Reviewerly/exHarmony huggingface dataset:
♫ MuSiQue: Multihop Questions via Single-hop Question Composition

Abstract Multihop reasoning remains an elusive goal as existing multihop benchmarks are known to be largely solvable via shortcuts. Can we create a question answering (QA) dataset that, by construction, requires proper multihop reasoning? To this end, we introduce a bottom–up approach that systematically selects composable pairs of single-hop questions that are connected, that is, where one reasoning step critically relies on information from another. This bottom–up methodology lets us explore a vast space of questions and add stringent filters as well as other mechanisms targeting connected reasoning. It provides fine-grained control over the construction process and the properties of the resulting k-hop questions. We use this methodology to create MuSiQue-Ans, a new multihop QA dataset with 25K 2–4 hop questions. Relative to existing datasets, MuSiQue-Ans is more difficult overall (3× increase in human–machine gap), and harder to cheat via disconnected reasoning (e.g., a single-hop model has a 30-point drop in F1). We further add unanswerable contrast questions to produce a more stringent dataset, MuSiQue-Full. We hope our datasets will help the NLP community develop models that perform genuine multihop reasoning.1
Machine–Deep–Ensemble Learning Model for Classifying Cybersickness Caused by Virtual Reality Immersion

This study aims to classify cybersickness (CS) caused by virtual reality (VR) immersion through a machine–deep–ensemble learning model. The heart rate variability and respiratory signal parameters of 20 subjects were measured, while watching a VR video for ∼5 minutes. After the experiment, the subjects were examined for CS and questioned to determine their CS states. Based on the results, we constructed a machine–deep–ensemble learning model that could identify and classify VR immersion CS among subjects. The ensemble model comprised four stacked machine learning models (support vector machine [SVM], k-nearest neighbor [KNN], random forest, and AdaBoost), which were used to derive prediction data, and then, classified the prediction data using a convolution neural network. This model was a multiclass classification model, allowing us to classify subjects' CS into three states (neutral, non-CS, and CS). The accuracy of SVM, KNN, random forest, and AdaBoost was 94.23 percent, 92.44 percent, 93.20 percent, and 90.33 percent, respectively, and the ensemble model could classify the three states with an accuracy of 96.48 percent. This implied that the ensemble model has a higher classification performance than when each model is used individually. Our results confirm that CS caused by VR immersion can be detected as physiological signal data with high accuracy. Moreover, our proposed model can determine the presence or absence of CS as well as the neutral state. Clinical Trial Registration Number: 20-2021-1.
A Blockchain Definition to Clarify its Role for the Internet of Things

The term blockchain is used for disparate projects, ranging from cryptocurrencies to applications for the Internet of Things (IoT). The concept of blockchain appears therefore blurred, as the same technology cannot empower applications with extremely different requirements, levels of security and performance. This position paper elaborates on the theory of distributed systems to advance a clear definition of blockchain allowing us to clarify its possible role in the IoT. The definition binds together three elements that, as a whole, delineate those unique features that distinguish the blockchain from other distributed ledger technologies: immutability, transparency and anonymity. We note that immutability-which is imperative for securing blockchains-imposes remarkable resource consumption. Moreover, while transparency demands no confidentiality, anonymity enhances privacy but prevents user identification. As such, we raise the concern that these blockchain features clash with the requirements of most IoT applications where devices are power-constrained, data needs to be kept confidential, and users to be clearly identifiable. We consequently downplay the role of the blockchain for the IoT: this can act as a ledger external to the IoT architecture, invoked as seldom as possible and only to record the aggregate results of myriads of local (IoT) transactions that are most of the time performed off-chain to meet performance and scalability requirements.
Application of Posture Recognition Service System Based on Information Fusion Smart Sensor in Dance Training

With the development and extension of the Internet of Things technology, many fields have already involved the Internet of Things technology, and its application in dance training has gradually emerged. In order to guide dance training and solve the hidden dangers of dance training, this article mainly introduces the application of posture recognition service system based on information fusion intelligent sensor in dance training. This article uses human body detection, computer interactive Boosting algorithm calculations and establishes standard dance poses. The difference between the trainer’s posture and the standard posture can be clearly seen on the posture recognition service platform. The error is only less than 5%, and it can also help the trainer find their own mistakes in time. Dance and correct your posture in time, and it can reduce the error rate of difficult dances and reduce the error rate of 10%-20%. In addition, through the survey on the satisfaction survey of the security services of the gesture recognition service platform, the satisfaction rate is as high as 90%. Thereby, while ensuring safe training, the dance quality and level of trainers are improved.
Dual-Tree Genetic Programming for Few-Shot Image Classification

