Reviewerly/exHarmony huggingface dataset:
Controlling Sequential Hybrid Evolutionary Algorithm by Q-Learning [Research Frontier] [Research Frontier]

Many state-of-the-art evolutionary algorithms (EAs) can be categorized as sequential hybrid EAs, in which various EAs are sequentially executed. The timing to switch from one EA to another is critical to the performance of the hybrid EA because the switching time determines the allocation of computational resources and thereby it helps balance exploration and exploitation. In this article, a framework for adaptive parameter control for hybrid EAs is proposed, in which the switching time is controlled by a learned agent rather than a manually designed scheme. First the framework is applied to an adaptive differential evolution algorithm, LSHADE, to control when to use the scheme to reduce the population. Then the framework is applied to the algorithm that won the CEC 2018 competition, i.e., the hybrid sampling evolution strategy (HSES), to control when to switch from the univariate sampling phase to the Covariance Matrix Adaptation Evolution Strategy phase. The agents for parameter control in LSHADE and HSES are trained by using Q-learning and deep Q-learning to obtain the learned algorithms Q-LSHADE and DQ-HSES. The results of experiments on the CEC 2014 and 2018 test suites show that the learned algorithms significantly outperform their counterparts and some state-of-the-art EAs.
Privacy preservation for spatio-temporal data in Mobile Crowdsensing scenarios

Mobile Crowdsensing has become an important paradigm in the last decade for on-demand monitoring scenarios in Smart Cities and vehicular networks, when the deployment of a dedicated sensor network is no longer affordable. To foster the participation of a large user base, it is common to reward them on top of the amount and the quality of data provided. Regardless of the MCS policy adopted, this requires the crowdsourcer to keep track of the participants. Since the contributed data inherently carries sensitive spatio-temporal information, privacy problems arise if a malicious entity gains access to it; still, in some cases, the spatio-temporal precision is crucial for the benefit of the application and cannot be distorted. In this paper we propose a privacy preserving framework for opportunistic MCS scenarios that includes data collection and rewarding phases. The framework both retains the precision of spatio-temporal information and limits the sensitivity of information disclosed through an algorithm that clusters the data points into low correlated sets. The framework is agnostic about how correlation is calculated, and we propose three exemplary correlation functions. We evaluate our framework against six real world datasets, assessing its efficacy and envisioning its implementation in practical deployments.
How Does the Use of AI in HRM Contribute to Improved Business Performance?

The chapter aims to review the usage of artificial intelligence in human resource management (HRM) to enhance organizational performance. The current study is a conceptual effort that integrates several streams of literature to give critical review of the interaction between AI and HRM in modern organizations. The present review enables industrialists to think of AI as a collection or combination of numerous IT configurations in the many company business areas rather than as a single technology because the success of AI depends on a number of important factors being combined. The authors found that the use of artificial intelligence has helped the organizations in enhancing their performance and assists in gaining competitive advantage.
Vote or not? How language mimicry affect peer recognition in an online social Q&amp;A community

As Q&A Communities become not only a source of information, but an interaction platform where people gain social recognition, the research into factors impacting viewers’ peer recognition of online contribution helps identify an effective model for such interaction. This study focuses on finding the connection between language mimicry and the peer recognition of online contributions. We collected a total of 13,109 contributions from Fluther, an online Q&A community. Language features including linguistic similarity, text resemblance and peer recognition of online contributions were analysed, with the contributors’ followers and past responses serving as moderators. Linear regression was adopted to provide empirical evidence. This study demonstrates that language mimicry between the contributor and the information seeker has significant positive effect on the peer recognition of online contributions by the viewers. Moreover, the number of contributors’ past expertise mitigates the positive influence of linguistic similarity and text resemblance while their popularity strengthens the influence. It is a novel direction as prior language mimicry studies mainly focused on dyadic communication, whereas this research study aims to examine the influence of language mimicry on peer recognition of online contribution. The study extends the literature on language mimicry and provides practical implications.
Print vs. electronic resources: influence of demographical characteristics of undergraduates on preferences and choices under diverse circumstances

