Reviewerly/exHarmony huggingface dataset:
Noise-robust optimization of quantum machine learning models for polymer properties using a simulator and validated on the IonQ quantum computer

Quantum machine learning for predicting the physical properties of polymer materials based on the molecular descriptors of monomers was investigated. Under the stochastic variation of the expected predicted values obtained from quantum circuits due to finite sampling, the methods proposed in previous works did not make sufficient progress in optimizing the parameters. To enable parameter optimization despite the presence of stochastic variations in the expected values, quantum circuits that improve prediction accuracy without increasing the number of parameters and parameter optimization methods that are robust to stochastic variations in the expected predicted values, were investigated. The multi-scale entanglement renormalization ansatz circuit improved the prediction accuracy without increasing the number of parameters. The stochastic gradient descent method using the parameter-shift rule for gradient calculation was shown to be robust to sampling variability in the expected value. Finally, the quantum machine learning model was trained on an actual ion-trap quantum computer. At each optimization step, the coefficient of determination [Formula: see text] improved equally on the actual machine and simulator, indicating that our findings enable the training of quantum circuits on the actual quantum computer to the same extent as on the simulator.
NATIONAL AGRICULTURAL PRODUCT AND ITS GROWTH PROSPECTS

Agrarian growth is relevant for Russia in the face of geopolitical challenges. We introduce a new concept of national agricultural product to determine the potential for agricultural growth. We propose a methodology for assessing the national agricultural product. The national agricultural product is the volume of agricultural products produced, taking into account its availability, sufficiency and quantity to meet the needs of the country’s population. Using this technique, we estimated the national agricultural product of Russia and developed a forecast for the production of the main types of agricultural products based on ARIMA (Auto-Regressive Integrated Moving Average) econometric models. We consider these data not optimistic. Russia will not be able to achieve breakthrough agricultural growth. The agricultural production index will decline moderately. Grain production will also have a slight negative trend. Milk production will increase by 300 thousand tons annually. This will not allow reaching the level of 90% self-sufficiency in the country. We also predict a moderate decline in meat production. Therefore, Russian agriculture will not be able to enter the trajectory of dynamic development and will not be able to adequately respond to political and economic events. We believe that it is necessary to change approaches to managing agricultural growth. To do this, we have developed a strategy for managing the economic growth of agriculture. We used fuzzy cognitive logic technologies. We have proposed a set of strategic initiatives for the dynamic growth of the national agricultural product. We substantiated the strategy on the basis of forecasts of the parameters of the national agricultural product.
Graph-based research field analysis by the use of natural language processing: An overview of German energy research

To stop climate change caused by the anthropogenic greenhouse effect, the amount of research project funding to develop and demonstrate solutions for carbon emission reduction has increased. Focused topics and procedures of research projects are always presented in text-based descriptions. Natural language processing offers the possibility to process and analyze data like this. In this work, we used natural language processing to extract keywords from descriptions of energy research projects with the algorithms TextRank and TF-IDF which has not been done for this kind of data basis before. A survey-based validation showed TF-IDF to be better suited for our data basis. Extracted keywords were used to conduct a keyword network analysis and calculate static and dynamic indices of the words concerning their recent importance and development over the past two decades. Further insights are shown by allocating the research projects’ amounts of funding to the extracted keywords. We found energy research to be more focused on individual components in the past. The last years were characterized by projects on energy systems and the interaction of renewable energy technologies and their integration into existing infrastructure. Finally, the results were compared with governmental research programs and we could analyze a comprehensive agreement.
Energy-efficient Management of Data Centers using a Renewable-aware Scheduler

Leveraging on-site renewable sources like solar and wind provides ample opportunities on developing environmental friendly and energy-efficient data centers. Evidence shows that renewable-aware job schedulers conserve energy by adjusting the arrangement of non-urgent workload according to renewable energy states. We propose an energy management system with a renewable-aware scheduler called REDUX3, which offers a smart way of managing the energy supply of data centers powered by the grid and renewable energy. Due to the intermittent nature of renewable energy resources, REDUX3 judiciously back-fills workload when renewable energy is sufficient, and defer workload to the next time slot if renewable energy is at outage state. As an integrated and smarter update from our previous work [1], [2] and [3], REDUX3 also orchestrates distribute UPS devices (i.e., recharge or discharge) to allocate energy resources when (1) grid price is at low or high states or (2) renewable energy generation is at a low or fluctuating level. Compared with the existing strategies, REDUX3 demonstrates a prominent capacity of boosting renewable energy utilization.
ForkMV: Mean-and-Variance Estimation of Fork-Join Queuing Networks for Datacenter Applications

