Miracle-dz/UniFAR-FTUs huggingface dataset:
BREAST CANCER CLASSIFICATION USING NEURAL NETWORK APPROACH : MLP AND RBF

The classification of breast cancer is a medical application that poses a great challenge for researchers and scientists. The use of learning machine and artificial intelligence techniques has revolutionized the process of diagnosis and prognosis of the breast cancer. The aim of our study is to propose an approach for breast cancer distinguishing between different classes of breast cancer. This approach is based on the Wisconsin Diagnostic and Prognostic Breast Cancer datasets for feature selection, and the classification of different types of breast cancer using neural network approach, and especially the multi layer perceptron MLP and the radial basis function RBF. The data set consists of nine features that represent the input layer to the neural network. The neural network will classify the input features into two classes of cancer type (benign and malignant). The proposed approach tested on the database, resulted in 97 % succession rate of classification using RBF neural network. Neural network approach and especially the RBF technique seems an efficient method for classification in medical applications and especially for the breast cancer
Contextual object categorization with energy-based model

Object categorization is a hot issue of an image mining. Contextual information between objects is one of the important semantic knowledge of an image. However, the previous researches for an object categorization have not made full use of the contextual information, especially the spatial relations between objects. In addition, the object categorization methods, which generally use the probabilistic graphical models to implement the incorporation of contextual information with appearance of objects, are almost inevitable to evaluate the intractable partition function for normalization. In this work, we introduced fully-connected fuzzy spatial relations including directional, distance and topological relations between object regions, so the spatial relational information could be fully utilized. Then, the spatial relations were considered as well as co-occurrence and appearance of objects by using energy-based model, where the energy function was defined as the region-object association potential and the configuration potential of objects. Minimizing the energy function of whole image arrangement, we obtained the optimal label set about the image regions and addressed the evaluation of intractable partition function in conditional random fields. Experimental results show the validity and reliability of this proposed method.
Topic Augmented Neural Network for Short Text Conversation

Message response matching is an important task within retrieval-based chatbots. We present a topic augmented neural network(TANN), comprising a sentence embedding layer, a topic embedding layer, and a matching layer, to match messages and response candidates. TANN inherits the benefits of neutral networks on matching sentence pairs, and leverages extra topic information and their corresponding weights as prior knowledge into a matching process. In TANN, the sentence embedding layer embeds an input message and a response into a vector space, while the topic embedding layer forms a topic vector by a linear combination of the embedding of topic words whose weights are determined by both themselves and the message vector. The message vector, the response vector, and the topic vector are then fed to the matching layer to calculate a matching score. The extensive evaluation of TANN, using large human annotated data sets, shows that TANN outperforms simple neutral network methods, while beating other typical matching models with a large margin.
Enhanced Growth Performance and Salinity Tolerance in Transgenic Switchgrass via Overexpressing Vacuolar Na+ (K+)/H+ Antiporter Gene (PvNHX1)

Switchgrass (Panicum virgatum L.) has been increasingly recognized as one of the most valuable perennial bioenergy crop. To improve its biomass production, especially under salt stress, we isolated a putative vacuolar Na+ (K+)/H+ antiporter gene from switchgrass and designated as PvNHX1. Subcellular localization revealed that this protein was localized mainly on the vacuole membrane. The PvNHX1 was found to be expressed throughout the entire growth period of switchgrass, exhibited preferentially expressed in the leaf tissue, and highly induced by salt stress. Transgenic switchgrass overexpressing PvNHX1 showed obvious advantages with respect to plant height and leaf development compared to the wild-type (WT) and transgenic control (EV, expressing the empty vector only) plants, suggesting PvNHX1 may serve as a promoter in switchgrass growth and development. Moreover, transgenic switchgrass were more tolerant than control plants with better growth-related phenotypes (higher shoot height, larger stem diameter, longer leaf length, and width) and physiological capacities (increased proline accumulation, reduced malondialdehyde production, preserved cell membrane integrity, etc.) under high salinity stress. Furthermore, the genes related to cell growth, flowering, and potassium transporters in transgenic switchgrass exhibited a different expression profiles when compared to the control plants, indicating a pivotal function of PvNHX1 in cell expansion and K+ homeostasis. Taken together, PvNHX1 is essential for normal plant growth and development, and play an important role in the response to salt stress by improving K+ accumulation. Our data provide a valuable foundation for further researches on the molecular mechanism and physiological roles of NHXs in plants.
A 200 mA low-area, low-noise, low-dropout linear regulator for monolithic active pixel sensors

