Reviewerly/exHarmony huggingface dataset:
Lighting Up a 1 km Fault near a Hydraulic Fracturing Well Using a Machine Learning-Based Picker

Abstract The development of portable nodal array in the recent years greatly improved the seismic monitoring ability across multiple scales. The dense arrays also directly benefit microseismic monitoring by providing relatively low-cost surface recordings. However, the rapid growth of seismic data is accompanied by the increased demand for efficient seismic phase picking. On the other hand, machine learning-based phase picking techniques achieved high stability and accuracy, showing promising potential to replace human labors and traditional automatic pickers. In this study, we applied a state-of-the-art package on newly collected nodal array data around a hydraulic fracturing well in southwestern China. The array consists of up to 85 nodes with an average station spacing of less than a kilometer. Within the hydraulic fracturing stimulation periods, we detected ∼3000 seismic events with magnitude down to ∼−2. After waveform cross-correlation-based relocation, the 1979 relocated events clearly light up a 1 km long fault structure and several fractures. Furthermore, the frequency–magnitude distribution of the catalog exhibits weak bilinear features with relatively low b-value (0.88) and a moderate coefficient of variation (Cv ∼2). The nature and origin of the observed earthquake cluster are then discussed and defined based on the industrial information, high-resolution earthquake catalog, and basic statistics. Finally, we summarized our experience and provided recommendations for applying similar approaches to other local scale, surface microseismic monitoring scenarios.
Human Action Recognition-Based IoT Services for Emergency Response Management

Emergency incidents can appear anytime and any place, which makes it very challenging for emergency medical services practitioners to predict the location and the time of such emergencies. The dynamic nature of the appearance of emergency incidents can cause delays in emergency medical services, which can sometimes lead to vital injury complications or even death, in some cases. The delay of emergency medical services may occur as a result of a call that was made too late or because no one was present to make the call. With the emergence of smart cities and promising technologies, such as the Internet of Things (IoT) and computer vision techniques, such issues can be tackled. This article proposes a human action recognition-based IoT services architecture for emergency response management. In particular, the architecture exploits IoT devices (e.g., surveillance cameras) that are distributed in public areas to detect emergency incidents, make a request for the nearest emergency medical services, and send emergency location information. Moreover, this article proposes an emergency incidents detection model, based on human action recognition and object tracking, using image processing and classifying the collected images, based on action modeling. The primary notion of the proposed model is to classify human activity, whether it is an emergency incident or other daily activities, using a Convolutional Neural Network (CNN) and Support Vector Machine (SVM). To demonstrate the feasibility of the proposed emergency detection model, several experiments were conducted using the UR fall detection dataset, which consists of emergency and other daily activities footage. The results of the conducted experiments were promising, with the proposed model scoring 0.99, 0.97, 0.97, and 0.98 in terms of sensitivity, specificity, precision, and accuracy, respectively.
Cyber banishment: an old sanction for virtual spaces

In this paper, I present the punishment called cyberbanishment. This sanction refers to the old-fashioned punishment of forbidding a person to be present in specific places, but instead of physical locations this sanction concerns digital spheres. The proposition of introducing cyberbanishment is based on a few observations. First, the current practices of punishment are problematic, and there is a need for alternative punishments. Second, this form of sanction may be a proper response to crimes committed online, like hate crimes or punishable threats. Third, the online sphere becomes more important each year, and this was even amplified by the recent pandemic. The development of virtual realities and metaverses could contribute to this trend. Fourth, as an instrument imposed by courts, cyberbanishment may increase judicial control over the limitations of constitutionally protected freedoms in the online world, which is currently decided mainly by the platforms themselves.
A novel robust IMM filter for jump Markov systems with heavy-tailed process and measurement noises

In some tracking application scenarios, the performances of the traditional interacting multiple model (IMM) approach may degrade dramatically since the presence of outliers in process and measurement noises. Aiming at solving the filtering problem for jump Markov systems containing heavy-tailed process and measurement noises, a new robust student's t-based Gaussian approximate filter utilizing the IMM filtering framework is presented in this paper. Firstly, the heavy-tailed process and measurement noises are assumed to obey student's t-distributions. Secondly, the degree of freedom parameters and the scale matrices are modeled as Gamma distributions and inverse-Wishart distributions respectively. Finally, the system state, degree of freedom parameters, scale matrices and the mode probabilities are inferred simultaneously by introducing the variational Bayesian (VB) theory. Maneuvering target tracking simulation verifies that the designed algorithm achieves superior estimation performances among the state-of-the-art filters.
Location- and Physical-Activity-Based Application for Japanese Vocabulary Acquisition for Non-Japanese Speakers

