Reviewerly/exHarmony huggingface dataset:
A Novel Social Collaboration Platform for Enhancing Energy Awareness

Building managers and stakeholders should begin establishing cultures and practices that encourage collaborative impact toward energy awareness. In this context, a novel social collaboration platform is suggested provisioning best practices and recommendations for energy-efficient user actions. The platform is equipped with social collaboration tools that will link relevant parties via direct or indirect communication using sound, video, text, and context-aware knowledge. Moreover, it provides support for recommendations, prescriptions, and identification of best practices for proactivity, as well as best practices and proposals for energy efficiency and the well-being of building occupants. Furthermore, the platform provides search functionality that allows quick access to the preferred content regarding the posts and users and also provides filter options for locations and building groups that enhance the scalability of the system.
Ensuring Reliability in Smart Building IoT Operations Through Real-Time Holistic Data Treatment

Efficient smartification in buildings pre-requires highly reliable data. Modern AI applications consider real-time emergent responsiveness, which renders control safety of outmost importance. A number of sensors and other devices are installed that retrieve data on energy consumption, indoor conditions, and other information. By analyzing this data, smart home devices are able to customise user experiences while optimizing energy use while enhancing security in a real-time manner. In this paper, a holistic data treatment framework for Smart Building IoT applications is presented. The proposed framework considers a multi-factorial anomaly detection and treatment. The implemented functional filtering pipeline consists of statistical filtering rules for detecting, recognizing and mitigating the most common anomalies observed in the historical data. First a methodology for estimating missing data values based on available data is considered. Moreover, an outlier detection and healing mechanism is also integrated to improve the accuracy and reliability of the analysed results. The results showcase that the clean data are of better quality for further exploitation.
Handwritten Word Recognition Using Deep Learning Methods

The handwriting recognition field has preoccupied the scientific community for several years. The complexity encountered in this sector is due to the fact that each individual has a unique way of writing. Various methods have been examined and evaluated throughout the years, with the sole purpose of achieving sustainable results. The introduction of neural networks, specifically the use of convolutional neural networks (CNN) and recurrent neural networks (RNN), has presented dependable results in the handwriting recognition field. In this paper, we introduce a model that recognizes handwritten words without pre-segmenting the words into characters. The model consists of a CNN for the extraction of features, a RNN for the prediction procedure and a final layer (CTC) for decoding the prediction. We conducted many experiments on the well-known IAM handwriting database and attained an accuracy of 77.22% and a character error rate (CER) of 10.4%.
Local Maximal Equality-Free Periodicities

In this paper, we study local maximal antiperiodicities. Given a string X and an integer k, we compute the maximal k-antiperiodicity starting at every position of X; that is, we identify a maximum-length sequence of distinct factors, where each is of length k. The space and time complexity of the algorithm is linear.
Principal Component Modes of Reservoir Dynamics in Reservoir Computing

In Reservoir Computing, signals or sequences are fed into a set of interconnected non-linear units (neurons) with capabilities for storing information (reservoir). The reservoir generates an expanded representation of the input, which is subsequently mapped onto the desired output using a trained output layer (readout). However, despite their success in various experimental tasks, the dynamics of the reservoir are not yet well understood. In this paper we introduce a new technique, based on the well known Singular Value Decomposition (SVD), to obtain the main dynamic modes of the reservoir when excited with an input signal. We conduct experiments using Echo State Networks (ESN) to demonstrate the technique’s potential and its ability to decompose input signals into Principal Component Modes as expanded by the reservoir. We expect that this approach will open new possibilities in its application to the field of visual analytics in process state visualisation, determination of attribute vectors, and detection of novelties. Furthermore, this technique could serve as a foundation for a better understanding of the reservoir’s dynamic state that could help in other areas of research, such as domain shift or continual learning.
Ensemble Machine Learning Models for Breast Cancer Identification

The advances in the Machine Learning (ML) domain, from pattern recognition to computational statistical learning, have increased its utility for breast cancer as well by contributing to the screening strategy of diverse risk factors with complex relationships and personalized early prediction. In this work, we focused on Ensemble ML models after using the synthetic minority oversampling technique (SMOTE) with 10-fold cross-validation. Models were compared in terms of precision, accuracy, recall and area under the curve (AUC). After the experimental evaluation, the model that prevailed over the others was the Rotation Forest achieving accuracy, precision and recall equal to 82% and an AUC of 87.4%.
Semantic Interoperability for Managing Energy-Efficiency and IEQ: A Short Review