To meet the low-material and low-noise requirements, the power supply of the monolithic active pixel sensors must be improved. This paper presents a built-in low-dropout (LDO) linear regulator to internally supply the analog power. A novel compensation strategy is proposed. Since a series RC network and an adaptively biased buffer are utilized, the location of the poles and zeros vary with that of the output pole. Employing one relative small compensation capacitor, the regulator is stable in the full range of load current. The regulator is designed and simulated in a standard 0.35 μm CMOS process. The chip area is 239 μm × 168 μm. The maximum load current of 200 mA can be obtained. The output spectral noise densities are 320 nV/ V Hz and 110 nV/ V Hz at 100 Hz and 1 kHz, respectively.
Exploring and visualizing academic social networks

We demonstrate the ReaSoN portal, consisting of interactive web-based tools for visualizing, exploring, querying, and integrating academic social networks. We describe how these networks are automatically extracted from bibliographic and citation databases, discuss notions of visibility in such networks which enable a rich set of social network analysis, and demonstrate our novel tools for the visualization and exploration of social networks.
Microblog Sentiment Analysis Algorithm Research and Implementation Based on Classification

Under the background of today's information age, microblog obtains a rapid development. With the news on the microblog updating, in order to avoid the users getting lost in the ocean of information, emotion analysis of the information becomes urgent and important. This paper based on the implementation of microblog emotion mining of Bayesian classifier and SVM classification algorithm, making comparison through the analysis of the experimental results in processing speed and accuracy, has a reference value.
The neuropsychological and psychosocial development of children and adolescents with lipoid proteinosis

Lipoid proteinosis (LiP) is a rare hereditary disease, which often results in bilateral, symmetrical and circumscribed calcifications in the mesial temporal region (especially the amygdala). While studies on the neuropsychological and neuropsychiatric difficulties of adults with LiP have been published, a lack of research focusing exclusively on the neuropsychological and psychosocial development of children and adolescents with LiP was identified. A heterogeneous group of five children and adolescents with LiP, ranging in age from 4 to 17 years, and who represented the entire known population of children and adolescents with LiP in South Africa, was assessed with standardized neuropsychological measures and behaviour checklists. Two control participants were matched to each LiP participant according to IQ, home language, rightor left-handedness, sex, race and geographic environment (urban/rural). Each child or adolescent with LiP was compared separately with the control participants matched to them, as well as with the norm groups on which the various instruments were standardized. Variable results were obtained, but in general the children and adolescents with LiP performed significantly worse (practical significance) compared with controls on measures of memory, facial emotion recognition and executive function. Three of the LiP participants also adapted less well socially than their control participants did. All the LiP participants presented with behaviour problems, although the severity and types of behaviour problems varied. Two of the participants in this study presented with amygdala lesions that may have influenced their scores on neuropsychological measures and the ratings of their behaviour, but this possibility can be substantiated only by further research that includes the imaging of controls. The study provides a baseline assessment for future longitudinal and developmental research on LiP; therefore, the study can be regarded as a pilot study.
Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs

Intrusion is the violation of information security policy by malicious activities. Intrusion detection (ID) is a series of actions for detecting and recognising suspicious actions that make the expedient acceptance of standards of confidentiality, quality, consistency, and availability of a computer based network system. In this paper, we present a new approach consists with merging of feature selection and classification for multiple class NSL-KDD cup 99 intrusion detection dataset employing support vector machine (SVM). The objective is to improve the competence of intrusion classification with a significantly reduced set of input features from the training data. In supervised learning, feature selection is the process of selecting the important input training features and removing the irrelevant input training features, with the objective of obtaining a feature subset that produces higher classification accuracy. In the experiment, we have applied SVM classifier on several input feature subsets of training dataset of NSL-KDD cup 99 dataset. The experimental results obtained showed the proposed method successfully bring 91% classification accuracy using only three features and 99% classification accuracy using 36 features, while all 41 training features achieved 99% classification accuracy.
Onboard and parts-based object detection from aerial imagery