Purpose This paper delineates the appraising and comparing the circumstances that may influence the choices and preferences of the undergraduates of National Institutes of Technology (NITs) of the north-western region of India for using library’s print versus electronic resources. The author investigated the circumstances in knowing whether different situations impact their choice for using print or electronic resources concerning their demographical characteristics for their academic learning. Design/methodology/approach The study used a survey of undergraduates at six NITs in the north-western region of India. The responses were analysed using Chi-Square, Mann–Whitney and Kruskal–Wallis statistical analysis tests, via SPSS software. Findings The study revealed that respondents preferred a mix of both forms of sources for their academic studies under different circumstances. The results reveal that the institution of respondents affected the choice of print resources over e-resources and vice versa in many different circumstances. Respondents’ year of study affected the choice of using print over e-resources and vice versa in certain situations. The residential backdrop affects the choice of using e-resources over print in some situations. Gender of respondents affects the choice of using e-resources over print under some situations. Practical implications The study will further aid librarians at six NITs in the north-western region of India to know whether print resources still have the same importance among library users or their preference switched towards using electronic resources. Originality/value This study offers new perspectives on the use of print verses electronic resources among the undergraduates at six NITs in the north-western region of India so that the libraries can procure desired resources rather than moving with the market trends.
Captioning Based Image Using Euclidean Distance and ResNet-50

Past few years researchers have focused on using the attention and transformer model to access image insights and provide deep descriptions for an input image. Although these methods have shown greater improvementsover the regular methodology of using recurrent neural networks. But still, it is under the great influence of the gradient vanishing issue. Our way of tackling this issue is to minimize the use of gradient-intensive task and replace it with distance-mapping tasks. This means that our model predicts the output on the basis of “Euclidian Distance”. To accomplish this first we use pretrained neural network to extract features and then use K Nearest neighbor to cluster image with similar features together. Here the model is used to gather low level object information to generate relevant caption so that model can work on the top efficiency and also the generated caption is natural. To fulfill this requirement, we make use for our feature extractor model and clustering model to find the closest resembling image to our query image and return its most relevant captions
ML Approaches to Detect Email Spam Anamoly

Email Spamming has been recognized as one of the most dangerous cyber-attacks these days. As email is more encouraging platform for the communication mechanism these days, it is accessible to everyone across the world with the help of internet. Hence it has to be protected in order to reduce the cyber-attacks which involve loss of organizational property. The previous spam-filtering technologies include humanly detection of certain keywords and blocking the spam-sending domains which are recognizable. Spamming of emails is on the rise as the number of internet users grows, resulting in the leakage of personal information from users. Thus, detecting these email spams is critical in order to reduce illegal and unethical behavior, as well as phishing and fraud. As a result, ongoing research into email spam detection has been conducted utilizing a variety of machine learning algorithms with varying levels of accuracy. Using the required techniques of machine learning in this project, the regular words which are used in spam emails are easily identified using the pre-occupied data set called stop-words. This proposed system tries to recognize a recurrent word group which are used mostly that are classed as spam using machine learning techniques. The machine learning model that has been using is an early-trained model with feedback that can tell the difference between a correct and an ambiguous output.
Construct an Intelligent Querying System in Education based on Ontology Integration

E-learning is an online educational system using technological and electronic devices through the internet. Using e-learning educational systems, learners can refer to documents and communicate directly with teachers to effectuate the acquisition of knowledge. In this paper, the method for creating an intelligent querying system for e-learning is proposed. The knowledge base of this system is organized based on the integrating of ontology and the structure of database. Some searching issues for knowledge content, such as studying and resolving knowledge searches based on classification of knowledge, are studied and solved. This method is applied to build an intelligent querying system on the course of Database Foundation in Information Technology (IT) curriculum at university. This system aims to support students to review lessons and understand more the knowledge of courses by their self-learning. The experimental results show that it would be expected to contribute in supporting online-based learning facilities for students.
Experimental Analysis of U-Net and Mask R-CNN for Segmentation of Synthetic Liquid Spray

In digital image processing, segmentation is a process by which we can partition an image based on some variables to extract necessary elements. Unlike typical objects, it is complicated to segment dynamic objects from a synthetic fluid dataset where properties like position and shape change over time. Experiments on image segmentation over this dataset are conducted using U-Net (semantic segmentation) and Mask R-CNN (instance segmentation) to compare their results. The training dataset is generated from seven labelled images through data augmentation. Training on 1000 and validating on 200 images, Mask R-CNN achieved more epochs quickly. Around 1000 epochs for Mask R-CNN and 500 epochs for U-Net, both models reached a similar result in terms of F1 score and can segment the object in the new images.
Accelerating Data Serialization/Deserialization Protocols with In-Network Compute

