Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Interpretation of Convolutional Neural Networks for Text Classification

A long-standing obstacle accompanying the growing popularity of convolutional neural networks (CNNs) is the lack of interpretability, which is essential to explain the decision-making process and diagnose the model’s behavior. In this paper, we present a mathematical decomposition to translate the output of CNN for text classiﬁcation into an ngram-level score matrix and a word-level score matrix, revealing how various parts of input sentences contribute to the ﬁnal prediction quantitatively. By exploiting the derived ngram-level score matrix, we perform extensive experiments to demonstrate how n-gram features are learned via the convolutional layer. We re-ﬁne previous intuitions about the behavior of ﬁlters and perform a deep investigation into their underlying properties. By leveraging the resulting word-level score matrix, we propose two visualization methods in either a global view or a local view to show how the model highlights the relative importance of inputs to arrive at a particular result. Moreover, we show how to perform adversarial attacks with word-level importance scores, and we achieve higher success rate than the baseline. Consequently, by interpreting the model in the form of score matrices, we are able to zoom in on the black boxes of CNN-based text classiﬁcation models and present a comprehensive analysis of their behaviors.
Quantitative trait loci map for growth and morphometric traits using a channel catfish × blue catfish interspecific hybrid system1

Head length, head depth, head width, body depth, body width, caudal depth, and caudal width and total length and BW were measured for 71 backcross full sibs between the interspecific backcross F1 (female channel catfish [Ictalurus punctatus] × male blue catfish [Ictalurus furcatus]) female × blue catfish male. Body measurements were corrected for both size and the relationship between relative body shape and size, which is critical but usually ignored in fish research. Amplified fragment length polymorphism analysis was used for construction of a QTL map with 44 linkage groups. Eleven of 44 linkage groups had at least 1 significant QTL (P ≤ 0.05) and 11 of 44 at P = 0.10. Linkage group 19 was unique as it had multiple QTL for every trait measured, except for caudal width for which no QTL was identified on any linkage group. Approximately half of the markers measured were associated with positive effects (increase in size) on the traits and half had negative effects (decrease in size). Linkage groups 5, 9, 18, 20, 39, and 40 were significant for multiple traits and always had a trait negative effect. Total length is represented on the map by the most linkage groups and the most markers. The linkage relationships found among BW, total length, and the 7 morphometric traits indicated that multiple trait marker-assisted selection to simultaneously increase BW body depth, body width, and caudal depth while decreasing the head traits with the goal to increase body weight and carcass yield would be very difficult. Multiple genetic enhancement approaches would likely be needed to simultaneously improve BW and body conformation.
A Weighted GCN with Logical Adjacency Matrix for Relation Extraction

Graph convolutional network (GCN), with its capability to update the current node features according to the features of its ﬁrst-order adjacent nodes and edges, has achieved impressive performance in dependency capturing. But some important nodes from which we should ﬁgure out the dependencies are not ﬁrst-order reachable, which calls for multi-layer GCNs for indirect relevance capturing. In this paper, we propose a novel weighted graph convolutional network by constructing a logical adjacency matrix which effectively solves the feature fusion of multi-hop relation without additional layers and parameters for relation extraction task. And we apply an Entity-Attention mechanism to enrich the entity pairs with more focused semantic information. Experimental results on TACRED and SemEval 2010 task 8 show that our model can take better advantage of the structural information in the dependency tree and produce better results than previous models.
Strategic Behavior in Energy and Reserve Co-optimizing T & D Markets with EV Participants : Long Version with Appendices

