Reviewerly/exHarmony huggingface dataset:
Automatic Waveform Quality Control for Surface Waves Using Machine Learning

Abstract Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise. This process has usually been completed by human experts labeling each waveform visually, which is time consuming and tedious for large data sets. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, K-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. To speed up signal quality assessment, we trained these five machine learning (ML) methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show that the two trained models agree with labels from human analysts but required only 0.4% of the time. Although the original (human) quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our investigations demonstrate the capability of the automated processing using these two ML models to reduce outliers in surface-wave-related measurements without human quality control screening.
Mining Logical Arithmetic Expressions From Proper Representations

Logical-arithmetic expressions are convenient for describing phenomena due to their expressiveness and comprehensibility. Therefore, we propose to target mining logical arithmetic expressions through a novel task called Logical-arithmetic expression mining (LAEM). Its goal is to discover expressive logical expressions that are representative for a database. It accepts a complex database as input and returns a set of representative expressions for the database. Driven by the success of machine learning models to recognize complex patterns, we argue that a thorough modeling of the learned representations could be exploited for generating interesting and representative mathematical expressions. To address this, in this paper we propose Soft dEcision Tree for logical arithmetic Expressions miNing (SEEN), an algorithm based on representation learning for generating logical expressions. Our mining mechanism partitions the learned representation space and assigns self-labels. Then, we use the self-labels to train a multivariate soft decision tree from which we generate logical arithmetic expressions. A comprehensive experimental study on 2 diverse real-world datasets shows that the proposed method is able to generate interesting expressions. The implementation is publicly available1.1 https://github.com/ekosman/SEEN
Diagnosing and Preventing Instabilities in Recurrent Video Processing

Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution. In this work, we focus on their stability as dynamical systems and show that they tend to fail catastrophically at inference time on long video sequences. To address this issue, we (1) introduce a diagnostic tool which produces input sequences optimized to trigger instabilities and that can be interpreted as visualizations of temporal receptive fields, and (2) propose two approaches to enforce the stability of a model during training: constraining the spectral norm or constraining the stable rank of its convolutional layers. We then introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stable Rank Normalization for Convolutional layers</i> (SRN-C), a new algorithm that enforces these constraints. Our experimental results suggest that SRN-C successfully enforces stablility in recurrent video processing models without a significant performance loss.
Beyond 3DMM: Learning to Capture High-Fidelity 3D Face Shape

3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori. However, previous reconstructed 3D faces suffer from degraded visual verisimilitude due to the loss of fine-grained geometry, which is attributed to insufficient ground-truth 3D shapes, unreliable training strategies and limited representation power of 3DMM. To alleviate this issue, this paper proposes a complete solution to capture the personalized shape so that the reconstructed shape looks identical to the corresponding person. Specifically, given a 2D image as the input, we virtually render the image in several calibrated views to normalize pose variations while preserving the original image geometry. A many-to-one hourglass network serves as the encode-decoder to fuse multiview features and generate vertex displacements as the fine-grained geometry. Besides, the neural network is trained by directly optimizing the visual effect, where two 3D shapes are compared by measuring the similarity between the multiview images rendered from the shapes. Finally, we propose to generate the ground-truth 3D shapes by registering RGB-D images followed by pose and shape augmentation, providing sufficient data for network training. Experiments on several challenging protocols demonstrate the superior reconstruction accuracy of our proposal on the face shape.
The Design of the Exercise Load Monitoring System Based on Internet of Things

Exercise is of great help to human health, and exercise load monitoring is also to ensure the physical and mental health of athletes. Therefore, this paper designs an exercise load detection system based on Internet of things technology. We introduce the design of heart rate acquisition part, mobile application part, and web server in detail. There are many places to weigh and consider in the design process. The design idea of the system is introduced step by step, and the hardware system deployment architecture and the overall software architecture of the system are designed. In particular, the monitoring of exercise heart rate is very suitable for the system requirements studied in this paper. The system design in this paper can achieve the effect of human sports load detection.
Dense and well-connected subgraph detection in dual networks

