Miracle-dz/UniFAR-FTUs huggingface dataset:
Environment aware ADL recognition system based on decision tree and activity frame

Abstract The interest towards robots for elderly care has been growing in the last years. Systems aiming to integrate robot interactive components and the user’s activity recognition system are increasing as well. This work presents an activity aware intelligent system that supports user in his/her daily life tasks. The proposed system aims to integrate three important aspects into a smart house application (environment monitoring, user activity recognition and user friendly interaction). The information gathered from sensors across the environment is structured as the state of the environment in a compacted form called activity frame. This specific frame is used by a predictor (based on the decision tree method), in order to recognize the activities that have been performed by the user inside his/her domestic environment. The recognized activity is used by an user-interactive component, which uses the predicted behavior as a guideline for its interaction planner. The presented activity recognition system was tested with the data provided by different smart home projects, and the recognition rate for the proposed predictor has high recognition rate compared to other similar ones. The architecture described by the sensory network allows the system to be easily implemented in real time in a smart house context.
Combination Effect of Silver Nanoparticles and Histone Deacetylases Inhibitor in Human Alveolar Basal Epithelial Cells

Although many treatment strategies have been reported for lung disease, the mechanism of combination therapy using silver nanoparticles (AgNPs) and histone deacetylases inhibitors (HDACi) remains unclear. Therefore, innovative treatment strategies are essential for addressing the therapeutic challenges of this highly aggressive lung cancer. AgNPs and HDACi seem to be the best candidates for anticancer therapy because of their anti-proliferative effect in a variety of cancer cells. First, we synthesized AgNPs using wogonin as a reducing and stabilizing agent, following which the synthesized AgNPs were characterized by various analytical techniques. The synthesized AgNPs exhibited dose- and size-dependent toxicity towards A549 cells. Interestingly, the combination of AgNPs and MS-275 significantly induces apoptosis, which was accompanied by an increased level of reactive oxygen species (ROS); leakage of lactate dehydrogenase (LDH); secretion of TNFα; dysfunction of mitochondria; accumulation autophagosomes; caspase 9/3 activation; up and down regulation of pro-apoptotic genes and anti-apoptotic genes, respectively; and eventually, induced DNA-fragmentation. Our findings suggest that AgNPs and MS-275 induce cell death in A549 lung cells via the mitochondrial-mediated intrinsic apoptotic pathway. Finally, our data show that the combination of AgNPs and MS-275 is a promising new approach for the treatment of lung cancer and our findings contribute to understanding the potential roles of AgNPs and MS-275 in pulmonary disease. However, further study is warranted to potentiate the use of this combination therapy in cancer therapy trials.
Analysis of Iterated Greedy Heuristic for Vertex Clique Covering

The aim of the vertex clique covering problem (CCP) is to cover the vertices of a graph with as few cliques as possible. We analyse the iterated greedy (IG) algorithm for CCP, which was previously shown to provide strong empirical results for real-world networks. It is demonstrated how the techniques of analysis for randomised search heuristics can be applied to IG, and several practically relevant results are obtained. We show that for triangle-free graphs, IG solves CCP optimally in expected polynomial time. Secondly, we show that IG ﬁnds the optimum for CCP in a speciﬁc case of sparse random graphs in expected polynomial time with high probability. For Barab´asi-Albert model of scale-free networks, which is a canonical model explaining the growth of social, biological or computer networks, we obtain that IG obtains an asymptotically optimal approximation in polynomial time in expectation. Last but not least, we propose a slightly modiﬁed variant of IG, which guarantees expected polynomial-time convergence to the optimum for graphs with non-overlapping triangles.
The association between blood pressure and long-term outcomes of patients with ischaemic cardiomyopathy with and without surgical revascularization: an analysis of the STICH trial

