Miracle-dz/UniFAR-FTUs huggingface dataset:
LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection

In this paper we describe our participation in the SemEval-2018 task 3 Shared Task on Irony Detection. We have approached the task with our low dimensionality representation method (LDR), which exploits low dimensional features extracted from text on the basis of the occurrence probability of the words depending on each class. Our intuition is that words in ironic texts have different probability of occurrence than in non-ironic ones. Our approach obtained acceptable results in both subtasks A and B. We have performed an error analysis that shows the difference on correct and incorrect classified tweets.
Computer-Assisted Crowd Transcription of the U.S. Census with Personalized Assignments for Better Accuracy and Participation

Our Open Genealogy Data census transcription project is intended to make valuable census data more readily available to researchers, digital libraries, and others. We use automatic handwriting recognition to bootstrap our census database, facilitating searches even in the early stages of the project while manual transcription is underway. We provide a web-based interface for crowd-sourced transcription of the census to complete and correct the database. In an effort to improve both volunteer participation and transcription accuracy, we provide default transcription assignments based on transcribers' own genealogical family lines or geographical locations to improve the likelihood that the transcribers will be familiar with the names and places in their assignments.
Fist american sign language recognition using leap motion sensor

The paper aims to solve the problem of the fist signs in American Sign Language because their recognition is not perfect due to the Euclidian distances of the fingertip to palm position used in 3D of those signs are similar, so it is difficult to recognize them. We therefore propose a system of fist American Sign Language recognition with a bare hand in depth plane by using 3D non-contact motion sensor. In this system, two patterns of the polygon area between the consecutive fingertip positions with palm position in depth plane by Shoelace formula are used to identify the fist sign language: 1) six triangles area, 2) one hexagon area, then a decision tree is applied to classify the alphabets. The results showed the 7 alphabets in fist ASL using the researcher's hand. The accuracy of the method proposed is approximately 96.1%.
struc2gauss: Structure Preserving Network Embedding via Gaussian Embedding

Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: structure preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on the local structural information, i.e., neighborhood information. However, neighborhood information does not capture the global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on both synthetic and real-world data sets demonstrate that struc2gauss effectively captures the global structural information while state-of-the-art network embedding methods fails to, outperforms other methods on the structure-based clustering task and provides more information on uncertainties of node representations.
Measuring Frame Instance Relatedness

Frame semantics is a well-established framework to represent the meaning of natural language in computational terms. In this work, we aim to propose a quantitative measure of relatedness between pairs of frame instances. We test our method on a dataset of sentence pairs, highlighting the correlation between our metric and human judgments of semantic similarity. Furthermore, we propose an application of our measure for clustering frame instances to extract prototypical knowledge from natural language.
Design and Hardware Implementation of Decoder Architectures for Polar Codes

Polar codes are a new class of forward error-correcting codes recently discovered by Arıkan. They are the first codes with an explicit construction to provably achieve capacity, a theoretical limit governing the transmission of information in the presence of noise, for a wide variety of communication channels. Since polar codes have a very regular construction, they also lend themselves very well to hardware implementations. This thesis presents the design work underlying the development of two generations of hardware decoders for polar codes based upon the successive-cancellation algorithm. Making use of SRAM-based semi-parallel architectures, those designs allow very large polar codes to be implemented on Altera Stratix IV and Stratix V FPGA targets: up to N = 2 for the first generation, and N = 2 for the second-generation design. The second-generation decoder also features variable quantization levels to reduce its memory footprint, look-ahead to improve throughput, a redesigned partial-sum encoder based on a novel semi-parallel design for increased scalability, and an overlapped frame loading mechanism allowing full-speed operation of the decoder with a single input buffer.
Effect of postdeposition annealing on the electrical properties of β-Ga2O3 thin films grown on p-Si by plasma-enhanced atomic layer deposition

