Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Causal Dependencies by using Natural Language Processing: A Survey

Identification of cause-effect relations in the domain is crucial for construction of its correct model, and especially for the Topological Functioning Model (TFM). Key elements of the TFM are functional characteristics of the system and cause-effect relations between them. Natural Language Processing (NLP) can help in automatic processing of textual descriptions of functionality of the domain. The current research illustrates results of a survey of research papers on identification and extracting cause-effect relations from text using NLP and other techniques. The survey shows that expression of cause-effect relations in text can be very different. Sometimes the same language constructs indicate both causal and non-causal relations. Hybrid solutions that use machine learning, ontologies, linguistic and syntactic patterns as well as temporal reasoning show better results in extracting and filtering cause-effect pairs. Multi cause and multi effect domains still are not very well studied.
JSOptimizer: An Extensible Framework for JavaScript Program Optimization

JavaScript has become a popular programming language. It is widely used in both client-side and server-side programming in web applications. The robustness and performance of JavaScript programs become vital. Unfortunately, real-world JavaScript programs often suffer from various issues. In this work, we present nine issue patterns derived from open-source projects and propose a general static analysis framework, JSOptimizer, to help detect such patterns of issues and optimize the code accordingly. Comparing to existing work, JSOptimizer is not only highly extensible but also performs code optimizations automatically. We applied JSOptimizer to seven real open-source JavaScript projects and five bugs detected by it have been confirmed by developers. Besides, we conducted a case study based on a popular project and found that addressing the issues detected by our framework can speed up the original project by over 300%. This shows the usefulness of JSOptimizer.
Big BiRD: A Large, Fine-Grained, Bigram Relatedness Dataset for Examining Semantic Composition

Bigrams (two-word sequences) hold a special place in semantic composition research since they are the smallest unit formed by composing words. A semantic relatedness dataset that includes bigrams will thus be useful in the development of automatic methods of semantic composition. However, existing relatedness datasets only include pairs of unigrams (single words). Further, existing datasets were created using rating scales and thus suffer from limitations such as in consistent annotations and scale region bias. In this paper, we describe how we created a large, fine-grained, bigram relatedness dataset (BiRD), using a comparative annotation technique called Best–Worst Scaling. Each of BiRD’s 3,345 English term pairs involves at least one bigram. We show that the relatedness scores obtained are highly reliable (split-half reliability r= 0.937). We analyze the data to obtain insights into bigram semantic relatedness. Finally, we present benchmark experiments on using the relatedness dataset as a testbed to evaluate simple unsupervised measures of semantic composition. BiRD is made freely available to foster further research on how meaning can be represented and how meaning can be composed.
Research in the Wild via Performance: Challenges, Ethics and Opportunities

 Performance can be combined with interactive, online and immersive video as a way of conducting research in the wild. This affords the researcher opportunities to engage with participants in a way that can be surprisingly intimate, reactive to live intervention, and scaffolded by aesthetic content in order to shape how participants engage with the research context. It does, however, pose particular challenges with regards to evaluation practices, challenges which vary depending upon whether the researcher favours more traditional methods such as participant interviews, covert or overt observation or quantitative analysis, or whether s/he functions as a participant in the shared experience enabling him/her to explore the engagement in an autoethnographic, self-situated way. In this half-day workshop, we seek to bring together designers who use live performance in combination with video as a method for conducting HCI research. Through discussion and experience sharing we aim to tackle practical and logistic challenges, ethical quandaries, and evaluatory pitfalls when working in this way. By crafting and deploying a live performance intervention during the workshop, we will tease out nuances of understanding public performance research to better make sense of human-computer interaction in a wide range of contexts.
Mitochondrial donation is “not unsafe,” review confirms

A third scientific review by the UK Human Fertilisation and Embryology Authority has confirmed that no evidence exists indicating that two mitochondrial donation techniques are unsafe.1 Scientists and medical charities are now urging the UK government to act swiftly to change the law during the current parliament. The regulator’s scientific panel reiterated the conclusion of its two earlier reviews, carried out in 2011 and 2013, that there was no evidence that the two techniques, maternal spindle transfer and pronuclear transfer, are unsafe. These two methods allow the transmission of both parents’ nuclear DNA but involve replacing abnormal mitochondria with normal mitochondria from a donor oocyte. The panel concluded that the techniques were potentially useful for people wishing to have their own genetically related child but whose offspring were at risk of severe or lethal genetic disease because of mutations in the mitochondrial DNA carried by the mother. Mutations in the mitochondrial DNA can cause a range of rare but serious diseases that can be fatal. …
Exploring Online Video Watching Behaviors

