Reviewerly/exHarmony huggingface dataset:
Gently down the stream(ing): Can digital literacy help turn the tide on the climate crisis?

The development of digital literacy and digital competencies are widely recognised as essential in an increasingly digitised world.However, as we begin to come to terms with the extent of the climate crisis facing the world, this paper asks the question: is it time for digital literacy to focus its attention on the impact digital activity has on the environment?This desk-based research piece begins by examining popular digital tools and services used for entertainment and work purposes, and highlights their environmental impact and potential mitigating factors.Following this, definitions of digital literacy are examined to reveal a complex set of competencies which, as yet do not engage with the environmental impact of activity.The paper then examines widely used digital literacy frameworks and exposes their relative lack of engagement with this area.Finally, the paper urges that we begin to discuss environmental impact as a component of digital literacy.It proposes ways to achieve this with students, using the language of existing digital literacy definitions.It also recommends the inclusion of environmental impact as a separate strand in digital literacy frameworks.
A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images

With the increasingly serious eutrophication of inland water, the frequency and scope of harmful cyanobacteria blooms are increasing, which affects the ecological balance and endangers human health. The aim of this study was to propose an alternative method for the quantification of cyanobacterial concentrations in water by correlating multispectral data. The research object was the cyanobacteria in Erhai Lake, Dali, China. Ten monitoring sites were selected, and multispectral images and cyanobacterial concentrations were measured in Erhai Lake from September to November 2021. In this study, multispectral data were used as independent variables, and cyanobacterial concentrations as dependent variables. We performed curve estimation, and significance analysis for the independent variables, and compared them with the original variable model. Here, we chose about four algorithms to establish models and compare their applicability, including Multivariable Linear Regression (MLR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Extreme Learning Machine (ELM). The prediction performance was evaluated by the coefficient of determination (R2), Root-Mean-Square Error (RMSE), and Mean Relative Error (MRE). The results showed that the variable analysis model outperformed the original variable model, the ELM was superior to other algorithms, and the variable analysis model based on the ELM algorithm achieved the best results (R2 = 0.7609, RMSE = 4197 cells/mL, MRE = 0.044). This study confirmed the applicability of cyanobacterial concentrations prediction using multispectral data, which can be characterized as a quick and easy methodology, and the deep neural network has great potential to predict the concentration of cyanobacteria.
The Detection of Video Shot Transitions Based on Primary Segments Using the Adaptive Threshold of Colour-Based Histogram Differences and Candidate Segments Using the SURF Feature Descriptor

Aim: Advancements in multimedia technology have facilitated the uploading and processing of videos with substantial content. Automated tools and techniques help to manage vast volumes of video content. Video shot segmentation is the basic symmetry step underlying video processing techniques such as video indexing, content-based video retrieval, video summarization, and intelligent surveillance. Video shot boundary detection segments a video into temporal segments called shots and identifies the video frame in which a shot change occurs. The two types of shot transitions are cut and gradual. Illumination changes, camera motion, and fast-moving objects in videos reduce the detection accuracy of cut and gradual transitions. Materials and Methods: In this paper, a novel symmetry shot boundary detection system is proposed to maximize detection accuracy by analysing the transition behaviour of a video, segmenting it initially into primary segments and candidate segments by using the colour feature and the local adaptive threshold of each segment. Thereafter, the cut and gradual transitions are fine-tuned from the candidate segment using Speeded-Up Robust Features (SURF) extracted from the boundary frames to reduce the algorithmic complexity. The proposed symmetry method is evaluated using the TRECVID 2001 video dataset, and the results show an increase in detection accuracy. Result: The F1 score obtained for the detection of cut and gradual transitions is 98.7% and 90.8%, respectively. Conclusions: The proposed symmetry method surpasses recent state-of-the-art SBD methods, demonstrating increased accuracy for both cut and gradual transitions in videos.
Mitigating Power and Memory Constraints on a Venusian Seismometer

