Reviewerly/exHarmony huggingface dataset:
A Systematic Literature Review on Earthquake Detector

Around the world, earthquakes continue to be a significant cause for death, injury and disruption. Recently, Malaysia startled by Sabah earthquake that struck Ranau, Sabah on 5 June 2015 with moment magnitude reported 6.0   by the United States Geological Survey while the Malaysia Meteorological department has reported the magnitude to be 5.9   at a depth approximately 10 km with it is epicenter approximately 15 km North of Ranau and which lasted for 30 seconds. There are four objectives in this review paper which are review the vary keywords used in research, studies the different types of earthquakes detector that are appropriate, review the effectiveness of earthquake detectors and the effects of installation on environment and lastly examination the output produced by the earthquake detectors through the created/ existing detectors. There are four phases involved in this part which are identification, screening, eligibility, included. Each of phases shows the flow to determine references from resources search. After identify, based on all this steps, there are only 35 articles that included in qualitative synthesis. All findings studies were discussing and analyzed based on keyword used, types of detectors, output that produce from earthquake detectors and its effects on humans, technologies and environments. From studies that has been run, there are several ideas will be recommend in this part for future studies and for the benefit of researcher. Firstly, the devices will produce the location of the nearest earthquake protection center. Next, earthquake detectors can detect heat from humans or animal within a range of distances.
Identitas: Semantics-free and human-readable identifiers

In this article, we present a new approach to identifiers, one that aims to improve the management of ontologies. This approach overcomes some of the main flaws associated with the existing approaches, providing alternative solutions. Ontology identifiers are the key for each entity defined in an ontology, and enable a unique and persistent reference to each term. The form of identifiers has been the subject of discussion, which has resulted in a number of different schemes. It is often recommended that identifiers for ontology terms should be semantics-free or meaningless. One practice is to use numeric identifiers, starting at one and working upwards. However, this has a number of disadvantages: it does not allow for concurrent development, is relatively hard to read, and it is difficult to detect errors when an identifier is misused. From the perspective of development, solving these issues could facilitate the process of building and managing ontologies. Here, we suggest random identifiers to enable concurrent development, while exploiting the proquint library to overcome the problems of memorability and pronounceability. Finally, a checksum is implemented to prevent the occurrence of errors while accessing relatively similar identifiers. Availability and Implementation: The software is available from https://github.com/Nizal-Shammry/identitas-j. It has been integrated into environments for ontology development such as Tawny-OWL and Protégé.
Tighter Generalization Bounds for Iterative Privacy-Preserving Algorithms

This paper studies the relationship between generalization and privacy preservation of machine learning in two steps. We first establish an alignment between the two facets for any learning algorithm. We prove that $(\varepsilon, \delta)$-differential privacy implies an on-average generalization bound for a multi-sample-set learning algorithm, which further leads to a high-probability bound for any learning algorithm. We then investigate how the iterative nature shared by most learning algorithms influences privacy preservation and further generalization. Three composition theorems are proved to approximate the differential privacy of an iterative algorithm through the differential privacy of its every iteration. Integrating the above two steps, we eventually deliver generalization bounds for iterative learning algorithms. Our results are strictly tighter than the existing works. Particularly, our generalization bounds do not rely on the model size which is prohibitively large in deep learning. Experiments of MLP, VGG, and ResNet on MNIST, CIFAR-10, and CIFAR-100 are in full agreement with our theory. The theory applies to a wide spectrum of learning algorithms. In this paper, it is applied to the Gaussian mechanism as an example.
Responding to Living-Off-the-Land Tactics using Just-In-Time Memory Forensics (JIT-MF) for Android

Digital investigations of stealthy attacks on Android devices pose particular challenges to incident responders. Whereas consequential late detection demands accurate and comprehensive forensic timelines to reconstruct all malicious activities, reduced forensic footprints with minimal malware involvement, such as when Living-Off-the-Land (LOtL) tactics are adopted, leave investigators little evidence to work with. Volatile memory forensics can be an effective approach since app execution of any form is always bound to leave a trail of evidence in memory, even if perhaps ephemeral. Just-in-Time Memory Forensics (JIT-MF) is a recently proposed technique that describes a framework to process memory forensics on existing stock Android devices, without compromising their security by requiring them to be rooted. Within this framework, JIT-MF drivers are designed to promptly dump in-memory evidence related to app usage or misuse. In this work, we primarily introduce a conceptualized presentation of JIT-MF drivers. Subsequently, through a series of case studies involving the hijacking of widely-used messaging apps, we show that when the target apps are forensically enhanced with JIT-MF drivers, investigators can generate richer forensic timelines to support their investigation, which are on average 26% closer to ground truth.
APLIKASI RENTAL MOBIL BERBASIS ANDROID DI DIRGA RENT SRAGEN

