Reviewerly/exHarmony huggingface dataset:
FedCostWAvg: A New Averaging for Better Federated Learning

We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community. Our method addresses the problem of how to aggregate multiple models that were trained on different data sets. Conceptually, we propose a new way to choose the weights when averaging the different models, thereby extending the current state of the art (FedAvg). Empirical validation demonstrates that our approach reaches a notable improvement in segmentation performance compared to FedAvg.
On Re-engineering the X.509 PKI with Executable Specification for Better Implementation Guarantees

The X.509 Public-Key Infrastructure (PKI) standard is widely used as a scalable and flexible authentication mechanism. Flaws in X.509 implementations can make relying applications susceptible to impersonation attacks or interoperability issues. In practice, many libraries implementing X.509 have been shown to suffer from flaws that are due to noncompliance with the standard. Developing a compliant implementation is especially hindered by the design complexity, ambiguities, or under-specifications in the standard written in natural languages. In this paper, we set out to alleviate this unsatisfactory state of affairs by re-engineering and formalizing a widely used fragment of the X.509 standard specification, and then using it to develop a high-assurance implementation. Our X.509 specification re-engineering effort is guided by the principle of decoupling the syntactic requirements from the semantic requirements. For formalizing the syntactic requirements of X.509 standard, we observe that a restricted fragment of attribute grammar is sufficient. In contrast, for precisely capturing the semantic requirements imposed on the most-widely used X.509 features, we use quantifier-free first-order logic (QFFOL). Interestingly, using QFFOL results in an executable specification that can be efficiently enforced by an SMT solver. We use these and other insights to develop a high-assurance X.509 implementation named CERES. A comparison of CERES with 3 mainstream libraries (i.e., mbedTLS, OpenSSL, and GnuTLS) based on 2 million real certificate chains and 2 million synthetic certificate chains shows that CERES rightfully rejects malformed and invalid certificates.
Emulating Foveated Path Tracing

At full resolution, path tracing cannot be deployed in real-time based on current graphics hardware due to slow convergence times and noisy outputs, despite recent advances in denoisers. In this work, we develop a perceptual sandbox emulating a foveated path tracer to determine the eccentricity angle thresholds that enable imperceptible foveated path tracing. In a foveated path tracer the number of rays fired can be decreased, and thus performance can be increased. For this study, due to current hardware limitations prohibiting real-time path-tracing for multiple samples-per-pixel, we pre-render image buffers and emulate foveated rendering as a post-process by selectively blending the pre-rendered content, driven by an eye tracker capturing eye motion. We then perform three experiments to estimate conservative thresholds of eccentricity boundaries for which image manipulations are imperceptible. Contrary to our expectation of a single threshold across the three experiments, our results indicated three different average thresholds, one for each experiment. We hypothesise that this is due to the dissimilarity of the methodologies, i.e., A-B testing vs sequential presentation vs custom adjustment of eccentricities affecting the perceptibility of peripheral blur among others. We estimate, for the first time for path tracing, specific thresholds of eccentricity that limit any perceptual repercussions whilst maintaining high performance. We perform an analysis to determine potential computational complexity reductions due to foveation in path tracing. Our analysis shows a significant boost in path-tracing performance (≥ 2x − 3x) using our foveated rendering method as a result of the reduction in the primary rays.
Fast Trajectory Optimization for Gliding Reentry Vehicle Based on Improved Sparrow Search Algorithm

Abstract In order to solve the problem of low convergence accuracy and easy to fall into local optimization when solving the reentry vehicle trajectory optimization problem for existing algorithms, an improved sparrow search algorithm (OTRSSA) is proposed. Firstly, the basic sparrow search algorithm is improved by the methods of opposition-based learning, adaptive T-distribution and random walk to improve the optimization accuracy and stability, and increase the global search ability, and the performance verification is carried out in 12 benchmark functions; secondly, the 3-DOF motion model of reentry trajectory optimization problem is established and transformed into multi-dimensional function optimization problem; finally, OTRSSA is applied to solve the trajectory optimization problem. The simulation results show that the optimization performance and convergence speed of OTRSSA are better, and a reentry trajectory with the farthest range and satisfying constraints can be obtained quickly.
DGPF:A Dialogue Goal Planning Framework for Cognitive Service Conversational Bot

