Reviewerly/exHarmony huggingface dataset:
Learning-Based Joint QoE Optimization for Adaptive Video Streaming Based on Smart Edge

The latest increase in HTTP-based adaptive video streaming over the Internet enables a growing number of clients to compete for a shared bottleneck bandwidth. This competition may affect users’ Quality of Experience (QoE) negatively, especially in terms of fairness and stability. This paper presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flex-Steward</i> , a solution that performs multi-client joint QoE optimization for adaptive video streaming during bottleneck bandwidth sharing. Joint QoE optimization refers to improving QoE fairness among clients with various video devices and availing from differentiated services with different priorities. Flex-Steward deploys an adaptive bitrate delivery algorithm based on Neural Networks (NN) and reinforcement learning at the network edge. It relies on a trained NN model to make appropriate bitrate recommendations in terms of video chunks to be requested by clients sharing the same bottleneck bandwidth. Flex-Steward is assessed in comparison with alternative state-of-the-art algorithms under different network conditions using a real-life prototype. Results show how Flex-Steward reduces the unfairness in terms of joint QoE optimization with between 10.9% and 41.7%.
Relay-Assisted Over-the-Air Federated Learning

Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge. To improve the communication efficiency of FL, over-the-air computation allows a large number of mobile devices to concurrently upload their local models. Due to wireless channel fading, the model aggregation error at the edge server is dominated by the weakest channel among all devices, causing severe straggler issues. In this paper, we propose a relay-assisted over-the-air FL scheme to address the straggler issue. In particular, we adopt a half-duplex relay to assist the devices in uploading the local model updates to the edge server. Our scheme exploits the direct transmissions by the devices and the device-relay cooperative diversity for over-the-air model aggregation. Then, we study the transceiver design in the relay-assisted FL system. The strong coupling between the design variables renders the optimization of such a system challenging. To tackle this issue, we propose an alternating-optimization-based algorithm to optimize the transceiver and relay operation. Numerical results show that our design achieves faster convergence compared with state-of-the-art schemes.
HeapCheck: Low-cost Hardware Support for Memory Safety

Programs written in C/C++ are vulnerable to memory-safety errors like buffer-overflows and use-after-free. While several mechanisms to detect such errors have been previously proposed, they suffer from a variety of drawbacks, including poor performance, imprecise or probabilistic detection of errors, or requiring invasive changes to the ISA, binary-layout, or source-code that results in compatibility issues. As a result, memory-safety errors continue to be hard to detect and a principal cause of security problems. In this work, we present a minimally invasive and low-cost hardware-based memory-safety checking framework for detecting out-of-bounds accesses and use-after-free errors. The key idea of our mechanism is to re-purpose some of the “unused bits” in a pointer in 64-bit architectures to store an index into a bounds information table that can be used to catch out-bounds errors and use-after-free errors without any change to the binary layout. Using this memory-safety checking framework, we enable HeapCheck, a design for detecting Out-of-bounds and Use-after-free accesses for heap-objects, that are responsible for the majority of memory-safety errors in the wild. Our evaluations using C/C++ SPEC CPU 2017 workloads on Gem5 show that our solution incurs 1.5% slowdown on average, using an 8 KB on-chip SRAM cache for caching bounds-information. Our mechanism allows detection of out-of-bounds errors in user-code as well as in unmodified shared-library functions. Our mechanism has detected out-of-bounds accesses in 87 lines of code in the SPEC CPU 2017 benchmarks, primarily in Glibc v2.27 functions, that, to our knowledge, have not been previously detected even with popular tools like Address Sanitizer.
Collision-Free Navigation in Human-Following Task Using a Cognitive Robotic System on Differential Drive Vehicles

As human–robot collaboration increases tremendously in real-world applications, a fully autonomous and reliable mobile robot for the collaboration has been a central research topic and investigated extensively in a large number of studies. One of the most pressing issues in such topic is the collision-free navigation that has a moving goal and unknown obstacles under the unstructured environment. In this article, a cognitive robotic system (CRS) is proposed for the robot to navigate itself to the moving target person without obstacle collision. This CRS consists of a cognitive agent, which is created based on the Soar cognitive architecture to reason its current situation and make action decision for the robot to avoid obstacles and reach the target position, and a speed planning module, which is based on dynamic window approach (DWA) to generate appropriate linear and angular velocities for driving the robot’s motors. For the implementation of the proposed system, we use a differential drive wheel robot equipped with two ultrawideband (UWB) sensors and a color depth camera as the experimental platform. Finally, to evaluate the performance of our system in actual operating conditions, we conduct experiments with a scenario that includes main tasks: avoiding consecutive unknown obstacles and turning at corner while the robot follows continuously human user along the corridor.
Memory transformer with hierarchical attention for long document processing

