Reviewerly/exHarmony huggingface dataset:
Benchmarking the Accuracy of Algorithms for Memory-Constrained Image Classification

Convolutional Neural Networks, or CNNs, are the state of the art for image classification, but typically come at the cost of a large memory footprint. This limits their usefulness in edge computing applications, where memory is often a scarce resource. Recently, there has been significant progress in the field of image classification on such memory-constrained devices, with novel contributions like the ProtoNN, Bonsai and FastGRNN algorithms. These have been shown to reach up to 98.2% accuracy on optical character recognition using MNIST-10, with a memory footprint as little as 6KB. However, their potential on more complex multi-class and multi-channel image classification has yet to be determined. In this paper, we compare CNNs with ProtoNN, Bonsai and FastGRNN when applied to 3-channel image classification using CIFAR-10. For our analysis, we use the existing Direct Convolution algorithm to implement the CNNs memory-optimally and propose new methods of adjusting the FastGRNN model to work with multi-channel images. We extend the evaluation of each algorithm to a memory size budget of 8KB, 16KB, 32KB, 64KB and 128KB to show quantitatively that Direct Convolution CNNs perform best for all chosen budgets, with a top performance of 65.7% accuracy at a memory footprint of 58.23KB.
A Time Optimal Reactive Collision Avoidance Method for UAVs Based on a Modified Collision Cone Approach

UAVs or Unmanned Aerial Vehicles are an upcoming technology which has eased human lifestyles in many ways. Due to this trend future skies have a risk of getting congested. In such a situation time optimal collision avoidance would be extremely vital to travel in a shortest possible time by avoiding collisions. The paper proposes a novel method for time optimal collision avoidance for UAVs. The proposed algorithm is constructed as a three-stage approach based on the Collision Cone method with slight modifications. A sliding mode controller is used as the control law for the navigation. Mathematical proofs are included to verify the time optimality of the proposed method. The efficiency and the applicability of the work carried out is confirmed by both simulation and experimental results. An automated Matrice 600 Pro hexacopter has been used for the experiments.
Storing Dynamical Data Using Natural Language Addressing

Abstract The present paper refers to the possibilities for storing dynamical data from tracking moving objects. The urgency of this problem is determined primarily by the need for electronic registration, storage and processing of large volumes of semi-structured dynamical data in various fields of practice. The new problem raised, is the way in which the relationships between the data are described. The capabilities of modern relational database management systems do not allow rapid readjustment in accordance with the changes in practice. The emergence of new types of data connections requires readjustment, which takes time and requires a certain level of skill. In this article we consider an approach based on “Natural language addressing”, which allows solving these problems and we will offer a conceptual design (prototype) of an extension to existing systems, which facilitates the work, especially with dynamical semi-structured data and implicit relationships between them.
An Algorithmic Framework for Decentralised Matrix Factorisation

We propose a framework for fully decentralised machine learning and apply it to latent factor models for top-N recommendation. The training data in a decentralised learning setting is distributed across multiple agents, who jointly optimise a common global objective function (the loss function). Here, in contrast to the client-server architecture of federated learning, the agents communicate directly, maintaining and updating their own model parameters, without central aggregation and without sharing their own data. This framework involves two key contributions. Firstly, we propose a method to extend a global loss function to a distributed loss function over the distributed parameters of the decentralised system; secondly, we show how this distributed loss function can be optimised using an algorithm that operates in two phases. In the learning phase, a large number of steps of local learning are carried out by each agent without communication. In a following sharing phase, neighbouring agents exchange messages that enable a batch update of local parameters. Thus, unlike other decentralised algorithms that require some inter-agent communication after one (or a few) model updates, our algorithm significantly reduces the number of messages that need to be exchanged during learning. We prove the convergence of our framework and demonstrate its effectiveness using both the Weighted Matrix Factorisation and Bayesian Personalised Ranking latent factor recommender models. We demonstrate empirically the performance of our approach on a number of different recommender system datasets.
A recommendation system for offers in telecommunications

Recommender Systems are recently applied in many various areas such as entertainment, education, science, and especially e-commerce. In this paper, we propose an approach for building a recommender system in telecommunications for the telecom data about the historical behavior of consumers in six months. Our collected data spans over 6 months and contains how many times each offer has been purchased by a customer in each month and revenue generated by him/her in that month. Our recommender system consists of three main modules. In the segmentation module, we segment our customers into homogeneous groups, consumption wise. In the recommendation module, we utilize the Alternative Least Squares method to determine candidate offers. In the business rule module, we apply business rules to finalize the best telecom offers out of these candidate offers to recommend to each customer. Thanks to big data platforms, our recommender system attains a running time of approximately 10 seconds to 15 seconds to handle one million customers.
An Efficient HPRA-Based Multiclient Verifiable Computation: Transform and Instantiation

