Reviewerly/exHarmony huggingface dataset:
Combating Imbalance in Network Traffic Classification Using GAN Based Oversampling

With the proliferation of encrypted traffic, machine learning (ML) based network traffic classification (NTC) has become the mainstream method. However, most studies ignored two issues. On the one hand, Internet traffic presents a natural uneven distribution. On the other hand, machine learning algorithms generally aim to achieve the highest overall accuracy without considering class imbalance. This leads to severe performance degradation of existing ML-based NTC schemes when facing imbalanced scenarios. In this paper, we design a novel Generative Adversarial Network (GAN) architecture to generate traffic samples, in which the addition of the classifier and the pretraining module makes the generation process more stable and effective. We propose an end-to-end framework for imbalanced traffic classification, named ITCGAN, which can generate traffic samples for minority classes to adaptively rebalance the original traffic and simultaneously train the optimal classifier. We evaluate its effectiveness on the public ISCXVPN2016 dataset based on the global metrics and individual metrics. The results show that our method performs well in imbalanced NTC tasks, fully alleviating the performance degradation (a 10.27-percentage-point improvement to the precision of the most minority class). Meanwhile, it surpasses five state-of-the-art oversampling methods.
SEVerity: Code Injection Attacks against Encrypted Virtual Machines

Modern enterprises increasingly take advantage of cloud infrastructures. Yet, outsourcing code and data into the cloud requires enterprises to trust cloud providers not to meddle with their data. To reduce the level of trust towards cloud providers, AMD has introduced Secure Encrypted Virtualization (SEV). By encrypting Virtual Machines (VMs), SEV aims to ensure data confidentiality, despite a compromised or curious Hypervisor. The SEV Encrypted State (SEV-ES) extension additionally protects the VM’s register state from unauthorized access. Yet, both extensions do not provide integrity of the VM’s memory, which has already been abused to leak the protected data or to alter the VM’s control-flow. In this paper, we introduce the SEVerity attack; a missing puzzle piece in the series of attacks against the AMD SEV family. Specifically, we abuse the system’s lack of memory integrity protection to inject and execute arbitrary code within SEV-ES-protected VMs. Contrary to previous code execution attacks against the AMD SEV family, SEVerity neither relies on a specific CPU version nor on any code gadgets inside the VM. Instead, SEVerity abuses the fact that SEV-ES prohibits direct memory access into the encrypted memory. Specifically, SEVerity injects arbitrary code into the encrypted VM through I/O channels and uses the Hypervisor to locate and trigger the execution of the encrypted payload. This allows us to sidestep the protection mechanisms of SEV-ES. Overall, our results demonstrate a success rate of 100% and hence highlight that memory integrity protection is an obligation when encrypting VMs. Consequently, our work presents the final stroke in a series of attacks against AMD SEV and SEV-ES and renders the present implementation as incapable of protecting against a curious, vulnerable, or malicious Hypervisor.
Optimal Pre-Filtering for Improving Facebook Shared Images

Online Social Networks (OSNs) have attracted a huge number of users, who store and share various images on a daily basis. As a well-known fact, most OSN platforms apply a series of lossy operations on the uploaded images, which could severely degrade the quality of the shared images, negatively affecting the user experiences. In this work, we consider the problem of significantly improving OSN-shared images through applying an optimal pre-filtering prior to image sharing, without any cooperation from the OSN platform itself. Facebook, as one of the most popular and representative OSNs, is chosen as the platform to present our designed pre-filtering strategy. We first treat Facebook as a black box, and thoroughly recover its mechanism of processing color images. Based on the precise knowledge on the image processing pipeline on Facebook, we design the pre-filter under an optimization framework, minimizing the end-to-end distortion between the shared image and the original one. Compared with the directly shared images, our proposed pre-filtering-then-sharing strategy brings significant improvements in terms of both quantitative and qualitative metrics. Extensive experimental results are provided to show the superiority of our proposed method. Finally, we discuss the strategy on how to extend our proposed technique to other OSN platforms.
Emergency Management Case-Based Reasoning Systems: A Survey of Recent Developments

With the frequent occurrence of natural and man-made disasters, emergency management has become an active research field aiming at saving lives and reducing environmental and economic losses. Due to the complexity of crisis situations, emergency managers need to be assisted in making critical and effective decisions. Case-based reasoning (CBR) methodology has been widely adopted to support emergency decision makers in their tasks. This paper presents a comprehensive literature review of recent emergency management CBR systems reported in peer-reviewed journals and ICCBR conference proceedings between 2000 and 2020. Recent development trends of emergency management CBR systems are identified in terms of their purposes, application contexts and techniques used for their development. Finally, opportunities to improve emergency management CBR systems are outlined.
Scale and appearance variation enhanced siamese network for thermal infrared target tracking

