Reviewerly/exHarmony huggingface dataset:
A Survey on Cyber Security Research in the Field of Agriculture Technology

The agriculture technology sector is dependent on a lot of technology in both day to day operations of farms and for transporting and selling goods. Smart farms and precision agriculture are quickly becoming the norm in several parts of the world. However, the increased digitization also brings vulnerable technology that can be exploited with malicious intent. As is often the case, technology and innovation comes first and cyber security after. However, having secure systems and networks are essential also in agritech as there will be no sustainable smart farming without appropriate digital security. This article presents a literary review of the recent research on cyber security in agriculture technology. After screening, the review included 19 research articles published from 2017 and after. The findings show that the majority of research on these topics are of threats and potential attacks from a technological and theoretical perspective on cyber security. However, empirical research on the farmers perspective and knowledge, as well as agriculture technology security requirements and regulations, are missing. Our research concludes that there is a lack of observational studies that examine the combined use of technology in agriculture from a cyber security perspective.
Hate Speech is not Free Speech: Explainable Machine Learning for Hate Speech Detection in Code-Mixed Languages

The increase in connectivity provided by social media platforms comes with several disadvantages. It has become surprisingly easy for ill-intentioned individuals to stalk, harass and threaten others online on the basis of race, gender, religion, etc. Artificial intelligence models provide a valuable solution to the problem by automatically filtering such content. However, research for hate speech detection in low-resource languages is still nascent. To promote research in the area, several shared tasks are held in various languages and attract contributions with novel approaches. In this study, we use the English and Hindi code-mixed datasets provided by the HASOC Identification of Conversational Hate-Speech in Code-Mixed Languages (ICHCL) shared task to train and evaluate machine learning models and compare the performance of different vectorization techniques and model parameters. The Explainable Artificial Intelligence (XAI) technique Local Interpretable Model Agnostic Explanation (LIME) is also applied to the model results to ascertain if the model is behaving in a coherent and logical manner. The highest accuracy achieved is 67.61 % using Bernoulli Naive Bayes with count vectorizer and TF-IDF. The analysis suggests that many of the models may be heavily influenced by the presence of slur words while classifying a statement as hateful and offensive.
Organizing projects with blockchain through a decentralized autonomous organization

Blockchain and its related concept of decentral autonomous organization (DAO) is starting to influence project management. But how might project management supported by blockchain technology look like? And how would such a new form change and affect traditional project management? Not many concepts have been designed or even implemented yet. We chose an experimental framework to answer the first aspects of these questions. We developed a Decentralized Autonomous Project Organization (DAPO) and conducted an experiment to study the impact of blockchain on traditional project management. We show that such a blockchain-based approach can support the management of simple projects. Further, a fair and clear incentive scheme seems crucial and influences the way team members engage in the work. Also, more decentralized project management increases the importance of social aspects-related project management principles such as teamwork, self-organization, and cultural aspects, while principles related to budget, objectives, and schedule remain unchanged.
Raspberry Pi-based design of intelligent household classified garbage bin

Until now, waste pollution is a global problem. The first and foremost part of the solution is waste classification, as it can effectively reduce the output of waste, so it is particularly important to classify household garbage in the household. However, classification of household garbage is a difficult task for people, and there are not enough designs and researches on automatic garbage classification bins at home. In this study, we designed and built a light and convenient intelligent garbage bin using devices such as motors, turn plates and a camera for classifying household garbage. Compared with the traditional garbage classification methods, two improvements in the algorithm are made, including the combination of the EfficientNetB2 model with the parallel mixed-attention mechanism and the design of the background noise removal algorithm. Finally, the algorithm is disposed to the Raspberry Pi to compose the complete classification system. The accuracy of this classification system in the test set was 93.38% and the results show that this bin can effectively distinguish recyclable waste, kitchen waste, hazardous waste and other waste, and is suitable for daily life garbage identification at home.
A Novel Registration Method for a Mixed Reality Navigation System Based on a Laser Crosshair Simulator: A Technical Note