With the development of human-machine dialogue technology, more and more companies have launched their cognitive service products, such as Virtual Personal Assistant (VPA), smart speakers, shopping guide robots, etc. However, in these practical applications, most of the bots passively respond to user's utterances, lacking user preference knowledge and the proactive consciousness to lead the dialogue. Therefore, it is essential that bots proactively and naturally lead the dialogue from chitchat to service recommendation to meet user's requirements. To address this challenge, bots not only needs to detect the user's dialogue goal in real time, but also needs to plan a goal sequence based on user profile. In this paper, we propose DGPF, a Dialogue Goal Planning Framework. DGPF plans a reasonable goal sequence grounded on user's interests and personal KB before the conversation, additionally predicts user's true intent (i.e. dialogue goal) and judges whether the goal is completed based on the utterances during the conversation. DGPF includes a novel joint learning model that can simultaneously fix the two sub-tasks of goal completion estimation as well as current goal prediction, and improve each other's performance interactively. Our experimental results on the open dataset DuRecDial have been significantly improved compared to the baseline, which proves the effectiveness of our framework.
The influence of gender in the use of Augmented Reality in Education: A Systematic Literature Review

Augmented Reality (AR) is a technology that is gaining a greater prominence in schools and universities in recent years. For this reason, it is necessary to know the scientific production on this technological resource based on the gender influence shown by the students. The objective of this research is to analyze the repercussion that the gender of the students presents with the use of Augmented Reality in different educational stages, in the Scopus and Web of Science (WoS) databases. To do this, a systematic review of the literature was carried out using the Kitchenham protocol, analysing a total of 66 articles published between 2017 and 2021. The main findings show that gender presents an important significant difference in relation to student motivation. However, there are no relevant or considerable differences regarding the acceptance and acquisition of knowledge.
Side-Channel Attacks on Query-Based Data Anonymization

A longstanding problem in computer privacy is that of data anonymization. One common approach is to present a query interface to analysts, and anonymize on a query-by-query basis. In practice, this approach often uses a standard database back end, and presents the query semantics of the database to the analyst.
We Built This Circuit: Exploring Threat Vectors in Circuit Establishment in Tor

Traffic analysis attacks against the Tor network are a persisting threat to the anonymity of its users. The technical capabilities of attacks against encrypted Internet traffic have come a long way. Although the current state-of-the-art predicts high precision and accuracy for website fingerprinting and end-to-end confirmation, the concepts of these attacks often solely focus on their technical capabilities and ignore the operational requirements that are mandatory to get access to transmissions. In this work, we introduce three novel stepping-stone attacks that enable an adversary to (i) gain additional information about monitored connections, (ii) manipulate the Tor connection build-up, and (iii) conduct a targeted Denial-of-Service attack within the Tor infrastructure. All attacks exploit core defensive features of Tor and, consequently, are hard to patch. At the same time, our attacks are in line with standard attacker models for traffic analysis attacks. We demonstrate the feasibility of all three attacks in simulations and empirical case studies and emphasize their pivotal role in preparing a realistic setting for end-to-end confirmation attacks.
IoT Unbalanced Traffic Classification System Based on Focal_Attention_LSTM

With the development of the Internet of Things, communication between diverse devices has become a common practice. The huge growth of IoT devices and the different characteristics of IoT traffic patterns have attracted attention to traffic classification solutions to solve the problems that arise in IoT applications. This paper proposed an IoT traffic classification system using deep learning method. The main work of our framework, Focal_Attention_LSTM, is as follows: (1) Apply the LDA feature processing algorithm. (2) Adapted the well-performed attention mechanism to the IoT traffic classification area (3) Modified focal loss is used to address common traffic category unbalances and hard example learning problems. We evaluate our system with IoT dataset BoT-IoT. The experimental results show that our system achieves 3.6% improvements compared to traditional feature processing methods and deep learning models, and has effectively solved the traffic category unbalance problem.
Screen content image quality assessment using distortion-based directional edge and gradient similarity maps

With the prevalent use of applications like Facebook, Twitter, and remote control applications, assessing the quality of the screen content images (SCIs) has become one of the critical fields of researches in image processing. In this paper, we develop a novel full-reference image quality assessment (IQA) method, called distortion-based directional edge and gradient similarity maps (DDEGSM) method, to evaluate the quality of SCIs by efficiently incorporating the effect of two challenging distortion types: contrast change (CC) and color saturation change (CSC). The proposed DDEGSM method has four main steps. First, we form an edge similarity map by extracting two edge and gradient features using the image’s gradient in 12 directions and the Laplacian of Gaussian filter. Next, we use the difference of Gaussian filter to weigh the strength of each pixel in the edge similarity map. Then, we examine the distorted image using two efficient approaches to find out whether the image contains the CC or CSC distortion. Finally, we calculate the quality score of the input SCI from the distortion-based feature maps using a pooling strategy. Our extensive experiments on three commonly-used SCI datasets indicate the proposed method is superior to the state-of-the-art full-reference IQA methods and is more consistent with the subjective assessments.
Static and Dynamic Human Activity Detection Using Multi CNN-ELM Approach