Few-shot image classification (FSIC) is an important but challenging task due to high variations across images and a small number of training instances. A learning system often has poor generalization performance due to the lack of sufficient training data. Genetic programming (GP) has been successfully applied to image classification and achieved promising performance. This article proposes a GP-based approach with a dual-tree representation and a new fitness function to automatically learn image features for FSIC. The dual-tree representation allows the proposed approach to have better searchability and learn richer features than a single-tree representation when the number of training instances is very small. The fitness function based on the classification accuracy and the distances of the training instances to the class centroids aims to improve the generalization performance. The proposed approach can deal with different types of FSIC tasks with various numbers of classes and different image sizes. The results show that the proposed approach achieves significantly better performance than a large number of state-of-the-art methods on nine 3-shot and 5-shot image classification datasets. Further analysis shows the effectiveness of the new components of the proposed approach, its good searchability, and the high interpretability of the evolved solutions.
Lightweight adaptive weighted network for single image super-resolution

Deep learning has been successfully applied to the single-image super-resolution (SISR) task with superior performance in recent years. However, most convolutional neural network (CNN) based SR models have a large number of parameters to be optimized, which requires heavy computation and thereby limits their real-world applications. In this work, a novel lightweight SR network, named Adaptive Weighted Super-Resolution Network (LW-AWSRN), is proposed to address this issue. A novel local fusion block (LFB) is developed in LW-AWSRN for efficient residual learning, which consists of several stacked adaptive weighted residual units (AWRU) and a local residual fusion unit (LRFU). Moreover, an adaptive weighted multi-scale (AWMS) module is proposed to make full use of features for the reconstruction of HR images. The AWMS module includes several convolutions with multiple scales, and the redundancy scale branch can be removed according to the contribution of adaptive weights for the lightweight network. The experimental results on the commonly used datasets show that the proposed LW-AWSRN achieves superior performance on × 2, × 3, × 4, and × 8 scale factors compared to state-of-the-art methods with similar parameters and computational overhead. It suggests that LW-AWSRN has a better trade-off between reconstruction quality and model size.
Word2vec Skip-Gram Dimensionality Selection via Sequential Normalized Maximum Likelihood

In this paper, we propose a novel information criteria-based approach to select the dimensionality of the word2vec Skip-gram (SG). From the perspective of the probability theory, SG is considered as an implicit probability distribution estimation under the assumption that there exists a true contextual distribution among words. Therefore, we apply information criteria with the aim of selecting the best dimensionality so that the corresponding model can be as close as possible to the true distribution. We examine the following information criteria for the dimensionality selection problem: the Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), and Sequential Normalized Maximum Likelihood (SNML) criterion. SNML is the total codelength required for the sequential encoding of a data sequence on the basis of the minimum description length. The proposed approach is applied to both the original SG model and the SG Negative Sampling model to clarify the idea of using information criteria. Additionally, as the original SNML suffers from computational disadvantages, we introduce novel heuristics for its efficient computation. Moreover, we empirically demonstrate that SNML outperforms both BIC and AIC. In comparison with other evaluation methods for word embedding, the dimensionality selected by SNML is significantly closer to the optimal dimensionality obtained by word analogy or word similarity tasks.
Optimal nonparametric inference via deep neural network

Deep neural network is a state-of-art method in modern science and technology. Much statistical literature have been devoted to understanding its performance in nonparametric estimation, whereas the results are suboptimal due to a redundant logarithmic sacrifice. In this paper, we show that such log-factors are not necessary. We derive upper bounds for the L2 minimax risk in nonparametric estimation. Sufficient conditions on network architectures are provided such that the upper bounds become optimal (without log-sacrifice). Our proof relies on an explicitly constructed network estimator based on tensor product B-splines. We also derive asymptotic distributions for the constructed network and a relating hypothesis testing procedure. The testing procedure is further proved as minimax optimal under suitable network architectures.
Application of K-Nearest Neighbor (KNN) Algorithm for Human Action Recognition

