Miracle-dz/UniFAR-FTUs huggingface dataset:
gIBIS: a hypertext tool for exploratory policy discussion

This paper describes an application specific hypertext system designed to facilitate the capture of early design deliberations. It implements a specific method, called Issue Based Information Systems (IBIS), which has been developed for use on large, complex design problems. The hypertext system described here, gIBIS (for graphical IBIS), makes use of color and a high speed relational database server to facilitate building and browsing typed IBIS networks. Further. gIBIS is designed to support the collaborative construction of these networks by any number of cooperating team members spread across a local area network. Early experiments suggest that the IBIS method is still incomplete, but there is a good match between the tool and method even in this experimental version.
Integrating Knowledge-Supported Search into the INCEpTION Annotation Platform

Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains. It disambiguates mentions, introduces cross-document coreferences, and the resources contribute extra information, e.g. taxonomic relations. Such tasks benefit from text annotation tools that integrate a search which covers the text, the annotations, as well as the knowledge resource. However, to the best of our knowledge, no current tools integrate knowledge-supported search as well as entity linking support. We address this gap by introducing knowledge-supported search functionality into the INCEpTION text annotation platform. In our approach, cross-document references are created by linking entity mentions to a knowledge base in the form of a structured hierarchical vocabulary. The resulting annotations are then indexed to enable fast and yet complex queries taking into account the text, the annotations, and the vocabulary structure.
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning

This paper describes an approach to solve implicit emotion classification with the use of pre-trained word embedding models to train multiple neural networks. The system described in this paper is composed of a sequential combination of Long Short-Term Memory and Convolutional Neural Network for feature extraction and Feedforward Neural Network for classification. In this paper, we successfully show that features extracted using multiple pre-trained embeddings can be used to improve the overall performance of the system with Emoji being one of the significant features. The evaluations show that our approach outperforms the baseline system by more than 8% without using any external corpus or lexicon. This approach is ranked 8th in Implicit Emotion Shared Task (IEST) at WASSA-2018.
Bmi‐1 high‐expressing cells enrich cardiac stem/progenitor cells and respond to heart injury

Bmi‐1 gene is well recognized as an oncogene, but has been recently demonstrated to play a role in the self‐renewal of tissue‐specific stem cells. By using Bmi‐1GFP/+ mice, we investigated the role of Bmi‐1 in cardiac stem/progenitor cells and myocardial repair. RT‐PCR and flow cytometry analysis indicated that the expression of Bmi‐1 was significantly higher in cardiac side population than the main population from CD45−Ter119−CD31− heart cells. More Sca‐1+ cardiac stem/progenitor cells were found in Bmi‐1 GFPhi subpopulation, and these Bmi‐1 GFPhi heart cells showed the potential of differentiation into SMM+ smooth muscle‐like cells and TnT+ cardiomyocyte‐like cells in vitro. The silencing of Bmi‐1 significantly inhibited the proliferation and differentiation of heart cells. Otherwise, myocardial infarction induced a significantly increase (2.7‐folds) of Bmi‐1 GFPhi population, mainly within the infarction and border zones. These preliminary data suggest that Bmi‐1hi heart cells are enriched in cardiac stem/progenitor cells and may play a role in myocardial repair.
Visualizing Group Dynamics based on Multiparty Meeting Understanding

Group discussions are usually aimed at sharing opinions, reaching consensus and making good decisions based on group knowledge. During a discussion, participants might adjust their own opinions as well as tune their attitudes towards others’ opinions, based on the unfolding interactions. In this paper, we demonstrate a framework to visualize such dynamics; at each instant of a conversation, the participants’ opinions and potential influence on their counterparts is easily visualized. We use multi-party meeting opinion mining based on bipartite graphs to extract opinions and calculate mutual influential factors, using the Lunar Survival Task as a study case.
Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes

In this work, we appropriate the popular tool of Gaussian processes to solve the problem of reconstructing networks from time-series perturbation data. To this end, we propose a construction for multivariate Gaussian processes to describe the continuous-time trajectories of the states of the network entities. We then show that this construction admits a state-space representation for the network dynamics. By exploiting Kalman filtering techniques, we are able to infer the underlying network in a computationally efficient manner.
The Zebra striped network file system

Zebra is a network file system that increases throughput by striping file data across multiple servers. Rather than striping each file separately, Zebra forms all the new data from each client into a single stream, which it then stripes using an approach similar to a log-structured file system. This provides high performance for writes of small files as well as for reads and writes of large files. Zebra also writes parity information in each stripe in the style of RAID disk arrays; this increases storage costs slightly but allows the system to continue operation even while a single storage server is unavailable. A prototype implementation of Zebra, built in the Sprite operating system, provides 4--5 times the throughput of the standard Sprite file system or NFS for large files and a 20%-3x improvement for writing small files.
How Social Ties Influence Hurricane Evacuation Behavior

