Reviewerly/exHarmony huggingface dataset:
Anomaly Detection using Supervised Learning and Multiple Statistical Methods

The presence of anomalies or outliers within time-series data can have a detrimental effect on the efficiency of automated decision-making applications. For example, in the context of vehicular traffic flow, various services reliant on traffic data may be negatively impacted by anomalies. This paper presents an automated anomaly detection method based on supervised Long-Short Term Memory (LSTM) neural network and statistical analysis. We train LSTM neural network to predict non-robust statistical properties and combine them with robust properties to determine the anomalies in time-series data. The proposed method relies on segmentation and tunable parameters for anomaly test. We measure the efficacy of our method in terms of Precision, Recall, and F-measure. The metrics approach to 100% for certain instances. We also analyzed the performance on the prevalence of anomalies and on varying specific parameters of the model.
A 3D reactive collision avoidance algorithm for underactuated underwater vehicles

Abstract Avoiding collisions is an essential goal of the control system of autonomous vehicles. This paper presents a reactive algorithm for avoiding obstacles in a three‐dimensional space, and shows how the algorithm can be applied to an underactuated underwater vehicle. The algorithm is based on maintaining a constant avoidance angle to the obstacle, which ensures that a guaranteed minimum separation distance is achieved. The algorithm can thus be implemented without knowledge of the obstacle shape. The avoidance angle is designed to compensate for obstacle movement, and the flexibility of operating in 3D can be utilized to implement traffic rules or operational constraints. We exemplify this by incorporating safety constraints on the vehicle pitch and by making the vehicle seek to move behind the obstacle, while also minimizing the required control effort. The underactuation of the vehicle induces a sway and heave movement while turning. To avoid uncontrolled gliding into the obstacle, we account for this movement using a Flow frame controller, which controls the direction of the vehicle's velocity rather than just the pitch and yaw. We derive conditions under which it is ensured that the resulting maneuver is safe, and these results are verified trough simulations and through full‐scale experiments on the Hugin HUS autonomous underwater vehicle. The latter demonstrates the performance of the proposed algorithm when applied to a case with unmodeled disturbances and sensor noise, and shows how the modular nature of the collision avoidance algorithm allows it to be applied on top of a commercial control system.
ASPPR: A New Assembly Sequence and Path Planner/Replanner for Monotone and Nonmonotone Assembly Planning

The Assembly Sequence and Path Planning (ASPP) problem concerns with computing a sequence of assembly motions for constituent parts of an assembled final product while finding collision-free and short paths for parts from their initial location to their final position. ASPP is a challenging problem due to being NP-hard, and is an important subproblem of the Assembly Planning problem, which is encountered frequently in the manufacturing industry and takes up more than half of the total production time. In this paper, a new method called Assembly Sequence and Path Planner/Replanner (ASPPR) is presented to solve the ASPP problem. ASPPR has a sequence-planning component, which is a simple greedy heuristic that in each iteration tries to locally minimize geometric interferences between parts being assembled along the main directions, and a path-planning component, which employs a sampling-based stochastic path planner to plan short paths for parts while avoiding workspace obstacles. Thanks to its replanning feature, the ASPPR method is able to identify and resolve cases where already-assembled parts impede the movements of subsequent parts. While majority of the methods in the literature deal with monotone problems in workspaces without obstacles, the proposed method has the advantage of considering obstacles in the workspace, allowing planning translational and rotational movements for parts, and handling nonmonotone assembly sequence plans, in which the parts need to be relocated to one or more intermediate positions before moving to their final assembled position. To test and measure the effectiveness of the ASPPR, eight problems (a mixture of monotone and non-monotone, in 2D and 3D, and benchmark and new) were solved with five different sampling-based algorithms (three existing and two new) in the path planning component, and the results of 30 runs for each case were thoroughly compared and analyzed. Analytical and statistical analyses showed that our newly proposed unidirectional and bidirectional variants of the original unidirectional and bidirectional Rapidly Exploring Random Trees (RRT) outperformed other planners in the Total Path Length, Total Number of Nodes, Total Number of Edges, Total Number of Collision Checks, and Total Time performance criteria.
Integrating Source-Channel and Attention-Based Sequence-to-Sequence Models for Speech Recognition