Abstract The nearest term pathway to the deployment of a seismometer on Venus is an instrument that can operate under ambient surface conditions on battery power. We conduct a series of studies on combined hardware and software approaches to maximize the quality of data returned under the likely restrictions of minimal on-board data storage and only being able to transmit in real time during a small fraction of a multimonth deployment. We assess likely Venus seismicity by examining different terrestrial analog settings; we find that likely Venus analog settings all fall within about an order of magnitude of mean Earth in terms of seismicity level. We use the seismic record from a station in central Alaska as a Venus surrogate for algorithm development. We tested various transmission triggers and developed a simple low-memory algorithm that mimics the common terrestrial long-term average/short-term average trigger. If the seismometer can operate in coordination with an orbiter that can remotely turn off data transmission, then the frequency content of a few seconds of data can be used to distinguish small, nearby earthquakes from large, distal ones, and total data transmission can be tuned to favor the latter. If an orbiter can also turn on transmission for other nearby seismometers, it would further enhance the ability to distinguish small- and large-magnitude earthquakes autonomously and increase the chances of capturing the initial onset of significant events.
Performing in-situ analytics: Mining frequent patterns from big IoT data at network edge with D-HARPP

Big IoT data is inherently distributed, high-dimensional, irregular, and sparse in nature. Fog computing model in its original form is by no means the optimal solution for mining big IoT data. However, utilizing the network edge for mining tasks, such as enabling edge and IoT devices to mine locally frequent patterns can significantly improve the mining performance. Additionally, edge devices capable of performing distributed job processing could utilize the model to the fullest. But resource poorness of edge and IoT devices needs lightweight pattern mining algorithms. This paper presents Distributed HARnessing the Power of Powersets for Mining Frequent Itemsets (D-HARPP), a spark-based distributed algorithm to mine frequent co-occurring itemsets in big IoT data. Unlike state-of-the-art distributed algorithms, D-HARPP makes a single pass over the data and does not create candidate itemsets; thus, achieves significantly better runtime and consumes the least memory. Moreover, performance of D-HARPP is not deteriorated at lower minimum support thresholds. These distinguishing characteristics make D-HARPP an optimal choice for Spark-enabled edge and IoT devices. D-HARPP has outperformed Spark-Apriori, another distributed algorithm by significant margins, both in terms of runtime and memory consumption, particularly on sparse datasets.
CNN based Deep Learning model for Deepfake Detection

In the recent period there has been massive progress in synthetic image generation and manipulation which significantly raises concerns for its ill applications towards society. This would result in spreading false information, leading to loss of trust in digital content. This paper introduces an automated and effective approach to get facial expressions in videos, and especially focused on the latest method used to produce hyper realistic fake videos: Deepfake. Using faceforenc++ dataset for training our model, we achieved more that 99% successful detection rate in Deepfake, Face2Face, faceSwap and neural texture. Regular image forensics techniques are usually not very useful, because of the strong deterioration of data due to the compression. Thus, this paper follows a layered approach with first detecting the subject with the help of existing facial recognition networks followed by extracting facial features using CNN, then passing through the LSTM layer, where we make use of our temporal sequence for face manipulation between frames. Finally use of the Recycle-GAN which internally makes use of generative adversarial networks to merge spatial and temporal data.
Implementation of Particle Swarm Optimization for Solving Optimal Control Problem of a Flexible Robotic Manipulator

This paper proposes using particle swarm optimization (PSO) to solve the optimal control problem (OCP) in the form of an ordinary differential equation (ODE). For state functions, these standard differential equations are linear to regulated coefficients. Because of the ordinary bilinear differential equations, optimal control methods are commonly used for these problems by considering the proposed performance index and a flexible manipulator as a model. A flexible manipulator (SLFM) is taken to use Optimization techniques (PSO) to assess and measure its performance. The results obtained by the PSO are compared with the DE, which has shown prominent results.
Video-based Sign Language Recognition using Recurrent Neural Networks

Deaf-mute people regularly confront communication challenges with people who can hear and speak as the primary means of communication they use is sign language. People often do not have the time and knowledge required to interpret gestures of people relying on sign language and hence either the people feel neglected or rely on a professional interpreter. This could be problematic, however, especially in emergency situations like murder, road accident, robbery, etc. where an interpreter might not be immediately available and there is an urgent need to communicate. In this paper, we study sign language recognition by implementing deep learning techniques on the open-source Word-Level American Sign Language (WLASL) Dataset. We perform hand recognition on the video frames followed by feature extraction using state-of-the-art convolutional neural networks and then perform classification on the image sequence by using a recurrent neural network. We compare the performance of several architectures by using various metrics like validation and test accuracy, precision, recall and F1-score. We also test the working of the system on different sizes of label set to check possible scalability.
Cryptocurrency Analysis and Forecasting

