Reviewerly/exHarmony huggingface dataset:
Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads

With the rise of artificial intelligence (AI) in recent years and the subsequent increase in complexity of the applied models, the growing demand in computational resources is starting to pose a significant challenge. The need for higher compute power is being met with increasingly more potent accelerator hardware as well as the use of large and powerful compute clusters. However, the gain in prediction accuracy from large models trained on distributed and accelerated systems ultimately comes at the price of a substantial increase in energy demand, and researchers have started questioning the environmental friendliness of such AI methods at scale. Consequently, awareness of energy efficiency plays an important role for AI model developers and hardware infrastructure operators likewise. The energy consumption of AI workloads depends both on the model implementation and the composition of the utilized hardware. Therefore, accurate measurements of the power draw of respective AI workflows on different types of compute nodes is key to algorithmic improvements and the design of future compute clusters and hardware. Towards this end, we present measurements of the energy consumption of two typical applications of deep learning models on different types of heterogeneous compute nodes. Our results indicate that 1. contrary to common approaches, deriving energy consumption directly from runtime is not accurate, but the consumption of the compute node needs to be considered regarding its composition; 2. neglecting accelerator hardware on mixed nodes results in overproportional inefficiency regarding energy consumption; 3. energy consumption of model training and inference should be considered separately – while training on GPUs outperforms all other node types regarding both runtime and energy consumption, inference on CPU nodes can be comparably efficient. One advantage of our approach is the fact that the information on energy consumption is available to all users of the supercomputer and not just those with administrator rights, enabling an easy transfer to other workloads alongside a raise in user-awareness of energy consumption.
Early Detection of Abandonment Signs in Interactive Novels with a Randomized Forest Classifier

Interactive applications are becoming increasingly popular to gather feedback from users in different fields (e.g., Urbanism, Design, Economy, or Sociology). However, it is difficult to keep users engaged with an application to provide high-quality answers, as there are plenty of competitors for their attention (e.g. other applications) and their attention time is short. In this context, the interactive adaptation of applications to the actual interaction with users is a key element to improve users’ engagement. It allows modifying the interface and story of the application to make it more attractive to the users while they play with it. This paper addresses this issue with early detection of potential signs of fatigue or abandonment by users in interactive visual novels. It applies Randomized Forests over a variety of events common in this type of application and analyzes which of them are best predictors of those signs. The results with a variety of novels and the selected features show promising results (a minimum accuracy of 81%).
Intelligent Planning Modeling and Optimization of UAV Cluster Based on Multi-Objective Optimization Algorithm

As a flight tool integrating carrier and reconnaissance, unmanned aerial vehicles (UAVs) are applied in various fields. In recent years, mission planning and path optimization have become the most important research focuses in the field of UAVs. With the continuous maturity of artificial intelligence technology, various search algorithms have been applied in the field of unmanned aerial vehicles. However, these algorithms have certain defects, which lead to problems, such as large search volume and low efficiency in task planning, and cannot meet the requirements of path planning. The objective optimization algorithm has a good performance in solving optimization problems. In this paper, the intelligent planning model of UAV cluster was established based on multi-objective optimization algorithm, and its path is optimized. In the aspect of modeling, this paper studied and analyzed online task planning, search rules and cluster formation control using an agent-based intelligent modeling method. For mission planning and optimization, it combined multi-objective optimization algorithm to build the model from three aspects of mission allocation, route planning and planning evaluation. The final simulation results showed that the UAV cluster intelligent planning modeling method and path optimization method based on multi-objective optimization algorithm met the requirements of route design and improved the path search efficiency with 2.26% task completion satisfaction.
Toward Trusted IoT by General Proof-of-Work

Internet of Things (IoT) is used to describe devices with sensors that connect and exchange data with other devices or systems on the Internet or other communication networks. Actually, the data not only represent the concrete things connected but also describe the abstract matters related. Therefore, it is expected to support trust on IoT since blockchain was invented so that trusted IoT could be possible or, recently, even metaverse could be imaginable. However, IoT systems are usually composed of a lot of device nodes with limited computing power. The built-in unsolved performance and energy-consumption problems in blockchain become more critical in IoT. The other problems such as finality, privacy, or scalability introduce even more complexity so that trusted IoT is still far from realization, let alone the metaverse. With general Proof of Work (GPoW), the energy consumption of Bitcoin can be reduced to less than 1 billionth and proof of PowerTimestamp (PoPT) can be constructed so that a global even ordering can be reached to conduct synchronization on distributed systems in real-time. Therefore, trusted IoT is possible. We reintroduce GPoW with more mathematic proofs so that PoPT can be optimal and describe how PoPT can be realized with simulation results, mining examples and synchronization scenario toward trusted IoT.
Effect of Context on Smartphone Users’ Typing Performance in the Wild

