Reviewerly/exHarmony huggingface dataset:
Using Machine Learning Approaches to Identify Exercise Activities from a Triple-Synchronous Biomedical Sensor

Human activity recognition (HAR) is the method for identifying a person’s activity using sensors that sense and capture movements during activity. HAR is an evolving area of research in computing, applied to sensors found in smartphones, smartwatches, and cameras. This paper illustrates how to identify some types of human activities using a BioStamp sensor that consists of an inertial sensors-based smart patch. Our dataset has five activities: stair climbing, jogging, skipping, kettlebell lifting, and passing a basketball. We employed both supervised and unsupervised learning approaches to perform HAR. For supervised learning, the dataset was divided into 67% training data and 33% testing data to train and evaluate the classification models. We evaluated the models on the accuracy, F1-score, precision, and recall. Random forest (F1-score = 97%) performed best followed by artificial neural networks (F1-score = 94%) and lastly, Gradient boosting (F1-score = 93%). We performed feature extraction on the original dataset and then performed unsupervised learning on the extracted features to obtain a Sørensen index value of 0.74, which shows good cluster separation. Geriatric or motor disease patients often use the BioStamp nPoint sensor and we demonstrate that their activities can be identified fairly accurately using machine learning approaches.
Comparison of 2D topological codes and their decoding performances

Topological quantum codes are favored because they allow suitable qubit layouts for practical implementation. An N-qubit topological code can be decoded by minimum-weight perfect matching (MWPM) with complexity O(poly(N)). Recently it is shown that various quantum codes, including topological codes, can be decoded by an adapted belief propagation with memory effects (denoted MBP) with complexity almost linear in N. In this paper, we show that various two-dimensional topological codes, CSS or non-CSS, regardless of the layout, can be decoded by MBP, including color codes and a family of twisted XZZX codes. We will comprehensively compare these codes in terms of code efficiency and decoding performance, assuming perfect error syndromes.
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation

Multilingual neural machine translation models are trained to maximize the likelihood of a mix of examples drawn from multiple language pairs.The dominant inductive bias applied to these models is a shared vocabulary and a shared set of parameters across languages; the inputs and labels corresponding to examples drawn from different language pairs might still reside in distinct subspaces.In this paper, we introduce multilingual crossover encoder-decoder (mXEncDec) to fuse language pairs at an instance level.Our approach interpolates instances from different language pairs into joint 'crossover examples' in order to encourage sharing input and output spaces across languages.To ensure better fusion of examples in multilingual settings, we propose several techniques to improve example interpolation across dissimilar languages under heavy data imbalance.Experiments on a large-scale WMT multilingual dataset demonstrate that our approach significantly improves quality on English-to-Many, Many-to-English and zero-shot translation tasks (from +0.5 BLEU up to +5.5 BLEU points).Results on code-switching sets demonstrate the capability of our approach to improve model generalization to out-of-distribution multilingual examples.We also conduct qualitative and quantitative representation comparisons to analyze the advantages of our approach at the representation level.
The underground explosion point high precision measurement method based on multidimensional information fusion of vibration sensors

Purpose The purpose of this paper is to solve the problem that the location of the initiation point cannot be measured accurately in the shallow underground space, this paper proposes a method, which is based on fusion of multidimensional vibration sensor information, to locate single shallow underground sources. Design/methodology/approach First, in this paper, using the characteristics of low multipath interference and good P-wave polarization in the near field, the adaptive covariance matrix algorithm is used to extract the polarization angle information of the P-wave and the short term averaging/long term averaging algorithm is used to extract the first break travel time information. Second, a hybrid positioning model based on travel time and polarization angle is constructed. Third, the positioning model is taken as the particle update fitness function of quantum-behaved particle swarm optimization and calculation is performed in the hybrid positioning model. Finally, the experiment verification is carried out in the field. Findings The experimental results show that, with root mean square error, spherical error probable and fitness value as evaluation indicators, the positioning performance of this method is better than that without speed prediction. And the positioning accuracy of this method has been improved by nearly 30%, giving all of the three tests a positioning error within 0.5 m and a fitness less than 1. Originality/value This method provides a new idea for high-precision positioning of shallow underground single source. It has a certain engineering application value in the fields of directional demolition of engineering blasting, water inrush and burst mud prediction, fuze position measurement, underground initiation point positioning of ammunition, mine blasting monitoring and so on.
Virtual image array generated by Risley prisms for three-dimensional imaging

