Reviewerly/exHarmony huggingface dataset:
A Stochastic Approximation Approach for Foresighted Task Scheduling in Cloud Computing

With the increasing and elastic demand for cloud resources, finding an optimal task scheduling mechanism become a challenge for cloud service providers. Due to the time-varying nature of resource demands in length and processing over time and dynamics and heterogeneity of cloud resources, existing myopic task scheduling solutions intended to maximize the performance of task scheduling are inefficient and sacrifice the long-time system performance in terms of resource utilization and response time. In this paper, we propose an optimal solution for performing foresighted task scheduling in a cloud environment. Since a-priori knowledge from the dynamics in queue length of virtual machines is not known in run time, an online reinforcement learning approach is proposed for foresighted task allocation. The evaluation results show that our method not only reduce the response time and makespan of submitted tasks, but also increase the resource efficiency. So in this thesis a scheduling method based on reinforcement learning is proposed. Adopting with environment conditions and responding to unsteady requests, reinforcement learning can cause a long-term increase in system’s performance. The results show that this proposed method can not only reduce the response time and makespan but also increase resource efficiency as a minor goal.
DisCoPy: Monoidal Categories in Python

We introduce DisCoPy, an open source toolbox for computing with monoidal categories.The library provides an intuitive syntax for defining string diagrams and monoidal functors.Its modularity allows the efficient implementation of computational experiments in the various applications of category theory where diagrams have become a lingua franca.As an example, we used DisCoPy to perform natural language processing on quantum hardware for the first time.
Blockchain-facilitated sharing to advance outbreak R&amp;D

Technology may help overcome nontechnological barriers
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text

Recent work has described neural-network-based agents that are trained with reinforcement learning (RL) to execute language-like commands in simulated worlds, as a step towards an intelligent agent or robot that can be instructed by human users. However, the optimisation of multi-goal motor policies via deep RL from scratch requires many episodes of experience. Consequently, instruction-following with deep RL typically involves language generated from templates (by an environment simulator), which does not reflect the varied or ambiguous expressions of real users. Here, we propose a conceptually simple method for training instruction-following agents with deep RL that are robust to natural human instructions. By applying our method with a state-of-the-art pre-trained text-based language model (BERT), on tasks requiring agents to identify and position everyday objects relative to other objects in a naturalistic 3D simulated room, we demonstrate substantially-above-chance zero-shot transfer from synthetic template commands to natural instructions given by humans. Our approach is a general recipe for training any deep RL-based system to interface with human users, and bridges the gap between two research directions of notable recent success: agent-centric motor behavior and text-based representation learning.
Kymatio: Scattering Transforms in Python

The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks. All transforms may be executed on a GPU (in addition to CPU), offering a considerable speed up over CPU implementations. The package also has a small memory footprint, resulting inefficient memory usage. The source code, documentation, and examples are available undera BSD license at https://www.kymat.io/
Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data.
Wheelchair Neuro Fuzzy Control and Tracking System Based on Voice Recognition

Autonomous wheelchairs are important tools to enhance the mobility of people with disabilities. Advances in computer and wireless communication technologies have contributed to the provision of smart wheelchairs to suit the needs of the disabled person. This research paper presents the design and implementation of a voice controlled electric wheelchair. This design is based on voice recognition algorithms to classify the required commands to drive the wheelchair. An adaptive neuro-fuzzy controller has been used to generate the required real-time control signals for actuating motors of the wheelchair. This controller depends on real data received from obstacle avoidance sensors and a voice recognition classifier. The wheelchair is considered as a node in a wireless sensor network in order to track the position of the wheelchair and for supervisory control. The simulated and running experiments demonstrate that, by combining the concepts of soft-computing and mechatronics, the implemented wheelchair has become more sophisticated and gives people more mobility.
HiveFive: Immersion Preserving Attention Guidance in Virtual Reality

Recent advances in Virtual Reality (VR) technology, such as larger fields of view, have made VR increasingly immersive. However, a larger field of view often results in a user focusing on certain directions and missing relevant content presented elsewhere on the screen. With HiveFive, we propose a technique that uses swarm motion to guide user attention in VR. The goal is to seamlessly integrate directional cues into the scene without losing immersiveness. We evaluate HiveFive in two studies. First, we compare biological motion (from a prerecorded swarm) with non-biological motion (from an algorithm), finding further evidence that humans can distinguish between these motion types and that, contrary to our hypothesis, non-biological swarm motion results in significantly faster response times. Second, we compare HiveFive to four other techniques and show that it not only results in fast response times but also has the smallest negative effect on immersion.
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning

Sample efficiency and scalability to a large number of agents are two important goals for multi-agent reinforcement learning systems. Recent works got us closer to those goals, addressing non-stationarity of the environment from a single agent's perspective by utilizing a deep net critic which depends on all observations and actions. The critic input concatenates agent observations and actions in a user-specified order. However, since deep nets aren't permutation invariant, a permuted input changes the critic output despite the environment remaining identical. To avoid this inefficiency, we propose a 'permutation invariant critic' (PIC), which yields identical output irrespective of the agent permutation. This consistent representation enables our model to scale to 30 times more agents and to achieve improvements of test episode reward between 15% to 50% on the challenging multi-agent particle environment (MPE).
Implementation of High-Performance Blockchain Network Based on Cross-Chain Technology for IoT Applications