Human Activity Recognition (HAR) is leading-edge in today's research field which has its applications in multiple research areas, some of those are Smart Health, Security and Ambient Assisted Living, etc. In today’s ubiquitous computing, HAR can be accomplished by espousing deep learning techniques that replace traditional analytical techniques that depend on the extraction of handcrafted features and classification methods. This work employed the Hierarchical Multi Convolution—Extreme Learning Machine approach for the classification of human activities. In the Hierarchical Multi CNN approach, the root CNN is employed to categorize the activities into static and dynamic activities. In the next level, two CNN-ELM are used to classify static activities into laying down, stand and sit; and classifies dynamic activities into Walking, Walking Downstairs, and walking upstairs. CNN-ELM approach exhibits its major advantages: CNN extracts the features from the dataset which confiscates expert knowledge in extracting features and ELM classifies the transitional results. This framework is evaluated on the UCI-HAR dataset and achieves an accuracy of 96.86%.
Dynamic Conflict Resolution of IoT Services in Smart Homes

We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. The proposed framework employs a preference extraction model based on a temporal proximity strategy. We design a preference aggregation model using a matrix factorization-based approach (i.e., singular value decomposition). The concepts of current resident item matrix and ideal resident item matrix are introduced as key criteria to cater to the conflict resolution framework. Finally, a set of experiments on real-world datasets are conducted to show the effectiveness of the proposed approach.
Towards the automatic restructuring of structural aesthetic design of Android user interfaces

End-user engagement heavily relies on the aesthetic design of the application's user interfaces. Designers are keen to create user interfaces that are usable and appealing. However, fundamental design issues , such as inconsistent padding and margins, cluttered user interfaces, and a high variation of element sizes, are too frequent in a UI design.. Prior studies provided ready-to-implement user interface alternatives. However, the aesthetic quality of these alternative designs is not guaranteed, and it limits the creativity of designers. Therefore, we present in this study an automated approach for restructuring a user interface structural design based on its data model. Our framework checks the violation of 13 generic structural design standards provided by Google Material Design. Then, the framework provides a set of recommendations for each violated guideline based on the specifications of the evaluated MUI. As a proof of concept, we used the tool ADDET to evaluate the quality of the original and restructured versions of 511 user interfaces in terms of the number of aesthetic defects and aesthetic properties. The results revealed a significant positive difference with a mean of 0.59 for the benefit of restructured user interfaces with an improved set of 7 quality metrics. We have also found that our approach could improve to an extent the accessibility of some designs by providing bigger element sizes.
What a SHAME

It is estimated that by the year 2024, the total number of systems equipped with voice assistant software will exceed 8.4 billion devices globally. While these devices provide convenience to consumers, they suffer from a myriad of security issues. This paper highlights the serious privacy threats exposed by information leakage in a smart assistant's encrypted network traffic metadata. To investigate this issue, we have collected a new dataset composed of dynamic and static commands posed to an Amazon Echo Dot using data collection and cleaning scripts we developed.
Enhancing Multimodal Affect Recognition with Multi-Task Affective Dynamics Modeling

Accurately recognizing students’ affective states is critical for enabling adaptive learning environments to promote engagement and enhance learning outcomes. Multimodal approaches to student affect recognition capture multi-dimensional patterns of student behavior through the use of multiple data channels. An important factor in multimodal affect recognition is the context in which affect is experienced and exhibited. In this paper, we present a multimodal, multitask affect recognition framework that predicts students’ future affective states as auxiliary training tasks and uses prior affective states as input features to capture bi-directional affective dynamics and enhance the training of affect recognition models. Additionally, we investigate cross-stitch networks to maintain parameterized separation between shared and task-specific representations and task-specific uncertainty-weighted loss functions for contextual modeling of student affective states. We evaluate our approach using interaction and posture data captured from students engaged with a game-based learning environment for emergency medical training. Results indicate that the affective dynamics-based approach yields significant improvements in multimodal affect recognition across four different affective states.
Quality control decision of government procurement of elderly care service based on multi‐index fusion of Pythagoras TOPSIS: Perspective of complex network