There are various mobile applications to support foreign-language learning. While providing interactive designs and playful games to keep learners interested, these applications do not focus on motivating learners to continue learning after a long time. Our goal for this study was to develop an application that guides learners to achieve small goals by creating small lessons that are related to their real-life situations, with a main focus on vocabulary acquisition. Therefore, we present MiniHongo, a smartphone application that recognizes learners’ current locations and activities to compose lessons that comprise words that are strongly related to the learners’ real-time situations and can be studied in a short time period, thereby improving user motivation. MiniHongo uses a cloud service for its database and public application programming interfaces for location tracking. A between-subject experiment was conducted to evaluate MiniHongo, which involved comparing it to two other versions of itself. One composed lessons without location recognition, and the other composed lessons without location and activity recognition. The experimental results indicate that users have a strong interest in learning Japanese with MiniHongo, and some difference was found in how well users could memorize what they learned via the application. It is also suggested that the application requires improvements.
Pose ResNet: 3D Human Pose Estimation Based on Self-Supervision

The accurate estimation of a 3D human pose is of great importance in many fields, such as human-computer interaction, motion recognition and automatic driving. In view of the difficulty of obtaining 3D ground truth labels for a dataset of 3D pose estimation techniques, we take 2D images as the research object in this paper, and propose a self-supervised 3D pose estimation model called Pose ResNet. ResNet50 is used as the basic network for extract features. First, a convolutional block attention module (CBAM) was introduced to refine selection of significant pixels. Then, a waterfall atrous spatial pooling (WASP) module is used to capture multi-scale contextual information from the extracted features to increase the receptive field. Finally, the features are input into a deconvolution network to acquire the volume heat map, which is later processed by a soft argmax function to obtain the coordinates of the joints. In addition to the two learning strategies of transfer learning and synthetic occlusion, a self-supervised training method is also used in this model, in which the 3D labels are constructed by the epipolar geometry transformation to supervise the training of the network. Without the need for 3D ground truths for the dataset, accurate estimation of the 3D human pose can be realized from a single 2D image. The results show that the mean per joint position error (MPJPE) is 74.6 mm without the need for 3D ground truth labels. Compared with other approaches, the proposed method achieves better results.
Cyber attack detection in healthcare data using cyber-physical system with optimized algorithm

A medical cyber-physical system (MCPS) integrates medical sensor devices with cyber (information) components, which creates a sensitive approach and provides security. The MCPS plays a vital role in hospitals by detecting attacks and protecting patients' medical information. Many research projects have been carried out to detect attacks on the generation of medical information in MCPS. The issues with existing algorithms are their inefficiency and time-consuming maximization of error rates. To overcome the challenges, this paper proposes detecting the attack using the fuzzy C-Means algorithm with artificial bee colony optimization (FCM-ABC). The novelty of the work is flexibility in deciding whether data points belong to actual users or attackers using the fuzzy c means degree [0,1] measuring technique of cluster formation. ABC is used in self-organizing the clusters with ABC collective intelligence. It monitors health information using sensor networks. The MCPS model interconnects the sensor devices remotely and collects the information. SVM has an accuracy rate of 76.32%, FCM has an accuracy rate of 81.34%, LSTM has an accuracy rate of 86.22%, and our proposed work, FCM-ABC, has an accuracy rate of 93.34%.
Reverse Reconciliation for Optimal Error Correction in Quantum Key Distribution

In this work, we introduce a new method for the establishment of a symmetric secret key through the reconciliation process in QKD systems that, we claim, is immune to the error rate of the quantum channel and, therefore, has an efficiency of 100% since it does not present losses during the distillation of secret keys. Furthermore, the secret rate is scaled to the square of the number of pulses on the destination side. The method only requires a single data exchange from Bob over the classic channel. We affirmed that our results constitute a milestone in the field of QKD and error correction methods at a crucial moment in the development of classical and quantum cryptanalytic algorithms. We believe that the properties of our method can be evaluated directly since it does not require the use of complex formal-theoretical techniques. For this purpose, we provide a detailed description of the reconciliation algorithm. The strength of the method against PNS and IR attacks is discussed. Furthermore, we define a method to analyze the security of the reconciliation approach based on frames that are binary arrays of 2×2. As a result, we came to the conclusion that the conjugate approach can no longer be considered secure, while we came up with a way to increase the secret gain of the method with measured bits.
Chatbot Using Deep Learning

