Reviewerly/exHarmony huggingface dataset:
Uncovering Hidden Instructions in Armv8-A Implementations

Though system and application level security has received and continue to receive significant attention, interest in hardware security has spiked only in the last few years. The majority of recently disclosed hardware security attacks exploit well known and documented hardware behaviours such as speculation, cache and memory timings, etc. We observe that security exploits in undocumented hardware behaviour can have even more severe consequences as such behaviour is rarely verified and protected against.
VPALG

Paper-publication venue prediction aims to predict candidate publication venues that effectively suit given submissions. This technology is developing rapidly with the popularity of machine learning models. However, most previous methods ignore the structure information of papers, while modeling them with graphs can naturally solve this drawback. Meanwhile, they either use hand-crafted or bag-of-word features to represent the papers, ignoring the ones that involve high-level semantics. Moreover, existing methods assume that the venue where a paper is published as a correct venue for the data annotation, which is unrealistic. One paper can be relevant to many venues. In this paper, we attempt to address these problems above and develop a novel prediction model, namelyVenue Prediction with Abstract-Level Graph (Vpalg xspace), which can serve as an effective decision-making tool for venue selections. Specifically, to achieve more discriminative paper abstract representations, we construct each abstract as a semantic graph and perform a dual attention message passing neural network for representation learning. Then, the proposed model can be trained over the learned abstract representations with their labels and generalized via self-training. Empirically, we employ the PubMed dataset and further collect two new datasets from the top journals and conferences in computer science. Experimental results indicate the superior performance of Vpalg xspace, consistently outperforming the existing baseline methods.
OpenAttHetRL

Learning the latent representations of entities based on their relationships and the data associated with them is an essential task in many applications such as ranking, recommendation systems, graph-based team formation, keyword search, and many more. However, the majority of existing techniques learn the latent representations of either network or textual data. Structural embedding techniques suffer from the sparsity of real-world networks. Attributes of nodes are a source of rich information to ameliorate network embedding vectors which are overlooked in the literature. Thus, most existing network representation learning tools capture structural information. This paper introduces an open-source toolkit called OpenAttHetRL to learn the latent representations of entities based on their both network and textual data in an end-to-end fashion. OpenAttHetRL is easy to employ and adapt for a variety of tasks including ranking, recommendation systems, and expert finding. OpenAttHetRL aims to provide a unified toolkit for data pre-processing, building and training models, and performing predictions for a downstream task. It employs a graph convolution network to capture the relationships among entities and a kernel pooling technique to preserve the similarity of their textual data in the embedding space. We use expert finding in community question answering systems to demonstrate how OpenAttHetRL can be trained to get latent representations of questions, their askers, tags, and answerers and find potential answerers of new questions.
Machine Learning Voice Synthesis for Intention Electromagnetic Interference Injection in Smart Speaker Devices

This work presents the effectiveness of using machine learning (ML) synthesized voice samples to control smart speaker devices through radiated intentional electromagnetic interference (I-EMI). In previous works, the feasibility of using I-EMI to control smart speaker devices was shown. However, devices that are trained to only recognize a single person’s voice or only execute certain commands from that person will not be as susceptible to this attack. By training a generative adversarial network (GAN) using samples of the target’s voice, this security feature can be bypassed directly, increasing the feasibility of the attack.
Real-time detection of game handball foul based on target detection and skeleton extraction

Video-assisted refereeing technology (VAR) has been widely used in various large football events. The introduction of this technology can effectively reduce the misjudgment rate and ensure fairness. However, the essence of this technology is to use video slow action playback. For foul judgment, human eyes are still used. In addition, the use of this technology will interrupt the process of the game and reduce the net time of about 4 % of the game, which will greatly reduce the user's viewing experience. In this paper, the target detection and pose extraction technology of artificial intelligence in recent years are integrated into the traditional VAR, and the migration learning of the YOLO network is carried out to realize real-time detection. The specific parameters of handball detection are as follows: recall 0.840, precision 0.913, and detection speed 8.2 frames/second.
Extended quasi-cyclic constructions of quantum codes and entanglement-assisted quantum codes

Constructions of quantum codes and entanglement-assisted quantum codes with good parameters via classical codes is an important task for quantum computing and quantum information. In this paper, by a family of one-generator quasi-cyclic codes, we provide quasi-cyclic extended constructions that preserve the self-orthogonality to obtain stabilizer quantum codes. As for the computational results, some binary and ternary stabilizer codes with good parameters are constructed. Moreover, we present methods to construct maximal-entanglement entanglement-assisted quantum codes by means of the class of quasi-cyclic codes and their extended codes. As an application, some good maximal-entanglement entanglement-assisted quantum codes are obtained and their parameters are compared.
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision

