Reviewerly/exHarmony huggingface dataset:
Managing Audio Monitoring Data with SIMON – Concept for Data Administration, Online Repository and Dissemination

Automated observations of natural occurrences play a key role in monitoring biodiversity worldwide. With the development of affordable hardware like the AudioMoth (Hill et al. 2019) acoustic logger, large scale and long-term monitoring has come within reach. However, data management and dissemination of monitoring data remain challenging, as the development of software and the infrastructure for the management of monitoring data lag behind. We want to fill this gap, providing a complete audio monitoring solution from affordable audio monitoring hardware, custom data management tools and storage infrastructure based on open source hard- and software, biodiversity information standards and integrable interfaces. The S cientific M onitoring Data Management and On line Repository (SIMON) consists of a portable data collector and a connected online repository. The data collector, a device for the automated extraction of the audio data from the audio loggers in the field, stores the data and metadata in an internal cache. Once connected to the internet via WiFi or a cable connection, the data are automatically uploaded to an online repository for automated analysis, annotation, data management and dissemination. To prevent SIMON from becoming yet another proprietary storage, the FAIR principles ( F indable, A ccessible, I nteroperable, and R e-usable) Wilkinson et al. (2016) are at the very core of data managed in the online repository. We plan to offer an API (application programming interface) to disseminate data to established data infrastructures. A second API will allow the use of external services for data enrichment. While in the planning phase, we would like to take the opportunity to discuss with domain experts the requirements and implementation of different standards—namely ABCD (Access to Biological Collections Data task group, Biodiversity Information Standards (TDWG) 2007), Darwin Core (Darwin Core Task Group, Biodiversity Information Standards (TDWG) 2009) and Darwin Core Archive (Remsen et al. 2017)—connecting to external services and targeting data infrastructures.
The Complexity of Boolean State Separation (Technical Report)

For a Boolean type of nets $τ$, a transition system $A$ is synthesizeable into a $τ$-net $N$ if and only if distinct states of $A$ correspond to distinct markings of $N$, and $N$ prevents a transition firing if there is no related transition in $A$. The former property is called $τ$-state separation property ($τ$-SSP) while the latter -- $τ$-event/state separation property ($τ$-ESSP). $A$ is embeddable into the reachability graph of a $τ$-net $N$ if and only if $A$ has the $τ$-SSP. This paper presents a complete characterization of the computational complexity of \textsc{$τ$-SSP} for all Boolean Petri net types.
ECG PRACTICE WITH SELF-GENERATION OF DIAGNOSES IMPROVES POST-TEST PERFORMANCE AND FLUENCY OVER MULTIPLE CHOICE

Despite advances in electronic teaching methods, medical trainee performance on ECG interpretation remains suboptimal. Robust evidence-based ECG teaching strategies are lacking. Prior education literature suggests that self-generation of diagnoses is more effective than multiple choice in practicing learned skills, where self-generated items are remembered better than items provided in a list. In this study, we examine the effect of self-generation on learning ECG interpretation skills by comparing it to the standard practice of using multiple choice questions.
Multi-scale Superpixel Based Homomorphic Filtering for the Component Image Enhancement

Aiming at the low accuracy of visual measurement caused by the nonuniform illumination image of components during industrial processing and assembly, a multi-scale superpixel based homomorphic filtering method for the component image enhancement is proposed. Firstly, the image is segmented into multiple subblocks using superpixel segmentation in different scales. Secondly, different subblocks are homomorphically filtered at each scale. Finally, images of different scales which have been filtered are weighted according to the standard deviation and fused to get the enhanced image. The experimental results show that the proposed method can effectively improve the average value and average gradient value of the enhanced image.
X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation

We suggest to represent an X-Field -a set of 2D images taken across different view, time or illumination conditions, i.e., video, light field, reflectance fields or combinations thereof-by learning a neural network (NN) to map their view, time or light coordinates to 2D images. Executing this NN at new coordinates results in joint view, time or light interpolation. The key idea to make this workable is a NN that already knows the "basic tricks" of graphics (lighting, 3D projection, occlusion) in a hard-coded and differentiable form. The NN represents the input to that rendering as an implicit map, that for any view, time, or light coordinate and for any pixel can quantify how it will move if view, time or light coordinates change (Jacobian of pixel position with respect to view, time, illumination, etc.). Our X-Field representation is trained for one scene within minutes, leading to a compact set of trainable parameters and hence real-time navigation in view, time and illumination.
Aspect Based Sentimental Analysis On Online Reviews System

