Reviewerly/exHarmony huggingface dataset:
Energy-Efficient VM Scheduling in the Cloud Environment using Reinforcement Learning

Cloud data centers consume a huge amount of energy in the form of electrical energy for their operation. They also emit carbon dioxide and impact the balance of nature. This management of exponentially increasing load and the minimization of energy use along with the impact on the environment is the biggest challenge a cloud service provider (CSP) faces. CSPs establish and maintain data center farms, which enable the delivery of cloud services to millions of clients. The reduction in energy usage by data centers while also minimizing the number of service level agreement (SLA) violations is a major challenge. In this work, we have proposed a reinforcement learning (RL)-based dynamic virtual machine (VM) consolidation mechanism wherein the host load is predicted by considering previous and current host utilization. The learning agent chooses a suitable-power mode for the hosts. Load balancing is done for the over-utilized hosts and dynamic VM consolidation is performed for the under-utilized hosts. The VM scheduling is performed using an energy-aware best fit method. Ourproposed model shows a significant drop in the number of SLA violations and energy consumption when compared to the ARIMA model.
Privacy-preserving distribution and access control of personalized healthcare data

The popularity of wearable smart healthcare devices has led to the emergence of a new service paradigm. However, in order to improve service quality, the manufacturers and online service providers collect massive data. This is a big concern as medical data is extremely sensitive. A few schemes have been proposed to overcome this problem. But, they suffer from security risks and overall increased complexity. Also, there was no implicit entity authentication and data integrity involved. We address these problems by allowing rectified data access through a directing authority, known as the transcryptor, using polymorphic encryption. Entity authentication and data integrity are achieved by smartly organizing data access and key information packets. The performance of the proposed approach is tested over different modalities data with varying size, whereas the security analysis is demonstrated using a challenge-response game model. The comparison with state-of-the-art schemes illustrates superiority of the proposed approach.
Yolo-based Realtime Object Detection using Interleaved Redirection of Time-Multiplexed Streamline of Vision Snapshot for Lightweighted Embedded Processors

The most essential function for fully automated driving is object recognition, as the car has to avoid obstacles. However currently, applying the technology can be challenging as they cost a lot of time and has lack of accuracy for embedded processors for automobiles. In this paper, we suggest time-multiplexed streamline of vision snapshot based on YOLOv3 for lightweighted embedded processors. As YOLOv3 process image detection with a single neural network in 1 evaluation, it can run fast. However, for lightweighted embedded processors, YOLO running on real time still can be heavy. By training YOLOv3 and Tiny YOLOv3, we lightened image detection program. As there are some latancies while running YOLO only on the lightweighted processor, ls1028 , we designed a socket communication program to communicate with tiny edge device based on python. The tiny edge device is connected with webcam and it sends snapshot to the processor by socket. When processor receives the snapshot, it detects objects using tiny YOLOv3. Then processor sends the coordinate information to tiny edge device using socket. Tiny edge device can draw a rectangle line above detected object. As a result, by using this system, we reduced webcam latency and some delay time drawing lines above the object and able to run YOLO on real time with lightweighted embedded processor.
Simulation of a Two-level Control System for Nonlinear Dynamic Objects with a Neurofuzzy Adaptive Regulator

The article deals with the construction of a two-level control system for dynamic objects with nonlinear properties, functioning under conditions of a priori uncertainty. The lower level of control includes local controllers that provide the closed-loop system with parametric invariance to variations in the parameters of the object and compensate for the effects of external and internal disturbances. For the upper control level, it is proposed to apply control algorithms based on methods of fuzzy logic and neural networks. A comparative analysis of the results obtained with the classical adaptive controller is carried out.
Efficient Training Method for Phrase Extraction Models using Natural Language Explanations

Phrase extraction is an information extraction task that extracts words or phrases in a specific category from text data, which is used in various downstream NLP technologies, including named entity recognition (NER), terminology extraction, question answering, dialogue systems, and information integration. While we need a large amount of annotated corpus for training a phrase extractor using machine learning, building such a corpus requires a lot of manual annotation work by domain experts. This research aims to reduce the annotation cost by developing a method that trains a phrase extractor using natural language explanations from experts. The proposed method transforms the natural language explanations into labeling functions, which allows us to make pseudo annotated corpus from a set of raw sentences. We empirically show the effectiveness of the model through experimental results.
Graph Connectivity in Log Steps Using Label Propagation