This paper compares Transmission and Distribution (T&D) network day-ahead market (DAM) algorithms clearing energy and reserves under various cases of information available to either a centralized or distributed decision maker. Flexible Distributed Energy Resource (DER) loads transacting hourly energy and reserves are market participants in addition to conventional generators and inelastic demand. DERs are connected to distribution networks where line losses are significantly higher than in the high voltage transmission network. Distributed-algorithm-based Market clearing with self-scheduling DERs is required for computation and information communication tractability. Under certain conditions of information available to flexible DER loads, strategic behavior is possible. This paper’s contribution is the investigation of the likelihood and severity of DER strategic behavior. It investigates the Nash Equilibrium achieved by selfscheduling of profit maximizing DER-market-participants adapting iteratively to hourly day-ahead energy and reserve marginal-cost-based prices set by collaborating T&D Independent System Operators. The Nash Equilibria obtained under different cases of distribution network information available to individual DERs or their aggregator are compared to social welfare maximizing DER schedules obtained by an “all knowing” Centralized Market Operator (CMO). Comparison of Nash and Social Welfare maximizing Equilibria across the aforementioned information availability cases, indicates that under some -possibly impractical -information availability cases, self-scheduling DERs can achieve a higher profit than under a social welfare maximizing CMO, a result magnified by DER collusion through an aggregator. We perform numerical experiments on a simple T&D network modeling salient characteristics of EV mobility, battery electrochemistry charging constraints and distribution network losses.
Sentiment Analysis Engine for Cambodian Music Industry Re-building

During Khmer Rouge Regime, Cambodian pop music was completely forgotten since 90% of artists were killed. After recovering from war since 1979, the music started to grow again in 1990. However, Cambodian popular music dynamic and flows are observably directed by the multifaceted socioeconomic, political and creative forces. The major problems are the plagiarism and piracy which have been prevailing for years in the industry. Recently, the consciousness of the need to preserve Khmer original songs from both fans and artist have been increased and become a new trend for Cambodia young population. Still, the music quality is in the limit state. To increase the mind-set, the feedbacks and inspiration are needed. The study suggested a music ranking website using sentiment analysis which data were collected from Production Companies Facebook Pages' posts and comments. The study proposed an algorithm which translates from Khmer to English, doing sentiment analysis and generate the ranking. The result showed 80% accuracy of translation and sentiment analysis on the proposed system. The songs that rank high in the system are the songs which are original and fit the occasion in Cambodia. With the proposed ranking algorithm, it would help to increase the competitive advantage of the musical productions as well as to encourage the producers to compose the new songs which fit the particular activities and event.
Visual-speech Synthesis of Exaggerated Corrective Feedback

To provide more discriminative feedback for the second language (L2) learners to better identify their mispronunciation, we propose a method for exaggerated visual-speech feedback in computer-assisted pronunciation training (CAPT). The speech exaggeration is realized by an emphatic speech generation neural network based on Tacotron, while the visual exaggeration is accomplished by ADC Viseme Blending, namely increasing Amplitude of movement, extending the phone's Duration and enhancing the color Contrast. User studies show that exaggerated feedback outperforms non-exaggerated version on helping learners with pronunciation identification and pronunciation improvement.
Knowledge Base Repair: From Active Integrity Constraints to Active TBoxes

In the database literature it has been proposed to resort to active integrity constraints in order to restore database integrity. Such active integrity constraints consist of a classical integrity constraint together with a set of preferred update actions that can be triggered when the constraint is violated. In this overview paper we start by reviewing the main repairing routes that have been proposed in the literature. We do so from the perspective of Dynamic Logic, viewing active integrity constraints as programs that test whether a constraint is violated and if so perform appropriate update actions. We then discuss how these ideas can be adapted to Description Logics. We assume extensions of TBox axioms by update actions that denote the preferred ways an ABox should be repaired in case of inconsistency with the axioms of the TBox. The extension of the TBox axioms with these update actions constitute new, active TBoxes.
Cognitive Logics - Features, Formalisms, and Challenges

