Reviewerly/exHarmony huggingface dataset:
Enhancing the convolution-based knowledge graph embeddings by increasing dimension-wise interactions

Knowledge graph embedding learns distributed low-dimensional representations for the elements in knowledge graphs, so that knowledge can be conveniently integrated into various tasks and smart systems. Recently, convolutional neural network has been introduced to embedding technique and obtained impressive achievements in link prediction task. ConvKB, a recently proposed method, captured the global dimension-wise interactions in facts with the convolutional filters. However, ConvKB failed to learn the local interactions between the entity and relation embedding. Moreover, rich interactions among feature maps are neglected in the existing convolutional embedding models. In this paper, based on ConvKB, we propose ConvD which models the local relationships in facts and integrates the cross-channel information based on the dimension-wise interactions to further improve the performance. From the experimental results, ConvD obtains scores that are 96% and 5% better than ConvKB on MRR and Hits@10 in the link prediction task. Furthermore, ConvD surpassed state-of-the-art baselines on WN18RR and achieved competitive results on FB15k-237 respectively.
An Overview of Security Materials in Banknotes and Analytical Techniques in Detecting Counterfeits

Counterfeiting or forged imitation of banknotes is a perpetual practice engulfing global economies. This not only poses challenges for the material scientists to come forth with advanced security materials but also demands veracious forensic examination to detect counterfeits. The present article pursues novel efforts in summarizing a study that lays focus on the recent optical and analytical examinations being used for the characterization and detection of chemical profiles of authentic and counterfeited banknotes. The article briefs the trends in banknote materials, security paper manufacturing process, security inks used for printing, and types of the security printing process in banknote practices. Reported literature shows the introduction of new anti-counterfeiting materials viz. magnetically-responsive photonic anti-counterfeiting watermarks, and fluorescent nanoparticles that can be used as anti-counterfeiting inks, anti-stokes inks, metameric inks, etc. Analytical techniques such as IR spectroscopy, Raman spectroscopy, Mossbauer spectroscopy, X-ray diffraction, X-ray fluorescence, LIBS, XRF, ELDI-MS, EASI/DESI-MS, HPLC, VSC, AFM, etc. in conjunction with different chemometrics approaches have been critically discussed. The study also presents the future scope in banknote examination like the use of hyper spectral imaging and sensor-based counterfeit detection systems.
AEGAN: Generating imperceptible face synthesis via autoencoder‐based generative adversarial network

Abstract Face recognition (FR) systems based on convolutional neural networks have shown excellent performance in human face inference. However, some malicious users may exploit such powerful systems to identify others' face images disclosed by victims' social network accounts, consequently obtaining private information. To address this emerging issue, synthesizing face protection images with visual and protective effects is essential. However, existing face protection methods encounter three critical problems: poor visual effect, limited protective effect, and trade‐off between visual and protective effects. To address these challenges, we propose a novel face protection approach in this article. Specifically, we design a generative adversarial network (GAN) framework with an autoencoder (AEGAN) as the generator to synthesize the protection images. It is worth noting that we introduce an interpolation upsampling module in the decoder in order to let the synthesized protection images evade recognition by powerful convolution‐based FR systems. Furthermore, we introduce an attention module with a perceptual loss in AEGAN to enhance the visual effects of synthesized images by AEGAN. Extensive experiments have shown that AEGAN not only can maintain the comfortable visual quality of synthesized images but also prevent the recognition of commercial FR systems, including Baidu and iKLYTEK.
Service Re-Selection for Disruptive Events in Mobile Environments: A Heuristic Technique for Decision Support at Runtime

Abstract Modern service-based processes in mobile environments are highly complex due to the necessary spatial–temporal coordination between multiple participating users and the consideration of context information. Due to the dynamic nature of mobile environments, disruptive events occur at runtime, which require a re-selection of the planned service compositions respecting multiple users and context-awareness. Thereby, when re-selecting services the features performance, solution quality, solution robustness and alternative solutions are essential and contribute to the efficacy of service systems. This paper presents an optimization-based heuristic technique based on a stateful representation that uses a region-based approach to re-select services considering multiple users, context information and in particular disruptive events at runtime. The evaluation results, which are based on a real-world scenario from the tourism domain, show that the proposed heuristic is superior compared to competing artifacts.
On the Methodology for Estimating the Relationship of Disturbances Using Digital Signal Processing in Relation to Measurements in Supersonic Flows

