OpenRaiser/Intern-Atlas huggingface dataset:
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification
Venue: arXiv cs.LG 2021
Year: 2021

Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces (MMI) and their application for e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Therefore, obtaining high generalization quality of a trained sEMG decoder is quite challenging. Usually, machine learning based sEMG decoders are either trained on subject-specific data, or at least recalibrated for each user, individually. Even though, deep learning algorithms produced several state of the art results for sEMG decoding,however, due to the limited amount of availability of sEMG data, the deep learning models are prone to overfitting. Recently, transfer learning for domain adaptation improved generalization quality with reduced training time on various machine learning tasks. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on a new subjects data, and compare their performance to subject-specific models. To the best of our knowledge, this is the first study that thoroughly investigated weight-initialization based transfer learning for sEMG classification and compared transfer learning to subject-specific modeling. We tested our models on three publicly available databases under various settings. On average over all settings, our transfer learning approach improves 5~\%-points on the pretrained models without fine-tuning and 12~\%-points on the subject-specific models, while being trained on average 22~\% fewer epochs. Our results indicate that transfer learning enables faster training on fewer samples than user-specific models, and improves the performance of pretrained models as long as enough data is available.
V2V-Based Task Offloading and Resource Allocation in Vehicular Edge Computing Networks
Venue: arXiv cs.DC 2021
Year: 2021

In the research and application of vehicle ad hoc networks (VANETs), it is often assumed that vehicles obtain cloud computing services by accessing to roadside units (RSUs). However, due to the problems of insufficient construction quantity, limited communication range and overload of calculation load of roadside units, the calculation mode relying only on vehicle to roadside units is difficult to deal with complex and changeable calculation tasks. In this paper, when the roadside unit is missing, the vehicle mobile unit is regarded as a natural edge computing node to make full use of the excess computing power of mobile vehicles and perform the offloading task of surrounding mobile vehicles in time. In this paper, the OPFTO framework is designed, an improved task allocation algorithm HGSA is proposed, and the pre-filtering process is designed with full consideration of the moving characteristics of vehicles. In addition, vehicle simulation experiments show that the proposed strategy has the advantages of low delay and high accuracy compared with other task scheduling strategies, which provides a reference scheme for the construction of Urban Intelligent Transportation in the future.
Towards Automated Sample Collection and Return in Extreme Underwater Environments
Venue: arXiv cs.RO 2021
Year: 2021

In this report, we present the system design, operational strategy, and results of coordinated multi-vehicle field demonstrations of autonomous marine robotic technologies in search-for-life missions within the Pacific shelf margin of Costa Rica and the Santorini-Kolumbo caldera complex, which serve as analogs to environments that may exist in oceans beyond Earth. This report focuses on the automation of ROV manipulator operations for targeted biological sample-collection-and-return from the seafloor. In the context of future extraterrestrial exploration missions to ocean worlds, an ROV is an analog to a planetary lander, which must be capable of high-level autonomy. Our field trials involve two underwater vehicles, the SuBastian ROV and the Nereid Under Ice (NUI) hybrid ROV for mixed initiative (i.e., teleoperated or autonomous) missions, both equipped 7-DoF hydraulic manipulators. We describe an adaptable, hardware-independent computer vision architecture that enables high-level automated manipulation. The vision system provides a 3D understanding of the workspace to inform manipulator motion planning in complex unstructured environments. We demonstrate the effectiveness of the vision system and control framework through field trials in increasingly challenging environments, including the automated collection and return of biological samples from within the active undersea volcano, Kolumbo. Based on our experiences in the field, we discuss the performance of our system and identify promising directions for future research.
Space-Efficient FPT Algorithms
Venue: arXiv cs.DS 2021
Year: 2021

We prove algorithmic results showing that a number of natural parameterized problems are in the restricted-space parameterized classes Para-L and FPT+XL. The first class comprises problems solvable in f(k) n^{O(1)} time using g(k) + O(log n)) bits of space (k is the parameter and n is the input size; f and g are computable functions). The second class comprises problems solvable under the same time bound, but using g(k) log n bits of space instead. Earlier work on these classes has focused largely on their structural aspects and their relationships with various other classes. We complement this with Para-L and FPT+XL algorithms for a restriction of Hitting Set, some graph deletion problems where the target class has an infinite forbidden set characterization, a number of problems parameterized by vertex cover number, and Feedback Vertex Set.
Fast Graph Subset Selection Based on G-optimal Design
Venue: arXiv cs.IT 2021
Year: 2021