Mixed Reality Navigation (MRN) is pivotal in augmented reality-assisted intelligent neurosurgical interventions. However, existing MRN registration methods face challenges in concurrently achieving low user dependency, high accuracy, and clinical applicability. This study proposes and evaluates a novel registration method based on a laser crosshair simulator, evaluating its feasibility and accuracy. A novel registration method employing a laser crosshair simulator was introduced, designed to replicate the scanner frame's position on the patient. The system autonomously calculates the transformation, mapping coordinates from the tracking space to the reference image space. A mathematical model and workflow for registration were designed, and a Universal Windows Platform (UWP) application was developed on HoloLens-2. Finally, a head phantom was used to measure the system's target registration error (TRE). The proposed method was successfully implemented, obviating the need for user interactions with virtual objects during the registration process. Regarding accuracy, the average deviation was 3.7 ± 1.7 mm. This method shows encouraging results in efficiency and intuitiveness and marks a valuable advancement in low-cost, easy-to-use MRN systems. The potential for enhancing accuracy and adaptability in intervention procedures positions this approach as promising for improving surgical outcomes.
Blockchain-based biometric identity management

Abstract In recent years, face biometrics recognition systems are a wide space of a computer usage which is mostly employed for security purpose. The main purpose of the face biometrics recognition system is to authenticate a user from a given database. Due to the widespread expansion of the surveillance cameras and facial recognition technology, a robust face recognition system required. The recognition system needs to store a large number of training samples in any storage unit, that time hackers can access and control that data. So, Protecting and managing sensitive data is essential object. This requires a technique that preserve the privacy of individuals, maintain data integrity, and prevent information leakage. The storage of biometric templates on centralized servers has been associated with potential privacy risks. To address this issue, we have developed and implemented a proof-of-concept facial biometric identification system that uses a private Blockchain platform and smart contract technology. So, the proposed approach is presented a secure and tamper-proof from data breaches as well as hacks with data availability, by using the Blockchain platform to store face images. This paper aims to utilize Blockchain technology to identify individuals based on their biometric traits, specifically facial recognition system makes it tamper-proof (immutable) ensuring security. The system consists of enrolment and authentication phases. Blockchain technology uses peer-to-peer communication, cryptography, consensus processes, and smart contracts to ensure the security. The proposed approach was tested on two popular datasets: CelebFaces Attributes (CelebA) and large-scale face UTKFace datasets. The experimental results indicate that the system yields highly performance outcomes, as evidenced by the Equal Error Rate (EER) values of 0.05% and 0.07% obtained for the CelebA and UTKFace datasets, respectively. The system was compared to three baseline methods and scored the lowest Equal Error Rate.
Deep Ultrasound Denoising Using Diffusion Probabilistic Models

Ultrasound images are widespread in medical diagnosis for musculoskeletal, cardiac, and obstetrical imaging due to the efficiency and non-invasiveness of the acquisition methodology. However, the acquired images are degraded by acoustic (e.g. reverberation and clutter) and electronic sources of noise. To improve the Peak Signal to Noise Ratio (PSNR) of the images, previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound. Herein, a method based on the recent Denoising Diffusion Probabilistic Models (DDPM) is proposed. It iteratively enhances the image quality by eliminating the noise while preserving the speckle texture. It is worth noting that the proposed method is trained in a completely unsupervised manner, and no annotated data is required. The experimental blind test results show that our method outperforms the previous nonlocal means denoising methods in terms of PSNR and Generalized Contrast to Noise Ratio (GCNR) while preserving speckles.
A novel ensemble learning approach for fault detection of sensor data in cyber-physical system

Cyber-physical systems (CPS) play a pivotal role in various critical applications, ranging from industrial automation to healthcare monitoring. Ensuring the reliability and accuracy of sensor data within these systems is of paramount importance. This research paper presents a novel approach for enhancing fault detection in sensor data within a cyber-physical system through the integration of machine learning algorithms. Specifically, a hybrid ensemble methodology is proposed, combining the strengths of AdaBoost and Random Forest with Rocchio’s algorithm, to achieve robust and accurate fault detection. The proposed approach operates in two phases. In the first phase, AdaBoost and Random Forest classifiers are trained on a diverse dataset containing normal and faulty sensor data to develop individual base models. AdaBoost emphasizes misclassified instances, while Random Forest focuses on capturing complex interactions within the data. In the second phase, the outputs of these base models are fused using Rocchio’s algorithm, which exploits the similarities between faulty instances to improve fault detection accuracy. Comparative analyses are conducted against individual classifiers and other ensemble methods to validate the effectiveness of the hybrid approach. The results demonstrate that the proposed approach achieves superior fault detection rates. Additionally, the integration of Rocchio’s algorithm significantly contributes to the refinement of the fault detection process, effectively leveraging the strengths of AdaBoost and Random Forest. In conclusion, this research offers a comprehensive solution to enhance fault detection capabilities in cyber-physical systems by introducing a novel ensemble framework. By synergistically combining AdaBoost, Random Forest, and Rocchio’s algorithm, the proposed methodology provides a robust mechanism for accurately identifying sensor data anomalies, thus bolstering the reliability and performance of cyber-physical systems across a multitude of critical applications.
Cognitive abilities and visual complexity impact first impressions in five-second testing

