Reviewerly/exHarmony huggingface dataset:
A novel approach for Spam Email Filtering Using Machine Learning

Spam emails also known as unsolicited emails (maybe commercial or maybe not) i.e. those mails which are sent without our request or concern. Email spam is the practice of sending unwanted emails, mostly contains commercial messages to randomly generated persons. In the internet email spam is widespread because of such low cost of sending emails other than any other means of communication. It is important to filter spam emails because most of the malicious activities performed in the internet done through email spamming. Though there are many spam filters are available we still get huge amount of spam emails. This is not because the filters are not accurate ; effective; the reason is the generation of quick and effective counters of the algorithm used in the filters. In our project we used mainly three supervised learning algorithms namely Linear SVC, Multinomial NB, and k-NN to implement the filter. We used these algorithms to train the system about spam email by using the feature called word count vector which is generated by processing a dataset filled with existing emails containing both spam and legitimate emails. The full process of the project and the result of the execution by implementing the three models/algorithms are discussed.
The role of sensors in the production of smart city spaces

Smart cities build on the idea of collecting data about the city in order for city administration to be operated more efficiently. Within a research project gathering an interdisciplinary team of researchers – engineers, designers, gender scholars and human geographers – we have been working together using participatory design approaches to explore how paying attention to the diversity of human needs may contribute to making urban spaces comfortable and safe for more people. The project team has deployed sensors collecting data on air quality, sound and mobility in a smart city testbed in Norrköping, Sweden. While these sensors are meant to capture an accurate ‘map’ of the street and what is going on along it, our interdisciplinary conversations around the sensors have revealed the heterogeneity both of smart city planning and spatial formulations of the city. The discussions have given rise to questions regarding the work that goes into constructing the sensor box itself, as well as the work of deploying it, and how these influence the ‘map’ that the sensors produce. In this paper, we draw on Lefebvre to explore how the sensors themselves produce smart spaces. We analyze how the box depends on perceived space to function (e.g. requiring electricity), and simultaneously it produces conceptualizations of space that are influenced by the materiality of the box itself (e.g. sensors being affected by heat and noise). Further, we explore how the (in)visibility of sensor technology influences lived space.
Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT

Abstract In recent years, with the development of Unmanned Aerial Vehicle (UAV) and Cloud Internet-of-Things (Cloud IoT) technology, data collection using UAVs has become a new technology hotspot for many Cloud IoT applications. Due to constraints such as the limited power life, weak computing power of UAV and no-fly zones restrictions in the environment, it is necessary to use cloud server with powerful computing power in the Internet of Things to plan the path for UAV. This paper proposes a coverage path planning algorithm called Parallel Self-Adaptive Ant Colony Optimization Algorithm (PSAACO). In the proposed algorithm, we apply grid technique to map the area, adopt inversion and insertion operators to modify paths, use self-adaptive parameter setting to tune the pattern, and employ parallel computing to improve performance. This work also addresses an additional challenge of using the dynamic Floyd algorithm to avoid no-fly zones. The proposal is extensively evaluated. Some experiments show that the performance of the PSAACO algorithm is significantly improved by using parallel computing and self-adaptive parameter configuration. Especially, the algorithm has greater advantages when the areas are large or the no-fly zones are complex. Other experiments, in comparison with other algorithms and existing works, show that the path planned by PSAACO has the least energy consumption and the shortest completion time.
Clouds of words as a didactic tool in literary education of primary school children

