Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the Grammar Feedback of Two Automated Writing Evaluation Systems: My Access and Criterion

ESL/EFL students need to enhance writing skills to pass various language tests. To help teachers reduce their teaching load and to give students faster feedback, several commercial AWE (automated writing evaluation) tools are available. Although these innovative tools have become popular, few empirical studies have examined the quality of feedback generated by these systems. There is a pressing need to better understand the strengths and weaknesses of these widely used AWE systems. In this paper, we examined the grammar feedback provided by two well-known systems, Vantage My Access and ETS Criterion. The purpose of this paper is to uncover the strengths and weaknesses of the feedback provided by these two systems. Automated feedback messages on 269 student essays were collected and analyzed. The results showed that these two systems can provide about 30 different types of feedback messages. However, My Access provided many false alarm messages. Criterion performed much better than My Access, but it still failed to deal with several common errors related to word order, modals, tenses, collocations, conjuncts, word choice, and pronouns. It is clear that the two AWE systems do not provide satisfactory help to ESL students. Teachers and students who use these new tools should be aware of these problems and limitations.
Hubless Nearest Neighbor Search for Bilingual Lexicon Induction

Bilingual Lexicon Induction (BLI) is the task of translating words from corpora in two languages. Recent advances in BLI work by aligning the two word embedding spaces. Following that, a key step is to retrieve the nearest neighbor (NN) in the target space given the source word. However, a phenomenon called hubness often degrades the accuracy of NN. Hubness appears as some data points, called hubs, being extra-ordinarily close to many of the other data points. Reducing hubness is necessary for retrieval tasks. One successful example is Inverted SoFtmax (ISF), recently proposed to improve NN. This work proposes a new method, Hubless Nearest Neighbor (HNN), to mitigate hubness. HNN differs from NN by imposing an additional equal preference assumption. Moreover, the HNN formulation explains why ISF works as well as it does. Empirical results demonstrate that HNN outperforms NN, ISF and other state-of-the-art. For reproducibility and follow-ups, we have published all code.
A Robust NN Ensemble Framework for Intrusion Detection Systems

The Internet continues to grow in importance for service deliveries for businesses, government, and private entities (i.e. via social networks). Governments and businesses of all sizes have reported that internet connectivity is crucial in their day-to-day operation. They have also reported that they have been affected in some way by cyber attacks. Security measures and policies are developed to counter known threats and risks. However there may be unintended weaknesses in a security measure, or new previously unknown types of attacks to which a security measure is vulnerable, or new Internet services and Internet protocols for which security implications are not yet fully understood. A set of security measures may hence be insufficient to provide a reliable assurance against attacks. This highlights the importance of systems that can detect network intrusions, which is the first step to respond to security incidents. An Intrusion Detection System (IDS) is a runtime security mechanism to detect malicious attempts at a system or a network. Much research efforts are needed to devise effective and robust IDS. Current IDSes either lack accuracy, reliability or speed such that attacks are mainly detected by a person rather than by automated systems. As a consequence, attacks may remain undetected for a long time or may be detected after damage has already been done. One hypothesis in this thesis is that the problem with IDS is in part due to a lack of better understanding of relationships between normal and attack behaviours. A second hypothesis in this thesis is that many current approaches to IDS are highly specialized and may hence not be sufficiently robust to changes in protocols, network services, and attack patterns. A third hypothesis in this thesis is that some current approaches to IDS are not sufficiently scalable to allow realtime or just-in-time detection of attacks. This thesis proposes and investigates a neural network ensemble approach to IDS that consists of a High Resolution Self-Organising Map (HRSOM) and a classifier such as MLP or Fully Recursive Neural Network (FRPN). To our knowledge, this is the first HRSOM and FRPN applications for IDS. The HRSOM is used for the projection of data such that relationships between attack and normal patterns can be identified via visual inspection. It will be found that the mappings of an HRSOM show the rationale of common IDS strategies and highlight feature spaces where a high degree of confusions occur. The HRSOM thus identifies situations where failure to detect attack samples or failure to correctly classify samples are more likely. The thesis also devises an alternative method
Using LSTMs to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learning

To ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segment-level phonotactic acquisition, we compare the performance of two recurrent neural network models of phonotactic learning: one that has access to distinctive features at the start of the learning process, and one that does not. Though the predictions of both models are significantly correlated with human judgments of non-words, the feature-naive model significantly outperforms the feature-aware one in terms of probability assigned to a held-out test set of English words, suggesting that distinctive features are not obligatory for learning phonotactic patterns at the segment level.
DOES DESIGNING HAVE A COMMON COGNITIVE BEHAVIOR INDEPENDENT OF DOMAIN AND TASK: A META-ANALYSIS OF DESIGN PROTOCOLS

This paper presents the results of a meta-analysis of three sets of previous, independent protocol studies of student designers from different disciplines carrying out different design challenges. This analysis is used to confirm or deny three hypotheses about commonalities of cognitive behavior that are independent of domain and task. The previous protocol studies cover the domains of mechanical engineering and computer science. The design challenges cover three different mechanical design challenges and three different software design challenges. The meta-analysis tested the following three hypotheses describing cognitive behavior: 1. Quantitative cognitive measures describing “structure” of the artifact would be statistically the same amongst the three groups; and 2. Quantitative cognitive measures describing “structure behavior” of the artifact would be statistically the same amongst the three groups; and 3. Quantitative cognitive measures describing “expected behavior” of the artifact would be statistically the same amongst the three groups. The meta-analysis used quantitative measures of the cumulative occurrence of the six design issues in the FBS ontology in these existing protocols as the foundation for the determination of commonality in testing the three hypotheses. Based on the evidence from these sets of protocols studies hypothesis 1 is fully supported, hypothesis 2 is partially supported and hypothesis 3 is not supported.
Resource Sharing Efficiency in Network Slicing

The economic sustainability of future mobile networks will largely depend on the strong specialization of its offered services. Network operators will need to provide added value to their tenants, by moving from the traditional one-size-fits-all strategy to a set of virtual end-to-end instances of a common physical infrastructure, named network slices, which are especially tailored to the requirements of each application. Implementing network slicing has significant consequences in terms of resource management: service customization entails assigning to each slice fully dedicated resources, which may also be dynamically reassigned and overbooked in order to increase the cost-efficiency of the system. In this paper, we adopt a data-driven approach to quantify the efficiency of resource sharing in future sliced networks. Building on metropolitan-scale real-world traffic measurements, we carry out an extensive parametric analysis that highlights how diverse performance guarantees, technological settings, and slice configurations impact the resource utilization at different levels of the infrastructure in presence of network slicing. Our results provide insights on the achievable efficiency of network slicing architectures, their dimensioning, and their interplay with resource management algorithms at different locations and reconfiguration timescales.
Semantic Representation of Motion for Tracing Object in Surveillance System

Recent proliferation of multimedia data necessitates effective and efficient methods of retrieving of multimedia data. Especially, intelligent surveillance system needs semantic multimedia data processing. However, these studies are significantly insufficient to enable a complete semantic recognition in regard to semantic-based video retrieval. Current technology focuses on analysis of low-level characteristics such as color, texture, shape, and trajectory of an object and on applications of those characteristics. In this paper, we develop a semantic representation in human language to reduce the semantic gap between low-level and high-level characteristics; with consideration of not only the low-level characteristics but also the high-level characteristics with the use of the human language. Particularly, this paper focuses on semantic representation by using topological and directional relations between non-moving and moving objects for intrusion detection system using Closed-Circuit Television (CCTV). To this end, we capitalize on size relation of the object based on the previous studies to suggest 3D spatio-temporal relation. We also intend to reduce semantic gap by mapping with the use of predicates. This paper extends our previous work [17] published on SeCIHD 2011.
Controllable Text Simplification with Lexical Constraint Loss

We propose a method to control the level of a sentence in a text simplification task. Text simplification is a monolingual translation task translating a complex sentence into a simpler and easier to understand the alternative. In this study, we use the grade level of the US education system as the level of the sentence. Our text simplification method succeeds in translating an input into a specific grade level by considering levels of both sentences and words. Sentence level is considered by adding the target grade level as input. By contrast, the word level is considered by adding weights to the training loss based on words that frequently appear in sentences of the desired grade level. Although existing models that consider only the sentence level may control the syntactic complexity, they tend to generate words beyond the target level. Our approach can control both the lexical and syntactic complexity and achieve an aggressive rewriting. Experiment results indicate that the proposed method improves the metrics of both BLEU and SARI.
Leveraging Local and Global Patterns for Self-Attention Networks

