Reviewerly/exHarmony huggingface dataset:
Readability of Reading Passages in English Textbooks and the Thai National Education English Test: A Comparative Study

Readability has long been regarded as a significant aspect in English language teaching as it provides the overall picture of a text’s difficulty level, especially in the context of teaching and testing. Readability is a practical consideration when making decisions on materials to match a text with target readers’ proficiency. However, few studies have compared the readability levels of teaching and testing materials in terms of the difficulty of passages. The present study, therefore, aims to explore the readability levels and the linguistic characteristics of reading passages in English textbooks and the Thai National Education English Test based on three readability formulas and eight aspects of linguistic characteristics as provided by the Coh-Metrix computational tool. Two sets of corpora were generated and analyzed by using Coh-Metrix as the main instrument. The obtained data from the reading passages compiled in the English textbooks and the Thai national education English test were compared to explore the significant differences. The results revealed a mismatch in the readability levels and linguistic characteristics. Passages from the English textbooks are easier than those used in the English test. It is recommended that all stakeholders in both teaching and testing administration be aware of the different levels of readability between reading passages. More considerations should be made when preparing the teaching and testing materials because a suitable difficulty level will ensure that students receive the most benefit from the materials. Moreover, an incongruity could affect students’ learning and testing performance.
Fuzzy Geographically Weighted Clustering dengan Gravitational Search Algorithm pada Kasus Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Tengah

One of the indicators of the success of social welfare development in Central Java was decreasing the population of people with social welfare problems (PMKS). One exertion that can be done was grouping or clustering the areas in Central Java-based on 26 indicators of PMKS. Fuzzy Geographically Weighted Clustering (FGWC) algorithm is a clustering analysis that observing the effect of the area. However, FGWC has a limitation in the initialization centroid phase that makes it trapped to local optimal. The limitation can be addressed with the Gravitational Search Algorithm (GSA) approach. The purpose of GSA was to optimize the value objective function. This research applied FGWC-GSA on PMKS in Central Java Province contained 26 indicators. Some validity indexes were applied to determine the best cluster. This research clustering the areas of Central Java into two clusters. The first cluster contained 24 districts and cities, and the second cluster contained 11 districts
Applying Phonological Features in Multilingual Text-To-Speech

This study investigates whether phonological features can be applied in text-to-speech systems to generate native and non-native speech in English and Mandarin. We present a mapping of ARPABET/pinyin to SAMPA/SAMPA-SC and then to phonological features. We tested whether this mapping could lead to the successful generation of native, non-native, and code-switched speech in the two languages. We ran two experiments, one with a small dataset and one with a larger dataset. The results proved that phonological features could be used as a feasible input system, although further investigation is needed to improve model performance. The accented output generated by the TTS models also helps with understanding human second language acquisition processes.
Remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor.

Remote attestation is a protocol to verify that a remote algorithm satisfies security properties, allowing to establish dynamic root of trust. Modern architectures for remote attestation combine signature or MAC primitives with hardware monitors to enforce secret confidentiality.
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators

This paper introduces an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated shapes. Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy. We show how to develop the unsupervised loss via a spectral decomposition of the Hessian of the ARAP energy. Our loss nicely decouples pose and shape variations through a robust norm. The loss admits simple closed-form expressions. It is easy to train and can be plugged into any standard generation models, e.g., variational auto-encoder (VAE) and auto-decoder (AD). Experimental results show that our approach outperforms existing shape generation approaches considerably on public benchmark datasets of various shape categories such as human, animal and bone.
Model Dashboard Information System untuk Peningkatan Kualitas Pengelolaan Jurnal Ilmiah

This study aims to develop a model of dashboard information system to improve the quality of scientific journal management. The method used to develop the model of the dashboard is prototyping. The dashboard was also developed using the Pivot Table, Histogram/ Column Chart, Line Plot/ Line Chart, and Area Chart approach. For case study, the information displayed in the dashboard which from data recorded in the MAVIB Journal. The information used includes information about the score of articles that have been received, website visitor in the form of a summary report View Page Statcounter. It can be downloaded in Excel form. The Prototype Dashboard Information System and the addition of Additional Menu in OJS were made using figma, so that it becomes an illustration for designing and updating the appearance of OJS website pages. The information displayed in the dashboard is used to make decisions in order to improve the management of scientific journals. The results show that the dashboard model successfully displays strategic information, tactical information and transactional information. This information can be used by journal managers to make decisions in improving the quality of journal management.
Neural Transition System for End-to-End Opinion Role Labeling.