The study reveals the possibilities of using the words’ cloud in the literary education of primary school children. The authors consider the possibility of using a cloud of words to visualize the keywords of the text for the translation of the work of art, the interpretation of the main idea and the characteristics of the artistic image. The words’ cloud can also be used as a reference summary to answer questions about the content of the work or to present the results of a school project by students. Moreover, in reading lessons, this tool can be used as a tool to identify the topic of the lesson. The study reveals the possibilities of the didactic tool of the words’ cloud for the development of speech of primary school children, in particular, in composing their own texts and editing them, the ability to explore words that the student uses too often and avoid tautology. Using the method of a problem situation and visually demonstrating information through a cloud of words, the teacher pro-motes the activation of students’ mental activity, the development of creative abilities and critical thinking.The study tested the use of the electronic resource WordArt in primary school – a website for creating a “word’s cloud” and proved its effectiveness in reflection, as well as creating an image of the main idea of the lesson, general conversation, its use as didactic material. The authors used the technology of learning using a cloud office package Google Drive to write a draft of their own statement of primary school children. The authors investigated the use of the service in the lesson of literary reading during the organization of reading activities in the following areas: in the “cloud of words” to encrypt the topic of the lesson; using the cloud as visual material or as basic information to explain new material; encrypt certain words from the text in the cloud, students’ task to guess the work; create a cloud of words of positive and negative characters of the work; write a story on the topic; create an “encrypted postcard” to the writer or hero of the work. In the course of experimental work, the effectiveness of the use of this didactic tool in the lessons of literary reading in primary school during the analysis of texts in order to identify the most important associations of students was confirmed.
Improving text classification with transformers and layer normalization

More than 25,000 injuries and 25 fatalities occur each year due to unstable furniture tip-over incidents. Classifying these furniture tip-over incidents is an essential task for understanding incident patterns and building safer products. For example, this classification can help standards development organizations (SDOs) and policy makers discover hidden insights, which can be used to develop standards and regulations that help improve furniture and make homes safer. Since 2000, the U.S. Consumer Product Safety Commission (CPSC) has published data related to consumer product injuries. The amount of data has grown rapidly, and the process of manually reviewing and classifying individual incidents has correspondingly become very resource intensive. This paper proposes an improved method that employs a combination of natural language processing (NLP) techniques and machine learning (ML) algorithms to classify textual data. Machine learning models can help reduce time and effort by streamlining incident narrative classification for determining whether incidents are related to furniture tip-overs. Challenges often presented by real-world data sets (such as the CPSC data used in our experiment) include imbalanced target classes and narratives requiring domain knowledge, since the data sets contain abbreviations and jargon. Using out-of-the-box, default classification models such as bidirectional encoder representations from transformers (BERT) might not yield adequate results. Our proposed method adds layer normalization and dropout layers to a transformer-based language model, which achieves better classification results than using a transformer-based language alone with imbalanced classes. We carefully measure the impact of hidden layers in order to fine-tune the model.
A System for the Promotion of Traceability and Ownership of Health Data Using Blockchain

The advent of wireless technologies and the development of more and better globally connected mobile devices, leverage real time health monitoring.Mobile health (m-health) promise to deliver health services anytime and anywhere, improving user convenience and enabling faster diagnoses without the need to travel to healthcare facilities.The use of m-health applications on mobile devices with the support of cloud computing is nowadays a technology trend that has many advantages, but also poses several challenges, especially on the data storage and privacy.Blockchain technology is an exponentially growing technology used in various research areas from finance, voting mechanisms, production chains, among others.This technology provides important characteristics such as immutability, non-repudiation, transparency, and reducing the need for intermediaries.Hence, this paper presents a novel approach for blockchain technology applied to m-health systems.This proposal allows an easy and fast integration with other health systems or applications allowing a patient-user to access his electronic health record in a more secure way.The data is traceable throughout the system, however maintaining the necessary anonymity.Hence, a prototype for a blockchain-based solution using Hyperledger Fabric was developed.This implementation enables the creation of a chronologically organized and immutable health data record.To create an anonymous storage system, the proposed system uses two separate database components that maintain data traceability through sets of IDs stored in the blockchain.Furthermore, the development of the proposed system was evaluated in terms of performance and network configurations of the Hyperledger Fabric.
Sequential Measurements for Quantum-Enhanced Magnetometry in Spin Chain Probes