This paper proposes a novel automatic speech recognition (ASR) framework called Integrated Source-Channel and Attention (ISCA) that combines the advantages of traditional systems based on the noisy source-channel model (SC) and end-to-end style systems using attention-based sequence-to-sequence models. The traditional SC system framework includes hidden Markov models and connectionist temporal classification (CTC) based acoustic models, language models (LMs), and a decoding procedure based on a lexicon, whereas the end-to-end style attention-based system jointly models the whole process with a single model. By rescoring the hypotheses produced by traditional systems using end-to-end style systems based on an extended noisy source-channel model, ISCA allows structured knowledge to be easily incorporated via the SC-based model while exploiting the complementarity of the attention-based model. Experiments on the AMI meeting corpus show that ISCA is able to give a relative word error rate reduction up to 21% over an individual system, and by 13% over an alternative method which also involves combining CTC and attention-based models.
Accelerometer-based Hand Gesture Recognition for Human-Robot Interaction

This paper presents a computational solution towards human-robot interaction using a wrist-mounted tri-axial accelerometer. This is tackled as a three-fold gesture recognition problem with a gesture set including six different gestures, namely right, front, left, back, up and circle. Given the sparsity of gestures, an adaptive segmentation technique is employed as a means of spotting potential segments of interest within the signal. Relevant features are a posteriori calculated from the spotted segments. Ultimately, a range of five state-of-the-art classifiers are employed for the classification of the gestures. The results achieved, with an average classification accuracy of 95.85%, show a great contribution towards modern human-robot interaction technologies.
Education in and for the Belt and Road Initiative:

This paper is an experiment in collective writing conducted in Autumn 2019 at the Faculty of Education at Beijing Normal University. The experiment involves 12 international masters' students reading the course based on the Belt and Road Initiative (BRI), their professor Michael Peters, visiting professor Petar Jandrić, and a mix of senior Chinese and Western scholars. To successfully complete the course, the students were required to produce a 3000-word paper of publishable quality. As part of this writing process we decided to engage in the experiment of collective writing where we aimed to produce a single paper consisting of the abstracts. This collective paper was developed in 7 steps. (1) Students submitted their 250-word abstracts. (2) Students were introduced into the methodology of collective writing, and 2 student-editors – Ogunniran Moses Oladele and Benjamin Green – volunteered to work on the paper. (3) Michael Peters wrote the introduction. (4) Abstracts were expanded to 500 words and integrated into a single document. (5) Petar Jandrić began to edit the paper and write a conclusion. (6) Students presented their abstracts in the class, where Michael Peters and Petar Jandrić provided direct feedback. (6) Revised abstracts were again integrated into a single document by student editors, and proofread / copy-edited in several exchanges with the instructors (7) The paper was subject to the process of open review, and the reviewer's comments were included in the paper. Resulting from months of collective work, the final paper provides a wide range of views ad perspectives to the question of education as a part of the BRI initiative.
Using an in-Ear Wearable to Annotate Activity Data across Multiple Inertial Sensors

Wearable activity recognition research needs benchmark data, which rely heavily on synchronizing and annotating the inertial sensor data, in order to validate the activity classifiers. Such validation studies become challenging when recording outside the lab, over longer stretches of time. This paper presents a method that uses an inconspicuous, ear-worn device that allows the wearer to annotate his or her activities as the recording takes place. Since the ear-worn device has integrated inertial sensors, we use cross-correlation over all wearable inertial signals to propagate the annotations over all sensor streams. In a feasibility study with 7 participants performing 6 different physical activities, we show that our algorithm is able to synchronize signals between sensors worn on the body using cross-correlation, typically within a second. A comfort rating scale study has shown that attachment is critical. Button presses can thus define markers in synchronized activity data, resulting in a fast, comfortable, and reliable annotation method.
Anomalous Telecom Customer Behavior Detection and Clustering Analysis Based on ISP’s Operating Data

Mobile networks and smart phones have become ubiquitous in our daily life.Large amount of customer related telecom data from various sources are generated every day, from which diversified behavior patterns can be revealed, including some anomalous behaviors that are vicious.It becomes increasingly important to achieve both efficient and effective customer behavior analysis based on the telecom big data.In this paper, the Multi-faceted Telecom Customer Behavior Analysis (MTCBA) framework for anomalous telecom customer behavior detection and clustering analysis is proposed.In this framework, we further design the hierarchical Locality Sensitive Hashing-Local Outlier Factor (hierarchical LSH-LOF) scheme for suspicious customer detection, and the Autoencoders with Factorization Machines (FM-AE) structure for dimension reduction to achieve more efficient clustering.Hierarchical LSH-LOF is an improved algorithm of LOF, in which we design a hierarchical LSH process that selects the approximate k nearest neighbors from coarse to fine by gradually narrowing down the scope.Experiments show its superiority over KD-tree w.r.t searching speed.FM-AE exploits Factorization Machines for learning second order feature interactions, which we prove to be useful by designing comparative experiments with five dimension reduction algorithms.With the proposed MTCBA framework, efficient and effective telecom customer behavior analysis including anomalous customer behavior detection and clustering analysis is performed on the real world telecom operating data provided by one of the major Internet service providers (ISPs) in China.Meanwhile, interpretable clustering results of six clusters are obtained to provide valuable information for the precision marketing of telecom operators, criminal combating, and social credit system construction.
Variational Level Set Evolution for Non-rigid 3D Reconstruction from a Single Depth Camera