Self-attention networks have received increasing research attention. By default, the hidden states of each word are hierarchically calculated by attending to all words in the sentence, which assembles global information. However, several studies pointed out that taking all signals into account may lead to overlooking neighboring information (e.g. phrase pattern). To address this argument, we propose a hybrid attention mechanism to dynamically leverage both of the local and global information. Specifically, our approach uses a gating scalar for integrating both sources of the information, which is also convenient for quantifying their contributions. Experiments on various neural machine translation tasks demonstrate the effectiveness of the proposed method. The extensive analyses verify that the two types of contexts are complementary to each other, and our method gives highly effective improvements in their integration.
Expanding the Usage of Web Archives by Recommending Archived Webpages Using Only the URI

EXPANDING THE USAGE OF WEB ARCHIVES BY RECOMMENDING ARCHIVED WEBPAGES USING ONLY THE URI Lulwah M. Alkwai Old Dominion University, 2019 Director: Dr. Michele C. Weigle Web archives are a window to view past versions of webpages. When a user requests a webpage on the live Web, such as http://tripadvisor.com/where_to_t ravel/, the webpage may not be found, which results in an HyperText Transfer Protocol (HTTP) 404 response. The user then may search for the webpage in a Web archive, such as the Internet Archive. Unfortunately, if this page had never been archived, the user will not be able to view the page, nor will the user gain any information on other webpages that have similar content in the archive, such as the archived webpage http://classy-travel.net. Similarly, if the user requests the webpage http://hokiesports.com/football/ from the Internet Archive, the user will only find the requested webpage, and the user will not gain any information on other webpages that have similar content in the archive, such as the archived webpage http://techsideline.com. In this research, we will build a model for selecting and ranking possible recommended webpages at a Web archive. This is to enhance both HTTP 404 responses and HTTP 200 responses by surfacing webpages in the archive that the user may not know existed. First, we detect semantics in the requested Uniform Resource Identifier (URI). Next, we classify the URI using an ontology, such as DMOZ or any website directory. Finally, we filter and rank candidates based on several features, such as archival quality, webpage popularity, temporal similarity, and content similarity. We measure the performance of each step using different techniques, including calculating the F1 to measure of different tokenization methods and the classification. We tested the model using human evaluation to determine if we could classify and find recommendations for a sample of requests from the Internet Archive’s Wayback Machine access log. Overall, when selecting the full categorization, reviewers agreed with 80.3% of the recommendations, which is much higher than “do not agree” and “I do not know”. This indicates the reviewer is more likely to agree on the recommendations when selecting the full categorization. But when selecting the first level only, reviewers only agreed with 25.5% of the recommendations. This indicates that having deep level categorization improves the performance of finding relevant recommendations.
Spatial Aggregation Facilitates Discovery of Spatial Topics

Spatial aggregation refers to merging of documents created at the same spatial location. We show that by spatial aggregation of a large collection of documents and applying a traditional topic discovery algorithm on the aggregated data we can efficiently discover spatially distinct topics. By looking at topic discovery through matrix factorization lenses we show that spatial aggregation allows low rank approximation of the original document-word matrix, in which spatially distinct topics are preserved and non-spatial topics are aggregated into a single topic. Our experiments on synthetic data confirm this observation. Our experiments on 4.7 million tweets collected during the Sandy Hurricane in 2012 show that spatial and temporal aggregation allows rapid discovery of relevant spatial and temporal topics during that period. Our work indicates that different forms of document aggregation might be effective in rapid discovery of various types of distinct topics from large collections of documents.
4D cone-beam CT image reconstruction of Varian TrueBeam v1.6 projection images for clinical quality assurance of stereotactic ablative radiotherapy to the lung

On-board cone-beam computed tomography (CBCT) imaging integrated with medical linear accelerators offers a viable tool for tumor localization just prior to radiation treatment delivery. However, the exact tumor location during treatment is not well-defined due to respiratory motion. This is taken into account during treatment planning by adding margins to the visible tumor volume defining the high dose region. The respiratory motion used to optimize the treatment plan is not guaranteed to be reproducible on the day of treatment, suggesting that the high dose region may not fully contain the tumor at all points of its trajectory during treatment. In this thesis, to image the tumor at the different portions of the breathing cycle, CBCT projections were binned by the respiratory signal at their time of acquisition. Reconstructing each bin created a 3D image depicting the tumor at one point of its trajectory. Combining the binned reconstructions added in a temporal component, defining a 4D-CBCT. 4D-CBCT reconstructions were performed on 6 stereotactic ablative radiotherapy (SABR) lung cancer patients. Imaging was performed using the Varian TrueBeam (v1.6) and respiratory information was captured with the infra-red camera-based Varian real-time position management (RPM) system. Both analytical and iterative reconstruction algorithms, and image quality metrics were used for a comparative study. Tumor motion was measured by tracking the visible tumor volume centroid from each 4D-CBCT image. The high dose regions defined during treatment planning were compared to the 4DCBCT tumor volume during its trajectory using an overlap metric to determine if
Tree Communication Models for Sentiment Analysis

Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously. However, traditional tree-LSTMs capture only the bottom-up dependencies between constituents. In this paper, we propose a tree communication model using graph convolutional neural network and graph recurrent neural network, which allows rich information exchange between phrases constituent tree. Experiments show that our model outperforms existing work on bidirectional tree-LSTMs in both accuracy and efficiency, providing more consistent predictions on phrase-level sentiments.
Nutrition-Related Mobile Apps in the China App Store: Assessment of Functionality and Quality

Background There are an increasing number of mobile apps that provide dietary guidance to support a healthy lifestyle and disease management. However, the characteristics of these nutrition-related apps are not well analyzed. Objective This study aimed to evaluate the functionality and quality of nutrition-related apps in China. Methods Mobile apps providing dietary guidance were screened in the Chinese iOS and Android app stores in November 2017, using stepwise searching criteria. The first screening consisted of extracting information from the app descriptions. Apps that (1) were free, (2) contain information on diet and nutrition, and (3) were last updated after January 1, 2016, were downloaded for further analysis. Nutritional functionalities were determined according to the Chinese Dietary Guidelines framework. Market-related functionalities were developed from previous studies and tailored to downloaded apps. The quality of apps was assessed with the user version of the Mobile App Rating Scale (uMARS). Results Out of 628 dietary guidance apps screened, 44 were nutrition-related. Of these, guidance was provided on diet exclusively (11/44, 25%), fitness (17/44, 39%), disease management (11/44, 25%), or maternal health (5/44, 11%). Nutritional functionalities included nutritional information inquiry (40/44, 91%), nutrition education (35/44, 80%), food record (34/44, 77%), diet analysis (34/44, 77%), and personalized recipes (21/44, 48%). Dietary analysis and suggestions mainly focused on energy intake (33/44, 75%) and less on other factors such as dietary structure (10/44, 23%). Social communication functionalities were available in 42 apps (96%), user incentives were supported in 26 apps (59%), and intelligent recognition technology was available in 8 apps (18%). The median score for the quality of the 44 apps, as determined on a 5-point uMARS scale, was 3.6 (interquartile range 0.7). Conclusions Most nutrition-related apps are developed for health management rather than for dietary guidance exclusively. Although basic principles of energy balance are used, their nutritional functionality was relatively limited and not individualized. More efforts should be made to develop nutrition-related apps with evidence-based nutritional knowledge, comprehensive and personalized dietary guidance, and innovative technology.
Generating fuzzy if-then rules from trained neural networks: linguistic analysis of neural networks

We propose a fuzzy-arithmetic-based approach for extracting fuzzy if-then rules from multilayer feedforward neural networks. For pattern classification problems, our approach extracts fuzzy if-then rules such as "If x/sub 1/ is small and x/sub 2/ is large then Class 1 with CF=0.9" where CF is the grade of certainty. In order to determine the consequent class and the grade of certainty of a fuzzy if-then rule, first an input vector of linguistic values is presented to a trained neural network. The input vector consists of linguistic values in the antecedent part of the fuzzy if-then rule (e.g. (small, large) in the case of the above fuzzy if-then rule). Next fuzzy outputs from the neural network are calculated by fuzzy arithmetic. Then the consequent class and the grade of certainty of the fuzzy if-then rule are determined by an inequality relation between the fuzzy outputs.
Controlling Grammatical Error Correction Using Word Edit Rate

When professional English teachers correct grammatically erroneous sentences written by English learners, they use various methods. The correction method depends on how much corrections a learner requires. In this paper, we propose a method for neural grammar error correction (GEC) that can control the degree of correction. We show that it is possible to actually control the degree of GEC by using new training data annotated with word edit rate. Thereby, diverse corrected sentences is obtained from a single erroneous sentence. Moreover, compared to a GEC model that does not use information on the degree of correction, the proposed method improves correction accuracy.