Reconstructing 4D vehicular activity (3D space and time) from cameras is useful for autonomous vehicles, commuters and local authorities to plan for smarter and safer cities. Traffic is inherently repetitious over long periods, yet current deep learning-based 3D reconstruction methods have not considered such repetitions and have difficulty generalizing to new intersection-installed cameras. We present a novel approach exploiting longitudinal (long-term) repetitious motion as self-supervision to reconstruct 3D vehicular activity from a video captured by a single fixed camera. Starting from off-the-shelf 2D keypoint detections, our algorithm optimizes 3D vehicle shapes and poses, and then clusters their trajectories in 3D space. The 2D keypoints and trajectory clusters accumulated over long-term are later used to improve the 2D and 3D keypoints via self-supervision without any human annotation. Our method improves reconstruction accuracy over state of the art on scenes with a significant visual difference from the keypoint detector's training data, and has many applications including velocity estimation, anomaly detection and vehicle counting. We demonstrate results on traffic videos captured at multiple city intersections, collected using our smartphones, YouTube, and other public datasets.
Multimodal Machine Translation Enhancement by Fusing Multimodal-attention and Fine-grained Image Features

With recent development of the multimodal machine translation (MMT) network architectures, recurrent models have effectively been replaced by attention mechanism and the translation results have been enhanced with the assistance of fine-grained image information. Although attention is a powerful and ubiquitous mechanism, different number of attention heads and granularity image features aligned by attention have an impact on the quality of multimodal machine translation. In order to address above problems, this paper proposes a multimodal machine translation enhancement by fusing multimodal-attention and fine-grained image features method which builds some submodels by introducing different granularity of image features to the multimodal-attention mechanism with different number of heads. Moreover, these sub-models are randomly fused and fusion models are obtained. The experimental results on the Multi30k dataset that the pruned attention heads lead to the improvement of translation results. Finally, our fusion model obtained the best results according to the automatic evaluation metrics BLEU compared with sub-models and some baselines.
Adaptive Modulation for Wireless Federated Learning

In wireless federated learning, the unreliable communication has a significant impact on the convergence rate and learning latency, which cannot be ignored. To cope with this problem, we propose a novel modulation selection mechanism to achieve the balance between learning latency and convergence rate loss caused by stochastic channel error. Different from the traditional one, the modulation mode in wireless FL system should be adjusted according to devices’ computing power, channel conditions, and training data importance. Then, an optimization problem to maximize the learning efficiency is formulated to obtain the optimal modulation scheme. Finally, extensive experiments are implemented to demonstrate the effectiveness of the proposed mechanism.
Context meta-reinforcement learning via neuromodulation

Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic representations in an agent's policy network (obtained via reasoning about task context, model parameter updates, or both). However, obtaining rich dynamic representations for fast adaptation beyond simple benchmark problems is challenging due to the burden placed on the policy network to accommodate different policies. This paper addresses the challenge by introducing neuromodulation as a modular component to augment a standard policy network that regulates neuronal activities in order to produce efficient dynamic representations for task adaptation. The proposed extension to the policy network is evaluated across multiple discrete and continuous control environments of increasing complexity. To prove the generality and benefits of the extension in meta-RL, the neuromodulated network was applied to two state-of-the-art meta-RL algorithms (CAVIA and PEARL). The result demonstrates that meta-RL augmented with neuromodulation produces significantly better result and richer dynamic representations in comparison to the baselines.
Modernization Framework to Enhance the Security of Legacy Information Systems

Due to various issues such as lack of agility, low performance, security issues, and high maintenance costs, the organization replaces its legacy information system (LIS). However, with the expansion of information technology, the security of the old system has received great attention. The protection of legacy data and information is critical to the organization. However, achieving safety through modernization, redevelopment, or redesign of LIS is a time-consuming and costly solution, especially in small and medium enterprises (SMEs). In addition, newly developed systems often lose inherent business rules, data integrity, and user trust. In this paper, we propose a Security Modernization Framework (SMF) to inject security measures into LIS without modernizing the existing solution. Fundamentally speaking, SMF is a collection of methods and technologies that enhance the security structure of LIS to protect applications and old data. SMF consists of two layers of security control: data audit trail and user access authorization. This contribution has two key advantages. First, it can help SMEs protect their data and applications from unauthorized access. Second, it is the lowest cost solution to implement security measures in LIS instead of modernizing or replacing the system. SMF has used the oracle technology for authentication, but the examples are also shown in pseudocode to facilitate easy positioning of other technologies.
Comparison Of Image Compression Analysis Using Deep Autoencoder And Deep CNN Approach