Cryptocurrencies are becoming a well-known and commonly acknowledged kind of substitute trade money. Most monetary businesses now include cryptocurrency. Accordingly, cryptocurrency trading is widely regarded as the most of prevalent and capable types of lucrative investments. However, because this financial sector is already known for its extreme volatility and quick price changes, over brief periods of time. For such constantly changing nature of crypto trends and price, it has become a necessary part for traders and crypto enthusiast to get a detailed analysis before investing. Also, the construction of a precise and dependable forecasting model is regarded vital for portfolio management and optimization. In this paper we propose a web system, which will help to understand cryptocurrency in a more statistical way. Proposed system focuses mainly on four coins : Bitcoin, Ethereum, Dogecoin and Shiba Inu performing analysis and forecasting on all the four coins. System will also do statistical comparison between the coins. Analysis and comparison is carried out using python libraries and modules whereas LSTM and ARIMA are used for forecasting. Extensive research was conducted using real-time and historical information, on four key cryptocurrencies, two of which had the greatest market capitalization, notably Bitcoin and Ethereum, while the other, Dogecoin and Shiba Inu, that had a significant growth in market capitalization over the previous year. In comparison to old fully-connected deep neural networks, the suggested model may employ mixed crypto data more proficiently, minimizing overfitting and computing costs.
Transfer Learning: Convolutional Neural Network-AlexNet Achieving Face Recognition

Nowadays, Machine-based face recognition is becoming very commonplace, robust and dependable system that is widely employed in numerous cases for access control. As in traditional approach, face recognition needs the extraction of face features prior to classification and recognition, which affects recognition rate. We employ Face Verification checks whether the pictures are associated with a single individual, whereas Face Identification must identify a specific face from a collection of known profiles in the system. To tackle this question, this paper incorporates the CNN structure Alexnet to obtain face identification.Throughout this article, we perform facial recognition using transfer learning in a Siamese network composed of 2 comparable CNNs. A pair of 2 face picture is fed into the Siamese network as input, after which the network learns the traits of this pair of pictures.Next the network is trained using the PRelu activation function to find the ideal learning algorithm and maximal values. Then, the face was identified and categorized. Library Multi-Spectral Face Data - set and Library 2D Faceprint Database were used to test the methodology, it enhances the accuracy of face recognition when compared to algorithms trained on datasets with a particular dataset and a specific spectrum’s recognition rate peaked up to 98 percent.
Yoga Correction Using Machine Learning

Yoga incorporates breathing exercises, meditation and poses designed to encourage relaxation and reduce stress. For the benefits of yoga, we should be able to perform the yoga asana with accuracy, correct movements and posture is really important. Incorrectly stretching your body or doing wrong movements can be detrimental to your health, this can lead to acute pain and long standing chronic problems. The proposed application performs yoga correction using OpenCV and the MediaPipe library. Pose detection and Pose correction would be the two important parts to make the system accurate and faultless. OpenCV and MediaPipe work simultaneously for gaining the expected output in real time.
Video to Text Summarisation and Timestamp Generation to Detect Important Events

With the advent of modern technology and the subsequent rise of efficient storage devices we are witnessing a rise in the number of media that is available to us. Among the most common media, the only one that takes up huge spaces on physical storage devices are videos. The primary reason for that is the addition of higher resolution videos and a greater frame rate. It is quite necessary to come up with summarisation techniques that help us understand the most important parts of the video. Apart from that, summarisation also helps us skip the non-essential parts of the video. This technology can be utilised to cut short on the time wasted on searching through the most relevant parts of the video. This paper tries to focus on the fundamental problem of summarising long videos and converting them into shorter sections that can effectively convey the same content if one were to see the entire video. Introducing timestamps also helps the viewer in jumping to the crucial events of the video. This paper makes use of deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). These serve as a means of comparing different frames and generating end results.
MeritRank: Sybil Tolerant Reputation for Merit-based Tokenomics

Decentralized reputation schemes present a promising area of experimentation in blockchain applications.These solutions aim to overcome the shortcomings of simple monetary incentive mechanisms of naive tokenomics.However, there is a significant research gap regarding the limitations and benefits of such solutions.We formulate these trade-offs as a conjecture on the irreconcilability of three desirable properties of the reputation system in this context.Such a system can not be simultaneously generalizable, trustless, and Sybil resistant.To handle the limitations of this trilemma, we propose MeritRank: Sybil tolerant feedback aggregation mechanism for reputation.Instead of preventing Sybil attacks, our approach successfully bounds the benefits of these attacks.Using a dataset of participants' interactions in MakerDAO, we run experiments to demonstrate Sybil tolerance of MeritRank.Decay parameters of reputation in MeritRank: transitivity decay and connectivity decay, allow for a fine-tuning of desirable levels of reputation utility and Sybil tolerance in different use contexts.
Performance and Cost Evaluation of Public Blockchain: An NFT Marketplace Case Study