In this study, the machine learning algorithm, K-Nearest Neighbor (KNN) is introduced for human action recognition. A wearable sensor is employed to collect the acceleration signals, which correspond to six types of human actions, including walking, walking upstairs, walking downstairs, sitting, standing and laying. The signals are normalized and randomly divided into a training set and a test set. A classifier is trained using KNN and the training set. It is aimed to recognize human actions when given acceleration signals. We apply this classifier to the test set and studies the relationship between the value k in KNN and the recognition accuracy. The numerical results indicate that with an appropriate value of k, the recognition accuracy can reach 96.70%, which can meet the engineering requirements. This study not only puts forward an approach for human action recognition but also exhibits the broad prospect of machine learning methods for solving recognition problems.
A Novel Three-Dimensional Path Planning Method for Fixed-Wing UAV Using Improved Particle Swarm Optimization Algorithm

This paper proposed an improved particle swarm optimization (PSO) algorithm to solve the three-dimensional problem of path planning for the fixed-wing unmanned aerial vehicle (UAV) in the complex environment. The improved PSO algorithm (called DCA <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mo>∗</mo> </math> PSO) based dynamic divide-and-conquer (DC) strategy and modified <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>A</mi> <mo>∗</mo> </math> algorithm is designed to reach higher precision for the optimal flight path. In the proposed method, the entire path is divided into multiple segments, and these segments are evolved in parallel by using DC strategy, which can convert the complex high-dimensional problem into several parallel low-dimensional problems. In addition, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>A</mi> <mo>∗</mo> </math> algorithm is adopted to generated an optimal path from the particle swarm, which can avoid premature convergence and enhance global search ability. When DCA <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mo>∗</mo> </math> PSO is used to solve the large-scale path planning problem, an adaptive dynamic strategy of the segment selection is further developed to complete an effective variable grouping according to the cost. To verify the optimization performance of DCA <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mo>∗</mo> </math> PSO algorithm, the real terrain data is utilized to test the performance for the route planning. The experiment results show that the proposed DCA <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mo>∗</mo> </math> PSO algorithm can effectively obtain better optimization results in solving the path planning problem of UAV, and it takes on better optimization ability and stability. In addition, DCA <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mo>∗</mo> </math> PSO algorithm is proved to search a feasible route in the complex environment with a large number of the waypoints by the experiment.
CNN-Based Multi-Modal Camera Model Identification on Video Sequences

Identifying the source camera of images and videos has gained significant importance in multimedia forensics. It allows tracing back data to their creator, thus enabling to solve copyright infringement cases and expose the authors of hideous crimes. In this paper, we focus on the problem of camera model identification for video sequences, that is, given a video under analysis, detecting the camera model used for its acquisition. To this purpose, we develop two different CNN-based camera model identification methods, working in a novel multi-modal scenario. Differently from mono-modal methods, which use only the visual or audio information from the investigated video to tackle the identification task, the proposed multi-modal methods jointly exploit audio and visual information. We test our proposed methodologies on the well-known Vision dataset, which collects almost 2000 video sequences belonging to different devices. Experiments are performed, considering native videos directly acquired by their acquisition devices and videos uploaded on social media platforms, such as YouTube and WhatsApp. The achieved results show that the proposed multi-modal approaches significantly outperform their mono-modal counterparts, representing a valuable strategy for the tackled problem and opening future research to even more challenging scenarios.
Tighter Security for Schnorr Identification and Signatures: A High-Moment Forking Lemma for $${\varSigma }$$-Protocols

The Schnorr identification and signature schemes have been amongst the most influential cryptographic protocols of the past three decades. Unfortunately, although the best-known attacks on these two schemes are via discrete-logarithm computation, the known approaches for basing their security on the hardness of the discrete logarithm problem encounter the “square-root barrier”. In particular, in any group of order p where Shoup’s generic hardness result for the discrete logarithm problem is believed to hold (and is thus used for setting concrete security parameters), the best-known t-time attacks on the Schnorr identification and signature schemes have success probability $$t^2/p$$ , whereas existing proofs of security only rule out attacks with success probabilities $$(t^2/p)^{1/2}$$ and $$(q_{\mathsf {H}} \cdot t^2/p)^{1/2}$$ , respectively, where $$q_{\mathsf {H}}$$ denotes the number of random-oracle queries issued by the attacker. We establish tighter security guarantees for identification and signature schemes which result from $$\varSigma $$ -protocols with special soundness based on the hardness of their underlying relation, and in particular for Schnorr’s schemes based on the hardness of the discrete logarithm problem. We circumvent the square-root barrier by introducing a high-moment generalization of the classic forking lemma, relying on the assumption that the underlying relation is “d-moment hard”: The success probability of any algorithm in the task of producing a witness for a random instance is dominated by the d-th moment of the algorithm’s running time. In the concrete context of the discrete logarithm problem, already Shoup’s original proof shows that the discrete logarithm problem is 2-moment hard in the generic-group model, and thus our assumption can be viewed as a highly-plausible strengthening of the discrete logarithm assumption in any group where no better-than-generic algorithms are currently known. Applying our high-moment forking lemma in this context shows that, assuming the 2-moment hardness of the discrete logarithm problem, any t-time attacker breaks the security of the Schnorr identification and signature schemes with probabilities at most $$(t^2/p)^{2/3}$$ and $$(q_\mathsf {H}\cdot t^2/p)^{2/3}$$ , respectively.
Role of cloud computing in management and education