Ga2O3 dielectric thin films were deposited on (111)-oriented p-type silicon wafers by plasma-enhanced atomic layer deposition using trimethylgallium and oxygen plasma. Structural analysis of the Ga2O3 thin films was carried out using grazing-incidence x-ray diffraction. As-deposited films were amorphous. Upon postdeposition annealing at 700, 800, and 900 °C for 30 min under N2 ambient, films crystallized into β-form monoclinic structure. Electrical properties of the β-Ga2O3 thin films were then investigated by fabricating and characterizing Al/β-Ga2O3/p-Si metal–oxide-semiconductor capacitors. The effect of postdeposition annealing on the leakage current densities, leakage current conduction mechanisms, dielectric constants, flat-band voltages, reverse breakdown voltages, threshold voltages, and effective oxide charges of the capacitors were presented. The effective oxide charges (Qeff) were calculated from the capacitance–voltage (C-V) curves using the flat-band voltage shift and were found as 2.6 × 1012, 1.9 × 1012, and 2.5 × 1012 cm−2 for samples annealed at 700, 800, and 900 °C, respectively. Effective dielectric constants of the films decreased with increasing annealing temperature. This situation was attributed to the formation of an interfacial SiO2 layer during annealing process. Leakage mechanisms in the regions where current increases gradually with voltage were well fitted by the Schottky emission model for films annealed at 700 and 900 °C, and by the Frenkel–Poole emission model for film annealed at 800 °C. Leakage current density was found to improve with annealing temperature. β-Ga2O3 thin film annealed at 800 °C exhibited the highest reverse breakdown field value.Ga2O3 dielectric thin films were deposited on (111)-oriented p-type silicon wafers by plasma-enhanced atomic layer deposition using trimethylgallium and oxygen plasma. Structural analysis of the Ga2O3 thin films was carried out using grazing-incidence x-ray diffraction. As-deposited films were amorphous. Upon postdeposition annealing at 700, 800, and 900 °C for 30 min under N2 ambient, films crystallized into β-form monoclinic structure. Electrical properties of the β-Ga2O3 thin films were then investigated by fabricating and characterizing Al/β-Ga2O3/p-Si metal–oxide-semiconductor capacitors. The effect of postdeposition annealing on the leakage current densities, leakage current conduction mechanisms, dielectric constants, flat-band voltages, reverse breakdown voltages, threshold voltages, and effective oxide charges of the capacitors were presented. The effective oxide charges (Qeff) were calculated from the capacitance–voltage (C-V) curves using the flat-band voltage shift and were found as 2.6 × 1012.
Quantifying Consumer Costs of Insecure Internet of Things Devices

As consumer Internet of Things (IoT) devices proliferate, we must grapple with the consequences of inexpensive, difficult-to-secure products. Malicious actors may use vulnerable IoT devices to snoop on consumers, cause devices to malfunction, or degrade or deny access to services. Violations of consumer confidentiality and integrity, although significant, are just one type of problem. Cybercriminals also exploit vulnerabilities in IoT products to build “botnets” of thousands of devices that can attack and shut down governments, infrastructure providers, and businesses. Attacks on the availability of information technology—such as DDoS attacks—create unique harms that accrue to both the targets of the attacks and to the consumers whose IoT devices enable the attacks. There is a dearth of literature measuring the harms to consumers resulting from DDoS attacks and, because these consumer costs are elusive, regulators have struggled to enact policies that could prompt manufacturers to design more secure IoT devices. We use the framework proposed by Anderson et al. (2013) to identify the costs to consumers in the context of DDoS attacks. This report provides empirical measurements of three of these costs by infecting several consumer IoT devices with the Mirai malware and measuring how the devices use electricity and bandwidth resources in non-infected and infected states. We observe only small increases in electricity consumption of infected devices but significant increases in bandwidth usage in infected devices when compared with non-infected devices operating normally. We also find that infected devices cause a degraded user experience for the device owner, as devices that are involved in attacks can interfere with the owner’s use of both the device and the network to which it is connected. Based on these increased resource consumptions costs, we then examine the costs to consumers of insecure IoT devices through the lens of three case studies. We first investigate the consumer costs of large-scale distributed denial of service attacks on Dyn, Inc. resources and the ​KrebsOnSecurity​ website that were caused by IoT botnets in 2016. We also present a hypothetical worst-case scenario attack to uncover potential damages that could arise given a large pool of insecure IoT devices. Finally, we explore potential implications of these issues and discuss regulations that could be used to promote a more secure IoT ecosystem.
Prevalence of Workplace Violence Against Health-Care Professionals in China: A Comprehensive Meta-Analysis of Observational Surveys

Background: In China, workplace violence (WPV) toward health-care professionals has been a major concern, but no meta-analysis on this topic has been published. This study is a meta-analysis of the pooled prevalence of WPV against health-care professionals in China and its associated risk factors. Method: English- (PubMed, PsycINFO, and Embase) and Chinese-language (Chinese National Knowledge Infrastructure, WanFang, and SinoMed) databases were systematically searched. Data on the prevalence of WPV and the subtypes of violence experienced by health-care professionals in China were extracted and pooled using random-effects models. Findings: A total of 47 studies covering 81,771 health-care professionals were included in the analyses. The overall prevalence of WPV from 44 studies with available data was 62.4% (95% confidence interval [CI] = [59.4%, 65.5%]). The estimated prevalence of physical violence, psychological violence, verbal abuse, threats, and sexual harassment were 13.7% (95% CI [12.2%, 15.1%]), 50.8% (95% CI [46.2%, 55.5%]), 61.2% (95% CI [55.1%, 67.4%]), 39.4% (95% CI [33.4%, 45.4%]), and 6.3% (95% CI [5.3%, 7.4%]), respectively. Males were more likely to experience WPV than females. Interpretation: WPV against health-care professionals appears to be a significant issue in China. Relevant policies and procedures related to WPV should be developed. Staff should be provided with adequate training, education, and support to implement violence management policies to ensure safety at the workplace.
Assessment of the Antidiabetic Potential of the Ethanolic Extract of Date Palm (Phoenix Dactylifera) Seed in Alloxan-Induced Diabetic Rats

