Reviewerly/exHarmony huggingface dataset:
AI Enabled Resources Scheduling in Cloud Paradigm

Cloud Computing was evolved as one of the paradigm, which gives services to users in a utility-based manner. Services of cloud computing were extended to various fields and applications. Due to the enormous number of users, flexibility and easy to use nature of cloud paradigm, many of companies are trying to migrate towards cloud paradigm but from a cloud provider perspective it is a difficult to job to handle or schedule these heterogeneous workloads, which are coming onto cloud console. Therefore, it is important for a cloud provider to employ a task scheduling mechanism, which should be more proactive based on the nature of workloads coming onto cloud interface and how effectively they are scheduled onto suitable virtual resources. Many of existing scheduling algorithms used nature or bio inspired techniques to model schedulers as scheduling problem in cloud paradigm is a classical NP-Hard problem but still to make a schedule for a task onto a suitable VM based on its processing capacity while minimizing its makespan, energy consumption and other operational costs is still a tedious job as incoming user requests are highly dynamic in nature. In this paper, we have used a deep reinforcement learning technique i.e. DDQN model to make decisions of scheduling in cloud paradigm while checking incoming requests and underlying resources for every task. Task priorities are evaluated for all incoming tasks and prioritized tasks are fed to our scheduler and based on imposed conditions our scheduler will make decisions effectively. This entire research implemented on cloudsim. Extensive simulations are conducted by generating workload randomly and from realtime workload traces. Finally, our proposed scheduler is evaluated against existing baseline approaches i.e. Round Robin, FCFS, and Earliest Deadline first. From Simulation results, our proposed approach shown a huge impact over existing baseline approaches in terms of makespan, Energy consumption.
Introduction to Testing

This chapter considers the different types of tests that you might want to perform with the systems you develop in Python. It also introduces Test-Driven Development.
Efficient Resource Allocation in Virtualized Cloud Platforms Using Encapsulated Virtualization Based Ant Colony Optimization (EVACO)

Virtualization in cloud computing ensures maximum utilization of the computational resources by creating virtual instances. In this paper, we have jointly considered virtualization and ant colony optimization (ACO) to propose a novel encapsulated virtualization-based ACO (EVACO) technique that allocates the computational resources efficiently. We have developed an efficient mathematical model that modifies the traditional ACO and encapsulated it in virtualized cloud platform. The objective is to optimize the resource allocation so that execution can be efficient. The novelty of the proposed model has been established through extensive simulations and comparative study. Our algorithm took less execution time in compare to some popular benchmark algorithms. Moreover, the number of iterations and virtual resources required to complete tasks were less than that of other algorithms. It establishes the optimality of our proposed approach.
Design of 3-D Pipe Routing for Internet of Things Networks Using Genetic Algorithm

The problem of routing paths has been studied systematically for several years. The problem is quite deterministic when there are only one start and ending point and there are many well-known path-finding algorithms like Dijkstra’s, A*, Maze algorithm. But for these algorithms both efficiency and correctness fall terribly when applied on more than two connection points (pair of connection points). To overcome these problems, a genetic algorithm (GA) which is an evolutionary-based biologically inspired technique is used. It tries to search global minimal from the overall solution by slowly improving over the previous solution. GA has already proved useful to solve many complex engineering problems. It is mostly used for searching and optimization related problems. The existing methods for a similar path routing problem for internet of things (IoTs) using GA show that finding an exact solution is somewhere difficult as GA uses heuristic approach that yields better solutions. The application of the genetic algorithm is immense and it is widely used in the industries. One such application of GA is the installment of pipe in 3-dimensional space. There are many more related problems like finding the shortest path in a network and the design of a circuit. The complexity of each of the problems is like the traveling salesman problem, which is an np-hard problem that means it’s hard to find an exact solution and will require a heuristic algorithm to get a reasonable solution. In this chapter, we have proposed an algorithm that gives significant results for optimizing the 3-D pipe routing in a given space with constraints such as length, cost and number of bends of the pipe. The result shows that in a first iteration the proposed algorithm optimizes the results by approximately 50% in comparison to the existing methods.
Sockets in Python

A socket is an end point in a communication link between separate processes. In Python, sockets are objects which provide a way of exchanging information between two processes in a straightforward and platform independent manner. In this chapter we will introduce the basic idea of socket communications and then present a simple socket server and client application.
A Covert Communication Method Adapted to Social Media Based on Time Modulation of Bullet Comments