Laptop and desktop computers are frequently used to watch online videos from a wide variety of services. From short YouTube clips, to television programming, to full-length films, users are increasingly moving much of their video viewing away from television sets towards computers. But what are they watching, and when? We set out to understand current video use on computers through analyzing full browsing histories from a diverse set of online Americans, finding some temporal differences in genres watched, yet few differences in the length of videos watched by hour. We also explore topics of videos, how users arrive at online videos through referral links, and conclude with several implications for the design of online video services that focus on the types of content people are actually watching online.
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs

We discuss three novel insights about dropout for DNNs with ReLUs: 1) dropout encourages each local linear piece of a DNN to be trained on data points from nearby regions; 2) the same dropout rate results in different (effective) deactivation rates for layers with different portions of ReLUdeactivated neurons; and 3) the rescaling factor of dropout causes a normalization inconsistency between training and test when used together with batch normalization. The above leads to three simple but nontrivial modifications resulting in our method “jumpout. ” Jumpout samples the dropout rate from a monotone decreasing distribution (e.g., the right half of a Gaussian), so each local linear piece is trained, with high probability, to work better for data points from nearby than more distant regions. Jumpout moreover adaptively normalizes the dropout rate at each layer and every training batch, so the effective deactivation rate on the activated neurons is kept the same. Furthermore, it rescales the outputs for a better trade-off that keeps both the variance and mean of neurons more consistent between training and test phases, thereby mitigating the incompatibility between dropout and batch normalization. Jumpout significantly improves the performance of different neural nets on CIFAR10, CIFAR100, Fashion-MNIST, STL10, SVHN, ImageNet-1k, etc., while introducing negligible additional memory and computation costs.
How Stress and Mental Workload are Connected

Mental Workload (MWL) can be both good and bad; we can thrive under high MWL, or our performance can drop if the demands become either too low or too high. Similarly, stress is not always bad, short term stress can be beneficial to overcome a challenge or dangerous situation. In our research, we have seen both people that enjoy high workload, and people that feel stressed by it, but we do not know whether that experience of stress significantly affects our measurements. Our recent results show that fNIRS measurements are affected by stress (measured by SSSQ). This paper seeks to discuss the relationship between these concepts, discussing examples of where similar influencing factors appear within models of both Stress and Mental Workload, as well as within subjective measures of them. We conclude that future work must consider participants' experiences of both Stress and Mental Workload, as well as other cognitive concepts, when trying to estimate them from physiological measures.
Dynamic Switching Networks

The concept of emergence is a powerful concept to explain very complex behaviour by simple underling rules. Existing approaches of producing emergent collective behaviour have many limitations making them unable to account for the complexity we see in the real world. In this paper we propose a new dynamic, non-local, and time independent approach that uses a network like structure to implement the laws or the rules, where the mathematical equations representing the rules are converted to a series of switching decisions carried out by the network on the particles moving in the network. The proposed approach is used to generate patterns with different types of symmetry.
Cloud-based Conversational Agents for User Acquisition and Engagement

The benefits of cloud computing have driven different companies from diverse sectors to migrate their products and services to the cloud. In the last decade many businesses have adopted web and mobile applications to offer better customer service as well as used social networks for advertisements and marketing campaigns aiming to acquire, engage and retain their customers. This paper presents a case study combining the areas of chatbots, cloud computing and customer service, acquisition and engagement targeting the gastronomy industry; it evaluates and compares the implementation of a chatbot as a cloud-native application (Platform as a Service) versus one built utilizing an authoring tool (Software as a Service); and it demonstrates how a gastronomic business could attract with ease new customers by interacting with them using chatbots embedded into instant messaging apps.
Bias in Filipino Newspapers? Newspaper Sentiment Analysis of the 2017 Battle of Marawi