The fastest deterministic algorithms for connected components take logarithmic time and perform superlinear work on a Parallel Random Access Machine (PRAM). These algorithms maintain a spanning forest by merging and compressing trees, which requires pointer-chasing operations that increase memory access latency and are limited to shared-memory systems. Many of these PRAM algorithms are also very complicated to implement. Another popular method is “leader-contraction” where the challenge is to select a constant fraction of leaders that are adjacent to a constant fraction of non-leaders with high probability, but this can require adding more edges than were in the original graph. Instead we investigate label propagation because it is deterministic, easy to implement, and does not rely on pointer-chasing. Label propagation exchanges representative labels within a component using simple graph traversal, but it is inherently difficult to complete in a sublinear number of steps. We are able to overcome the problems with label propagation for graph connectivity. We introduce a surprisingly simple framework for deterministic, undirected graph connectivity using label propagation that is easily adaptable to many computational models. It achieves logarithmic convergence independently of the number of processors and without increasing the edge count. We employ a novel method of propagating directed edges in alternating direction while performing minimum reduction on vertex labels. We present new algorithms in PRAM, Stream, and MapReduce. Given a simple, undirected graph [Formula: see text] with [Formula: see text] vertices, [Formula: see text] edges, our approach takes O(m) work each step, but we can only prove logarithmic convergence on a path graph. It was conjectured by Liu and Tarjan (2019) to take [Formula: see text] steps or possibly [Formula: see text] steps. Our experiments on a range of difficult graphs also suggest logarithmic convergence. We leave the proof of convergence as an open problem.
An Integrated Expert System with a Supervised Machine Learning based Probabilistic Approach to Play Tic-Tac-Toe

Tic-Tac-Toe, also known as Noughts and Crosses, is a widely popular game among people of all ages. In recent times, due to the rapid development of Artificial Intelligence (AI) based algorithms, AI in Games has become an interesting topic for research in both academia and industry. Due to the complicated yet competent nature of AI algorithms, the design and implementation of such AI-driven approaches in games are challenging and time intensive. In this regard, we propose a supervised Machine Learning (ML)-based approach that contributes in designing an innovative and less complex Tic-Tac-Toc expert system. Integrating AI and ML in the solution process will lead the concerned community toward a more lightweight and computationally efficient systems for playing games. In this study, we propose a novel algorithmic solution by combining an ensemble-based boosting approach and rule-based inference to build a probabilistic expert system that strategically chooses the best optimal move for next possible state of the game. A benchmark dataset containing 255,168 unique game states of Tic Tac Toe was utilized at training stage. The proposed strategy is able to successfully settle a draw against never-loosing MiniMax algorithm in 18 standard test cases.
A Framework for Decentralized Parallel Complex Event Processing on Heterogeneous Infrastructures

Low-latency stream processing applications (SPA) distribute the event streams on parallel instances of the application’s operators to avoid queuing. Each operator has a set of processing nodes for its instances. Edge and fog infrastructures promise, among other things, low communication latency and are thus of interest for low-latency SPAs. Yet, they include heterogeneous, even volatile nodes which makes the parallel processing of SPAs challengingAvailable solutions for parallel SPAs often assume homogeneity and stable infrastructures.Thus, we aim to balance the stream processing among heterogeneous, instable infrastructures using a decentralized control mechanism. We propose a decentralized, dynamic, and lightweight approach to re-balance SPA-load according to the nodes current performance, while minimizing latencies. In doing so, we introduce the Coda-EFC framework for window-based stream processing on heterogeneous distributed infrastructures. It dynamically adapts its load balancing to node performance without computing quickly outdated schedules and aims for latency-optimal load balancing. We introduce two adaptive load balancing strategiesone tailored for heterogeneous, dynamic infrastructures, the other an adaptation of a state-of-the art approach.
Prediction of Human Physical Activities With Consumer Smartphone Sensor Data

Sensor-dependent recognition of human activity (HAR) is an exciting and challenging research topic. It is commonly used in many areas, such as sports, health, criminal investigations, etc. Billions of people now have smartphones with sensors such as accelerometers and Gyroscopes that can easily record data related to a person’s activity. Those data can be helpful to build a monitoring system. Classical machine learning algorithms use human-engineered feature data. However, it requires domain expertise and signal processing techniques to get the best feature from the time series data recorded from sensors. It takes a tremendous amount of time and effort to get the best features for human activity recognition. But nowadays, the recent advantages of deep learning make it easier for everyone to detect human activity faster and more accurately. Deep learning models can be used to construct a suitable monitoring system for human activity prediction. This paper proposed a Deep LSTM (long short term memory) model for human activity recognition. The performance of the proposed model is evaluated in terms of accuracy and F1 score. The proposed approach requires lesser number of parameters for real time activity detection.
Pretty good quantum fractional revival in paths and cycles

