Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient context-sensitive pointer analysis for c programs

This paper proposes an efficient technique for context-sensitive pointer analysis that is applicable to real C programs. For efficiency, we summarize the effects of procedures using partial transfer functions. A partial transfer function (PTF) describes the behavior of a procedure assuming that certain alias relationships hold when it is called. We can reuse a PTF in many calling contexts as long as the aliases among the inputs to the procedure are the same. Our empirical results demonstrate that this technique is successful—a single PTF per procedure is usually sufficient to obtain completely context-sensitive results. Because many C programs use features such as type casts and pointer arithmetic to circumvent the high-level type system, our algorithm is based on a low-level representation of memory locations that safely handles all the features of C. We have implemented our algorithm in the SUIF compiler system and we show that it runs efficiently for a set of C benchmarks.
CORRELATION BETWEEN SPIRITUAL ATTITUDE AND HOPE WITH QUALITY OF LIFE IN ADOLESCENTS WITH CHRONIC DISEASE

Background and aim: The survival of children with chronic disease has increased in recent years, and focusing on their quality of life, instead of immediate interventions is recommended. This cross - correlation study was conducted to determine the relationship between spiritual attitude and hope with the quality of life of adolescents with chronic disease and special needs. Methods: The research population consisted of 134 adolescents with chronic disease and special needs, who were hospitalized in different wards of or visited in clinics of the Children’s Medical Center, the Cancer Institute, the Mofid and the Masih Daneshvari hospitals. 134 adolescents were selected using the convenience sampling method and based on the inclusion criteria of the study. The data collection tools included "the Demographic Characteristics Questionnaire", "Adolescents’ Spiritual Attitude Scale", "Adolescents’ Hope Scale" and "Children’s Quality of life Checklist". The reliability and validity of these scales were examined. The data were analyzed with the SPSS19 software using descriptive tests, the Spearman correlation coefficient and regression analysis. Findings: In this study, 134, 13-18 years old adolescents with chronic disease were studied with a mean±SD age of 15/03±2/03. The majority of the research samples (56/7%) were male. The majority of the samples (41%) were adolescents with chronic respiratory disorders, who were hospitalized in the wards. Aaverage spiritual attitude was 163/8±45/21 for boys and 166/52±27/17 for girls. The mean of hope was 83/61±17/8 in boys and 83/83±18/8 in girls, and the average quality of life was 62/05±16/16 in boys and 67±15/02 in girls. There was not significant relationship between the study variables and sex, but there was negative significant relationship between age and spiritual attitude (r=-0/245, p<0/05). Spiritual attitude (p<0/05, r=0/184), and hope (p<0/05, r=0/176) had a significant positive relationship with the quality of life. There was also a significant positive relationship between spiritual attitude and hope (p<0/05, r=0/406). The regression analysis showed spirituality, kind of admission and father education, can explain 14/6% of the changes in the quality of life. Conclusion: The findings indicates that issues such as purposefulness in life, belief in God, and optimism, as constituents of spiritual attitude and hope, can improve the quality of life of adolescents with chronic disease. Please cite this article as: Heidari Sangelaji M, Rassouli M, Shirinabadi Farahani A, Shakeri N, Ilkhani M. Correlation between spiritual attitude and hope with quality of life in adolescents with chronic disease. Med Ethics J 2016; 10(34): 143-163.
Chapter 8. Task effects in second language sentence processing research

This chapter explores the ways in which secondary tasks utilized in on-line sentence processing experiments can influence second language (L2) learners’ processing behavior. After a brief introduction to sentence processing and the self-paced reading methodology, we present data from a self-paced reading study that investigates how task type (comprehension questions vs. grammaticality judgment tasks) affect intermediate L2 Spanish learners processing of two types of grammatical violations: noun-adjective gender agreement and subject-verb inversion in wh-questions. Our findings suggest that, for some structures, certain secondary tasks commonly used in sentence processing research can indeed influence how L2 learners’ process morpho-syntactic violations during comprehension. We discuss the implications of our findings for L2 learning research, as well as for L2 instruction.
An Edge-oriented Framework for Saliency Detection

Confusing visual appearance and scattered small-scale patterns commonly exist in natural images, which forms a challenge for prior saliency detection methods. Inspired by the sensitivity to edge information of Human Visual Systems, we propose a universal edge-oriented framework to improve the performance of existing salient detection methods. Firstly, edge probability map is extracted from images and utilized to get edge-based over segmentation. Secondly, merging segments by a hierarchical model to generate edge regions. Finally, the proposed framework turns saliency detection to assign a saliency value to each edge region. Experimental results demonstrate the effectiveness of our framework.
Detection and Analysis of Online Extremist Communities

