Reviewerly/exHarmony huggingface dataset:
Systematic evaluation of deep face recognition methods

Face recognition is an important task in both academia and industry. With the development of deep convolutional neural networks, many deep face recognition methods have been proposed and have achieved remarkable results. However, these methods show great diversity among their datasets, network architectures, loss functions, and parameter learning strategies. For those who want to apply these technologies to establish a deep face recognition system, it is bewildering to evaluate which improvements are more suitable and effective. This study systematically summarizes and evaluates the state-of-the-art face recognition methods. However, unlike general reviews, on the basis of a survey, this study presents a comprehensive evaluation framework and measures the effects of multifarious settings in five components, including data augmentation, network architecture, loss function, network training, and model compression. Based on the experimental results, the influences of these five components on the deep face recognition are summarized. In terms of the datasets, a high sample-identity ratio is conducive to generalization, but it leads to increased difficulty for the training to converge. For the network architecture, deep ResNet has an advantage over other designs. Various normalization operations in the network are also necessary. For the loss function, whose performance is closely related to network design and training conditions. The angle-margin loss has a higher upper bound performance, but the traditional Euclidean-margin loss has a stable performance in limited training condition and shallower network. In terms of the training strategy, the step-declining learning rate and large batch size are recommended for recognition tasks. Furthermore, this study compares several popular model compression methods and shows that MobileNet has advantages over the others in terms of both compression ratio and robustness. Finally, a detailed list of recommended settings is provided.
Color image segmentation using adaptive hierarchical-histogram thresholding

Histogram-based thresholding is one of the widely applied techniques for conducting color image segmentation. The key to such techniques is the selection of a set of thresholds that can discriminate objects and background pixels. Many thresholding techniques have been proposed that use the shape information of histograms and identify the optimum thresholds at valleys. In this work, we introduce the novel concept of a hierarchical-histogram, which corresponds to a multigranularity abstraction of the color image. Based on this, we present a new histogram thresholding—Adaptive Hierarchical-Histogram Thresholding (AHHT) algorithm, which can adaptively identify the thresholds from valleys. The experimental results have demonstrated that the AHHT algorithm can obtain better segmentation results compared with the histon-based and the roughness-index-based techniques with drastically reduced time complexity.
Sharing Manipulated Heart Rate Feedback in Collaborative Virtual Environments

We have explored the effects of sharing manipulated heart rate feedback in collaborative virtual environments. In our study, we created two types of different virtual environments (active and passive) with different levels of interactions and provided three levels of manipulated heart rate feedback (decreased, unchanged, and increased). We measured the effects of manipulated feedback on Social Presence, affect, physical heart rate, and overall experience. We noticed a significant effect of the manipulated heart rate feedback in affecting scariness and nervousness. The perception of the collaborator's valance and arousal was also affected where increased heart rate feedback perceived as a higher valance and lower arousal. Increased heart rate feedback decreased the real heart rate. The type of virtual environments had a significant effect on social presence, heart rate, and affect where the active environment had better performances across these measurements. We discuss the implications of this and directions for future research.
Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning

(1) Background: The segmentation of cell nuclei is an essential task in a wide range of biomedical studies and clinical practices. The full automation of this process remains a challenge due to intra- and internuclear variations across a wide range of tissue morphologies, differences in staining protocols and imaging procedures. (2) Methods: A deep learning model with metric embeddings such as contrastive loss and triplet loss with semi-hard negative mining is proposed in order to accurately segment cell nuclei in a diverse set of microscopy images. The effectiveness of the proposed model was tested on a large-scale multi-tissue collection of microscopy image sets. (3) Results: The use of deep metric learning increased the overall segmentation prediction by 3.12% in the average value of Dice similarity coefficients as compared to no metric learning. In particular, the largest gain was observed for segmenting cell nuclei in H&amp;E -stained images when deep learning network and triplet loss with semi-hard negative mining were considered for the task. (4) Conclusion: We conclude that deep metric learning gives an additional boost to the overall learning process and consequently improves the segmentation performance. Notably, the improvement ranges approximately between 0.13% and 22.31% for different types of images in the terms of Dice coefficients when compared to no metric deep learning.
Dynamic Obstacle Avoidance for UAVs Using a Fast Trajectory Planning Approach

