Reviewerly/exHarmony huggingface dataset:
Phenology Probes: Exploring Human-Nature Relations for Designing Sustainable Futures

Understanding and relating with nature is increasingly important in design as we aim to address the risks posed by anthropogenic climate change. Design methods are often human-centered and provide us with opportunities to reshape existing methods to foreground nature. Inspired by phenology, the study of cyclic biological events, this paper presents phenology probes: a method to study the multifaceted relations between humans and nature and how they are manifested in a particular time and place. Our phenology probe kit consisted of 3 activities: spatial mapping, phenology snapshotting, and phenology wheels. In deploying these probes with 20 gardeners we identified key relations with nature: Personal historic, multisensory, emotional, global, and climate activist relations. We discuss how these relations help to bring nonhuman stakeholders and cyclic biological events to the design process and directions for future work to further enhance the agency of nonhumans in interaction design.
Design and Implementation of Supermarket Management System Based on Database

In the process of operation, supermarkets are characterized by frequent supply and delivery, a large amount of order data, and frequent staff turnover, but the data generated needs to be managed uniformly. Manual management of these complex data has not adapted to the trend of the development, the realization of standardized data, and automatic management process has gradually become the requirements of the supermarkets that can better manage their business. The supermarket management information system realizes the classification of goods, the recording of suppliers and consumers, the recording of a large number of the order information, and their distribution information. Through the analysis of orders, it promotes the decision-making of supermarket management and sales. At the same time, the supermarket management system also greatly reduces the workload of supermarket order administrators, who can simply query the order information they want to view. In addition, this supermarket management information system also realized the supermarket staff management function, employee administrators can easily complete the supermarket staff information increase, delete, modify, and view tasks. This system is very convenient for managing, inputting, outputting, and finding the data to make the supermarket data specific, visualizations, and rationalization. As for the background database, this system uses Oracle. In the aspect of the software, various configurations in computers including input and output capacity, internal memory, and external memory capacity can meet the requirements of users.
Analysis and research on backscattering effect of underwater imaging

In laser underwater imaging, backscattered light is the main component received by the detector. Backscattered light reduces the contrast of underwater image target and restricts the application of underwater image. The stripe laser scanning imaging method can meet the requirements of underwater laser imaging system to increase the field of view and reduce the influence of backscattered light. In this paper, combined with the transmission characteristics of laser in underwater, we establish the calculation model of backscattered light, This model is of guiding significance to analyze the influence of illumination system on imaging quality and the optimization of light source in underwater imaging system.
Efficient and Secure Data Sharing Scheme on Interoperable Blockchain Database

Interoperable Blockchain Database (IBD) can enable users to execute transactions for sharing data stored in various blockchains maintained by different organizations or communities in a transparent manner. However, compared to traditional distributed databases, IBD can hardly provide high-level security and scalability, which are caused by many factors, such as system architecture, consensus protocol, and interactive pattern. Among them, the consensus protocol is the most critical factor, since the credibility of consensus nodes inside the corresponding blockchains are difficult to be guaranteed. Additionally, the consensus protocol directly affects the verification efficiency for given transactions in IBD. In this paper, we formally concern the problem of secure data sharing in IBD. We present a scheme named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hybridchain</i> to execute transactions for sharing data securely and efficiently. We first propose a novel concept named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interoperable Consensus Group</i> (ICG) which organizes a set of basic consensus nodes into a group, each of which is responsible for managing at least one local blockchain. Then, we present an interoperable cross-chains consensus protocol to achieve eventual consistency of blockchain transactions. We conduct extensive experiments, and the evaluation results show that our proposed approach achieves superior performance.
FlexBNN: Fast Private Binary Neural Network Inference With Flexible Bit-Width