Unified opinion role labeling (ORL) aims to detect all possible opinion structures of `opinion-holder-target' in one shot, given a text. The existing transition-based unified method, unfortunately, is subject to longer opinion terms and fails to solve the term overlap issue. Current top performance has been achieved by employing the span-based graph model, which however still suffers from both high model complexity and insufficient interaction among opinions and roles. In this work, we investigate a novel solution by revisiting the transition architecture, and augment it with a pointer network (PointNet). The framework parses out all opinion structures in linear-time complexity, meanwhile breaks through the limitation of any length of terms with PointNet. To achieve the explicit opinion-role interactions, we further propose a unified dependency-opinion graph (UDOG), co-modeling the syntactic dependency structure and the partial opinion-role structure. We then devise a relation-centered graph aggregator (RCGA) to encode the multi-relational UDOG, where the resulting high-order representations are used to promote the predictions in the vanilla transition system. Our model achieves new state-of-the-art results on the MPQA benchmark. Analyses further demonstrate the superiority of our methods on both efficacy and efficiency.
Regex Queries over Incomplete Knowledge Bases

We propose the novel task of answering regular expression queries (containing disjunction ($\vee$) and Kleene plus ($+$) operators) over incomplete KBs. The answer set of these queries potentially has a large number of entities, hence previous works for single-hop queries in KBC that model a query as a point in high-dimensional space are not as effective. In response, we develop RotatE-Box -- a novel combination of RotatE and box embeddings. It can model more relational inference patterns compared to existing embedding based models. Furthermore, we define baseline approaches for embedding based KBC models to handle regex operators. We demonstrate performance of RotatE-Box on two new regex-query datasets introduced in this paper, including one where the queries are harvested based on actual user query logs. We find that our final RotatE-Box model significantly outperforms models based on just RotatE and just box embeddings.
Multisensory Flow Experience Through Augmented-Virtual Reality Interactive Technology Education

The industry of education has appeared to be found as little bit slow at embracing the effectiveness and usefulness of new technologies. As for now, the advancement of technology in the field of learning is highly appreciated and anticipated as well. The concerning people have now started to share and exchange their technology hub in order to develop hi-tech communities that is facilitating educational system. Having a fair access to knowledge is indeed a human right. In this regard, advent of augmented and virtual reality education to facilitate learning around the world has helped the students get engaged in experiential learning. The learning is faster, better, retained and has caused at making improved decisions. Industrialization that changed the entire world could have not been possible without technological advancement and innovation. Virtual reality technology comes under the expertise of next generation that will benefit both the educators and the learners.
Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning

This work proposes an optimization-based manipulation planning framework where the objectives are learned functionals of signed-distance fields that represent objects in the scene. Most manipulation planning approaches rely on analytical models and carefully chosen abstractions/state-spaces to be effective. A central question is how models can be obtained from data that are not primarily accurate in their predictions, but, more importantly, enable efficient reasoning within a planning framework, while at the same time being closely coupled to perception spaces. We show that representing objects as signed-distance fields not only enables to learn and represent a variety of models with higher accuracy compared to point-cloud and occupancy measure representations, but also that SDF-based models are suitable for optimization-based planning. To demonstrate the versatility of our approach, we learn both kinematic and dynamic models to solve tasks that involve hanging mugs on hooks and pushing objects on a table. We can unify these quite different tasks within one framework, since SDFs are the common object representation. Video: https://youtu.be/ga8Wlkss7co
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant portions of the internet, and are frequently introduced with only minimal documentation. In this work we provide some of the first documentation for the Colossal Clean Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl. We begin by investigating where the data came from, and find a significant amount of text from unexpected sources like patents and US military websites. Then we explore the content of the text itself, and find machine-generated text (e.g., from machine translation systems) and evaluation examples from other benchmark NLP datasets. To understand the impact of the filters applied to create this dataset, we evaluate the text that was removed, and show that blocklist filtering disproportionately removes text from and about minority individuals. Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.
Exploring Temporal Coherence for More General Video Face Forgery Detection