With the rise of the Internet of Things and Smart Home industries, there is a real opportunity to increase the energy efficiency of buildings and improve the indoor experience of their occupants. However, as these industries continue to grow, so does the number of data sources in the energy sector in recent years. This can lead to suboptimal exploitation of these data and even to dualities and misunderstandings. As a result, semantic interoperability in the energy sector is now more necessary than ever. Combining event processing to handle data quantities, semantics to manage numerous data streams, and background ontologies will increase prompt identification of all information. In this context, this short review aims to explore state-of-the-art semantic ontologies and their utilization in the energy sector, with an additional emphasis on the indoor environment and air quality. Furthermore, a semantically enriched framework for a smart home will be proposed.
Visual Analytics Tools for the Study of Complex Problems in Engineering and Biomedicine

In this article, we present the main lines of an ongoing research project funded by the Spanish government. The project proposes research on visual analytics techniques for solving complex problems in engineering and biomedicine. We outline the characteristics of complex problems that make it difficult for machine learning approaches to tackle them. Next, we present the benefits of solutions that exploit the synergy between machine learning and data visualization through interactive mechanisms for solving such problems. Finally, we briefly present the approaches being worked on in this project to achieve the objectives and the results achieved so far. We hope that these ideas and approaches will serve as inspiration for other projects or applications in the field.
An XAI Approach to Deep Learning Models in the Detection of DCIS

The results showed that XAI could indeed be used as a proof of concept to begin discussions on the implementation of assistive AI systems within the clinical community.
Analyzing User Reviews in the Tourism &amp; Cultural Domain - The Case of the City of Athens, Greece

Tourism is an important economic activity for many countries and the ability of understanding visitor needs as they evolve over time is a priority for all involved stakeholders. The analysis of textual reviews written by travelers on various online platforms may be a valuable tool in this direction. In this work, we showcase the potential of this idea by examining 8 well-known attractions in the City of Athens, Greece. After retrieving the relevant data from two popular online services, we employ a state-of-the-art transformer-based language model for two tasks; the extraction of distinctive keywords and phrases out of the free-text reviews and the assignment of a sentiment score to each review. Based on this information, we can associate certain keywords and phrases with specific sentiment values and monitor their evolution over time, in the context of specific touristic & cultural places. The analysis that follows explores the potential of this idea in more detail.
Cerebral Vessel Tree Estimation from Non-contrast CT using Deep Learning Methods

Non-contrast computed tomography (NCCT) is the primary first-line neuroimaging technique in the clinical workflow for patients with suspected ischemic stroke. We present a deep learning model to estimate the cerebral vessel tree from the NCCT instead of subsequently performed contrast-enhanced imaging techniques, e.g. computed tomography angiography (CTA). We employ a volumetric sliding window approach and feed the patches to a 3D U-Net. This U-Net has two outputs, a probability map that indicates vessel presence and a prediction of the corresponding CTA patch. The CTA regression target is used in addition to the supervised segmentation to optimize the 3D U-Net in a GAN-like manner in order to generate more realistic estimations for the vessel tree. Comparing our proposed model with the current state of the art for this task, a 2D U-Net operating on axial NCCT slices, we were able to slightly increase quantitative overlap metrics as well as achieve notably improved qualitative results w.r.t. spatial continuity of the segmented vessel tree.
Cross-modality Training Approach for CT Super-resolution Network

In this work, we propose a U-Net-based super-resolution neural network, SRU-Net, to create emulated high spatial resolution (eHR) CT images from low spatial resolution (LR) CT images. As resolution could be defined by the modulation transfer function in CT reconstruction, we propose the novel approach based on CT reconstruction kernels to create realistic multi-detector CT (MDCT) synthetic LR images from high-resolution cone-beam CT (CBCT) scans. Keeping a constant sampling grid size of 0.20 × 0.20mm2, we reconstruct two types of MDCT-like LR images and one corresponding HR image from the same CBCT raw data and train two models respectively. We validated the performance of the trained models on unseen LR CBCT images. We then applied the trained network to MDCT images. Mean squared error, structural similarity index measures and peak signal-to-noise ratio of two models show significant improvements (p < 0.001) in the eHR images.
Label Efficient Classification in Liquid Biopsy Data by Self-supervision