Abstract : The almost endless amount of full-motion video (FMV) data collected by Unmanned Aerial Vehicles (UAV) and similar sources presents mounting challenges to human analysts, particularly to their sustained attention to detail despite the monotony of continuous review. This digital deluge of raw imagery also places unsustainable loads on the limited resource of network bandwidth. Automated analysis onboard the UAV allows transmitting only pertinent portions of the imagery, reducing bandwidth usage and mitigating operator fatigue. Further, target detection and tracking information that is immediately available to the UAV facilitates more autonomous operations, with reduced communication needs to the ground station. Experimental results proved the utility of our onboard detection system a) through bandwidth reduction by two orders of magnitude and b) through reduced operator workload. Additionally, a novel parts-based detection method was developed. A whole-object detector is not well suited for deformable and articulated objects, and susceptible to failure due to partial occlusions. Parts detection with a subsequent structural model overcomes these difficulties, is potentially more computationally efficient (smaller resource footprint and able to be decomposed into a hierarchy), and permits reuse for multiple object types. Our parts-based vehicle detector achieved detection accuracy comparable to whole-object detection, yet exhibiting said advantages.
SCRIPTING ARGUMENTATION IN TECHNOLOGY- ENHANCED LEARNING: A PROPOSED SYSTEM ARCHITECTURE

Argumentative knowledge construction is an important component of critical thinking. Learners who collaboratively argue about a subject can potentially develop domain knowledge and have the opportunity to use evidence critically in order to make sense of the subject under consideration. Moreover learners have to take into account claims and evidence of each other during argumentation and in this way they develop mental models for argument construction and sequence. However, research indicates that learners tend to be inconsistent in distinguishing claims and theories from evidence that support them. Furthermore they rarely know how to efficiently construct knowledge through argumentation. In this regard, the use of collaboration scripts can assist learners to understand and participate in argumentative discourse while facilitating the process of argumentative knowledge construction. Unfortunately, this need for scripting argumentation is poorly reflected in the design of computer-based environments which mostly do not incorporate scripts. This paper provides a unifying view of the functional characteristics of existing argumentation tools and also analyzes the major features of collaboration scripts. Furthermore the paper presents a proposed system architecture for a computer-based tool that supports argumentative knowledge construction integrating also collaboration scripts for guiding students' individual and group work. The architecture provides a coarse-grained description of the system components and describes user facilities that need to be considered in a script-based system.
Chemical characteristics of inorganic ammonium salts in PM 2.5 in the atmosphere of Beijing (China)

Abstract. The atmospheric concentrations of gaseous HNO 3 , HCl and NH 3 and their relative salts have been measured during two field campaigns in the winter and in the summer of 2007 at Beijing (China), as part of CAREBEIJING (Campaigns of Air Quality Research in Beijing and Surrounding Region). In this study, annular denuder technique used with integration times of 2 and 24h to collect inorganic and soluble PM 2.5 without interferences from gas–particle and particle–particle interactions. The results were discussed from the standpoint of temporal and diurnal variations and meteorological effects. Fine particulate Cl − , NH 4 + and SO 4 2− exhibited distinct temporal variations, while fine particulate NO 3 − did not show much variation with respect to season. Daily mean concentrations of fine particulate NH 4 + and SO 4 2− were higher during summer (12.30 μg m −3 and 18.24 μg m −3 , respectively) than during winter (6.51 μg m −3 and 7.50 μg m −3 , respectively). Daily mean concentrations of fine particulate Cl − were higher during winter (2.94 μg m −3 ) than during summer (0.79 μg m −3 ), while fine particulate NO 3 − showed similar both in winter (8.38 μg m −3 ) and in summer (9.62 μg m −3 ) periods. The presence of large amounts of fine particulate NO 3 − even in summer are due to higher local and regional concentrations of NH 3 in the atmosphere available to neutralize H 2 SO 4 and HNO 3 , which is consistent with the observation that the measured particulate species were neutralized. The composition of fine particulate matter indicated the domination of (NH 4 ) 2 SO 4 during winter and summer periods. In addition, the high relative humidity conditions in summer period seemed to dissolve a significant fraction of HNO 3 and NH 3 enhancing fine particulate NO 3 − and NH 4 + in the atmosphere. All measured particulate species showed diurnal similar patterns during the winter and summer periods with higher peaks in the early morning, especially in summer, when humid and stable atmospheric conditions occurred. These diurnal variations were affected by wind direction suggesting regional and local source influences. The fine particulate species were correlated with NO x and PM 2.5 , supporting the hypothesis that traffic may be also an important source of secondary particles.
FPGA implementation of FHSS-FSK modulator

This In this paper, we present the Binary Frequency Shift Keying (BFSK) modulator using the Frequency Hopping Spread Spectrum (FHSS) operating in the European band ISM 863-870 MHz. This modulator is intended for short range wireless applications, such as the wireless network sensors. The modulator generates a 7 MHz wide single-sideband, frequency hopped spread spectrum waveform for wireless transmission in the 863-870 MHz. This modulator is designed using the Direct Digital Frequency Synthesizer (DDFS) which enables us to generate BFSK signal with the hopping frequencies. Low power DDFS architecture is presented. It uses a smaller lookup table for sine and cosine functions compared with existing systems using a minimum additional hardware. A DDFS with -88 dBc spectral purity, 41.29 Hz frequency resolution and 10 bits output data for sine function generation is being implemented in an FPGA.
A Novel Application of Gamification for Collecting High-Level Design Information