Image compression is a fundamental technique in digital image processing used to decrease the space used for storage of digital images and videos, which will help to increase the storage space and for efficient transmission. Nowadays many deep learning techniques have produced promising results on image compression field. However, traditional compression techniques have introduced many compression artifacts problem. To solve this problem we have compared two deep learning approaches for image compression. One method is based on Deep Autoencoder technique and other is based on deep convolutional neural network (deep CNN) approach. Autoencoder structure is a popular choice to do end-to-end compression and deep CNN is the most popular neural network model for the application of any basic deep learning technique. The performance of two methods are compared based on Peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Based on the performance evaluation methods result, it is evident that deep Autoencoder technique is more advantageous than deep CNN technique.
To Be or not to Be, Tail Labels in Extreme Multi-label Learning

EXtreme Multi-label Learning (XML) aims to predict each instance its most relevant subset of labels from an extremely huge label space, often exceeding one million or even larger in many real applications. In XML scenarios, the labels exhibit a long tail distribution, where a significant number of labels appear in very few instances, referred to as tail labels. Unfortunately, due to the lack of positive instances, the tail labels are intractable to learn as well as predict. Several previous studies even suggested that the tail labels can be directly removed by referring to their label frequencies. We consider that such violent principle may miss many significant tail labels, because the predictive accuracy is not strictly consistent with the label frequency especially for tail labels. In this paper, we are interested in finding a reasonable principle to determine whether a tail label should be removed, not only depending on their label frequencies. To this end, we investigate a method named Nearest Neighbor Positive Proportion Score (N2P2S) to score the tail labels by annotations of the instance neighbors. Extensive empirical results indicate that the proposed N2P2S can effectively screen the tail labels, where many preserved tail labels can be learned and accurately predicted even with very few positive instances.
Local Intrinsic Dimensionality and Graphs: Towards LID-aware Graph Embedding Algorithms

Local intrinsic dimensionality (LID) has many important applications in the field of machine learning (ML) and data mining (DM). Existing LID models and estimators have mostly been applied to data points in Euclidean spaces, enabling LID-aware ML/DM algorithms for tabular data. To the best of our knowledge, prior research works have not considered LID for designing or evaluating graph-based ML/DM algorithms. In this paper, we discuss potential applications of LID to graph-structured data considering graph embeddings and graph distances. Then, we propose NC-LID – a LID-related measure for quantifying the discriminatory power of the shortest-path distance with respect to natural communities of nodes as their intrinsic neighborhoods. It is shown how NC-LID can be utilized to design LID-elastic graph embedding algorithms based on random walks by proposing two LID-elastic variants of Node2Vec. Our experimental evaluation on real-world graphs demonstrates that NC-LID can point to weak parts of Node2Vec embeddings that can be improved by the proposed LID-elastic extensions.
Elliptical Curve Cryptography Design Principles

The factors decomposition problem for large numbers and the discrete logarithm problem in finite field are two mathematical theories used in cryptography of public key. In recent public key cryptography, such as RSA cryptography, large-number factor decomposition problems are commonly exploited. RSA has had several issues with the advancement in processor hardware and fast computing technologies. With small public key, bandwidth of network is inadequate, and the capacity to withstand attack is excellent, the elliptic curve discrete logarithm problem is used in cryptography. The article delves into the strategy values of elliptic curve cryptography, the system's key insides, the steady elliptic curve collection process, and a thorough examination of its application.
Triplet Loss for Effective Deployment of Deep Learning Based Driver Identification Models

Recent studies have shown that both the quantity and quality of the driving data are crucial for the success of deep learning based driver identification solutions. This is not only true for the training of deep neural networks, but also for their deployment. In the deployment phase the models are applied to identify new drivers that are different from the drivers in the training phase. New data is required to re-train the models and adjust them to fit the new drivers. While the focus of recent studies has been mostly on the training phase, this study focuses on the deployment phase. Inspired by face and fingerprint recognition solutions, in this paper we propose a solution that does not require model retraining in the deployment phase. Instead, only a small amount of example driving data from each driver is required. More specifically, we propose an approach that utilises the triplet loss function to learn a mapping from vehicular sensor data to an embedding space, where the distance between two embeddings represents the drivers' similarity. The resulting network can be directly used for identifying new drivers without re-training.
Medical Image Segmentation with Imperfect 3D Bounding Boxes