Although current face manipulation techniques achieve impressive performance regarding quality and controllability, they are struggling to generate temporal coherent face videos. In this work, we explore to take full advantage of the temporal coherence for video face forgery detection. To achieve this, we propose a novel end-to-end framework, which consists of two major stages. The first stage is a fully temporal convolution network (FTCN). The key insight of FTCN is to reduce the spatial convolution kernel size to 1, while maintaining the temporal convolution kernel size unchanged. We surprisingly find this special design can benefit the model for extracting the temporal features as well as improve the generalization capability. The second stage is a Temporal Transformer network, which aims to explore the long-term temporal coherence. The proposed framework is general and flexible, which can be directly trained from scratch without any pre-training models or external datasets. Extensive experiments show that our framework outperforms existing methods and remains effective when applied to detect new sorts of face forgery videos.
Fingerprinting Multi-exit Deep Neural Network Models via Inference Time

Transforming large deep neural network (DNN) models into the multi-exit architectures can overcome the overthinking issue and distribute a large DNN model on resource-constrained scenarios (e.g. IoT frontend devices and backend servers) for inference and transmission efficiency. Nevertheless, intellectual property (IP) protection for the multi-exit models in the wild is still an unsolved challenge. Previous efforts to verify DNN model ownership mainly rely on querying the model with specific samples and checking the responses, e.g., DNN watermarking and fingerprinting. However, they are vulnerable to adversarial settings such as adversarial training and are not suitable for the IP verification for multi-exit DNN models. In this paper, we propose a novel approach to fingerprint multi-exit models via inference time rather than inference predictions. Specifically, we design an effective method to generate a set of fingerprint samples to craft the inference process with a unique and robust inference time cost as the evidence for model ownership. We conduct extensive experiments to prove the uniqueness and robustness of our method on three structures (ResNet-56, VGG-16, and MobileNet) and three datasets (CIFAR-10, CIFAR-100, and Tiny-ImageNet) under comprehensive adversarial settings.
Biden ending Keystone pipeline would kill thousands of American jobs

The Biden administration shutting down construction of the Keystone XL pipeline could end thousands of American construction jobs.
Toward Software-Defined Moving Target Defense for Secure Service Deployment Enhanced with a User-Defined Orchestration

In recent years, cloud native computing, which involves the deployment of scalable applications enhanced with containers, microservices, and serverless functions, has been actively studied to maximize its efficiency, flexibility, and economic feasibility. In this regard, studies on the security of the cloud native computing environment have been conducted. Among various studies on the security of these systems, moving target defense (MTD), which is an area of research that blocks various security threats in advance by actively changing the main properties of the protected target to deceive attackers, has been actively studied and developed. However, cloud native computing is highly dynamic; it is difficult to apply MTD technologies that actively change static system properties. Therefore, a software-defined MTD framework was designed for easier application of MTD technology to the cloud native environment. In this study, the user-defined adaptability of the software-defined MTD framework was implemented, and it was verified that the properties of the target service were changed according to previously defined mutation properties.
Character Recognition Based on Euclidean Distance Calculation Over Predictive and Centroid Coordinates

Now a day people are living with internet technology but those technologies brings many problems to the people through many hacking techniques. Image spam is the one among them. In the earlier stages, hackers used to annoy targeted victims with their fabricated text called spam text. Hackers are passing their bogus information on many ways such as advertising, spam emails, buttons, query distributions etc. From which spam emails are very specific to attack and they are filtered by text based filter. Then attackers nurtured their attacks on new way i.e., spreading spam mails by images. Those images are non related content to the concerned users on their corresponding mails or any web pages. Because of those spam images, text based filter couldn’t identify spam texts. On the basis of an image’s features, Attackers used to embed their spam text or mischief coded links into some of the attracted images. To identify spam contents from an image, security functions of a system must be able to recognize the characters imbedding on any images. This research paper is going to present views on image spam, Data mining approaches for dataset analysis, proposed optical character recognizer model and implementation of character recognition from images using Euclidean distance values.
Impact Free and XBEE Trans Processing For Mobile Robot Using Fuzzy Logic Approach

