Reviewerly/exHarmony huggingface dataset:
Low Power, Rugged Edge Computing provides a low cost, powerful solution for on the ground remote sensing in extreme environments

Ground based remote sensing devices increasingly incorporate low cost single board computers such as a Raspberry Pi to capture and analyze images and data from the environment. Useful and cheap as these devices are, they are not designed for use in extreme conditions and as a consequence often suffer from early failure. Here we describe a system that incorporates a commercially available rugged Edge Computer running embedded Linux that is designed to operate in remote and extreme environments. The AVERT system&amp;#160; (Anticipating Volcanic Eruptions in&amp;#160; Real Time) developed at Columbia University in New York and funded by the Moore Foundation uses solar and wind powered Sensor nodes configured in a spoke and hub architecture currently operating on two volcanoes overseen by the Alaska Volcano Observatory in the Aleutian Islands, Alaska. Multiple Nodes distributed around the volcanoes are each controlled by an Edge Computer which manages and monitors local sensors, processes and parses their data via radio link to a central Hub and schedules system components to wake and sleep to conserve power. The Hub Edge Computer collects and assembles data from multiple Nodes and passes it via satellite, cellular modem or radio links to servers located elsewhere in the world or cloud for near real-time analysis. The local computer enables us to minimize local power demand to just a few watts in part due to the extremely low power sleep modes that are incorporated into these devices. For instance, a Node incorporating a webcam, IRCam, weather station, Edge Computer, network switch, communications radio and power management relays draws only 4.5W on average. In addition, this level of local computing power and a mature Linux operating environment enables us to run AI algorithms at source that process image and other data to flag precursory indicators of an impending eruption. This also helps to reduce data volume passed across the network at times of low network connectivity. We can also remotely interrogate any part of the system and implement new data schemes to best monitor and react to ongoing events. Future work on the AI algorithm development will incorporate local multisensor data analytics to enhance our anticipatory capability.
The Swedish National Seismic Network - Infrastructure, Data Processing and Products

In recent years the Swedish National Seismic Network (SNSN) made an increased effort to modernize station and communication equipment, and thereby has significantly improved continuous real-time data availability and data quality.&amp;#160; Currently, the SNSN is operating 67 permanent broadband seismic stations evenly distributed in the South, along the Eastern shore and the North of Sweden. In addition, a temporay network of 13 stations was deployed in 2021 for a 3-year period to monitor small earthquakes associated with a linear cluster of events at the Western cost of the Gulf of Bothnia. SNSN transmits continuous real-time data to networks in the neighboring countries (Norway, Denmark, Finland, Germany) and in turn receives and processes data from about 120 stations abroad.Compared to many other countries, Sweden has a relatively low seismicity. This makes it all the more important to focus on small seismic events in order to map crustal structures and processes and to provide a data basis for reasonable long-term seismic hazard assessments. Turning to small events means to deal with many events and most of them being man-made seismic events (blasts related to quarries, underground mines, road/tunnel constructions, etc). Within the last 22 years SNSN has recorded and analyzed about 170,000 seismic events out of which only 11,000 (~6.5%) were classified as natural events. Automatic event processing and event type classification are of the essence in order to cope with the amount of data and to decrease the workload of the analysts.SNSN is running four different and independent automatic processing routines in parallel: SeisComp (SC), Earthworm (EW), MSIL and a migration stack algorithm (MS). The main purpose of SC and EW is to detect and locate events in realtime. Both systems are set up to be very sensitive in order to detect as small events as possible, which on the other side also increases the probability to generate spurious events. To counterbalance that we generate a common event catalogue (i.e. events that were located both by SC as well as EW) which turnes out to be very reliable. The common event bulletin captures events as small as about ML1 and contains almost all events ML &gt; 1.5.&amp;#160; MSIL and MS are running in offline and delayed mode which allows the backfilling of potential data gaps, before processing. These systems are catching events down to about magnitude ML0. All events of the common bulletin and the MSIL bulletin are subject to an automatic Neural Network event typ classification into earthquakes, blasts and mining-induced events. In a final step all events classified as earthquakes, significant blasts (felt events or events of special interest) or events with unclear cassification are reviewed by SNSN analysts and are being made available on the SNSN web page. In the framework of EPOS-Sweden, SNSN will make available the waveform and metadata data of the permanent network via FDSN-services.
ANALYSIS OF THE IMPLEMENTATION OF MVVM ARCHITECTURE PATTERN ON PERFORMANCE OF IOS MOBILE-BASED APPLICATIONS