Real-time path planning is crucial to the dexterity of UAVs when traversing through environments with unknown obstacles. In this paper, we proposed such a real-time planning algorithm that can be used in cluttered environments. The real-time computation is achieved by using dimensionality reduction as well as the rolling optimization. We test the algorithm both in the Gazebo simulation and in two real experiments. The results show that our algorithm can maintain a relatively high speed flight while avoiding obstacles successfully.
Dual attention convolutional network for action recognition

Action recognition has been an active research area for many years. Extracting discriminative spatial and temporal features of different actions plays a key role in accomplishing this task. Current popular methods of action recognition are mainly based on two-stream Convolutional Networks (ConvNets) or 3D ConvNets. However, the computational cost of two-stream ConvNets is high for the requirement of optical flow while 3D ConvNets takes too much memory because they have a large amount of parameters. To alleviate such problems, the authors propose a Dual Attention ConvNet (DANet) based on dual attention mechanism which consists of spatial attention and temporal attention. The former concentrates on main motion objects in a video frame by using ConvNet structure and the latter captures related information of multiple video frames by adopting self-attention. Their network is entirely based on 2D ConvNet and takes in only RGB frames. Experimental results on UCF-101 and HMDB-51 benchmarks demonstrate that DANet gets comparable results among leading methods, which proves the effectiveness of the dual attention mechanism.
Construction of weightwise perfectly balanced Boolean functions with high weightwise nonlinearity

In this paper, we firstly introduce a kind of weightwise almost perfectly balanced Boolean functions. And then, a construction of weightwise perfectly balanced Boolean functions on 2q+2 variables is given by modifying the support of the weightwise almost perfectly balanced functions, where q is a non-negative integer. The algebraic degree, the weightwise nonlinearity, and the algebraic immunity of the newly constructed weightwise perfectly balanced functions are discussed at the end of this paper.
The Research Data Alliance: Benefits and Challenges of Building a Community Organization

The Research Data Alliance (RDA) is a community-driven organization dedicated to the development and use of technical, social, and community infrastructure promoting data sharing and data-driven exploration.RDA is particularly important for the global academic community where research infrastructure is often ad hoc, may have a short shelf-life, and hard to fund.At its launch in 2013, RDA struck a chord.Since then, RDA has attracted more than 9400 members from 130plus countries and developed infrastructure used by groups all over the world.One of its founders is
Collaborative Distillation for Top-N Recommendation

Knowledge distillation (KD) is a well-known method to reduce inference latency by compressing a cumbersome teacher model to a small student model. Despite the success of KD in the classification task, applying KD to recommender models is challenging due to the sparsity of positive feedback, the ambiguity of missing feedback, and the ranking problem associated with the top-N recommendation. To address the issues, we propose a new KD model for the collaborative filtering approach, namely collaborative distillation (CD). Specifically, (1) we reformulate a loss function to deal with the ambiguity of missing feedback. (2) We exploit probabilistic rank-aware sampling for the top-N recommendation. (3) To train the proposed model effectively, we develop two training strategies for the student model, called the teacher-and the student-guided training methods, selecting the most useful feedback from the teacher model. Via experimental results, we demonstrate that the proposed model outperforms the state-of-the-art method by 5.5-29.7% and 4.8-27.8% in hit rate (HR) and normalized discounted cumulative gain (NDCG), respectively. Moreover, the proposed model achieves the performance comparable to the teacher model.
Experiencing the Sense of Presence within an Educational Desktop Virtual Reality

This study examines the sense of presence in MaxWhere desktop virtual reality.Thirty-one people participated in the research.The participants spend about fifteen minutes in the virtual environment.For measuring presence, the Igroup Presence Questionnaire (IPQ) was used.The results showed that more automatic navigation positively relates to spatial presence.This research also measured the participants' level of experience with the VR software.A significant difference was found in the spatial presence and experienced realism: the more experienced users gave higher ratings on both subscales.At the same time, the involvement and the general presence scores were similar in the two groups, which is advantageous in education as the presence is positively related to the learning outcome.
Detecting Political Bots on Twitter during the 2019 Finnish Parliamentary Election