Natural disasters bring enormous costs every year, both in terms of lives and materials. Evacuation from potentially affected areas stands out among the most critical factors that can reduce mortality and vulnerability to crisis. We know surprisingly little about the factors that drive this important and often life-saving behavior, though recent work has suggested that social capital may play a critical and previously underestimated role in natural disaster preparedness. Moving beyond retrospective self-reporting and vehicle count estimates, we use social media data from a large number of Facebook users to examine connections between levels of social capital and evacuation behavior. This work is the first of its kind, examining these phenomena across three major U.S. disasters-Hurricane Harvey, Hurricane Irma, and Hurricane Maria-with data on over 1.5 million social media users. Our analysis confirms that, holding confounding factors constant, several aspects of social capital are correlated with whether or not an individual evacuates. Higher levels of bridging and linking social ties correlate strongly with evacuation. However, these social capital related factors are not significantly associated with the rate of return after evacuation.
FACSHuman a Software to Create Experimental Material by Modeling 3D Facial Expression

FACSHuman is a software that allows researchers to create, through three-dimensional modeling, experimental material that can be used in nonverbal communication and emotional facial expressions researches. It thus offers the possibility of practically manipulating all the Action Units presented in the Facial Action Coding System[6]. But also, the morphological parameters of the entire body and face.
Coumarins and flavones from the fruit and root extracts of Micromelum integerrimum

Abstract A phytochemical investigation of the fruit and root extracts of Micromelum integerrimum resulted in the isolation and identification of a new compound, integerravone (1), together with 23 known compounds (2–24). Their structures were characterized by spectroscopic methods as well as comparisons made from the literature. Compounds 2, 3–15, 17–18 and 20–23 were evaluated for their cytotoxicities against the colon cancer cell line, HCT116. All of them were inactive at 50 µM. Most of the phenolic compounds were evaluated for their antioxidant activity using the DPPH assay. Compounds 14 and 22–24 showed antioxidant activity with IC50 values ranging from 24.83-135.05 µM. Graphical Abstract
Characterizing Online Public Discussions through Patterns of Participant Interactions

Public discussions on social media platforms are an intrinsic part of online information consumption. Characterizing the diverse range of discussions which can arise is crucial for these platforms, as they may seek to organize and curate them. This paper introduces a framework to characterize public discussions, relying on a representation that captures a broad set of social patterns which emerge from the interactions between interlocutors, comments, and audience reactions. We apply our framework to study public discussions on Facebook at two complementary scales. First, at the level of individual discussions, we use it to predict a discussion's future trajectory, anticipating future antisocial actions (such as participants blocking each other) and forecasting the discussion's growth. Second, we systematically analyze the variation of discussions across thousands of Facebook sub-communities, revealing subtle differences (and unexpected similarities) in how people interact when discussing online content. We further show that this variation is driven more by participant tendencies than by the content triggering these discussions.
Caring for IT Security

Despite being considered a fundamental issue in the design, use, and appropriation of digital technologies, IT security has found but little attention in CSCW so far. Approaches in Human-Computer Interaction and Software Engineering do not account appropriately for the weave of dispersed practices that it takes to 'do' IT security---practices that involve a heterogeneous set of actors and unfold at diverse sites and across organizational, legal, and professional boundaries. In this paper we propose to conceive of IT security through the lens of care, a notion that we draw from Science and Technology Studies. Caring for IT security requires continuous, often invisible work that relies upon tinkering and experimentation and addresses perennial oscillations between in-/securities. Caring for IT security, then, engages with established accountabilities and cultivates a moral stance that refrains from blaming insecurities upon single actors. We conclude with outlining a caring approach to IT security for CSCW.
CONTROLLING BLOOD GLUCOSE LEVELS IN PATIENTS WITH TYPE 1 DIABETES USING FITTED Q-ITERATIONS AND FUNCTIONAL FEATURES

Type 1 Diabetes is characterized by the lack of insulin-producing beta cells in the pancreas. The artificial pancreas promises to alleviate the burdens of self-management. While the physical components of the system – the continuous glucose monitor and insulin pump – have experienced rapid advances, a technological bottleneck remains in the control algorithm, which is responsible for translating data from the former into instructions for the latter. In this work, we propose to bring machine learning techniques to bear upon the challenges of blood glucose control. Specifically, we employ reinforcement learning to learn an optimal insulin policy. Learning is generalized using nonparametric regression with functional features, exploiting information contained in the shape of the glucose curve. Our algorithm is model-free, data-driven and personalized. In-silico simulations with T1D models demonstrate the potential of the proposed algorithm.
PICA: Proactive Intelligent Conversational Agent for Interactive Narratives

