Reviewerly/exHarmony huggingface dataset:
FT-MDnet: A Deep-Frozen Transfer Learning Framework for Person Search

Matching manually cropped pedestrian images between queries and candidates, termed as person re-identification, has achieved significant progress with deep convolutional neural networks. Recently, a topic called ‘person search’ is proposed for the end-to-end application of re-identification technologies. It integrates object detection and person re-identification and aims to both locate and match pedestrians on a gallery of raw images. However, the design and implementation of such kind of hybrid network are difficult and computationally consuming in real practical situations. In order to fasten the design and ease the implementation, this paper proposes a deep-frozen transfer learning framework, named FT-MDnet, to extract re-identification features from a pre-trained detection network in two steps. First, using a channel-wise attention mechanism, a network called adaptive transfer learning network (ATLnet) is used to convert the sharing data of the underlying detection network to a re-identification feature map. Then, a multi-branch feature representation network called multiple descriptor network (MDnet) is proposed to extract re-identification features from the re-identification feature map. Our proposed solution has been verified on different types of mainstream detection networks, including YOLOv3, YOLOv4, Mask RCNN, and CenterNet. The experimental results show that our solution outperforms all other person search solutions by a large margin. It proves that the feature representations of detection networks are highly compatible with re-identification, and the proposed framework effectively extracts these features out. To encourage further research, we have made our framework open source.
HSA-Net: Hidden-State-Aware Networks for High-Precision QoS Prediction

The high-precision QoS (quality of service) prediction is based on the comprehensive perception of state information of users and services. However, the current QoS prediction approaches have limited accuracy, for most state information of users and services (i.e., network speed, latency, network type, and more) are hidden due to privacy protection. Therefore, this article proposes a hidden-state-aware network (HSA-Net) that includes three steps called hidden state initialization, hidden state perception, and QoS prediction. A hidden state initialization approach is developed first based on the latent dirichlet allocation (LDA). After that, a hidden-state perception approach is proposed to abstract the initialized hidden state by fusing the known information (e.g., service ID and user location). The perception approach consists of four hidden-state perception (HSP) modes (i.e., known mode, object mode, hybrid mode and overall mode) implemented to generate explainable and fused features through four adaptive convolutional kernels. Finally, the relationship between the fused features and the QoS is discovered through a fully connected network to complete the high-precision QoS prediction process. The proposed HSA-Net is evaluated on two real-world datasets. According to the results, the HSA-Net's mean absolute error (MAE) index reduced by 3.67% and 28.84%, whereas the root mean squared error (RMSE) index decreased by 3.07% and 7.14% compared with ten baselines on average in the two datasets.
ViTAWiN - Developing Multiprofessional Medical Emergency Training with Mixed Reality

Multi-user Virtual Reality (VR) has shown potential to provide a secure and collaborative environment for medical emergency training. The actual implementation and effective use of such technology depends on ergonomic (e.g., presence, usability) factors as well as solving challenges concerning the integration into existing training environments and multiple medical professions. This paper explores on how to extend a virtual training environment while addressing two professional groups, i.e., paramedics and emergency nurses. In an iterative and interdisciplinary development case we report on how a a comprehensive user-centered requirements analysis leads to the design of multi professional medical scenarios applied to an extended VR prototype. A user study with 30 paramedic students and 4 nursing trainees indicates adequate functionality with good usability and confirms effectiveness of our process. Based on our findings we propose a future research agenda concerning social and haptic interactions between trainees and patients for interprofessional medical mixed reality (MR) training.
Automated Video Labelling: Identifying Faces by Corroborative Evidence

We present a method for automatically labelling all faces in video archives, such as TV broadcasts, by combining multiple evidence sources and multiple modalities (visual and audio). We target the problem of ever-growing online video archives, where an effective, scalable indexing solution cannot require a user to provide manual annotation or supervision. To this end, we make three key contributions: (1) We provide a novel, simple, method for determining if a person is famous or not using image-search engines. In turn this enables a face-identity model to be built reliably and robustly, and used for high precision automatic labelling; (2) We show that even for less-famous people, image-search engines can then be used for corroborative evidence to accurately label faces that are named in the scene or the speech; (3) Finally, we quantitatively demonstrate the benefits of our approach on different video domains and test settings, such as TV shows and news broadcasts. Our method works across three disparate datasets without any explicit domain adaptation, and sets new state-of-the-art results on all the public benchmarks.
How smooth is quantum complexity?