This paper presents a novel application of gamification for collecting high-level design descriptions of objects. High-level design descriptions entail not only superficial characteristics of an object, but also function, behavior, and requirement information of the object. Such information is difficult to obtain with traditional data mining techniques. For acquisition of high-level design information, we investigated a multiplayer game, “Who is the Pretender? ” in an offline context. Through a user study, we demonstrate that the game offers a more fun, enjoyable, and engaging experience for providing descriptions of objects than simply asking people to list them. We also show that the game elicits more high-level, problem-oriented requirement descriptions and less low-level, solution-oriented structure descriptions due to the unique game mechanics that encourage players to describe objects at an abstract level. Finally, we present how crowdsourcing can be used to generate game content that facilitates the gameplay. Our work contributes towards acquiring high-level design knowledge that is essential for developing knowledge-based CAD systems.Copyright © 2015 by ASME
The neurotrophic effects of PACAP in PC12 cells: control by multiple transduction pathways

Pituitary adenylate cyclase‐activating polypeptide (PACAP) and vasoactive intestinal polypeptide (VIP) are closely related members of the secretin superfamily of neuropeptides expressed in both the brain and peripheral nervous system, and they exhibit neurotrophic and neurodevelopmental effects in vivo. Like the index member of the Trk receptor ligand family, nerve growth factor (NGF), PACAP promotes the differentiation of PC12 cells, a well‐established cell culture model, to investigate neuronal differentiation, survival and function. Stimulation of catecholamine secretion and enhanced neuropeptide biosynthesis are effects exerted by PACAP at the adrenomedullary synapse in vivo and on PC12 cells in vitro through stimulation of the specific PAC1 receptor. Induction of neuritogenesis, growth arrest, and promotion of cell survival are effects of PACAP that occur in developing cerebellar, hippocampal and cortical neurons, as well as in the more tractable PC12 cell model. Study of the mechanisms through which PACAP exerts its various effects on cell growth, morphology, gene expression and survival, i.e. its actions as a neurotrophin, in PC12 cells is the subject of this review. The study of neurotrophic signalling by PACAP in PC12 cells reveals that multiple independent pathways are coordinated in the PACAP response, some activated by classical and some by novel or combinatorial signalling mechanisms.
TOWARDS A CLOUD-BASED APPROACH FOR SPAM URL DEDUPLICATION FOR BIG DATASETS

Spam emails are often used to advertise phishing websites and lure users to visit such sites. URL blacklisting is a widely used technique for blocking malicious phishing websites. To prepare an effective blacklist, it is necessary to analyze possible threats and include the identified malicious sites in the blacklist. However, the number of URLs acquired from spam emails is quite large. Fetching and analyzing the content of this large number of websites are very expensive tasks given limited computing and storage resources. To solve the problem of massive computing and storage resource requirements, we need a highly distributed and scalable architecture, where we can provision additional resources to fetch and analyze on the fly. Moreover, there is a high degree of redundancy in the URLs extracted from spam emails, where more than one spam emails contain the same URL. Hence, preserving the contents of all the websites causes significant storage waste. Additionally, fetching content from a fixed IP address introduces the possibility of being reversed blacklisted by malicious websites. In this paper, we propose and develop CURLA – a Cloud-based spam URL Analyzer, built on top of Amazon Elastic Computer Cloud (EC2) and Amazon Simple Queue Service (SQS). CURLA allows deduplicating large number of spam-based URLs in parallel, which reduces the cost of establishing equally capable local infrastructure. Our system builds a database of unique spam-based URL and accumulates the content of these unique websites in a central repository. This database and website repository will be a great resource to identify phishing websites and other counterfeit websites. We show the effectiveness of our architecture using real-life, large-scale spam-based URL data.
Continuous Plan Management Support for Space Missions: the RAXEM Case

This paper describes RAXEM, an AI-based system developed to support human mission planners in the daily task to plan uplink commands for an interplanetary spacecraft. The intelligent environment of RAXEM has been designed to support the users in analyzing the problem and taking planning decisions as a result of an interactive process. The system combines different ingredients like integrating flexible automated algorithms, promoting user active participation during problem solving, and guaranteeing continuity of work practice. The paper touches upon all these aspects and comments on how a key factor for success has been the integration of intelligent technology to continuously support mission plan management.