Smartphones play a crucial role in daily activities, however, situationally-induced impairments and disabilities (SIIDs) can easily be experienced depending on the context. Previous studies explored the effect of context but mainly done in controlled environments with limited research done in the wild. In this article, we present an in-situ remote user study with 48 participants’ keyboard interaction on smartphones including the performance and context details. We first propose an automated approach for error detection by combining approaches introduced in the literature and with a follow-up study, show that the accuracy of error detection is improved. We then investigate the effect of context on the typing performance based on five dimensions: environment, mobility, social, multitasking, and distraction, and reveal that the context affects participants’ error rate significantly but with individual differences. Our main contribution is providing empirical evidence with an in-situ study showing the effect of context on error rate.
SecureTVM: A TVM-based Compiler Framework for Selective Privacy-preserving Neural Inference

Privacy-preserving neural inference helps protect both the user input data and the model weights from being leaked to others during the inference of a deep learning model. To achieve data protection, the inference is often performed within a secure domain, and the final result is revealed in plaintext. Nevertheless, performing the computations in the secure domain incurs about a thousandfold overhead compared with the insecure version, especially when the involved operations of the entire model are mapped to the secure domain, which is the computation scheme adopted by the existing works. This work is inspired by the transfer learning technique, where the weights of some parts of the model layers are transferred from a publicly available, pre-built deep learning model, and it opens a door to further boost the execution efficiency by allowing us to do the secure computations selectively on parts of the transferred model. We have built a compiler framework, SecureTVM, to automatically translate a trained model into the secure version, where the model layers to be protected can be selectively configured by its model provider. As a result, SecureTVM outperforms the state of the art, CrypTFlow2, by a factor of 55 for the transfer learning model. We believe that this work takes a step forward toward the practical uses of privacy-preserving neural inference for real-world applications.
Robust self-supervised learning for source-free domain adaptation

Source-free domain adaptation (SFDA) is from unsupervised domain adaptation (UDA) and do apply to the special situation in reality that the source domain data is not accessible. In this subject, self-supervised learning is widely used in previous works. However, inaccurate pseudo-labels are hardly avoidable and that degenerates the adapted target model. In this work, we propose an effective method, named RS2L (robust self-supervised learning), to reduce the negative impact due to inaccurate pseudo-labels. Two strategies are adapted. The first is called structure-preserved pseudo-labeling strategy which generates much better pseudo-labels by stored predictions of k-closest neighbors. Another is self-supervised learning with mask. We use threshold masks to select samples for different operations, i.e., self-supervised learning and structure-preserved learning. For different masks, the threshold values are different. So it is not excluded that some samples participate in both two operations. Experiments on three benchmark datasets show that our method achieves the state-of-the-art results.
Latent feature learning via autoencoder training for automatic classification configuration recommendation

The Combined Algorithm Selection and Hyperparameter Optimization problem, in short, CASH, seeks the most suitable classifiers and hyperparameters for the underlying classification problems. In current literature, the common approaches in dealing with CASH problem are conducted via search-based methods such as sequential model-based optimization (SMBO) along with various active tests. Different from current existing approaches, in this paper, we propose a new method by incorporating the so-called denoising autoencoder (DAE) approach into meta-learning (MtL) for automatic configuration (both algorithms and their hyperparameters) recommendation, which appears to be quite effective compared to standard search-based approaches. More specifically, we set up the configuration search space for CASH and produce the metadata, and generate the classification performance on a set of collected historical datasets. Then both encoder and decoder in the DAE system are trained with the masked metadata as inputs and the unmasked metadata as targets to extract the subtle latent variables of metadata and recover the unmasked inputs subsequently. Under our framework, the performance over the entire configuration space can be predicted effectively through two different settings, and the configuration with the highest predictive performance is thus recommended. The first recommendation approach is by inactivating some inputs and then to recover their entries via the trained encoder and decoder for new problems, while in the second approach, the relationship between the acquired latent variables and the meta-features of historical datasets via kernel multivariate multiple regression (MMR) is enacted, leading to the performance estimation of new datasets being pursued directly through MMR and the decoder of DAE without requiring any new configuration evaluations. An automatic classification configuration recommendation system, including 81 historical problems and 11 common classifiers with a total of 4983 configurations, is established to show the effectiveness of our proposed approach. The comparative results on 45 testing problems demonstrate that our proposed model has the superior recommendation capacity in terms of the baselines for existing MtL as well as other search-based approaches.
Generalized zeroing neural dynamics model for online solving time-varying cube roots problem with various external disturbances in different domains

