Miracle-dz/UniFAR-FTUs huggingface dataset:
A red team/blue team assessment of functional analysis methods for malicious circuit identification

Recent advances in hardware security have led to the development of FANCI (Functional Analysis for Nearly-Unused Circuit Identification), an analysis algorithm that identifies stealthy, malicious circuits within hardware designs that can perform backdoor operations to compromise security. Evaluations of such methods using benchmarks and academically known attacks are not always equivalent to the dynamic attack scenarios that can arise in the real world. For this reason, we apply a red team/blue team approach to stress-test the abilities of the FANCI prototype. In the Embedded Systems Challenge (ESC) 2013, teams from research groups from multiple continents created designs with backdoors hidden in them as part of a red team effort to circumvent FANCI. Notably, these backdoors were not placed into a priori known designs. The red team was allowed to create arbitrary, unspecified designs. Two interesting results came out of this effort. The first was that FANCI was surprisingly resilient to this wide variety of attacks and was not circumvented by any of the stealthy backdoors created by the red teams. The second result is that frequent-action backdoors, which are non-stealthy backdoors, were often successful. These results emphasize the importance of combining FANCI with a reasonable degree of validation testing. The blue team efforts also exposed some areas where the FANCI prototype could be made more performant, which motivates further development of the prototype.
Location Prediction of Twitter Users using Wikipedia

The mining of user generated content in social media has proven very effective in domains ranging from personalization and recommendation systems to crisis management. The knowledge of online users’ locations makes their tweets more informative and adds another dimension to their analysis. Existing approaches to predict the location of Twitter users are purely data-driven and require large training data sets of geo-tagged tweets. The collection and modelling process of tweets can be time intensive. To overcome this drawback, we propose a novel knowledge based approach that does not require any training data. Our approach uses information in Wikipedia, about cities in the geographical area of our interest, to score entities most relevant to a city. By semantically matching the scored entities of a city and the entities mentioned by the user in his/her tweets, we predict the most likely location of the user. Using a publicly available benchmark dataset, we achieve 3% increase in accuracy and 80 miles drop in the average error distance with respect to the state-of-the-art approaches.
Calibration of Credit Derivative Pricing Models by Relative Entropy Minimization : Modelling Term Structure of Default Correlations

The paper develops a non-parametric approach to calibration of credit derivative pricing models, where the payoff is contingent on defaults by multiple reference entities. The applied methodology is to find the joint probability distribution of times to default by the reference entities closest (in the sense of relative entropy) to the distribution being calibrated among all equivalent distributions that satisfy imposed calibration conditions. The research focuses on calibration to forward default probabilities and forward default correlations, as well as to conditional probabilities of the next default given the number of reference entities that have already defaulted. However, the results hold for a variety of alternative calibration conditions, such as market prices of other derivatives with payoffs dependent on default of some of the reference entities of the priced financial derivative. It is shown that it is possible to perform calibration to any consistent term structure of forward default probabilities, forward default correlations and forward conditional probabilities of the next default. The impact of the term structure of the default probabilities and correlations on the calibrated distribution is studied. Considering the calibration technique as a pricing methodology, for a wide range of credit derivatives it is shown that the price after calibration is infinitely differentiable as a function of the calibration conditions; the explicit expressions for partial derivatives are found. The developed approach can by applied to calibration of both structural and reduced-form models.
Supporting Academic Engagement in Boys and Girls

Understanding how social support and anxiety influence academic engagement in boys and girls is important to ensure that we effectively promote academic engagement. This study examined the relationship between gender, social support, anxiety, and academic engagement in elementary and junior high school students. Students in Grades 5 to 9 (N = 1,904) completed self-reports measuring academic engagement, anxiety, and perceived social support from family, friends, and school staff. Results indicated that girls were more likely to perceive social support and to score higher on the anxiety scale than boys were. Grade level was a significant predictor of academic engagement for boys but not for girls, while anxiety classification was a significant predictor of academic engagement for girls but not for boys. This study highlights the importance of understanding the multiple factors that influence academic engagement to provide targeted prevention and intervention strategies and how these factors differ for boys and girls.
Mechanism and Clinical Significance of IL-6 Combined with TNF-ÃÂ± orIL-1 for the Induction of Acute Phase Proteins SAA and CRP in ChronicInflammatory Diseases

