Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Paraphrase Identification Using Supervised Learning Methods Against Various Datasets and Features

Paraphrase identification is the task of identifying the meaning similarity between two text segments given in natural language. It is the primary task essential for natural language understanding. Past work in paraphrase identification primarily focused on machine learning based approaches which are evaluated on any single type of dataset. In this work, paraphrase identification is modeled as the task of binary classification using different classifiers in a supervised manner. Performance of proposed supervised paraphrase identification models are evaluated against two different datasets namely, Twitter paraphrase corpus and Microsoft Research Paraphrase corpus. Evaluation is carried out by means of standard evaluation measures on different experimental setup with lexical, syntactic and semantic features. The proposed paraphrase identification approach achieves competitive results compare to other state-of-the-art machine learning approaches.
Mapping knowledge structure by keyword co-occurrence and social network analysis: Evidence from Library Hi Tech between 2006 and 2017

The purpose of this paper is to present the knowledge structure based on the articles published in Library Hi Tech. The research hotspots are expected to be revealed through the keyword co-occurrence and social network analysis. ,Data sets based on publications from Library Hi Tech covering the time period from 2006 to 2017 were extracted from Web of Science and developed as testbeds for evaluation of the CiteSpace system. Highly cited keywords were analyzed by CiteSpace which supports visual exploration with knowledge discovery in bibliographic databases. ,The findings suggested that the percentage of publications in the USA, Germany, China, and Canada are high. Further, the most popular keywords identified in Library Hi Tech were: “service,” “technology,” “digital library,” “university library,” and “academic library. ” Finally, four research issues were identified based on the most-cited articles in Library Hi Tech.,While keyword plays an important role in scientific research, limited studies paid attention to the keyword analysis in librarian research. The contribution of this study is to systematically explore the knowledge structure constructed by the keywords in Library Hi Tech.
Architecture Analysis and Simulation Research of Distributed Generation for Flexible Distribution Network

Flexible distribution network, which can accept distributed power, is a key feature of smart distribution network in the future. Firstly, the basic structure of distribution system with soft open point (SOP) is established in this paper. PV, fan and energy storage are connected to the AC or DC area of distribution network according to the characteristics of each distributed power. Then, this paper do researches on the mathematical model of SOP and its main operational control strategy. Finally, in this paper, the simulation experiments of feeder output optimal equalization, uninterruptible power supply and energy scheduler for flexible distribution network are carried out by the flexible switching of control mode and operational strategy of converter. The result of simulation verifies that the system structure of AC and DC flexible distribution network with SOP has the advantages of receiving distributed power and flexible control.
Work-in-Progress: Secure Non-volatile Memory with Scratch Pad Memory using Dual Encryption Mode

This paper proposes a secure non-volatile main memory (NVMM) with a scratch pad memory (SPM) management compiler to reduce the number of bit flips in NVMM. The main idea is to categorize data to write-intensive and non write-intensive, and apply the CTR mode encryption for write intensive data and the ECB mode encryption for non write intensive data. The proposed scheme is secure against deterministic chosen plaintext physical attacks. Our experimental results show that the proposed dual encryption scheme reduces the number of flipped bits by 31.8% compared with ECB mode only encryption.
Lexicon Generation Using Genetic Algorithm for Aspect-Based Sentiment Analysis

Sentiment analysis is the task of extracting and analyzing opinions expressed in comments written in social media and websites and is performed to assist users and stakeholders to better understand the public opinion on a subject. In this paper, the main contribution is a lexicon generation method using genetic algorithm for aspect level sentiment analysis. The proposed method produces a lexicon which can be used in polarity detection of aspects in written comments and reviews. The proposed method is an extension over the ALGA algorithm developed to work on tweet level and in this paper, it is modified to fit in aspect level problems. The results of experiment on the SemEval-2014 dataset proves that the proposed lexicon can improve F-measure of polarity detection on subjective comments by at least 1.9 percentage points in Laptop domain compared to prominent lexicons such as AFINN and NRC.
Variability in DNA Methylation and Generational Plasticity in the Lombardy Poplar, a Single Genotype Worldwide Distributed Since the Eighteenth Century