We present a compact three-dimensional (3D) imaging system based on a virtual image array, which employs a single camera and Risley prisms. The original multi-perspective images are captured by the fixed camera with a dynamically deflected boresight. By applying planar homographies to original images, we obtain light field samples from a planar virtual image array. Moreover, a depth estimation framework on the epipolar-plane image (EPI) is adopted to generate 3D profiles of scenes. In the experiment, two-dimensional (2D) projections from a 5 × 5 virtual image array are obtained to exhibit the 3D imaging capability. The system has the potential to achieve stereovision in numerous applications such as industrial vision and visual navigation.
The Importance of Digital Literacy in Quadratic Equations, Strategies Used, and Issues Faced by Educators

The purpose of this study is to identify the teaching styles and problems that are faced by teachers in cultivating digital literacy skills for the topic of quadratic equations. This study helps to overcome and refine all shortcomings that occur in the process of instilling digital literacy skills into the topic of quadratic equations. This study employed a qualitative approach by adopting the structured interview method that involved four participants from three districts in Malaysia. The interview data were analyzed using the ATLAS.ti 8 software by dividing the transcripts into small codes based on thematic analysis. The findings show that the importance of digital literacy skills in quadratic equation comprises the aspect of motivation, space saving, conducive environment, fostering thinking skills, and diversity of resources. The results have also indicated that the teachers’ teaching approach is based on existing hardware and software such as the Microsoft software, Google software, teaching aids, the Internet resources, and mobile device applications. Among the constraints that are faced by the teachers include the use of technology in calculations, communicating information, issues in exploring the diversity of information, issues in mathematical modelling, and technical limitations. Accordingly, all emerging themes and codes are summarized using schematic diagrams. A major implication of this research is that it serves as a catalyst in cultivating digital literacy among generation Z such as using the TI-Nspire software to solve mathematical problems.
Edges and Boundary detection of Mammography images in earlier stages through Non-Convex border optimization of segmentation thresholding Algorithm

Despite the most significant cancer that occurs worldwide is breast cancer for women. All almost all parts of the country, breast cancer detection increase every year, and the death rate of breast cancer is nearly (20–27) % in India. The input mammography image i.e left side and right side of the breast segment and optimize concerning pixel shape and intensity. Furthermore, the proposed method of segmentation thresholding algorithm optimizes and classifies the mammographic image through two methods. The first method of optimization of segmentation image through the convex border of mammographic images and detect the edges and boundary. The second method of optimization image through the non-convex border and detect edges and boundaries. Finally, the result is processed through a segmentation thresholding algorithm to delineate the edges and boundaries in the mammographic images and helps the radiologist to detect earlier stages of breast cancer. The net result outer performs the existing algorithm and proposed in the state of art to detect breast cancer in earlier stage and help the Radiologist for accuracy in cancer detection. The achieved result of the segmentation thresholding algorithm through the convex and non-convex border of image optimization is about 93% of the accuracy.
Shedding New Light on the Language of the Dark Web

The hidden nature and the limited accessibility of the Dark Web, combined with the lack of public datasets in this domain, make it difficult to study its inherent characteristics such as linguistic properties.Previous works on text classification of Dark Web domain have suggested that the use of deep neural models may be ineffective, potentially due to the linguistic differences between the Dark and Surface Webs.However, not much work has been done to uncover the linguistic characteristics of the Dark Web.This paper introduces CoDA, a publicly available Dark Web dataset consisting of 10000 web documents tailored towards text-based Dark Web analysis.By leveraging CoDA, we conduct a thorough linguistic analysis of the Dark Web and examine the textual differences between the Dark Web and the Surface Web.We also assess the performance of various methods of Dark Web page classification.Finally, we compare CoDA with an existing public Dark Web dataset and evaluate their suitability for various use cases.
A New Multistage Encryption Scheme Using Linear Feedback Register and Chaos-Based Quantum Map

With the increasing volume of data transmission through insecure communication channels, big data security has become one of the important concerns in the cybersecurity domain. To address these concerns and keep data safe, a robust privacy-preserving cryptosystem is necessary. Such a solution relies on chaos encryption algorithms over standard cryptographic methods that possess multistage encryption levels, including high speed, high security, low compute overheads, and procedural power, among other characteristics. In this work, a secure image encryption scheme is proposed using linear feedback shift register (LFSR) and chaos-based quantum chaotic map. The focus of the scheme is mainly dependent on the secret keys from the input of the algorithm. The threat landscape, the statistical test analysis, along critical comparisons with other schemes indicate that the presented algorithm is significantly secure and is resistant to a wide range of different attacks such as differential and statistical attacks. The proposed method has sufficiently higher sensitivity and security when compared to existing encryption algorithms. Several security parameters validated the security of proposed work such as correlation coefficient analyses among the neighboring pixels, entropy, the number of pixels change rate (NPCR), unified average change intensity (UACI), mean square error (MSE), brute force, key sensitivity, and peak signal to noise ratio (PSNR) analyses. The randomness of the ciphers produced by the proposed technique is also passed through NIST-800-22. The results of NIST indicate that the ciphers are highly random and do not produce any type of periodicity or pattern.
Controlled remote implementation of operators via hyperentanglement