In the face of quality problems in government procurement of elderly care services, mastering relationships between quality‐influencing factors and identifying key factors is beneficial for managers to make correct quality control decisions. From the complex networks' perspective, quality‐influencing factors of government purchasing elderly care services are obtained by case analysis, and the influencing factors' network topology is constructed. Importance indexes of nodes are determined with complex network theory, and multi‐indexes are fused by the Pythagoras TOPSIS to evaluate the importance of each factor. The results reveal key factors in the quality of government procurement of elderly care services.
Analyzing Blockchain integrated architectures for effective handling of IoT-ecosystem transactions

The IoT ecosystem envisions a new world fully integrated by smart devices exchanging data to enable more automated, accurate, and timely decisions. Nonetheless, this idealized scenario may be only implemented if the system database allow smart devices’ transactions to be efficiently processed and security is guaranteed. To tackle this challenge, this paper then analyzes three Blockchain integrated architectures, covering an assemblage of different popular IoT-application scenarios. The performance evaluation is based on analytical queueing models, simulations, and theoretical discussion. The results show that efficiency and security requirements may be met when the Blockchain system is suitably configured, mainly observing a trade-off between data integrity and response time. In this sense, our main contribution is to yield valuable subsidies which can act as guidance for the development of real IoT-ecosystem projects. At last, general conclusions and directions for further research close this paper.
Two-step gradient-assisted phase-shifting demodulation algorithm for fast 3D reconstruction

Abstract The conventional multi-frequency heterodyne method is one of the most widely used methods in non-contact 3D measurement. However, it needs to project different phase-shifting patterns with different frequencies, so a large number of patterns are required. For most conventional methods, the fringe period number of the projected patterns is usually small due to its limited noise tolerance, though a larger fringe period number always means higher accuracy. We propose a two-step phase-shifting demodulation algorithm based on an intensitygradient. In this method, only two patterns for each frequency are required. With the intensity gradient of the two patterns, we obtain the wrapped phase of each frequency. Next, the absolute phase is retrieved from the three wrapped phases with the heterodyne algorithm. Because only two patterns are required for each frequency, the proposed method is more robust and has higher measuring speed compared with the traditional three-frequency four-step heterodyne method. Simulations and experiments prove the feasibility and effectiveness of the method, and demonstrate that the proposed method extends the noise tolerance and achieves highprecision with only half of the patterns required by the traditional three-frequency four-step method.
Real-Time Traffic Sign Detection Based on Improved YOLO V3

The automatic recognition technology of traffic signs is crucial in the process of autonomous driving. This paper aims to solve the low recognition rate of traffic signs due to complex background interference. This study realizes the improvement of detection accuracy of the trained YOLOv3 model by adjusting the relevant parameters in the YOLOv3 model, retraining the data set, and finding the appropriate threshold value, and finally realizing the accurate recognition of traffic signs.
Real Life Violence Detection in Surveillance Videos using Spatiotemporal Features

Automatic violence detection has remarkable importance from practical and academic point of view. Generally speaking, detecting violence in a crowded locality, via computational approaches, is challenging owing to rapid movements, overlapping characteristics, obstructed scenery, and scattered backgrounds. Fortunately, Deep Learning techniques can detect anomalies to a certain extent. Furthermore, their popularity, as a paradigm to detect violence, is growing at a tremendous pace. The aim of such approaches is to develop a method that recognizes violence and evokes an alarm so that immediate assistance can be provided. This paper is aong the same line of thought. This article presents a Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) based approach for violence detection by learning the detailed features in videos. The spatio-temporal features extracted from the combination of InceptonV3 pre-trained model and late LSTM architecture yielded a 97.5% accuracy thereby, proving its superiority over existing methods in literature.
Variable selection consistency of Gaussian process regression

Bayesian nonparametric regression under a rescaled Gaussian process prior offers smoothness-adaptive function estimation with near minimax-optimal error rates. Hierarchical extensions of this approach, equipped with stochastic variable selection, are known to also adapt to the unknown intrinsic dimension of a sparse true regression function. But it remains unclear if such extensions offer variable selection consistency, that is, if the true subset of important variables could be consistently learned from the data. It is shown here that variable consistency may indeed be achieved with such models at least when the true regression function has finite smoothness to induce a polynomially larger penalty on inclusion of false positive predictors. Our result covers the high-dimensional asymptotic setting where the predictor dimension is allowed to grow with the sample size. The proof utilizes Schwartz theory to establish that the posterior probability of wrong selection vanishes asymptotically. A necessary and challenging technical development involves providing sharp upper and lower bounds to small ball probabilities at all rescaling levels of the Gaussian process prior, a result that could be of independent interest.