The paper describes a digital signal processing technique for determining the relationship between disturbances in a supersonic flow and pulsations of the boundary layer of a flat plate model. Estimates of the error of the proposed data processing method are given, the results of an experiment conducted to demonstrate the method on real data are presented.
Integration of magnetic and geological field data into geological mapping and rutile mineralization targets in the Minta locality (Haute-Sanaga, Cameroon)

This study combines the processing and interpretation of magnetic data with geological fieldwork. The purpose is to establish the geological map and rutile mineralization targets map of Minta area (Haute-Sanaga, Cameroon). To this, a strategic geological field survey was first conducted. It highlighted some geological structures, especially magnetite quartzites, which is considered as the potential sources of primary rutile mineralization. Several methods were applied to the magnetic data, including upward continuation (UC), vertical derivative (DZ), analytical signal (AS) and horizontal gradient magnitude (HGM). The regional-residual separation method based on the UC was used to develop the residual map which was very helpful to understand the distribution of magnetic anomalies related to rutile mineralization in this area. The structural model of the study area was established by combining the analysis of the AS and HGM maxima with the analysis of the topographic model. Since magnetic relief variation characterizes the lithological information changes according to the filtered map considered, a set of categorization processes based on the anomaly signals was applied to each of filtered maps according to the geological information sought. It is based on the interpretation of structural models, combined with previous works and the spatial distribution of geological data collected. This process produced a set of partial lithological models, which were then combined with the interpreted structural model to produce the geological map of the study area. This map also shows the spatial distribution of the various targets of potential rutile mineralization interpreted on the DZ model as rectilinear anomalies, with a signal range of 0.78–0.88 nT/m.
The Effects of Mobile Device Use and Presence on Perceptions of a Conversation Partner

This study examined the effects of mobile device use, and its mere presence, on in-person conversations. The study utilized an experimental design to replicate and advance existing scholarship on the impact of co-present device use and mere device presence on a conversation. This study examined how the presence of an unused device may hinder conversation, and the results did not replicate the mere presence effect. The study found participants reacted negatively to a confederate’s phone use, regardless of whether it was self- or externally initiated.
One-way deficit and Holevo quantity of generalized n-qubit Werner state

Originated from the work extraction in quantum systems coupled to a heat bath, quantum deficit is a kind of significant quantum correlations like quantum entanglement. It links quantum thermodynamics with quantum information. We analytically calculate the one-way deficit of the generalized $n$-qubit Werner state. We find that the one-way deficit increases as the mixing probability $p$ increases for any $n$. For fixed $p$, we observe that the one-way deficit increases as $n$ increases. For any $n$, the maximum of one-way deficit is attained at $p=1$. Furthermore, for large $n$ ($2^n \rightarrow \infty$), we prove that the curve of one-way deficit versus $p$ approaches to a straight line with slope $1$. We also calculate the Holevo quantity for the generalized $n$-qubit Werner state, and show that it is zero.
A PRticle filter algorithm for nonparametric estimation of multivariate mixing distributions

Predictive recursion (PR) is a fast, recursive algorithm that gives a smooth estimate of the mixing distribution under the general mixture model. However, the PR algorithm requires evaluation of a normalizing constant at each iteration. When the support of the mixing distribution is of relatively low dimension, this is not a problem since quadrature methods can be used and are very efficient. But when the support is of higher dimension, quadrature methods are inefficient and there is no obvious Monte Carlo-based alternative. In this paper, we propose a new strategy, which we refer to as PRticle filter, wherein we augment the basic PR algorithm with a filtering mechanism that adaptively reweights an initial set of particles along the updating sequence which are used to obtain Monte Carlo approximations of the normalizing constants. Convergence properties of the PRticle filter approximation are established and its empirical accuracy is demonstrated with simulation studies and a marked spatial point process data analysis.
AAEE‐Net: Attention‐guided aggregation and error‐aware enhancement network for accurate and efficient stereo matching

Abstract Stereo matching is a fundamental and long‐standing task in computer vision. Although learning‐based stereo matching algorithms have made remarkable progress, two major challenges still persist. Firstly, existing cost aggregation methods that use stacked three‐dimensional convolutions are complex, leading to heavy computation and memory costs. Secondly these methods continue to struggle with establishing reliable matches in weakly matchable such as that edges and thin structures. To overcome these limitations, we propose an accurate and efficient network called Attention‐guided Aggregation and Error‐aware Enhancement Network (AAEE‐Net). Our approach involves designing an Attention‐guided Aggregation Mechanism (AAM) based on simple image features. This mechanism uses attention weights generated from image features to guide cost aggregation with a more efficient and effective strategy. Additionally, we propose an Error‐aware Enhancement Module (EEM) that refines the raw disparity by combining high‐frequency information from the original image and warp error between the left and right views. EEM enables the network to learn error correction capabilities that produce excellent subtle details and sharp edges. The experimental results on the SceneFlow and KITTI benchmark datasets demonstrate that AAEE‐Net achieves state‐of‐the‐art performance with low inference time. The qualitative results show that AAEE‐Net significantly improves predictions, especially for thin structures.
Evaluating the convergence of tabu enhanced hybrid quantum optimization