Five-second testing is a method commonly used by user research professionals to assess users' first impressions of user interfaces or product designs. Its rule of thumb, that five seconds is generally the right amount of time for users to report realistic and relevant first impressions, misrepresents the reality of human cognition. Users possess disparate levels of cognitive ability for processing stimuli that possess varied visual complexity. We conducted a complex experiment where participants complete an evaluation of their cognitive ability – working memory and perceptual speed. They are shown website stimuli of varied complexity over differing time periods (2/5/10 seconds) to answer a representative list of questions typical for evaluation of first impressions. We show that first impression feedback is rendered inconsistent by cognitive ability and visual complexity. Visually complex stimuli viewed for too short a time link to a problem with identifying the web page purpose. Participants with varied perceptual speed produce comparable results at 5 seconds, but at lower working memory, they provide less verbose answers and recall less information. These findings suggest that to receive relevant first impression feedback, the time for which participants are shown stimuli should be adapted to cognitive abilities and stimulus complexity.
Improving First-stage Retrieval of Point-of-interest Search by Pre-training Models

Point-of-interest (POI) search is important for location-based services, such as navigation and online ride-hailing service. The goal of POI search is to find the most relevant destinations from a large-scale POI database given a text query. To improve the effectiveness and efficiency of POI search, most existing approaches are based on a multi-stage pipeline that consists of an efficiency-oriented retrieval stage and one or more effectiveness-oriented re-rank stages. In this article, we focus on the first efficiency-oriented retrieval stage of the POI search. We first identify the limitations of existing first-stage POI retrieval models in capturing the semantic-geography relationship and modeling the fine-grained geographical context information. Then, we propose a Geo-Enhanced Dense Retrieval framework for POI search to alleviate the above problems. Specifically, the proposed framework leverages the capacity of pre-trained language models (e.g., BERT) and designs a pre-training approach to better model the semantic match between the query prefix and POIs. With the POI collection, we first perform a token-level pre-training task based on a geographical-sensitive masked language prediction and design two retrieval-oriented pre-training tasks that link the address of each POI to its name and geo-location. With the user behavior logs collected from an online POI search system, we design two additional pre-training tasks based on users’ query reformulation behavior and the transitions between POIs. We also utilize a late-interaction network structure to model the fine-grained interactions between the text and geographical context information within an acceptable query latency. Extensive experiments on the real-world datasets collected from the Didichuxing application demonstrate that the proposed framework can achieve superior retrieval performance over existing first-stage POI retrieval methods.
An intelligent chatbot design and implementation model using long short-term memory with recurrent neural networks and attention mechanism

Assistant Conversational Agents (chatbots) are becoming increasingly complex and sophisticated since they are enabled to understand and respond to human language. Chatbots have varying organizational functions, from customer support to personal assistance, and have dominated the digital market as they are efficient and effective. Chatbots can collect valuable information from customers through interaction which can result in a better customer experience, but an inappropriate or inadequate response can also lead to the loss of customers. Therefore, the chatbots must be intelligent enough to provide the most efficient and effective response to positively impact the customers and add value to the business and organization. Over the years, several streams of research have been conducted, and multiple chatbots have been created, but they have their limitation, which has been discussed in this article. We suggest a method to upgrade the performance of the chatbots so that they can respond to the user query with better accuracy. The proposed model shows the implementation of a chatbot using Long Short-Term Memory (LSTM), attention mechanism, Bag of Words (BOW), and beam search decoding. The sequence-to-sequence (Seq2Seq) architecture with an LSTM encoder and decoder has been used. The dialog dataset is preferred to train and test the model, and the BleuN algorithm is used to determine the chatbot's accuracy.
Enhancing blockchain security through natural language processing and real-time monitoring