Thermal infrared target tracking has recently attracted much attention. Although various methods have been proposed, they are usually sensitive to the target appearance and scale variation which will lead to tracking deviation. Therefore, in this paper, we propose a novel scale and appearance variation enhanced siamese network to solve the above problem. First, we introduce the dilated convolution module into siamese network to enlarge the receptive field of network while maintaining the resolution of feature maps. Hence, the model adaptability to the target with different scales could be enhanced. In addition, we devise a target template library update strategy based on the tracking results of historical frames to reduce the dependence of the network on the initially given exemplar. In this way, the appearance change of targets could be better described so as to overcome the tracking deviation problem over time. Qualitative and quantitative results on PTB-TIR, LSOTB-TIR and VOT-TIR2015 datasets demonstrate that our proposed model is able to achieve superior thermal infrared target tracking results. Especially, when the target appearance and scale have significant variations, our model shows outstanding performance than other state-of-the-art methods.
Scholarly journal publishing standards, policies and guidelines

Abstract It is easy to launch and run a journal but, more difficult to ensure that it adheres to best practices and provides a sustainable, secure, validated and respected venue for research. On launching a new journal, it is vital that the publisher and editors create suitable policies, technical systems and procedures before the inaugural call for papers is sent out. This article reviews the plethora of guidance that is available but scattered across multiple websites and draws together the key issues required by any new journal. The key areas considered are: journal and article identifiers; ethical and practical policies; technical decisions and considerations; systems for submission, publication and archiving; and promotion and marketing. The article provides a snapshot of the issues that all new publishers and editors need to consider.
Maya: An artificial intelligence based smart toy for pre-school children

Toys are valuable educational tools that, while entertaining, they also help in childhood development. Traditional instructional materials do not adequately take into account children’s individual development abilities. New instructional materials should be developed so that children can obtain education in accordance with their own learning pace and learning processes. The possible benefits to be drawn from smart devices that cater to more than one sensory organ simultaneously and the inferences to be drawn from scenarios utilizing such instructional materials are amazing. The aim of this study was to develop a smart toy using artificial intelligence in order to support the concept development of preschool children in terms of numbers, shapes, animals and colors. In this study, the design and development process of the smart toy Maya is explained. Maya is a humanoid robot that supports children in learning at their own pace and can determine what and how to teach and to which child. It is an intelligent tutoring system that can perform image processing and natural language processing in the teaching process. Maya is being able to recognize its user, to interact, and to manage learning activities according to the learning speed of the child. A design-based research method was used in the study. A needs analysis was conducted during the design phase of the smart toy, and the attributes that a toy should provide to help the acquisition of the concept by children were determined. A toy prototype was developed within the framework of the determined criteria. Preschool teachers were interviewed about the prototype toy. Improvements were made to the toy in response to its perceived flaws as well as other feedback, and the second iteration of the smart toy was developed. The toy’s shortcomings were discovered through interviews and were then removed, and the toy was given its final form. An interactive smart toy which can be used for both individual and group activities was developed at the end of research. In this study, the educational, hardware and software components of the smart toy are emphasized, and a framework for the smart toy design and the development process is provided.
Chinese Medical Named Entity Recognition using CRF-MT-Adapt and NER-MRC

Medical Named Entity Recognition is a fundamental component of understanding the medical free-text notes in Electronic Health Records, and it has become a popular research topic in both academia and industry. The China Conference on Knowledge Graph and Semantic Computing (CCKS) organizes a challenge for Medical Named Entity Recognition, aiming at extracting medical entity mentions and categorizing them into pre-defined classes. We propose a Multi-Task sequence labeling model with Adaptive Loss Weighting (CRF-MT-Adapt) to address the issue of low recall and a Named Entity Recognition model based on Machine Reading Comprehension (NER-MRC) to address the issue of long-span entity mentions. We experimentally demonstrate the state-of-the-art performance of the two proposed models and the ensemble even surpasses the strong baselines by at least 2% F-score. On the official test set, our best submission achieves an F-score of 90.51% and 95.96% under strict and relaxed criteria respectively.
Self-Supervised Visibility Learning for Novel View Synthesis