In this paper we have proposed the working of Assistant conversational agent(Chatbot) using deep learning concepts with the utilization of tensorflow library. The LSTM is used overhere, so that the input taken with more than 30-40 words in a sentence canbe replied or answerd with more adequate conversation. The movie dataset used to train the model is taken from Cornell. The model isdesigned to perform a movie dialogue prediction conversation between the user and chatbot. The main aim is to increase the accuracy and estimation of the model. In the proposed model, we have developed a Seq2Seq AI Chatbot with attention mechanism using LSTM and libraries like tenserflow.
CatSight, a direct path to proper multi-variate time series change detection: perceiving a concept drift through common spatial pattern

Abstract Detecting changes in data streams, with the data flowing continuously, is an important problem which Industry 4.0 has to deal with. In industrial monitoring, the data distribution may vary after a change in the machine’s operating point; this situation is known as concept drift, and it is key to detecting this change. One drawback of conventional machine learning algorithms is that they are usually static, trained offline, and require monitoring at the input level. A change in the distribution of data, in the relationship between the input and the output data, would result in the deterioration of the predictive performance of the models due to the lack of an ability to generalize the model to new concepts. Drift detecting methods emerge as a solution to identify the concept drift in the data. This paper proposes a new approach for concept drift detection—a novel approach to deal with sudden or abrupt drift, the most common drift found in industrial processes-, called CatSight. Briefly, this method is composed of two steps: (i) Use of Common Spatial Patterns (a statistical approach to deal with data streaming, closely related to Principal Component Analysis) to maximize the difference between two different distributions of a multivariate temporal data, and (ii) Machine Learning conventional algorithms to detect whether a change in the data flow has been occurred or not. The performance of the CatSight method, has been evaluated on a real use case, training six state of the art Machine Learning (ML) classifiers; obtained results indicate how adequate the new approach is.
The Data Privacy Protection Method for Hyperledger Fabric Based on Trustzone

Hyperledger Fabric is a distributed ledger solution platform based on a modular architecture. The cryptographic algorithm is the core of the platform to ensure the security and tamper-resistant of the data on the chain. However, the original Fabric platform lacks the protection of user’s keys and cryptographic operations. To this end, this paper proposes a data privacy protection method for Hyperledger Fabric based on Trustzone technology, which places the user‘s key and the cryptographic operation process of private data in the trusted execution environment for protection. The experimental results based on the existing blockchain network show that the scheme can effectively ensure the security of data encryption process and key static storage, greatly reduce the trusted computing base and the attack surface. The performance loss is within an acceptable range.
Locality-Sensitive Hashing of Soft Biometrics for Efficient Face Image Database Search and Retrieval

As multimedia technology has advanced in recent years, the use of enormous image libraries has dramatically expanded. In applications for image processing, image retrieval has emerged as a crucial technique. Content-based face image retrieval is a well-established technology in many real-world applications, such as social media, where dependable retrieval capabilities are required to enable quick search among large numbers of images. Humans frequently use faces to recognize and identify individuals. Face recognition from official or personal photos is becoming increasingly popular as it can aid crime detectives in identifying victims and criminals. Furthermore, a large number of images requires a large amount of storage, and the process of image comparison and matching, consequently, takes longer. Hence, the query speed and low storage consumption of hash-based image retrieval techniques have garnered a considerable amount of interest. The main contribution of this work is to try to overcome the challenge of performance improvement in image retrieval by using locality-sensitive hashing (LSH) for retrieving top-matched face images from large-scale databases. We use face soft biometrics as a search input and propose an effective LSH-based method to replace standard face soft biometrics with their corresponding hash codes for searching a large-scale face database and retrieving the top-k of the matching face images with higher accuracy in less time. The experimental results, using the Labeled Faces in the Wild (LFW) database together with the corresponding database of attributes (LFW-attributes), show that our proposed method using LSH face soft biometrics (Soft BioHash) improves the performance of face image database search and retrieval and also outperforms the LSH hard face biometrics method (Hard BioHash).
Crowdmeta: Crowdsourcing truth inference with meta-Knowledge transfer

Crowdsourcing provides a fast and low-cost solution to collect annotations for training data in computer vision. However, there are two challenges in crowdsourced image annotation: First, when crowdsourced workers perform annotation tasks in an unfamiliar domain, their accuracy will dramatically decline due to the lack of expertise; Second, the difficulties of tasks may be different due to the noises in images, which is only related to the features of images themselves and will affect the judgment of workers. It is well known that transferring knowledge from relevant domains can form a better representation for training samples, which benefits the estimation of workers' expertise in truth inference models. However, the existing knowledge transfer processes for crowdsourcing require a considerable number of well-collected samples in source domains. Comprehensively considering the above issues, this paper proposes a novel probabilistic model for crowdsourcing truth inference, which fuses few-shot meta-learning and transfer learning. The proposed model transfers meta-knowledge from the source domain to form better high-level representations of the instances in the target domain. Simultaneously utilizing both high-level representations and instance features, the quality of workers and the difficulty of instances can be better modeled and inferred. Experimental results on a number of datasets show that the proposed model not only outperforms the state-of-the-art models but also significantly reduces the number of instances required in the source domain.
Difficulties to digitalize: ambidexterity challenges in law firms