Aspect extraction is SA's (Sentiment Analysis) most critical and thoroughly studied process toconduct an accurate classification of feelings. Massive amounts of studies over the last decade haveconcentrated on identifying and removing elements. Products have centralized distribution channels, inwhich certain apps sometimes operate close to the latest product to be created. Any e-commerce businessenterprise needs to analyze user / customer feedback and find out what customers feel for providing betterproducts and services for them. Since wide reviews may often involve remarks in a consolidated way when apatron gives his thoughts on distinctive product attributes within the equivalent summary, it is difficult tofigure out the exact feeling. The key components of these software are provided in their release, and thisprovides a valuable tool for management to enhance the consistency of their very own system'sspecifications. The aim was to classify the aspects of the target entities given and the feeling conveyed foreach aspect. First, we are implementing a framework based on supervised classification, which is tightlyrestricted and utilizes the training sets as the only knowledge source. Therefore, the key terms derived aremapped with different aspects of an object in order to conduct the aspect-specific sentiment analysis oncustomer reviews. Synthetic and actual data set experiments show positive outcomes as opposed to currentsentiment analysis approaches.
A design method for a passive reflectionless transmission-line model based on the cochlea through parameter optimization techniques

A passive reflectionless transmission-line model is able to reproduce the physiological characteristics of the cochlea via parameter tuning. In this paper, we propose a quantitative design method for the passive reflectionless transmission-line model using parameter optimization techniques. The proposed design can achieve the overall physiological characteristics of the cochlea.
전근대 역사교육과 교재의 성격

History education in pre-modern Korea has been largely influenced by China. From the Three Kingdoms Period to the Goryeo Dynasty, Wujing (五經) and history books of China were the main textbooks in history education. In the Goryeo Dynasty, however, history education was emphasized compared to contemporary China. In the Joseon Dynasty, systematic history education was conducted as a result of the widespread use of history textbooks for children containing the Neo-Confucian values.Dongmongsunseup(童蒙先習), meanwhile, was the first children''s textbook to independently deal with Korean history. However, in the true sense of the word, the first Korean history textbook for children is Ginyeonaram(紀年兒覽), which was influenced by Silhak(實學). The authors of the book broke away from the traditional framework of Confucian education and made history an independent field of education. They also organized Korean and Chinese history equally. Like encyclopedists did, setting up a new academic hierarchy means not just editing, but epistemological change and innovation of knowledge.
MemShield: GPU-Assisted Software Memory Encryption

Cryptographic algorithm implementations are vulnerable to Cold Boot attacks, which consist in exploiting the persistence of RAM cells across reboots or power down cycles to read the memory contents and recover precious sensitive data. The principal defensive weapon against Cold Boot attacks is memory encryption. In this work we propose MemShield, a memory encryption framework for user space applications that exploits a GPU to safely store the master key and perform the encryption/decryption operations. We developed a prototype that is completely transparent to existing applications and does not require changes to the OS kernel. We discuss the design, the related works, the implementation, the security analysis, and the performances of MemShield.
Learning constraints for the epistemic graphs approach to argumentation

Epistemic graphs are a proposal for modelling how agents may have beliefs in arguments and how beliefs in some arguments may influence the beliefs in others. The beliefs in arguments are represented by probability distributions and influences between arguments are represented by logical constraints on these probability distributions. This allows for various kinds of influence to be represented including supporting, attacking, and mixed, and it allows for aggregation of influence to be captured, in a context-sensitive way. In this paper, we investigate methods for learning constraints, and thereby the nature of influences, from data. We evaluate our approach by showing that we can obtain constraints with reasonable quality from two publicly available studies.
Does having access to uncertainty information improve geologic interpretation? You tell us!

