Miracle-dz/UniFAR-FTUs huggingface dataset:
Composite Model Checking with Type Specific Symbolic Encodings

We present a new symbolic model checking technique, which analyzes temporal properties in multi-typed transition systems. Speciically, the method uses multiple type-speciic data encodings to represent system states, and it carries out xpoint computations via the corresponding type-speciic symbolic operations. In essence, diierent symbolic encodings are uniied into one composite model checker. Any type-speciic language can be included in this framework { provided that the language is closed under Boolean connectives, propositions can be checked for satissability, and relational images can be computed. Our technique relies on conjunctive partitioning of transition relations of atomic events based on variable types involved, which allows independent computation of one-step pre-and post-conditions for each variable type. In this paper we demonstrate the eeectiveness of our method on a nontrivial data-transfer protocol, which contains a mixture of integer and Boolean-valued variables. The protocol operates over an unreliable channel that can lose, duplicate or reorder messages. Moreover, the protocol's send and receive window sizes are not speciied in advance; rather, they are represented as symbolic constants. The resulting system was automatically veriied using our composite model checking approach, in concert with a conservative approximation technique.
Planar metric rectification from perspective image via vertical vanishing point

We propose a method for estimating the rectification homography of ground plane image using the plane's vertical vanishing point. First, the method computes the image of the horizon by a vertical prism pole in the image. Then, the two imaged circular points of the ground plane are computed from the image of absolute conic and the imaged horizon. At last, the image rectification is computed from the conic dual to the circular points of the ground plane by singular value decomposition method. Experimental results show that the angle measurement error in the rectified image is less than 1.8°, which means the method is accurate and can be applied in various applications.
Flashover forecasting on high-voltage insulators with a backpropagation neural net

The flashover forecast problem on high-voltage insulators is addressed with a backpropagation neural network paradigm. The importance of analysis of artificial neural networks (ANNs) dedicated to forecast problems is emphasized. This analysis includes adequate training-data management and optimum topology design. The direct (or automatic) design approach is compared with the design-with-analysis approach, and the results are presented as seven guidelines for new designs of ANNs for prediction.
Toward a Robust Diversity-Based Model to Detect Changes of Context

Being able to automatically and quickly understand the user context during a session is a main issue for recommender systems. As a first step toward achieving that goal, we propose a model that observes in real time the diversity brought by each item relatively to a short sequence of consultations, corresponding to the recent user history. Our model has a complexity in constant time, and is generic since it can apply to any type of items within an online service (e.g. profiles, products, music tracks) and any application domain (e-commerce, social network, music streaming), as long as we have partial item descriptions. The observation of the diversity level over time allows us to detect implicit changes. In the long term, we plan to characterize the context, i.e. to find common features among a contiguous sub-sequence of items between two changes of context determined by our model. This will allow us to make context-aware and privacy-preserving recommendations, to explain them to users. As this is an on-going research, the first step consists here in studying the robustness of our model while detecting changes of context. In order to do so, we use a music corpus of 100 users and more than 210,000 consultations (number of songs played in the global history). We validate the relevancy of our detections by finding connections between changes of context and events, such as ends of session. Of course, these events are a subset of the possible changes of context, since there might be several contexts within a session. We altered the quality of our corpus in several manners, so as to test the performances of our model when confronted with sparsity and different types of items. The results show that our model is robust and constitutes a promising approach.
Dietary Risk Factors for Sporadic Creutzfeldt-Jakob Disease: A Confirmatory Case-Control Study