We describe implementing and testing a security monitoring system for Blockchain-based applications by performing Log Analysis through natural language processing (NLP) techniques and enabling queries via REST API. The study focuses on the Hyperledger Fabric framework, a highly reliable and scalable open-source software platform for private blockchains. The real-time monitoring system aims to detect and prevent attack scenarios on the network structure and smart contracts, such as DDOS, Sybil, and Eclipse. The vulnerability analysis is extended to inspecting Docker containers to detect network-based attacks. Although the case study focuses on Hyperledger Fabric, the techniques used are general and applicable to all Blockchain-based systems and fulfill the need to provide adequate monitoring capabilities for blockchain implementations.
Leveraging Meta-Learning To Improve Unsupervised Domain Adaptation

Abstract Unsupervised Domain Adaptation (UDA) techniques in real-world scenarios often encounter limitations due to their reliance on reducing distribution dissimilarity between source and target domains, assuming it leads to effective adaptation. However, they overlook the intricate factors causing domain shifts, including data distribution variations, domain-specific features and nonlinear relationships, thereby hindering robust performance in challenging UDA tasks. The Neuro-Fuzzy Meta-Learning (NF-ML) approach overcomes traditional UDA limitations with its flexible framework that adapts to intricate, nonlinear domain gaps without rigid assumptions. NF-ML enhances domain adaptation by selecting a UDA subset and optimizing their weights via a neuro-fuzzy system, utilizing meta-learning to efficiently adapt models to new domains using previously acquired knowledge. This approach mitigates domain adaptation challenges and bolsters traditional UDA methods’ performance by harnessing the strengths of multiple UDA methods to enhance overall model generalization. The proposed approach shows potential in advancing domain adaptation research by providing a robust and efficient solution for real-world domain shifts. Experiments on three standard image datasets confirm the proposed approach’s superiority over state-of-the-art UDA methods, validating the effectiveness of meta-learning. Remarkably, the Office+Caltech 10, ImageCLEF-DA and combined digit datasets exhibit substantial accuracy gains of 30.9%, 6.8% and 10.9%, respectively, compared with the best-second baseline UDA approach.
“Hard” or “Soft”

Recent research has shown that the shape, timbre, and intensity of a sound influence the perception of its timing at the micro level of rhythm. In this case study of contemporary Norwegian jazz, we investigate to what extent jazz musicians intentionally use sonic features to shape the micro level of rhythm in their performances. First, we provide an overview of existing research into microrhythm in jazz and auditory perception studies. Then we present results from interviews with five expert jazz musicians about how playing techniques and sound qualities of specific instruments are utilized to influence the perception of timing. We also analyze two selected performances by the musicians to explore the effects of interaction between sonic features and timing in a musical context. The article concludes by discussing the results in the context of findings from empirical research into microlevel auditory perception regarding the perceptual center of sounds (P-center) and auditory stream segregation. The study demonstrates the need to move related research beyond the temporal domain, and to cultivate a more holistic approach to what constitutes groove in jazz and related genres. Keywords: timing, microrhythm, intensity, timbre, sound shape, P-center, stream segregation
Automated Mapping of Adaptive App GUIs from Phones to TVs

With the increasing interconnection of smart devices, users often desire to adopt the same app on quite different devices for identical tasks, such as watching the same movies on both their smartphones and TVs. However, the significant differences in screen size, aspect ratio, and interaction styles make it challenging to adapt Graphical User Interfaces (GUIs) across these devices. Although there are millions of apps available on Google Play, only a few thousand are designed to support smart TV displays. Existing techniques to map a mobile app GUI to a TV either adopt a responsive design, which struggles to bridge the substantial gap between phone and TV, or use mirror apps for improved video display, which requires hardware support and extra engineering efforts. Instead of developing another app for supporting TVs, we propose a semi-automated approach to generate corresponding adaptive TV GUIs, given the phone GUIs as the input. Based on our empirical study of GUI pairs for TVs and phones in existing apps, we synthesize a list of rules for grouping and classifying phone GUIs, converting them to TV GUIs, and generating dynamic TV layouts and source code for the TV display. Our tool is not only beneficial to developers but also to GUI designers, who can further customize the generated GUIs for their TV app development. An evaluation and user study demonstrate the accuracy of our generated GUIs and the usefulness of our tool.