Performance efficiency is important in mobile application development because mobile devices have limitations in terms of power and resources. Performance efficiency can be improved by applying architecture patterns. In this paper, we use the Model View ViewModel (MVVM) architecture. The application of the architecture is carried out to analyze how practical the application of the MVVM architecture pattern is in increasing performance efficiency in the mobile application. Performance efficiency is measured based on CPU usage, memory usage, and execution Time. The case study shows that the CPU usage and execution Time on MVVM are smaller than Base architecture pattern from the AR Ruler. This is due to the third-party library RxSwift in the MVVM architecture that increases the application's response so that CPU usage and execution time is better than Base architecture pattern. However, the existence of the third-party library RxSwift has a negative impact on memory usage, resulting in higher memory usage than the Base Architecture Pattern. The MVVM pattern is highly recommended for mobile application development to improve performance efficiency.
Some quantum MDS codes from GRS codes

The construction of quantum maximum-distance-separable (MDS) codes has become one of the major goals in quantum coding theory. In this paper, we construct several classes of Hermitian self-orthogonal generalized Reed–Solomon codes. Based on these classical MDS codes, we obtain several new classes of quantum MDS codes with large minimum distance. It turns out that these constructed quantum MDS codes have fewer constraints on the selection of code length, and in some cases, have larger minimum distance than previous literature. Notably, some of the distance parameters of our codes are greater than (q/2)+1.
Prediction of a Sprint Delivery’s Capabilities in Iterative-based Software Development

Iterative-based software development has been frequently implemented in working environment. A modern era software project demands that the product is delivered on every sprint development. Hence, the execution of a sprint requires ample supervision and capabilities to deliver a high quality product at the end of the software project development. This research’s purpose is to give support for a software project’s supervisor or owner in predicting the end product’s capability by knowing the performance level of each sprint. The method proposed for this purpose is to build a prediction model utilizing a number of features in a form of characteristics from a dataset containing software project iterations. The proposed model is built using Random Forest Regressor as a main method, with KNN (K-Nearest Neighbors) and Decision Tree Regressor being the comparison methods. Testing results show that compared to KNN and Decision Tree, Random Forest Regressor yields the best performance through its steady results on every stage progression of all tested software projects.
Passive seismic and infrasonic monitoring at the Mefite d’Ansanto deep-CO2 degassing site (Southern Apennines, Italy).

The role of fluids in the preparatory phase of major earthquakes and in the evolution of aftershocks and swarms in space and time is well-documented. In particular, numerous studies evidence the primary role that mantle-derived fluids play in the generation of large upper crustal earthquakes in extensional domains, where crustal-scale faults act as preferential hydraulic pathways.&amp;#160;&amp;#160;We focus on the Mefite D'Ansanto degassing site, the largest low-temperature non-volcanic CO2 emission in the world, located at the northern tip of the Mw6.9 1980 Irpinia faults. The study area experienced strong historical earthquakes (1702, 1732 and 1930 M6+ earthquakes) but it is characterized by a relatively low background seismicity rate with respect to the nearby Sannio and Irpinia regions.&amp;#160;&amp;#160;&amp;#160;&amp;#160;To collect high-quality microseismicity data in this key sector of the southern Apennine extensional belt and investigate the relationship among seismicity, crustal fluids, and physical-hydraulic properties of the crust, we installed in July 2021 (up to May 2023) a temporary network composed of 10 stations equipped with short-period velocimeters (5 sec). The temporary network covers an area of approximately 30x30 km2 surrounding the Mefite d&amp;#8217;Ansanto site and integrates with the numerous permanent stations of the INGV and ISNet networks located at the boundary of the survey area.&amp;#160;Within the Mefite area, we also deployed a temporary seismo-acoustic dense array to study two CO2 vents. The seismo-acoustic array is composed of 5 infrasonic stations equipped with IST-2018 broadband microphones developed by The ISTerre (Universit&amp;#233; Savoie Mont Blanc, France), in addition to one seismo-acoustic station equipped with a co-located digital broadband seismometers (120s). The array is positioned approximately at the vertices of a star, with an aperture of about 50 meters. The deployment lasted for 1 week at the end of May 2022, allowing us to sample the emission site during &amp;#8220;dry&amp;#8221; weather conditions.&amp;#160;We show first results of the analysis of seismicity recorded by the temporary network applying both standard (STA/LTA) detection algorithms or innovative enhanced techniques such as cross-correlation based template-matching algorithms and/or Deep-Learning-Phase-Recognition methods.The activities are developed in the framework of the multidisciplinary project FURTHER (https://progetti.ingv.it/en/further).
Fast Earthquake Magnitude Estimation using HR-GNSS time series: a Deep Learning approach