Efficient data communication is a major goal for scalable and cost-effective use of datacenter and HPC system resources. To let applications communicate efficiently, exchanged data must be serialized at the source and deserialized at the destination. The serialization/deserialization process enables exchanging data in a language- and machine-independent format. However, serialization/deserialization overheads can negatively impact application performance. For example, a server within a microservice framework must deserialize all incoming requests before invoking the respective microservices. We show how data deserialization can be offloaded to fully programmable Smart-NICs and performed on the data path, on a per-packet basis. This solution avoids intermediate memory copies, enabling on-the-fly deserialization. We showcase our approach by offloading Google Protocol Buffers, a widely used framework to serialize/deserialize data. Our evaluation demonstrates that, by offloading data deserialization to the NIC, we can achieve up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4. 8\mathrm{x}$</tex> higher throughput than a single AMD Ryzen 7 CPU. We then show through microservice throughput modeling how we can improve the overall throughput by pipelining the deserialization and actual application activities with PsPIN.
PharmaChain 2.0: A Blockchain Framework for Secure Remote Monitoring of Drug Environmental Parameters in Pharmaceutical Cold Supply Chain

Cold chain logistics play an important aspect in storing, preserving and transporting of cargo which is highly sensitive to environmental parameters surrounding it. Not han-dling these medicinal products in the recommended environment either during transportation or storage can cause adverse effects such as degradation of potency of the drug, the stability of drugs and in some cases may even lead to serious consequences on the health and well being of the consumer. Once a product leaves the supplier and enters the cold supply chain, monitoring and controlling the surroundings of the shipment will be a difficult task which can be resolved by using latest technologies like Internet of Things (IoT), However, due to the resource constraints of such IoT devices, it is very easy to manipulate the data and provide falsified information by any adversary in order to disrupt the system. Hence, a robust architecture called PharmaChain 2.0, which is capable of efficiently and securely monitor and control the ambient parameters of shipments in cold chain using IoT, and a cloud architecture and IoT friendly Proof-of-Authentication consensus based blockchain technologies to increase confidence of safe consumption for end consumers.
The Improvement of Stress Level Detection in Twitter: Imbalance Classification Using SMOTE

This study developed a model to improve stress level detection using Synthetic Minority Oversampling Technique (SMOTE) imbalanced data classification. SMOTE is a method to address imbalanced datasets to oversample the minority class. The data collected from Twitter may seem vague mainly due to the massive amount of data. This research used the framework model of Data, Experts Data Annotation, Text Pre-processing, and Text Representation and Classification. The Bag of Word (BoW), Term Frequency-Inverse Document Frequency (TFIDF), and Lemma were used for the text representation. The data were collected only from Twitter under certain circumstances. The Subject Matter Experts (SMEs) on mental health problems have annotated the text from the tweets based on four levels: Normal, Mild, Moderate, and Severe. The data group for the Normal stress level was relatively large compared to the other groups. Due to the imbalanced data group, the SMOTE technique was used for data argumentation. The result showed that the model classification using Support Vector Machine with SMOTE increased by improving the cardinality of the minority class label through the significant Macro Avg Recall and Macro Avg F1-Score analysis results compared to the baseline.
Machine Learning-based Oddity Detection of Smoke and Gas Sensor Data in a Large Gated Community

Oddity detection identifies strange events or observations that are statistically distinct from the rest of the data and can cause concern. The paper proposes a method to find oddities from an extensive dataset made up of data from smoke and gas sensors of the large gated community to identify a potential hazard (such as a fire) in the home. The dataset was created using a Python Script, and oddities were found using machine learning techniques with the PYCARET module. By applying an oddity score to each data sample, methods for detecting anomalies based on monsters, namely, the Local Outlier Factor (LOF) and the Isolation Forest (I-Forest) algorithms, are used. The entire program was developed using Google Colaboratory (also known as Colab), an open-source cloud service.
RACL: A robust adaptive contrastive learning method for conversational satisfaction prediction

• We propose the TAM-GCN method to embed users' potential preferences in conversation. • We propose the TAIA method to model user's conversational perception and experience. • We propose a robust adaptive contrastive learning method RACL for CSP. • Experiments on four datasets show the superior prediction performance of RACL.
Enhance Performance of Ad-hoc Search via Prompt Learning

Recently, pre-trained language models (PTM) have achieved great success on ad hoc search. However, the performance decline in low-resource scenarios demonstrates the capability of PTM has not been inspired fully. As a novel paradigm to apply PTM to downstream tasks, prompt learning is a feasible scheme to boost PTM's performance by aligning the pre-training task and downstream task. This paper investigates the effectiveness of the standard prompt learning paradigm on the ad hoc search task. Based on various PTMs, two types of prompts are tailored for the ad hoc search task. Overall experimental results on the MS Marco dataset show the credible better performance of our prompt learning method than fine-tuning based methods and another previous prompt learning based model. Experiments conducted in various resource scenarios show the stability of prompt learning. RoBERTa and T5 deliver better results compared to BM25 using 100 training queries utilizing prompt learning, while fine-tuning based methods need more data. Further analysis shows the significance of the uniformity of tasks' format and adding continuous tokens into training in our prompt learning method.
Enriching Social Sharing for the Dementia Community: Insights from In-Person and Online Social Programs