Background: Reports exist of the allopathic use of dates as a hypoglycemic, antioxidant and anti-diabetic agent. Date palm seed have also been found to be of nutritional and medicinal value.Aim: this study was aimed at evaluating the anti-diabetic potential of the ethanolic extract of date palm seed in alloxan-induced diabetic rats.Methodology: Proximate composition of date palm seed was determined using standard methods. Ethanolic extract of date palm seed was prepared by crude extraction protocol. The total flavonoids, phenolic contents and total antioxidant capacity of the seed extract was determined. Also, the scavenging activity of the extract using 1, 1- diphenyl-2-picrylhydrazyl (DPPH), nitric oxide (NO) and the reducing power was also evaluated. Thirty wistar rats were randomly divided into five groups of six rats each. Group A was the control group, group B received date palm seed extract only (200 mg/kg bw) i.p, group C, D and E were the diabetic groups that received alloxan (150 mg/kg bw) intra peritoneally; group C was the diabetic control group, group D and group E were treated with glibenclamide (5 mg/kg bw) and date palm seed extract (400 mg/kg bw) intraperitoneally for fourteen days. At the end of the experiment, the lipid profile parameters (HDL, LDL, VLDL, cholesterol and triglyceride) levels were determined from serum samples of the animals. Levels of glucose and antioxidant parameters (SOD, CAT, GSH and MDA) were also analysed from the serum samples of the experimental animals.Results: Results of proximate analysis revealed fiber to be highest (61.75%), carbohydrate (20.95%), fat (8.55%), moisture (3.40%), ash (2.79%) and protein (2.63%). The total flavonoid and phenolic content of date palm seed was found to be 45.28 ± 0.32 mg/100 g and 28.22 ± 0.43 mg/100 g respectively while the total antioxidant capacity was 30.11 ± 0.21 mg/100 g. There was a significant (P 0.05) increase in the HDL of the diabetic treated groups as compared to the diabetic control group. A significant (P<0.05) increase was observed in the SOD, CAT, GSH levels while there was a significant (P<0.05) decrease in the MDA level of the diabetic group treated with date palm seed extract as compared to the diabetic control group.Conclusion: This study suggests the anti-diabetic potential of date palm seed extract which might be due to its hypolipidemic, hypoglycemic and antioxidant properties.
Deep Sequencing Revealed a CpG Methylation Pattern Associated With ALDH1L1 Suppression in Breast Cancer

Hypermethylation of promoter CpG islands is generally recognized epigenetic mechanism responsible for gene silencing in cancer. However, molecular details on how this epigenetic mark triggers the process of gene downregulation are still elusive. Here, we used deep bisulfite sequencing and qPCR analysis to investigate the pattern of CpG methylation of ALDH1L1 promoter region and its association with the gene expression level in 16 paired breast cancer (BC) samples of different clinical stages. Expression of ALDH1L1 gene was suppressed in all examined BC samples up to 200-fold, and average hypermethylation level of the promoter region correlated positively with ALDH1L1 downregulation. We determined the role of every individual CpG site within the ALDH1L1 promoter, including upstream untranscribed region, first untranslated exon, and the start of the first intron, in aberrant gene expression by correlation analysis. The search revealed CpG sites which methylation has the highest impact on intensity of gene transcription. The majority of such CpG sites are located in a compact region in the first intron of the ALDH1L1 gene. These results assist in unraveling of dynamic nature of CpG promoter hypermethylation as well as demonstrate the efficiency of deep bisulfite sequencing in search for novel epigenetic markers in cancer.
Object-Level Representation Learning for Few-Shot Image Classification