As a basic problem of the nonlinear dynamic model, the online solution of time-varying cube root problem (TVCRP) is widely used in science and engineering. However, the practical system is frequently disturbed by the external factors, which inevitably leads to unknown disturbances in the solution process. Therefore, addressing the TVCRP with high accuracy in non-ideal environment (unknown disturbances) is the basis of solving the nonlinear dynamic model. In this work, a noise-suppression zeroing neural dynamics (NSZND) model is investigated and developed from the control perspective to resolve the time-varying or time-invariant cube root problem with the different disturbances (bounded and unbounded disturbances) in real/complex domain. Moreover, the generalized noise-suppression zeroing neural dynamics model with various activation functions is discussed and designed for eliminating the interference of different disturbances to improve the precision and noise immunity of the NSZND model. The global/exponential convergence of the developed models with various disturbances are proved by theoretical analyses. With unbounded disturbance (linear noise), the solving accuracy of the NSZND model is about 101 and 103 times superior to the gradient neural dynamics model and the zeroing neural dynamics model. Finally, the proposed NSZND model is extended to the tensor cube root problem, and the feasibility of the proposed model is verified in this work. Beyong that, different fractals are generated by using the proposed model to solve the cube root problem in the complex domain, which provides an interesting idea for advertising design and computer graphics.
Configurations of rectangles in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msubsup><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math>

Let Fq be a finite field of order q. In this paper, we study the distribution of rectangles in a given set in Fq2. More precisely, for any 0<δ≤1, we prove that there exists an integer q0=q0(δ) with the following property: if q≥q0 and A is a multiplicative subgroup of Fq⁎ with |A|≥q2/3, then any set S⊂Fq2 with |S|≥δq2 contains at least ≫|S|4|A|2q5 rectangles with side-lengths in A. We also consider the case of rectangles with one fixed side-length and the other in a multiplicative subgroup A.
An Enhanced Deep Learning Approach for Smartphone-Based Human Activity Recognition in IoHT

Human activity recognition (HAR) uses sensor-based technology to predict human activity using sensor-generated time-series data. According to recent studies, researchers have been drawn to the area of HAR as the use of mobile devices with various sensors has increased in several research areas in health care that includes the identification of gait abnormality in brain or neurological disorder subjects, designing techniques for clinical gait analyses of a disabled and elderly person, etc. HAR is especially essential on the Internet of healthcare things (IoHT) because of the increasing growth of the Internet of Things (IoT) technology incorporated in numerous smart products and wearable technology (such as smartwatches and smartphones) that have a significant impact on the life of human. A deep neural network (DNN) comprising a bidirectional gated recurrent unit (BiGRU) and two convolutional layers is proposed in this research. The model used could automatically extract and identify activity features using a few of the architecture parameters. The raw data obtained using smartphone sensors is sent into a two layer BiGRU followed by two CNN layers in the proposed architecture. The model’s performance is assessed using two publicly available datasets (UCI-HAR and WISDM). The observations indicate that the reliability and activity identification capability of the proposed model is better than earlier findings.
Cheap and secure metatransactions on the blockchain using hash-based authorisation and preferred batchers

Smart contracts are self-executing programs running in the blockchain allowing for decentralised storage and execution without a middleman. On-chain execution is expensive, with miners charging fees for distributed execution according to a cost model defined in the protocol. In particular, transactions have a high fixed cost. We present MultiCall, a transaction-batching interpreter for Ethereum that reduces the cost of smart contract executions by gathering multiple users’ transactions into a batch. Our current implementation of MultiCall includes the following features: the ability to emulate Ethereum calls and create transactions, both from MultiCall itself and using an identity unique to the user; the ability to cheaply pay Ether to other MultiCall users; and the ability to authorise emulated transactions on behalf of multiple users in a single transaction using hash-based authorisation rather than more expensive signatures. This improves upon a previous version of MultiCall. Our experiments show that MultiCall provides a saving between 57% and 99% of the fixed transaction cost compared with the standard approach of sending Ethereum transactions directly. Besides, we also show how to prevent an economic attack exploiting the metatransaction feature, describe a generic protocol for hash-based authorisation of metatransactions, and analyse how to minimise its off-chain computational and storage cost.
Improved Tweet Sentiment Analysis by Features Weight Optimize by GWO and Classify by XG-Boost