Aims This study’s primary purpose was to determine whether earlier findings suggesting an association between sporadic Creutzfeldt-Jakob disease (sCJD), a transmissible spongiform encephalopathy of humans and specific dietary components could be replicated. The a priori hypotheses were that consumption of (i) foods likely to contain organ tissue and (ii) raw/rare meat are associated with increased sCJD risk. Study Design Population-based case-control study. Place and Duration of Study Department of Neurology, School of Medicine, Loma Linda University, Loma Linda, CA, USA; 4 years. Methodology An 11-state case-control study of pathologically confirmed, definite sCJD cases, matched controls, and a sample of control-surrogates was conducted. Ninety-six percent (106/110) of the case data was obtained in 1991-1993, prior to variant CJD publicity. Results Using control self-responses, consumption of hot dogs, sausage, pepperoni, kielbasa, “other” canned meat, poultry liver, any stomach/intestine, beef stomach/intestine, any organ tissue, and beef organ tissue was individually associated with increased sCJD risk; odds ratios (OR) ranged from 2.4 to 7.2 (0.003 <p<0.025). Rare/raw meat consumption was associated with sCJD (OR=2.0; p<0.05). Greater consumption of hot dogs, bologna, salami, sausage, pepperoni and kielbasa was associated with significantly higher risk. The OR for gizzard consumption was 7.6, p<0.04. Bologna, salami, any liver, beef liver and pork stomach/intestine were marginally associated with sCJD: ORs ranged from 1.7 to 3.7; 0.05 <p< 0.10. Brain consumption was not associated with an elevated risk. Analyses using control-surrogate data indicate that use of the control self-responses did not bias the results away from the null hypothesis. Conclusions The a priori hypotheses were supported. Consumption of various meat products may be one method of transmission of the infectious agent for sCJD.
Comparison of Two Mindfulness-Based Cognitive Therapies and Acupuncture on the Pain and Depression Index in a Case with Lobular Carcinoma: A Single Case Experimental Study

Introduction: Breast cancer is considered to be the most common diagnosis of cancer in the world. Psychological distresses help to increase the mortality rate in breast cancer and psychotherapy plays an important role in managing syndromes. On the other hand, the effectiveness of acupuncture on psychological indices in patients with breast cancer has a research support. So far, no study has been done to compare the two approaches in patients with breast cancer. Case Presentation: in a single-case experimental design in the form of ABCABC design with multiple baselines with follow-up of 6 months (TCTR20180605001), during January to October 2015, a 37-year-old woman with infiltrating lobular carcinoma (ILC) with metastasis invasion of axillary lymph nodes with dysthymic syndromes, was selected through targeted sampling, and after twenty-one days of evaluation (A1), she was treated for three weeks with auricular acupuncture (B1). After six months, the second phase of the assessment in the form of baseline of second intervention was performed for three weeks (A2) and then three weeks mindfulness-based cognitive therapy (MBCT) (B2) was performed. Phase C1 was performed as follow-up acupuncture and six months after mindfulness cognitive therapy follow-up (C2) was done. Pain index was considered as the primary outcome and depression index as secondary outcome and the results were evaluated through the control of the trend of the multiple baselines through visual analysis. Conclusions: Data analysis showed that acupuncture was associated with a reduction in pain in the patient (P 0.05). Also, the mindfulness significantly reduced the depression and pain index (all < 0.05). However, the effectiveness of these treatments was not maintained during the follow-up phase (all < 0.05). These findings can be useful in convergence of therapeutic models in order to formulate effective interventions strategies and to be considered as a link between psychological and biological approaches.
The first compact objects in the Λ-dominated universe

We calculate the evolution of a low-mass (M≤ 105 M⊙) spherically symmetric density perturbation in the Ωbh2= 0.02, ΩM= 0.35, ΩΛ= 0.65, h= 0.72 universe. The results are compared with those that assume no cosmological constant and a flat, dark-matter-dominated universe. We include thermal processes and non-equilibrium chemical evolution of the collapsing gas. We find that direct formation of bound objects with such masses by z= 8 is unlikely so in fact they may form only through fragmentation of greater objects. This is in stark contrast with the Ω= 1 pure cold dark matter cosmology, where low-mass objects form abundantly at redshifts z>10.
Effect of Platform Used for Data Collection on Open-Ended Response Quality

Abstract Although the validity of collecting quantitative data using electronic platforms has been widely accepted, there has been concern that qualitative data collected on an electronic platform may not provide the same depth and richness in response quality as data collected using the pen-and-paper format. This article compares the quality of responses to open-ended questions collected using the pen-and-paper platform with responses collected using an electronic platform. Data for a program evaluation study were collected using either the pen-and-paper platform, an iPad, or an iPad Mini at a series of after-hours events for adults at the Center of Science and Industry, a Midwest science center. Metrics were designed to calculate a comprehensive response score consisting of the breadth of information and the richness of detail in open-ended item responses, allowing us to compare the overall response quality between the three platforms. Results indicate that qualitative data collected using electronic devices are as broad and detailed as data collected using traditional pen-and-paper techniques.
Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate

Prediction of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles’ dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by numerous factors (features), and counting the known features leads to a dataset with 300 features. This large number of features and high redundancy within the dataset makes the prediction task very difficult and inaccurate. In this study, dimensionality reduction techniques were applied in order to simplify the task and eliminate irrelevant and redundant features. A heterogeneous pool of several regression algorithms were independently tested and evaluated. In addition, several ensemble methods were tested in order to improve the accuracy of prediction. The empirical results revealed that the proposed evolutionary weighted ensemble method offered the lowest margin of error and significantly outperformed the individual algorithms and the other ensemble techniques.
Disruption of the Cerebral White Matter Network Is Related to Slowing of Information Processing Speed in Patients With Type 2 Diabetes

Patients with type 2 diabetes often show slowing of information processing. Disruptions in the brain white matter network, possibly secondary to vascular damage, may underlie these cognitive disturbances. The current study reconstructed the white matter network of 55 nondemented individuals with type 2 diabetes (mean age, 71 ± 4 years) and 50 age-, sex-, and education-matched controls using diffusion magnetic resonance imaging–based fiber tractography. Graph theoretical analysis was then applied to quantify the efficiency of these networks. Patients with type 2 diabetes showed alterations in local and global network properties compared with controls (P < 0.05). These structural network abnormalities were related to slowing of information processing speed in patients. This relation was partly independent of cerebrovascular lesion load. This study shows that the approach of characterizing the brain as a network using diffusion magnetic resonance imaging and graph theory can provide new insights into how abnormalities in the white matter affect cognitive function in patients with diabetes.
Fast Distributed Slicing without Requiring Uniformity ∗

Peer-to-peer (P2P) systems have become popular as a means of sharing resources efficiently within large sets of end-user platforms. Among their many applications are file sharing [4, 8], storage [10] and Voice-over-IP (VoIP) [12]. Ideally, such systems should be able to perform well even when the peers are heterogeneous with respect to performance-critical attributes such as bandwidth or storage capacity. Many recent studies have identified heavy-tailed distributions of storage space and bandwidth, and modal distributions of uptime [11, 13]. In systems that ignore such heterogeneity, two serious problems may arise. First, the unpredictable distribution of resources can adversely affect the service quality. VoIP applications, for instance, should avoid routing calls through nodes that are highly loaded or have low bandwidth. Second, when less capable peers are overloaded, the performance of the overall system can be affected. For example the fully decentralized P2P overlay Gnutella [4] suffered from congestion problems with increased system size, not only because of its inefficient structure for lookups, but also because the system made no distinction between bandwidth capabilities of nodes during a query flood. So severe was this problem that Gnutella was extensively revamped to create a two-level structure, promoting peers with longer lifetime and greater bandwidth capabilities to play a more active role, these peers are often referred to as so-called super-peers [8]. Other Kazaa-like applications [8, 12] have deployed similar distinction between users to improve the service quality. Identifying a set of nodes that have similar attributes or capabilities (for example bandwidth, processing power, storage, space or uptime) in a completely decentralized manner is thus an important challenge. Not only do systems tend to be large, but peers can arrive and depart
Getting Better Information Quality By Assessing And Improving Information Quality Management

Information quality has already become a decisive factor in information dependent business. Much has been said about the growing importance of data and information quality, and many researching lines over the last decade have looked at specific data and information quality issues from different standpoints. However data and information quality goes beyond the definition of data quality dimensions, there is still lack of an integrative framework, which can guide organizations in the assessment and improvement of data and information quality in a coordinated and global way. In this paper a framework to fill this gap is proposed. This framework is based on the Information Management Process (IMP) concept and it consists of two main components: an Information Quality Management Model structured in Maturity Levels (CALDEA) and an Assessment and Improvement Methodology (EVAMECAL). The methodology allows the assessment of an IMP in terms of maturity levels given by CALDEA, which can also be used as guidance for improvements. In the paper, a tool for automating the assessment and improvement is also briefly described.
Attendance Management System

The system is designed to help the admin of the department to monitor the students of their regularity in the class with their attendance report of the class. The system tracks the student with respect to the data given by the staff pertaining presence for their class conducted, depending on which the system evaluates the performance of a student in conducting the theory classes. It provides facilities to maintain the student reports obtained for every class. This project is going to help us to reduce the traditional paper-based data maintenance and verification/validation. This system will benefit for both the admin and the staff. The system will help the staff to know the percentages of attendance and also track the records of previous years. Keywords: Attendance, Data, Student, Class, System
Monocyte Differentiation towards Protumor Activity Does Not Correlate with M1 or M2 Phenotypes