We present a framework for real-time 3D reconstruction of non-rigidly moving surfaces captured with a single RGB-D camera. Based on the variational level set method, it warps a given truncated signed distance field (TSDF) to a target TSDF via gradient flow without explicit correspondence search. We optimize an energy that contains a data term which steers towards voxel-wise alignment. To ensure geometrically consistent reconstructions, we develop and compare different strategies, namely an approximately Killing vector field regularizer, gradient flow in Sobolev space and newly devised accelerated optimization. The underlying TSDF evolution makes our approach capable of capturing rapid motions, topological changes and interacting agents, but entails loss of data association. To recover correspondences, we propose to utilize the lowest-frequency Laplacian eigenfunctions of the TSDFs, which encode inherent deformation patterns. For moderate motions we are able to obtain implicit associations via a term that imposes voxel-wise eigenfunction alignment. This is not sufficient for larger motions, so we explicitly estimate voxel correspondences via signature matching of lower-dimensional eigenfunction embeddings. We carry out qualitative and quantitative evaluation of our geometric reconstruction fidelity and voxel correspondence accuracy, demonstrating advantages over related techniques in handling topological changes and fast motions.
L<sub>1</sub> norm based pedestrian detection using video analytics technique

Abstract Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. In general, detection is made with two different phases, feature extraction and classification. Also, features for detection of pedestrian are already are available such as optimal feature model. But still required is an improvement in detection by reducing the execution time and false positive. The proposed model has three different phases, that is, background subtraction, feature extraction, and classification. In spite of giving entire information into feature extraction, the system gives only a useful information (foreground image) by twin background model. Then the foreground image moves to the feature extraction and classifies the pedestrian. For feature extraction, histogram of orientation gradient (HOG) L 1 normalization has been used. This will increase the detection accuracy and reduce the computation time of a process. In addition, false positive rate has been minimized.
A survey of methods and tools used for interpreting Random Forest

Interpretability of highly performant Machine Learning [ML] methods, such as Random Forest [RF], is a key tool that attracts a great interest in datamining research. In the state of the art, RF is well-known as an efficient ensemble learning (in terms of predictive accuracy, flexibility and straightforwardness). Moreover, it is recognized as an intuitive and intelligible approach regarding to its building process. However it is also regarded as a Black Box model because of its hundreds of deep decision trees. This can be crucial for several fields of study, such as healthcare, biology and security, where the lack of interpretability could be a real disadvantage. Indeed, the interpretability of the RF models is, generally, necessary in such fields of applications because of different motivations. In fact, the more the ML users grasp what is going on inside a ML system (process and resulting model), the more they can trust it and take actions based on the knowledge extracted from it. Furthermore, ML models are increasingly constrained by new laws that require regulation and interpretation of the knowledge they provide.Several papers have tackled the interpretation of RF resulting models. It had been associated with different aspects depending on the specificity of the issue studied as well as the users concerned with explanations. Therefore, this paper aims to provide a survey of tools and methods used in literature in order to uncover insights in the RF resulting models. These tools are classified depending on different aspects characterizing the interpretability. This should guide, in practice, in the choice of the most useful tools for interpretation and deep analysis of the RF model depending on the interpretability aspect sought. This should also be valuable for researchers who aim to focus their work on the interpretability of RF, or ML in general.
Eye-tracking for human-centered mixed reality: promises and challenges