The Fork-Join structure underlays many distributed computing applications in data centers. In this paper, we develop a technique, called ForkMV, to estimate the mean and variance of request response time for Fork-Join queuing networks (FJQNs) with both short-tailed and long-tailed service time distributions and arbitrary request fanout degrees (i.e., the number of Fork nodes). Specifically, for an FJQN with any given service time distribution of practical interests, ForkMV is able to estimate the mean and variance of request response time, accurate enough to facilitate effective resource allocation for data center applications. The test results indicate that in the entire range of the fanout degrees being tested (i.e., [1], [4000]), ForkMV is able to estimate the mean response time within 5 % and 15 % and variance response time within 15% and 10% of the simulation results for short-tailed exponential service distribution and long-tailed truncated Pareto distribution, respectively, at the 90 % load or higher.
GARDENPRO - Android Mobile Application for Home Garden Management Using Machine Learning Augmented Reality and IOT

IoT is an important technology for Agriculture. When it comes to machine learning-based decisions related to agriculture the farmers can take precise decisions according to the sensor data collected from IoT. But most people who live in urban areas of Sri Lanka were adopted to agriculture because of COVID 19 pandemic situation. So most of the people have limited space for gardening, and newcomers haven't proper knowledge of what are the crops suitable for their area of garden space and relevant instructions to grow the crops which they like and environment factors needed. And they haven't understood the type of fertilizers that need for the crop. Almost nowadays people haven't much time for watering and care the plants that they like to grow in their garden. Garden Pro Android Application had introduced a feature Machine Learning based crop recommendation system for newcomers to home gardening. By using this feature of the app user can get an idea about crops that are suitable for their garden area, gardening instructions. And soil moisture also fertilizers and the application shows real-time temperature, soil moisture, and humidity, water level required for the crop that collected from IoT sensors and additionally the 3D View of the plants that predicted according to the soil conditions visualize to the user by Augmented reality technology.
Short And Low Resolution Deepfake Video Detection Using CNN

Recently, convincing deepfake videos are growing very fast that can delude even the trained experts. These deepfake videos have huge impacts all over the world covering the political, social, and personal lives. The state-of-the-art machine learning studies are demonstrating noticeable success to detect fake videos in high resolution and long-time video data while the same performance is not observed in low resolution and short-time clips. In this work, we have trained a convolutional neural network (CNN) that demonstrates mentionable accuracy in detecting fake videos in low-resolution and short-time video data. We have exploited Kaggle Deepfake Detection Challenge (DFDC) and the Face Forensics++ datasets in our experiment. Our model shows 94.93% accuracy in detecting fake videos for the DFDC dataset while the same is 93.2% for FaceForensics++ Dataset. We evaluated our models by different performance metrics and compared the performance with state-of-the-art methods. Our model demonstrates comparable performance.
Recruitment Scam Detection Using Gated Recurrent Unit

Recruitment advertisements in-cluding fake or fraudulent job advertisements is delivered to any job seekers and lead them to lose their money or personal information by frauds. With mass digitization and inter-net access scammers are coming up with new ideas and recruitment fraud is one of them. By sending false recruitment emails, SMS texts, fake websites, fake social media profiles, fake job posts, online recruiting services such as LinkedIn, or unsolicited emails purporting to be from well-known organizations are common methods of recruitment fraud. To identify fake or fraudulent job advertisements, we have pro-posed a one layer gated recurrent unit (GRU) model that can classify scams and real re-cruitments. We also evaluate the efficiency and achieved 93.51% of AUC score on Employment Scam Aegean Dataset (EMSCAD) dataset.
A Hierarchical Spatial–Temporal Cross-Attention Scheme for Video Summarization Using Contrastive Learning

Video summarization (VS) is a widely used technique for facilitating the effective reading, fast comprehension, and effective retrieval of video content. Certain properties of the new video data, such as a lack of prominent emphasis and a fuzzy theme development border, disturb the original thinking mode based on video feature information. Moreover, it introduces new challenges to the extraction of video depth and breadth features. In addition, the diversity of user requirements creates additional complications for more accurate keyframe screening issues. To overcome these challenges, this paper proposes a hierarchical spatial-temporal cross-attention scheme for video summarization based on comparative learning. Graph attention networks (GAT) and the multi-head convolutional attention cell are used to extract local and depth features, while the GAT-adjusted bidirection ConvLSTM (DB-ConvLSTM) is used to extract global and breadth features. Furthermore, a spatial-temporal cross-attention-based ConvLSTM is developed for merging hierarchical characteristics and achieving more accurate screening in similar keyframes clusters. Verification experiments and comparative analysis demonstrate that our method outperforms state-of-the-art methods.
A Multi-level Mesh Mutual Attention Model for Visual Question Answering

