Reviewerly/exHarmony huggingface dataset:
Accelerating Evolutionary Neural Architecture Search via Multifidelity Evaluation

Evolutionary neural architecture search (ENAS) has recently received increasing attention by effectively finding high-quality neural architectures, which however consumes high computational cost by training the architecture encoded by each individual for complete epochs in individual evaluation. Numerous ENAS approaches have been developed to reduce the evaluation cost, but it is often difficult for most of these approaches to achieve high evaluation accuracy. To address this issue, in this article, we propose an accelerated ENAS via multifidelity evaluation termed MFENAS, where the individual evaluation cost is significantly reduced by training the architecture encoded by each individual for only a small number of epochs. The balance between evaluation cost and evaluation accuracy is well maintained by suggesting a multifidelity evaluation, which identifies the potentially good individuals that cannot survive from previous generations by integrating multiple evaluations under different numbers of training epochs. Besides, a population initialization strategy is devised to produce diverse neural architectures varying from ResNet-like architectures to Inception-like ones. As shown by experiments, the proposed MFENAS takes only 0.6 GPU days to find the best architecture holding a 2.39% test error rate, which is superior to most state-of-the-art neural architecture search approaches. And the architectures transferred to CIFAR-100 and ImageNet also exhibit competitive performance.
A semi-supervised semantic-enhanced framework for scientific literature retrieval

Scientific literature retrieval provides convenience for researchers to find scientific literature related to the query. It is an important part of scientific research to search related papers given a paper title as query. However, for scientific literature retrieval tasks, most of the existing retrieval methods do not consider sentence-level semantic matching so that the retrieval performance is limited. With the success of neural networks, neural information retrieval methods have been widely studied and achieved good retrieval results. In this paper, we propose a semi-supervised semantic-enhanced scientific literature retrieval framework. The framework is composed of two networks: a self-attention convolutional encoder-decoder network and a sentence-level attention scientific literature retrieval network. By joint training of the two networks, the proposed semi-supervised semantic-enhanced scientific literature retrieval model can fully capture the rich semantic information of scientific text data and leverages human labeled scientific text data to improve the discriminativeness of the learned semantic representation. The retrieval results on two scientific literature datasets demonstrate that the proposed method significantly and consistently outperforms the other baseline methods.
Multi-image Super-Resolution Algorithm Supported on Sentinel-2 Satellite Images Geolocation Error

Every year, the hottest seasons are marked by forest fires. Monitoring these forest areas is more effective with the help of satellite imagery, since ground operations are hampered by vegetation density and height, making them less productive and more expensive. However, nowadays, freely available imagery from Sentinel-2 satellite has a maximum spatial resolution of 10 meters per pixel, a low resolution to identify small or thin structures in the image, such as roads, bridges, buildings, rivers, fuel breaks, among others.To improve image's resolution, a new super-resolution algorithm, named KGEONP - K Geographically Nearest Pixels, is proposed in this paper. It benefits from Sentinel-2 regular observations (it has a revisit of 5 days) and the georeferencing error of its images (whose maximum value is 1.5 pixels). KGEONP seeks to add as much information as possible to the super-resolved image, by using data from K-nearest pixels and their spatial distance for computing each new pixel's value.KGEONP was applied to Sentinel-2 images to increase resolution by a factor of 10 and was compared to state-of-the-art super-resolution techniques. It showed quite satisfactory results, with the capacity of increasing resolution and maintaining the structural data of the source images.
LR-GD-RNS: Enhanced Privacy-Preserving Logistic Regression Algorithms for Secure Deployment in Untrusted Environments

The protection of data processing is emerging as an essential aspect of data analytics, machine learning, delegation of computation, Internet of Things, medical and financial analysis, smart cities, genomics, non-disclosure searching, among others. Often, they use sensitive information that cannot be protected by traditional cryptosystems. Homomorphic Encryption (HE) schemes and secure Multi-Party Computation (MPC) are considered suitable solutions for privacy protection. In this paper, we propose and analyze the performance of three homomorphic Logistic Regression (LR) models with Gradient Descent (GD) algorithms based on the Residue Number System (RNS). We compare their performance with four traditional non-homomorphic versions, one homomorphic algorithm based on RNS with Batch GD, and two state-of-the-art homomorphic algorithms. To validate our approach, we consider six public datasets of different medicine domains (diabetes, cancer, drugs, etc.) and genomics. We use a 5-fold cross-validation technique for a fair comparison in terms of the solution quality and training time. The results show that propose homomorphic solutions have similar accuracy with non-homomorphic algorithms, increased classification performance, and decreased training time compared with the state-of-the-art HE algorithms.
Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox

The use of Hierarchical Fuzzy Systems (HFS) has been well acknowledged as a good approach in reducing the complexity and improving the interpretability of fuzzy logic systems (FLS). Over the past years, many fuzzy logic toolkits have been made available for type-1, interval type-2 and general type-2 fuzzy logic systems under different programming languages. However, it is still challenging for people, especially for those who are not expert in fuzzy systems or programming, to build models based on HFSs. The main reason could be the lack of practical tools and examples of using HFSs. This paper presents a step-by-step guide to the implementation of an HFS with the open-source toolbox, FuzzyR, utilising the R Programming Language.
An Automated University Gate Pass Monitoring System Using Deep Learning

Surveillance cameras are necessary to find and track a person in the technology world for various security purposes. The work aims to design an auto gate pass system using face recognition technology based on real-time video processing. Various photographs of the student at different environmental conditions have been collected and stored in the database. While exiting from the campus students need to show their face in front of the camera and the proposed system recognizes the student by comparing the details stored in the database. After recognizing a student, the next step is to fetch the student's record from the database and make the entry of a person into e-register. Meanwhile, the automated system will send a notification to the respective student. To track the whole action, the system provides a graphical user interface (GUI), which helps to monitor all activities more easily.
E-puck Mobile Robot Obstacles Avoidance Controller Using the Fuzzy Logic Approach

This paper presents the development of the E-puck mobile robot controller using a fuzzy logic approach for obstacle avoidances in various static environments. The WEBOTS simulator was selected to test E-puck performances in finding a free path to the desired target while avoiding obstacles within the environment. This E-puck model is equipped with eight proximity sensors and driven by two DC motors that will be the input and output to the development of the fuzzy inference system of the fuzzy logic controller. The execution of fuzzy rules depended on the input and output membership function which consists of the sensor distance, velocity, and turning angle of the E-puck. The results proved that the E-puck robot can avoid the obstacles successfully while heading towards the goal point in several test environments. Different numbers and shapes of obstacles were allocated within the environment. The trajectory and time taken were recorded to validate the effectiveness and robustness of the proposed fuzzy logic controller in avoiding obstacles. The trajectory indicates the safe path obtained and the time taken most when the E-puck finds a path in a complex environment as compared to the simple environment. The local minima problem can also be avoided using fuzzy control.
Region Coverage Flight Path Planning Using Multiple UAVs to Monitor the Huge Areas

The development of a coverage flight planner for multiple multicopters used in a wide-area survey, such as inspecting the storage site of radioactive decontaminated soil or searching for victims in disaster response, is proposed. For these multiple multicopter application cases, effective coverage flight path planning with no collisions is needed. In this paper, we developed two parts of the system where the region is separated by two approaches and quasi-optimal flight paths for each region. As a result, each of the two region divisions showed a good equally divided region, and a flight path designed on the two region divisions was also efficiently generated. Flight tests of two multicopters with the generated paths were also conducted.
CASH: A Credit Aware Scheduling for Public Cloud Platforms

Distributed data processing frameworks such as Hadoop, Tez, Spark, and Flink are exclusively used by public cloud tenants for executing large scale data analytics applications in various domains including but not limited to content management, financial sector, healthcare etc. These frameworks slice a job into a number of smaller tasks, which are then executed by a job scheduler on a multi-node compute cluster. While making scheduling decisions, the State-of-art schedulers employed in these frameworks assume hardware resources such as CPU, disk I/O and network I/O to offer a fixed service rate. However, in a public cloud environment, many of these resources are associated with burstable service rates. More specifically, the resources offer a guaranteed baseline service rate with an option to burst above their baseline rate by expending accumulated burst credits. Being unaware about this underlying hardware burstability, schedulers tend to make sub-optimal task placement decisions, thereby adversely affecting the job completion times, leading to higher deployment costs.In this paper, we propose CASH, a burst credit aware scheduler, which is cognizant about the burst credits associated with the individual hardware resources in the public cloud cluster. Through coarse grained task annotations depicting the burst credit demand of individual tasks and dynamically monitoring the credits for the underlying resources, CASH performs optimal task placement decisions. We prototype CASH on YARN, Hadoop, and Tez, and extensively evaluate it using both batch and streaming workloads. Our experimental results with CASH show CPU-credit based instances, like AWS T3, are a viable cost effective alternative when compared to self-managed offerings like Amazon EMR, for running large scale batch workloads. Furthermore, we demonstrate that CASH can accelerate streaming SQL queries on a large Hive database by up to 39.4% , leading to public cloud cost savings by up to 22%.
A Touch of Indonesian Local Wisdom for the Concept of User Interface (UI) Design of Nusantara Traditional Wastra Digital Repository (NTWDR)