Dense subgraph discovery is a fundamental problem in graph mining whose goal is to extract a dense subgraph from a given graph, and it has a wide range of applications [18]. However, numerous real-world applications, ranging from computational biology and computational neuroscience to computational social science, take as input a dual graph, namely a pair of graphs on the same set of nodes. Despite the large number of such applications, research on dense subgraph discovery has focused on a single graph input, with few notable exceptions [9, 22, 35, 36]. In this work, we contribute to this line of research by studying the following novel algorithmic problem:Given a pair of graphs G, H on the same set of nodes V, how do we find a subset of nodes S ⊆ V that induces a well-connected subgraph in G and a dense subgraph in H?Our formulation generalizes previous research [11, 44, 45], by enabling to control the connectivity constraint on G. We propose a mathematical formulation and prove that it is solvable exactly in polynomial time. We compare our method to state-of-the-art competitors and find empirically that controlling the connectivity constraint enables the practitioner to obtain information that is otherwise inaccessible. Finally, we show that our proposed mining tool can be used to better understand how users interact on Twitter and connectivity aspects of human brain networks with and without Autism Spectrum Disorder (ASD).MSC codesdense subgraph discoveryk-edge connectivitydual graphalgorithm designgraph mining
Scalable RBAC model for large-scale applications with automatic user-role assignment

Access control is one of the essential security requirements of any information system.Role-based access control (RBAC) has been the most popular access control model so far.However, in-advance, manual, and time-consuming role assignment process makes it inefficient in large data-sharing applications.Extension to RBAC models using attributes, attribute-based access control (ABAC) model, and combination of access control models with other techniques has become an emerging research area in access control.Recent research either focuses on standardising the ABAC model or making RBAC fine-grained, dynamic, and context-aware.A manual user-role assignment is still an open problem for RBAC that limits it for extending.In large-scale applications, a large number of users makes the manual user-role assignment process complicated and difficult.In this paper, we present an attribute-based framework to make RBAC's user-role assignment process automatic by extending it for large-scale applications.
NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

Abstract We introduce the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute was funded in 2020 as part of a new initiative from the NSF to advance foundational AI research across a wide variety of domains. To date AI2ES is the only NSF AI institute focusing on environmental science applications. Our institute focuses on developing trustworthy AI methods for weather, climate, and coastal hazards. The AI methods will revolutionize our understanding and prediction of high-impact atmospheric and ocean science phenomena and will be utilized by diverse, professional user groups to reduce risks to society. In addition, we are creating novel educational paths, including a new degree program at a community college serving underrepresented minorities, to improve workforce diversity for both AI and environmental science.
Prototype-Based Interpretation of the Functionality of Neurons in Winner-Take-All Neural Networks

Prototype-based learning (PbL) using a winner-take-all (WTA) network based on minimum Euclidean distance (ED-WTA) is an intuitive approach to multiclass classification. By constructing meaningful class centers, PbL provides higher interpretability and generalization than hyperplane-based learning (HbL) methods based on maximum inner product (IP-WTA) and can efficiently detect and reject samples that do not belong to any classes. In this article, we first prove the equivalence of IP-WTA and ED-WTA from a representational power perspective. Then, we show that naively using this equivalence leads to unintuitive ED-WTA networks in which the centers have high distances to data that they represent. We propose ±ED-WTA that models each neuron with two prototypes: one positive prototype, representing samples modeled by that neuron, and a negative prototype, representing the samples erroneously won by that neuron during training. We propose a novel training algorithm for the ±ED-WTA network, which cleverly switches between updating the positive and negative prototypes and is essential to the emergence of interpretable prototypes. Unexpectedly, we observed that the negative prototype of each neuron is indistinguishably similar to the positive one. The rationale behind this observation is that the training data that are mistaken for a prototype are indeed similar to it. The main finding of this article is this interpretation of the functionality of neurons as computing the difference between the distances to a positive and a negative prototype, which is in agreement with the BCM theory. Our experiments show that the proposed ±ED-WTA method constructs highly interpretable prototypes that can be successfully used for explaining the functionality of deep neural networks (DNNs), and detecting outlier and adversarial examples.
Research on The Application of Blockchain in The Supply Chain From The Perspective of Big Data

The quality monitoring of products and services has always plagued the theoretical and practical circles, and blockchain technology has significant technical advantages in solving this problem. However, there is currently no literature that systematically analyzes the research hotspots and practical frontiers in this field. Aiming at a large number of relevant literature, this article uses the analysis method of scientific knowledge graphs from the perspective of big data to conduct quantitative and qualitative analysis from research hotspots and research frontiers. The results show that the research content of blockchain traceability is very extensive, involving multiple fields. The research hotspots are mainly smart contracts, the Internet of Things and supply chain; the future should focus on the research of other industries in the supply chain besides the food industry, and should also focus on theory and practice. In addition, corresponding system construction and infrastructure construction should also be strengthened. The research results have certain reference value for theoretical research and related practice.
Blockchain and Distributed Ledgers: Mathematics, Technology, and Economics