The development of high quality medical image segmentation algorithms depends on the availability of large datasets with pixel-level labels. The challenges of collecting such datasets, especially in case of 3D volumes, motivate to develop approaches that can learn from other types of labels that are cheap to obtain, e.g. bounding boxes. We focus on 3D medical images with their corresponding 3D bounding boxes which are considered as series of per-slice non-tight 2D bounding boxes. While current weakly-supervised approaches that use 2D bounding boxes as weak labels can be applied to medical image segmentation, we show that their success is limited in cases when the assumption about the tightness of the bounding boxes breaks. We propose a new bounding box correction framework which is trained on a small set of pixel-level annotations to improve the tightness of a larger set of non-tight bounding box annotations. The effectiveness of our solution is demonstrated by evaluating a known weakly-supervised segmentation approach with and without the proposed bounding box correction algorithm. When the tightness is improved by our solution, the results of the weakly-supervised segmentation become much closer to those of the fully-supervised one.
Data-driven vehicle speed detection from synthetic driving simulator images

Despite all the challenges and limitations, vision-based vehicle speed detection is gaining research interest due to its great potential benefits such as cost reduction, and enhanced additional functions. As stated in a recent survey [1], the use of learning-based approaches to address this problem is still in its infancy. One of the main difficulties is the need for a large amount of data, which must contain the input sequences and, more importantly, the output values corresponding to the actual speed of the vehicles. Data collection in this context requires a complex and costly setup to capture the images from the camera synchronized with a high precision speed sensor to generate the ground truth speed values. In this paper we explore the use of synthetic images generated from a driving simulator (e.g., CARLA) to address vehicle speed detection using a learning-based approach. We simulate a virtual camera placed over a stretch of road, and generate thousands of images with variability corresponding to multiple speeds, different vehicle types and colors, and lighting and weather conditions. Two different approaches to map the sequence of images to an output speed (regression) are studied, including CNN-GRU and 3D-CNN. We present preliminary results that support the high potential of this approach to address vehicle speed detection.
Gram-Elites: N-Gram Based Quality-Diversity Search

In the context of procedural content generation via machine learning (PCGML), quality-diversity (QD) algorithms are a powerful tool to generate diverse game content. A branch of QD uses genetic operators to generate content (e.g. MAP-Elites). Problematically, levels generated with these operators have no guarantee of matching the style of a game. This can be addressed by incorporating whether a level is generable by an n-gram into the fitness function. Unfortunately, this leads to wasted computation and poor results. In this work, we introduce n-gram genetic operators, which produce only solutions that are generable by the n-gram model; we call MAP-Elites combined with these operators Gram-Elites. We test on a tile-based side-scrolling platformer, vertical platformer, and roguelike. For all three, n-gram operators outperform standard operators and random n-gram generation, finding more usable (i.e. completable and generable) solutions at a faster rate. By integrating structure into operators, instead of fitness, these genetic operators could be beneficial to QD in PCGML.
Multi-Modal RGB-D Scene Recognition Across Domains

Scene recognition is one of the basic problems in computer vision research with extensive applications in robotics. When available, depth images provide helpful geometric cues that complement the RGB texture information and help to identify discriminative scene image features.Depth sensing technology developed fast in the last years and a great variety of 3D cameras have been introduced, each with different acquisition properties. However, those properties are often neglected when targeting big data collections, so multi-modal images are gathered disregarding their original nature. In this work, we put under the spotlight the existence of a possibly severe domain shift issue within multi-modality scene recognition datasets. As a consequence, a scene classification model trained on one camera may not generalize on data from a different camera, only providing a low recognition performance. Starting from the well-known SUN RGB-D dataset, we designed an experimental testbed to study this problem and we use it to benchmark the performance of existing methods.Finally, we introduce a novel adaptive scene recognition approach that leverages self-supervised translation between modalities. Indeed, learning to go from RGB to depth and vice-versa is an unsupervised procedure that can be trained jointly on data of multiple cameras and may help to bridge the gap among the extracted feature distributions. Our experimental results confirm the effectiveness of the proposed approach.