Serum amyloid A (SAA) and C-reactive protein (CRP) are two major acute phase proteins whose serum concentrations increase in response to inflammation, though the exact mechanism underlying this induction remains unknown. Dysregulated production of interleukin (IL)-6 plays a pathogenic role in various inflammatory diseases such as rheumatoid arthritis, Castleman’s disease and systemic juvenile idiopathic arthritis. IL-6 blocking therapy with an anti-IL-6 receptor antibody (tocilizumab) can ameliorate clinical symptoms and abnormal laboratory findings, and completely normalize CRP and SAA levels in rheumatoid arthritis patients. Conversely, therapy for blocking tumor necrosis factor (TNF)-α, another key pathogenic factor in rheumatoid arthritis, barely lowers CRP and SAA to within their normal range, suggesting that IL-6 and TNF-α play different roles in the induction of acute phase protein expression. To clarify the different pathogenic roles and mechanisms of IL-6 and TNF-α or IL-1 in the induction of CRP and SAA in chronic inflammatory diseases, we investigated the transcriptional regulation mechanisms of SAA and CRP using hepatoma-derived cell lines in vitro, and related these mechanisms to the different effects of IL-6 and TNF-α blocking therapies on serum SAA and CRP in rheumatoid arthritis patients in vivo. We propose transcriptional regulation models for inflammatory cytokine-induced SAA and CRP expression that explain why IL-6 plays an essential role in the induction of SAA and CRP in the presence of inflammation.
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models

In this paper, we address an important step toward our goal of automatic musical accompaniment-the segmentation problem. Given a score to a piece of monophonic music and a sampled recording of a performance of that score, we attempt to segment the data into a sequence of contiguous regions corresponding to the notes and rests in the score. Within the framework of a hidden Markov model, we model our prior knowledge, perform unsupervised learning of the data model parameters, and compute the segmentation that globally minimizes the posterior expected number of segmentation errors. We also show how to produce "online" estimates of score position. We present examples of our experimental results, and readers are encouraged to access actual sound data we have made available from these experiments.
Position management system for an indoor group of forklifts with a sensor network

A position management system for forklifts involved in a warehouse storage task is proposed. With a simple, low-cost laser beam sensor and reflective stickers, the position of the forklift can be established. Furthermore, by combining probability estimation algorithms, the possible location of forklifts can be managed and the accuracy of positioning can be improved through the exchange of information on a sensor network. With this system, forklift operations are easier, and the cost of a warehouse navigation system decreases.
Automated type-3 clone oracle using Levenshtein metric

Clone detection techniques quality and performance evaluation require a system along with its clone oracle, that is a reference database of all accepted clones in the investigated system. Many challenges, including finding an adequate clone definition and scalability to industrial size systems, must be overcome to create good oracles. This paper presents an original method to construct clone oracles based on the Levenshtein metric. Although other oracles exist, this is the largest known oracle for type-3 clones that was created by an automated process on massive data sets. The method behind the creation of the oracle as well as actual oracles characteristics are presented. Discussion of the results in relation to other ways of building oracles is also provided along with future research possibilities.
Effects of Sediment Nitrogen Availability and Plant Density on Interactions Between the Growth of Hydrilla Verticillata and Potamogeton Americanus. Aquatic Plant Control Research Program.

Abstract : This study examines the growth of Hydrilla verticillata (L.f) Royle and Potamogeton americanus C. and S. on both nitrogen-poor and nitrogen-rich sediments and assesses the nature and degree of interspecific interactions between the two species. Extensive greenhouse experiments conducted from May through July 1989 employed sediments differing initially only in sediment nitrogen (fertilized containers: 0.21 + or - 0.01 mg exchangeable N per gram dry sediment; unfertilized containers: 0.01 + or -0.00 mg exchangeable N per gram dry sediment). Species were grown monotypically, as well as in 50:50 mixtures, on each sediment type. Although shoot production did not differ between species in monoculture, shoot production in Hydrilla was diminished by 60 to 76 percent in the presence of Potamogeton. Likewise, canopy development for Hydrilla was reduced in mixtures compared to monocultures, but Potamogeton did not show a similar reduction in canopy. High sediment nitrogen increased canopy development by 50 percent in both species. Above-ground biomass for monotypically grown species was similarly low on unfertilized sediment (6 to 8 g per container). However, on fertilized sediment, aboveground biomass in Potamogeton (approaching 25 g per container) was significantly greater than in Hydrilla (approximately 15 g per container). Aquatic plants, Macrophyte, Biomass, Nitrogen, Density, Production, Interspecific interaction, Sediment.
Key-frame extraction of wildlife video based on semantic context modeling

In recent work on image and video retrieval there seems to be a shift of focus from low-level feature extraction to producing high-level semantic representation of scenes. This paper presents a framework that produces semantic context features from video frames which are then employed for key-frame extraction. Working with wildlife video frames, the framework starts with image segmentation, followed by low-level feature extraction and classification of the image blocks extracted from image segments. Based on the image block labels in the neighbourhood a co-occurrence matrix is then constructed to represent the semantic context of the scene. The semantic co-occurrence matrices then undergo binarization and principal component analysis for dimension reduction, forming the feature vectors used in a one-class classifier that extracts the key-frames. Experiments show that the utilization of high-level semantic features result in better key-frame extraction when compared with methods using low-level features only.
A constraint-based bottom-up counterpart to DCG

