Miracle-dz/UniFAR-FTUs huggingface dataset:
Layout to Logic Defect Analysis for Hierarchical Test Generation

As shown by previous studies, shorts between the interconnect wires should be considered as the predominant cause of failures in CMOS circuits. Fault models and tools for targeting these defects, such as the bridging fault test pattern generators have been available for a long time. However, this paper proposes a new hierarchical approach based on critical area extraction for identifying the possible shorted pairs of nets on the basis of the chip layout information, combined with logic-level test pattern generation for bridging faults. Experiments on real design layouts will show that only a fraction of all the possible pairs of nets have non-zero shorting probabilities. Furthermore, it will also be proven at the logic-level that nearly all such bridging faults can be tested by a simple and robust one-pattern logic test. The methods proposed in this paper are supported by a design flow implementing existing commercial and academic CAD software.
Stimulating Inquisitiveness: Teachers at Orthodox Protestant Schools about their Roles in Religious Socialization

Abstract Previous research showed that strong religious communities wish for cohesion and cooperation between pedagogical environments in religious socialization. This explorative and descriptive study examines what Dutch orthodox Protestant primary school teachers perceive to be their role in comparison with others, such as parents. We extensively interviewed 16 teachers. Their professional ideals show that being an agent in religious socialization matters to them. Teachers experience cooperation with other agents with the purpose of children becoming committed Christians. However, they perceive their roles to differ in five ways. Stimulating inquisitiveness could be seen as exemplary for the teacher’s role.
Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals with Dyslexia Consistent with a Multiple Deficit Model

Neuroimaging has become a powerful way of studying in vivo brain function and structure. The aim here is to comprehensively review Reid’s fMRI study which is the first to use a multiple case approach to investigate individual differences among 18 participants with dyslexia (DPs) and 16 control participants (CPs) and to directly test the predictions of the main dyslexia theories on reading deficit. The results show that the neural correlates of reading deficit for all DPs (except one) are consistent with more than one theory, supporting a multiple deficit model. Striking individual differences between DPs were found; even if the neural correlates of reading deficit in two DPs were consistent with the same theory, the affected brain areas could differ. To make progress, research on causes of reading deficit in dyslexia would need to (1) focus on the multiple deficit model, (2) use neuroimaging to test a further refined set of brain areas (including areas hypothesised by other dyslexia theories) in longitudinal designs, (3) control the effects of co-occurring neurodevelopmental disorders, (4) use high-field MRI (including diffusion techniques), multiband fMRI and MEG with optically pumped magnetometers, (5) progress imaging genetics and (6) pursue neuroimaging intergenerational transmission of brain circuity.
Cyclotomic polynomial filters based on cascaded recursive building blocks with minimum number of additions

The design of low-complexity filters based on the first 104 Cyclotomic Polynomials (CPs) has received great interest because CPs have coefficients belonging to the set {−1, 0, 1}, thus yielding simple and efficient multiplierless structures. Recently, the design of CP filters has been extended in a search space containing the first 200 CPs and it has been shown that their recursive z-transfer functions still have coefficients in the set {−1, 0, 1}, resulting in complexity reductions especially for narrow passband filters. In this paper, it is shown that any filter with arbitrarily narrow passband can be designed regardless of the CP's indexes such that the resulting cascade of recursive building blocks has minimum number of additions.
Rectifying the Misinformation in Social Network

This paper proposes a rumor rectification model considering the context when rumor in the social network has propagated for a period of time such that it is too late to block the rumor. We give an explanation that existing models on rumor blocking are not appropriate in this case. TIM + is an efficient approximation algorithm in influence maximization problem, however it handles the case when there is only one influence cascade in the network. We modify it to suit the two cascades case in our problem and find obvious effect comparing with the random solution.
DynaTune: Circuit-level optimization for timing speculation considering dynamic path behavior

Traditional circuit design focuses on optimizing the static critical paths no matter how infrequently these paths are exercised dynamically. Circuit optimization is then tuned to the worst-case conditions to guarantee error-free computation but may also lead to very inefficient designs. Recently, there are processor works that over-clock the chip to achieve higher performance to the point where timing errors occur, and then error correction is performed either through circuit-level or microarchitecture-level techniques. This approach in general is referred to as Timing Speculation. In this paper, we propose a new circuit optimization technique "DynaTune" for timing speculation based on the dynamic behavior of a circuit. DynaTune optimizes the most dynamically critical gates of a circuit and improves the circuit's throughput under a fixed power budget. We test this proposed technique with two timing speculation schemes — Telescopic Unit (TU) and Razor Logic (RZ). Experimental results show that applying DynaTune on the Leon3 processor can increase the throughput of critical modules by up to 13% and 20% compared to the timing-speculative and non-timing-speculative results optimized by Synopsys Design Compiler, respectively. For MCNC benchmark circuits, DynaTune combined with TU can provide 9% and 20% throughput gains on average compared to timing-speculative and non-timing-speculative results optimized by Design Compiler. When combined with RZ, DynaTune can achieve 8% and 15% throughput gains on average for above experiments. Categories and Subject Descriptors B.6. 3 [Hardware]: Design Aids — Optimization. General Terms Algorithms, Performance, Design, Experimentation
Personalization and Community Communication for Customer Support