In this paper, we introduce the tabu enhanced hybrid quantum optimization metaheuristic approach useful for optimization problem-solving on a quantum hardware. We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model. The results of numerical evaluation of the algorithm on quantum hardware as well as on a classical semiconductor hardware model are also demonstrated.
Patch merging refiner embedding UNet for image denoising

Image denoising is a fundamental task in low-level computer vision. The well-known CNN-UNet model with encoder and decoder has shown excellent denoising performance. The encoder adopts downsample modules such as pooling, convolution and patch merging to reduce the feature map size. The downsample module is very important for feature transformation between adjacent feature layers. However, these plain downsample modules are incapable of removing noise during feature transformation. It is expected that the downsample module can play the role of removing noise while executing feature transformation. Therefore, we design a patch merging refiner (PMR) downsample module, which utilizes subspace projection to learn a set of restoration basis from the feature space and projects the patch merging feature onto such space, to remove noise and retain the authentic information of feature space while executing feature transformation. Subsequently, from the perspective of the non-local mechanism, we carry out group convolution (GC) block module on PMR to restore high-frequency detail. Finally, by integrating the two modules into the commonly-used UNet model, we construct a PMR-UNet architecture. Experiments on synthetic and real-world noise images demonstrate that our architecture has stronger anti-noise power and surpasses the state-of-the-art denoisers in both quantitative measure and visual perception.
BLUFADER: Blurred face detection &amp; recognition for privacy-friendly continuous authentication

Authentication and de-authentication phases should occur at the beginning and end of secure user sessions, respectively. A secure session requires the user to pass the former, but the latter is often underestimated or ignored. Unattended or dangling sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, researchers focused on automated de-authentication systems, either as a stand-alone mechanism or as a result of continuous authentication failures. Unfortunately, no single approach offers security, privacy, and usability. Face-recognition methods, for example, may be suitable for security and usability, but they violate user privacy by continuously recording their actions and surroundings. In this work, we propose BLUFADER, a novel continuous authentication system that takes advantage of blurred face detection and recognition to fast, secure, and transparent de-authenticate users, preserving their privacy. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection and recognition continuously. To evaluate BLUFADER's practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. The de-authentication system was trained and evaluated using the former, while the latter was used to appraise the privacy and increase variance at training time. To guarantee the privacy-preserving effectiveness of the selected physical blurring filter, we show that state-of-the-art deblurring models are not able to revert our physical blur. Further, we demonstrate that our approach outperforms state-of-the-art methods in detecting blurred faces, achieving up to 95% accuracy. Moreover, BLUFADER effectively de-authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements. Last, our continuous authentication face recognition module based on Siamese Neural Network preventively protect users from adversarial attacks, enhancing the overall system security.
An extension of the numerical range over finite fields

Studying linear algebra over finite fields is fascinating since many properties of the real and complex numbers do not carry over directly to finite fields. In particular, the finite fields are not ordered, we do not have a notion of positivity, and we have a nonzero characteristic. Despite this, it is possible to develop interesting results related to an inner product like structure, norms, and numerical ranges. One of the challenges is that many nonzero vectors turn out to have a "norm" of zero. We give a formula for the number of vectors with norm zero, and analyze the effect that these zero norm vectors have in derailing some classical results over the real and complex numbers. In particular, we give examples of matrices for which the classical definition of a numerical range need not contain the eigenvalues of the matrix itself, and then extend the definition of numerical range and show that this new definition does indeed encompass all the eigenvalues.
Meanwhile, In Russia … Russian Internet Memes and Viral Video

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsJonathan Z. LudwigJonathan Z. Ludwig, Oklahoma State University, Stillwater, OK, USA. Email: drjonathanzludwig@gmail.com
Focusing on Governance for a Real Client in a Content Strategy Course

