Reviewerly/exHarmony huggingface dataset:
Real-time monitoring of physical education classroom in colleges and universities based on open IoT and cloud computing

The teaching effect of college physical education classroom needs to be combined with artificial intelligence system. From the actual situation, the current college physical education classroom is mostly based on manual teaching and manual management, so the teaching effect is not good. In order to change the traditional teaching mode and improve the classroom detection effect, based on the open Internet of Things and cloud computing technology, this paper builds a real-time monitoring system of college physical education classroom, and proposes a number of new and improved algorithms, which provide a theoretical and technical basis for the application of automatic identity positioning in large scenes. Moreover, this study obtains field scenes through field image data collection and field data processing, and then combines the regional scenes with field measured data to verify accuracy and trends to obtain students’ morphological characteristics. In addition, this paper designs practical experiments to verify the system performance. The research results show that the intelligent system constructed in this paper has certain effects and can be applied to physical education.
An Improved C4.5 Algorithm using Principle of Equivalent of Infinitesimal and Arithmetic Mean Best Selection Attribute for Large Dataset

Scaling up the data-mining classification algorithms to very large datasets has been attracting growing interest now a days. Many techniques have been employed to improve those algorithms but efficient data-mining classification algorithms that have a minimal decrease in accuracy with little increase in time complexity remain very important. The C4.5 algorithm is one of the data mining classification algorithms that have been used for uncovering hidden patterns and gleaning useful and novel knowledge in such large datasets. This work proposes a new C4.5 data mining algorithm with a lesser time complexity for large dataset compared with traditional C.45 algorithm, but however for smaller dataset traditional C.45 algorithm has lesser time complexity. The new algorithm was improved using Principle of Equivalent of Infinitesimal and Arithmetic Mean Best Selection Attribute.
Mapping the Big Data Landscape: Technologies, Platforms and Paradigms for Real-Time Analytics of Data Streams

The `Big Data' of yesterday is the `data' of today. As technology progresses, new challenges arise and new solutions are developed. Due to the emergence of Internet of Things applications within the last decade, the field of Data Mining has been faced with the challenge of processing and analysing data streams in real-time, and under high data throughput conditions. This is often referred to as the Velocity aspect of Big Data. Whereas there are numerous reviews on Data Stream Mining techniques and applications, there is very little work surveying Data Stream processing paradigms and associated technologies, from data collection through to pre-processing and feature processing, from the perspective of the user, not that of the service provider. In this article, we evaluate a particular type of solution, which focuses on streaming data, and processing pipelines that permit online analysis of data streams that cannot be stored as-is on the computing platform. We review foundational computational concepts such as distributed computation, fault-tolerant computing, and computational paradigms/architectures. We then review the available technological solutions, and applications that pertain to data stream mining as case studies of these theoretical concepts. We conclude with a discussion of the field of data stream processing/analytics, future directions and research challenges.
BitSystolic: A 26.7 TOPS/W 2b~8b NPU With Configurable Data Flows for Edge Devices

Efficient deployment of deep neural networks (DNNs) emerges with the exploding demand for artificial intelligence on edge devices. Mixed-precision inference with both compressed model and reduced computation cost enlightens a way for accurate and efficient DNN deployments. Despite obtaining mixed-precision DNN models at the algorithmic level, there still lacks sufficient hardware support. In this work, we propose BitSystolic, a neural processing unit based on a systolic array structure. In BitSystolic, the numerical precision of both weights and activations can be configured in the range of 2 - 8 bits, fulfilling different requirements across mixed-precision models and tasks. Moreover, BitSystolic can support various data flows presented in different types of neural layers (e.g., convolution, fully-connected, and recurrent neural layers) and adaptive optimization of data reuse by switching between the matrix-matrix mode and vector-matrix mode. We designed and fabricated the proposed BitSystolic composed of a $16\times 16$ systolic array. Our measurement results show that BitSystolic features the unified power efficiency of up to 26.7 TOPS/W with 17.8 mW peak power consumption across various layer types.
Fast Sentiment Analysis Algorithm Based on Double Model Fusion