Advancements in deep learning enable neural network (NN) inference to be a service, but service providers and clients want to keep their inputs secret for privacy protection. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Private Inference</i> is the task of evaluating NN without leaking private inputs. Existing secure multiparty computation (MPC)-based solutions mainly focus on fixed bit-width methodology, such as 32 and 64 bits. Binary Neural Network (BNN) is efficient when evaluated in MPC and has achieved reasonable accuracy for commonly used datasets, but prior private BNN inference solutions, which focus on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Boolean Circuits</i> , are still costly in communication and run-time. In this paper, we introduce FLEXBNN, a fast private BNN inference framework using three-party computation (3PC) in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Arithmetic Circuits</i> against semi-honest adversaries with honest-majority. In FLEXBNN, we propose to employ flexible and small bit-width equipped with a seamless bit-width conversion method and design several specific optimizations towards the basic operations: i) We propose bit-width determination methods for Matrix Multiplication and Sign-based Activation function. ii) We integrate Batch Normalization and Max-Pooling into the Sign-based Activation function for better efficiency. iii) More importantly, we achieve seamless bit-width conversion within the Sign-based Activation function with no additional cost. Extensive experiments illustrate that FLEXBNN outperforms state-of-the-art solutions in communication, run-time, and scalability. On average, FLEXBNN is 11× faster than XONN (USENIX Security’ 19) in LAN, 46× (resp. 9.3×) faster than QUOTIENT (ACM CCS’19) in LAN (resp. WAN), 10× faster than BANNERS (ACM IH&MMSec’21) in LAN, and 1.1-2.9× (resp. 1.5-2.7×) faster than FALCON (semi-honest, PoPETs’21) in LAN (resp. WAN), and improves the respective communication by 500×, 127×, and 1.3-1.5× compared to XONN, BANNERS, and FALCON.
A Comparative Study on Approaches for Text Quality Prediction using Machine Learning and Natural Language Processing

The popularity of developer support forums is rising. Although open-source knowledge is a precious resource for many developers, there may be challenges with the standard of the information obtained. The advancement of semantic segmentation systems has expanded quickly in response to the unprecedented growth of digital resources. The quality of questions, comments, answers, and views are difficult to distinguish on the Q&A platforms like Stack Overflow, Bloom Fire, Answer Hub, Quora, Yahoo, etc. The automatic extraction of expressive features is made possible by recently developed machine learning algorithms, which take advantage of the most important innovations in advanced learning techniques. The technological increase in these approaches has spawned a wide range of approaches for encoding natural language into machine-interpretable data. In this paper, an in-depth examination of these methodologies used to solve quality-related problems is done. Algorithms like Logistic Regression, Multiple Linear Regression, Fully CNN, Support Vector Machine, Random Forest, FastText Classifier, and BiLSTM are studied and analyzed. Lastly, a relative analysis is achieved primarily focused totally on innovative consequences obtained from specific methods.
A Cybersecurity Evaluation Model (CSEM) for Indian SMEs Working in a Virtual Team Environment

During COVID-19 pandemic, there has been unprecedented increase in the number of employees working outside an organisations IT infrastructure due to the use of personal devices. The scale and sophistication of cyberattacks also continue to increase post-COVID-19 and it has become critical for SMEs (Small and Medium Sized Enterprises) to safeguard their information and IT assets. COVID19 proved to be a major catalyst for the adoption of digital approaches to remote working that many organisations did not previously believe to be feasible. The systems are becoming increasingly exposed to cyber-attacks as a result of remote access technology and cloud networks. The literature points to a gap in the existing knowledge to address the cybersecurity requirements for SMEs in India working in a virtual setup. The purpose of this paper is to develop a cybersecurity evaluation model (CSEM) that can be leveraged by SMEs which will eventually help them assess their cyber-risk portfolio. Based on the research project and the methodology used in the past for similar research, a quantitative approach will be chosen for this research. This research requires the researcher to roll out an online survey, which will enable the participants to evaluate cybersecurity risks by responding to the survey questionnaire. Analysing and implementing a CSEM will not only assist SMEs in identifying their strengths and weaknesses but will also include simple best practice guidelines for effectively plugging their cybersecurity flaws while working remotely.
A Neural Network Model for Matrix Factorization: Dimensionality Reduction

One of the most commonly used approaches to deal with complex high-dimensional datasets is dimensionality reduction. In this scenario, a shallow neural network model for non-negative matrix factorization has been developed for low rank approximation. We have used hierarchical learning to ma-nipulate the ubiquity of nonnegative input data to generate part-based, sparse, and meaningful representations. A modification of the He initialization technique has been proposed to initialize the weights while maintaining the non-negative criterion of the model. A necessary modification of the ReLU activation function has been made to suppress all neurons in a layer from adjusting their weights simultaneously. Regularization has been used in the model's objective function to reduce the risk of overfitting. To demonstrate the efficacy of the proposed model, we have analyzed and compared the results with six well known dimensionality reduction methods on five popular datasets for clustering. We have also discussed the computational complexity of the model.
On the use of XAI for CNN Model Interpretation: A Remote Sensing Case Study