Logic is responsible for scientiﬁc progress in many disciplines. In particular, computer science and AI would be impossible without it. Classical logics have long been accepted as a normative framework for human reasoning to capture correct reasoning. However, many psychological ﬁndings such as the Wason Selection Task have demonstrated that classical logic cannot serve as a possible descriptive language for the human inference process, which is the aim of what we call a cognitive logic . Recently, some nonmonotonic logics have been employed to explain human inferences for some well-known examples. In this paper, we discuss possible features of cognitive logics, present ﬁrst results, and highlight new challenges.
An extended radar relative calibration adjustment (eRCA) technique for higher-frequency radars and range–height indicator (RHI) scans

Abstract. This study extends the relative calibration adjustment technique for calibration of weather radars to higher-frequency radars as well as range–height indicator (RHI) scans. The calibration of weather radars represents one of the most dominant sources of error for their use in a variety of fields including quantitative precipitation estimation and model comparisons. While most weather radars are routinely calibrated, the frequency of calibration is often less than required, resulting in miscalibrated time periods. While full absolute calibration techniques often require the radar to be taken offline for a period of time, there have been online calibration techniques discussed in the literature. The relative calibration adjustment (RCA) technique uses the statistics of the ground clutter surrounding the radar as a monitoring source for the stability of calibration but has only been demonstrated to work at S- and C-band for plan-position indicator (PPI) scans at a constant elevation. In this work the RCA technique is modified to work with higher-frequency radars, including Ka-band cloud radars. At higher frequencies the properties of clutter can be much more variable. This work introduces an extended clutter selection procedure that incorporates the temporal stability of clutter and helps to improve the operational stability of RCA for relatively higher-frequency radars. The technique is also extended to utilize range–height scans from radars where the elevation is varied rather than the azimuth. These types of scans are often utilized with research radars to examine the vertical structure of clouds. The newly extended technique (eRCA) is applied to four Department of Energy Atmospheric Radiation Measurement (DOE ARM) weather radars ranging in frequency from C- to Ka-band. Cross comparisons of three co-located radars with frequencies C, X, and Ka at the ARM Cloud, Aerosol, and Complex Terrain Interactions (CACTI) site show that the technique can determine changes in calibration. Using an X-band radar at the ARM Eastern North Atlantic (ENA) site, we show how the technique can be modified to be more resilient to clutter fields that show increased variability, in this case due to sea clutter. The results show that this technique is promising for a posteriori data calibration and monitoring.
Improving Neural Machine Translation with Linearized Dependency Tree Decoder

: In spite of achieving signiﬁcant performance in recent years, there are some existing issues that Sequence-to-Sequence Neural Machine Translation still does not solve completely. Two of them are translation for long sentences and the over-translation. To address these two problems, we propose an approach that utilize more syntactic information, syntactic dependency information, so that the output is generated based on more abundant information. In addition, the outputs are presented not as a simple sequence of tokens but as a linear grammatical tree structure. In addition, the output of the model is presented not as a simple sequence of tokens but as a linearized tree construction. Experiments on the Europarl-v7 dataset of French-to-English translation demonstrate that our proposed method can produce dependency relations between words in the target language and improve BLEU scores by 1.57 and 2.40 on datasets consisting of sentences with up to 50 and 80 tokens, respectively. Furthermore, the proposed method also solved the ine ﬀ ective translation for long sentences and repetition problems in Neural Machine Translation.
Pathogenesis of ovarian cancer