Consumers often have the need to use social media to transmit secret information, but such communication often faces distortion due to lossy compression and limited sending frequency. In order to solve these issues, this paper proposes a covert communication method based on the time modulation of online video bullet comments. Bullet comment is a kind of time-anchored on-screen comment, which is a popular feature for online video platforms. The proposed method converts the absolute time and relative time of the bullet comments to carry the index value of the loaded bullet comments and encrypted characters. The receiver converts the time information of the obtained bullet comments into index values and uses them to extract the secret message. The experimental results show that the proposed method is suitable for multiple online video platforms. Compared with the existing covert communication methods based on social media, the secure operation frequency of the proposed method is at least 9.06 times higher when sending a secret message of the same length. The security of the proposed method is evaluated by the metrics designed according to the characteristics of the large-scale bullet comments.
Functional ideographies are composite semiotic systems

All sufficiently large functional notations (ideographic or otherwise) are composites of discrete, structured elements (e.g., phonemes, morphemes, numerals). We must consider not only the modality but also the structure of the existing, workable ideographic/semasiographic systems we know (e.g., musical and numerical notation) to establish the cognitive limitations militating against humans memorizing and standardizing domain-general ideographies that would parallel written language.
Ideography in interaction

The standardization account predicts short message service (SMS) interactions, allowed by current technology, will support the use and conventionalization of ideographs. Relying on psycholinguistic theories of dialogue, we argue that ideographs (such as emoji) can be used by interlocutors in SMS interactions, so that the main contributor can use them to accompany language and the addressee can use them as stand-alone feedback.
Usability of Augmented Reality Technology in Situational Telementorship for Managing Clinical Scenarios: Quasi-Experimental Study

Telementorship provides a way to maintain the professional skills of isolated rural health care workers. The incorporation of augmented reality (AR) technology into telementoring systems could be used to mentor health care professionals remotely under different clinical situations.This study aims to evaluate the usability of AR technology in telementorship for managing clinical scenarios in a simulation laboratory.This study used a quasi-experimental design. Experienced health professionals and novice health practitioners were recruited for the roles of mentors and mentees, respectively, and then trained in the use of the AR setup. In the experiment, each mentee wearing an AR headset was asked to respond to 4 different clinical scenarios: acute coronary syndrome (ACS), acute myocardial infarction (AMI), pneumonia severe reaction to antibiotics (PSRA), and hypoglycemic emergency (HE). Their mentor used a laptop to provide remote guidance, following the treatment protocols developed for each scenario. Rating scales were used to measure the AR's usability, mentorship effectiveness, and mentees' self-confidence and skill performance.A total of 4 mentors and 15 mentees participated in this study. Mentors and mentees were positive about using the AR technology, despite some technical issues and the time required to become familiar with the technology. The positive experience of telementorship was highlighted (mean 4.8, SD 0.414 for mentees and mean of 4.25, SD 0.5 for mentors on the 5-point Likert scale). Mentees' confidence in managing each of the 4 scenarios improved after telementoring (P=.001 for the ACS, AMI, and PSRA scenarios and P=.002 for the HE scenario). Mentees' individual skill performance rates ranged from 98% in the ACS scenario to 97% in the AMI, PSRA, and HE scenarios.This study provides evidence about the usability of AR technology in telementorship for managing clinical scenarios. The findings suggest the potential for this technology to be used to support health workers in real-world clinical environments and point to new directions of research.
Local government cyber insecurity: Causes and recommendations for improvement

Abstract In this paper, we address several facets of the problem we call local government cyber insecurity—a problem that plagues such governments across the nation, if not the world. We describe this problem and discuss its manifestations in local governments. This is followed by our analysis of why, on average, local government cybersecurity is managed and practiced so poorly. Next, we discuss several constraints on local governments that may help to explain why so many of these governments are not able to provide highly effective cybersecurity. We then discuss steps that local governments can and should take to improve their cybersecurity, including adopting dedicated cybersecurity budgets, adopting several highly recommended cybersecurity policies, and following best cybersecurity practices.
Face deblurring based on regularized structure and enhanced texture information