"Blockchain and Distributed Ledgers: Mathematics, Technology, and Economics." Quantitative Finance, 22(1), pp. 23–24
Remittances, Financial Sector Development, Institutions and Economic Growth in the ECOWAS Region

We investigate the relationship between remittances, financial sector development, institutions, and economic growth in a panel of 15 Economic Community of West African States (ECOWAS) over the period 2000-2017. The empirical evidence is based on the Two-Stage Least Squares Instrumental Variable (2SLS-IV) estimator, which provided two main findings. First, measures of remittances themselves had negative and significant effects on economic growth in the ECOWAS sub-region. The interactive effects of remittances and measures of financial sector development promote growth in the sub-region, thus supporting the complementarity hypothesis. Second, measures of institutional quality had positive and significant effects on the growth of countries in the ECOWAS area. Meanwhile, the interactive terms of remittances and institutional quality show support for the substitutability hypothesis. Therefore, remittances substitute for the presence of weak institutions in the ECOWAS area. Based on the preceding, we suggest the need for ECOWAS countries to further broaden the roles of financial sector institutions inside the remitting process to enhance savings mobilisation and channel the remitted funds into productive and growth-enhancing activities. Moreover, policymakers in ECOWAS countries need to strengthen governance institutions, which could increase the developmental benefits of remittances.
Dynamic thresholding for video anomaly detection

Anomaly detection is one of the most important applications in video surveillance that involves the temporal localisation of anomaly events in unannotated video sequences. By learning the normal patterns to generate frames and calculating their reconstruction error relative to the ground truth, a frame can be recognised as being abnormal if the reconstruction error exceeds a threshold. Most existing works use a fixed threshold that computes over all the testing data to determine the anomalies. However, fixed threshold strategy cannot address the challenges brought by the dynamic environment, e.g. changes in illumination conditions. In this paper, a dynamic thresholding algorithm (DTA) is proposed, which is fully data-driven and capable of automatically determining thresholds such that the developed anomaly detection system can flexibly adapt to different scenarios. The proposed DTA is independent of the backbone network and can be easily incorporated into most existing video anomaly detection models to help identify the appropriate thresholds. On both synthetic and real-world datasets, the experimental results show that with the proposed DTA, the video anomaly detection methods achieve a better performance considering the changes in dynamic environment.
Model Stealing Defense against Exploiting Information Leak through the Interpretation of Deep Neural Nets

Model stealing techniques allow adversaries to create attack models that mimic the functionality of black-box machine learning models, querying only class membership or probability outcomes. Recently, interpretable AI is getting increasing attention, to enhance our understanding of AI models, provide additional information for diagnoses, or satisfy legal requirements. However, it has been recently reported that providing such additional information can make AI models more vulnerable to model stealing attacks. In this paper, we propose DeepDefense, the first defense mechanism that protects an AI model against model stealing attackers exploiting both class probabilities and interpretations. DeepDefense uses a misdirection model to hide the critical information of the original model against model stealing attacks, with minimal degradation on both the class probability and the interpretability of prediction output. DeepDefense is highly applicable for any model stealing scenario since it makes minimal assumptions about the model stealing adversary. In our experiments, DeepDefense shows significantly higher defense performance than the existing state-of-the-art defenses on various datasets and interpreters.
Evaluating the accuracy of forced alignment across Mandarin varieties

Forced alignment is widely used in phonetics to align transcripts with acoustic signals. These tools are trained on specific language varieties; it is unclear if they generalize to others. Previous research on English by MacKenzie and Turton (2020) finds good agreement between automated and human alignments for varieties that differ from the training variety. Such evaluation has only been carried out for English. We evaluate the level of human-aligner agreement on four Mandarin varieties (Canto, Shanghai, Beijing, and Tianjin). For each variety, two recordings from the HUB5 Corpus (LDC 1998) were aligned manually and by the Montreal Forced Aligner [McAuliffe et al. (2017)] using acoustic models trained on Beijing, Wuhan, and Hekou Mandarin [Schultz (2002)]. We find strong agreement between human and machine-aligned phone boundaries, with 17 ms as the median onset displacement. A mixed model identifies little variation across varieties or according to speech rate, but significant interindividual variation. Notably, despite the generally close agreement between the machine and human alignments, for two of the speakers, more than 10% of the alignments are displaced by over 100 ms. In sum, the Mandarin forced-aligner yields reliable alignments for out-of-training varieties, but manual checking of the results is still crucial.
Priority-Based Virtual Machine Selection Algorithm in Cloud Computing