Non Fungible tokens (NFTs) are receiving unprecedented attention among digital creators and traders. This technology allows creators to certify their digital assets on blockchain as a decentralized, immutable, and transparent database. They can transfer the ownership of NFTs, which can easily be traced without the risk of manipulation. The trading volume for NFTs has surged to $10 billion in the third quarter of 2021. Although the high complexity of the consensus algorithm in blockchain ensures better security, it imposes higher transaction costs and limited scalability. To alleviate high transaction fees, energy inefficiency, and delays, tens of public blockchain platforms with different consensus protocols are being proposed as alternatives for NFT marketplaces. A crucial design choice for such an NFT marketplace is, in fact, to select the best public blockchain platform. In this work, we evaluate the cost and the performance of three public blockchain platforms, Fantom, Avalanche, and Polygon, in a use case for minting and transferring NFTs. We present machine learning models to predict the transaction cost and the throughput for the three platforms as decision parameters to choose the most appropriate platform. Our experimental results in terms of transaction fees and throughput demonstrate that the Polygon network is more efficient than the other two platforms.
Concept-Based Label Distribution Learning for Text Classification

Abstract Text classification is a crucial task in data mining and artificial intelligence. In recent years, deep learning-based text classification methods have made great development. The deep learning methods supervise model training by representing a label as a one-hot vector. However, the one-hot label representation cannot adequately reflect the relation between an instance and the labels, as labels are often not completely independent, and the instance may be associated with multiple labels in practice. Simply representing the labels as one-hot vectors leads to overconfidence in the model, making it difficult to distinguish some label confusions. In this paper, we propose a simulated label distribution method based on concepts (SLDC) to tackle this problem. This method captures the overlap between the labels by computing the similarity between an instance and the labels and generates a new simulated label distribution for assisting model training. In particular, we incorporate conceptual information from the knowledge base into the representation of instances and labels to address the surface mismatching problem when instances and labels are compared for similarity. Moreover, to fully use the simulated label distribution and the original label vector, we set up a multi-loss function to supervise the training process. Expensive experiments demonstrate the effectiveness of SLDC on five complex text classification datasets. Further experiments also verify that SLDC is especially helpful for confused datasets.
Federated Learning With Privacy-Preserving Ensemble Attention Distillation

Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particularly relevant for clinical applications since patient data are usually not allowed to be transferred out of medical facilities, leading to the need for FL. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they also require numerous rounds of synchronized communication and, more importantly, suffer from a privacy leakage risk. We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation in this work. The central model is learned from local knowledge via ensemble attention distillation. Our technique uses decentralized and heterogeneous local data like existing FL approaches, but more importantly, it significantly reduces the risk of privacy leakage. We demonstrate that our method achieves very competitive performance with more robust privacy preservation based on extensive experiments on image classification, segmentation, and reconstruction tasks.
Correlation between the Quality of Talent Training and Regional Economic Development Based on Multivariate Statistical Analysis Model

Entering the era of knowledge economy, the economic value of talent education is more and more emphasized, and it contributes more and more to the local economy and injects new vitality into local economic development. However, there are still some difficulties in the synergistic development of talent education and local economy, such as the low degree of integration of "industry, profession, and employment" and the lack of synergy between schools and enterprises, which affects the high-quality development of talent education and restricts the long-term development of regional economy. This paper incorporates machine learning algorithms to construct a multivariate statistics analysis model based on the correlation analysis model between talent training quality and regional economic development. Based on the multi-variate statistical analysis theory and method, the qualitative analysis and quantitative analysis are closely combined to scientifically pick up the main indicators affecting the comprehensive quality of talent cultivation and overcome the drawbacks of artificial subjective assignment of values. Through the judicious selection of evaluation indexes, a scientific and fair evaluation model is established, and the targets of talent cultivation are combined with regional economic development, in a comprehensive analysis, which provides a new means for the development of the regional economy.