Abstract Visual question answering is a complex multimodal task involving images and text, with broad application prospects in human–computer interaction and medical assistance. Therefore, how to deal with the feature interaction and multimodal feature fusion between the critical regions in the image and the keywords in the question is an important issue. To this end, we propose a neural network based on the encoder–decoder structure of the transformer architecture. Specifically, in the encoder, we use multi-head self-attention to mine word–word connections within question features and stack multiple layers of attention to obtain multi-level question features. We propose a mutual attention module to perform information exchange between modalities for better question features and image features representation on the decoder side. Besides, we connect the encoder and decoder in a meshed manner, perform mutual attention operations with multi-level question features, and aggregate information in an adaptive way. We propose a multi-scale fusion module in the fusion stage, which utilizes feature information at different scales to complete modal fusion. We test and validate the model effectiveness on VQA v1 and VQA v2 datasets. Our model achieves better results than state-of-the-art methods.
Semi‐supervised interactive fusion network for MR image segmentation

Abstract Background Medical image segmentation is an important task in the diagnosis and treatment of cancers. The low contrast and highly flexible anatomical structure make it challenging to accurately segment the organs or lesions. Purpose To improve the segmentation accuracy of the organs or lesions in magnetic resonance (MR) images, which can be useful in clinical diagnosis and treatment of cancers. Methods First, a selective feature interaction (SFI) module is designed to selectively extract the similar features of the sequence images based on the similarity interaction. Second, a multi‐scale guided feature reconstruction (MGFR) module is designed to reconstruct low‐level semantic features and focus on small targets and the edges of the pancreas. Third, to reduce manual annotation of large amounts of data, a semi‐supervised training method is also proposed. Uncertainty estimation is used to further improve the segmentation accuracy. Results Three hundred ninety‐five 3D MR images from 395 patients with pancreatic cancer, 259 3D MR images from 259 patients with brain tumors, and four‐fold cross‐validation strategy are used to evaluate the proposed method. Compared to state‐of‐the‐art deep learning segmentation networks, the proposed method can achieve better segmentation of pancreas or tumors in MR images. Conclusions SFI‐Net can fuse dual sequence MR images for abnormal pancreas or tumor segmentation. The proposed semi‐supervised strategy can further improve the performance of SFI‐Net.
Preventing or Mitigating Adversarial Supply Chain Attacks

The world is currently strongly connected through both the internet at large, but also the very supply chains which provide everything from food to infrastructure and technology. The supply chains are themselves vulnerable to adversarial attacks, both in a digital and physical sense, which can disrupt or at worst destroy them. In this paper, we take a look at two examples of such successful attacks to put the idea of Supply Chain Attacks into perspective, and analyse how EU and national law can prevent these attacks or otherwise punish companies which do not try to mitigate them at all possible costs. We find that the current types of national regulation are not technology specific enough, and cannot force or otherwise mandate the correct parties who could play the biggest role in preventing supply chain attacks to do everything in their power to mitigate them. But, current EU law is on the right path, and further \textcolorblack development of this may be what is necessary to combat these large threats, as national law may fail at properly regulating companies when it comes to cybersecurity.
Natural Disaster Analytics using High Resolution Satellite Images

Throughout history, natural calamities have taken a toll on both property and life. Forest fires, hurricanes, floods, earthquakes, and tornadoes are all responsible for substantial damages, and often render access to the affected areas difficult. One of the challenges, particularly in remote areas, is accurately assessing the severity of the disaster. In this paper, we propose a solution to the challenge of determining the severity of property damage through the analysis building change within satellite imagery. We determine the severity of the loss by training deep neural networks to count buildings which were destroyed in multitemporal satellite imagery of the affected area. We demonstrate, through experimental results, that a model composed of a Single Shot Detector (SSD) and a Feature Pyramid Network (FPN) trained with Focal Loss is able to detect buildings through satellite imagery with improved performance compared to conventional object detection models. For evaluation, we use the xView dataset, which consists of high resolution satellite images containing building labels. We evaluate three different object detection models, namely: (i) SSD, (ii) Faster R-CNN (Regional-based Convolutional Neural Network), and (iii) SSD+FPN with Focal Loss. Our findings show that SSD+FPN with focal loss model achieves mAP improvements of 15% and 20% increase in detecting buildings in comparison to Faster R-CNN and standard SSD models, respectively. The improved mAP metric is a reflection of our more accurate detection and localization of buildings from remotely sensed imagery.
Performance analysis of U-Net with hybrid loss for foreground detection