Transformers have attracted lots of interest from the researchers. Up to now, transformers achieved state-of-the-art results in a wide range of natural language processing tasks such as different sequence modeling tasks like language understanding, text summarization and translation, and definitely more transformers to come. Still, transformers has their limitations in the tasks requiring long document processing. This paper introduces a new version of transformer, a Sentence level transformer with global memory pooling and hierarchical attention to cope with long text. We replace self-attention of vanilla transformer with multi-head attention between memory and a sequence, and also add a decoder sequence selector on the top of the encoder output. In our architecture sentences are encoded in parallel and then summarized with soft-attention on every decoding step. Proposed model was validated in machine translation task. We hypothesize that attaching memory slots to each sequence improves the quality of translation, besides tuning the model on context-aware data set by using pre-trained sequence-level weights will help to get more precise translation and promote translating long documents. Results show that extending each sentence with a memory slot and employing the attention over the encoder outputs improves translation results.
Optimal video handling in on-line hand gesture recognition using Deep Neural Networks

Deep Neural Networks (DNNs) are machine learning models with a myriad of uses, such as enabling tools that support professional workers or facilitating new modes of human-computer communication. Hand gesture recognition from RGB video feed is one of the most important relevant tasks where DNN models tend to excel, with many application domains typically demanding their real-time execution (e.g., in human-robot interaction scenarios). However, several operational parameters of similar models, especially concerning the way the input video data are handled, have not yet been sufficiently investigated with a clear goal of identifying best practices. For instance, various design choices about how video frames are fed to the DNN model during its training and its evaluation (e.g., using the traditional temporal subsampling per video approach, or employing a temporal sliding window) directly affect method accuracy. This paper aims at empirically finding optimal strategies regarding such operational parameters for input video handling when training and evaluating learning models that perform real-time, on-line gesture recognition’ using typical CNN-based approaches on a relevant dataset.
Efficient Encoder-Decoder Network With Estimated Direction for SAR Ship Detection

Synthetic aperture radar (SAR) image ship detection has important applications in marine surveillance. There are two limitations when applying advanced detection methods naively for SAR ship detection. First, most detectors construct the model as an encoder and rely on the feature pyramid network (FPN) head for accurate prediction, which may lead to high computational costs. Second, the background noises in the ground truth (annotated as rectangular bounding boxes) of angular ships bring difficulties for model training. To meet these challenges, we propose an efficient encoder&#x2013;decoder network with estimated direction for ship detection in SAR images. First, we present an anchor-free encoder&#x2013;decoder model that can efficiently extract multiple-level features. Second, we formulate ship detection as a multitask learning problem, including a bounding box prediction and a ship direction regression. The estimated ship direction can weakly supervise and benefit ship detection. Furthermore, we develop a center-weighted labeling method for overlapped annotations. Comprehensive experiments on SAR-Ship-Detection and SSDD datasets show that our method achieves state-of-the-art performance with a high running speed.
Semantic Segmentation for High-Resolution Remote-Sensing Images via Dynamic Graph Context Reasoning

Semantic segmentation for high-resolution remote-sensing (HRRS) images is one of the most challenging tasks in remote-sensing images understanding. Capturing long-range dependencies in feature representations is crucial for semantic segmentation. Recent graph-based global reasoning networks (<i>GloRe</i>) focus on modeling the global contextual relationship between latent nodes based on fully connected graph in interaction space. However, such a dense operation is susceptible to redundant features. Most importantly, it treats each node equally, ignoring the contextual relationship between nodes in graphs. In this work, we propose to explore more effective contextual representations in semantic segmentation by introducing dynamic graph contextual reasoning module over <i>GloRe</i>, dubbed DGCR. It incorporates local semantic information that represents the relationships between nodes to perform long-range contextual reasoning. More specifically, to provide effectively and flexible reasoning in graph-based reasoning approaches, we construct <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor (KNN) graphs rather than fully connected graphs using only the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> closest nodes depends on pairwise semantic distance. Extensive experiments on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam datasets demonstrate the effectiveness and superiority of our proposed DGCR module over other state-of-the-art methods.
Advanced uncertainty based approach for discovering erasable product patterns