Abstract Image deblurring is an essential problem in computer vision. Due to highly structured and special facial components (e.g. eyes), most general image deblurring methods and face deblurring methods failed to yield more explicit structure and facial details, resulting in too smooth, uncoordinated and distorted face structure. Considering the unique face texture and sufficient facial details, we present an effective face deblurring network by exploiting more regularized structure and enhanced texture information (RSETNet). We first incorporate the face parsing network with fine-tuning to obtain more accurate face structure, and we present the feature adaptive denormalization (FAD) to regularize the facial structure as a condition of auxiliary to generate more harmonious and undistorted face structure. Meanwhile, to improve the generated facial texture information, we propose a new Laplace depth-wise separable convolution (LDConv) and multi-patch discriminator. Compared with existing methods, our face deblurring method could restore face structure more accurately and with more facial details. Experiments on two public face datasets have demonstrated the effectiveness of our proposed methods in terms of qualitative and quantitative indicators.
Time-warping invariant quantum recurrent neural networks via quantum-classical adaptive gating

Abstract Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNNs), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations. This paper builds on quantum RNNs (QRNNs), a dynamic model with quantum memory, to introduce a novel class of temporal data processing quantum models that preserve invariance to time-warping transformations of the (classical) input-output sequences. The model, referred to as time warping-invariant QRNN (TWI-QRNN) , augments a QRNN with a quantum–classical adaptive gating mechanism that chooses whether to apply a parameterized unitary transformation at each time step as a function of the past samples of the input sequence via a classical recurrent model. The TWI-QRNN model class is derived from first principles, and its capacity to successfully implement time-warping transformations is experimentally demonstrated on examples with classical or quantum dynamics.
Enabling Perception-Driven Optimization in Networking

Service providers struggle to catch up with the rapid growth in bandwidth and latency demand of Internet videos and other applications. An essential contributor to this resource contention is the assumption that users are equally sensitive to service quality everywhere, so any low-quality incidents must be avoided. However, this assumption is not true. For example, our work and other parallel efforts have shown that more video users can be served with better quality of experience (QoE) if we embrace the fact that the QoE's sensitivity to video quality varies greatly with the video content. To unleash such benefits, the application systems must be driven by not only system measurement data but also user feedback data that capture users' perceptions of service quality. In this short paper, I will highlight some of our recent efforts toward the efficient collection of user feedback and enabling perception-driven optimization for Internet applications.
Learning-based Optimal Quantum Switch Scheduling

In this paper, we consider the problem of optimal scheduling for quantum switches with dynamic demand and random entanglement successes. Different from prior results that often focus on (known) fixed entanglement success probabilities, we assume zero prior knowledge about the entanglement success probabilities and allow them to vary from time to time in an adversarial manner. We propose a learning-based algorithm QSSoftMW based on the framework developed in [1], which combines adversarial learning and Lyapunov queue analysis. We show that QSSoftMW is able to automatically adapt to the changing system statistics and ensure quantum switch stability.
Toward Fast Query Serving in Key-Value Store Migration with Approximate Telemetry

Distributed key-value stores scale data analytical processing by spreading data across nodes. Frequent migration of key-value shards between online nodes is a key technique to react to dynamic workload changes for load balancing and service elasticity. During migration, the data is split between a source and a destination, making it difficult to query the exact location. Existing solutions aiming to provide real-time read and write query capabilities during migration may require querying both source and destination servers, doubling the compute/network resources. In this paper, we explore a simple yet effective measurement approach to track the key-value migration status, in order to improve the query-serving performance under migration. In our preliminary prototype, we use a Bloom filter on the destination server to keep track of individual key-value pairs that have been successfully migrated. For key-value pairs that have yet migrated, the information stored in the Bloom filter enables fast forwarding to the source server without the need to check the database. We prototype this design on a local cluster with Redis deployments. Our preliminary results show that this approximate measurement-based design minimizes query losses during migration.
Low latency computing for time stretch instruments

Abstract Time stretch instruments have been exceptionally successful in discovering single-shot ultrafast phenomena such as optical rogue waves and have led to record-speed microscopy, spectroscopy, lidar, etc. These instruments encode the ultrafast events into the spectrum of a femtosecond pulse and then dilate the time scale of the data using group velocity dispersion. Generating as much as Tbit per second of data, they are ideal partners for deep learning networks which by their inherent complexity, require large datasets for training. However, the inference time scale of neural networks in the millisecond regime is orders of magnitude longer than the data acquisition rate of time stretch instruments. This underscores the need to explore means where some of the lower-level computational tasks can be done while the data is still in the optical domain. The Nonlinear Schrödinger Kernel computing addresses this predicament. It utilizes optical nonlinearities to map the data onto a new domain in which classification accuracy is enhanced, without increasing the data dimensions. One limitation of this technique is the fixed optical transfer function, which prevents training and generalizability. Here we show that the optical kernel can be effectively tuned and trained by utilizing digital phase encoding of the femtosecond laser pulse leading to a reduction of the error rate in data classification.
Efficient Multi-Scale Cosine Attention Transformer for Image Super-Resolution