Purpose This paper aims to understand what prevents established law firms from embracing digitalization and discusses barriers to solving the emerging ambidexterity problem. Law firms have been organized in the same way for decades. However, digital opportunities are emerging and new competitors are challenging established firms. This presents established law firms with an ambidexterity problem: How can law firms simultaneously uphold their successful way of working while entering a new world of digitalization, artificial intelligence (AI) and machine learning? Design/methodology/approach Previous research suggests that law firms are slow in digital transformation, compared to other Professional Service Firms (PSFs). In this paper, the authors explore why this happens. Interview data from representatives in law firms are complemented with data from architects as well as legal industry data and field notes. The data have been analyzed to spot patterns and emerging themes. Findings The authors find that established law firms face structural and cultural barriers to applying ambidextrous solutions. When comparing law firms with architecture firms, the authors see that while established architecture firms have combined digital exploration with ongoing exploitation, established law firms have focused on exploitation, leaving digital exploration to new legal tech firms. This difference can be attributed to industry context and professional culture. Originality/value This paper shows that both structural and contextual ambidexterity is a challenge for established law firms. This paper contributes to the understanding of barriers to embrace digital technology, and supports practitioners in efforts to remove these barriers.
Virtual nature makes knowledge beautiful

This is a perspective that presents a viewpoint on immersive informal learning applications built as digital twins of the natural world. Such applications provide multimodal, interactive, immersive, embodied, and sensory experiences unique and different from typical game art environments because they are geospatial visualizations of data and information derived from ecological field plot studies, geographical information systems, drone images, and botanically correct 3D plant models visualized in real-time interactive game engines. Since they are constructed from geometric objects, they can programmatically self-express semantic data and connect to knowledge stores on the Internet to create a web of knowledge for both exploration of a virtual environment as a natural landscape and for exploration of connected knowledge stores for informal learning at the moment of curiosity. This design is exceptionally powerful for informal learning as it supports the innate human desire to understand the world. This paper summarizes the construction methods used for creating three digital twins of natural environments and the informal learning applications created and distributed, namely, augmented reality (AR), virtual reality (VR), and virtual field trips. Informal learning outcomes and emotional reactions are evaluated using mixed-methods research studies to understand the impact of design factors. Visual fidelity and navigational freedom are combined to increase learning outcomes and many effective and emotional outcomes as well. Access to facts and story increase learning outcomes, and applications evaluated as beautiful are correlated with emotional reactions of awe and wonder, and awe and wonder are correlated with higher learning gains. Beauty is correlated with other system-wide subjective evaluations largely accepted as important to create a context conducive to support learning outcomes, such as calmness, excitement, and curiosity, a desire to share, and a desire to create. This paper summarizes the highlights of the author’s prior work to give the reader a perspective on the body of work.
Investigating the impact of calibration on the quality of explanations

Abstract Predictive models used in Decision Support Systems (DSS) are often requested to explain the reasoning to users. Explanations of instances consist of two parts; the predicted label with an associated certainty and a set of weights, one per feature, describing how each feature contributes to the prediction for the particular instance. In techniques like Local Interpretable Model-agnostic Explanations (LIME), the probability estimate from the underlying model is used as a measurement of certainty; consequently, the feature weights represent how each feature contributes to the probability estimate. It is, however, well-known that probability estimates from classifiers are often poorly calibrated, i.e., the probability estimates do not correspond to the actual probabilities of being correct. With this in mind, explanations from techniques like LIME risk becoming misleading since the feature weights will only describe how each feature contributes to the possibly inaccurate probability estimate. This paper investigates the impact of calibrating predictive models before applying LIME. The study includes 25 benchmark data sets, using Random forest and Extreme Gradient Boosting (xGBoost) as learners and Venn-Abers and Platt scaling as calibration methods. Results from the study show that explanations of better calibrated models are themselves better calibrated, with ECE and log loss for the explanations after calibration becoming more conformed to the model ECE and log loss. The conclusion is that calibration makes the models and the explanations better by accurately representing reality.