A bstract The “quantum complexity” of a unitary operator measures the difficulty of its construction from a set of elementary quantum gates. While the notion of quantum complexity was first introduced as a quantum generalization of the classical computational complexity, it has since been argued to hold a fundamental significance in its own right, as a physical quantity analogous to the thermodynamic entropy. In this paper, we present a unified perspective on various notions of quantum complexity, viewed as functions on the space of unitary operators. One striking feature of these functions is that they can exhibit non-smooth and even fractal behaviour. We use ideas from Diophantine approximation theory and sub-Riemannian geometry to rigorously quantify this lack of smoothness. Implications for the physical meaning of quantum complexity are discussed.
SkeletonNetV2: A Dense Channel Attention Blocks for Skeleton Extraction

Geometrical analysis of a shape through skeletonization has some of very important high- and low-level application which includes tracking, manipulation, retrieval, representation, registration, recognition, and compression. The task of skeletonization is defined as the generation of the medial axis of the shape while preserving its original topology and geometry. While the earlier approaches are mainly based on extracting the skeleton and then pruning the unwanted branches, the present study proposes a novel convolutional neural network based method to perform this task. The proposed architecture is an encoder-decoder network that leverages the benefits of the coordinated convolutional layer and multi-level supervision to prevent the loss of information between the extracted skeleton and the ground truth. The dense attention block is used as the backbone block in the encoder and decoder block. This architecture is performing better than the state of art on not only skeletonization of image tasks but also skeletonization from the point cloud. This method achieved an F1 score of 0.7961 on the Pixel Skeleton dataset and a Chamfer Distance (CD) score of 1.9561 on the Point skeleton dataset.
Spatially Adaptive Feature Refinement for Efficient Inference

Spatial redundancy commonly exists in the learned representations of convolutional neural networks (CNNs), leading to unnecessary computation on high-resolution features. In this paper, we propose a novel Spatially Adaptive feature Refinement (SAR) approach to reduce such superfluous computation. It performs efficient inference by adaptively fusing information from two branches: one conducts standard convolution on input features at a lower spatial resolution, and the other one selectively refines a set of regions at the original resolution. The two branches complement each other in feature learning, and both of them evoke much less computation than standard convolution. SAR is a flexible method that can be conveniently plugged into existing CNNs to establish models with reduced spatial redundancy. Experiments on CIFAR and ImageNet classification, COCO object detection and PASCAL VOC semantic segmentation tasks validate that the proposed SAR can consistently improve the network performance and efficiency. Notably, our results show that SAR only refines less than 40% of the regions in the feature representations of a ResNet for 97% of the samples in the validation set of ImageNet to achieve comparable accuracy with the original model, revealing the high computational redundancy in the spatial dimension of CNNs.
An Overview of Laser Injection against Embedded Neural Network Models

For many IoT domains, Machine Learning and more particularly Deep Learning brings very efficient solutions to handle complex data and perform challenging and mostly critical tasks. However, the deployment of models in a large variety of devices faces several obstacles related to trust and security. The latest is particularly critical since the demonstrations of severe flaws impacting the integrity, confidentiality and accessibility of neural network models. However, the attack surface of such embedded systems cannot be reduced to abstract flaws but must encompass the physical threats related to the implementation of these models within hardware platforms (e.g., 32-bit microcontrollers). Among physical attacks, Fault Injection Analysis (FIA) are known to be very powerful with a large spectrum of attack vectors. Most importantly, highly focused FIA techniques such as laser beam injection enable very accurate evaluation of the vulnerabilities as well as the robustness of embedded systems. Here, we propose to discuss how laser injection with state-of-the-art equipment, combined with theoretical evidences from Adversarial Machine Learning, highlights worrying threats against the integrity of deep learning inference and claims that join efforts from the theoretical AI and Physical Security communities are a urgent need.
In Your Face: Person Identification Through Ratios and Distances Between Facial Features

These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. This approach investigates the human face identification based on frontal images by producing ratios from distances between the different features and their locations. Moreover, this extended version includes an investigation of identification based on side profile by extracting and diagnosing the feature sets with geometric ratio expressions which are calculated into feature vectors. The last stage involves using weighted means to calculate the resemblance. The approach considers an explainable Artificial Intelligence (XAI) approach. Findings, based on a small dataset, achieve that the used approach offers promising results. Further research could have a great influence on how faces and face-profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate, and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89. This work is an extended version of the paper submitted in ACIIDS 2020.
Introduction of Electronic Technology into Education