Nowadays, as the number of textual data is exponentially increasing, sentiment analysis has become one of the most significant tasks in natural language processing (NLP) with increasing attention.Traditional Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference.In this paper, we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient optimization.First of all, FastText was trained to get the basic classification model, which can generate pre-trained word vectors as a by-product.Secondly, Bidirectional Long Short-Term Memory Network (Bi-LSTM) utilizes the generated word vectors for training and then merges with FastText to make comprehensive sentiment analysis.By combining FastText and Bi-LSTM, we have developed a new fast sentiment analysis, called FAST-BiLSTM, which consistently achieves a balance between performance and speed.In particular, experimental results based on the real datasets demonstrate that our algorithm can effectively judge sentiments of users' comments, and is superior to the traditional algorithm in time efficiency, accuracy, recall and F1 criteria.
Flexible and Adaptive Fairness-aware Learning in Non-stationary Data Streams

Artificial intelligence (AI)-based decision-making systems are employed nowadays in an ever growing number of online as well as offline services-some of great importance. Depending on sophisticated learning algorithms and available data, these systems are increasingly becoming automated and data-driven. However, these systems can impact individuals and communities with ethical or legal consequences. Numerous approaches have therefore been proposed to develop decision-making systems that are discrimination-conscious by-design. However, these methods assume the underlying data distribution is stationary without drift, which is counterfactual in many realworld applications. In addition, their focus has been largely on minimizing discrimination while maximizing prediction performance without necessary flexibility in customizing the tradeoff according to different applications. To this end, we propose a learning algorithm for fair classification that also adapts to evolving data streams and further allows for a flexible control on the degree of accuracy and fairness. The positive results on a set of discriminated and non-stationary data streams demonstrate the effectiveness and flexibility of this approach.
Fast Modular Multipliers for Supersingular Isogeny-Based Post-Quantum Cryptography

As one of the postquantum protocol candidates, the supersingular isogeny key encapsulation (SIKE) protocol delivers promising public and secret key sizes over other candidates. Nevertheless, the considerable computations form the bottleneck and limit its practical applications. The modular multiplication operations occupy a large proportion of the overall computations required by the SIKE protocol. The VLSI implementation of the high-speed modular multiplier remains a big challenge. In this article, we propose three improved modular multiplication algorithms based on an unconventional radix for this protocol, all of which cost about 20% fewer computations than the prior art. Besides, a multiprecision scheme is also introduced for the proposed algorithms to improve the scalability in hardware implementation, resulting in three new algorithms. We then present very efficient high-speed constant-time modular multiplier architectures for the six algorithms. It is shown that these new architectures can be extensively pipelined and highly optimized to obtain high throughput and low latency. The field-programmable gate array (FPGA) implementation results show that all proposed multipliers achieve much higher throughput than previous designs, but the increase in resources is relatively small. In addition, the multipliers without the multiprecision scheme have very low latency, which is very friendly to high-speed applications of the SIKE protocol.
Instance selection in medical datasets: A divide-and-conquer framework

Instance selection is an important problem in medical data mining. It focuses on selecting representative data samples from a given training set, whereas unrepresentative (or noisy) data samples are filtered out. This reduces the size of the training set, which then requires less storage space. In addition, when the instance selection algorithm was carefully chosen, a reduction in the training set so that it contains less noisy data can usually make the classifiers perform better than the ones without considering instance selection. In the literature, many instance selection algorithms have been proposed. However, different algorithms tend to use different criteria to determine the noisy data, making it difficult to find the best algorithm for different domain datasets. In other words, some algorithms may perform better than the others for some specific domain datasets, but may perform worse than others over other domain datasets. Instead of developing a novel algorithm that performs better than most other algorithms, this paper introduces a divide-and-conquer based instance selection (DCIS) framework that aims to improve the performance of each specific instance selection algorithm per se. Two well-known algorithms, i.e., DROP3 and IB3, are used as the baseline, and various small and large scale medical datasets are used in the experiments. Our results show that when DROP3 and IB3 are used to perform instance selection based on the DCIS framework, there is an improvement in the performance of the k-NN and SVM classifiers over the ones by the DROP3 and IB3 baselines, respectively.
Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks

Sharing recommendation is becoming ubiquitous at almost every e-commerce website, where a user will be recommended a list of users when he wants to share something with others. With the tremendous growth of online shopping users, sharing recommendation confronts several distinct difficulties: 1) how to establish a unified recommender model for large numbers of sharing scenarios; 2) how to handle with long-tail even cold start scenarios with limited training data; 3) how to incorporate social influence in order to make more accurate recommendations.
Entropy-based Sample Selection for Online Continual Learning