With the latest developments in deep neural networks, the convolutional neural network (CNN) has made considerable progress in the area of foreground detection. However, the top-rank background subtraction algorithms for foreground detection still have many shortcomings. It is challenging to extract the true foreground against complex background. To tackle the bottleneck, we propose a hybrid loss-assisted U-Net framework for foreground detection. A proposed deep learning model integrates transfer learning and hybrid loss for better feature representation and faster model convergence. The core idea is to incorporate reference background image and change detection mask in the learning network. Furthermore, we empirically investigate the potential of hybrid loss over single loss function. The advantages of two significant loss functions are combined to tackle the class imbalance problem in foreground detection. The proposed technique demonstrates its effectiveness on standard datasets and performs better than the top-rank methods in challenging environment. Moreover, experiments on unseen videos also confirm the efficacy of proposed method.
Railway Track Crack and Key Detection Robot using IoT

An autonomous railway track fault detection system inspects every large and small-sized metals to detect any faults or defects in the railway track. This system can be actively used by the railway key man, who walks 8km everyday to inspect the defects. The main aims of the proposed study is to design and develop a system to effectively inspect, locate and report about the cracks or any other metallic damages to the appropriate authority. The proposed robot includes two Laser transmitter, Raspberry Pi 3 model, GPS (Global Positioning System) module, GSM (Global System for Mobile Communication) module and image processing-based track detection framework. Laser transmitter and receiver will identify the crack present in the track. The camera captures the damage in the railway lines, and later the captured image is utilized for analysis. GPS delivers the signal to Raspberry Pi 3, and further the GSM modules send an alarm message with the longitudinal coordinates of the region to the authorized contact that has already been saved in the robot.
Mixed Reality in Multiuser Participatory Design: Case Study of the Design of the 2022 Nordic Pavilion Exhibition at the Venice Biennale

The case study documents the design process of the physical and digital versions of the heritage-valued Nordic pavilion at the Venice Biennale. The case facilitated a multiuser collaboration in mixed reality (MR), studying the technologies’ influence upon user interactions and design decision making. Retitled as the ‘Sami Pavilion’ in tribute to the Sami artists from Norway, Sweden, and Finland, which the exhibition featured, the case study took place between 2019 and 2022, primarily during the COVID-19 pandemic. The context of the case study prompted a need to explore MR methods to overcome travel restrictions. While MR has shown some interesting utility in design research, the literature indicates the need for more concrete case work. It also was necessary to design a custom solution for multiuser collaboration. As the transferability of predictive design decisions in MR to the physical building relied upon replication between user experiences, the case embodied interesting challenges to prevalent Nordic architectural theory, particularly that of ‘genius loci’ or the ‘spirit of place’, which was a strong component of the heritage value of the building and, thus, an important design narrative. The case study documents how artworks and positions of artworks were tested in various configurations within the MR model by curators and designers to simulate the spatial experience of the design options. Several key design decisions were made based on the unique vantage points offered in MR. The MR model was then used to generate 2D technical documentation and installation instructions, which were installed on site. Studies to check the relationship between the MR model and the finished, physical result were conducted. Findings depicted a high degree of transferability between the MR model and the physical exhibition while noting discrepancies between the field of view (FOV) in the MR and physical spaces in which objects felt smaller in the real-life, built result. Possibilities and limitations for future MR implementation in the design and construction of complex projects in heritage situations are discussed in addition to implications for current architectural theory regarding place and experience from a non-dualistic perspective.
An Efficient System for the Prediction of House Prices using a Neural Network Algorithm

The process of projecting house prices involves making estimations regarding how house prices will vary in the future. This is an essential aspect of the process. The price of a home is determined by a variety of different aspects, some of which include the location of the property, the overall land area, and the population of the surrounding area. In order to estimate the range of possible costs associated with purchasing a house, the Deep Learning methodology is used. In addition to this, alternative approaches are used for estimating, such as linear regression, key-value network analysis, and random forest. In the final stage of this process, the results obtained from the models utilised are evaluated and contrasted.
Smart Financial Real-Time Control System Implementation based on Artificial Intelligence and Data Mining

Smart financial real-time control system implementation based on artificial intelligence and data mining is studied in the paper. Innovation and reform are important objectives for the operation and development of enterprises. With the construction, reform and innovation of intelligent financial management system, financial staff not only need to do a good job in budget management, cost control, system income data analysis, but also need to pay attention to the upgrading and maintenance of the financial management system functions. Enterprises can use the management model to adapt to the current dynamic and diversified market economic environment to ensure that smart financial management can create more economic value for the enterprise. The data mining and the AI models are integrated to construct the efficient model. It can be reflected that the designed model is intelligent and efficient.