Epithelial ovarian cancer (EOC) is a heterogeneous disease with five histotypes: serous (high-grade and low-grade), endometrioid, clear cell and mucinous carcinoma. New evidences suggest that the majority of EOC are of extra-ovarian origin. We used next-generation sequencing to investigate the cell of origin of high-grade serous ovarian carcinoma (HGSOC) and mucinous ovarian carcinoma (MOC). We analyzed exome-wide sequence and structural analyses of multiple tumor samples from five individuals with advanced stage sporadic HGSOC. Our results suggest that ovarian cancer is a disease of the fallopian tubes, with the development of p53 signatures and serous tubal intraepithelial carcinoma as early events. The subsequent formation of a cancer in the ovaries represents a seeding event from a primary tumor in the fallopian tube that already contains sequence and structural alterations in key driver genes, including TP53, PI3K pathway, and BRCA1/BRCA2 genes. Our work could have implication for screening and early diagnosis of HGSOC. In a separate work, we used unsupervised clustering of gene-expression profile of different histotype of ovarian tumors, their eutopic tissues (ovarian surface epithelium and fallopian tube) and single-cell RNA-sequencing of primordial germ cells (PGCs). We observed that mucinous ovarian tumors (borderline and carcinoma) cluster more closely to PGCs than their eutopic tissue of origin. Our work brings a new and plausible explanation of the clinical and epidemiologic characteristics of MOC and could help into developing new target therapies for this rare and chemoresistant tumor.
An automatic Clustering Method based on Maximum Distance

Summary Traditional k-means clustering algorithm needs to determine cluster number and select initial cluster centers in advance,considering the shortcomings of k-means algorithm,the paper proposed an improved efficient clustering algorithm.The algorithm does not require pre-determined number of clusters and initial cluster centers,select cluster centers according to the principle of maximum distance,divide cluster according to the principle of minimum distance,and determine the optimal cluster partition according to the distance evaluation function.The improved algorithm avoids the choice of the value of cluster number and initial centers.Hence this method can produce more accurate clustering results than the standard k-means algorithm.Experimental results show that the improved algorithm has good performance and high time efficiency.
DFraud3- Multi-Component Fraud Detection freeof Cold-start

Fraud review detection is a hot research topic inrecent years. The Cold-start is a particularly new but significant problem referring to the failure of a detection system to recognize the authenticity of a new user. State-of-the-art solutions employ a translational knowledge graph embedding approach (TransE) to model the interaction of the components of a review system. However, these approaches suffer from the limitation of TransEin handling N-1 relations and the narrow scope of a single classification task, i.e., detecting fraudsters only. In this paper, we model a review system as a Heterogeneous InformationNetwork (HIN) which enables a unique representation to every component and performs graph inductive learning on the review data through aggregating features of nearby nodes. HIN with graph induction helps to address the camouflage issue (fraudsterswith genuine reviews) which has shown to be more severe when it is coupled with cold-start, i.e., new fraudsters with genuine first reviews. In this research, instead of focusing only on one component, detecting either fraud reviews or fraud users (fraudsters), vector representations are learnt for each component, enabling multi-component classification. In other words, we are able to detect fraud reviews, fraudsters, and fraud-targeted items, thus the name of our approach DFraud3. DFraud3 demonstrates a significant accuracy increase of 13% over the state of the art on Yelp.
Rotor Currents Command of The Doubly Fed Induction Generator Used in The Wind Energy

1 www.wjir. org Abstract—The command of the doubly fed induction generator by the rotor currents plays a key role in the control of the stator active and reactive powers, So the concept of the command rests on the precise control of the rotor currents closed-loop, the implementation of this command allows to check the doubly fed induction generator used in the wind energy with the possibility of working in a loop of power open to the exit of this command which is represented by the exit of the doubly fed induction generator (DFIG), Regulators PI used are simple and precise regulators, this type of control of the rotor currents closed-loop adjusts the sliding of DFIG what gives a good adjustment of the stator powers and rotor powers generated by the DFIG, the compensation of the terms of coupling is made by the synthesis of all the regulators PI used in the command. The role of these regulators PI is the control of the stator active and reactive powers and the compensation of the cyclic rotor inductance and the cyclic stator inductance. Index Terms —Command, DFIG, grid, loads, Matlab-Simulink.
"Shrinkage Estimation for Multivariate Hidden Markov Mixture Models"