The emergence of Twitter as a significant social media network and its strong interest among sentiment analysis experts are big talking points. Despite its recent focus, state-of-the-art Twitter sentiment analysis methods are known to perform very badly with claimed classification accuracies that are frequently around 70%, which negatively impacts the ability to use the sentiment information generated from the techniques. This study looks at the difficulties involved with Twitter, research the literature to find out whether the solutions were addressed challenges. We perform a benchmark assessment to evaluate the state-of-the-art in Twitter sentiment analysis. In this paper analysis of sentiment 140 dataset and classify by XG-Boost with feature optimization by XG-Boost and GWO. Result shows that in proposed approach accuracy improves 2–3%, precision 4%, recall 5%, and F-sore3%.
The Voice and Gesture to Speech Conversion in Indian Regional Sign Language

This project helps mute people to interact with the rest of the world using sign language. Communication is an important aspect of human beings. Interaction between normal people and an impaired person is very difficult because of communication barriers. This work includes a voice-based and text-based interaction approach. An interpreter is responsible for helping impaired individuals understand what is being said in a variety of situations. The main feature of this project is that it can be used to predict sign language and translate it to voice so that normal people can understand sign language without the need of learning them. In this project, gesture to speech translation is done for Hindi as well as Tamil language, Hindi being the nation's most spoken language, and Tamil being the language used in most parts of southern India has increased the requirement of this model. For future scope, the model created in this project can be used to develop an application that involves not one but two-way communication.
Toward Human-Robot Cooperation: Unsupervised Domain Adaptation for Egocentric Action Recognition

With the advent of collaborative manipulators, the community is pushing the limits of human-robot interaction with novel control, planning, and task allocation strategies. For a purposeful interaction, however, the robot is also required to understand and predict the action of the human not only at a kinematic level (i.e. motion estimation), but also at an higher level of abstraction (i.e. action recognition), ideally from the human own perspective. Dealing with egocentric videos comes with the benefit that the data source already embeds an intrinsic attention mechanism, driven by the focus of the user. However, the deployment of such technology in realistic use-cases cannot ignore the large variability of background characteristics when changing environment, resulting in a domain shift in features space not learnable from labels at training time. In this paper, we discuss a method to perform Domain Adaptation with no external supervision, which we test on the EPIC-Kitchens-100 UDA Challenge in Action Recognition. More specifically, we move from our previous work on Relative Norm Alignment and extend the approach to unlabelled target data, enabling a simpler adaptation of the model to the target distribution in an unsupervised fashion. To this purpose, we enhanced our framework with multi-level adversarial alignment and with a set of losses aimed at reducing the classifier's uncertainty on the target data. Extensive experiments demonstrate how our approach is capable to perform Multi-Source Multi-Target Domain Adaptation, thus minimising both temporal (i.e. different recording times) and environmental (i.e. different kitchens) biases.
Survey on Collaborative Filtering Technique for Recommender System Using Deep Learning

Automated systems like Recommenders are developed to recommend item suggestions to consumers abiding a variety of conditions. They predict ratings of the most likely product a user will buy intuitively or based on interest. Collaborative filtering (CF) is one of the most common methods for designing recommender systems. It works on a collection of users’ established preferences to make suggestions or predictions for unknown users. This survey study provides a comprehensive insight into the most up-to-date state-of-the-art approaches for efficient filtering and successful recommendation. In addition, the survey article studies different types of deep learning-based collaborative filtering algorithms and implementation techniques used in different recommender system applications to overcome cold start and data sparsity challenges.
IoT-Enabled RFID-Based Library Management and Automatic Book Recommendation System Using Collaborative Learning

Libraries are a valuable resource for learning. A person must acquire and retain knowledge. However, the current library system has several disadvantages, including manual management of books by librarians, tedious tasks of book searching, and difficulty to track the unreturned books. Thus, this article presents the automatic library management system (LMS) based on radio frequency identification (RFID) and the Internet of Things (IoT). The proposed system helps to identify and track a large number of tagged books using radio waves. The proposed LMS provides an interactive portal to display the availability of books in the library, book issue facility, tracking of issued books, book return facility, etc. The LMS portal also provides book recommendations based on the K-Means clustering algorithm and collaborative learning. The IoT helps to collect the data on the cloud platform so that remote access to the library system. This action simplifies the process of borrowing, updating, and returning books using RFID tags.