With the development of technology, the network structure has changed a lot. Many people regard the Internet of Things as the next-generation network structure, which means all the embedded devices can communicate with each other directly. However, some problems remain in IoT before it can be applied in a large scale. Blockchain, which has become a hot research topic in recent years, may be one of the solutions. However, currently, the transaction speed of blockchain is still a disadvantage compared to traditional transaction methods. This paper focuses on to implement a high-performance blockchain platform. After investigation of the current blockchain consensus algorithm and blockchain architecture, we propose: (1) an improved blockchain consensus algorithm, which is implemented based on the mortgage model instead of probability model; (2) a cross-chain protocol with transverse expansion capacity, which would support the message transmission among chains; (3) a high-performance cross-chain blockchain network structure, which could handle more than 1000 transactions per second per chain by verification. Experiments have been carried out, and shown that the cross-chain blockchain network structure we provided is feasible to meet the requirement of large-scale distributed IoT applications.
Collaboration Based Multi-Label Propagation for Fraud Detection

Detecting fraud users, who fraudulently promote certain target items, is a challenging issue faced by e-commerce platforms. Generally, many fraud users have different spam behaviors simultaneously, e.g. spam transactions, clicks, reviews and so on. Existing solutions have two main limitations: 1) the correlations among multiple spam behaviors are neglected; 2) large-scale computations are intractable when dealing with an enormous user set. To remedy these problems, this work proposes a collaboration based multi-label propagation (CMLP) algorithm. We first introduce a general-purpose version that involves collaboration technique to exploit label correlations. Specifically, it breaks the final prediction into two parts: 1) its own prediction part; 2) the prediction of others, i.e. collaborative part. Then, to accelerate it on large-scale e-commerce data, we propose a heterogeneous graph based variant that detects communities on the user-item graph directly. Both theoretical analysis and empirical results clearly validate the effectiveness and scalability of our proposals.
Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

In this paper, we present the task description and discuss the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to identify whether the sound emitted from a target machine is normal or anomalous. The main challenge of this task is to detect unknown anomalous sounds under the condition that only normal sound samples have been provided as training data. We have designed this challenge as the first benchmark of ASD research, which includes a large-scale dataset, evaluation metrics, and a simple baseline system. We received 117 submissions from 40 teams, and several novel approaches have been developed as a result of this challenge. On the basis of the analysis of the evaluation results, we discuss two new approaches and their problems.
Rethinking the Value of Labels for Improving Class-Imbalanced Learning

Real-world data often exhibits long-tailed distributions with heavy class imbalance, posing great challenges for deep recognition models. We identify a persisting dilemma on the value of labels in the context of imbalanced learning: on the one hand, supervision from labels typically leads to better results than its unsupervised counterparts; on the other hand, heavily imbalanced data naturally incurs "label bias" in the classifier, where the decision boundary can be drastically altered by the majority classes. In this work, we systematically investigate these two facets of labels. We demonstrate, theoretically and empirically, that class-imbalanced learning can significantly benefit in both semi-supervised and self-supervised manners. Specifically, we confirm that (1) positively, imbalanced labels are valuable: given more unlabeled data, the original labels can be leveraged with the extra data to reduce label bias in a semi-supervised manner, which greatly improves the final classifier; (2) negatively however, we argue that imbalanced labels are not useful always: classifiers that are first pre-trained in a self-supervised manner consistently outperform their corresponding baselines. Extensive experiments on large-scale imbalanced datasets verify our theoretically grounded strategies, showing superior performance over previous state-of-the-arts. Our intriguing findings highlight the need to rethink the usage of imbalanced labels in realistic long-tailed tasks. Code is available at https://github.com/YyzHarry/imbalanced-semi-self.
A Novel Nonlinear Algorithm for Non-Gaussian Noises and Measurement Information Loss in Underwater Navigation

The ocean environment is complex and changeable because of all kinds of noise interferences, such as salt cliffs, ships around and other electromagnetic interferences, so the measurement information is prone to be lost. It is difficult to describe the complex noise and the acquisition probability of measurement information. In this paper, a continuous discrete variational Bayesian filter (CD VBF) is proposed to solve the problems of the heavy tailed noises and measurement random loss for state estimation. The variational Bayesian (VB) approach can effectively estimate the state vector, scale matrices, degree of freedom (DOF) parameters, Bernoulli random variables and the acquisition probability of measurement information. The performances of the proposed algorithm and traditional algorithms are tested in simulations and underwater experiments. The simulation results illustrate that the CD VBF has better localization accuracy and robustness. The experimental results demonstrate that the localization accuracy is improved by CD VBF and an optimal solution of iteration number is acquired.
High-resolution bathymetry by deep-learning-based image superresolution