Choi, Katz, Kumaresan, and Cid put forward the conception of multiclient noninteractive verifiable computation (MVC), enabling a group of clients to outsource computation of a function of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>f</mi> </math> . CKKC’s MVC is impractical due to their dependence on fully homomorphic encryption (FHE) and garbled circuits (GCs). In this paper, with the goal of satisfying practical requirements, a general transform is presented from the homomorphic proxy re-authenticator (HPRA) of Deler, Ramacher, and Slamanig to MVC schemes. MVC constructions in this particular study tend to be more efficient once the underlying HPRA avoids introducing FHE and GCs. By deploying the transform to DRS’s HPRA scheme, a specific MVC scheme for calculating the linear combinations of vectors has been proposed. It can be understood that it is the first feasible and implementable MVC scheme so far, and the instantiation solution has a great advantage in efficiency compared with related works.
Lightweight DCT-Like Domain Forensics Model for Adversarial Example

Since the emergence of adversarial examples brings great security threat to deep neural network which is widely used in various fields, their forensics become very important. In this paper, a lightweight model for the forensics of adversarial example based on DCT-like domain is proposed. The DCT-like layer realizes the block conversion of data from the spatial domain to the frequency domain. Together with the color space transformation layer and the residual layer, the DCT-like layer realizes the simulation of JPEG quantization error. The feature statistical layer is used to obtain the statistical feature values of the feature map output by the frequency-division convolution, and at the same time, it also contains learnable hyperparameters. The group BN strategy ensures the effectiveness of the DCT-like layer and the feature statistical layer and promotes the accuracy of forensics. Experiments show that the proposed model not only reaches the highest accuracy we know, but also it only needs to train for one epoch to get a high-performance.
High Performance Computing for Understanding Natural Language

The amount of user-generated text available online is growing at an ever-increasing rate due to tremendous progress in enlarging inexpensive storage capacity, processing capabilities, and the popularity of online outlets and social networks. Learning language representation and solving tasks in an end-to-end manner, without a need for human-expert feature extraction and creation, has made models more accurate and much more complicated in the number of parameters, requiring parallelized and distributed resources high-performance computing or cloud. This chapter gives an overview of state-of-the-art natural language processing problems, algorithms, models, and libraries. Parallelized and distributed ways to solve text understanding, representation, and classification tasks are also discussed. Additionally, the importance of high-performance computing for natural language processing applications is illustrated by showing details of a few specific applications that use pre-training or self-supervised learning on large amounts of data in text understanding.
BEAF: A Blockchain and Edge Assistant Framework with Data Sharing for IoT Networks

Edge computing is emerging as an innovative technology that brings data processing and storage to end users, further leading to scale, decentralization and safety to IoT netWorks. It improves the quality of services but meanwhile introduces great challenges such as data security, latency, etc. Fortunately, blockchain technology can improve security issues of edge computing in IoT networks as it alloWs only trusted IoT nodes to interact With each other. To better address the security risks of data sharing problem and improve the credibility of data, this paper presents a Blockchain and Edge computing Assistant security FrameWork (BEAF) With data sharing for IoT netWorks. With blockchain and edge computing, BEAF supports both decentralization and data tracing after data sharing. In addition, BEAF can achieve access control and share data to specific node. We also conduct security analysis and shoiv that BEAF provides confidentiality, availability, data integrity, etc. In addition, We develop the base layer through Hyperledger Fabric technology, and evaluate the performance of BEAF in terms of stability, scalability and efficiency.
Designing Intelligent Tutoring Systems With AI

This chapter describes the research problems inherent the design of effective intelligent tutoring systems (ITS) based on cognitive neuroscience research (brain-based approach) and evidence-based education. Effective student-ITS interaction requires a thorough understanding of the brain processes that underpin learning. The knowledge of these principles allows you to select optimal pedagogical strategies to monitor and guide the process. AI-based tutors have great potential in constantly adapting teaching content and tactics to the changing cognitive needs of the individual student in order to foster deep understanding, increase motivation, and develop a sense of self-efficacy in the learner. The brain-based approach can give ITSs a significant increase in effectiveness in promoting learning.
Data-driven Distributed State Estimation and Behavior Modeling in Sensor Networks