Abstract Distributed quantum computation is a good solution for salable quantum computation within a quantum network each node of which just contains reasonably a few number of qubits. Controlled implementation of operators on states of a remote node is thus necessary. In this paper we propose protocols for three kinds of tasks of controlled implementation of operators on remote photon states via one hyperentangled Greenberger–Horne–Zeilinger state assisted with cross-Kerr nonlinearities: one with general operators and photon states in spatial degree of freedom (DOF), another one also with general operators but the photon state being in polarization DOF and the third one with a limited subset of operators acting on photon state in both spatial and polarization degrees of freedom. All the protocols are deterministic and performed in two steps under quantum control in each step.
Securing content-based image retrieval on the cloud using generative models

Content-based image retrieval (CBIR) with deep neural networks (DNNs) on the cloud has tremendous business and technical advantages to handle large-scale image repositories. However, cloud-based CBIR service raises challenges in image data and DNN model security. Typically, users who wish to request CBIR services on the cloud require their input images remaining confidential. On the other hand, image owners may intentionally (or unintentionally) upload adversarial examples to the cloud servers, which potentially leads to the misbehavior of CBIR services. Generative Adversarial Networks (GANs) can be utilized to defense against such malicious behavior. However, the GANs model, if not well protected, can be easily abused by the cloud to reconstruct the users' original image data. In this paper, we focus on the problem of secure generative model evaluation and secure gradient descent (GD) computation in GANs. We propose two secure generative model evaluation algorithms and two secure minimizer protocols. Furthermore, we propose and implement Sec-Defense-Gan, a secure image reconstruction framework which can keep the image data, the generative model details and corresponding outputs confidential from the cloud. Finally, We carried out a set of benchmarks over two public available image datasets to show the performance and correctness of Sec-Defense-Gan.
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs

Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing.We find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring more performance gain than other tasks e.g.MT and summarization in the text-to-AMR transition even with much less data.2) To make a better fit for AMR, data from auxiliary tasks should be properly "AM-Rized" to PseudoAMR before training.Knowledge from shallow level parsing tasks can be better transferred to AMR Parsing with structure transform.3) Intermediate-task learning is a better paradigm to introduce auxiliary tasks to AMR parsing, compared to multitask learning.From an empirical perspective, we propose a principled method to involve auxiliary tasks to boost AMR parsing.Extensive experiments show that our method achieves new state-ofthe-art performance on different benchmarks especially in topology-related scores.Code and models are released at https://github.com/PKUnlp-icler/ATP.
Comparing Teleportation Methods for Travel in Everyday Virtual Reality

The number of everyday virtual reality (VR) applications is increasing at a remarkable pace. Perhaps the most fundamental interaction in these applications is the ability to travel throughout virtual environments in which users find themselves immersed. Teleportation is often used to support travel in VR applications. While many methods exist for implementing teleportation, relatively little research has been done to compare such methods. In this paper, we describe an experiment to compare four teleportation methods for travel in everyday virtual reality. We found that, for general use, experienced VR users prefer to control a virtual arc with their hand to indicate the location and direction of orientation to which they want to teleport. However, teleporting a single step at a time in the direction of view may support more natural movement and encourage shorter travel paths, but at the expense of longer travel times.
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation

Pre-trained language models have demonstrated superior performance in various natural language processing tasks.However, these models usually contain hundreds of millions of parameters, which limits their practicality because of latency requirements in real-world applications.Existing methods train small compressed models via knowledge distillation.However, performance of these small models drops significantly compared with the pretrained models due to their reduced model capacity.We propose MoEBERT, which uses a Mixture-of-Experts structure to increase model capacity and inference speed.We initialize MoEBERT by adapting the feed-forward neural networks in a pre-trained model into multiple experts.As such, representation power of the pre-trained model is largely retained.During inference, only one of the experts is activated, such that speed can be improved.We also propose a layer-wise distillation method to train MoEBERT.We validate the efficiency and effectiveness of MoEBERT on natural language understanding and question answering tasks.Results show that the proposed method outperforms existing task-specific distillation algorithms.For example, our method outperforms previous approaches by over 2% on the MNLI (mismatched) dataset.
MCL: Mixed-Centric Loss for Collaborative Filtering