Fast magnitude estimation of large earthquakes has been a key task for warning systems. In the last decades, Global Navigation Satellite Systems data with high-rate sampling (&amp;#8805;1 Hz; HR-GNSS) have provided us with useful information from displacement time series for analyzing large earthquakes; especially when the signals of earthquakes recorded in inertial sensors are saturated. Hence, improving algorithms to contribute to the fast analyses of the HR-GNSS data has been a recent challenge.In this work, we propose a deep-learning-based algorithm for earthquake magnitude estimation, which was trained by thousands of synthetic displacement time series corresponding to Mw &gt; 6.5 earthquake signals. We adapted the model to a variable number of stations and lengths of the time series as input. Thus, it is possible to apply the algorithm without any restriction on the number of stations, and the flexibility in the length of the input time series facilitates the inclusion of data not only from local stations but also from regional stations if required. The influence of attributes such as noise, magnitude, number of stations, epicentral distance, and length of input time series on the model performance was evaluated. We aim to generalize this approach to the magnitude estimation of earthquakes from different tectonic regions. The robustness of the model was tested with both synthetic and real earthquake signals.
Spatial and Temporal Patterns in Eastern-Alpine Seismicity

We exploit a new template matching based catalogue to study the spatialand temporal patterns of seismicity in the Eastern and Southern Alps. Datafrom the AlpArray Swath-D network from late 2017 to late 2019 were used toenhance the resolution of the seismic catalogues provided by localagencies. The template matching method was implemented using our ownGPU-accelerated algorithm to deal with the large data volume. All eventsare relocated using waveform-based picking methods.Based on this result, we study statistical characteristics of the extendedseismicity catalogue, which now has a magnitude of completeness of Mc=0.6and contains about 7,500 seismic events. We analyse the main spatial andtemporal features of seismicity revealed by this novel dataset. Finally,we analyse specific event clusters that originated from the templatematching method, and seek to link event interconnectivity to geometricalproperties of the clusters.
Pantone's color of the year 2023 is Viva Magenta 18-1750

Pantone has designated Viva Magenta 18-1750 to be the color of the year 2023
Performance analysis of edge, fog and cloud computing paradigms for real-time video quality assessment and phishing detection

Purpose The purpose of this paper is to perform a comparative analysis of the delay associated in running two real-time machine learning-based applications, namely, a video quality assessment (VQA) and a phishing detection application by using the edge, fog and cloud computing paradigms. Design/methodology/approach The VQA algorithm was developed using Android Studio and run on a mobile phone for the edge paradigm. For the fog paradigm, it was hosted on a Java server and for the cloud paradigm on the IBM and Firebase clouds. The phishing detection algorithm was embedded into a browser extension for the edge paradigm. For the fog paradigm, it was hosted on a Node.js server and for the cloud paradigm on Firebase. Findings For the VQA algorithm, the edge paradigm had the highest response time while the cloud paradigm had the lowest, as the algorithm was computationally intensive. For the phishing detection algorithm, the edge paradigm had the lowest response time, and the cloud paradigm had the highest, as the algorithm had a low computational complexity. Since the determining factor for the response time was the latency, the edge paradigm provided the smallest delay as all processing were local. Research limitations/implications The main limitation of this work is that the experiments were performed on a small scale due to time and budget constraints. Originality/value A detailed analysis with real applications has been provided to show how the complexity of an application can determine the best computing paradigm on which it can be deployed.
Research Progress on Non-Thermophysical Fresh Keeping Technology of Fruits and Vegetables Based on Nutrition and Safety

With the rapid development of the economy and the improvement of living standards, people not only have an increasing demand for fresh fruits and vegetables but also have become increasingly strict on the nutrition, safety, and quality of fruits and vegetables.However, the characteristics of high nutrition and high moisture make them easily susceptible to microbial infection, which can seriously reduce their nutritional value and economic value and even cause food safety problems.Therefore, from the perspective of nutrition and safety, this study examines the application and research of non-thermal physical preservation technologies, such as packaging, irradiation, and cold plasma, in the postharvest storage of fruits and vegetables, and forecasts the research and development trends of fruit preservation technology.
Towards Automated Fiqh School Authorship Attribution