Online social networks have become a powerful venue for political activism. In many cases large, insular online communities form that have been shown to be powerful diffusion mechanisms of both misinformation and propaganda. In some cases these groups users advocate actions or policies that could be construed as extreme along nearly any distribution of opinion, and are thus called Online Extremist Communities (OECs). Although these communities appear increasingly common, little is known about how these groups form or the methods used to influence them. The work in this thesis provides researchers a methodological framework to study these groups by answering three critical research questions: How can we detect large dynamic online activist or extremist communities? What automated tools are used to build, isolate, and influence these communities? What methods can be used to gain novel insight into large online activist or extremist communities? These group members social ties can be inferred based on the various affordances offered by OSNs for group curation. By developing heterogeneous, annotated graph representations of user behavior I can efficiently extract online activist discussion cores using an ensemble of unsupervised machine learning methods. I call this technique Ensemble Agreement Clustering. Through manual inspection, these discussion cores can then often be used as training data to detect the larger community. I present a novel supervised learning algorithm called Multiplex Vertex Classification for network bipartition on heterogeneous, annotated graphs. This methodological pipeline has also proven useful for social botnet detection, and a study of large, complex social botnets used for propaganda dissemination is provided as well. Throughout this thesis I provide Twitter case studies including communities focused on the Islamic State of Iraq and al-Sham (ISIS), the ongoing Syrian Revolution, the Euromaidan Movement in Ukraine, as well as the alt-Right.
Retrodirective Rotman Lens Antenna

The purpose of this paper is to present the theoretical design of a passive retrodirective array architecture based on a microstrip Rotman lens. The true time delay property of the beamforming network is exploited to perform automatic high gain antenna pointing with broadband capability. The analysis and performance optimisation is carried out using Remcom Rotman Lens Designer (RLD) software and CST Microwave Studio (MWS) and requires design features not normally considered when the lens is operated as a beamforming network for an array. The structure, which has 21 beam and 12 array ports, is designed to minimise bistatic beam pattern squinting and monostatic RCS ripple over the frequency range 8 – 12 GHz.
Hybrid Friend Recommendation Approach based on Clustering and Similarity Index

: With the change in internet, way of using it is also changing. Internet not only just provide a way of interaction through operating system but also involving in different fields like artificial intelligence, machine learning etc. Social networking site is mostly used and popular platform of internet. It creates connectivity among people with similar features. Internet service in social networking is making it essential in life of people. Keeping in mind about user nature and interest makes it easy to recommend similar characteristic friend. It also provides a way of enhancing business, promoting products, getting current new, updates etc. It helps to be in touch with our contacts by recommending them similar characteristics of user. Here, a hybrid recommendation model has been proposed and developed to explore the similarity between users based on lifestyle basis. It is the combination of K-mean Clustering and Similarity Weight Calculation to explore the more precise and absolute results. The complete solution is developed using Java technology and evaluated on basis of precision, recall and f-score.
Agreement between nurses regarding patients' risk for developing pressure ulcer Concordância entre enfermeiros quanto ao risco dos pacientes para úlcera por pressão

Objectives: To evaluate the agreement between nurses regarding classification and assessment of patients’ risk for developing pressure ulcer. Methods: A descriptive exploratory study conducted with 22 nurses in four intensive care units of Brazilian university hospitals. The Braden Scale was used for assessment and classification of the patients’ risk for developing pressure ulcer. To assess agreement, we considered the score obtained by the researcher, nurse specialist, as gold standard parameter for comparison with scores punctuated by assisting nurses. Results: There was general agreement among nurses only in the assessment of the subscales Sensory Perception, Mobility, Friction & Shear. For classification into risk levels, there was agreement in only two units. Conclusion: There were differences of agreement between assisting nurses and difference in classification of patients into risk levels.
Exploitation of linguistic tools in semantic extraction - a design

The paper addresses a problem of information extraction from Czech texts from the Web. The method described in the paper exploits existing linguistic tools created originally for a syntactically annotated corpus, Prague Dependency Treebank (PDT 2.0). We propose a system which captures text of web-pages, annotates it linguistically by PDT tools, extracts data and stores the data in an ontology. We report on initial experiments in the domain of reports of traffic accidents. These experiments are promising, e.g. enabling summarization of the number of injured people.
Understanding large scale online environments with qualitative methods