Cloud computing technology has gained the attention of researchers in recent years. Almost every application is using cloud computing in one way or another. Virtualization allows running many virtual machines on a single physical computer by sharing its resources. Users can store their data on datacenter and run their applications from anywhere using the internet and pay as per service level agreement documents accordingly. It leads to an increase in demand for cloud services and may decrease the quality of service. This paper presents a priority-based selection of virtual machines by cloud service provider. The virtual machines in the cloud datacenter are configured as Amazon EC2 and algorithm is simulated in cloud-sim simulator. The results justify that proposed priority-based virtual machine algorithm shortens the makespan, by 11.43 % and 5.81 %, average waiting time by 28.80 % and 24.50%, and cost of using the virtual machine by 21.24% and 11.54% as compared to FCFS and ACO respectively, hence improving quality of service.
Resource Allocation in Cloud Computing for Energy Efficiency

Cloud computing is a paradigm in which we have virtualized computer systems that deliver services, processing, storage, network, and other fundamental computing resources. Cloud computing enables low cost, device location independence, high reliability, scalability and sustainability. This paper describes the present state of cloud computing research by examining literature, identifying current study trends. We have analyzed the resource allocation method and concluded. It typically designs for high performance that supports the peak resource requirements. After several analyses the power consumption of data center and cloud systems as increased almost several times. There is a lack of research that addresses challenges of managing multiple resources with objective of allocating enough resources for each work load to optimizing power consumption. These papers survey various types of resource allocation algorithms that improve the cloud Infrastructure.
An Enhancement of Underwater Images using DCP and CLAHE Algorithm

The lack of resource requirement in this population world, we are in a position to require another resources. In this regard, ocean is one of our sustenance. It is the exact platform for various applications like, transport, food, energy etc., but still we are surveyed partly at all aspects. One of the main focus of challenge is scattering of light as it penetrate from air to water which presents us with a bluish background while studying the scenery. In this, added to this there is a hazy appearance in the visuals and calls for Image Enhancement techniques. Here, Dark Channel Prior(DCP) is used to remove the haze and noise induced by the bluish environment. However, this proposal of method is also used to increase darkness of the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is used on the RGB image to enhance the contrast and intensity of the image. Finally, we get visually pleasing result, colour correlation method is carried out. The experimental result shows that a enhanced underwater image from the base image, and mostly useful to analyze and monitoring the underwater images.
Hidden Markov Model Based Odia Numeral Recognition Using Moment and Structural Features

Optical character recognition (OCR) is a strategy to perceive character from optically checked and digitized pages. OCR plays an important role for Indian script research. The official language of the state Odisha is Odia. OCR face an incredible difficulties to recognize Odia language due to similar shape characters, their complex nature, the complicated way in which they combine form to compound character, use of Matra etc. Each character and numbers are passed through several modules like binarization, noise removal, segmentation, line segmentation, word segmentation, skeletonization, deskewing, thinning, thickening. The input picture is standardized to a size of 50 x 50 2D pictures. HMM is a stochastic process which has utilized in various applications for example speech recognition, Handwriting recognition, Gesture recognition. In this paper we utilized HMM to recognize the Odia character and numbers. Hidden Markov Model have many advantages such as resistant to noise, handle contrast recorded as a hard copy and the HMM devices are effectively accessible. In our proposed method we have developed an efficient recognition algorithm using Hidden Markov model based on moment based and structural feature to recognize Odia characters and numerals.
Predicting missing data for data integrity based on the linear regression model

Multiple linear regression is an important data analysis technique. Based on this technique, we propose a new method for predicting missing data items and detecting possible errors in the data. The proposed method has a key feature that it can be used to predict not only just one missing item, but also two or more missing items within a certain tolerance. At the same time, we perform a few experiments to prove the feasibility of our proposed method. The results of our experiments show that our method can indeed predict one or more missing items within an acceptable range and find the error of the original data.