In the absence of genetic diversity, plants rely on the capacity of phenotypic plasticity to cope with shifts in environmental conditions. Understanding the mechanisms behind phenotypic plasticity and how local phenotypic adjustments are transferred to clonal offspring, will provide insight into its ecological and evolutionary significance. Epigenetic changes have recently been proposed to play a crucial role in rapid environmental adaptation. While the contribution of epigenetic changes to phenotypic plasticity has been extensively studied in sexual reproducing model organisms, little work has been done on vegetative generations of asexual reproducing plant species. We studied the variability of DNA methylation and bud set phenology of the Lombardy poplar (Populus nigra cv. Italica Duroi), a cultivated tree representing a single genotype worldwide distributed since the eighteenth century. Bud set observations and CpG methyl polymorphisms were studied on vegetative offspring resulting from cuttings grown for one season in a common glasshouse environment. The cuttings were collected from 60 adult Lombardy poplars growing in different environments. The physiological condition of the cuttings was determined by measuring weight and nutrient condition. Methylation sensitive amplified polymorphisms were used to obtain global patterns of DNA methylation. Using logistic regression models, we investigated correlations among epigenotype, bud phenology, and the climate at the home site of the donor trees, while accounting for physiological effects. We found significant epigenetic variation as well as significant variation in bud phenology, in the absence of genetic variation. Remarkably, phenology of bud set observed at the end of the growing season in the common environment was significantly correlated with climate variables at the home site of the mother trees, specifically the average temperature of January and monthly potential evapotranspiration. Although we could not directly detect significant effects of epigenetic variation on phenology, our results suggest that, in the Lombardy poplar, epigenetic marks contribute to the variation of phenotypic response that can be transferred onto asexually reproduced offspring resulting in locally adapted ecotypes. This contributes to the growing evidence that epigenetic-based transgenerational inheritance might be relevant for adaptation and evolution in contrasting or rapidly changing environments.
Adversarial Sampling and Training for Semi-Supervised Information Retrieval

Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the models, whereas too many non-clicked documents may harm effectiveness of the models and efficiency of training. In addition, recent neural network-based models are vulnerable to adversarial examples due to the linear nature in them. To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback. Our key idea is (i) to augment clicked examples by adversarial training for better generalization and (ii) to obtain very informational non-clicked examples by adversarial sampling and training. Experiments are performed on benchmark data sets for common ad-hoc retrieval tasks such as Web search, item recommendation, and question answering. Experimental results indicate that the proposed approaches significantly outperform strong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search task.
A Novel Approach for Resource Provisioning in Cloud Using Load Comfort Index and VM Demand: A Preview

Organizations as well as Individuals are now-a-days moving to the Cloud due to advantages such as Resource Availability at cheaper rates, Maintenance free Infrastructure, Most Recent updated applications, Anywhere and anytime resource provisioning, Ubiquitous Networking, etc. However there are drawbacks associated with the advantages such as High Power Consumption, Hidden Datacenter Location, Security, Allotment of Resources and Partial Load on Hosts. So, to solve some of these problems in this paperwe aim to address a burning issue of resource allotment to consumer based on novel approach of resource indexing has been proposed. Proposed scheme not only reduce energy consumption but also overcome the issue of poor resource provisioning. Here in this approach, the incoming user requests will be consider based on parameters like whether process is critical or not, required partial resource or full allotment Etc. The system not only support better resource allotment but also provides more energy efficient environment to Cloud providers.
Image Caption via Visual Attention Switch on DenseNet

We introduce a novel approach that is used to convert images into the corresponding language descriptions. This method follows the most popular encoder-decoder architecture. The encoder uses the recently proposed densely convolutional neural network (DenseNet) to extract the feature maps. Meanwhile, the decoder uses the long short time memory (LSTM) to parse the feature maps to descriptions. We predict the next word of descriptions by taking the effective combination of feature maps with word embedding of current input word by “visual attention switch”. Finally, we compare the performance of the proposed model with other baseline models and achieve good results.
MultiNetCom: An Extended Framework for Communities Detection in Multi-relational Networks

Community detection in multi-relational networks is an important computational task; its complexity increases with the size of the network and also with the number of connection types. Although in nature many networks are multi-relational, the easiest way to approach them in practice is to consider them single-relational, by transforming all connection types into a single one and by compressing the multiple connections between two nodes into a single connection. This way, the critical aspects of the multi-relational nature of the network are lost and therefore the results produced are not general enough and sometimes their interpretation can lead to false conclusions. We concentrate here on multi-relational biological networks in particular and show the differences in communities found in these networks by a standard algorithm that reduces the network and by an extended algorithm (which we propose) that treats the network as multi-relational. We believe that this work is a proof of concept and will be considered by researchers working in this field for validating their biological findings.
The Hidden Shape of Stories Reveals Positivity Bias and Gender Bias

To capture the shape of stories is crucial for understanding the mind of human beings. In this research, we use word emdeddings methods, a widely used tool in natural language processing and machine learning, in order to quantify and compare emotional arcs of stories over time. Based on trained Google News word2vec vectors and film scripts corpora (N =1109), we form the fundamental building blocks of story emotional trajectories. The results demonstrate that there exists only one universal pattern of story shapes in movies. Furthermore, there exists a positivity and gender bias in story narratives. More interestingly, the audience reveals a completely different preference from content producers.
Influence Indexing of Developers, Repositories, Technologies and Programming Languages on Social Coding Community GitHub