We address the problem of novel view synthesis (NVS) from a few sparse source view images. Conventional image-based rendering methods estimate scene geometry and synthesize novel views in two separate steps. However, erroneous geometry estimation will decrease NVS performance as view synthesis highly depends on the quality of estimated scene geometry. In this paper, we propose an end-to-end NVS framework to eliminate the error propagation issue. To be specific, we construct a volume under the target view and design a source-view visibility estimation (SVE) module to determine the visibility of the target-view voxels in each source view. Next, we aggregate the visibility of all source views to achieve a consensus volume. Each voxel in the consensus volume indicates a surface existence probability. Then, we present a soft ray-casting (SRC) mechanism to find the most front surface in the target view (i.e., depth). Specifically, our SRC traverses the consensus volume along viewing rays and then estimates a depth probability distribution. We then warp and aggregate source view pixels to synthesize a novel view based on the estimated source-view visibility and target-view depth. At last, our network is trained in an end-to-end self-supervised fashion, thus significantly alleviating error accumulation in view synthesis. Experimental results demonstrate that our method generates novel views in higher quality compared to the state-of-the-art.
MultiSoma: Distributed Embodiment with Synchronized Behavior and Perception

Human behavior and perception are optimized for a single body. Yet, the human brain has plasticity, which allows us to extend our body schema. By utilizing technology like robotics or virtual reality (VR) , we can modify our body parts or even add a new body to our own while retaining control over these parts. However, the update of body cognition when controlling multiple bodies has not been well examined. In this study, we explore the task performance and body cognition of humans when they have multiple full bodies as an extended embodiment. Our experimental system allows a participant to control up to four bodies at the same time and perceive sensory information from them. The participant experiences synchronizing behavior and vision perception in a virtual environment. We set up two tasks for multiple bodies and evaluated the cognition of these bodies by their task performances and subjective ratings. We found that humans can have the sense of body ownership and agency for each body when controlling multiple bodies simultaneously. Distributed embodiment has the potential to extend human behavior in cooperative work, parallel work, group behavior, and so on.
Generating Manga from Illustrations via Mimicking Manga Creation Workflow

We present a framework to generate manga from digital illustrations. In professional mange studios, the manga create workflow consists of three key steps: (1) Artists use line drawings to delineate the structural outlines in manga storyboards. (2) Artists apply several types of regular screentones to render the shading, occlusion, and object materials. (3) Artists selectively paste irregular screen textures onto the canvas to achieve various background layouts or special effects. Motivated by this workflow, we propose a data-driven framework to convert a digital illustration into three corresponding components: manga line drawing, regular screen-tone, and irregular screen texture. These components can be directly composed into manga images and can be further retouched for more plentiful manga creations. To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner. We conduct both perceptual user study and qualitative evaluation of the generated manga, and observe that our generated image layers for these three components are practically usable in the daily works of manga artists. We provide 60 qualitative results and 15 additional comparisons in the supplementary material. We will make our presented manga dataset publicly available to assist related applications.
Discriminative deep semi-nonnegative matrix factorization network with similarity maximization for unsupervised feature learning

• We propose a DSNnet optimization model with non-convex equality constraints for unsupervised representation learning. • We design a neural network based algorithm to solve the proposed DSNnet model. • We introduce a novel similarity measurement between input data and learned features as a regularization term of DSNnet. Deep Semi-NMF (DSN), which learns hierarchical representations by stacking multiple layers Semi-NMF, shows competitive performance in unsupervised data analysis. However, the features learned from DSN always lack of representativity and discriminativity. In this paper, we build a novel Deep Semi-NMF network (DSNnet) to address the issues of DSN. Specifically, DSNnet contains multiple fully-connected layers, in which the activation function of each layer adopts Smoothly Clipped Absolute Deviation (SCAD). The non-negative hidden features are computed forwardly, while the network parameters are updated by the stochastic gradient descent method. Moreover, to enhance the discriminativity of features, we suggest simultaneously minimizing the reconstruction error of input and output, and maximizing the similarity between input and learned features. The proposed similarity measurement, which consists of global geometric similarity and local pointwise similarity, encourages the compactness between similar points and separateness between dissimilar points in the feature space, and is beneficial to preserve intrinsic information of original data. Extensive experiments conducted on several datasets illustrate the superiority of the proposed approach in comparison with state-of-the-art methods.
Emotion analysis of Portuguese Political Parties Communication over the covid-19 Pandemic

In this paper, we explore the use of emotions in the Portuguese political parties' (with a seat in the Portuguese Parliament) communication as expressed by their official Twitter accounts, as of March 2020. The chosen period of our investigation is particularly interesting because political parties had a chance to communicate their views during a pandemic situation and over a period of one year. These views include possible solutions to face the crisis and their comments on the development of the whole situation. Using a standard lexicon we classified the amount of particular emotions in different tweets. Using this method we plotted the average positivity and negativity along time per party. We also analyzed the impact of each emotion to classify positivity using the present corpus. Finally, we considered some important words regarding the pandemic and their average positivity score. The analysis allows us to identify different approaches to participation in social media according to different strategies, more than political ideology.
Autonomous 3D geometry reconstruction through robot-manipulated optical sensors