Graph sampling theory extends the traditional sampling theory to graphs with topological structures. As a key part of the graph sampling theory, subset selection chooses nodes on graphs as samples to reconstruct the original signal. Due to the eigen-decomposition operation for Laplacian matrices of graphs, however, existing subset selection methods usually require high-complexity calculations. In this paper, with an aim of enhancing the computational efficiency of subset selection on graphs, we propose a novel objective function based on the optimal experimental design. Theoretical analysis shows that this function enjoys an $α$-supermodular property with a provable lower bound on $α$. The objective function, together with an approximate of the low-pass filter on graphs, suggests a fast subset selection method that does not require any eigen-decomposition operation. Experimental results show that the proposed method exhibits high computational efficiency, while having competitive results compared to the state-of-the-art ones, especially when the sampling rate is low.
Robustness and risk management via distributional dynamic programming
Venue: arXiv cs.LG 2021
Year: 2021

In dynamic programming (DP) and reinforcement learning (RL), an agent learns to act optimally in terms of expected long-term return by sequentially interacting with its environment modeled by a Markov decision process (MDP). More generally in distributional reinforcement learning (DRL), the focus is on the whole distribution of the return, not just its expectation. Although DRL-based methods produced state-of-the-art performance in RL with function approximation, they involve additional quantities (compared to the non-distributional setting) that are still not well understood. As a first contribution, we introduce a new class of distributional operators, together with a practical DP algorithm for policy evaluation, that come with a robust MDP interpretation. Indeed, our approach reformulates through an augmented state space where each state is split into a worst-case substate and a best-case substate, whose values are maximized by safe and risky policies respectively. Finally, we derive distributional operators and DP algorithms solving a new control task: How to distinguish safe from risky optimal actions in order to break ties in the space of optimal policies?
FPGA Based Accelerator for Neural Networks Computation with Flexible Pipelining
Venue: arXiv cs.AR 2021
Year: 2021

FPGA is appropriate for fix-point neural networks computing due to high power efficiency and configurability. However, its design must be intensively refined to achieve high performance using limited hardware resources. We present an FPGA-based neural networks accelerator and its optimization framework, which can achieve optimal efficiency for various CNN models and FPGA resources. Targeting high throughput, we adopt layer-wise pipeline architecture for higher DSP utilization. To get the optimal performance, a flexible algorithm to allocate balanced hardware resources to each layer is also proposed, supported by activation buffer design. Through our well-balanced implementation of four CNN models on ZC706, the DSP utilization and efficiency are over 90%. For VGG16 on ZC706, the proposed accelerator achieves the performance of 2.58x, 1.53x and 1.35x better than the referenced non-pipeline architecture [1], pipeline architecture [2] and [3], respectively.
Machine Learning in Congestion Control: A Survey on Selected Algorithms and a New Roadmap to their Implementation
Venue: arXiv cs.NI 2021
Year: 2021

With the emergence of new technologies, computer networks are becoming more structurally complex, diverse and heterogenous. The increasing discrepancy (among the interconnected networks) in data rates, delays, packet loss, and transmission scenarios, influence significantly the dynamics of congestion control (CC) parametrization. In contrast to the traditional endto-end CC algorithms that rely on strict rules, new approaches aim to involve machine learning in order to continuously adapt the CC to real-time network requirements. However, due to the high computational complexity and memory consumption, the feasibility of these schemes may still be questioned. This paper surveys selected machine-learning based approaches to CC and proposes a roadmap to their implementation in computer systems, by using dataflow computing and Gallium Arsenide (GaAs) chips.
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning
Venue: arXiv cs.LG 2021
Year: 2021

Inferring reward functions from demonstrations and pairwise preferences are auspicious approaches for aligning Reinforcement Learning (RL) agents with human intentions. However, state-of-the art methods typically focus on learning a single reward model, thus rendering it difficult to trade off different reward functions from multiple experts. We propose Multi-Objective Reinforced Active Learning (MORAL), a novel method for combining diverse demonstrations of social norms into a Pareto-optimal policy. Through maintaining a distribution over scalarization weights, our approach is able to interactively tune a deep RL agent towards a variety of preferences, while eliminating the need for computing multiple policies. We empirically demonstrate the effectiveness of MORAL in two scenarios, which model a delivery and an emergency task that require an agent to act in the presence of normative conflicts. Overall, we consider our research a step towards multi-objective RL with learned rewards, bridging the gap between current reward learning and machine ethics literature.
An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation
Venue: arXiv cs.IR 2021
Year: 2021