Software development is taking advantage of social media and revolutionizing the software development in a startling way. Sharing of code has evolved as social coding paradigm through various well-known social coding communities such as GitHub, Stack Overflow, etc. In this research work, Social coding community GitHub is used to identify most influential Developers, Repositories, technologies and programming languages. Social network analysis and Social data analysis concepts have been used to obtain the desired outcome. Social network analysis approaches have been used to identify developers and repositories influencer index. Five kinds of networks-Developer-Developer Network (D-D), Repository-Repository Network (R-R), Repository-Developer Network (R-D), Developer-Follower Network (D-F) and a star Network are formed and analyzed. Further, Trend analysis of GitHub platform has been performed to identify latest trending technologies and most popular programming languages used by the developers for their projects in the past few years.
Motion Planning and Control of an Autonomous Mobile Robot

This paper introduces an efficient motion planning method for autonomous mobile robots, which is based on the rapidly exploring random tree (RRT) algorithm. RRT belongs to the class of sampling-based algorithm and is widely used to solve the motion path planning problem of mobile robots. In this context, a motion planning method for a differential driven mobile robot is discussed which includes an improved RRT variant with a closed loop postprocess for the generation of control sequence for the robot. The motion planning subsystem is integrated with the associated controller. The RRT based motion planning subsystem runs an onward simulation using a system model and the controller to determine the state trajectory. It uses the controller in two different ways: 1) To Eliminate the error in the position between reference and actual path 2) To eliminate the error in the orientation. It utilises a PI controller and a pure pursuit controller. Thus, the controller not only facilitates the execution of the motion plan but also ensures reduction in error in the predicted trajectory ensuring acceptable performance.
Three Dimensional Space Vector Modulation for Three Phase Four Leg Inverters - A Review

This paper discusses three dimensional space vector modulation techniques (3DSVM) for three phase four leg inverters and its development over the past two decades. Three dimensional Space vector Modulation is the successor of conventional two dimensional space vector modulation (2DSVM), with an additional ability to deal unbalanced three phase system. An unbalanced three phase system demands a third dimension to represent zero sequence current in addition to conventional $a$ and $\mathbf{\beta}$ frames. Here analysis of unbalanced three phase load system, types of three wire three phase systems, general methodology of 3DSVM, types of modulation techniques $\alpha\beta\gamma$ and abc coordinate system, and other new improved systems like gh $\gamma$ system are discussed.
Impact of Resistive Open and Bridge Defects on the SET Robustness of Standard CMOS Combinational Logic

The robustness of standard CMOS combinational logic gates to Single Event Transients (SETs), in the presence of resistive open and resistive bridge defects, was investigated. Analysis was performed with SPICE simulations, using the resistors for modeling the open and bridge defects, and a standard double-exponential current source for modeling the SET effects. Two simple circuits based on NAND gate, designed in IHP's 130 nm bulk CMOS process, were employed for this study. It was demonstrated that, for certain input logic levels, the intra- and inter-gate resistive open and bridge defects may lead to significant decrease of the gate's critical charge, and thus to the increase of its soft error rate (SER) by more than one order of magnitude. Also, it was shown that the SET pulse width may significantly increase due to the resistive defects. Simulation results have confirmed that the resistive open defects have a stronger impact on the SET robustness of standard logic gates than the resistive bridge defects.
TextToHBM: A Generalised Approach to Learning Models of Human Behaviour for Activity Recognition from Textual Instructions

There are various knowledge-based activity recognition approaches that rely on manual deﬁnition of rules to describe user behaviour. These rules are later used to generate computational models of human behaviour that are able to reason about the user behaviour based on sensor observations. One problem with these approaches is that the manual rule deﬁ-nition is time consuming and error prone process. To address this problem, in this paper we outline an approach that learns the model structure from textual sources and later optimises it based on observations. The approach includes extracting the model elements and generating rules from textual instructions. It then learns the optimal model structure based on observations in the form of manually created plans and sensor data. The learned model can then be used to recognise the behaviour of users during their daily activities. We illustrate the approach with an example from the cooking domain.
Multi-label Text Classification with Deep Neural Networks