Car rental business is a business in the field of transportation which is currently developing, but the general management of thedata is still done manually. Initially this manual system was not a problem, but along with the increasing number of cars obtained and theincreasing number of tenants making transactions, the data increased more and more, thus managing data manually became ineffective andinefficient, so as to ensure accuracy in transaction recording, security in data storage, as well as accuracy and speed in presentinginformation, a computer-based car rental management information system needs to be built. Making this car rental management informationsystem starts with collecting data collected with the system to be built, then analyzing it, and proceed with designing the system based on theresults of the analysis, the next step is to replace the program in accordance with the design results. After the program has been written, a testwill be carried out to ensure that the program is made as expected. The last step of this activity is to create a documentation system that ismade in the form of research reports. The results of this study are a computer-based car rental application that can be implemented, and isexpected to be able to overcome all problems in data processing in the car rental businessKeywords : E-Rental, time, efficient
Finding Missing Person Using AI

Nowadays to find a missing person is very hard task, although we all are updated by social media, it requires numerous paper work to be done and it is time consuming process and also after doing this hard work, there are not much chances of proper result. This project gives out a system, which helps both police department and public by speeding up the process of searching using face recognition. Therefore, how this system works is that when the person goes missing the respective guardian of that person can upload the image, which then will   get store in our database. Next is, the face recognition model in our system will find a match of that person in the database. If a match is found, it will be notified to the police and the guardian of that person.
Unit Testing of ETSI Enhanced Aacplus General Audio Codec

The Enhanced aacplus codec(EAC) is specified in ISO standards. The standard converts an incoming MPEG 4 bitstream to pulse code modulation sample output. Thecoding makesuseofamethod forcodingaudio which is advanced(AAC), it makes use of a replica in the Spectral band (SBR) and parameters of speech in coding. The parametric coding facilitates low bitrates while encoding stereo. Error Concealment techniques used while coding minimize frame loss. The codec uses and encoder and a decoder. In this project code is available from ETSI which is compiled and tested on a linux machine. In the absence of real time testing the encoder uses a wav file as input which converts it into 3gp file format. This 3gp bitstream file is later used by the decoder to get back the original wav file. A series of typical wav files are identified as test stimulus. The recovered wav files are verified for correctness using three matlab scripts. Testing is performed meticulously to verify the correctness of the standard.
Adversarial Machine Learning

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning - the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques. © 2011 ACM.
Text Identification System for Translation of English Language

India is a multilinguistic country. People of different states speak different languages but all Indians are not polyglots. English is called as universal language and Kannada is one of Dravidian languages that is used in India. The majority of people in India, especially who live in villages, cannot read, write and understand English language. Translation of any language that is done by human is time consuming. Therefore, there is a need for implementation of Machine translation systems that will translate English language to Kannada language. This paper describes translation system that enhances the knowledge of society without language barrier by translating text in English language to Kannada language by giving the meaning of the word. We can click the picture of any English text and give Image as input to translation system. The system translates English text to Kannada text by extracting characters from an image by using tesseract software. Tesseract is optical engine that scan image containing text and extract characters from image and convert them into editable from. Extracted text which is in English language might have non dictionary words that may or may not be translated properly to Kannada language by existing system. It also identifies such non dictionary words compares with hybrid database and replaces these words with exact Kannada word.
Late Pleistocene Dual Process Minds

The global dispersal of prehistoric ancient humans from Africa to North America, and the existence of artistic innovation evidenced in the Late Pleistocene are, by now, parts of a familiar and fascinating story. But the explanation of how our human career was possible cries out for clarification. In this chapter, I argue that dual process theory can provide the needed explanation. My claim will be that the advent of System-2 reasoning running offline, aided by executive cognitive control and language, and facilitated by neural plasticity, made possible the remarkable human dispersal from Africa to North America by way of the Middle East and Asia. System-1 modular adaptations, together with System-2 reasoning, gave rise to the flexible, culturally-informed, mental operations that were essential to shape the psychologically modern mind. The result was, inter alia, the surprising Late Pleistocene dispersal of ancient humans to North America.
Accelerating Object-Sensitive Pointer Analysis by Exploiting Object Containment and Reachability (Artifact)