Using electronic media for interaction with customers enables enterprises to integrate customer activity and knowledge about customers resulting in a high-quality relationship thus turning buying processes more efficient. In this paper we present findings and concepts from two projects. A study of the potential for online personalization conducted in Switzerland gives hints about the readiness of companies for the adoption of personalization tools and about some challenges in implementing these. In a basic research project in Germany we are investigating how personalization and community communication can be used in a specific context with a focus on the selection and design of individualized products. This paper provides an overview of personalization and community communication in customer support and highlights some issues in the context of the projects.
INDEPENDENT COMPONENT ANALYSIS BASED ON BLIND SOURCE SEPARATION BY USING MARKOVIAN AND INVERTIBLE FILTER MODEL

In this process blind sources are analyzed independently and the independent component analysis separates the underlying sources from the given mixture. Before to this process many more methods are used for blind sources they have non-Gaussian and sample dependences, this method can exploit both properties jointly. This proposed system uses mutual information rate that is used to analysis and derivation of algorithms. In this process, two types of source models are used for entropy rate estimation they are Markovian and another one is invertible filter model that gives the general independent component analysis (ICA). Under the Markovian source model, the entropy rate equals the difference between two joint entropies. Under the invertible filter source model, the source is generated by an invertible filter that is driven independently and identically distributed random process, the entropy rate of the source equals the entropy of the driving process under some constraints. The proposed Fast ICA algorithm is presented for Entropy estimation by using MATLAB2009 Software.
Spectroscopic confirmation of high-redshift supernovae with the ESO VLT

We present VLT FORS1 and FORS2 spectra of 39 candidate high-redshift supernovae that were discovered as part of a cosmological study using type Ia supernovae (SNe Ia) over a wide range of redshifts. From the spectra alone, 20 candidates are spectrally classified as SNe Ia with redshifts ranging from z = 0.212 to z = 1.181. Of the remaining 19 candidates, 1 might be a type II supernova and 11 exhibit broad supernova-like spectral features and/or have supernova-like light curves. The candidates were discovered in 8 separate ground-based searches. In those searches in which SNe Ia at z {approx} 0.5 were targeted, over 80 percent of the observed candidates were spectrally classified as SNe Ia. In those searches in which SNe Ia with z > 1 were targeted, 4 candidates with z > 1 were spectrally classified as SNe Ia and later followed with ground and space based observatories. We present the spectra of all candidates, including those that could not be spectrally classified as supernova.
Exploring the Role of Instant Messaging in a Global Software Development Project

Communication plays a vital role in software development projects. Globally distributed teams use a mix of different communication channels to get the work done. In this paper, we report on an empirical study of a team distributed across Denmark and India. This paper explores the integration of formal documentation, bug-tracking systems and email with informal communication on Instant Messaging (IM), screen sharing, and audio conversations. Whenever overlap times occur, informal communication can take place at the same time in different sites, and it can effectively complement formal documentation. Our analysis provides an indication that IM can play a special role in such socio-technical communication systems: IM acts as a real time glue between different channels. The communication through IM also provides a means to build trust and social relationships with co-workers.
Can the Ambiance of a Place be Determined by the User Profiles of the People Who Visit It?

Recently, several online social networking sites (OSNs; e.g., FourSquare) have emerged that allow people to record and broadcast their presence at physical locations. We examined the viability of using images of frequent patrons listed on an establishment’s FourSquare page to infer the ambiance of the establishment. In Study 1, 10 judges independently rated 25 bars and 24 cafes in Austin, Texas. Ratings were based on the profile pictures of 25 randomly selected persons who frequented each establishment. Ratings tapped ambient physical and psychological qualities (e.g., loud, creepy), typical-patron personalities (e.g., extraverted), and likely patron activities (e.g., dancing). Results indicated considerable inter-judge consensus across the attributes rated (mean ICCs averaged .32, .69. and .33 for the ambiance, patron-personality, and patron-activity variables respectively). In Study 2, a second team of 10 observers visited each target establishment during business hours and rated each location on the same set of variables. These ratings also showed strong inter-judge consensus (mean ICCs averaged .69, .79. and .62 for the ambiance, patron-personality, and patron-activity variables respectively). Correlations between the ratings obtained in Studies 1 and 2 showed surprisingly strong convergence suggesting that the profile photos conveyed valid information about the target establishments.
Judgementoring and other threats to realizing the potential of school‐based mentoring in teacher education