The development of information technology has given an important impetus to the development of many sectors of development, including education. One of the conditions for improving learning outcomes in terms of new approaches and requirements is the introduction of information technology.&#x0D; The 21st century is called informational (knowledge-based, information technology, etc.). At this time of increasing information flow and rapid technological development, there is a need for cooperation and exchange of information and knowledge. In 2019, Mongolia was ranked 14th in Asia in the ICT Development Index in a keynote speech at the Mongolia International Digital User Conference.&#x0D; The use of active teaching methods improves the knowledge and skills of students. Active learning is learning that engages learners in the learning process and allows them to think about what they are doing and find ways to do it. Active learning is about helping students learn for themselves, not teaching them. Since the development of computer technology and the emergence of the Internet, scientists and educators in developed and developing countries of the world have conducted a wide range of experimental studies on the use of electronic technology and electronic materials in the learning process. Depending on the type of information technology used in training, it is divided into: e-learning, mobile learning, u-learning, blended learning, and more. The study mentioned in the article is a blended form of study, and in recent years, it has become commonplace in the best universities in the world to combine full-time education with online education at the same level.&#x0D; The study of methods and ways of introducing electronic technology in education are of practical importance. In this article, we present the results of some studies carried out on the example of teaching the subject of engineering graphics at MGUNT.
Multiple Description Coding for Best-Effort Delivery of Light Field Video Using GNN-Based Compression

In recent years, Light Field (LF) video has grabbed much attention as an emerging form of immersive media. LF collects, through a lens matrix, light information emanating in every direction, and obtains rich information about the scene, providing users with an immersive 6 Degrees of Freedom (DoF) experience. The visual content between different viewpoints is highly homogenized, suggesting the possibility of good compression and encoding. However, most fixed-structure LF coding schemes are difficult to adapt to the real-time requirements of different LF applications and best-effort network conditions causing packet loss. In this paper, we propose a dynamic adaptive LF video transmission scheme that can achieve high compression and yet provide near-distortion-free LF video when the network condition is stable. Additionally, for unstable network conditions a description scheduling algorithm is proposed, which can decode the LF video with the highest possible quality even if partial data cannot be received completely and/or timely. We achieve this by designing a Multiple Description Coding (MDC) based solution to transport the LF video compressed by a Graph Neural Network (GNN) model. Experimental results show that the scheduling algorithm can improve the quality of the decoding results by 3% to 15%. Compared with other similar schemes, our system greatly improves the reliability of the video streaming system against packet loss/error and supports heterogeneous receivers.
User-based network embedding for opinion spammer detection

Due to the huge commercial interests behind online reviews, a tremendous amount of spammers manufacture spam reviews for product reputation manipulation. To further enhance the influence of spam reviews, spammers often collaboratively post spam reviews within a short period of time, the activities of whom are called collective opinion spam campaign. The goals and members of the spam campaign activities change frequently, and some spammers also imitate normal purchases to conceal the identity, which makes the spammer detection challenging. In this paper, we propose an unsupervised network embedding-based approach to jointly exploiting different types of relations, e.g., direct common behavior relation, and indirect co-reviewed relation to effectively represent the relevances of users for detecting the collective opinion spammers. The average improvements of our method over the state-of-the-art solutions on dataset AmazonCn and YelpHotel are [14.09%,12.04%] and [16.25%,12.78%] in terms of AP and AUC, respectively.
BAPM: Block Attention Profiling Model for Multi-tab Website Fingerprinting Attacks on Tor

Website fingerprinting attacks on Tor pose an security issue in anonymity privacy, in which attackers can identify websites visited by victims through passively capturing and analyzing encrypted packet traces. Although related works have been studied over a long period, most of them focus on single-tab packet traces which only contain one page tab’s data. However, users often open multiple page tabs successively when browsing the web, and multi-tab packet traces generated will corrupt common single-tab attacks. Existing multi-tab attacks still depend on an elaborate feature engineering, besides, they fail to exploit the overlapping area which contains the mixed data of two adjacent page tabs, thus suffering from the information lost or confusion. In this paper, we propose a Block Attention Profiling Model named BAPM as a new multi-tab attacking model. Specifically, BAPM fully utilizes the whole multi-tab packet trace including the overlapping area to avoid information lost. It generates a tab-aware representation from direction sequences and performs the block division to separate mixed page tabs as clearly as possible, thus relieving the information confusion. Then the attention-based profiling is used to group blocks belonging to the same page tab and finally multiple websites are simultaneously identified under a global view. We compare BAPM with state of the art multi-tab attacks, and BAPM outperforms comparison methods even with larger overlapping area. The effectiveness of model design is also validated through ablation, sensitivity and generalization analysis.
Addressing critical failure factors and barriers in implementing Lean Six Sigma in Italian public hospitals