The word Fiqh (Islamic jurisprudence) refers to the body of Islamic law (Shari’ah). A large volume of Fiqh literature has been generated over the past thirteen hundred years, some of which texts have unknown authors. The importance of identifying the Fiqh School emanates from its importance in offering an authenticated interpretation of fundamental sources. The traditional method for identifying the Fiqh School for a certain text is either by knowledge of the school affiliation the author or by close reading of the text by Fiqh scholars. This method is costly in terms of the time and human effort involved. An alternative to this manual approach is automated identification of Fiqh school texts using stylometric analysis. In this study we investigate the extent to which stylometric features can be used as predictors for Fiqh school authorship of a given text. We explore a corpus of Arabic Fiqh texts using unsupervised cluster analysis and supervised machine learning. The results of our study show that the Fiqh schools have distinctive text style features that can be used to indicate authorship. The observations from the cluster analysis experiment using a number of different distance measures are visualized using network graphs. The best clustering in terms of Fiqh school division was achieved by the Classic Delta distance measure and Eder’s Delta distance measure. The results from the supervised experiment comparing the four classification algorithms: Support Vector Machines (SVM), Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Delta show that supervised classification using SVM produces the highest average accuracy at 97.5% for the task of Fiqh school prediction.
Automatic Method to Build a Dictionary for Class-Based Translation Systems

Mis-translation or dropping of proper nouns reduces the quality of machine translation or speech translation output. In this paper, we propose a method to build a proper noun dictionary for the systems which use class-based language models. The method consists of two parts: training data building part and word classifier training part. The first part uses bilingual corpus which contain proper nouns. For each proper noun, the first part finds out the class which gives the highest sentence-level automatic evaluation score. The second part trains CNN-based word class classifier by using the training data yielded by the first step. The training data consists of source language sentences with proper nouns and the proper nouns’ classes which give the highest scores. The CNN is trained to predict the proper noun class given the source side sentence. Although, the proposed method does not require the manually annotated training data at all, the experimental results on a statistical machine translation system show that the dictionary made by the proposed method achieves comparable performance to the manually annotated dictionary.
Deep Learning-Based Screen Text Detection and Recognition for Onboard Maintenance Systems

Due to the numerous alarm entries and dense text on the interface of the onboard maintenance system (OMS), it is still challenging for current algorithm to achieve complete and accurate text recognition based on screen. This paper proposes a deep learning method that employs YOLOv3, CRNN and a post-processing module. This method first uses YOLOv3 to locate some text which we really concerned; then the feature map of the positioning area is aligned and fed to the CRNN text recognition module to obtain the text detection result, and finally the text matching module based on the minimum edit distance is used to solve some hard cases. During the experiment, we also discussed the limits of each module and made some improvements. The experimental results demonstrate that the approach we proposed has an accuracy of 99.95% and a recall of 95.74%, indicating that our algorithm can solve the problems of incomplete localization and inaccurate text recognition, and can meet practical application scenarios.
The Utility of Hierarchical Phrase-Based Model Machine Translation for Low Resource Languages

The paper uses a hierarchical phrase-based model to develop Statistical Machine Translation (SMT) Systems for four low resourced South Asian languages. South Asian languages predominantly use traditional statistical and neural machine approaches to translate into another language (mainly English). However, translation accuracy is not much higher as South Asian languages lack in necessary natural language resources and tools; hence classified as low resourced languages. Any SMT system needs large parallel corpora for actual performance. So, the non-availability of corpora constraints the success in machine translation of those languages. Another reason for poor translation quality is grammatical differences between South Asian languages and English: morphological richness and different sentence structure. But traditional SMT systems use the default distortion reordering model to reorder the sentences independent of their context. To overcome this problem, hierarchical phrase model translation, which uses grammar rules formed by the Synchronous Context-Free Grammar, is proposed. This paper considers English to Tamil, Tamil to English, Malayalam to English, English to Malayalam, Tamil to Sinhala and Sinhala to Tamil translations. In the end, we evaluate the system using BLEU as the evaluation metric. The hierarchical phrase-based model shows better results compared to the traditional approach between Tamil-English and Malayalam-English pairs. For Sinhala to Tamil, it achieves 11.18 and 10.73 for vice-versa.
Building Personalized Language Models Through Language Model Interpolation

Social media users differ in how they write such as writing style and topics. This suggests that personalized language models—language models tailored to a specific person—could outperform a single generic language model. One challenge, however, is that language models typically require a large volume of text to train on, but for many people such a volume of text is not available. In this paper, we train n-gram and neural language models on relatively large in-domain background corpora, and on relatively small amounts of text from individual social media users, specifically authors of blogs. In experiments with interpolated language models, we find that, although user-specific language models trained on a small amount of text from a user perform relatively poorly, they can be interpolated with language models trained on a large background corpus to give improvements over either approach on its own. We further find that n-gram and neural language models are complementary, and can be interpolated to give improvements over either approach used individually. Our evaluation considers perplexity, and two evaluation measures motivated by next word suggestion on smart-phones. We find that although perplexity is widely used for intrinsic evaluation of language models, it is a poor indicator of performance in terms of these other measures.