Nowadays, the prevalence of sensor networks has enabled tracking of the states of dynamic objects for a wide spectrum of applications from autonomous driving to environmental monitoring and urban planning. However, tracking realworld objects often faces two key challenges: First, due to the limitation of individual sensors, state estimation needs to be solved in a collaborative and distributed manner. Second, the objects' movement behavior model is unknown, and needs to be learned using sensor observations. In this work, for the first time, we formally formulate the problem of simultaneous state estimation and behavior learning in a sensor network. We then propose a simple yet effective solution to this new problem by extending the Gaussian process-based Bayes filters (GPBayesFilters) to an online, distributed setting. The effectiveness of the proposed method is evaluated on tracking objects with unknown movement behaviors using both synthetic data and data collected from a multi-robot platform.
Verifying Rapid Increasing of Mega-Solar PV Power Plants in Japan by Applying a CNN-Based Classification Method to Satellite Images

Since the huge earth quake in 2011 in Japan, the Japanese government has strongly encouraged growth of renewable energy use. As a result, even we focus on only mega-solar photovoltaic (PV) power plants (> 1,000 kW), the amount of the power generation significantly increased. Because such rapid increasing of the PV power plants changes land use widely, verification of the spreading of the PV power plants should be essential for assessing economic/environmental issues. In this study, to verify the increasing of the PV power plants based on observations, we applied a method that can detect the PV power plants efficiently with a convolutional neural network to Landsat-8 images. From the detection, we successfully identified the increasing of the area of the PV power plants quantitatively at the same time identifying the location of each PV power plant in many prefectures in the capital region of Japan.
Evaluation Methodologies for Virtual Reality and Physical Test Environments for Spaceflight Design

NASA human factor design engineers wanted to examine if there would be any differences in testing low-fidelity conceptual designs in a physical environment compared to a virtual environment. An evaluation of two identical environments was conducted with subject matter experts (SMEs). Results indicated that when testing a design concept at this early stage, a high correlation between the two environments exists, meaning SMEs found little to no difference when evaluating a design in either a physical or a virtual environment. There are advantages and limitations to both environments. The virtual world gave the experts a better sense of the microgravity space and the relationships of space and human presence that are difficult to simulate in a 1-g physical environment. However, the interaction between human and mechanics is better enhanced in the physical world compared to the virtual world. These advantages and limitations of each environment are important; thus, at this early design life cycle phase, virtual reality shows great promise as an evaluation environment for testing early design concepts that will cost less, give more options, and increase designer’s time to design.
Parcel Tracking System Using Barcode Scanner with Verified Notification

Abstract The emergence of electronic commerce online shopping has significantly increased the use of mail-order within Kolej University Poly-Tech Mara (KUPTM) communities. The tracking system provided by the courier services offers the status of delivery for each parcel until it reaches the designated destination. However, inefficiencies of managing the parcels at KUTPM may causing lost or misplaced. Therefore, the study offers an innovative approach where the study intended to invent a parcel tracking delivery and management systems for KUPTM communities. In particular, this invention relates to computerized tracking of delivered parcels using the barcode or tracking number that will notify recipients via Short Message Service (SMS) notification. The primary purpose of this study is to develop an efficient and secured system called the Parcel Tracking System. The system used a web-based service for managing and tracking the parcel. The development method of this system uses PHP and JavaScript programming languages in Adobe Dreamweaver platform with MySQL programme as the backend of the system. This paper focuses on the development process of the Parcel Tracking System. The findings found that the efficiencies of parcel tracking and security management are significantly improved. The application of Parcel Tracking System is promising and efficient for KUPTM communities. It is evidently that the proposed system was found successful and beneficial for KUPTM communities in tracking and managing parcels efficiently.
Modeling knowledge and functional intent for context-aware pragmatic analysis

Nikhita Vedula is an Applied Scientist at Amazon Alexa Science. She obtained her PhD in Computer Science and Engineering from the Ohio State University in August 2020, advised by Professor Srinivasan Parthasarathy. She received her bachelor's degree from the National Institute of Technology, Nagpur, India in 2015. Her research interests are at the intersection of data mining, natural language processing and social computing. Over the course of her PhD, her research involved designing efficient and novel machine learning and computational linguistic techniques that extract, interpret and transform the vast, unstructured digital content into structured knowledge representations in diverse contexts. She has worked with researchers from interdisciplinary fields such as emergency response, marketing, sociology and psychology. She performed research internships at Nokia Bell Laboratories, Adobe Research and Amazon Alexa AI. Her work has been published at several top data mining conferences such as the Web Conference, SIGIR, WSDM and ICDM. Her work on detecting user intentions from their natural language interactions won the Best paper award at the Web Conference 2020. She was a recipient of a Graduate Research Award (2020), a Presidential Fellowship (2019) and a University Graduate Fellowship (2015) at the Ohio State University. She was also selected as a Rising Star in EECS (2019).
Evaluation of Algorithms and Security Protocols to Mitigate the Risks in Commercial Transactions carried out by Public Companies