Large scale online environments, in which there are hundreds of thousands, and often millions of links, present an emerging phenomenon where millions of people come together online to share and consume information. The resulting massive amounts of data have been a fertile ground for quantitative researchers. Qualitative studies of these environments are less common, suggesting that the scale and the constant change of these environments pose considerable challenges for qualitative researchers. We present an exploratory study into the challenges and opportunities experienced by researchers conducting qualitative research in large scale online environments, and a meta-analysis of papers from the ACM Digital Library that reveals how few published research studies use qualitative methods to investigate large-scale online environments. We discuss practical and theoretical issues arising from the unique stance of qualitative researchers in these environments.
Hepatoma cell functions modulated by NEK2 are associated with liver cancer progression

NEK2 (NIMA‐related expressed kinase 2) is a serine/threonine centrosomal kinase that acts as a critical regulator of centrosome structure and function. Aberrant NEK2 activities lead to failure in regulating centrosome duplication. NEK2 overexpression promotes tumorigenesis and is associated with poor prognosis in several cancers. Increased NEK2 expression during the late pathological stage has been detected in the Oncomine liver dataset and hepatocellular carcinoma (HCC) specimens. Elevated NEK2 protein is associated with poor overall survival in patients with HCC. However, the precise roles and mechanisms of NEK2 in liver cancer progression remain largely unknown. An earlier functional study revealed that NEK2 mediates drug resistance (cisplatin or lipo‐doxorubicin) via expression of an ABCC10 transporter. Active angiogenesis and metastasis underlie the rapid recurrence and poor survival of HCC. Results from the current study showed that NEK2 mediates tumor growth, metastasis and angiogenesis in vivo. NEK2‐mediated drug resistance was blocked by a specific PI3K or AKT inhibitor. Moreover, NEK2 mediated liver cancer cell migration via pAKT/NF‐κB signaling and matrix metalloproteinase (MMP) activation. Angiogenesis was induced via the same signaling pathway and IL‐8 stimulation. Our findings collectively indicate that NEK2 modulates hepatoma cell functions, including growth, drug resistance, metastasis and angiogenesis via downstream genes activation.
A Personalized Hybrid Recommendation Algorithm for Location-Based Service on Smart Campus

In order to provide location-based service for teachers and students, this paper proposes a personalized hybrid recommendation algorithm (GTRA) based on user’s geographical locations and tags. First, by analyzing the user's historical stay points and the length of stay time in different feature areas, the user's geographical adjacent user set is identified, which well solves scoring matrix sparse problem; then, we propose a tag growth strategy in which club administrators participate to give an effective solution to the problem of cold start; Finally, personalized push service was offered according to the user’s identity and the current location. Through the construction of personalized recommendation system and execution of the experiment, the algorithm proposed in this paper is superior to the traditional collaborative filtering algorithm, and it is suitable for campus application scenarios, which has some reference to the construction of smart campus in which personalized services for teachers and students are emphasized.
Self-production facilitates and adult input interferes in a neural network model of infant vowel imitation.

It is well known that greater amounts of adult input facilitate a child’s language development. Thus, one might expect that increased amounts of adult input would help an infant learn to accurately imitate the vowels of his/her native language. In addition, an infant’s own production of sounds during cooing, babbling, etc. is known to be important to the development of speech abilities. We simulate infant vowel development using a neural network that contains a layer of auditory neurons, a layer of motor neurons, and bidirectional connections linking these perceptual and motor layers. During an initial babbling phase, the system produces random motor activations, hears the acoustic consequences of these motor activations, and adjusts the weights between its auditory and motor layers in a Hebbian fashion. In simulations, passive auditory input from an external “caregiver” is also included during the babbling phase, and is used to update existing auditory-motor connections. In a testing phase, the model is given adult vowels as auditory input and asked to imitate them. Results indicate that self-productions do promote the development of the ability to imitate, but, somewhat counterintuitively, the more adult input this model receives during babbling, the less accurate its imitations are during test. Explanations and implications of this finding are discussed.
Online recognition of people recurrences with bag-of-features representation and automatic new-class labeling

In multicamera surveillance systems, tracking the same person across multiple cameras is an important technique. It is also desirable to recognize the individuals who have been previously observed with a single-camera monitor. This paper addresses this problem by recognizing the same individual in different tracks. The method that represents an object image using a bag of features has been commonly used in image retrieval and classification. In this paper, that approach is adapted for people image description, and support vector machines are employed for high classification performance. To get more reliable matches and support supervised learning in online operation, we propose a decision scheme to distinguish previously unseen individuals from recurrences so that the new classes can be automatically labeled. On this basis, an online recognition framework that applies incremental learning is also presented. We get promising results from the evaluation with more than 200 tracks of 70 different people.
Influence of Noisy Environment on the Speech Recognition Rate Based on the Altera FPGA