In the current day, information and communication technologies are tightly linked to provide ultimate business solutions. Cloud computing is one of these topics that has recently been hotly contested and carefully explored, but still has a lot of room for further research and growth. Technology-assisted learning is becoming more common, with most educational institutions across the globe using learning management systems, content management systems, virtual networks, and virtual machines to enhance student learning. In this day and age, educational institutions are even using private clouds to improve the student experience. Despite the fact that many educational institutions, colleges, and universities have effectively implemented and incorporated instructional technology, many of them still fall short in a variety of areas. The purpose of this research is to investigate the influence of cloud computing technology on educational institutions and corporate management.
Realistic image normalization for multi-Domain segmentation

Image normalization is a building block in medical image analysis. Conventional approaches are customarily employed on a per-dataset basis. This strategy, however, prevents the current normalization algorithms from fully exploiting the complex joint information available across multiple datasets. Consequently, ignoring such joint information has a direct impact on the processing of segmentation algorithms. This paper proposes to revisit the conventional image normalization approach by, instead, learning a common normalizing function across multiple datasets. Jointly normalizing multiple datasets is shown to yield consistent normalized images as well as an improved image segmentation when intensity shifts are large. To do so, a fully automated adversarial and task-driven normalization approach is employed as it facilitates the training of realistic and interpretable images while keeping performance on par with the state-of-the-art. The adversarial training of our network aims at finding the optimal transfer function to improve both, jointly, the segmentation accuracy and the generation of realistic images. We have evaluated the performance of our normalizer on both infant and adult brain images from the iSEG, MRBrainS and ABIDE datasets. The results indicate that our contribution does provide an improved realism to the normalized images, while retaining a segmentation accuracy at par with the state-of-the-art learnable normalization approaches.
IoU-guided Siamese region proposal network for real-time visual tracking

Recently, region proposal network (RPN) has been combined with the Siamese network for tracking and shown excellent accuracy and high efficiency. However, the low correlation between the classification score and localization accuracy in tracking has weakened the performance of the tracking model. In this paper, we propose an IoU-guided Siamese RPN (SiamIG) to address this problem. Specifically, SiamIG predicts the intersection-over-union (IoU) with the ground truth for each regressed anchor by using an IoU predictor. Then the predicted IoU is multiplied with the classification score to compute the final score, which is more suitable for localization accuracy. Furthermore, each anchor in the regression branch is assigned an IoU-based weighting factor such that the tracking accuracy can be further improved. Specifically, anchor boxes with high IoU are given more attention because of the IoU-based weighting factor, which helps the model pay more attention to an anchor box with a high IoU. Experiments demonstrate that the proposed method runs at over 200 fps and achieves up to 2.4%(AUC), 3%(EAO) and 2.3%(AUC) improvements over the baseline on the OTB-100, VOT-2016 and UAV123 datasets respectively.
Fuzzy Explainable Attention-based Deep Active Learning on Mental-Health Data

In this paper, we propose a fuzzy classification deep attention-based model that expands emotional lexicons by using linguistic properties of actual patient authored texts. The active learning methods can expand the trained dataset and fuzzy rules over some time. As a result, the model itself can reduce its labeling efforts for mental health application. Thus, the designed model can solve issues related to vocabulary sizes per class, data sources, methods of creation, and create a baseline for human performance levels. This paper also gives fuzzy explainability by visualizing weighted words. Our proposed method uses a similarity-based method that includes a subset of unstructured data as the training set. Next, using an active learning mechanism cycle, our method updates the training model using new training points. This cycle is repeatedly performed until an optimal solution is reached. The designed model also converts all unlabeled texts into the training set. Our in-depth experimental results show that the emotion-based expansion enhances the testing accuracy and helps to build quality rules.