A cut-off of 5 circulating tumor cells (CTCs) per 7.5 ml of blood in metastatic breast cancer patients is highly predictive of progression-free survival and overall survival. These rare events potentially reflect the disease and heterogeneity of the tumor and therefore need to be isolated and characterized from liquid biopsy samples. Identification of CTCs in peripheral blood samples is only partially automated by the rule-based commercially available Cellsearch® system. However, the system still requires manual post-processing and selection of CTCs from a large number of proposed cell image candidates. We propose a self-supervised (DINO), label-efficient combination of deep learning (DL) and support vector machine (SVM) to reliably identify CTCs. We evaluate the label data efficiency of our method in comparison to supervised DL and show that it consistently outperforms supervised state of the art models in terms of F1 score for different balanced subsets as well as for all available data.
Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images

Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be visualized using silver-based staining. The average number of AgNORs per nucleus has been shown to be a prognostic factor for predicting the outcome of many tumors. Since manual detection of AgNORs is laborious, automation is of high interest. We present a deep learning-based pipeline for automatically determining the AgNOR-score from histopathological sections. An additional annotation experimentwas conducted with six pathologists to provide an independent performance evaluation of our approach. Across all raters and images, we found a mean squared error of 0.054 between the AgNORscores of the experts and those of the model, indicating that our approach offers performance comparable to humans.
Exploring the Effects of Contrastive Learning on Homogeneous Medical Image Data

We investigate contrastive learning in a multi-task learning setting classifying and segmenting early Barrett’s cancer. How can contrastive learning be applied in a domain with few classes and low inter-class and inter-sample variance, potentially enabling image retrieval or image attribution? We introduce a data sampling strategy that mines per-lesion data for positive samples and keeps a queue of the recent projections as negative samples. We propose a masking strategy for the NT-Xent loss that keeps the negative set pure and removes samples from the same lesion. We show cohesion and uniqueness improvements of the proposed method in feature space. The introduction of the auxiliary objective does not affect the performance but adds the ability to indicate similarity between lesions. Therefore, the approach could enable downstream auto-documentation tasks on homogeneous medical image data.
Dataset Pruning using Evolutionary Optimization

Data is key to training deep neural networks. A common demand for individual data units is their abundance and diversity. However, it is barely investigated what is actually an informative data unit and how the amount of data relates to the neural network performance. In this study, we utilize evolutionary algorithms to optimize data usage during deep neural network training. We test multiple medical classification and segmentation datasets as being key tasks in medical imaging and found that this so-called dataset pruning removes rather unimportant data elements. Depending on how much we punished the incorporation of data, we found that across tasks and datasets, a critical amount of data is incorporated by the algorithm itself. This shows that future research not only needs to incorporate abundant data but rather relevant data.
Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays

Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems.We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients. The results show that AMIL approaches can handle very small amounts of tissue from a TMA and reach similar C-index performance compared to established survival prediction methods trained with highly discriminative clinical factors such as age, cancer grade, and cancer stage.
Client Selection Frameworks Within Federated Machine Learning: The Current Paradigm

Organisations are increasingly looking for ways to further utilise big data and the benefits that come with this. Previously, this role has been taken by traditional machine learning algorithms. However, these have drawbacks such as computation cost and privacy issues. Federated machine learning (FML) seeks to remedy the downfalls of traditional machine learning. Client selection is one way in which to further improve FML, as which clients that are chosen, and how they operate are a core part of its operation. This paper proposes a potential better way to operate a client selection framework, after reviewing the current literature within academia.
Organizational and Project-based Research Activity: the curriculum of the discipline for higher education applicants of the degree of Doctor of Philosophy in the specialty " 014 Secondary education "

The curriculum of the discipline “Organizational and Project-based Research Activity” is compiled in accordance with the educational and scientific program of training of the third level of higher education in the field of knowledge 01 “Education” for specialty 014 “Secondary Education”. The aim of the discipline “Organizational and Project-based Research Activity” is to form systemic comprehension of peculiarities of organizing and financing academic, sector-specific, university and corporative sectors of science in Ukraine and abroad among students.
Cryptography Using GPGPU