This paper introduce an approach to study the effects of different levels of environment noise on the recognition rate of speech recognition systems, which are not used any type of filters to deal with this issue. This is achieved by implementing an embedded SoPC (System on a Programmable Chip) technique with Altera Nios II processor for real-time speech recognition system. Mel Frequency Cepstral Coefficients (MFCCs) technique was used for speech signal feature extraction (observation vector). Model the observation vector of voice information by using Gaussian Mixture Model (GMM), this model passed to the Hidden Markov Model (HMM) as probabilistic model to process the GMM statistically to make decision on utterance words recognition, whether a single or composite, one or more syllable words. The framework was implemented on Altera Cyclone II EP2C70F896C6N FPGA chip sitting on ALTERA DE2-70 Development Board. Each word model (template) stored as Transition Matrix, Diagonal Covariance Matrices, and Mean Vectors in the system memory. Each word model utilizes only 4.45Kbytes regardless of the spoken word length. Recognition words rate (digit/0 to digit/10) given 100% for the individual speaker. The test was conducted at different sound levels of the surrounding environment (53dB to 73dB) as measured by Sound Level Meter (SLM) instrument.
Pain and physical activity in older depressed heart failure patients

Background: Heart failure (HF) is a costly and growing health problem that is routinely complicated by chronic pain and depression. The purpose of this study is to describe the characteristics of pain and pain management in depressed HF patients. Methods: This descriptive cross-sectional study analyzed data from 62 participants with depression and Class II-IV HF. Summary statistics were created for the study variables of interest from the Brief Pain Inventory and Rand-36. Results: Almost all participants (98%) had some pain in the past month and most had pain in the last 24 hours (66%). The median pain score was 4 (0-10 scale) with the majority reporting moderate to severe pain (63%). The median pain interference score was 4.42 (0-10 scale) with the majority (66%) reporting moderate to extreme interference. Medication to treat pain was used by all participants who reported pain with only 5% also using a non-pharmacologic treatment. Discussion: The majority of participants reported moderate or severe pain while also having moderate to extreme pain interference. Nonpharmacologic pain treatments were severely underutilized. Women were more likely to have higher levels of pain intensity and more pain interference than men suggesting that, additional screening for the impact of pain is especially
The Impact of Hands-On Simulation Laboratories on Teaching of Wireless Communications

Accepted by Editor Janice Whatley │Received: July 5, 2016│ Revised: September 12, December 23, 2016; January 30, February 14, February 27, 2017 │ Accepted: February 28, 2017. Cite as: Chou, T-S. , & Vanderbye, A. (2017). The impact of hands-on simulation laboratories on teaching of wireless communications. Journal of Information Technology Education: Innovations in Practice, 16, 69-90. Retrieved from http://www.informingscience. org/Publications/3674
New insight into the detection of high-impedance arcing faults on DC trolley systems

High-impedance arcing faults are difficult to detect with conventional switchgear, and the presence of these faults in coal mine power systems represents a significant fire hazard. Research was performed to identify plausible techniques that would discriminate between the high-impedance arcing faults and legitimate load currents on the DC trolley system. This paper briefly summarizes that effort, and focuses on the frequency characteristics of the arc current. After the arc was modelled as a stochastic process, good agreement was obtained between experimental observations and mathematical predictions.
Cluster based collaborative filtering with inverted indexing

CLUSTER BASED COLLABORATIVE FILTERING WITH INVERTED INDEXING Ozlem Nurcan Subakan M.S. in Computer Engineering Supervisor: Prof. Dr. Ozgur Ulusoy August, 2005 Collectively, a population contains vast amounts of knowledge and modern communication technologies that increase the ease of communication. However, it is not feasible for a single person to aggregate the knowledge of thousands or millions of data and extract useful information from it. Collaborative information systems are attempts to harness the knowledge of a population and to present it in a simple, fast and fair manner. Collaborative filtering has been successfully used in domains where the information content is not easily parse-able and traditional information filtering techniques are difficult to apply. Collaborative filtering works over a database of ratings for the items which are rated by users. The computational complexity of these methods grows linearly with the number of customers which can reach to several millions in typical commercial applications. To address the scalability concern, we have developed an efficient collaborative filtering technique by applying user clustering and using a specific inverted index structure (so called cluster-skipping inverted index structure) that is tailored for clustered environments. We show that the predictive accuracy of the system is comparable with the collaborative filtering algorithms without clustering, whereas the efficiency is far more improved.