In recent years, the impact of bots used for manipulating public opinion has become an increasingly prevalent topic in politics.Numerous sources have reported about the presence of political bots in social media sites such as Twitter.Compared to other countries, the influence of bots in Finnish politics has received little attention from media and researchers.This study aims to investigate the influence of bots on Finnish political Twitter, based on a dataset consisting of the accounts following major Finnish politicians before the Finnish parliamentary election of 2019.To identify the bots, we extend the existing models with the use of user-level metadata and state-of-art classification models.The results support our model as a suitable instrument for detecting Twitter bots.We found that, albeit there is a huge amount of bot accounts following major Finnish politicians, it is unlikely resulting from foreign entities' attempts to influence the Finnish parliamentary election.
Conversational Agents - Exploring Generative Mechanisms and Second-hand Effects of Actualized Technology Affordances

Many organisations jumped on the bandwagon and implemented conversational agents (CAs) as a new communication channel.Customers benefit from shorter resolution times, ubiquitous availability, and consistent and compliant responses.However, despite the hype around CAs and the various benefits for customers, we know little about the effects of external facing CAs on the human workforce.This is crucial to better manage the possible changes in the work organisation.Adopting a critical realist stance and using the lens of technology affordances we explore a) why users increasingly actualize CA affordances and b) the first and second-hand effects of affordance actualisation on customers and human employees.We conducted semi-structured interviews with 18 experts in the field and introduce the term affordance effects pairs describing the relationships between the first and second-hand effects.We further explain which generative mechanisms lead to an increasing actualization of affordances and the associated effects.
An Automatic Ontology Generation Framework with An Organizational Perspective

Ontologies have been known for their powerful semantic representation of knowledge.However, ontologies cannot automatically evolve to reflect updates that occur in respective domains.To address this limitation, researchers have called for automatic ontology generation from unstructured text corpus.Unfortunately, systems that aim to generate ontologies from unstructured text corpus are domain-specific and require manual intervention.In addition, they suffer from uncertainty in creating concept linkages and difficulty in finding axioms for the same concept.Knowledge Graphs (KGs) has emerged as a powerful model for the dynamic representation of knowledge.However, KGs have many quality limitations and need extensive refinement.This research aims to develop a novel domain-independent automatic ontology generation framework that converts unstructured text corpus into domain consistent ontological form.The framework generates KGs from unstructured text corpus as well as refine and correct them to be consistent with domain ontologies.The power of the proposed automatically generated ontology is that it integrates the dynamic features of KGs and the quality features of ontologies.
Video steganography based on DCT psychovisual and object motion

Steganography is a technique of concealing the message in multimedia data. Multimedia data, such as videos are often compressed to reduce the storage for limited bandwidth. The video provides additional hidden-space in the object motion of image sequences. This research proposes a video steganography scheme based on object motion and DCT-psychovisual for concealing the message. The proposed hiding technique embeds a secret message along the object motion of the video frames. Motion analysis is used to determine the embedding regions. The proposed scheme selects six DCT coefficients in the middle frequency using DCT-psychovisual effects of hiding messages. A message is embedded by modifying middle DCT coefficients using the proposed algorithm. The middle frequencies have a large hiding capacity and it relatively does not give significant effect to the video reconstruction. The performance of the proposed video steganography is evaluated in terms of video quality and robustness against MPEG compression. The experimental results produce minimum distortion of the video quality. Our scheme produces a robust of hiding messages against MPEG-4 compression with average NC value of 0.94. The proposed video steganography achieves less perceptual distortion to human eyes and it's resistant against reducing video storage.
Modulated Autocorrelation Convolution Networks for Automatic Modulation Classification Based on Small Sample Set

For modulation classification, hand-crafted approaches can generalize well from a few samples, yet deep learning algorithms require millions of samples to achieve the superior performance with purely data-driven manner. However for many practical problems only with small sample set (SSS) available, there still remains a challenge for deep learning. In this paper, we employ deep learning to solve the modulation classification task in a more practical setting, particularly suffering from the SSS problem and with low signal-to-noise ratios (SNRs). Novel modulated autocorrelation convolution networks (MACNs) are introduced to capture periodic representation for automatic modulation classification (AMC). In MACNs, modulated communication signals are classified with the periodic local features under an autocorrelation convolution criterion. Modulation filters are utilized to enhance the capacity of the convolution filters and compress the model. On a challenging SSS learning task in low SNRs, MACNs achieve state-of-the-art performance that outperforms the existing algorithms for AMC, while compressing the size of required storage space of convolutional filters by a factor of 8 compared with convolution neural networks (CNNs).
A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES

The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges associated with their nature. This paper presents a survey on several techniques of ultrasonography images segmentation including threshold based, region based, watershed, active contour and learning based techniques, their merits, and demerits. This can provide significant insights for CAD developers or researchers to advance this field.
A Blockchain based PKI Validation System based on Rare Events Management

Public key infrastructures (PKIs) are the cornerstone for the security of the communication layer of online services relying on certificate-based authentication, such as e-commerce, e-government, online banking, cloud services, and many others. A PKI is an infrastructure based on a hierarchical model, but the use of PKIs in non-hierarchical contexts has exposed them to many types of attacks. Here, we discuss weaknesses exploited in past attacks and we propose a solution based on an original consensus algorithm developed for use on blockchain technology. In this implementation we retain the full functionality around X.509 certificates, i.e., for the triad (server name, server address, X.509 server certificate), and demonstrate a mechanism for obtaining fast consensus. The main properties of the solution are that a consensus may be reached even when not all members of the involved PKI participate in a transaction, and that no advanced trust agreement among PKIs is needed. The proposed solution is able to detect PKI attacks and can distinguish errors from attacks, allowing precise management of anomalies.
Tunable refrigerator for nonlinear quantum electric circuits

The emerging quantum technological applications call for fast and accurate initialization of the corresponding devices to low-entropy quantum states. To this end, we theoretically study a recently demonstrated quantum-circuit refrigerator in the case of nonlinear quantum electric circuits such as superconducting qubits. The maximum refrigeration rate of transmon and flux qubits is observed to be roughly an order of magnitude higher than that of usual linear resonators, increasing flexibility in the design. We find that for typical experimental parameters, the refrigerator is suitable for resetting different qubit types to fidelities above 99.99% in a few or a few tens of nanoseconds depending on the scenario. Thus the refrigerator appears to be a promising tool for quantum technology and for detailed studies of open quantum systems.
Reversible Data Hiding Scheme Based on Adjusting Pixel Modulation and Block-Wise Compression for Encrypted Images

Reversible data hiding in encrypted image (RDHEI) is a technique that can provide the security and invisibility of their own information during the acquisition and sharing of them among multi-users.Among which, parametric binary tree labelling (PBTL) is a novel technique designed to serve for highcapacity RDHEI.However, considering the local smoothness of the image, its potential redundancy room has not been fully explored.In this paper, we propose an improved PBTL-RDHEI scheme (IPBTL-RDHEI in short).In IPBTL-RDHEI, an adjusting pixel modulation strategy is considered to reduce the probability of the overflow of pixels, making more embeddable pixels usage to carry secret data.Moreover, a blockwise rearrangement and compression (BRC) mechanism is introduced to decrease the length of the required auxiliary information.Experimental results confirm that the proposed scheme is able to achieve an average embedding rate as large as 1.827 bpp when the block size is set to 3×3.
Targeting rare earth element bearing mine tailings on Bangka Island, Indonesia, with Sentinel-2 MSI

A laboratory study on rare earth element bearing mine tailings, collected from Bangka Island, Indonesia, reported a new spectral absorption feature at 674 nm associated with Erbium. The present study aims to evaluate the capability of the European Space Agency’s Sentinel-2 MSI sensors to detect this absorption feature from space. An arithmetic band operation is performed on selected visible and near-infrared spectral bands of a Sentinel-2 image. The results show that Sentinel-2 MSI is capable of detecting the 674 nm Erbium-related absorption feature within the particular environmental setting of the study area.
Image deblurring using tri-segment intensity prior

Camera shake during exposure often introduces annoying blur of objects and deteriorates image quality. Existing image deblurring algorithms usually use intensity and gradient priors to alleviate the degree of blurring. However, these methods only consider the changes caused by the blur process in the low intensity range, omitting the changes caused by the blur process in the high and middle part of the intensity range. In this paper, we propose an effective blind image deblurring algorithm based on the three segments of intensity prior, i.e., low, middle and high parts. This work is motivated by the observation that the blur process destroys the sparsity of both ends of intensity, and meanwhile shrinks the distance between the two distinct gray levels. A fast numerical scheme is deployed for alternatingly computing the sharp image and the blur kernel using an image pyramid at the stage of kernel estimation. Extensive experiments on both synthetic and real-world blurred images demonstrate that our method performs favorably against the state-of-the-art image deblurring methods.