Purpose – The purpose of this article is to identify and examine root causes of the failure of school‐based mentoring to realize its full potential.Design/methodology/approach – The article draws on the re‐analysis of data from two major mixed‐method empirical studies carried out in England. It focuses on data generated from interviews with beginner teachers and mentors in both primary and secondary schools.Findings – The findings point to a failure to create appropriate conditions for effective mentoring in England at the level of the mentoring relationship, the school, and the national policy context.Practical implications – Implications of the findings include the need to achieve a greater degree of informed consensus on the meaning and purposes of mentoring in teacher education, and to ensure that mentors of beginner teachers are appropriately trained for the role.Originality/value – The article identifies the practice of judgemental mentoring or “judgementoring” as an obstacle to school‐based mentori.
TCP: tag correlating prefetchers

Although caches for decades have been the backbone of the memory system, the speed gap between CPU and main memory suggests their augmentation with prefetching mechanisms. Recently, sophisticated hardware correlating prefetching mechanisms have been proposed, in some cases coupled with some form of dead-block prediction. In many proposals, however correlating prefetchers demand a significant investment in hardware. In this paper we show that correlating prefetchers that work with tags instead of cache-line addresses are significantly more resource-efficient, providing equal or better performance than previous proposals. We support this claim by showing that per-set tag sequences exhibit highly repetitive patterns both within a set and across different sets. Because a single tag sequence can capture multiple address sequences spread over different cache sets, significant space savings can be achieved. We propose a tag-based prefetcher called a tag correlating prefetcher (TCP). Even with very small history tables, TCP outperforms address-based correlating prefetchers many times larger. In addition, we show that such a prefetcher can yield most of its performance benefits if placed at the L2 level of an aggressive out-of-order processor. Only if one wants prefetching all the way up to L1, is dead-block prediction required. Finally, we draw parallels between the two-level structure of TCP and similar structures for branch prediction mechanisms; these parallels raise interesting opportunities for improving correlating memory prefetchers by harnessing lessons already learned for correlating branch predictors.
Visual Perception Model for Online Target Marketing

A lot of studies in the information systems, advertising, and marketing literature have shown that for the past two decades online banner ad click-through rate is decreasing steadily (Idemudia et al. 2007). To address this issue, we develop the Visual Perception Model (VP) for Online Target marketing and then conducted an experiment by manipulating the independent variables (i.e. perceived security of online banner ads and match between web user’s need and banner ad contents). The theoretical background for the VP Model is the Visual Perception Theories. The VP Model shows that perceived usefulness of online banner ads have a positive and significant effect on the intention to click online banner ads. Also, the VP Model shows that both perceived security of online banner ads and match between web user’s need and banner ad contents has a positive and significant effects on perceived usefulness of online banner ads. The result provides support for the visual perception theories.
ProDom: Automated Clustering of Homologous Domains

The ProDom database is a comprehensive set of protein domain families automatically generated from the SWISS-PROT and TrEMBL sequence databases. An associated database, ProDom-CG, has been derived as a restriction of ProDom to completely sequenced genomes. The ProDom construction method is based on iterative PSI-BLAST searches and multiple alignments are generated for each domain family. The ProDom web server provides the user with a set of tools to visualise multiple alignments, phylogenetic trees and domain architectures of proteins, as well as a BLAST-based server to analyse new sequences for homologous domains. The comprehensive nature of ProDom makes it particularly useful to help sustain the growth of InterPro.
Transaction commitment at minimal communication cost

We consider the communication protocol for transaction commitment in a distributed database. Specifically, the connection between the structure of communication among the participating sites, and the communication network topology is investigated. In order to do so, the cost of transaction commitment is defined as the number of network hops that messages of the protocol must traverse. We establish the necessary cost for transaction commitment, and show that it is also sufficient. A simple distributed algorithm is presented to prove sufficiency. Our algorithm is also time-efficient, and in order to prove that we show that the timing of our algorithm is optimal within a natural class of commit-protocols.
Interactive Query Refinement for Boolean Search

Boolean search is still the method of choice for many kinds of professional search, such as constructing systematic reviews in legal and medical fields. It is effective for fast, high-recall document classification. Its drawback is the difficulty in crafting a Boolean query that captures semantically relevant documents. Ambiguous search terms lead to the inclusion of non-relevant documents. We propose a new exploratory document classification system that guides the user in creating a Boolean search query with both high recall and high precision. To increase recall, the system suggests semantically similar search terms. However, unlike other systems that use interactive query expansion, ours also includes a mechanism for removing non-relevant documents that inevitably get matched by an expanded query. The system explicitly identifies semantically ambiguous query terms that reduce the precision of the search. Removing non-relevant documents increases precision without reducing recall.
PPDM: A Privacy-Preserving Protocol for Cloud-Assisted e-Healthcare Systems