Newspapers provide factual reports on current events. However, news media has been shown to be ideologically biased, often negatively shaping the readers' point of view. News on controversial issues makes the bias of the newspaper or its writers more visible. This study aims to measure the objectivity of newspapers by classifying news articles from three newspaper agencies covering the 2017 Battle of Marawi in Southern Philippines. We used Aylien Sentiment Analysis Tool to detect the bias or polarity in each news article (whether positive, negative or neutral). Negative articles on Marawi dominated the three broadsheets (45.1% to 59.9%) while the neutral articles were the least frequent (16.1% to 21.2%). These results indicate that newspapers apply unequal space on the different sides of an issue, which may lead to unbalanced reporting. We also note that despite the varying number of total articles, the three papers applied the same proportion of positive, negative and neutral articles, which may imply collusion. The emergence of Big Data greatly increases the speed of gathering news articles on any given issue, while the Internet of Things enables readers and journalists to measure the objectivity of the news.
Remote Sensing in Urban Forestry: Recent Applications and Future Directions

Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote sensing, especially with its recent advances in sensors and data processing methods, has emerged as a premier and useful observational and analytical tool. This study summarises recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multi-source, multi-temporal and multi-scale inputs. It reviews how different sources of remotely sensed data offer a fast, replicable and scalable way to quantify urban forest dynamics at varying spatiotemporal scales on a case-by-case basis. Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency. For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality. Current attention given to multi-scale inputs remains limited; hence, future studies should be more aware of scale effects and cautiously draw conclusions.
PSSST! The Privacy System for Smart Service Platforms: An Enabler for Confidable Smart Environments

The Internet of Things and its applications are becoming increasingly popular. Especially Smart Service Platforms like Alexa are in high demand. Such a platform retrieves data from sensors, processes them in a back-end, and controls actuators in accordance with the results. Thereby, all aspects of our everyday life can be managed. In this paper, we reveal the downsides of this technology by identifying its privacy threats based on a real-world application. Our studies show that current privacy systems do not tackle these issues adequately. Therefore, we introduce PSSST! , a user-friendly and comprehensive privacy system for Smart Service Platforms limiting the amount of disclosed private information while maximizing the quality of service at the same time.
Recurrent models and lower bounds for projective syntactic decoding

The current state-of-the-art in neural graph-based parsing uses only approximate decoding at the training phase. In this paper aim to understand this result better. We show how recurrent models can carry out projective maximum spanning tree decoding. This result holds for both current state-of-the-art models for shift-reduce and graph-based parsers, projective or not. We also provide the first proof on the lower bounds of projective maximum spanning tree decoding.
From Manual to Machine-executable Model-based Systems Engineering via Graph-based Design Languages

Today, Model-Based Systems Engineering (MBSE) is widely used in the successful design of complex systems in various industries. It intends to provide a complete description of the design process of complex systems in form of a V-Model or one of its various extensions thereof (extended V-Model). While already several formal languages like Unified Modelling Language (UML) and Systems Modelling Language (SysML) are used to formally describe the design process and behavioural aspects of the design as well, models in UMor SysML are still mostly assembled and connected manually. Instead, graph-based design languages based on UML make use of the digital representation of the design process with the additional benefit of a repeatable execution encoded in an activity diagram within a rule set. This allows for a seamless transformation of a formerly mainly manual MBSE-approach towards a fully automated, machine-executable MBSE approach. The article will focus on the impact of graph-based design on that transition from classical MBSE towards machine-executable MBSE. We show this with the example of an automotive dashboard.
Highly Effective Arabic Diacritization using Sequence to Sequence Modeling

Arabic text is typically written without short vowels (or diacritics). However, their presence is required for properly verbalizing Arabic and is hence essential for applications such as text to speech. There are two types of diacritics, namely core-word diacritics and case-endings. Most previous works on automatic Arabic diacritic recovery rely on a large number of manually engineered features, particularly for case-endings. In this work, we present a unified character level sequence-to-sequence deep learning model that recovers both types of diacritics without the use of explicit feature engineering. Specifically, we employ a standard neural machine translation setup on overlapping windows of words (broken down into characters), and then we use voting to select the most likely diacritized form of a word. The proposed model outperforms all previous state-of-the-art systems. Our best settings achieve a word error rate (WER) of 4.49% compared to the state-of-the-art of 12.25% on a standard dataset.
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process

Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models. In this paper, we develop topic modeling with knowledge graph embedding (TMKGE), a Bayesian nonparametric model to employ knowledge graph (KG) embedding in the context of topic modeling, for extracting more coherent topics. Specifically, we build a hierarchical Dirichlet process (HDP) based model to flexibly borrow information from KG to improve the interpretability of topics. An efficient online variational inference method based on a stick-breaking construction of HDP is developed for TMKGE, making TMKGE suitable for large document corpora and KGs. Experiments on three public datasets illustrate the superior performance of TMKGE in terms of topic coherence and document classification accuracy, compared to state-of-the-art topic modeling methods.
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification

The recently released FEVER dataset provided benchmark results on a fact-checking task in which given a factual claim, the system must extract textual evidence (sets of sentences from Wikipedia pages) that support or refute the claim. In this paper, we present a completely task-agnostic pipelined system, AttentiveChecker, consisting of three homogeneous Bi-Directional Attention Flow (BIDAF) networks, which are multi-layer hierarchical networks that represent the context at different levels of granularity. We are the first to apply to this task a bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization. AttentiveChecker can be used to perform document retrieval, sentence selection, and claim verification. Experiments on the FEVER dataset indicate that AttentiveChecker is able to achieve the state-of-the-art results on the FEVER test set.
Vetting API Usages in C Programs with IMChecker

Libraries offer reusable functionality through application programming interfaces (APIs) with usage constraints such as call conditions and orders. Constraint violations, i.e., API misuses, commonly lead to bugs and even security issues. In this paper, we introduce IMChecker, a constraint-directed static analysis toolkit to vet API usages in C programs powered by a domain-specific language (DSL) to specify the API usages. First, we propose a DSL, which covers most API usage constraint types and enables straightforward but precise specification by studying real-world API-misuse bug patches. Then, we design and implement a static analysis engine to automatically parse specifications into checking targets, identify potential API misuses and prune the false positives with rich semantics. We have instantiated IMChecker for C programs with user-friendly graphic interfaces and evaluated the widely used benchmarks and real-world projects. The results show that IMChecker outperforms 4.78-36.25% in precision and 40.25-55.21% w.r. t. state-of-the-arts toolkits. We also found 75 previously unknown bugs in Linux kernel, OpenSSL and applications of Ubuntu, 61 of which have been confirmed by the corresponding development communities. Video: https://youtu.be/YGDxeyOEVIM
Variational Annealing of GANs: A Langevin Perspective

The generative adversarial network (GAN) has received considerable attention recently as a model for data synthesis, without an explicit specification of a likelihood function. There has been commensurate interest in leveraging likelihood estimates to improve GAN training. To enrich the understanding of this fast-growing yet almost exclusively heuristic-driven subject, we elucidate the theoretical roots of some of the empirical attempts to stabilize and improve GAN training with the introduction of likelihoods. We highlight new insights from variational theory of diffusion processes to derive a likelihood-based regularizing scheme for GAN training, and present a novel approach to train GANs with an unnormalized distribution instead of empirical samples. To substantiate our claims, we provide experimental evidence on how our theoretically-inspired new algorithms improve upon current practice.
TOI-CNN: a Solution of Information Extraction on Chinese Insurance Policy

Contract analysis can significantly ease the work for humans using AI techniques. This paper shows a problem of Element Tagging on Insurance Policy (ETIP). A novel Text-Of-Interest Convolutional Neural Network (TOI-CNN) is proposed for the ETIP solution. We introduce a TOI pooling layer to replace traditional pooling layer for processing the nested phrasal or clausal elements in insurance policies. The advantage of TOI pooling layer is that the nested elements from one sentence could share computation and context in the forward and backward passes. The computation of backpropagation through TOI pooling is also demonstrated in the paper. We have collected a large Chinese insurance contract dataset and labeled the critical elements of seven categories to test the performance of the proposed method. The results show the promising performance of our method in the ETIP problem.