Transformer has gained increasing attention in the field of computer vision due to its global modeling capability. However, its high computational cost and weak capture of local pixel information restrict its application in realistic scenes. To address these issues, we propose a multi-scale cosine attention Transformer network (MCATN) for single image super-resolution. Specifically, we propose a residual multi-scale Transformer group (RMTG), which consists of a local feature extraction module and a multi-scale Transformer block (MTB). RMTG is designed to capture the local information of features and model long-range dependencies. Within the MTB, the multi-scale cosine attention layer divides the input features into groups and further splits them into blocks of different sizes to enable fast cosine self-attention computation. MTB mitigates information loss during image reconstruction on feature block boundaries and prevents the network from being dominated by specific pixels. Extensive experimental results demonstrate that MCATN outperforms several state-of-the-art lightweight models in terms of accuracy.
ASLog: An Area-Efficient CNN Accelerator for Per-Channel Logarithmic Post-Training Quantization

Post-training quantization (PTQ) has been proven an efficient model compression technique for Convolution Neural Networks (CNNs), without re-training or access to labeled datasets. However, it remains challenging for a CNN accelerator to fulfill the efficiency potential of PTQ methods. A large number of PTQ techniques blindly pursue high theoretic compression effect and accuracy, ignoring their impact on the actual hardware implementation, which causes more hardware overhead than benefit. This paper introduces ASLog, a PTQ-friendly CNN accelerator that explores four key designs in an algorithm-hardware co-optimizing manner: the first practical 4-bit logarithmic PTQ pipeline SLogII, the multiplier-free arithmetic element (AE) design, the energy-efficient bias correction element (BCE) design, and the per-channel quantization friendly (PCF) architecture and dataflow. The proposed SLogII PTQ pipeline can push the limit of logarithmic PTQ to 4-bit with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 2.5% accuracy degradation on various image classification and face recognition tasks. Exploiting the approximate computing design and a novel encoding and decoding scheme, the proposed SLogII AE is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 40% lower in power and area consumption compared with a common 8-bit multiplier. The BCE and PCF design proposed in this paper are the first to consider the hardware impact of the widely-used per-channel quantization and bias correction technique, enabling an efficient PTQ-friendly implementation with a small hardware overhead. The ASLog is validated in a UMC 40-nm process, with 12.2 TOPS/W energy efficiency and 0.80 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> core area. The ASLog can achieve 336.3 GOPS/mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> area efficiency and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 500 OPs/Byte operational intensity, which map to over 1.85 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> and 1.12 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> improvement compared with the previous related works.
Reinforcement-and-Alignment Multispectral Object Detection Using Visible–Thermal Vision Sensors in Intelligent Vehicles

Multispectral object detection using visible–thermal vision sensors is a crucial method for enabling intelligent vehicles to perceive their environment accurately. However, fusing and complementing multispectral information is challenging because of problems such as miscalibration resulting from disparate camera fields of view (FOV) and pattern discrepancies in images captured at different wavelengths. To address these challenges, we propose a novel multispectral object detection system named the reinforcement- and alignment-based fusion model (RA-FMDet). This system integrates a cross-modality reinforcement (CMR) module with a hierarchical modality alignment (HMA) strategy. The CMR module uses hybrid transformer–convolutional neural network (CNN) architecture for the effective extraction of high-quality semantic representations from multispectral data. The HMA strategy addresses miscalibration at the pixel and anchor levels. To evaluate RA-FMDet, we established a comprehensive dataset covering various scenarios and conditions. This dataset was obtained by optimizing the hardware configuration of visible–thermal vision sensors and mitigating the shift between image modalities through the use of a modified calibration board. Extensive experiments conducted on the challenging KAIST dataset and the established dataset indicated that the proposed RA-FMDet outperformed state-of-the-art models in object detection and achieved mean average precision values of 92.60%, 93.37%, and 93.60% in the detection of cars, pedestrians, and motorcyclists, respectively.