Recently, deep reinforcement learning (RL) has proven its feasibility in solving combinatorial optimization problems (COPs). The learning-to-rank techniques have been studied in the field of information retrieval. While several COPs can be formulated as the prioritization of input items, as is common in the information retrieval, it has not been fully explored how the learning-to-rank techniques can be incorporated into deep RL for COPs. In this paper, we present the learning-to-rank distillation-based COP framework, where a high-performance ranking policy obtained by RL for a COP can be distilled into a non-iterative, simple model, thereby achieving a low-latency COP solver. Specifically, we employ the approximated ranking distillation to render a score-based ranking model learnable via gradient descent. Furthermore, we use the efficient sequence sampling to improve the inference performance with a limited delay. With the framework, we demonstrate that a distilled model not only achieves comparable performance to its respective, high-performance RL, but also provides several times faster inferences. We evaluate the framework with several COPs such as priority-based task scheduling and multidimensional knapsack, demonstrating the benefits of the framework in terms of inference latency and performance.
Evaluation of Cyber Attacks Targeting Internet Facing IoT : An Experimental Evaluation
Venue: arXiv cs.CR 2021
Year: 2021

The rapid growth of Information and Communication Technology (ICT) in the 21st century has resulted in the emergence of a novel technological paradigm; known as the Internet of Things, or IoT. The IoT, which is at the heart of today's smart infrastructure, aids in the creation of a ubiquitous network of things by simplifying interconnection between smart digital devices and enabling Machine to Machine (M2M) communication. As of now, there are numerous examples of IoT use cases available, assisting every person in this world towards making their lives easier and more convenient. With the latest advancement of IoT in variety of cyber-attacks that targets these pervasive IoT environments, which can even lead to jeopardizing the lives of peoples; that are involving with it. In general, this IoT can be considered as every digital object that is connected to the Internet for intercommunication. Hence in this regard in order to analyse cyber threats that come through the Internet, here we are doing an experimental evaluation to analyse the requests, received to exploit the opened Secure Shell (SSH) connection service of an IoT device, which in our case a Raspberry Pi devices, which connected to the Internet for more than six consecutive days. By opening the SSH service on Raspberry Pi, it acts as a Honeypot device where we can log and retrieve all login attempt requests received to the SSH service opened. Inspired by evaluating the IoT security attacks that target objects in the pervasive IoT environment, after retrieving all the login requests that made through the open SSH connection we then provide a comprehensive analysis along with our observations about the origin of the requests and the focus areas of intruders; in this study.
Improving Surrogate Gradient Learning in Spiking Neural Networks via Regularization and Normalization
Venue: arXiv cs.NE 2021
Year: 2021

Spiking neural networks (SNNs) are different from the classical networks used in deep learning: the neurons communicate using electrical impulses called spikes, just like biological neurons. SNNs are appealing for AI technology, because they could be implemented on low power neuromorphic chips. However, SNNs generally remain less accurate than their analog counterparts. In this report, we examine various regularization and normalization techniques with the goal of improving surrogate gradient learning in SNNs.
Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts
Venue: arXiv cs.CL 2021
Year: 2021

Language-enabled AI systems can answer complex, multi-hop questions to high accuracy, but supporting answers with evidence is a more challenging task which is important for the transparency and trustworthiness to users. Prior work in this area typically makes a trade-off between efficiency and accuracy; state-of-the-art deep neural network systems are too cumbersome to be useful in large-scale applications, while the fastest systems lack reliability. In this work, we integrate fast syntactic methods with powerful semantic methods for multi-hop explanation generation based on declarative facts. Our best system, which learns a lightweight operation to simulate multi-hop reasoning over pieces of evidence and fine-tunes language models to re-rank generated explanation chains, outperforms a purely syntactic baseline from prior work by up to 7% in gold explanation retrieval rate.
Latency in Mesh Networks
Venue: arXiv cs.NI 2021
Year: 2021

In this work, we consider latency in wireless networks, motivated by the need for reliable and low-latency wireless communication for applications such as vehicle-to-vehicle communication and industrial automation. The environment that these applications operate in is often highly dynamic, and, as a result, the wireless network technology used needs to be able to adapt to changing conditions, which makes mesh networking a good fit. Hence, in this work, we consider Thread and Bluetooth Mesh, two prominent mesh networking technologies. We measure one-way point-to-point latency using these technologies and show the cumulative probability distribution of the latency for varying payload size. We carry out the measurements under static conditions in an open-air environment. Our results show that Thread achieves lower latency than Bluetooth Mesh, especially for larger payloads. Furthermore, we show that Thread meets some of the latency targets set out for 5G. However, since Thread networks are slow to adapt to network changes, Thread may still not be suitable for application requiring low latency. Bluetooth mesh, on the other hand, does not meet any of the latency targets, although it is close when the payload is small (less than 11 bytes). Overall, there needs to be further optimization before these technologies can be used for reliable and low-latency communication in dynamic environments.
Can Machine Learning be Moral?
Venue: arXiv cs.CY 2021
Year: 2021