The dementia community faces major challenges in social engagements, which have been further complicated by the prolonged physical distancing measures due to the COVID-19 pandemic. Designing digital tools for in-person social sharing in family and care facility settings has been well explored, but comparatively little HCI work has focused on the design of community-based social technologies for virtual settings. We present our virtual fieldwork on remote social activities explored by one dementia community in response to the impacts of the pandemic. Building upon our previously published on-site fieldwork in this community, we expand on our initial publication by follow-up interviewing caregivers and facilitators and reflecting on a virtual social program. Through thematic analysis and contrasting in-person and online formats of the program, we deepened the understanding of virtual social engagements of the dementia community, examining their efforts to leverage physical objects and environments, enhance open and flexible experiences, and expand collaborative space. We propose to open new design opportunities through holistic approaches, including reimagining community social spaces, rethinking agency in people with dementia and caregivers, and diversifying HCI support across communities and stakeholders.
Memory-Tree Based Design of Optical Character Recognition in FPGA

As one of the fields of Artificial Intelligence (AI), Optical Character Recognition (OCR) systems have wide application in both industrial production and daily life. Conventional OCR systems are commonly designed and implement data computation on the basis of microprocessors; the performance of the processor relates to the effect of the computation. However, due to the “Memory-wall” problem and Von Neumann bottlenecks, the drawbacks of traditional processor-based computing for OCR systems are gradually becoming apparent. In this paper, an approach based on the Memory-Centric Computing and “Memory-Tree” algorithm has been proposed to perform hardware optimization of traditional OCR systems. The proposed algorithm was first designed in software implementation using C/C++ and OpenCV to verify the feasibility of the idea and then the RTL conversion of the algorithm was done using the Xilinx Vitis High Level Synthesis (HLS) tool to implement the hardware. This work chose Xilinx Alveo U50 FPGA Accelerator to complete the hardware design, which can be connected to the x86 CPU in the PC by PCIe to form heterogeneous computing. The results of the hardware implementation show that the system this work designed can recognize characters of English capital letters and numbers within 34.24 us. The power of FPGA is 18.59 W, which saves 77.87% of energy consumption compared to the 84 W of the processor in PC.
Generalized Type 2 Fuzzy Differential Evolution Applied to a Sugeno Controller

The papers using fuzzy logic have generated continuous improvements for applications, and an example of this is the use of generalized type 2 fuzzy systems in real-world problems. The key idea of this paper is to present a generalized type 2 fuzzy system for augmenting differential evolution with dynamic parameter variation in order to enhance its performance and convergence. A generalized type 2 Sugeno controller was implemented with the aim of enhancing the trajectory of a robot, and an analysis of the execution time and the errors obtained by ITAE, IAE, ITSE, ISEV, RMSE, and MSE is presented. In addition, a comparison with different levels of disturbance applied to the controller was performed with the goal of demonstrating the efficiency of a type 2 fuzzy system.
Mineral Texture Classification Using Deep Convolutional Neural Networks: An Application to Zircons From Porphyry Copper Deposits

Abstract The texture and morphology of igneous zircon indicates magmatic conditions during zircon crystallization and can be used to constrain provenance. Zircons from porphyry copper deposits are typically prismatic, euhedral, and strongly oscillatory zoned which may differentiate them from zircons associated with unmineralized igneous systems. Here, cathodoluminescence images of zircons from the Quellaveco porphyry copper district, Southern Peru, were collected to compare zircon textures between the premineralization Yarabamba Batholith and the Quellaveco porphyry copper deposit. Quellaveco porphyry zircons are prismatic, euhedral, and strongly oscillatory zoned, whereas the batholith zircons are subhedral‐anhedral with weaker zoning. We adopt a deep convolutional neural network (CNN) approach to demonstrate that a CNN can classify Quellaveco porphyry zircons with high success. We trial several CNN architectures to classify zircon images: LeNet‐5, AlexNet and VGG, including a transfer learning approach where we used the weights of a VGG model pretrained on the ImageNet data set. The VGG model with transfer learning is the most effective approach, with accuracy and receiver operating characteristic‐area under curve (ROC‐AUC) scores of 0.86 and 0.93, indicating that a Quellaveco porphyry zircon CL image can be ranked higher than a batholith zircon with 93% probability. Visualizing model layer outputs demonstrates that the CNN models can recognize crystal edges, zoning, and mineral inclusions. We trial implementing trained CNN models as unsupervised feature extractors, which can empirically quantify crystal textures and morphology. Therefore, deep learning provides a tool for the extraction of information from large, imaged‐based petrographic data sets which can facilitate petrologic and provenance studies.