It is uncertain whether products will be manufactured as defective products. Producing the defective products causes the waste of time and finance in the industrial fields. When the financial crisis occurs in the manufacturing plants, production lines of the less profitable products should be stopped. To overcome the financial crisis, it is required to find the production lines that manufacture the less profitable commodities and are likely to produce the faulty merchandises and remove the corresponding production lines in order to increase the profit. Erasable pattern mining has been suggested to analyze and solve the financial problems by discovering patterns with low profit. As databases become more diverse due to the environment and characteristics of data, demands for erasable pattern mining that processes the various databases increase. An uncertain database, which is one of diverse types of databases, includes the probability related to the item, such as the probability of existence and the probability of defects. Erasable pattern mining, which considers the probability of each item, can discover pattern results which are more suitable for purpose of users. Motivated by this, we propose an efficient tree-based erasable pattern mining algorithm from the uncertain database. Considering the probability of the item, the suggested algorithm extracts erasable patterns with low profits from the uncertain database. Since both profit of the product and the probability of the item are considered, the discovered patterns are more meaningful than the patterns found by the existing erasable pattern mining. Moreover, the proposed method improves the mining operations by utilizing a list structure as well as a tree structure. We performed a performance evaluation on diverse real and synthetic datasets in order to prove the proposed algorithm outperforms the existing erasable pattern mining algorithms. The differences in runtime, memory usage, and scalability are shown through the comparison of performance with other state-of-the-art algorithms and the suggested algorithm.
Learning 3D Semantic Scene Graphs with Instance Embeddings

Abstract A 3D scene is more than the geometry and classes of the objects it comprises. An essential aspect beyond object-level perception is the scene context, described as a dense semantic network of interconnected nodes. Scene graphs have become a common representation to encode the semantic richness of images, where nodes in the graph are object entities connected by edges, so-called relationships. Such graphs have been shown to be useful in achieving state-of-the-art performance in image captioning, visual question answering and image generation or editing. While scene graph prediction methods so far focused on images, we propose instead a novel neural network architecture for 3D data, where the aim is to learn to regress semantic graphs from a given 3D scene. With this work, we go beyond object-level perception, by exploring relations between object entities. Our method learns instance embeddings alongside a scene segmentation and is able to predict semantics for object nodes and edges. We leverage 3DSSG , a large scale dataset based on 3RScan that features scene graphs of changing 3D scenes. Finally, we show the effectiveness of graphs as an intermediate representation on a retrieval task.
A Blockchain-based Subscriber Data Management Scheme for 6G Mobile Communication System

The subscriber data management locates at the heart of the mobile network by providing identity management and granting subscriber access to various network services. The subscriber data is usually stored in the core network, the handling of which shall be compliant with regulatory requirements on data protection. Inspired by the current research on the privacy issues of Internet applications, this work is the first attempt to consider data management from mobile network architecture perspective with the goal of making data processing natively transparent. This work proposes a novel subscriber data management scheme based on the combination of Distributed Ledger Technology (DLT) and Distributed Hash Table (DHT) technologies. Moreover, corresponding procedures are defined to ensure subscriber’s control over personal subscription data (including authentication data) by implementing authentication, authorization and access control in the blockchain platform. To avoid blockchain bloat and privacy issues caused by transparency of blockchain, off-chain data storage architecture is adopted, and is realized in the form of DHT for its expressive features like scalability and look-up speed.
Domain-Specific Adaptation of CNN for Detecting Face Presentation Attacks in NIR

For the automotive industry moving towards personalized applications and experiences, the identification of the person inside vehicle is necessary; and it must be carried out in a secure manner. In this paper, we propose a unique face presentation attack detection (PAD) system for operation inside a passenger vehicle. A typical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vehicular</i> face PAD system is required to function with several constraints such as bounded sensing (imaging) capabilities, limited computing resources on embedded devices, real-time inference, and essentially, very high accuracy. In this work, we develop a face PAD system for automotive domain, relying on a single NIR camera, to continually verify whether the driver’s face is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bona-fide</i> or not. Our work has two main contributions: first, a lightweight face PAD framework has been developed using a 9-layer convolutional neural network (CNN). With its compact size and limited set of operators, it can be deployed in a resource constrained embedded device to achieve a near real-time inference. To alleviate the problem of limited training data (face PAD in NIR) for a given system, we develop an efficient mechanism to obtain this CNN through the combination of adaptation of domain-specific layers and task-specific fine-tuning of a base CNN. As the second contribution, we collect a large face PAD dataset with 5800+ videos, acquired in NIR (940 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nm</i> ) illumination, for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vehicular</i> use-cases. This dataset, named VFPAD, captures several real-world variations in terms of environmental settings, illumination, subject’s pose, and appearances. Based on the VFPAD dataset, we demonstrate that the proposed face PAD method achieves very high performance (overall accuracy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 98.0$ </tex-math></inline-formula> %), and also outperforms several baseline face PAD methods. The dataset will be shared with the wider scientific community for research purposes.
Balanced K-means using Quantum annealing