The ethics of Machine Learning has become an unavoidable topic in the AI Community. The deployment of machine learning systems in multiple social contexts has resulted in a closer ethical scrutiny of the design, development, and application of these systems. The AI/ML community has come to terms with the imperative to think about the ethical implications of machine learning, not only as a product but also as a practice (Birhane, 2021; Shen et al. 2021). The critical question that is troubling many debates is what can constitute an ethically accountable machine learning system. In this paper we explore possibilities for ethical evaluation of machine learning methodologies. We scrutinize techniques, methods and technical practices in machine learning from a relational ethics perspective, taking into consideration how machine learning systems are part of the world and how they relate to different forms of agency. Taking a page from Phil Agre (1997) we use the notion of a critical technical practice as a means of analysis of machine learning approaches. Our radical proposal is that supervised learning appears to be the only machine learning method that is ethically defensible.
Demand-Driven Asset Reutilization Analytics
Venue: arXiv cs.LG 2021
Year: 2021

Manufacturers have long benefited from reusing returned products and parts. This benevolent approach can minimize cost and help the manufacturer to play a role in sustaining the environment, something which is of utmost importance these days because of growing environment concerns. Reuse of returned parts and products aids environment sustainability because doing so helps reduce the use of raw materials, eliminate energy use to produce new parts, and minimize waste materials. However, handling returns effectively and efficiently can be difficult if the processes do not provide the visibility that is necessary to track, manage, and re-use the returns. This paper applies advanced analytics on procurement data to increase reutilization in new build by optimizing Equal-to-New (ETN) parts return. This will reduce 'the spend' on new buy parts for building new product units. The process involves forecasting and matching returns supply to demand for new build. Complexity in the process is the forecasting and matching while making sure a reutilization engineering process is available. Also, this will identify high demand/value/yield parts for development engineering to focus. Analytics has been applied on different levels to enhance the optimization process including forecast of upgraded parts. Machine Learning algorithms are used to build an automated infrastructure that can support the transformation of ETN parts utilization in the procurement parts planning process. This system incorporate returns forecast in the planning cycle to reduce suppliers liability from 9 weeks to 12 months planning cycle, e.g., reduce 5% of 10 million US dollars liability.
One-Step Abductive Multi-Target Learning with Diverse Noisy Label Samples
Venue: arXiv cs.LG 2021
Year: 2021

One-step abductive multi-target learning (OSAMTL) was proposed to handle complex noisy labels. In this paper, giving definition of diverse noisy label samples (DNLS), we propose one-step abductive multi-target learning with DNLS (OSAMTL-DNLS) to expand the methodology of original OSAMTL to better handle complex noisy labels.
Comparative Study of Cloud and Non-Cloud Gaming Platform: Apercu
Venue: arXiv cs.HC 2021
Year: 2021

Nowadays game engines are imperative for building 3D applications and games. This is for the reason that the engines appreciably reduce resources for employing obligatory but intricate utilities. This paper elucidates about a game engine and its foremost elements. It portrays a number of special kinds of contemporary game engines by way of their aspects, procedure and deliberates their stipulations with comparison.
Software Engineering Meets Network Engineering: Conceptual Model for Events Monitoring and Logging
Venue: arXiv cs.NI 2021
Year: 2021

Abstraction applied in computer networking hides network details behind a well-defined representation by building a model that captures an essential aspect of the network system. Two current methods of representation are available, one based on graph theory, where a network node is reduced to a point in a graph, and the other the use of non-methodological iconic depictions such as human heads, walls, towers or computer racks. In this paper, we adopt an abstract representation methodology, the thinging machine (TM), proposed in software engineering to model computer networks. TM defines a single coherent network architecture and topology that is constituted from only five generic actions with two types of arrows. Without loss of generality, this paper applies TM to model the area of network monitoring in packet-mode transmission. Complex network documents are difficult to maintain and are not guaranteed to mirror actual situations. Network monitoring is constant monitoring for and alerting of malfunctions, failures, stoppages or suspicious activities in a network system. Current monitoring systems are built on ad hoc descriptions that lack systemization. The TM model of monitoring presents a theoretical foundation integrated with events and behavior descriptions. To investigate TM modeling s feasibility, we apply it to an existing computer network in a Kuwaiti enterprise to create an integrated network system that includes hardware, software and communication facilities. The final specifications point to TM modeling s viability in the computer networking field.