Purpose During the past few decades, Lean Six Sigma (LSS) in the health-care sector has received increasing attention from both researchers and practitioners because it plays an imperative role in quality improvement and cost reduction initiatives. Although researchers have often focussed on evidence of model effectiveness through the study of performance indicators, too little attention has been given to the factors that lead to implementation failure and the causal relationships among them. This study aims to investigate the factors that may inhibit the successful implementation of the method by focussing on Italian public hospitals. Design/methodology/approach Through the use of the Delphi technique and fuzzy cognitive maps, this paper derives new and relevant results for researchers, hospital managers and policymakers. Findings The results show the factors with the greatest impact on LSS implementation and provide insight into the causal links and degrees of influence between critical failure factors and performance variables. Practical implications The findings could be considered useful, in particular, to hospital managers and policymakers, who could leverage the suggestions derived from the study to address LSS implementation. Originality/value This work overcomes a gap in the literature related to the absence of studies on the causal relationships between factors that determine the success or failure of LSS implementation.
Heuristics from bounded meta-learned inference.

Numerous researchers have put forward heuristics as models of human decision-making. However, where such heuristics come from is still a topic of ongoing debate. In this work, we propose a novel computational model that advances our understanding of heuristic decision-making by explaining how different heuristics are discovered and how they are selected. This model-called bounded meta-learned inference (BMI)-is based on the idea that people make environment-specific inferences about which strategies to use while being efficient in terms of how they use computational resources. We show that our approach discovers two previously suggested types of heuristics-one reason decision-making and equal weighting-in specific environments. Furthermore, the model provides clear and precise predictions about when each heuristic should be applied: Knowing the correct ranking of attributes leads to one reason decision-making, knowing the directions of the attributes leads to equal weighting, and not knowing about either leads to strategies that use weighted combinations of multiple attributes. In three empirical paired comparison studies with continuous features, we verify predictions of our theory and show that it captures several characteristics of human decision-making not explained by alternative theories. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Comparative Analysis of Data Visualization Libraries Matplotlib and Seaborn in Python

With the tremendous growth in the areas of computing, statistics, and mathematics has led to the rise of the emerging field of expertise, named ‘Data Science’. This paper focuses on the comparative study and evaluation of the data science libraries used in Python Programming Languages, named ‘Matplotlib’ and ‘Seaborn’. The sole purpose of this paper is to identify areas and evaluate the strengths and weaknesses of these libraries with the implementation of code and identify the classification of the univariate and multivariate plotting of data concerned with patterns of data visualization and computational modelling of data in the form of processed information using techniques of big data and data mining
Examining the influence of pre-service teachers’ digital native traits on their technology acceptance: A Serbian perspective

Many seem to believe that today's pre-service teachers as born after 1980 are digital natives, or that they are "native speakers" of the digital language. Nevertheless, there is no evidence that their digital native characteristics determine whether or not they would use technology in the classroom. Although not widely evaluated, the four-factor, 21-item Digital Nativity Assessment Scale (DNAS) was one of the first instruments to assess digital nativeness (DN). This study aim is to explore the influence of pre-service teachers' DN on their intention to use technology in the future classroom in Serbia, by evaluating the DNAS on Serbian sample and using it for measuring the DN. Six variables were incorporated to examine their mutual relationships based on technology acceptance model: digital nativeness, behavioral intention (BI), perceived usefulness (PU), perceived ease of use (PEU), subjective norm (SN), and technological complexity (TC). Data were collected from 688 pre-service teachers in Serbia. Exploratory factor analysis confirmed a four-factor model for the DNAS, and Serbian pre-service teachers demonstrated a high level of DN. To evaluate the hypothesized model structural equation modeling was utilized. The suggested model had a good fit for describing the BI of Serbian pre-service teachers, accounting for 27.1% of the variance in BI. It was found that direct dominant predictors of BI are digital native traits, perceived usefulness, and perceived ease of use. Significant influence of digital native traits on all other variables in the model was also confirmed. The implications for theory and practice are discussed.