In this paper, we propose a new quantum version of the Balanced K-means algorithm in the D-wave quantum annealer. D-wave 2000Q quantum computer has been used by many papers in the last few years to solve optimization problems and for finding the global minimum of the balanced K-means optimization problem. However, in this paper, we modify the quadratic unconstrained binary optimization (QUBO) formulation of the Balanced K-means that has been proposed in a recent paper. Our modification is trained on different data sets: Iris, Wine and Breast Cancer. Also, we performed a comparative analysis between the two approaches (our approach and the paper's approach) to find the one that assigns the largest number of data to clusters and we also use the Davies-Bouldi metric to prove that our method gives the best clustering.
Photon-assisted Phase Slips in Superconducting Nanowires

Phase slip is the intrinsic fluctuation of the order parameter and leads to the dissipation of superconductors. Here we propose a photon-assisted phase-slip model in superconducting nanowires to explain the phase-slip rate under photon irradiation. In this phenomenological model, incident photons destroy large quantities of Cooper pairs and reduce the free-energy barrier of phase slips, resulting in proliferation in the phase-slip events and leading to superconducting transition. The switching rates from the superconducting state of a niobium nitride nanowire under various photon irradiation (4.4 mW/m${}^{2}$--4.4 W/m${}^{2}$) and temperatures (2.3--3.7 K) are investigated through the distribution of switching currents in the experiment. The experimental data can be well fitted by our deduced expression of phase-slip rate after eliminating the influence of external noise. Our model develops the phase-slip theory under incident photons and is promising to reveal the intrinsic detection mechanism of the superconducting nanowire single-photon detector.
Heterogeneous Graph Neural Network with Distance Encoding

Heterogeneous graph neural network (HGNN) has shown superior performance and attracted considerable research interest. However, HGNN inherits the limitation of representational power from GNN via learning individual node embeddings based on their neighbors, largely ignoring the potential correlations between nodes. In fact, the complex correlation between nodes (e.g., distance) is crucial for many graph mining tasks. How to establish correlations between multiple node embeddings and improve the representational power of HGNN is still an open problem. To solve it, we propose a heterogeneous distance encoding (HDE) technique to fundamentally improve the representational power of HGNN. Specifically, we define heterogeneous shortest path distance to describe the relative distance between nodes, and then jointly encode such distances for multiple nodes of interest to establish their correlation. By simply injecting the encoded correlation into the neighbor aggregating process, we propose a novel distance encoding based heterogeneous graph neural network (called DHN), which is able to learn more expressive heterogeneous graph representations for downstream tasks. More importantly, the proposed DHN relies only on the graph structure and ensures the inductive ability of HGNN. Significant improvements over four real-world graphs demonstrate the representational power of HDE.
Cross-domain Mutual Information Adversarial Maximization

Domain adaptation challenges the problem where the source domain and the target domain have distinctive data distributions. Different from previous approaches which align the two domains by minimizing a distribution metric, in this paper, we report a new perspective of handling unsupervised domain adaptation. Specifically, we formulate domain adaptation as maximizing the obtained knowledge of the target domain through observing the source domain. Technically, we maximize the mutual information between the source domain features and the target domain features in a deep adversarial network. Firstly, we use a feature extraction network and a domain discriminator with opposite goals to form adversarial components, and learn the domain-invariant features between the source and target domains through adversarial training. Secondly, we use the optimization goal of maximizing the mutual information between cross-domain features to supervise the adversarial training process to ensure that the maximum target domain information can be obtained by observing the source domain features. Finally, we evaluate our method on four datasets: Office-31, ImageCLEF-DA, Office-Home, and VisDA-2017, and all achieve better performance than previous methods. We show that our method, named Cross-domain Mutual Information Adversarial Maximization (CMIAM), is a promising approach and able to outperform previous state-of-the-arts on various unsupervised domain adaptation tasks.