Today, with an ever-increasing number of computer users, the number of cyberattacks to steal data and invade privacy is of utmost importance. A group of applications uses the Advanced Encryption Standard (AES) to encrypt data for security reasons. This mainly concerns enterprises and businesses, which ultimately handle user data. But many implementations of the AES algorithm consume large amounts of CPU horsepower and are not up to the mark in terms of throughput. To tackle this problem, the proposed system makes use of GPUs, which are targeted for parallel applications. These enable parallel operations to be performed much faster than the CPU, ultimately increasing throughput and reducing resource consumption to some extent. The vital aspect of this approach is the speedup that is achieved due to massive parallelism. This research aims to implement AES encryption and decryption using CUDA and benchmark it on various compute devices.
Moving Pictures Feature Extraction Using Scheme Pixel Values Method

Technology allows users to store a variety of datasets. Among these datasets, retrieving the correct source from the vast respiratory system is one of the most critical tasks. This extraction process is used in various applications in the engineering, medicine, and science domains. Extracting the image attributes from the stored respiratory is one of the more complex processes. Due to the intricacy of the input set, many scholars find these techniques among the most difficult undertakings. The process is the consolidation of various other attribute sets. From this combination, extracting the specific domain or specific patterns is more difficult for the investigators. The work is made considerably more complicated inputs like video sets. The proposed work finds the best solution for extracting image datasets using image attributes as an input. Investigational results prove that the suggested system will work with various types of inputs.
Vehicle Collision Warning and Accident Detection Using Raspberry Pi and SSD MobileNetV1

The rise in fatal and permanently disabling road accidents is a severe public health issue. Human lives are frequently lost in road accidents when medical assistance arrives too slowly. Therefore, deaths from road accidents are more common. Several accident prevention systems can, to some extent, avoid accidents. Still, they lack in giving collision warning while driving and the ability to contact emergency services in the event of an accident. A collision warning is one of the great difficulties regarding active safety for automobiles on the road. In this paper, the proposed system is to design and implement collision warning and accident detection for automobiles. In the proposed work, the collision warning is done using object detection, and accident detection is implemented by Raspberry Pi, a vibrator sensor, GPS, and a Telegram bot. The detection of the vehicles is based on the Single-Shot Detection (SSD) MobileNetV1 algorithm. Suppose the vehicles did not maintain a minimum distance between each other. In that case, it will send a collision warning message to the user using a Telegram bot via a Wi-Fi network. If the user made accident, then the accident occurred message and location are sent to emergency services based on the vibration sensor and Global Positioning System module.
Cannotation MeasureUp to Detect Deepfake by Face Recognition via Long Short-Term Memory Networks Algorithm

In this computerized world, any individual can create deep fake videos, images, and audios, and it’s becoming a challenge to differentiate between the real and the fake ones. Such fake videos and images of a person that spread like wildfire on social media ultimately destroys the person's life by leaving a deep mark that is impossible to remove. Hence, it’s important to discern the fake and stop the circulation of these types of videos and images. Different technologies with different algorithms have been used to identify fake images. In this research, to distinguish fake and real images, long short-term memory network methods in deep learning technology are used to recognize faces from real-time dataset for better result because of its hidden layers and some inbuilt functions of this algorithm. Haar Cascade is used for detecting the images from real time; even though it’s an oldest one, it still has its own place in detecting face by providing more accuracy than others. Backpropagation is then used to feed both forward and backward, so that the values of bias can be reduced according to the predicted output. Sequence transformer makes the images get into cell state with continuous step-up time, so that the image will not collide while capturing. By using all these conditions, the output will be predicted with actual value which gives the desired outcome. Detecting the swapped and manipulated images from the real one is very difficult even though different algorithms are used. Therefore, long short-term memory networks are used in this research, because it's easy to identify fake images due to the continuous networks in the layer.
Efficient Sentiment Classification Model of Tweets Using an Adaptive Megaptera Whale Optimization LSTM Classifier

User sentiment analysis from the online social media has become an interest, and importantly, Twitter opens a valuable source for user emotions, opinions, attitudes, confessions, and so on. However, analyzing the massive reviews and posts dumped in the Twitter media is a tedious and computationally ineffective scenario, raising the demand for implementing the automatic sentiment analysis models. Accordingly, Megaptera whale optimization-based adaptive long short-term memory (MWO-based adaptive LSTM) classifier is proposed for sentiment analysis, where the significance of the research relies in the proposed adaptive Megaptera whale hunt optimization that optimizes the classifier parameters toward the effective classification performance. The analysis of the model based on the accuracy reveals that the proposed model acquires the maximal performance with 98.48% of accuracy.