Deep neural networks (DNNs) suffer from catastrophic forgetting, a rapid decrease in performance when trained on a sequence of tasks where only data of the most recent task is available. Most previous research has focused on the case where all data of a task is available simultaneously and boundaries between tasks are known. In this paper, we focus on the online setting where data arrives one-by-one or in small batches ordered by tasks and task boundaries are unknown. Avoiding catastrophic forgetting in such a setting is of great interest since it would allow DNNs to accumulate knowledge without the need to store all previously seen data even if task boundaries are unknown. For this, we propose a novel rehearsal algorithm for online continual learning that is derived from basic concepts of information theory. We demonstrate on commonly used data sets that our method can avoid catastrophic forgetting, achieve competitive results when compared with the current state-of-the-art and even outperform it in most cases.
Zero-Communication Reductions

We introduce a new primitive in information-theoretic cryptography, namely zero-communication reductions ( $$\textsc {zcr}$$ ), with different levels of security. We relate $$\textsc {zcr}$$ to several other important primitives, and obtain new results on upper and lower bounds. In particular, we obtain new upper bounds for PSM, CDS and OT complexity of functions, which are exponential in the information complexity of the functions. These upper bounds complement the results of Beimel et al. [BIKK14] which broke the circuit-complexity barrier for "high complexity" functions; our results break the barrier of input size for "low complexity" functions. We also show that lower bounds on secure $$\textsc {zcr}$$ can be used to establish lower bounds for OT-complexity. We recover the known (linear) lower bounds on OT-complexity [BM04] via this new route. We also formulate the lower bound problem for secure $$\textsc {zcr}$$ in purely linear-algebraic terms, by defining the invertible rank of a matrix. We present an Invertible Rank Conjecture, proving which will establish super-linear lower bounds for OT-complexity (and if accompanied by an explicit construction, will provide explicit functions with super-linear circuit lower bounds).
RS-Mask: Random Space Masking as an Integrated Countermeasure against Power and Fault Analysis

While modern masking schemes provide provable security against passive side-channel analysis (SCA), such as power analysis, single faults can be employed to recover the secret key of ciphers even in masked implementations. In this paper, we propose random space masking (RS-Mask) as a countermeasure against both power analysis and statistical fault analysis (SFA) techniques. In the RS-Mask scheme, the distribution of all sensitive variables, faulty and/or correct values is uniform, and it therefore protects the implementations against any SFA technique that exploits the distribution of intermediate variables, including fault sensitivity analysis (FSA), statistical ineffective fault analysis (SIFA) and fault intensity map analysis (FIMA). We implement RS-Mask on AES, and show that a SIFA attack is not able to identify the correct key. We additionally show that an FPGA implementation of AES, protected with RS-Mask, is resistant to power analysis SCA using Welch's t-test. The area of the RSMasked AES is about 3.5 times that of an unprotected AES implementation of similar architecture, and about 2 times that of a known FPGA SCA-resistant AES implementation. Finally, we introduce infective RS-Mask that provides security against differential techniques, such as differential fault analysis (DFA) and differential fault intensity analysis (DFIA), with a slight increase in overhead.
How do multilingual users search? An investigation of query and result list language choices

Abstract Many users of search systems are multilingual, that is, they are proficient in two or more languages. In order to better understand and support the language preferences and behaviors of such multilingual users, this paper presents a series of five large‐scale studies that specifically elicit language choices regarding search queries and result lists. Overall, the results from the studies indicate that users frequently make use of different languages (i.e., not just their primary language), especially when they are provided with choices (e.g., when provided with a secondary language query or result list choice). In particular, when presented with a mixed‐language list choice, participants choose this option to an almost equal extent compared to primary‐language‐only lists. Important factors leading to language choices are user‐, task‐ and system‐related, including proficiency, task topic, and result layout. Moreover, participants' subjective reasons for making particular choices indicate that their primary language is considered more comfortable, that the secondary language often has more relevant and trustworthy results, and that mixed‐language lists provide a better overview. These results provide crucial insights into multilingual user preferences and behaviors, and may help in the design of systems that can better support the querying and result exploration of multilingual users.
An Auction-Based Incentive Mechanism with Blockchain for IoT Collaboration

