI (NON overlapping EPIsode rule miner) is presented and experiments have been performed to compare its performance with state-of-the-art algorithms.
Cloud based scalable object recognition from video streams using orientation fusion and convolutional neural networks

Object recognition from live video streams comes with numerous challenges such as the variation in illumination conditions and poses. Convolutional neural networks (CNNs) have been widely used to perform intelligent visual object recognition. Yet, CNNs still suffer from severe accuracy degradation, particularly on illumination-variant datasets. To address this problem, we propose a new CNN method based on orientation fusion for visual object recognition. The proposed cloud-based video analytics system pioneers the use of bi-dimensional empirical mode decomposition to split a video frame into intrinsic mode functions (IMFs). We further propose these IMFs to endure Reisz transform to produce monogenic object components, which are in turn used for the training of CNNs. Past works have demonstrated how the object orientation component may be used to pursue accuracy levels as high as 93%. Herein we demonstrate how a feature-fusion strategy of the orientation components leads to further improving visual recognition accuracy to 97%. We also assess the scalability of our method, looking at both the number and the size of the video streams under scrutiny. We carry out extensive experimentation on the publicly available Yale dataset, including also a self generated video datasets, finding significant improvements (both in accuracy and scale), in comparison to AlexNet, LeNet and SE-ResNeXt, which are three most commonly used deep learning models for visual object recognition and classification.
Ten years of attacks on companies using visual impersonation of domain names

We identify over a quarter of a million domains used by medium and large companies within the .com registry. We find that for around 7% of these companies very similar domain names have been registered with character changes that are intended to be indistinguishable at a casual glance. These domains would be suitable for use in Business Email Compromise frauds. Using historical registration and name server data we identify the timing, rate, and movement of these look-alike domains over a ten year period. This allows us to identify clusters of registrations which are quite clearly malicious and show how the criminals have moved their activity over time in response to countermeasures. Although the malicious activity peaked in 2016, there is still sufficient ongoing activity to cause concern.
TULVCAN: Terahertz Ultra-broadband Learning Vehicular Channel-Aware Networking

Due to spectrum scarcity and increasing wireless capacity demands, terahertz (THz) communications at 0.1-10THz and the corresponding spectrum characterization have emerged to meet diverse service requirements in future 5G and 6G wireless systems. However, conventional compressed sensing techniques to reconstruct the original wideband spectrum with under-sampled measurements become inefficient as local spectral correlation is deliberately omitted. Recent works extend communication methods with deep learning-based algorithms but lack strong ties to THz channel properties. This paper introduces novel THz channel-aware spectrum learning solutions that fully disclose the uniqueness of THz channels when performing such ultra-broadband sensing in vehicular environments. Specifically, a joint design of spectrum compression and reconstruction is proposed through a structured sensing matrix and two-phase reconstruction based on high spreading loss and molecular absorption at THz frequencies. An end-to-end learning framework, namely compression and reconstruction network (CRNet), is further developed with the mean-square-error loss function to improve sensing accuracy while significantly reducing computational complexity. Numerical results show that the CRNet solutions outperform the latest generative adversarial network (GAN) realization with a much higher cosine and structure similarity measures, smaller learning errors, and 56% less required training overheads. This THz Ultra-broadband Learning Vehicular Channel-Aware Networking (TULVCAN) work successfully achieves effective THz spectrum learning and hence allows frequency-agile access.
Knowledge-based memetic algorithm for joint task planning of multi-platform earth observation system

With the increasing complexity of earth observation tasks, the single observation mode can no longer meet the current requirements for timeliness and accuracy. The joint use of multi-platform observation resources can give full play to the advantages of each platform and improve the efficiency of task execution. The research on joint task planning of multi-platform is the key to achieve the collaborative effect of multi-platform earth observation system. In this paper, a unified observation path model is proposed. The objective functions of tracking rate, coverage rate, recognition rate and surveillance rate are proposed respectively for moving target tracking, regional target search, multi-point target recognition and continuous target surveillance. To address the problem, a problem-specific knowledge-based memetic algorithm is proposed. The mechanism of combining swarm intelligence algorithm and local search algorithm in the framework of memetic algorithm is used to enhance the ability of exploration and exploitation. Problem-specific knowledge operators are introduced into global search and local search. It can increase the learning ability of the algorithm in the iterative process, and guide the algorithm to adopt reasonable evolutionary strategy. Finally, speed up convergence while avoiding premature maturity. Simulation experiments verify the superiority of the algorithm.
CoCon: Cooperative-Contrastive Learning