We initiate the study of pretty good quantum fractional revival in graphs, a generalization of pretty good quantum state transfer in graphs. We give a complete characterization of pretty good fractional revival in a graph in terms of the eigenvalues and eigenvectors of the adjacency matrix of a graph. This characterization follows from a lemma due to Kronecker on Diophantine approximation, and is similar to the spectral characterization of pretty good state transfer in graphs. Using this, we give complete characterizations of when pretty good fractional revival can occur in paths and in cycles.
A Study: Design Patterns Detection Approaches and Impact on Software Quality

The influence of design decisions on quality characteristics has been studied extensively in research with various viewpoints, aims, measurements, and quality attributes, resulting in contradictory and difficult-to-compare conclusions. Until now, the results on the effect of design patterns on software quality are controversial. There are two objectives of conducting this study. The first one is to analyze the impact of design patterns on software quality. The second is investigating the approaches used for detecting design patterns. All the analysis is done with the use of a technique called systematic literature review (SLR). The SLR findings demonstrate that pattern documentation, pattern class size, and pattern dispersion degree have a significant effect on the quality of software. Similarity scoring, graph-based, and machine learning-based approaches are the existing proposed methods for detecting design patterns. The results have shown that there is a need for benchmarking design patterns detection proposed approaches.
Survey on Object Detection Framework: Evolution of Algorithms

Object Detection is one among the most emerging and effective field of interest in the broad domain of Artificial Intelligence. Given a set of images or video (sequence of frames) as input, object detection involves detecting and identifying various range of artifacts in the corresponding image or frame. This in turn includes various categories of detecting an artifact like detecting an object based on its location or detecting an object based on its identity. This categorization of the object detection framework is important because of the ranges of application that's available where these techniques can be applied effectively. This paper establishes a detailed survey which explains the different techniques and algorithms that has paved our way into the object detection framework. Starting from the historical mathematical technique dynamic programming which followed geometric techniques to detect objects, techniques like SIFT, HOG which followed feature level detection and networks like D-CNN, R-CNN, RFCN, YOLO etc. which employs a pipeline of network to detect and segment instance of objects from the images.
Research on the Construction and Trust Issue of Asset Securitization Multiple Initiators Underlying Asset Pool Model Based on Blockchain

In recent years, with the rapid development of China's economy, the process of asset securitization has become faster and faster, and has now become one of the most important financing methods, and has made financing change from being based on overall creditworthiness to being based on some high-quality assets, thereby innovating the financing review and approval mechanism. Moreover, China's mortgage loans are developing rapidly, and the conditions for asset securitization are maturing, and the era of large-scale asset securitization is approaching. However, in the process of development, due to the long process of asset securitization and many types of products, there are problems such as opaque and asymmetrical information, and it is very easy to be out of supervision in the business process which can lead to the occurrence of many risks and harm the interests of investors. In response to these problems, most scholars currently believe that blockchain technology can help solve the difficulties in asset securitization issuance due to its characteristics of decentralization, without relying on trust and the difficulty of tampering with recorded information. However, in the current asset securitization platform based on blockchain technology in China, the underlying asset pool only includes one initiator's underlying assets, which leads to excessive pressure on individual enterprises to bear the platform costs, and only a small number of strong large enterprises can apply blockchain technology for asset securitization, and most SMEs cannot carry out asset securitization. Therefore, this paper aims at this pain point by analyzing the application of blockchain technology in the field of asset securitization, proposing an asset securitization model containing a pool of underlying assets from multiple initiators (SMEs), analyzing the factors affecting the trust between initiators and the role of mutual trust, and finally giving several methods to maintain and enhance the trust between initiators.
Invisible and readable checkerboard as watermark with temporally brightness modulation

This paper describes invisibility and readability in terms of the size of the basic unit of information that was attached to a displayed image by using our proposed method. The proposed method enables us to invisibly attach information to a displayed image on a flat panel display (FPD) and to extract the information from a captured video image of the displayed image. The information was attached to a single color component of the displayed image by using temporal and spatial brightness modulation. The information was arranged within a given rectangle area of the displayed image as a black and white checkerboard pattern. The checkerboard pattern was used because it is a typical example of a two-dimensional pattern with a high density. The checkerboard pattern consists of squares having the same side length. The side length of the square was changed to examine the invisibility and readability in terms of the size of the basic unit of information. First, we assessed invisibility to the human eye of the information attached to the displayed image on the FPD. The experimental results showed the range of the side length of the square that satisfied the invisibility of the information. We also examined readability of the attached information for a video camera. The experimental results showed a lower limit value of the side length of the square that satisfied the readability. The experimental results revealed the range of the side length of the squares that satisfied both the invisibility and readability of the information attached to the displayed image.
ICHC Framework