In this paper, we investigate the use of Explainable Artificial Intelligence (XAI) methods for the interpretation of two Convolutional Neural Network (CNN) classifiers in the field of remote sensing (RS). Specifically, the SegNet and Unet architectures for RS building information extraction and segmentation are evaluated using a comprehensive array of primary- and layer-attributions XAI methods. The attribution methods are quantitatively evaluated using the sensitivity metric. Based on the visualization of the different XAI methods, Deconvolution and GradCAM results in many of the study areas show reliability. Moreover, these methods are able to accurately interpret both Unet's and SegNet's decisions and managed to analyze and reveal the internal mechanisms in both models (confirmed by the low sensitivity scores). Overall, no single method stood out as the best one.
Digital Image Processing Using Noise Removal Technique: A Non-Linear Approach

An undesirable by-product of a captured image is Image Noise - random distribution of brightness and color misinformation. Denoising is one of the common components which is present in digital image processing techniques, Image processing has become an integral part of today's era amongst rapidly growing technologies. The process of manipulating images by using various algorithms exploring techniques to pre-process an image, improving its pictorial information for human interpretation. This research paper explores different types of image noise that can be present in a digital image and degradation by photo electronic noise, impulse (salt & pepper) noise and gaussian noise, thus reviews on linear approaches to denoise the image using 2D convolution filters and median non-linear filters.
Anomaly Detection Methods in Surveillance Videos: A Survey

Surveillance system continuously generates massive amount of video data in the newest technological era, analysing these data is a tedious task for security specialists. With the popularization of surveillance monitoring system and the evolution of information technology, how to immediately and without human interaction detect unusual behaviours in surveillance footage is becoming more and more crucial for smart cities and public safety. Finding of abnormal footage physically in these massive video recordings is a laborious work, as they do not happen often in the real world. This clearly shows the necessity of automated anomaly detection, afterward that can detect crimes and aid investigations. The progress of anomaly detection has substantially benefited from deep learning, and much outstanding work has been published on this subject. This survey paper provides a comprehensive review of various anomaly detection and recognition methods. Researchers will get a better perspective of anomaly detection task with GAN approach, fine-tuned approach, and keyframes extraction plus shallow network approach and also their issues as well.
Orchestrating the Resilience of Cloud Microservices Using Task-Based Reliability and Dynamic Costing

In recent years, cloud computing has grown in popularity as enterprises realize the technical and economic benefits of managing resources using an on-demand model. With this growth come user expectations of quality of service, availability and reliability. As the trend in cloud systems architecture moves from monolithic systems to microservices, opportunities exist to develop granular solutions for improving the reliability of the overall system. In this paper we propose an approach to the resilience of microservices-based cloud systems using task-based reliability, quantifying of the reliability requirement with respect to the marginal cost of additional redundancy and the cost of failure. Our approach enables the cloud orchestrator to manage replication of microservice containers to reach an optimal, cost-effective level of redundancy. Empirical testing showed our approach achieved a lower overall cost in comparison with a fixed-reliability approach under an exponential failure function.
BanglaSarc: A Dataset for Sarcasm Detection

Being one of the most widely spoken language in the world, the use of Bangla has been increasing in the world of social media as well. Sarcasm is a positive statement or remark with an underlying negative motivation that is extensively employed in today's social media platforms. There has been a significant improvement in sarcasm detection in English over the previous many years, however the situation regarding Bangla sarcasm detection remains unchanged. As a result, it is still difficult to identify sarcasm in bangla, and a lack of high-quality data is a major contributing factor. This article proposes BanglaSarc, a dataset constructed specifically for bangla textual data sarcasm detection. This dataset contains of 5112 comments/status and contents collected from various online social platforms such as Facebook, YouTube, along with a few online blogs. Due to the limited amount of data collection of categorized comments in Bengali, this dataset will aid in the of study identifying sarcasm, recognizing people's emotion, detecting various types of Bengali expressions, and other domains. The dataset is publicly available at https://www.kaggle.com/datasets/sakibapon/banglasarc.
OkkhorNet: A Deep Convolutional Neural Network to Classify Bengali Handwritten Characters