&amp;lt;p&amp;gt;We are a collaborative group of geoscientists and psychologists seeking to understand the influence of uncertainty information on geologic interpretation. We have developed a five-fold ranking system for characterizing uncertainty in the internal features of an outcrop. From least well constrained to best constrained, these are, Permissive, Suggestive, Presumptive, Compelling, and Certain. In some sense, Permissive and Certain are end members, because there is no variability within these categories. In contrast, the middle three categories - Suggestive, Presumptive, Compelling &amp;amp;#8211; have a range of possible values.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Permissive is the least certain form of evidence.&amp;amp;#160; Permissive suggests that a particular idea or interpretation cannot be ruled out, but it is also not the only available solution.&amp;amp;#160; Suggestive indicates that there is positive evidence for a particular interpretation, but that the evidence also allows the possibility for other interpretations.&amp;amp;#160; Presumptive &amp;amp;#8211; defined as &amp;amp;#8220;presumed in the absence of further information&amp;amp;#8220;&amp;amp;#8211; indicates that an interpretation is &amp;amp;#8220;more likely right than wrong&amp;amp;#8221;. Compelling indicates that the evidence is strongly supportive of the interpretation.&amp;amp;#160; That is, compelling evidence for an interpretation is based on a preponderance of positive evidence.&amp;amp;#160;&amp;amp;#160; Finally, Certain indicates that there is a direct and resolvable link between the evidence and a particular interpretation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Attaching uncertainty rankings to observational data has the potential to improve the sharing and combining of datasets within geoscience, and offers experts the opportunity to weight data (based on uncertainty) during geologic interpretation. At this poster, we are investigating how the availability of uncertainty rankings for strike and dip bedding measurements impacts the structural interpretation of folding rocks in Mecca Hills in Southern California. The geology of the Mecca Hills is often described as three distinct structural blocks (the platform, central, and basin blocks), all of which are highly exposed. The Central block is characterized by highly deformed stratigraphy of Palm Spring and underlying Miocene Mecca formations that define a series of en-echelon anticline/syncline pairs of varying frequency.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We invite expert geoscientists (who have completed at least a Master&amp;amp;#8217;s degree) to make structural interpretations of folds (e.g., hinge orientations). You will be provided drone imagery of anticline/syncline pairs, with strike and dip bedding measurements marked at different locations. Each measurement has a corresponding ranking of uncertainty in measurement quality. We will not collect any identifying information, but we will ask you to complete a brief demographic survey.&amp;lt;/p&amp;gt;
Blockchain Based Proof of Existence (PoE) Application for Educational Certificate Verification

In this digital era, most of the documents such as Educational Certificates, Memorandum of Understanding (MoU)s/Agreements, Driving Licenses, Health Records, Employee Service Records, Sale Deeds and Property Registration Records, Birth and Death Certificates, Tax Returns, and so on are being generated and maintained as digital artifacts. But, people are misusing this facility by generating and submitting fake documents. The organization or person, whoever is receiving the documents, is unable to verify the authenticity and integrity of the submitted documents, so verification of these documents is a challenging task. To address this problem, Blockchain technology can be leveraged. Blockchain technology provides document authenticity, ownership, immutability, and tamper-proofness to the data recorded in it. Out of these digital documents, educational certificates are one of the most important entities to be secured as people keep hearing news about fake certificates. C-DAC has considered this as an important use case and implemented a Blockchain based solution for it. The organization, which is issuing the educational certificates, should issue certificates through Blockchain. Once a certificate is recorded on the Blockchain, it is persistent. Anyone can verify it by providing the unique identifier of the certificate such as student roll number, transaction ID, or cryptographic hash of the certificate. This will ease the verification process for the document receivers for verifying the authenticity and integrity of the document. And it also eliminates the role of third party verification agencies, which saves cost and increases the efficiency of verification.
Multi-Level Complex Tasks in the System of Assessment Tools (On Example of a Physics Course)

The article discusses the problem of forming a system of assessment tools in the context of modular competence-based learning. The aim of the research is to find the optimal means for assessing the level of competence formation. In author’s opinion, one of the abovementioned means is complex tasks that include several multi-level components. Based on the analysis of ways to solve this problem, presented in the scientific and pedagogical literature, the author offers criteria for selecting tasks for the formation of a system of assessment tools aimed at diagnosing the level of formation of necessary competencies, shows the role of complex tasks for such diagnostics. Using the example of the physics course, their possible structure and the problems solved by individual elements of this structure are reviewed. Examples of such tasks are given, and it is shown that they allow to diagnose the level of formation of individual elements of competence, adjust teaching methods and motivate students to work actively, seek out additional information, promote awareness of the importance of discipline as a professional one. The results of the research generalize the personal experience of teaching physics at the Far Eastern Federal University and can be used by university teachers in creating their own system of assessment tools.
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing

In Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) for the generated SQL query is both crucial and challenging; the parser is required to connect the natural language (NL) question and the SQL query to the structured knowledge in the database. We formulate two linking processes to address this challenge: schema linking which links explicit NL mentions to the database and structural linking which links the entities in the output SQL with their structural relationships in the database schema. Intuitively, the effectiveness of these two linking processes changes based on the entity being generated, thus we propose to dynamically choose between them using a gating mechanism. Integrating the proposed method with two graph neural network-based semantic parsers together with BERT representations demonstrates substantial gains in parsing accuracy on the challenging Spider dataset. Analyses show that our proposed method helps to enhance the structure of the model output when generating complicated SQL queries and offers more explainable predictions.
LPI RADAR WAVEFORM CLASSIFICATION USING BINARY SVM AND MULTI-CLASS SVM BASED ON PRINCIPAL COMPONENTS OF TFI