Abstract Many industrial sectors face increasing production demands and the need to reduce costs, without compromising the quality. The use of robotics and automation has grown significantly in recent years, but versatile robotic manipulators are still not commonly used in small factories. Beside of the investments required to enable efficient and profitable use of robot technology, the efforts needed to program robots are only economically viable in case of large lot sizes. Generating robot programs for specific manufacturing tasks still relies on programming trajectory waypoints by hand. The use of virtual simulation software and the availability of the specimen digital models can facilitate robot programming. Nevertheless, in many cases, the virtual models are not available or there are excessive differences between virtual and real setups, leading to inaccurate robot programs and time-consuming manual corrections. Previous works have demonstrated the use of robot-manipulated optical sensors to map the geometry of samples. However, the use of simple user-defined robot paths, which are not optimized for a specific part geometry, typically causes some areas of the samples to not be mapped with the required level of accuracy or to not be sampled at all by the optical sensor. This work presents an autonomous framework to enable adaptive surface mapping, without any previous knowledge of the part geometry being transferred to the system. The novelty of this work lies in enabling the capability of mapping a part surface at the required level of sampling density, whilst minimizing the number of necessary view poses. Its development has also led to an efficient method of point cloud down-sampling and merging. The article gives an overview of the related work in the field, a detailed description of the proposed framework and a proof of its functionality through both simulated and experimental evidences.
An Active Transfer Learning (ATL) Framework for Smart Manufacturing with Limited Data: Case Study on Material Transfer in Composites Processing

Unprecedented advances in Machine Learning (ML), cloud computing and sensory technology promise to enable the manufacturing industry to respond rapidly to changes in marked needs while maintaining product quality and minimizing costs. Despite the unparalleled advantages that ML offers, critical limiting factors have prevented the exhaustive cultivation of ML in advanced manufacturing. Constant shifts in the process configuration and lack of sufficient fully-descriptive data restrict the performance of predictive ML models. This paper proposes to partly address these shortfalls with an active transfer learning (ATL) model that is applied to an aerospace composites manufacturing case study. The proposed ATL framework requires 1) developing an AI-based optimal experimental design using Active Learning (AL) to maximize the information gain from the limited number of allowable manufacturing trials, and 2) equipping the manufacturing process with a robust Transfer Learning (TL) model that is trained on limited available data and is immune to shifts in the process settings. The results suggest that uncertainty-based AL approaches can significantly decrease the dependency on large datasets for obtaining accurate process models. Furthermore, in comparison with traditional TL approaches, the proposed framework represents a practical solution to further reduce the necessity for generating expensive data in advanced manufacturing applications for developing reliable and transferable predictive models.
The effect of font width on eye movements during reading

Certain font features (e.g., letter width) can change the amount of space occupied by text in published works. Font styles/features are also known to affect reading eye movements (EM); however, few studies have examined these effects – and none used high-resolution displays. We examined the effects of font width on EMs by utilizing four fonts, from the Univers family, which varied in letter-width magnitude. Participants’ (n = 25) reading speed, saccade velocity, and the duration/number of fixations and saccades were recorded. The Ultra Condensed font significantly influenced readability and yielded: fewer fixations and saccades; longer fixation durations than the Roman and Extended fonts; and shorter saccade durations, relative to the other fonts. Readers efficiently adjusted their EMs such that no reading-speed differences were observed. The eye-tracking metrics revealed two trade-off effects: (1) fewer and shorter EMs and (2) more and longer EMs, which were revealed by the font-width manipulation.
Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning

Orthogonal frequency division multiplexing (OFDM) is one of the dominant waveforms in wireless communication systems due to its efficient implementation. However, it suffers from a loss of spectral efficiency as it requires a cyclic prefix (CP) to mitigate inter-symbol interference (ISI) and pilots to estimate the channel. We propose in this work to address these drawbacks by learning a neural network (NN)-based receiver jointly with a constellation geometry and bit labeling at the transmitter, that allows CP-less and pilotless communication on top of OFDM without a significant loss in bit error rate (BER). Our approach enables at least 18 % throughput gains compared to a pilot and CP-based baseline, and at least 4 % gains compared to a system that uses a neural receiver with pilots but no CP.