Aims Hypertension (HTN) is a well-known contributor to cardiovascular disease, including heart failure (HF) and coronary artery disease, and is the leading risk factor for premature death world-wide. A J- or U-shaped relationship has been suggested between blood pressure (BP) and clinical outcomes in different studies. However, there is little information about the significance of BP on the outcomes of patients with coronary artery disease and left ventricular dysfunction. This study aimed to determine the relationship between BP and mortality outcomes in patients with ischaemic cardiomyopathy. Methods and results The influence of BP during a median follow-up of 9.8 years was studied in a total of 1212 patients with ejection fraction ≤35% and coronary disease amenable to coronary artery bypass grafting (CABG) who were randomized to CABG or medical therapy alone (MED) in the STICH (Surgical Treatment for Ischaemic Heart Failure) trial. Landmark analyses were performed starting at 1, 2, 3, 4, and 5 years after randomization, in which previous systolic BP values were averaged and related to subsequent mortality through the end of follow-up with a median of 9.8 years. Neither a previous history of HTN nor baseline BP had any significant influence on long-term mortality outcomes, nor did they have a significant interaction with MED or CABG treatment. The landmark analyses showed a progressive U-shaped relationship that became strongest at 5 years (χ2 and P-values: 7.08, P = 0.069; 8.72, P = 0.033; 9.86; P = 0.020; 8.31, P = 0.040; 14.52, P = 0.002; at 1, 2, 3, 4, and 5-year landmark analyses, respectively). The relationship between diastolic BP (DBP) and outcomes was similar. The most favourable outcomes were observed in the SBP range 120-130, and DBP 75-85 mmHg, whereas lower and higher BP were associated with worse outcomes. There were no differences in BP-lowering medications between groups. Conclusion A strong U-shaped relationship between BP and mortality outcomes was evident in ischaemic HF patients. The results imply that the optimal SBP might be in the range 120-130 mmHg after intervention, and possibly be subject to pharmacologic action regarding high BP. Further, low BP was a marker of poor outcomes that might require other interactions and treatment strategies. Clinical Trial Registration URL: http://www.clinicaltrials. gov. Unique identifier: NCT00023595.
An Empirical Investigation of Error Types in Vietnamese Parsing

Syntactic parsing plays a crucial role in improving the quality of natural language processing tasks. Although there have been several research projects on syntactic parsing in Vietnamese, the parsing quality has been far inferior than those reported in major languages, such as English and Chinese. In this work, we evaluated representative constituency parsing models on a Vietnamese Treebank to look for the most suitable parsing method for Vietnamese. We then combined the advantages of automatic and manual analysis to investigate errors produced by the experimented parsers and find the reasons for them. Our analysis focused on three possible sources of parsing errors, namely limited training data, part-of-speech (POS) tagging errors, and ambiguous constructions. As a result, we found that the last two sources, which frequently appear in Vietnamese text, significantly attributed to the poor performance of Vietnamese parsing.
deepQuest: A Framework for Neural-based Quality Estimation

Predicting Machine Translation (MT) quality can help in many practical tasks such as MT post-editing. The performance of Quality Estimation (QE) methods has drastically improved recently with the introduction of neural approaches to the problem. However, thus far neural approaches have only been designed for word and sentence-level prediction. We present a neural framework that is able to accommodate neural QE approaches at these fine-grained levels and generalize them to the level of documents. We test the framework with two sentence-level neural QE approaches: a state of the art approach that requires extensive pre-training, and a new light-weight approach that we propose, which employs basic encoders. Our approach is significantly faster and yields performance improvements for a range of document-level quality estimation tasks. To our knowledge, this is the first neural architecture for document-level QE. In addition, for the first time we apply QE models to the output of both statistical and neural MT systems for a series of European languages and highlight the new challenges resulting from the use of neural MT.
Compression of Textual Column-Oriented Data

Column-oriented data are well suited for compression. Since values of the same column are stored contiguously on disk, the information entropy is lower if compared to the physical data organization of conventional databases. There are many useful light-weight compression techniques targeted at specific data types and domains, like integers and small lists of distinct values, respectively. However, compression of textual values formed by skewed and high-cardinality words is usually restricted to variations of the LZ compression algorithm. So far there are no empirical evaluations that verify how other sophisticated compression methods address columnar data that store text. In this paper we shed a light on this subject by revisiting concepts of those algorithms. We also analyse how they behave in terms of compression and speed when dealing with textual columns where values appear in adjacent positions.
MCDTB: A Macro-level Chinese Discourse TreeBank