A narrative relies on the imperfect knowledge of the user to create interactions between the characters that are ultimately used as a plot device to drive the narrative. This motivates our exploration of ways to encode this information, provides means for a user to both query and influence the knowledge, and guides the user based on a model of their experience. We developed PICA: a proactive intelligent conversational agent for interactive narratives that can guide users through such experiences. The underlying knowledge base is designed using a sub-symbolic architecture, which encodes belief models for multiple users and autonomous agents in addition to the actual story knowledge. We also developed a discourse module using Behavior Trees to intuitively design the proactive and reactive capabilities of PICA. We compare our approach to neural networks and symbolic knowledge bases and demonstrate its functionality.
Clustering of Transcriptomic Data for Identification of Cancer Subtypes

Cancer is a number of related yet highly heterogeneous diseases. Correct identification of cancer subtypes is critical for clinical decisions. The advance in sequencing technologies has made it possible to study cancer based on abundant genomics and transcriptomic (-omics) data. Such a data-driven approach is expected to address limitations and issues with traditional methods in identifying cancer subtypes. We evaluate the suitability of clustering--a data mining tool to study heterogenous data when there is a lack of sufficient understanding of the subject matters--in the identification of cancer subtypes. A number of popular clustering algorithms and their consensus are explored, and we find cancer subtypes identified by consensus clustering agree well with clinical studies.
Using meta-level compilation to check FLASH protocol code

Building systems such as OS kernels and embedded software is difficult. An important source of this difficulty is the numerous rules they must obey: interrupts cannot be disabled for "too long," global variables must be protected by locks, user pointers passed to OS code must be checked for safety before use, etc. A single violation can crash the system, yet typically these invariants are unchecked, existing only on paper or in the implementor's mind.This paper is a case study in how system implementors can use a new programming methodology, metalevel compilation (MC), to easily check such invariants. It focuses on using MC to check for errors in the code used to manage cache coherence on the FLASH shared memory multiprocessor. The only real practical method known for verifying such code is testing and simulation. We show that simple, system-specific checkers can dramatically improve this situation by statically pinpointing errors in the program source. These checkers can be written by implementors themselves and, by exploiting the system-specific information this allows, can detect errors unreachable with other methods. The checkers in this paper found 34 bugs in FLASH code despite the care used in building it and the years of testing it has undergone. Many of these errors fall in the worst category of systems bugs: those that show up sporadically only after days of continuous use. The case study is interesting because it shows that the MC approach finds serious errors in well-tested, non-toy systems code. Further, the code to find such bugs is usually 10-100 lines long, written in a few hours, and exactly locates errors that, if discovered during testing, would require several days of investigation by an experienced implementor.The paper presents 8 checkers we wrote, their application to five different protocol implementations, and a discussion of the errors that we found.
Constant-Round Nonmalleable Commitments from Any One-Way Function

We show unconditionally that the existence of commitment schemes implies the existence of constant-round nonmalleable commitments; earlier protocols required additional assumptions such as collision-resistant hash functions or subexponential one-way functions. Our protocol also satisfies the stronger notions of concurrent nonmalleability and robustness. As a corollary, we establish that constant-round nonmalleable zero-knowledge arguments for NP can be based on one-way functions and constant-round secure multiparty computation can be based on enhanced trapdoor permutations; also here, earlier protocols additionally required either collision-resistant hash functions or subexponential one-way functions.
A New Approach for Sparse Matrix Classification Based on Deep Learning Techniques

In this paper, a new methodology to select the best storage format for sparse matrices based on deep learning techniques is introduced. We focus on the selection of the proper format for the sparse matrix-vector multiplication (SpMV), which is one of the most important computational kernels in many scientific and engineering applications. Our approach considers the sparsity pattern of the matrices as an image, using the RGB channels to code several of the matrix properties. As a consequence, we generate image datasets that include enough information to successfully train a Convolutional Neural Network (CNN). Considering GPUs as target platforms, the trained CNN selects the best storage format 90.1% of the time, obtaining 99.4% of the highest SpMV performance among the tested formats.
Measuring sentence parallelism using Mahalanobis distances: The NRC unsupervised submissions to the WMT18 Parallel Corpus Filtering shared task