Seafloor mapping to create bathymetric charts of the oceans is important for various applications. However, making high-resolution bathymetric charts requires measuring underwater depths at many points in sea areas, and thus, is time-consuming and costly. In this work, treating gridded bathymetric data as digital images, we employ the image-processing technique known as superresolution to enhance the resolution of bathymetric charts by estimating high-resolution images from low-resolution ones. Specifically, we use the recently-developed deep-learning methodology to automatically learn the geometric features of ocean floors and recover their details. Through an experiment using bathymetric data around Japan, we confirmed that the proposed method outperforms naive interpolation both qualitatively and quantitatively, observing an eight-dB average improvement in peak signal-to-noise ratio. Deep-learning-based bathymetric image superresolution can significantly reduce the number of sea areas or points that must be measured, thereby accelerating the detailed mapping of the seafloor and the creation of high-resolution bathymetric charts around the globe.
Efficient Video Integrity Analysis Through Container Characterization

Most video forensic techniques look for traces within the data stream that are, however, mostly ineffective when dealing with strongly compressed or low resolution videos. Recent research highlighted that useful forensic traces are also left in the video container structure, thus offering the opportunity to understand the life-cycle of a video file without looking at the media stream itself. In this paper we introduce a container-based method to identify the software used to perform a video manipulation and, in most cases, the operating system of the source device. As opposed to the state of the art, the proposed method is both efficient and effective and can also provide a simple explanation for its decisions. This is achieved by using a decision-tree-based classifier applied to a vectorial representation of the video container structure. We conducted an extensive validation on a dataset of 7000 video files including both software manipulated contents (ffmpeg, Exiftool, Adobe Premiere, Avidemux, and Kdenlive), and videos exchanged through social media platforms (Facebook, TikTok, Weibo and YouTube). This dataset has been made available to the research community. The proposed method achieves an accuracy of 97.6% in distinguishing pristine from tampered videos and classifying the editing software, even when the video is cut without re-encoding or when it is downscaled to the size of a thumbnail. Furthermore, it is capable of correctly identifying the operating system of the source device for most of the tampered videos.
Mode-Level vs. Implementation-Level Physical Security in Symmetric Cryptography

Triggered by the increasing deployment of embedded cryptographic devices (e.g., for the IoT), the design of authentication, encryption and authenticated encryption schemes enabling improved security against side-channel attacks has become an important research direction. Over the last decade, a number of modes of operation have been proposed and analyzed under different abstractions. In this paper, we investigate the practical consequences of these findings. For this purpose, we first translate the physical assumptions of leakage-resistance proofs into minimum security requirements for implementers. Thanks to this (heuristic) translation, we observe that (i) security against physical attacks can be viewed as a tradeoff between mode-level and implementation-level protection mechanisms, and (ii) security requirements to guarantee confidentiality and integrity in front of leakage can be concretely different for the different parts of an implementation. We illustrate the first point by analyzing several modes of operation with gradually increased leakage-resistance. We illustrate the second point by exhibiting leveled implementations, where different parts of the investigated schemes have different security requirements against leakage, leading to performance improvements when high physical security is needed. We finally initiate a comparative discussion of the different solutions to instantiate the components of a leakage-resistant authenticated encryption scheme.
Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.
An Overview about the Cyberattacks in Grid and Like Systems

Like a sniper, the most dangerous cybersecurity threats are the ones you never see coming. Even with firewalls, antivirus solutions, and cybersecurity awareness training for your employees, cybercriminals still manage to exploit any vulnerabilities they can find. This could be because they exploit attack vectors that are known to your organization (but remain unaddressed for some reason) or because they’ve discovered vulnerabilities that are not yet known to you (what are known as zero-day exploits). Cyberattacks are not a matter of “if,” but “when” they will occur. Unless you somehow gain omniscience (if that happens, be sure to reach out and we can split the cost of a lotto ticket), there’s really no way for you to know every single vulnerability that exists on your network or within your organization. After all, security risks come in all shapes, sizes, attack vectors, and levels of potency in the digital world. And, considering that threats to cybersecurity are continually changing and adapting, it’s a challenge to keep up with them all. So, what can you do? You can take the time to learn about as many cybersecurity threats as possible and work to identify and address as many holes in your defenses that you possibly can.
Emotional sounds of crowds: spectrogram-based analysis using deep learning

Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots. A critical question concerning the innovative concept of crowd emotions is whether the emotional content of crowd sounds can be characterized by frequency-amplitude features, using analysis techniques similar to those applied on individual voices, where deep learning classification is applied to spectrogram images derived by sound transformations. In this work, we present a technique based on the generation of sound spectrograms from fragments of fixed length, extracted from original audio clips recorded in high-attendance events, where the crowd acts as a collective individual. Transfer learning techniques are used on a convolutional neural network, pre-trained on low-level features using the well-known ImageNet extensive dataset of visual knowledge. The original sound clips are filtered and normalized in amplitude for a correct spectrogram generation, on which we fine-tune the domain-specific features. Experiments held on the finally trained Convolutional Neural Network show promising performances of the proposed model to classify the emotions of the crowd.