Abstract This paper aims to create a concept for the UI (User Interface) design for the NTWDR (Nusantara Traditional Wastra Digital Repository) with a touch of Indonesian local wisdom focusing on the implementation of Visual Communication Design and the implementation of eight golden rules of UI web-based and mobile applications to the NTWDR (Nusantara Traditional Wastra Digital Repository). The research is essential as kick-off research to the UX (User Experience) research that will be conducted after the UI (User Interface) is done. The research adapted the Design Thinking method of IDEO and Stanford d.School: Empathize, Interpretation, Ideation, Experimentation, and Test. It was designed with a simple sitemap that easy to follow and directly to the core of data. The uses of Nusantara colors and the interweaving of the intangible culture values of each NTW (Nusantara Traditional Wastra) are visual concepts behind each page of the NTWDR UI (Nusantara Traditional Wastra Digital Repository User Interface) design. The Result is the enchanting visual design representing Nusantara’s diversity that attracts to be experienced and surfed.
Learning to Forget for Meta-Learning via Task-and-Layer-Wise Attenuation

Few-shot learning is an emerging yet challenging problem in which the goal is to achieve generalization from only few examples. Meta-learning tackles few-shot learning via the learning of prior knowledge shared across tasks and using it to learn new tasks. One of the most representative meta-learning algorithms is the model-agnostic meta-learning (MAML), which formulates prior knowledge as a common initialization, a shared starting point from where a learner can quickly adapt to unseen tasks. However, forcibly sharing an initialization can lead to conflicts among tasks and the compromised (undesired by tasks) location on optimization landscape, thereby hindering task adaptation. Furthermore, the degree of conflict is observed to vary not only among the tasks but also among the layers of a neural network. Thus, we propose task-and-layer-wise attenuation on the compromised initialization to reduce its adverse influence on task adaptation. As attenuation dynamically controls (or selectively forgets) the influence of the compromised prior knowledge for a given task and each layer, we name our method Learn to Forget (L2F). Experimental results demonstrate that the proposed method greatly improves the performance of the state-of-the-art MAML-based frameworks across diverse domains: few-shot classification, cross-domain few-shot classification, regression, reinforcement learning, and visual tracking.
Area Surveillance Using a UAV with Mounted Chaotic Camera

This research focuses on the problem of area surveillance, using a UAV with a mounted camera that is controlled chaotically. The chaotic motion of the camera is performed by using the values of a chaotic map, to generate random rotation angles for the camera head, which periodically captures photos of the area to be surveyed. In this way, the position of the camera footprint on the ground is unpredictable, and this makes the surveillance path impossible to predict for an adversary. Thus, this approach allows for increased security when required, since the use of unpredictable agents will make it hard to design infiltration strategies. Simulations are performed for several different scenarios, to evaluate the ground coverage of the chaotic camera, as compared to a camera that constantly looks directly below the UAV.
Comparative study on the readiness of Mobile Learning Application in Learning

Mobile application has been widely used as a learning aid in education. It could be the best learning service for adult’s learner. The benefits of mobile app include increasing learning accessibility, reducing learning costs, promoting lifelong learning and many others. However, early studies have reported that there are challenges in promoting mobile learning due to the readiness of the users. Therefore, this project aims to conduct a comparative study by examining the students’ readiness towards the use of mobile application in learning. The investigation will focus on 100 students from UTP, Malaysia and Uhamka, Indonesia. Questionnaire has been distributed using random sampling method. The results have shown that there was no significant difference between the two samples, hence, it can be concluded that both students from universities are ready for the use of mobile learning.
Fast filters for preprocessing and mass segmentation in mammography images

Digital mammography is a valuable technique for breast cancer detection, because it is safe, noninvasive and can reduce unnecessary biopsies. However, it is difficult to distinguish masses from normal or dense regions because of their morphological characteristics and ambiguous margins. Thus, improvement of image quality, highlighting the tissues details and performing mass segmentation are important tasks for early breast cancer diagnosis. This work presents a mini-Mammographic Image Analysis Society (MIAS) database preprocessing, system which combines classic and efficient techniques of Median, Wiener and Gaussian filters to remove salt and pepper, speckle and gaussian noise in mammography images. The experimental results indicates that the Gaussian filter outperforms other filtering techniques, as shown by evaluated by Peak Signal to Noise Ratio and Mean Square Error metrics.
Attacks on Vulnerable Web Applications