The Goal in this paper is to propose a cultural heritage data model and evolve towards the creation of a framework based on MongoDB that will allow to manage a JSON model representing the cultural heritage of a city ICHC (Intelligent Cultural Heritage of a City). This manuscript per the authors noticed that during the census of cultural heritage, the presence of human resources linked to heritage is not something that is represented in a smart engine of a framework. Which is why the goal is to integrate the human resource and therefore add a relational aspect to the NoSql documents so that the resulting framework can have a smart engine to link data.This model is a set of ICHD (Intelligent Cultural Heritage Document) which are JSON documents that represent of the different types of cultural heritage entities. Those documents will be managed in a MongoDB repository architecture that will allow to them, so that the microservices-based ICHC framework can offer a big data context that can handle a huge variety, volume and velocity of data and be based on distributed operations.
Japanese Election Prediction Based on Sentiment Analysis of Twitter Replies to Candidates

Election prediction based on Web information has attracted much research interest. One previous research for Japanese election prediction proposed two kinds of indicators including familiarity degree and support degree and performed machine learning to achieve the goal. In this paper, we explore new features including the number of Twitter replies to election candidates and the sentimental values of reply texts, and develop models that have higher performance. The results of experiments conducted for the Japanese House of Councillors election in 2019 show that our method can obtain a higher F-measure than the previous method, which indicates that the usage of reply texts and sentiment analysis can contribute to election prediction.
Fast spectral analysis for approximate nearest neighbor search

In large-scale machine learning, of central interest is the problem of approximate nearest neighbor (ANN) search, where the goal is to query particular points that are close to a given object under certain metric. In this paper, we develop a novel data-driven ANN search algorithm where the data structure is learned by fast spectral technique based on s landmarks selected by approximate ridge leverage scores. We show that with overwhelming probability, our algorithm returns the $$(1+\epsilon /4)$$ -ANN for any approximation parameter $$\epsilon \in (0,1)$$ . A remarkable feature of our algorithm is that it is computationally efficient. Specifically, learning k-length hash codes requires $$O((s^3+ns^2)\log n)$$ running time and $$O(d^2)$$ extra space, and returning the $$(1+\epsilon /4)$$ -ANN of the query needs $$O(k\log n)$$ running time. The experimental results on computer vision and natural language understanding tasks demonstrate the significant advantage of our algorithm compared to state-of-the-art methods.
PID Controller-Inspired Model Design for Single Image De-Raining

Deep learning based methods have achieved remarkable breakthroughs on the single image de-raining task. However, most of the current models are constructed by empirically designing black-box network architectures. These network architectures always lack sufficient interpretability, which limits their further improvements in de-raining performance. In this brief, inspired by the classical Proportional Integral Derivative (PID) controller and feedback mechanism in the automatic control community, we propose a novel de-raining network architecture to address the above issues. Specifically, since the PID controller can speed up the system convergence and eliminate the system steady-state error, this motivates us to mimic the signal processing flow of PID controller to provide an interpretable and reliable guideline on de-raining network designs. By casting the signal flow process in the PID controller as blueprints, we extend the PID controller to an almost parameter-free network module, named PID-IM with every component in the module one-to-one corresponding to each operation involved in PID controller. Equipped with PID-IM, our proposed network could efficiently explore and exploit the features of rain streaks in a recursive fashion. Extensive experiments on several benchmarks demonstrate that our method has best performance over other de-raining methods. In addition, by directly embedding our PID-IM into existing baseline networks, the de-raining performance can be significantly improved.
GPU-Accelerated Timing Simulation of Systolic-Array-Based AI Accelerators

Systolic arrays are currently used in autonomous systems such as self-driving cars to accelerate the enormous amount of matrix operations necessary for DNN inference. The reliability of such accelerators are of utmost importance since any loss in DNN accuracy due to erroneous calculations can have dire consequences. We propose a novel method to measure accuracy losses caused by arbitrary timing faults in systolic arrays. Our GPU-based simulation system enables for the first time a complete and accurate timing simulation of all inference-related matrix operations on large systolic arrays. A single consumer-grade GPU can simulate a LeNet-5 at a throughput of about 13s per inference. Furthermore, our simulation approach readily scales to larger DNNs and multiple GPUs.