The WMT18 shared task on parallel corpus filtering (Koehn et al., 2018b) challenged teams to score sentence pairs from a large high-recall, low-precision web-scraped parallel corpus (Koehn et al., 2018a). Participants could use existing sample corpora (e.g. past WMT data) as a supervisory signal to learn what a “clean” corpus looks like. However, in lower-resource situations it often happens that the target corpus of the language is the only sample of parallel text in that language. We therefore made several unsupervised entries, setting ourselves an additional constraint that we not utilize the additional clean parallel corpora. One such entry fairly consistently scored in the top ten systems in the 100M-word conditions, and for one task—translating the European Medicines Agency corpus (Tiedemann, 2009)—scored among the best systems even in the 10M-word conditions.
When CTC Training Meets Acoustic Landmarks

Connectionist temporal classification (CTC) provides an end-to-end acoustic model (AM) training strategy. CTC learns accurate AMs without time-aligned phonetic transcription, but sometimes fails to converge, especially in resource-constrained scenarios. In this paper, the convergence properties of CTC are improved by incorporating acoustic landmarks. We tailored a new set of acoustic landmarks to help CTC training converge more rapidly and smoothly while also reducing recognition error rates. We leveraged new target label sequences mixed with both phone and manner changes to guide CTC training. Experiments on TIMIT demonstrated that CTC based acoustic models converge significantly faster and smoother when they are augmented by acoustic landmarks. The models pretrained with mixed target labels can be further finetuned, resulting in phone error rates 8.72% below baseline on TIMIT. Consistent performance gain is also observed on WSJ (a larger corpus) and reduced TIMIT (smaller). With WSJ, we are the first to succeed in verifying the effectiveness of acoustic landmark theory on a mid-sized ASR task.
Non-Locality in Interactive Proofs

In multi-prover interactive proofs (MIPs), the verifier is usually non-adaptive. This stems from an implicit problem which we call ``contamination'' by the verifier. We make explicit the verifier contamination problem, and identify a solution by constructing a generalization of the MIP model. This new model quantifies non-locality as a new dimension in the characterization of MIPs. A new property of zero-knowledge emerges naturally as a result by also quantifying the non-locality of the simulator.
Arabizi sentiment analysis based on transliteration and automatic corpus annotation

Arabizi is a form of writing Arabic text which relies on Latin letters, numerals and punctuation rather than Arabic letters. In the literature, the difficulties associated with Arabizi sentiment analysis have been underestimated, principally due to the complexity of Arabizi. In this paper, we present an approach to automatically classify sentiments of Arabizi messages into positives or negatives. In the proposed approach, Arabizi messages are first transliterated into Arabic. Afterwards, we automatically classify the sentiment of the transliterated corpus using an automatically annotated corpus. For corpus validation, shallow machine learning algorithms such as Support Vectors Machine (SVM) and Naive Bays (NB) are used. Simulations results demonstrate the outperformance of NB algorithm over all others. The highest achieved F1-score is up to 78% and 76% for manually and automatically transliterated dataset respectively. Ongoing work is aimed at improving the transliterator module and annotated sentiment dataset.
Academic Social Network-Based Recommendation Approach for Knowledge Sharing

Academic information overload has brought researchers great difficulty due to the rapid growth of scientific articles. Methods have been proposed to help professional readers find relevant articles on the basis of their publications. Although effectively sharing publications is essential to spreading knowledge and ideas, few studies have focused on knowledge sharing from an author perspective. This study leverages the online academic social network to propose a recommendation approach for knowledge sharing. In our approach, we integrate researcher-level and document-level analyses in the same model. Our model works in two stages: 1) researcher-level analysis and 2) document-level analysis. The former combines research topic relevance, social relations, and research quality dimension, and the latter uses the machine learning method to learn the vector representation for each word. Online social behavior information is also leveraged to enhance readers' short-term interests. Our approach is deployed in ScholarMate, a prevalent academic social network. Compared with other baseline methods (CB, LDA, and part of the proposed approach), our approach significantly improves the accuracy of recommendations. Moreover, our method can disseminate papers efficiently to readers who have no publications.
Action Tube Extraction Based 3D-CNN for RGB-D Action Recognition

In this paper we propose a novel action tube extractor for RGB-D action recognition in trimmed videos. The action tube extractor takes as input a video and outputs an action tube. The method consists of two parts: spatial tube extraction and temporal sampling. The first part is built upon MobileNet-SSD and its role is to define the spatial region where the action takes place. The second part is based on the structural similarity index (SSIM) and is designed to remove frames without obvious motion from the primary action tube. The final extracted action tube has two benefits: 1) a higher ratio of ROI (subjects of action) to background; 2) most frames contain obvious motion change. We propose to use a two-stream (RGB and Depth) I3D architecture as our 3D-CNN model. Our approach outperforms the state-of-the-art methods on the OA and NTU RGB-D datasets.