Quantum sensors outperform their classical counterparts in their estimation precision, given the same amount of resources. So far, quantum-enhanced sensitivity has been achieved by exploiting the superposition principle. This enhancement has been obtained for particular forms of entangled states, adaptive measurement basis change, critical many-body systems, and steady state of periodically driven systems. Here, we introduce a different approach to obtain quantum-enhanced sensitivity in a many-body probe through utilizing the nature of quantum measurement and its subsequent wave function collapse without demanding prior entanglement. Our protocol consists of a sequence of local measurements, without reinitialization, performed regularly during the evolution of a many-body probe. As the number of sequences increases, the sensing precision is enhanced beyond the standard limit, reaching the Heisenberg bound asymptotically. The benefits of the protocol are multifold as it uses a product initial state and avoids complex initialization (e.g., prior entangled states or critical ground states) and allows for remote quantum sensing.
High-speed camera-based optical measurement methods for in-mesh tooth deflection analysis of thermoplastic spur gears

The engineering characteristics of polymer gears, such as wear and fatigue resistance, are dictated by the underlying processes that take place during the gear meshing phase. These are in turn dependent on the running load, speed, temperature, lubrication regime and other parameters. So far, the structural response of a meshing gear could only be analysed using numerical approaches, typically involving the finite element method. In this study, an optical experimental analysis approach is proposed to evaluate the in-mesh gear tooth deflection based on high-speed camera recordings. For this purpose, two image processing methods were implemented, namely the well-known Digital image correlation method and a newly proposed Edge displacement detection method. Using an experimental setup with a camera resolution of 1024×1024px, an exposure time of 40 μs, a frame rate of 2·104fps and a 2000 lm focused light source, a measurement accuracy of ±5μm could be achieved. A good correlation between both methods and finite element analysis results was confirmed. Furthermore, the newly proposed Edge displacement detection method offers high potential for experimental analysis of long-term wear and strain accumulation processes.
Energy Repowering Using Photovoltaic Microgrids - A Case Study in the Province of Manabí in Ecuador

Currently, in Ecuador, the participation of photovoltaic energy is practically symbolic. In the province of Manabí, generation continues to be carried out through the intensive use of fossil fuel, which is expensive, inefficient, and polluting. This happens in a territory with optimal potential. Hence, the objective of the work is to demonstrate the feasibility of introducing a photovoltaic microgrid through the case study. Due to its simplicity and versatility, a methodology was applied to generalize experiences in other territories with similar geographical and climatic conditions. Field research was applied, and the method corresponds to the descriptive-deductive method, using a Geographic Information System (GIS) as a geographic information manager. The PVsyst 6.8.6 tool was used for the technical design of the installation, and the HOMER software was used for the economic feasibility analysis. The results demonstrate the relevance of introducing the photovoltaic microgrid in the distributed generation mode to increase the quality of service and the system's efficiency, reduce energy costs, promote the preservation of natural resources, and reduce CO2 emissions environment.
Machine learning methods for the study of cybersickness: a systematic review

Abstract This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness.
Deepfake Video Detection with Spatiotemporal Dropout Transformer

While the abuse of deepfake technology has caused serious concerns recently, how to detect deepfake videos is still a challenge due to the high photo-realistic synthesis of each frame. Existing image-level approaches often focus on single frame and ignore the spatiotemporal cues hidden in deepfake videos, resulting in poor generalization and robustness. The key of a video-level detector is to fully exploit the spatiotemporal inconsistency distributed in local facial regions across different frames in deepfake videos. Inspired by that, this paper proposes a simple yet effective patch-level approach to facilitate deepfake video detection via spatiotemporal dropout transformer. The approach reorganizes each input video into bag of patches that is then fed into a vision transformer to achieve robust representation. Specifically, a spatiotemporal dropout operation is proposed to fully explore patch-level spatiotemporal cues and serve as effective data augmentation to further enhance model's robustness and generalization ability. The operation is flexible and can be easily plugged into existing vision transformers. Extensive experiments demonstrate the effectiveness of our approach against 25 state-of-the-arts with impressive robustness, generalizability, and representation ability.
A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot

To improve the finding path accuracy of the ant colony algorithm and reduce the number of turns, a jump point search improved ant colony optimization hybrid algorithm has been proposed in this article. Firstly, the initial pheromone concentration distribution gets from the jump points has been introduced to guide the algorithm in finding the way, thus accelerating the early iteration speed. The turning cost factor in the heuristic function has been designed to improve the smoothness of the path. Finally, the adaptive reward and punishment factor, and the Max–Min ant system have been introduced to improve the iterative speed and global search ability of the algorithm. Simulation through three maps of different scales is carried out. Furthermore, the results prove that the jump point search improved ant colony optimization hybrid algorithm performs effectively in finding path accuracy and reducing the number of turns.
FindNet: Can You Find Me? Boundary-and-Texture Enhancement Network for Camouflaged Object Detection

Camouflaged objects share very similar colors but have different semantics with the surroundings. Cognitive scientists observe that both the global contour (i.e., boundary) and the local pattern (i.e., texture) of camouflaged objects are key cues to help humans find them successfully. Inspired by the cognitive scientist's observation, we propose a novel boundary-and-texture enhancement network (FindNet) for camouflaged object detection (COD) from single images. Different from most of existing COD methods, FindNet embeds both the boundary-and-texture information into the camouflaged object features. The boundary enhancement (BE) module is leveraged to focus on the global contour of the camouflaged object, and the texture enhancement (TE) module is utilized to focus on the local pattern. The enhanced features from BE and TE, which complement each other, are combined to obtain the final prediction. FindNet performs competently on various conditions of COD, including slightly clear boundaries but very similar textures, fuzzy boundaries but slightly differentiated textures, and simultaneous fuzzy boundaries and textures. Experimental results exhibit clear improvements of FindNet over fifteen state-of-the-art methods on four benchmark datasets, in terms of detection accuracy and boundary clearness. The code will be publicly released.
Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection

Poor generalizability is a major barrier to clinical implementation of artificial intelligence in digital pathology. The aim of this study was to test the generalizability of a pretrained deep learning model to a new diagnostic setting and to a small change in surgical indication. A deep learning model for breast cancer metastases detection in sentinel lymph nodes, trained on CAMELYON multicenter data, was used as a base model, and achieved an AUC of 0.969 (95% CI 0.926-0.998) and FROC of 0.838 (95% CI 0.757-0.913) on CAMELYON16 test data. On local sentinel node data, the base model performance dropped to AUC 0.929 (95% CI 0.800-0.998) and FROC 0.744 (95% CI 0.566-0.912). On data with a change in surgical indication (axillary dissections) the base model performance indicated an even larger drop with a FROC of 0.503 (95%CI 0.201-0.911). The model was retrained with addition of local data, resulting in about a 4% increase for both AUC and FROC for sentinel nodes, and an increase of 11% in AUC and 49% in FROC for axillary nodes. Pathologist qualitative evaluation of the retrained model´s output showed no missed positive slides. False positives, false negatives and one previously undetected micro-metastasis were observed. The study highlights the generalization challenge even when using a multicenter trained model, and that a small change in indication can considerably impact the model´s performance.
Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy

The traditional Grey Wolf Optimization algorithm (GWO) has received widespread attention due to features of strong convergence performance, few parameters, and easy implementation. However, in actual optimization projects, there are problems of slow convergence speed and easy to fall into local optimal solution. The paper proposed a Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy (CG-GWO) in response to the above problems. The Cauchy-Gaussian mutation operator is introduced to increase the population diversity of the leader wolves and improve the global search ability of the algorithm. This work retains outstanding grey wolf individuals through the greedy selection mechanism to ensure the convergence speed of the algorithm. An improved search strategy was proposed to expand the optimization space of the algorithm and improve the convergence accuracy. Experiments are performed with 16 benchmark functions covering unimodal functions, multimodal functions, and fixed-dimension multimodal functions to verify the effectiveness of the algorithm. Experimental results show that compared with four classic optimization algorithms, particle swarm optimization algorithm (PSO), whale optimization algorithm (WOA), sparrow optimization algorithm (SSA), and farmland fertility algorithm (FFA), the CG-GWO algorithm shows better convergence accuracy, convergence speed, and global search ability. The proposed algorithm shows the same better performance compared with a series of improved algorithms such as the improved grey wolf algorithm (IGWO), modified Grey Wolf Optimization algorithm (mGWO), and the Grey Wolf Optimization algorithm inspired by enhanced leadership (GLF-GWO).