Text classification is a foundational task in natural language processing (NLP). Traditional methods rely heavily on human-designed features, while deep learning models based on neural networks can automatically capture contextual information. We explore and introduce various neural network architectures to extract information and key components in texts. An extensive set of experiments and comparisons on accuracy, speed, memory-consumption are conducted. Methods based on the proposed models won the first place in the Zhihu Machine Learning Challenge 2017. The code has been made publicly available1https://github.com/chenyuntc/PyTorchText.
Linking Source Code to Untangled Change Intents

Previous work [13] suggests that tangled changes (i.e., different change intents aggregated in one single commit message) could complicate tracing to different change tasks when developers manage software changes. Identifying links from changed source code to untangled change intents could help developers solve this problem. Manually identifying such links requires lots of experience and review efforts, however. Unfortunately, there is no automatic method that provides this capability. In this paper, we propose AutoCILink, which automatically identifies code to untangled change intent links with a pattern-based link identification system (AutoCILink-P) and a supervised learning-based link classification system (AutoCILink-ML). Evaluation results demonstrate the effectiveness of both systems: the pattern-based AutoCILink-P and the supervised learning-based AutoCILink-ML achieve average accuracy of 74.6% and 81.2%, respectively.
T-S Fuzzy Logic Control with Genetic Algorithm Optimization for Pneumatic Muscle Actuator

Based on the three elements model of pneumatic muscle actuators(PMA), this paper proposed a T-S fuzzy logic control with genetic algorithm optimization and achieved the trajectory tracking control of PMA. To guarantee the stability of control system, the Lyapunov direct method was used. And the LMI Toolbox of Matlab was used in this paper to solve linear matrix inequalities(LMls) and calculate the state feedback gains. Finally, the results of experiment demonstrated that, T-S fuzzy logic control with genetic algorithm optimization can achieve desired control performance, which overcome the chattering of trajectory tracking, reduced tracking error effectively and improved the accuracy of control.
A Case for Spraying Packets in Software Middleboxes

The standard approach adopted by software middleboxes to use multiple cores has long been to direct packets to cores at flow granularity. This, however, has significant shortcomings. First, it is inefficient, since it cannot use all cores when there is a small number of concurrent flows---which happens frequently. Second, asymmetry in flow distribution causes unfairness even with a larger number of flows. Yet, the current trend of higher-speed links and core-richer CPUs only aggravates these problems. In this paper, we propose a natural alternative: that middleboxes should direct packets to cores at a finer granularity. Our system, Sprayer, solves the fundamental problems of per-flow solutions and addresses the new challenges of handling shared flow state that come with packet spraying. Sprayer builds on the observation that most middleboxes only update flow state when connections start or finish; ensuring that all control packets from the same TCP connection are processed in the same core. We show that, when compared to the per-flow alternative, Sprayer significantly improves fairness and seamlessly uses the entire capacity, even when there is a single flow.
Thai Traffic Sign Detection and Recognition for Driver Assistance

Nowadays, driver assistance systems are embedded with some expensive cars, but more importantly, those systems are not able to recognize Thai traffic signs. This paper proposes a Thai traffic sign detection and recognition system. The proposed system is implemented with two main processes: Thai traffic sign detection and recognition. For the former process, a cascade classifier trained with histogram of oriented gradient (HOG) features is used to generate a trained model for a sign detector, and then Viola-Jones cascade detector is used to classify sign and non-sign objects of the input image. For the latter process, a linear support vector machine (SVM) learner trained with HOG features is used to generate the trained model for sign symbol recognition, and then a SVM class prediction is applied for recognizing the HOG features of the detected sign. Based on a real world data-set, the proposed system can correctly detectand recognize Thai traffic signs in near real time.
Dual-Mode CMOS Power Amplifier Based on Load-Impedance Modulation

This letter presents a dual-mode CMOS power amplifier (PA) that has an improved efficiency using load-impedance modulation in the low-power mode (LPM) and a fully differential operation in the high-power mode (HPM). For the LPM, the transistor in the negative path of the differential pair is turned ON, as a switch, to appropriately modulate the load impedance for the transistor in the positive path using the output balun. An external switch is deployed to turn $V_{\text {DD}}$ OFF for the negative path. In order to verify the proposed concept, a dual-mode CMOS PA IC was designed using a bulk CMOS process and an off-chip output balun, and it was evaluated using the 920-MHz narrow-band Internet of Things signal with a bandwidth of 200 kHz and a peak-to-average power ratio of 5.7 dB. For the HPM, the implemented PA exhibited a gain of 24.1 dB, a power-added efficiency (PAE) of 44.3%, and an adjacent channel leakage power ratio (ACLR) of −33.9 dBc at an average output power of 27.7 dBm. For the LPM, a gain of 19.3 dB, a PAE of 37.7%, and an ACLR of −34.9 dBc were obtained at an average output power of 21.7 dBm.