The security problems in electronic commerce in transactions are very evident in public companies. The objective of this research is to analyze security algorithms and protocols to mitigate risks and vulnerabilities within commercial transactions that help us identify computer attacks, whether internal or external. Deductive method and exploratory research were used to analyze the information from the reference articles and information available online; regarding different types of protocols and algorithms that protect the information in its entirety and confidentiality, thus providing the respective security to the information of public companies. It resulted in a control of the security and privacy factors, by having RSA algorithms with formulas to encrypt and decrypt public keys, combinations of commercial transactions, asymmetric and symmetric cryptography and the SET protocol, which facilitates the algorithms in decision-making within the organizations. It was concluded that the SET protocol is the best in facilitating greater security to mitigate vulnerabilities, threats and risks of its processes in public organizations.
Poster: Configuration Management for Internet Services at the Edge: A Data-Driven Approach

Internet services are increasingly pushed from the remote cloud to the edge sites close to data sources to offer fast response time and low energy footprint. However, software deployed at edge sites must be updated frequently. Performing updates as soon as they are available consumes a large amount of energy. Configuration management tools that install software updates and manage allowed staleness can inflate energy demands, especially when updates interrupt idle periods at the edge site and block processors from entering power-saving modes. Our research studies configuration management policies, their effect on energy footprint and strategies to optimize them. We have observed that policies yielding low energy footprint differ from site to site and over time. We propose a data-driven approach that uses data collected at each edge site to predict an energy-efficient policy and also guards against worst-case performance if data-driven predictions error occurs. We use a novel randomwalk approach to manage data-driven policies that yield a low footprint for a representative trace of updates observed at an edge site. We are setting up 4 edge service benchmarks powered by AI inference to create realistic software update traces.
QI-BRiCE: Quality Index for Bleeding Regions in Capsule Endoscopy Videos

With the advent in services such as telemedicine and telesurgery, provision of continuous quality monitoring for these services has become a challenge for the network operators. Quality standards for provision of such service... | Find, read and cite all the research you need on Tech Science Press
WITHDRAWN: Predicting class and pose for a face recognition application with simple and efficient reconstruction technique

Multiple pose recognition becomes a popular domain of research due to its wide range of applications i.e. monitoring and surveillance, authentication, criminology, and others. Several techniques were recently introduced for multiple pose recognition. In this paper, the main aim is to design a multiple pose based face recognition technique that accurately identifies the person's identity as well as their pose label. In this context, two variants of the multiple pose recognition technique are introduced. Among the first techniques is using the pose alignment and face reconstruction strategy, for recognition, using the least square technique as Least Square based Face Class and Pose (LSFCP). Moreover, it the second variant of the system is using the radial basis function (RBF) and Manhattan Distance. Additionally, it is usages a threshold value for recognizing the pose and person class more precisely as Least Square kernel-based Face Class and Pose (LSKFCP). The experiments were performed on a real-world dataset that includes 40 persons (i.e. identities) and each identity includes 10 poses. According to the gained results of LSFCP and LSKFCP, the LSKFCP found it more accurate and superior. Shortly, the LSKFCP is extended more for achieving higher accuracy on recognition.
Effect of pH and Concentration on Biosorption Malachite Green and Rhodamine B Dyes using Banana Peel (Musa balbisiana Colla) as Biosorbent

Abstract All Biosorption of Malachite green and Rhodamine B dyes using banana peel ( Musa balbisiana Colla) as a biosorbent has been carried out. The purpose of this study was to determine the effect of pH and concentration for biosorption of Malachite green and Rhodamine B by banana peel biosorbent ( Musa balbisiana Colla). The method used is the batch method. The maximum wavelength of Malachite green is 618 nm and Rhodamine B 555 nm measured by the UV-Vis Spectrophotometer. The results showed that the optimum conditions for Malachite green adsorption occurred at pH 3, a concentration of 100 ppm 8,0662 mg/g. The optimum conditions for Rhodamine B adsorption occurred at pH 3, a concentration of 120 ppm 16,6614 mg/g. The adsorption isotherm of Malachite green with banana peel (Musa balbisiana Colla) biosorbent tends to follow the Freundlich isotherm with a determinant coefficient (R 2 ) of 0.9996 and Rhodamine B 0,9884.
Research of Psychology Computerization Experiment

Abstract Efficient and accurate computer has become an indispensable tool in experimental research and teaching, and the computerized experimental methods of psychology have gradually diversified and standardized. This paper first introduces the application of computer-based experiment generation tools in psychology, and then describes the characteristics of computer-based experiments in psychology, such as single machine experiments, online experiments with a variety of devices. Finally, the paper introduces the generation system of psychological computerized experiment based on windows, and looks forward to the future of psychological computerized experiment in form, technology and application.