Since cognition has become an important topic in Electronic Warfare (EW) systems, Electronic Support Measures (ESM) are used to monitor, intercept and analyse radar signals. Low Probability of Intercept (LPI) radars is preferred to be able to detect targets without being detected by ES systems. Because of their properties as low power, variable frequency, wide bandwidth, LPI Radar waveforms are difficult to intercept with ESM systems. In addition to intercepting, the determination of the waveform types used by the LPI Radars is also very important for applying counter-measures against these radars. In this study, a solution for the LPI Radar waveform recognition is proposed. The solution is based on the training of Support Vector Machine (SVM) after applying Principal Component Analysis (PCA) to the data obtained by Time-Frequency Images (TFI). TFIs are generated using Choi-Williams Distribution. High energy regions on these images are cropped automatically and then resized to obtain uniform data set. To obtain the best result in SVM, the SVM Hyper-Parameters are also optimized. Results are obtained by using one-against-all and one-against-one methods. Better classification performance than those given in the literature have been obtained especially for lower Signal to Noise Ratio (SNR) values. The cross-validated results obtained are compared with the best results in the literature.
Integrated Geophysical study on the subsurface structural characterization of West Beni Suef area, Western Desert, Egypt

&amp;lt;p&amp;gt;The aim of this work is to study the subsurface structures in the west Beni Suef area of the Western Desert in Egypt and to determine their effects on surface geologic structures. A detailed land magnetic survey has been carried out for the total component of the geomagnetic field using two proton magnetometers. The necessary corrections concerning daily variation, the regional gradient and time variations have been applied. Then, the total magnetic intensity anomaly map (TMI) has been constructed and transformed to the reduced to the pole magnetic map (RTP). The reduction-to-pole magnetic and Bouguer anomaly maps were used to obtain regional extensions of this subsurface structure. Regional&amp;amp;#8211;residual separation is carried out using the power spectrum. Also, Edge detection techniques are applied to delineate the structure and hidden anomalies. Data analysis was performed using trend analysis, Euler deconvolution, the results indicate that the area is affected by tectonic forces in the N-S, NW-SE, NE-SW and E-W trends, which are correlated with the directions of surface geologic lineaments. In addition, depths to the basement rocks have been estimated using spectral analysis technique. The computed depths have been used to construct the basement relief map which resulted from gravity and magnetic data. They show that the depth to the basement rocks ranges from 2.3 km to 4.7 km.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;KEYWORDS&amp;lt;/strong&amp;gt;&amp;lt;strong&amp;gt;&amp;lt;br&amp;gt;&amp;lt;/strong&amp;gt;Land magnetic, Gravity, Euler deconvolution, Edge detection and Spectral analysis.&amp;lt;/p&amp;gt;
Self-grouping Convolutional Neural Networks

Although group convolution operators are increasingly used in deep convolutional neural networks to improve the computational efficiency and to reduce the number of parameters, most existing methods construct their group convolution architectures by a predefined partitioning of the filters of each convolutional layer into multiple regular filter groups with an equal spatial group size and data-independence, which prevents a full exploitation of their potential. To tackle this issue, we propose a novel method of designing self-grouping convolutional neural networks, called SG-CNN, in which the filters of each convolutional layer group themselves based on the similarity of their importance vectors. Concretely, for each filter, we first evaluate the importance value of their input channels to identify the importance vectors, and then group these vectors by clustering. Using the resulting \emph{data-dependent} centroids, we prune the less important connections, which implicitly minimizes the accuracy loss of the pruning, thus yielding a set of \emph{diverse} group convolution filters. Subsequently, we develop two fine-tuning schemes, i.e. (1) both local and global fine-tuning and (2) global only fine-tuning, which experimentally deliver comparable results, to recover the recognition capacity of the pruned network. Comprehensive experiments carried out on the CIFAR-10/100 and ImageNet datasets demonstrate that our self-grouping convolution method adapts to various state-of-the-art CNN architectures, such as ResNet and DenseNet, and delivers superior performance in terms of compression ratio, speedup and recognition accuracy. We demonstrate the ability of SG-CNN to generalise by transfer learning, including domain adaption and object detection, showing competitive results. Our source code is available at https://github.com/QingbeiGuo/SG-CNN.git.
Structure Learning for Cyclic Linear Causal Models

We consider the problem of structure learning for linear causal models based on observational data. We treat models given by possibly cyclic mixed graphs, which allow for feedback loops and effects of latent confounders. Generalizing related work on bow-free acyclic graphs, we assume that the underlying graph is simple. This entails that any two observed variables can be related through at most one direct causal effect and that (confounding-induced) correlation between error terms in structural equations occurs only in absence of direct causal effects. We show that, despite new subtleties in the cyclic case, the considered simple cyclic models are of expected dimension and that a previously considered criterion for distributional equivalence of bow-free acyclic graphs has an analogue in the cyclic case. Our result on model dimension justifies in particular score-based methods for structure learning of linear Gaussian mixed graph models, which we implement via greedy search.