E-healthcare systems have been increasingly facilitating health condition monitoring, disease modeling and early intervention, and evidence-based medical treatment by medical text mining and image feature extraction. Owing to the resource constraint of wearable mobile devices, it is required to outsource the frequently collected personal health information (PHI) into the cloud. Unfortunately, delegating both storage and computation to the untrusted entity would bring a series of security and privacy issues. The existing work mainly focused on fine-grained privacy-preserving static medical text access and analysis, which can hardly afford the dynamic health condition fluctuation and medical image analysis. In this paper, a secure and efficient privacy-preserving dynamic medical text mining and image feature extraction scheme PPDM in cloud-assisted e-healthcare systems is proposed. Firstly, an efficient privacy-preserving fully homomorphic data aggregation is proposed, which serves the basis for our proposed PPDM. Then, an outsourced disease modeling and early intervention is achieved, respectively by devising an efficient privacy-preserving function correlation matching PPDM1 from dynamic medical text mining and designing a privacy-preserving medical image feature extraction PPDM2. Finally, the formal security proof and extensive performance evaluation demonstrate our proposed PPDM achieves a higher security level (i.e., information-theoretic security for input privacy and adaptive chosen ciphertext attack (CCA2) security for output privacy) in the honest but curious model with optimized efficiency advantage over the state-of-the-art in terms of both computational and communication overhead.
Emotion categorization based on probabilistic Latent Emotion Analysis model

This paper proposes a probabilistic Latent Emotion Analysis (pLEA) model, which is an extension of probabilistic Latent Semantic Analysis (pLSA) model, to discover latent emotion topics considering theoretical and empirical concepts from psychology and art theory. The traditional pLSA models performed poorly since the statistical model are hardly narrowing the semantic gap between low-level image features and high-level emotion concepts. To address this problem, this paper introduces the affective concept from art theory to bridge the semantic gap. To be specific, the pLEA model first builds the relations between visual words and affective concepts. And then discovers the latent emotion topics based on the affective concepts. Meanwhile, we exploit the training images labels to modulate the learning process of latent emotion topics, aiming to make them similar to the actual image emotion labels. Once obtained the emotion topics, the appearing frequency of the corresponding emotion topics in a given image forms a histogram, which can be subsequently used in the scene categorization task via the Support Vector Machine (SVM) classifier. The proposed approach has been tested on the International Affective Picture System (IAPS) and a set of artistic photography and abstract paintings. The experimental results have demonstrated the effectiveness of the proposed method for eliciting image emotions.
Discovering Temporal Narrative Containers in Clinical Text

The clinical narrative contains a great deal of valuable information that is only understandable in a temporal context. Events, time expressions, and temporal relations convey information about the time course of a patient’s clinical record that must be understood for many applications of interest. In this paper, we focus on extracting information about how time expressions and events are related by narrative containers. We use support vector machines with composite kernels, which allows for integrating standard feature kernels with tree kernels for representing structured features such as constituency trees. Our experiments show that using tree kernels in addition to standard feature kernels improves F1 classification for this task.
Reduced HMGB 1-Mediated Pathway and Oxidative Stress in Resveratrol-Treated Diabetic Mice: A Possible Mechanism of Cardioprotection of Resveratrol in Diabetes Mellitus

Myocardial fibrosis and inflammation are intricately linked in diabetic cardiomyopathy (DCM), and resveratrol has been shown to attenuate oxidative stress, inflammation, and fibrosis in several cell types or animal models. High mobility group box 1 (HMGB 1), a proinflammatory cytokine, has been reported to regulate fibrosis and inflammation in various organs. Then the present study aimed to reveal the expression of HMGB 1-mediated signaling pathway and oxidative stress in resveratrol-treated diabetic mice. The significant increase in serum HMGB 1 concentration in diabetic mice was attenuated by treatment with resveratrol. Similarly, western blot analysis revealed a significant increase of HMGB 1 protein in monocytes and heart tissues of diabetic mice, and resveratrol partly normalized the changes. In addition, resveratrol abrogated the increased expression of HMGB 1-mediated signaling pathway, oxidative stress, fibrosis, and inflammation in diabetic hearts. In conclusion, inhibition of HMGB 1-mediated signaling pathway and oxidative stress may contribute to resveratrol-induced anti-inflammatory and antifibrotic effects in DCM.