The majority of recent work in latent Collaborative Filtering (CF) has focused on developing new model architectures to learn accurate user and item representations. Typically, a standard pairwise loss function (BPR, Triplet, etc.) is used in these models, and little exploration is done on how to optimally extract signals from the available preference information. In the implicit setting, negative examples are sampled, and these losses allocate weights that solely depend on the difference in user distance between observed (positive) and negative item pairs. This can ignore valuable global information from other users and items, and lead to sub-optimal results. Motivated by this problem, we propose a novel loss which first leverages mining to select the most informative pairs, followed by a weighing process to allocate more weight to harder examples. Our weighting process consists of four different components, and incorporates distance information from other users, enabling the model to better position the learned representations. We conduct extensive experiments and demonstrate that our loss can be applied to different types of CF models leading to significant gains with each type. In particular, by applying our loss to the graph convolutional architecture, we achieve new state-of-the-art results on four different datasets. Further analysis shows that through our loss the model is able to learn better user-item representation space compared to other losses. Full code for this work is available here: https://github.com/layer6ai-labs/MCL.
Predictors of The Subjective Effectiveness of Emergency Remote Teaching During The First Phase of The COVID-19 Pandemic

Worldwide school closures which were happening during the first phase of the COVID-19 pandemic in spring 2020 created a great lesson about the conditions for effective teaching. As temporary school closures seem very likely to occur in the forthcoming school year(s), at least locally, it is worthwhile to exploit this lesson to learn more about the capacities and resources that can be utilised in both emergency remote teaching (ERT) and regular teaching, beyond the pandemic. This research was initiated in the third week of ERT, on 13 April 2020, and involved 154 teachers in 87 schools in Poland. Its purpose was to identify predictors of subjective effectiveness of emergency remote teaching (SEERT) that might be important for remote teaching pedagogy in general. The results show that the best predictors of the SEERT are self-efficacy, digital competence and peer support from other teachers. The situation of the pandemic in a sense confirms the key role of transferable resources in the performance of teachers’ professional duties and sets a path for strengthening these in teacher training and continuing professional development programmes to increase capacity for both regular and remote teaching.
Mold-It: Understanding how Physical Shapes affect Interaction with Handheld Freeform Devices

Advanced technologies are increasingly enabling the creation of interactive devices with non-rectangular form-factors but it is currently unclear what alternative form-factors are desirable for end-users. We contribute an understanding of the interplay between the rationale for the form factors of such devices and their interactive content through think-aloud design sessions in which participants could mold devices as they wished using clay. We analysed their qualitative reflections on how the shapes affected interaction. Using thematic analysis, we identified shape features desirable on handheld freeform devices and discuss the particularity of three themes central to the choice of form factors: freeform dexterity, shape features discoverability and shape adaptability (to the task and context). In a second study following the same experimental set-up, we focused on the trade off between dexterity and discoverability and the relation to the concept of affordance. Our work reveals the shape features that impact the most the choice of grasps on freeform devices from which we derive design guidelines for the design of such devices.
On Survivorship Bias in MS MARCO

Survivorship bias is the tendency to concentrate on the positive outcomes of a selection process and overlook the results that generate negative outcomes. We observe that this bias could be present in the popular MS MARCO dataset, given that annotators could not find answers to 38--45% of the queries, leading to these queries being discarded in training and evaluation processes. Although we find that some discarded queries in MS MARCO are ill-defined or otherwise unanswerable, many are valid questions that could be answered had the collection been annotated more completely (around two thirds using modern ranking techniques). This survivability problem distorts the MS MARCO collection in several ways. We find that it affects the natural distribution of queries in terms of the type of information needed. When used for evaluation, we find that the bias likely yields a significant distortion of the absolute performance scores observed. Finally, given that MS MARCO is frequently used for model training, we train models based on subsets of MS MARCO that simulates more survivorship bias. We find that models trained in this setting are up to 9.9% worse when evaluated on versions of the dataset with more complete annotations, and up to 3.5% worse at zero-shot transfer. Our findings are complementary to other recent suggestions for further annotation of MS MARCO, but with a focus on discarded queries.