Motivated from a changing market environment over time, we consider high-dimensional data such as financial returns, generated by a hidden Markov model which allows for switching between different regimes or states. To get more stable estimates of the covariance matrices of the different states, potentially driven by a number of observations which is small compared to the dimension, we apply shrinkage and combine it with an EM-type algorithm. This approach will yield better estimates a more stable estimates of the covariance matrix, which allows for improved reconstruction of the hidden Markov chain. In addition to a simulation study and the analysis of a portfolio data set, we present a series of theoretical results which include a dimensionality asymptotics and which provide the motivation and theoretical foundation for certain techniques used by our method
A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy

With the rapid development of social networks, it has become extremely important to evaluate the propagation capabilities of the nodes in a network. Related research has wide applications, such as in network monitoring and rumor control. However, the current research on the propagation ability of network nodes is mostly based on the analysis of the degree of nodes. The method is simple, but the effectiveness needs to be improved. Based on this problem, this paper proposes a method that is based on Tsallis entropy to detect the propagation ability of network nodes. This method comprehensively considers the relationship between a node’s Tsallis entropy and its neighbors, employs the Tsallis entropy method to construct the TsallisRank algorithm, and uses the SIR (Susceptible, Infectious, Recovered) model for verifying the correctness of the algorithm. The experimental results show that, in a real network, this method can effectively and accurately evaluate the propagation ability of network nodes.
’ Response to ACP MS No . : acp-2019-669

The nitrogen stable isotopic composition in nitrate (δN-NO3) measured in polar ice cores has the potential to provide 10 constraints on past ultraviolet (UV) radiation and thereby total column ozone (TCO) due to the sensitivity of nitrate (NO3) photolysis to UV radiation. However, understanding the transfer of reactive nitrogen at the air-snow interface in Polar Regions is paramount for the interpretation of ice core records of δN-NO3 and NO3 mass concentrations. As NO3 undergoes a number of post-depositional processes before it is archived in ice cores, site-specific observations of δN-NO3 and air-snow transfer modelling are necessary in order to understand and quantify the complex photochemical processes at play. As part of the 15 Isotopic Constraints on Past Ozone Layer Thickness in Polar Ice (ISOL-ICE) project, we report new measurements of NO3 mass concentration and δN-NO3 in the atmosphere, skin layer (operationally defined as the top 5 mm of the snow pack), and snow pit depth profiles at Kohnen Station, Dronning Maud Land (DML), Antarctica. We compare the results to previous studies and new data, presented here, from Dome C, East Antarctic Plateau. Additionally, we apply the conceptual onedimensional model of TRansfer of Atmospheric Nitrate Stable Isotopes To the Snow (TRANSITS) to assess the impact of 20 photochemical processesNO3 recycling on that drive the archival of δN-NO3 and NO3 mass concentrations archived in the snow and firnpack. We find clear evidence of NO3 photolysis at DML, and confirmation of previous theoretical, field and laboratory studies that UV-photolysis is driving our hypothesis that UV-photolysis is driving NO3 recycling and redistribution at DML. Firstly, strong denitrification of the snow pack is observed through the δN-NO3 signature which evolves from the enriched snow pack (-3 to 100 ‰), to the skin layer (-20 to 3 ‰), to the depleted atmosphere (-50 to -20 ‰) corresponding to 25 mass loss of NO3 from the snow pack. Secondly, constrained by field measurements of snow accumulation rate, light attenuation (e-folding depth) and atmospheric NO3 mass concentrations, the TRANSITS model is able to reproduce our observed δN-NO3 observations in depth profiles. We find that NO3 is recycled three two times before it is archived (i.e., below the photic zone) in the snow pack below 15 cm and within 0.75 years (i.e., below the photic zone). Mean annual aArchived δN-NO3 and NO3 mass concentration values are 50 ‰ and 60 ng g-1, respectively, at the DML site. We report an 30 Formatted: Not Superscript/ Subscript Formatted: Not Superscript/ Subscript Formatted: Not Superscript/ Subscript