Object-sensitive pointer analysis for an object-oriented program can be accelerated if context-sensitivity can be selectively applied to some precision-critical variables/objects in the program. Existing pre-analyses, which are performed to make such selections, either preserve precision but achieve limited speedups by reasoning about all the possible value flows in the program conservatively or achieve greater speedups but sacrifice precision (often unduly) by examining only some but not all the value flows in the program heuristically. In this paper, we introduce a new approach, named Turner, that represents a sweet spot between the two existing ones, as it is designed to enable object-sensitive pointer analysis to run significantly faster than the former approach and achieve significantly better precision than the latter approach. Turner is simple, lightweight yet effective due to two novel aspects in its design. First, we exploit a key observation that some precision-uncritical objects can be approximated based on the object-containment relationship pre-established (by applying Andersen’s analysis). This approximation introduces a small degree yet the only source of imprecision into Turner. Second, leveraging this initial approximation, we introduce a simple DFA to reason about object reachability for a method intra-procedurally from its entry to its exit along all the possible value flows established by its statements to finalize its precision-critical variables/objects identified. We have validated Turner with an implementation in Soot against the state of the art using a set of 12 popular Java benchmarks and applications.
Learning How to Listen: Automatically Finding Bug Patterns in Event-Driven JavaScript APIs

Event-driven programming is widely practiced in the JavaScript community, both on the client side to handle UI events and AJAX requests, and on the server side to accommodate long-running operations such as file or network I/O. Many popular event-based APIs allow event names to be specified as free-form strings without any validation, potentially leading to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lost events</i> for which no listener has been registered and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dead listeners</i> for events that are never emitted. In previous work, Madsen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> presented a precise static analysis for detecting such problems, but their analysis does not scale because it may require a number of contexts that is exponential in the size of the program. Concentrating on the problem of detecting dead listeners, we present an approach to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn</i> how to use event-based APIs by first mining a large corpus of JavaScript code using a simple static analysis to identify code snippets that register an event listener, and then applying statistical modeling to identify anomalous patterns, which often indicate incorrect API usage. In a large-scale evaluation on 127,531 open-source JavaScript code bases, our technique was able to detect 75 anomalous listener-registration patterns, while maintaining a precision of 90.9% and recall of 7.5% over a validation set, demonstrating that a learning-based approach to detecting event-handling bug patterns is feasible. In an additional experiment, we investigated instances of these patterns in 25 open-source projects, and reported 30 issues to the project maintainers, of which 7 have been confirmed as bugs.
AESTHETICS EVOLUTION OF E-LEARNING INTERFACES

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 12077-12085ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.2525Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
IMPROVING STUDENTS’ READING COMPREHENSION BY USING JIGSAW TECHNIQUE OF THE SEVENTH GRADE SMP DARUSSALAM MEDAN

This study was aimed to find the empirical proof of the improving students reading comprehension by using jigsaw technique. The used of jigsaw technique could enhance students reading comprehension in case to found: (a) information in the text; (b) words meaning; and (c) main idea in paragraph. Jigsaw technique also made the students more active and motivated to learn reading comprehension in class. The method that was used was a quantitative method. The design of this study was experimental study. This study was conducted in SMP Darussalam Medan. The number of the sample of the study was 60 students of seventh  grade. The instrument of this study was tests. The tests were divided into two, there were pre-test and post-test. The data that were obtained from the tests were analyzed by using SPS statis t ic 25 program. The result of the study showed that the mean score of post test in control class was 72.33 while the mean score of post-test in experimental was 84.00.Based on the criteria of hypothesis test that if sig. 2 tailed (p) was lower than alpha (α) 0.05, then Ho (Null Hypothesis) was rejected and Ha (Alternative Hypothesis) was accepted. In this study, the result of sig. 2 tailed was 0.000 and it proved that p <α. It showed that there was a significant to improving students reading comprehension.
La educación en tiempos de pandemia. Emociones y percepciones en estudiantes de nivel medio superior y licenciatura