Web Application now days are an important part of our daily life. Attackers try to attack these web applications to gain valuable information about the users using these web applications. Attackers exploit vulnerabilities in a web application to gain user data. This paper describes in detail the most harmful attacks that occurs on vulnerable web applications that are: SQL Injection Attack, Cross-Site Scripting Attack (XSS) and Command Injection Attack and helps you understand mechanism for 3 different types of attacks how they take place, what threats they possess and what could be done to avoid them.
Scalable and Compact 3D Tensorized Photonic Neural Networks

We propose a scalable and compact 3D tensorized photonic neural network with high- radix (1024×1024) synaptic interconnections implemented by cascaded small-radix (10×10) photonic tensor-train cores. Our architecture exhibits 171.8-dB lower insertion loss and 582× fewer MZIs.
Cooperative Distributed Deep Neural Network Deployment with Edge Computing

Deep Neural Networks (DNNs) are widely used to analyze the abundance of data collected by massive Internet-of-Thing (IoT) devices. The traditional approaches usually send the data to the cloud and process the DNN inference on the powerful cloud servers but suffer from long network latency. Therefore, edge computing has emerged to reduce network latency by offloading the computation from the cloud to the edge. However, a single resource-constrained edge device is unable to process real-time DNN inference. Thus, we devise a collaborative edge computing system CoopAI to distribute DNN inference over several edge devices with a novel model partition technique to allow the edge devices to prefetch the required data in advance to compute the inference cooperatively in parallel without exchanging data. Subsequently, we present a new optimization problem to minimize the completion time of distributed DNN inference. An innovative algorithm is then proposed to intelligently partition the model into the proper number and sizes of blocks, deploy them on a suitable number of edge devices, and run them in different rounds. The numerical results manifest that our algorithm outperforms the traditional approach by 20%−30% on the completion time.
Scheduling Containers Rather Than Functions for Function-as-a-Service

Function-as-a-Service (FaaS) is a compelling technology that allows users to run functions in an event-driven way without concerns about server management. Container-based virtualization enables functions to run in a lightweight and isolated run-time environment, but frequent function executions accompanied with container initialization (cold starts) make the platform busy and unresponsive. For performance sake, warm starts, which is to execute functions on already initialized containers, are encouraged, and thus FaaS platforms make efforts to schedule functions to warm containers.From our experience operating an on-premise FaaS platform, we found that the existing scheduler showed poor performance and unstable behavior against multi-tenant and highly concurrent workloads. This paper proposes a novel FaaS scheduling algorithm, named FPCSch, that schedules Function-Pulling-Containers instead of scheduling functions to containers. As FPCSch lets containers continuously pull functions of the same type, cold starts decrease dramatically. Our evaluations show that Apache OpenWhisk equipped with FPCSch has many desirable features for FaaS platforms; (1) quite stable throughput against the multi-tenant workloads mixed by the increasing numbers of function types, (2) growing throughput for increasing concurrency, (3) uniformly load-balancing resource-intensive workloads, and (4) nearly proportional performance for scale-out.
Passphrases Beat Thermal Attacks: Evaluating Text Input Characteristics Against Thermal Attacks on Laptops and Smartphones

We investigate the effectiveness of thermal attacks against input of text with different characteristics; we study text entry on a smartphone touchscreen and a laptop keyboard. First, we ran a study (N = 25) to collect a dataset of thermal images of short words, websites, complex strings (special characters, numbers, letters), passphrases and words with duplicate characters. Afterwards, 20 different participants visually inspected the thermal images to attempt to identify the text input. We found that long and complex strings are less vulnerable to thermal attacks, that visual inspection of thermal images reveals different parts of the entered text (36% on average and up to 82%) even if the attack is not fully successful, and that entering text on laptops is more vulnerable to thermal attacks than on smartphones. We conclude with three learned lessons and recommendations to resist thermal attacks.
Instance Similarity Learning for Unsupervised Feature Representation

In this paper, we propose an instance similarity learning (ISL) method for unsupervised feature representation. Conventional methods assign close instance pairs in the feature space with high similarity, which usually leads to wrong pairwise relationship for large neighborhoods because the Euclidean distance fails to depict the true semantic similarity on the feature manifold. On the contrary, our method mines the feature manifold in an unsupervised manner, through which the semantic similarity among instances is learned in order to obtain discriminative representations. Specifically, we employ the Generative Adversarial Networks (GAN) to mine the underlying feature manifold, where the generated features are applied as the proxies to progressively explore the feature manifold so that the semantic similarity among instances is acquired as reliable pseudo supervision. Extensive experiments on image classification demonstrate the superiority of our method compared with the state-of-the-art methods. The code is available at https://github.com/ZiweiWangTHU/ISL.git.