Eye-tracking hardware and software are being rapidly integrated into mixed reality (MR) technology. Cognitive science and human-computer interaction (HCI) research demonstrate several ways eye-tracking can be used to gauge user characteristics, intent, and status as well as provide active and passive input control to MR interfaces. In this paper, we argue that eye-tracking can be used to ground MR technology in the cognitive capacities and intentions of users and that such human-centered MR is important for MR designers and engineers to consider. We detail relevant and timely research in eye-tracking and MR and offer suggestions and recommendations to accelerate the development of eye-tracking-enabled human-centered MR, with a focus on recent research findings. We identify several promises that eye-tracking holds for improving MR experiences. In the near term, these include user authentication, gross interface interactions, monitoring visual attention across real and virtual scene elements, and adaptive graphical rendering enabled by relatively coarse eyetracking metrics. In the far term, hardware and software advances will enable gaze-depth aware foveated MR displays and attentive MR user interfaces that track user intent and status using fine and dynamic aspects of gaze. Challenges, such as current technological limitations, difficulties in translating lab-based eye-tracking metrics to MR, and heterogeneous MR use cases are considered alongside cutting-edge research working to address them. With a focused research effort grounded in an understanding of the promises and challenges for eye-tracking, human-centered MR can be realized to improve the efficacy and user experience of MR.
Speeding up the Generation of Entangled State between a Superconducting Qubit and Cavity Photons via Counterdiabatic Driving

Abstract Optimal generation of entangled states is of critical significance for robust quantum information processing. An effective scheme is presented for speeding up the generation of an entangled state between a superconducting qubit and microwave photons via counterdiabatic driving. At a magic bias point, the first three levels of a charge‐phase quantum circuit constitute an effective qutrit. An entangled state based on adiabatic population transfer is first achieved. By the technique of shortcuts to adiabaticity, a counterdiabatic driving is applied to the qutrit, which then accelerates the entanglement generation significantly. Moreover, with the accessible decoherence rates, the rapid operations in a shortcut way are highly robust when compared with adiabatic manipulations. The scheme could offer a promising approach toward optimal preparation of entangled states with superconducting artificial atoms in circuit quantum electrodynamics, experimentally.
Anomaly Detection on Shuttle data using Unsupervised Learning Techniques

Many modern day applications require the ability to identify those observations or data that deviate from the ones that are considered to be normal by domain expert. Anomaly detection helps to identify these anomalies and once identified, then the system can take the necessary changes. In data mining, this problem is tackled using supervised and unsupervised machine learning techniques. Since in many practical applications, data used will have no labels, unsupervised learning techniques are well suited. This work was aimed at comparing various unsupervised anomaly detection techniques using performance metrics like precision, recall, F-score and area under the curve. The unsupervised learning techniques used in this work are One Class Support Vector Machine(OneClassSVM), Local Outlier Factor(LOF), Isolation Forest(IF) and Elliptic Envelope(EE). Shuttle and satellite datasets were used for experimentation. Performance of these unsupervised learning techniques were compared with supervised learning techniques such as SVM and k-NN. Results show that unsupervised learning techniques are on par or better for anomaly detection compared to supervised learning techniques for the shuttle and satellite datasets.
Expediting the Accuracy-Improving Process of SVMs for Class Imbalance Learning

To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., Different Error Costs (DEC) and Fuzzy SVM for Class Imbalance Learning (FSVM-CIL). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATEC</i> , a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATEC</i> distinguishes itself from all existing parameter tuning strategies by two main features: (1) it can evaluate how effective an error cost is in terms of classification accuracy; and (2) it changes the error cost in the right direction if it is not effective. Extensive experiments show that compared with the state-of-art methods, SVMs that are equipped with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATEC</i> can not only obtain comparable improvements in terms of F1 score of minority class, area under the precision-recall curve (AUC-PR) and area under the ROC curve (AUC-ROC) scores, but also outperform the grid-search parameter tuning strategy by two orders of magnitude in terms of the training time when a high F1 score is required.
A CNN-LSTM network with attention approach for learning universal sentence representation in embedded system

The model for obtaining universal sentence representation is getting larger and larger, making it unsuitable for small embedded systems. The paper presents an extended encoder-decoder model with introduced an attention mechanism for learning distributed sentence representation. We can extract the CNN encoder and apply to other NLP downstream tasks on small embedded systems. Inspired by the linguistic features of the word embeddings, the different dimensions of the sentence representation can be aligned to especially linguistic features. The decoder which decodes one word will focus on the partial dimension of sentence representation into a fixed-length vector where CNN is more effective than LSTM, especially on devices with limited computing power. Moreover, the expanded LSTM with attention mechanism as the decoder to learn multitask that reconstruct the original sentence and predict the next sentence. The model was trained on an extensive collection of a novel to learning sentence representation encoder. Finally, the small-scale CNN encoder obtained encouraging results on several benchmark datasets and multiple task.