In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus. Fol-lowing RST (Rhetorical Structure Theory), we annotate the macro discourse information, including discourse structure, nuclearity and relationship, and the additional discourse information, including topic sentences, lead and abstract, to make the macro discourse annotation more objective and accurate. Finally, we annotated 720 articles with a Kappa value greater than 0.6. Preliminary experiments on this corpus verify the computability of MCDTB.
Learning Sentiment Composition from Sentiment Lexicons

Sentiment composition is a fundamental sentiment analysis problem. Previous work relied on manual rules and manually-created lexical resources such as negator lists, or learned a composition function from sentiment-annotated phrases or sentences. We propose a new approach for learning sentiment composition from a large, unlabeled corpus, which only requires a word-level sentiment lexicon for supervision. We automatically generate large sentiment lexicons of bigrams and unigrams, from which we induce a set of lexicons for a variety of sentiment composition processes. The effectiveness of our approach is confirmed through manual annotation, as well as sentiment classification experiments with both phrase-level and sentence-level benchmarks.
Deep Bayesian Learning and Understanding

Given the current growth in research and related emerging technologies in machine learning and deep learning, it is timely to introduce this tutorial to a large number of researchers and practitioners who are attending COLING 2018 and working on statistical models, deep neural networks, sequential learning and natural language understanding. To the best of our knowledge, there is no similar tutorial presented in previous ACL/COLING/EMNLP/NAACL. This three-hour tutorial will concentrate on a wide range of theories and applications and systematically present the recent advances in deep Bayesian and sequential learning which are impacting the communities of computational linguistics, human language technology and machine learning for natural language processing.
Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models

The spread of disease through human populations is complex. The characteristics of disease propagation evolve with time, as a result of a multitude of environmental and anthropic factors, this non-stationarity is a key factor in this huge complexity. In the absence of appropriate external data sources, to correctly describe the disease propagation, we explore a flexible approach, based on stochastic models for the disease dynamics, and on diffusion processes for the parameter dynamics. Using such a diffusion process has the advantage of not requiring a specific mathematical function for the parameter dynamics. Coupled with particle MCMC, this approach allows us to reconstruct the time evolution of some key parameters (average transmission rate for instance). Thus, by capturing the time-varying nature of the different mechanisms involved in disease propagation, the epidemic can be described. Firstly we demonstrate the efficiency of this methodology on a toy model, where the parameters and the observation process are known. Applied then to real datasets, our methodology is able, based solely on simple stochastic models, to reconstruct complex epidemics, such as flu or dengue, over long time periods. Hence we demonstrate that time-varying parameters can improve the accuracy of model performances, and we suggest that our methodology can be used as a first step towards a better understanding of a complex epidemic, in situation where data is limited and/or uncertain.
Entity Coreference Resolution

—Entity coreference resolution is generally considered one of the most difﬁcult tasks in natural language understanding. Though extensively investigated for more than 50 years, the task is far from being solved. Its difﬁculty stems from its reliance on sophisticated knowledge sources and inference mechanisms. Nevertheless, signiﬁcant progress has been made on learning-based coreference research since its inception two decades ago. This paper provides an overview of the major milestones made in learning-based coreference research.
Parasympathetic Response Patterns are Associated with Metabolic Syndrome Among Older Women but Not Men

Abstract Background Little is known about the role of physiological stress responses in metabolic syndrome (MetS). Purpose To examine whether patterns of autonomic response to psychological stress are associated with MetS and whether this association is moderated by sex Methods 1121 men and women (Mage = 65.3 ± 6.77 years) with and without coronary artery disease (CAD) underwent an anger recall stressor task. Heart rate and heart-rate variability (HRV; HF, LF/HF) were assessed. Clusters of participants showing similar patterns of response across baseline, stress, and recovery periods were created using ACECLUS and FASTCLUS in SAS. Logistic regressions included clusters and interaction between clusters and sex as independent variables, controlling for relevant covariates. ANCOVAs were conducted in secondary analyses utilizing a continuous composite representation of MetS. Results Men and women showing greater tonic and phasic HR elevations were more likely to meet MetS criteria (OR = 1.45, [95% CI = 1.02–2.07], p = .037). HF-HRV cluster interacted significantly with sex (p < .001) to predict MetS. In women, those with significant parasympathetic withdrawal to stress and poor recovery were more likely to have MetS than women with a more moderate response (OR = 2.56, [95% CI = 1.23–5.41], p = .013). Women who displayed stress-related parasympathetic activation were also at greater risk of MetS (OR = 2.30, [95% CI = 1.30–4.07], p = .004). Results using a continuous measure of MetS were generally consistent with these findings. Conclusion Among older participants with CAD or other noncardiovascular disease, hyperreactivity to stress was associated with greater prevalence of MetS, particularly in women. Consistent with emerging literature, women who showed blunting or activation of parasympathetic responses to stress were similarly at greater risk.
Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew

This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs — fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89% accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks’ task performance.
Responding E-commerce Product Questions via Exploiting QA Collections and Reviews

Providing instant responses for product questions in E-commerce sites can significantly improve satisfaction of potential consumers. We propose a new framework for automatically responding product questions newly posed by users via exploiting existing QA collections and review collections in a coordinated manner. Our framework can return a ranked list of snippets serving as the automated response for a given question, where each snippet can be a sentence from reviews or an existing question-answer pair. One major subtask in our framework is question-based response review ranking. Learning for response review ranking is challenging since there is no labeled response review available. The collection of existing QA pairs are exploited as distant supervision for learning to rank responses. With proposed distant supervision paradigm, the learned response ranking model makes use of the knowledge in the QA pairs and the corresponding retrieved review lists. Extensive experiments on datasets collected from a real-world commercial E-commerce site demonstrate the effectiveness of our proposed framework.
Studying Semi-TCP and Its Application in Marine Internet

TCP protocol has good performance on the Internet, but its performance is significantly reduced when it is applied to Marine Internet (MI). How to improve the performance of TCP protocol in Marine Internet has become an important research topic. In this paper, an improved Semi-TCP is adopted for Marine Internet, and the implementation scheme of Semi-TCP congestion control is introduced. The exposed terminal problem and congestion control problem of high load networks are analyzed in detail. By using a timer, the congestion control algorithm is improved. Performance analysis and comparison of TCP-Lite, Semi-TCP-RTS, and improved Semi-TCP (Semi-TCP-RTS-V2) are carried out on Exata simulation platform, and the experimental results show that Semi-TCP-RTS-V2 has better transmission performance in ship ad hoc networks.
Few-Shot Charge Prediction with Discriminative Legal Attributes

Automatic charge prediction aims to predict the final charges according to the fact descriptions in criminal cases and plays a crucial role in legal assistant systems. Existing works on charge prediction perform adequately on those high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. Moreover, these exist many confusing charge pairs, whose fact descriptions are fairly similar to each other. To address these issues, we introduce several discriminative attributes of charges as the internal mapping between fact descriptions and charges. These attributes provide additional information for few-shot charges, as well as effective signals for distinguishing confusing charges. More specifically, we propose an attribute-attentive charge prediction model to infer the attributes and charges simultaneously. Experimental results on real-work datasets demonstrate that our proposed model achieves significant and consistent improvements than other state-of-the-art baselines. Specifically, our model outperforms other baselines by more than 50% in the few-shot scenario. Our codes and datasets can be obtained from https://github.com/thunlp/attribute_charge.
A Review on Low-Grade Thermal Energy Harvesting: Materials, Methods and Devices

Combined rejected and naturally available heat constitute an enormous energy resource that remains mostly untapped. Thermal energy harvesting can provide a cost-effective and reliable way to convert available heat into mechanical motion or electricity. This extensive review analyzes the literature covering broad topical areas under solid-state low temperature thermal energy harvesting. These topics include thermoelectricity, pyroelectricity, thermomagneticity, and thermoelasticity. For each topical area, a detailed discussion is provided comprising of basic physics, working principle, performance characteristics, state-of-the-art materials, and current generation devices. Technical advancements reported in the literature are utilized to analyze the performance, identify the challenges, and provide guidance for material and mechanism selection. The review provides a detailed analysis of advantages and disadvantages of each energy harvesting mechanism, which will provide guidance towards designing a hybrid thermal energy harvester that can overcome various limitations of the individual mechanism.
Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN

Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.