Handwritten letter classification of any given language has the potential to be used in various fields such as literature, educational institutions, digitization of government records etc. Bengali language with its complex sets of mixed characters, poses significant complexities in terms of automatic recognition of characters. In the Bengali character set, there are over 360 distinct characters among which a lot of similarities are present between different characters. Thus, the classification of these characters gets harder as the recognition system incorporates all these distinct characters. In recent years, a lot of research has been done to solve this problem on isolated datasets with significant results. Continuing the advancement in image processing, In this paper, we have proposed a custom CNN model which has been trained on Bangla Lekha Isolated dataset containing 1,66,106 images belong to 84 distinct classes with the capability to detect individual handwritten Bengali letters including digits, vowels, consonants and compound characters. Our proposed model was able to achieve 94.73% accuracy while using less number of parameters compared to existing popular models
Proposal to Improve Exercise Using the Fogg Behavior Model

This paper proposes an exercise improvement method utilizing the Fogg Behavior Model (FBM). FBM asserts that three elements, motivation, ability, and triggers, must be satisfied in order to persuade persons to do something. Existing works mainly value motivation. Many methods have been studied to increase user motivation utilizing behavioral economics, such as sending text messages to recipients. On the other hand, few studies have focused on ability and trigger. This method utilizes FBM to realize exercise improvement, taking all three factors into account. The proposed method examines the time-series data of the number of steps the target user takes, which represents the user's physical activity. It regards the user's ability high in the time period when the user's physical activity is high. At the timing, its triggers are given according to the user's Motivation. This enables us to promote exercises to users, having arranged an environment to facilitate users to make exercises. This paper explains a challenge that estimates a user's motivation for exercise from data acquired from a wearable device.
Fault Diagnosis of Track Circuit Based on KELM Optimized by SSA Algorithm

Aiming at the diversity of ZPW2000A jointless track circuit faults, an intelligent diagnosis method based on sparrow search algorithm(SSA) optimizing kernel extreme learning machine(KFLM) is proposed. Firstly, according to the basic structure of ZPW2000A jointless track circuit, parameters collected by the track circuit centralized signal monitoring(CSM) system are chosen as the fault data sample set. Secondly, SSA algorithm is used to optimize the kernel function parameter and the penalty coefficient of the KELM to obtain best fault classification accuracy. Finally, SSA-KELM model with optimal parameters is used to identify specific fault types. The Matlab simulation results show that the fault diagnosis accuracy of SSAKELM model reached 98.13%, which is superior to KELM model and PSO-KELM model. SSA-KELM model can effectively and accurately identify the fault types of track circuit, which provides a new solution for the development of intelligent railway fault diagnosis.
Cyborg Cockroaches can be Commanded to Play Soccer

Nowadays, cyborg insects are used for various applications such as search and rescue operations, environmental monitoring, and many others. However, the cyborg insect for entertainment purposes is quite new. Therefore, this study implements Madagascar Hissing Cockroach (MHC) as a cyborg insect platform for playing game purpose. A small size electronic backpack that can transmit IMU data and receive stimulation commands from a computer was developed. User Interface (UI) is developed for an efficient interface between a computer, human operator, and cyborg insects. A wireless gamepad is applied to provide the stimulation command from the human operator to the electronic backpack attached to the cockroach. The experimental result showed that the two cyborg cockroaches could be utilized for the entertainment purpose of playing soccer with a table tennis ball. Our experimental study successfully implemented cyborg cockroaches in the entertainment application.
Design Probes in a Pandemic: Two Tales of Hybrid Radical Placemaking from Ireland and Australia

Design probes, an essential research tool during the COVID-19 pandemic, are ancillary "personal" data gathering tools that enable researchers to enter the private world of research participants. This paper compares two case studies of design probes used during the pandemic for radical placemaking in hybrid digital-physical environments: Digital Art Summer School in Northrock, Ireland, with eleven participants, and Chatty Bench Project in Brisbane, Australia, with sixteen participants. The paper further expands on the design methodology of the probes and their deployment during the online radical placemaking projects. From the participant responses to the probes’ activities and interviews, both studies demonstrated that the probes fostered placemaking in digital environments during the pandemic. The paper concludes with three lessons on the potential of probes as a critical research instrument to enable creativity, build social capital and create bonds between people and places during uncertain and turbulent times.