Labeling videos at scale is impractical. Consequently, self-supervised visual representation learning is key for efficient video analysis. Recent success in learning image representations suggest contrastive learning is a promising framework to tackle this challenge. However, when applied to real-world videos, contrastive learning may unknowingly lead to separation of instances that contain semantically similar events. In our work, we introduce a cooperative variant of contrastive learning to utilize complementary information across views and address this issue. We use data-driven sampling to leverage implicit relationships between multiple input video views, whether observed (e.g. RGB) or inferred (e.g. flow, segmentation masks, poses). We are one of the firsts to explore exploiting inter-instance relationships to drive learning. We experimentally evaluate our representations on the downstream task of action recognition. Our method achieves competitive performance on standard benchmarks (UCF101, HMDB51, Kinetics400). Furthermore, qualitative experiments illustrate that our models can capture higher-order class relationships. The code is available at http://github.com/nishantrai18/CoCon.
Quantum properties of correlated emission laser with dephasing and phase fluctuation

We consider a two-photon laser in a cascade configuration coupled to an ordinary vacuum reservoir via a partially transmitting mirror. The impacts of the dephasing and the phase fluctuation on the entanglement, two-mode squeezing, and the intensity of the intracavity radiation are studied at steady-state. It so happens that the initial atomic coherence is the cause for the quantum properties of the cavity radiation even in the presence of the decoherence. We show that the dephasing rates and the phase fluctuations hurt the amount of squeezing and entanglement of the intracavity radiation. However, the dephasing rate leads to an increase in the intensity of the cavity radiation. Moreover, the phase fluctuation degrades the intensity of the intracavity radiation near to the initial maximum atomic coherence and it enhances otherwise.
Generative image inpainting with salient prior and relative total variation

Image inpainting is an important research direction of image processing. The generative adversarial network (GAN), which can reconstruct new reasonable content in the corrupted region, is the most interesting tool in current inpainting technologies. However, the previous deep methods generally need to be pre-added the binary mask representing the corruption location as the extra input. A novel inpainting algorithm which does not require additional external labels is proposed in this paper. The algorithm consists of two parts: corruption recognition module and content inpainting module, which can recognize and fill random corruption. In the recognizer, the salient object from the uncorrupted region is used as the prior for distinguishing corruption. In the inpainting module, a two-stage network is applied to reconstruct the image from coarse content to texture details. To avoid the misdetection in recognition which has a negative impact on the restoration in inpainting, we perform relative total variational filtering on the corrupted image, and use the salient map as the supervision of detail reconstruction. Qualitative and quantitative experiments on multiple datasets verify the effectiveness of our recognition module, the competitive advantage of our inpainting module, and the enlightening significance of our total algorithm in image inpainting.
Modern Privacy Risks and Protection Strategies in Data Analytics

Technology has transformed our socioeconomic life. We are more dependent on technology for our day-to-day activities like banking, e-commerce, retail, etc. Extensive usage of smart phone apps and enormous interest of people for social media has led to a digital data rich environment where significantly large-scale data is being generated and shared by organizations for better decision making and foster businesses through data analytics. However, data analytics involves privacy threats leading to disclosure of personal and sensitive data without the user's consent. Conventional data analytics involved analytics on the data as a whole using aggregate queries. Modern applications like recommendation systems, digital marketing, etc. involves analytics on person-specific individual records which is more harmful to individual privacy. In this paper, we examine various privacy-related risks, privacy preservation strategies with their potentials and limitations, also highlight the important aspects of many privacy legislations made in various countries including Personal Data Protection bill of India and European Union's General Data Protection Regulation (GDPR).
Exploiting Rich Event Representation to Improve Event Causality Recognition

Event causality identification is an essential task for information extraction that has attracted growing attention.Early researchers were accustomed to combining the convolutional neural network or recurrent neural network models with external causal knowledge, but these methods ignore the importance of rich semantic representation of the event.The event is more structured, so it has more abundant semantic representation.We argue that the elements of the event, the interaction of the two events, and the context between the two events can enrich the event's semantic representation and help identify event causality.Therefore, the effective semantic representation of events in event causality recognition deserves further study.To verify the effectiveness of rich event semantic representation for event causality identification, we proposed a model exploiting rich event representation to improve event causality recognition.Our model is based on multi-column convolutional neural networks, which integrate rich event representation, including event tensor representation, event interaction representation, and context-aware event representation.We designed various experimental models and conducted experiments on the Chinese emergency corpus, the most comprehensive annotation of events and event elements, enabling us to study the semantic representation of events from all aspects.The extensive experiments showed that the rich semantic representation of events achieved significant performance improvement over the baseline model on event causality recognition, indicating that the semantic representation of events plays an important role in event causality recognition.