Few-shot learning that trains image classifiers over few labeled examples per category is a challenging task. In this paper, we propose to exploit an additional big dataset with different categories to improve the accuracy of few-shot learning over our target dataset. Our approach is based on the observation that images can be decomposed into objects, which may appear in images from both the additional dataset and our target dataset. We use the object-level relation learned from the additional dataset to infer the similarity of images in our target dataset with unseen categories. Nearest neighbor search is applied to do image classification, which is a non-parametric model and thus does not need fine-tuning. We evaluate our algorithm on two popular datasets, namely Omniglot and MiniImagenet. We obtain 8.5\% and 2.7\% absolute improvements for 5-way 1-shot and 5-way 5-shot experiments on MiniImagenet, respectively. Source code will be published upon acceptance.
Detection and correction of geometric distortion in 3D MR images

Three-dimensional magnetic resonance medical images may contain scanner- and patient-induced geometric distortion. For qualitative diagnosis, geometric errors of a few millimeters are often tolerated. However, quantitative applications such as image-guided neurosurgery and radiotherapy can require an accuracy of a millimeter or better. We have developed a method to accurately measure scanner-induced geometric distortion and to correct the MR images for this type of distortion. The method involves a number of steps. First, a specially designed phantom is scanned that contains a large number of reference structures on positions with a manufacturing error of less than 0.05 mm. Next, the positions of the reference structures are automatically detected in the scanned images and a higher-order polynomial distortion-correction transformation is estimated. Then the patient is scanned and the transformation is applied to correct the patient images for the detected distortion. The distortion-correction method is explained in detail in this paper. The accuracy of the method has been measured with synthetically generated phantom scans that contain an exactly-known amount and type of distortion. The reproducibility of the method has been measured by applying it to a series of consecutive phantom scans. Validation results are briefly described in this paper, a more-detailed description is given in another submission to SPIE Medical Imaging 2001.
Boosting integer factoring performance via quantum annealing o sets TECHNICAL REPORT

D-Wave quantum computing systems now allow a user to advance or delay the annealing path of individual qubits through the anneal osets feature. Here we demonstrate the potential of this feature by using it in an integer factoring circuit. Osets allow the user to homogenize dynamics of various computational elements in the circuit. This gives a remarkable improvement over baseline performance, in some cases making the computation more than 1000 times faster. Boosting integer factoring performance via quantum annealing osets
Attribute Extraction by Combing Feature Ranking and Sequence Labeling

Due to the language characteristics, it is a challenge for the knowledge extraction of Chinese text documents. In this paper, an attribute extraction method based on feature ranking and sequence labeling is proposed. Firstly, we obtain the training corpus by annotating Wikipedia texts with the attribute information extracted from the information box of Wikipedia. To improve the quality of training corpus, the trigger keywords are filtered based on the information entropy. The attribute extraction is regarded as a sequence labeling problem, which exploits the multidimensional features such as part of speech and word context. Then, the conditional random field model is trained on the corpus to extract attributes from the unstructured texts. Experiment results show that our method can effectively improve the quality of training corpus using the keyword filtering technique, and hence improve the performance of attribute extraction. Compared with the rule-based attribute extraction methods, our method can be extended to other fields, which has better portability and expansibility.
Connecting branding and Corporate Social Responsibility : strategies to communicate values in an effective way

An integrated management of a company’s intangibles generates credibility and trust among stakeholders. This management begins with the strategic definition of corporate values. A corporate identity also includes social responsibility values that must take into account global issues required from companies. The way in which companies integrate these strategic values into their brand management is a key issue for organizations. Furthermore, corporate communication that includes the management of the company values contributes to the company’s differentiation. This article is the result of analysing intangibles (identity, CSR, branding and communication) in Spanish multinational companies using a qualitative methodology based on in-depth interviews with executive managers. Results show that companies must integrate responsible values into the company’s strategy and communicate, in a direct way, the impact generated at the company through this value management. Thereby, the company reinforces its global presence and social legitimacy in those countries in which it operates.
Deep contextual residual network for electron microscopy image segmentation in connectomics

The goal of connectomics research is to manifest the mechanisms and functions of neural system by using electron microscopy (EM). One of the biggest challenges in connectomic reconstruction is developing reliable neuronal membranes segmentation method to reduce the burden on manual neurite labeling and validation. In this paper, we put forward an effective deep learning approach to realize neuronal membranes segmentation in EM image stacks, which utilizes spatially efficient residual network and multilevel representations of contextual cues to achieve accurate segmentation performance. Furthermore, multicut is used as post-processing to optimize the outputs of network. Experimental results on the public dataset of ISBI 2012 EM Segmentation Challenge demonstrate the effectiveness of our approach in neuronal membranes segmentation. Our method now ranks top 3 among 88 teams and yields 0.98356 Rand Score as well as 0.99063 Information Score, which outperforms most of state-of-the-art methods.