Advanced mechanics is traveling its approach to development combination altering its definition to a field that concentrates on the production of robots that tackle responsibilities that are unsafe to individuals like disarming explosives, mines and investigating wrecks. Impact Free Robot works utilizing outer sensors like sonar sensors which helps the robot in staying away from impediments in front of it utilizing controlling component as microcontrollers just as its implanted code. The methodology in its implanted code is the alleged fluffy rationale which helps the robot makes more effective choices in regards to its objective looking for capacity.
Salinity-Based Spatial Evaluation of Groundwater Quality for Agricultural Use

Abstract The Miandoab Plain has the largest water reserve in the province of West Azerbaijan, northwest Iran. Groundwater resources along with surface-water meet the needs of urban, industrial, and agricultural sectors, and therefore, their quality should be examined. Water quality indices are useful tools for aquifer management. In this research, the groundwater quality of the Miandoab Plain for agricultural purposes was investigated. For this purpose, the concentrations of the ions Mg 2+ , Ca 2+ , Na + , Hco 3 - , So 4 2- , Cl - and the pH level were measured. The indices effective salinity and potential salinity as well as sodium adsorption ratio and electrical conductivity were analyzed to evaluate the salinity. The geostatistical analysis was performed using the GS + software, and the zoning maps of salinity hazard were prepared using ArcGIS. To prepare the maps, EC, ES, PS, and SAR as well as Mg 2+ , Ca 2+ , Na + , Hco 3 - , So 4 2 , and Cl - were selected based on the semi-variogram values ​​and cross-validation technique. The Cl - map was considered as the basis for preparing the groundwater quality maps of the region. The results showed that the groundwater quality in the east of the plain is suitable, in the central part can be recommended under constant supervision, and in the west is unsuitable for agriculture. In other words, according to the geography of the plain, the recharge area is the low-risk part of the plain and the salinity hazard increases toward the discharge area. The results can pave the way for the relevant organizations to plan for the agricultural and environmental sectors.
INVESTIGATING UNIVERSITY STUDENTS’ ABILITY IN PARAPHRASING PARAGRAPHS

This research examined university students’ ability in paraphrasing paragraphs. This descriptive quantitative research took students of the English Education Department of Universitas Riau as samples using cluster random sampling technique. The selected sample was a Class of the English Study Program, Universitas Riau, which consists of 34 students. The data were collected by administering an online test of paraphrasing paragraphs through Google Form. The collected data were analyzed by three raters and the results were calculated and classified by using adaptation guide rubric from Brown (2004). Three aspects of paraphrasing were assessed and analyzed: contents of information, choice of words and writing convention. This study found that the ability of the Students of English Study Program in paraphrasing paragraphs was an unacceptable level, with total points; 11.21 for Content Information aspect, 9.52 for Choice of Words aspect, and 9.75 for Writing Conventions aspect. In order to answer the research question on how bad the ability is, the only clear answer is that all facts that are found within the research are only referring to the ‘crisis’ of paraphrasing ability shown by the university students themselves. This finding showed that the university students’ ability in paraphrasing paragraphs is low, due to the facts that; First, they get a low-level score in all aspects of paraphrasing. Second, they still make many fatal mistakes in every aspect of paraphrasing like; skipping important details in paraphrasing that may lead to misinformation, misusing the vocabulary and diction in creating the words in paraphrasing, and severe problems in the grammar, punctuation, and spelling (writing conventions). Moreover, choice of words seems to be the most difficult aspect for the students since it had the lowest score than other aspects in paraphrasing. This research suggests that English students should be exposed to more practices in their academic writing subject about paraphrasing skills
Institutional Governance and Protection Motivation: Theoretical Insights into Shaping Employees’ Security Compliance Behavior in Higher Education Institutions in the Developing World

Higher education institutions (HEIs) are progressively computerized to deal with substantial academic and operational information. With the increase in enriched information systems (IS) comes the potential hazard of malicious exposure to internal and external threats. This academic sector is advancing in the implementation of technical security controls; however, behavioral influence is still a challenge in the information security domain. Information security policies (ISPs) are generally designed and developed to control employees’ working behavior, yet compliance with these documents is near to non-existent. This research paper describes an empirical test of the influence of institutional governance (IG) on protection motivation and planned behavior of employees in HEIs. Results were analyzed using structural equation modeling (SEM) techniques. Our findings confirm the significant contribution of IG in motivating protection behavior among employees of HEIs. This cultivated motivation encourages positive conduct in information security policy compliance (ISPC).