This article describes a graduate seminar on Content Strategy taught in the Fall of 2020 during the height of the COVID pandemic. Students worked totally online with a real client to develop a content strategy plan. This class was noteworthy because, unlike most classes that end up designing a logo, identity package, and look-n-feel approach to content strategy, this course ended up focusing on the much-overlooked emphasis on governance in an already well-established content strategy plan. Students conducted a persona research study (using Redish's approach) and built a UX journey map (using Kalbach's approach). They conducted a content audit (using Halverson's approach) and then used the data to determine what problems in content development really needed to be solved. These analyses showed that the client's principal needs actually dealt with governance issues rather than logos, branding, and content, so students researched and recommended suitable governance systems (primarily following Welchman's approach). Finally, they produced templates, sample content, and a content development plan for PCLS based on the new governance model provided.
TripCEAiR: A multi-loss minimization approach for surface EMG based airwriting recognition

Airwriting Recognition refers to the problem of identification of letters written in space with movement of the finger. It can be seen as a special case of dynamic gesture recognition wherein the set of gestures are letters in a particular language. Surface Electromyography (sEMG) is a non-invasive approach used to capture electrical signals generated as a result of contraction and relaxation of the muscles. sEMG has been widely adopted for gesture recognition applications. Unlike static gestures, dynamic gestures are user-friendly and can be used as a method for input with applications in Human Computer Interaction. There has been limited work in recognition of dynamic gestures such as airwriting, using sEMG signals and forms the core of the current work. In this work, a multi-loss minimization framework for sEMG based airwriting recognition is proposed. The proposed framework aims at learning a feature embedding vector that minimizes the triplet loss, while simultaneously learning the parameters of a classifier head to recognize corresponding alphabets. The proposed method is validated on a dataset recorded in the lab comprising of sEMG signals from 50 participants writing English uppercase alphabets. The effect of different variations of triplet loss, triplet mining strategies and feature embedding dimension is also presented. The best-achieved accuracy was 81.26% and 65.62% in user-dependent and independent scenarios respectively by using semihard positive and hard negative triplet mining. The code for our implementation will be made available at https://github.com/ayushayt/TripCEAiR
Exploring the teleported information in two two-level atoms interacting with a deformed cavity field

The possibility of teleporting a single-qubit system in an atomic system that interacts with a deformed cavity mode is being discussed. The average fidelity is used to investigate the quality of the teleported information in either ideal or deformed cavity. The quantum Fisher information is used as an estimator for the weight and phase angle parameters of the initial input state. The study shows that maximizing/minimizing the estimation degree and fidelity depends on the initial state settings of the atomic system, dipole–dipole coupling, and the estimated parameters. To maximize the information in the presence of a deformed cavity, one has to set large values of the atomic coupling dipole subsystem and reduce the deformation parameter.
Scale-Aware Domain Adaptation for Robust UAV Tracking

Siamese object tracking has facilitated diversified applications for autonomous unmanned aerial vehicles (UAVs). However, they are typically trained on general images with relatively large objects instead of small objects observed from UAV. The gap on object scale between the training and inference phases is prone to suboptimal tracking performance or even failure. To solve the gap issue and tailor general Siamese trackers for UAV tracking, this work proposes a novel scale-aware domain adaptation framework, i.e., ScaleAwareDA. Specifically, a contrastive learning-inspired network is proposed to guide the training phase of Transformer-based feature alignment for small objects. In this network, feature projection module is designed to avoid information loss of small objects. Feature prediction module is developed to drive the aforementioned training phase in a self-supervised way. In addition, to construct the target domain, training datasets with UAV-specific attributes are obtained by downsampling general training datasets. Consequently, this novel training approach can assist a tracker to represent objects in UAV scenarios more powerfully and thus maintain its robustness. Extensive experiments on three authoritative challenging UAV tracking benchmarks have demonstrated the superior tracking performance of ScaleAwareDA. In addition, quantitative real-world tests further attest to its practicality.
What should be encoded by position embedding for neural network language models?

Abstract Word order is one of the most important grammatical devices and the basis for language understanding. However, as one of the most popular NLP architectures, Transformer does not explicitly encode word order. A solution to this problem is to incorporate position information by means of position encoding/embedding (PE). Although a variety of methods of incorporating position information have been proposed, the NLP community is still in want of detailed statistical researches on position information in real-life language. In order to understand the influence of position information on the correlation between words in more detail, we investigated the factors that affect the frequency of words and word sequences in large corpora. Our results show that absolute position, relative position, being at one of the two ends of a sentence and sentence length all significantly affect the frequency of words and word sequences. Besides, we observed that the frequency distribution of word sequences over relative position carries valuable grammatical information. Our study suggests that in order to accurately capture word–word correlations, it is not enough to focus merely on absolute and relative position. Transformers should have access to more types of position-related information which may require improvements to the current architecture.