This article reports the results obtained in the research carried out during the pandemic produced by the SARS-CoV-2 or covid-19 virus, where the student sector was affected. This research focuses on the feelings and emotions resulting from confinement and online classes, as well as the difficulties and successes that arise in the implementation of this sudden teaching model in middle and upper-level students. Resulting in mostly obstacles and restrictions that disadvantage education in Mexico.
Spring Web Services

Spring Web Services (Spring WS) is focused on building contract-first SOAP web services, with flexible XML mappings, loose coupling between contract and implementation, and easy integration with Spring. It has an architecture similar to that of Spring MVC.
Secure Exam Management System for Android Systems Based on M-Learnings

The main aim of m-learning is not to convert all computer-based learning or to replace classroom to mobile format but to strengthen the existing strategy. M-learning is learning across multiple contexts, through social and content interactions using personal electronic devices. This project aims to design a secure exam management system which meets the essential security measures.
Trust and Corporate Innovation

This paper examines the relation between the level of societal trust and corporate innovation in the worldwide. Using data on firms located in 72 countries or regions from1992 to 2016, we quantify the country trust level and find strong evidence that firms in countries with a higher level of societal trust have higher R&D investment input. We also find that the impact of societal trust on firms’ innovation behavior is more pronounced in the firms with a higher level of financial constraints and in the countries with a poor intellectual property rights protection. Overall, our results highlight the effect of societal trust in shaping enterprise innovation behavior.
Investment Stratagem of It Employees

Saving and Investment are two key variables which play a significant role in economic growth, control of inflation, stability and promotion of employment especially in the context of developing countries. In general, to invest is to distribute money in the expectation of some benefit in the future. Actually, the present study identifies about the preferred investment avenues among IT investors from backward areas using their own self-assessment test. This paper analyse and found that IT sector employees consider more safety as well as good return on investment rather than taking risk, when compared to urban IT sector employees.
An Integrated Approach for Table Detection and Structure Recognition

Detecting and identifying the table structure is an important issue in document digitization. Although there have been many great strides based on current deep learning techniques, table structure identification is still a difficult and difficult problem, especially when solving the problem of digitizing text in practice. The paper proposes a solution to digitize table documents based on the Cascade R-CNN HRNet network to detect, classify tables and integrate image processing algorithms to improve table data identification results. The proposed algorithm proved effective on real data - the hydrometeorological station record book contains tables including simple and complex structures tables with over 98% accuracy.
Learning effectiveness improvement through mobile learning

This research was conducted to identify the ulitization of mobile learning in making learning effective during the pandemic at SMK Nurul Jadid Paiton Probolinggo, East Java, Indonesia. This research used a qualitative method approach with case study. The results showed that; the use of mobile learning in making learning effective during this pandemic is carried out through several stages, that is; planning, which includes the preparation of materials, methods, media, and evaluation techniques that will be used in learning activities. Furthermore, the results of the planning are implemented in mobile learning activities and evaluated according to the planned expectations.
Hypozonal gold mineralization in shear zone-hosted deposits driven by fault valve action and fluid mixing: the Nalunaq deposit, Greenland

Analyses of fluid inclusions in quartz from the Nalunaq gold deposit, Greenland, use to interpret hydrothermal fluid sources and gold transport and deposition mechanisms.
Disentangling factors in the placement of manner adverbials in German: the effect of distributional similarity

Author(s): Schafer, Martin; Gauza, Holger; Stolterfoht, Britta | Abstract: Processing differences between obligatory constituents like verbal complements and facultative ones like adjuncts are widely discussed in psycholinguistics. But the relative positional and semantic variability of adjuncts makes analyzing these differences a daunting challenge. Focusing on the intricate problem of the default position of manner adverbials in German, we present a re-analysis of a recent psycholinguistic study on their ordering preferences in which we explore the extent to which similarities between word embeddings can be used as stand-ins for shared semantic memory, representing the probability of seamless conceptual combination. In re-analyzing six experiments across different paradigms, the addition of the new predictors yields substantially better models which show that these factors considerably interact with established lexical and grammatical predictors.
Morphological Texture Description For Hyperspectral Images: Pattern Spectrum

A metrological extension of morphological granulometry for the hyperspectral domain is introduced in this work. This development is enabled by the latest study of a suitable ordering relation for hyperspectral images. With granulometry as a texture descriptor, a suitable similarity measure for it is also introduced. In addition to providing validation experiments to the extension, a preliminary result in a texture discrimination task can also be found in this work.