Macrophages facilitate breast cancer progression. Macrophages were initially classified as M1 or M2 based on their distinct metabolic programs and then expanded to include antitumoral (M1) and protumoral (M2) activities. However, it is still uncertain what markers define the pro- and antitumoral phenotypes and what conditions lead to their formation. In this study, monocytic cell lines and primary monocytes were subjected to commonly reported protocols of M1/M2 polarization and conditions known to engage monocytes into protumoral functions. The results showed that only IDO enzyme and CD86 M1 markers were upregulated correlating with M1 polarization. TNF-α, CCR7, IL-10, arginase I, CD36, and CD163 were expressed indistinguishably from M1 or M2 polarization. Similarly, protumoral engaging resulted in upregulation of both M1 and M2 markers, with conditioned media from the most aggressive breast cancer cell line promoting the greatest changes. In spite of the mixed phenotype, M1-polarized macrophages exhibited the highest expression/secretion of inflammatory mediators, many of which have previously been associated with breast cancer aggressiveness. These data argue that although the existence of protumoral macrophages is unquestionable, their associated phenotypes and the precise conditions driving their formation are still unclear, and those conditions may need both M1 and M2 stimuli.
Examining the 4th Grade Students’ Ability to Say “No” in the Framework of Life Studies Curriculum with Many-Facet Rasch Model

This study aims to reveal whether or not the ability to say “no”, which is tried to be taught to students in the 2005 life studies curriculum, has been gained by children with regards to various situations. The survey model was utilized in the study. The study was conducted with 4 th grade students who took the class of life studies in primary school 1 st , 2 nd and 3 rd grades and therefore are expected to have gained the ability to say “no”. In order to collect data, The Ability to Say “No”-Specific Cases Inventory (NSCI) developed by the researchers was employed. In order to be able to evaluate the responses given to the cases in the NSCI, the “NSCI Rubric” was developed. The data obtained from NSCI were analyzed according to the many-facet Rasch analysis using the FACETS packet program. The findings suggest that students may be able to demonstrate the basic ability to say “no” expected of them in response to specific cases; however they will not focus too much on the behavior of making an explanation about the reason why they have said “no”, or, on performing alternative behaviors after saying “no”.
A reconstructed event-based framework for analyzing community evolution

People never stopped research on social networks. Researching the dynamic characteristics of social networks, we can find the key points of the network and predict the tendency of human social activities. The event-based framework is proposed by Asur et al., and it provides a good direction for analyzing the community evolution. The classification of events is the first and most important step in the framework. But we find that it has some small drawbacks to make the full framework less meaningful and applicability. This paper presents the concept of community attributes, and reconstructs each event based on the community attributes. We also use the reconstructed framework to analyze the C_DBLP and Catalano networks, and find the classification of reconstructed events is better than the original one.
Caching in HTTP Adaptive Streaming: Friend or Foe?

Video streaming is a major source of Internet traffic today and usage continues to grow at a rapid rate. To cope with this new and massive source of traffic, ISPs use methods such as caching to reduce the amount of traffic traversing their networks and serve customers better. However, the presence of a standard cache server in the video transfer path may result in bitrate oscillations and sudden rate changes for Dynamic Adaptive Streaming over HTTP (DASH) clients. In this paper, we investigate the interactions between a client and a cache that result in these problems, and propose an approach to solve it. By adaptively controlling the rate at which the client downloads video segments from the cache, we can ensure that clients will get smooth video. We verify our results using simulation and show that compared to a standard cache our approach (1) can reduce bitrate oscillations (2) prevents sudden rate changes, and compared to a no-cache scenario (3) provides traffic savings, and (4) improves the quality of experience of clients.
Survey propagation at finite temperature: application to a Sourlas code as a toy model

In this paper we investigate a finite temperature generalization of survey propagation, by applying it to the problem of finite temperature decoding of a biased finite connectivity Sourlas code for temperatures lower than the Nishimori temperature. We observe that the result is a shift of the location of the dynamical critical channel noise to larger values than the corresponding dynamical transition for belief propagation, as suggested recently by Migliorini and Saad for LDPC codes. We show how the finite temperature 1RSB SP gives accurate results in the regime where competing approaches fail to converge or fail to recover the retrieval state.
histogram File Size Distribution Model in Enterprise File Server toward Efficient Operational Management

enterprise file server, we propose an estimation method for relationship between file number and cumulative file size in descending order of file size based on a model for file size distribution. We develop the model by weighted summation of multiple log normal distribution based on AIC. File size data from technical and non-technical divisions of a company show that our model fits well with observed distribution, and that the estimated relationship can be utilized for cost-effective operational management of file server.