The prosperous development of IoT has created tremendous opportunities to improve people's lives. Essentially, the core property of the IoT applications is the ability to perform collaborative tasks with data supplied by separate IoT managers. However, the fulfillment of collaborative tasks is driven by the participations of the IoT managers. Generally, the willingness can be activated with appropriate profit (or incentive cost). Thus, an efficient incentive mechanism is needed to motivate the IoT managers to participate in the collaboration. In this paper, we present a reverse auction-based incentive mechanism with the goal of minimizing and stabilizing incentive costs while maintaining adequate participants. To prevent the incentive cost explosion, a droppers recruiting scheme is leveraged to attract the inactive participants. A price verification strategy is designed to avoid bid cheating. Furthermore, we introduce blockchain to orchestrate the interactions between collaborative parties, so as to protect their privacy. Finally, we show the feasibility and efficiency of our proposed framework with simulation experiments and theoretical analysis.
Forecasting PV Panel Output Using Prophet Time Series Machine Learning Model

Due to climate change effects, the demand for renewable energy is growing immensely around the world. Photovoltaic (PV) panels are widely popular as a vital source of renewable energy all over the world as well as in Bangladesh. However, besides solar irradiance, the panel output is greatly affected by some of the weather parameters like temperature, humidity, wind, etc. Reliable forecasting of PV panel output is essential for capacity planning in advance to efficiently manage the energy distribution. This paper presents a method to forecast the PV panel output energy using a machine learning model, known as the Prophet Model used for a univariate time series forecasting. For this study, the PV panel generated data are collected from an outdoor experimental set-up throughout the full winter season in Bangladesh. Based on the data, forecasting of one-day-ahead PV panel short circuit current is done, and then the estimation of PV panel output energy is made. The results show the proposed forecasting method to be quite encouraging and reliable one while providing a higher coefficient of determination value with an average 0.9772 for one-day-ahead PV panel output energy forecasting.
Semi-Supervised 3D Human Pose Estimation by Jointly Considering Temporal and Multiview Information

Three-dimensional human pose estimation is usually conducted in a supervised manner.However, because collecting labeled 3D skeletons is expensive and time-consuming, semi-supervised methods that need much fewer amount of labeled 3D data are urgently demanded.Some semi-supervised learning methods propose to independently consider information from consecutive video frames, or frames simultaneously captured from multiple viewpoints.In this paper, we propose to jointly consider temporal information and multiview information in a unified adversarial learning framework.Given a 2D skeleton, a pose generator network is developed to estimate the corresponding 3D skeleton, and a camera network is developed to estimate camera parameters.The estimated 3D skeleton is evaluated by a critic network to examine whether the estimated one is a plausible 3D human pose or not.Based on the estimated camera parameters, the estimated 3D skeleton can be re-projected into a 2D skeleton, which should be similar to the input 2D skeleton.The ideas of re-projection and adversarial learning enable the scheme of self supervision.We design network architectures of the aforementioned networks to take 2D skeletons from multiple viewpoints in temporally consecutive frames.By jointly considering two types of information, we verify that performance can be largely improved.
Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning

This paper investigates the stochastic optimization problem focusing on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of the objective function for stochastic optimization in neural network models, along with a novel parallel computing strategy, coined the weighted aggregating stochastic gradient descent ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD</i> ). Following a theoretical analysis on the characteristics of the new objective function, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD</i> introduces a decentralized weighted aggregating scheme based on the performance of local workers. Without any center variable, the new method automatically gauges the importance of local workers and accepts them by their contributions. Furthermore, we have developed an enhanced version of the method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD+</i> , by (1) implementing a designed sample order and (2) upgrading the weight evaluation function. To validate the new method, we benchmark our pipeline against several popular algorithms including the state-of-the-art deep neural network classifier training techniques (e.g., elastic averaging SGD). Comprehensive validation studies have been conducted on four classic datasets: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CIFAR-100</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CIFAR-10</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fashion-MNIST</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MNIST</i> . Subsequent results have firmly validated the superiority of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD</i> scheme in accelerating the training of deep architecture. Better still, the enhanced version, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD+</i> , is shown to be a significant improvement over its prototype.