A new grammar formalism, CHR Grammars (CHRG), is proposed that provides a constraintsolving approach to language analysis, built on top of the programming language of Constraint Handling Rules in the same way as Definite Clause Grammars (DCG) on Prolog. CHRG works bottom-up and adds the following features when compared with DCG: – An inherent treatment of ambiguity without backtracking. – Robust parsing; do not give up in case of errors but return the recognized phrases. – A flexibility to produce and consume arbitrary hypotheses making it straightforward to deal with abduction, integrity constraints, operators à la assumption grammars, and to incorporate other constraint solvers. – Context-sensitive rules that apply for disambiguation, coordination in natural language, and tagger-like rules.
On the Cost of Data Analysis

Abstract A regression analysis usually consists of several stages, such as variable selection, transformation and residual diagnosis. Inference is often made from the selected model without regard to the model selection methods that preceeded it. This can result in overoptimistic and biased inferences. We first characterize data-analytic actions as functions acting on regression models. We investigate the extent of the problem and test bootstrap, jackknife, and sample-splitting methods for ameliorating it. We also demonstrate an interactive LISP-STAT system for assessing the cost of the data analysis while it is taking place.
Modeling Potential Impacts of Climate Change on Streamflow Using Projections of the 5th Assessment Report for the Bernam River Basin, Malaysia

Potential impacts of climate change on the streamflow of the Bernam River Basin in Malaysia are assessed using ten Global Climate Models (GCMs) under three Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). A graphical user interface was developed that integrates all of the common procedures of assessing climate change impacts, to generate high resolution climate variables (e.g., rainfall, temperature, etc.) at the local scale from large-scale climate models. These are linked in one executable module to generate future climate sequences that can be used as inputs to various models, including hydrological and crop models. The generated outputs were used as inputs to the SWAT hydrological model to simulate the hydrological processes. The evaluation results indicated that the model performed well for the watershed with a monthly R2, Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) values of 0.67, 0.62 and −9.4 and 0.62, 0.61 and −4.2 for the calibration and validation periods, respectively. The multi-model projections show an increase in future temperature (tmax and tmin) in all respective scenarios, up to an average of 2.5 °C for under the worst-case scenario (RC8.5). Rainfall is also predicted to change with clear variations between the dry and wet season. Streamflow projections also followed rainfall pattern to a great extent with a distinct change between the dry and wet season possibly due to the increase in evapotranspiration in the watershed. In principle, the interface can be customized for the application to other watersheds by incorporating GCMs’ baseline data and their corresponding future data for those particular stations in the new watershed. Methodological limitations of the study are also discussed.
Blockade of Inhibitors of Apoptosis Proteins in Combination with Conventional Chemotherapy Leads to Synergistic Antitumor Activity in Medulloblastoma and Cancer Stem-Like Cells

Background Medulloblastoma (MB) is the most common pediatric primary malignant brain tumor. Approximately one-third of MB patients succumb to treatment failure and some survivors suffer detrimental side effects. Hence, the purpose of this study is to explore new therapeutic regimens to overcome chemotherapeutic agent resistance or reduce chemotherapy-induced toxicity. Methods We detected the expression of inhibitors of apoptosis proteins (IAPs) in MB and CD133+ MB cell lines and MB tissues using immunoblotting and immunohistochemical staining. The antitumor effects of inhibitors against IAPs on MB or CD133+ MB cells were evaluated by MTT assay, Annexin V/PI analysis, and caspase-3/7 activity. Autophagy was assessed by the conversion of light chain (LC) 3-I to LC3-II and Cyto-ID autophagy detection kit. Results MB cells showed higher expression of IAPs compared to normal astrocytes and normal brain tissues. Conventional chemotherapeutic agents combined with small-molecule IAP inhibitors (LCL161 or LBW242) showed a synergistic effect in MB cells. Combined treatments triggered apoptosis in MB cells through activation of caspase-3/7 and autophagic flux simultaneously. In addition, we found that CD133+ MB cells with features of cancer stem cells displayed higher levels of X-linked inhibitor of apoptosis (XIAP) and cellular inhibitor of apoptosis 1/2 (cIAP1/2), and were hypersensitive to treatment with IAP inhibitors. Conclusions These results shed light on the biological effects of combination therapy on MB cells and illustrate that IAP inhibitors are more effective for CD133+ stem-like MB cells.
Experimental investigation of collective action

There is a long-standing difference amongst public economists between those who think that collective choice must be formally acknowledged, and those who derive their policy recommendations from a social planning framework in which politics plays no role. The purpose of this book is to contribute to a meaningful dialogue between these two groups, in the belief that the future of both political economy and of normative public finance lies somewhere between the two approaches. Some of the specific questions addressed in the book include: does public finance need political economy? Should collective choice play a role in the standard of reference used in normative public finance? What is a ‘failure' in a non-market or policy process? And what have we learned about the theory and practice of public finance from three decades of empirical research on public choice? The book also provides a practitioner's view of the political economy of redistribution.
Wavelet image inpainting based on Dictionary Learning with a Beta Process

The problem of image inpainting and wavelet image inpainting were presented in this study. Dictionary Learning with a Beta Process (BPDL) was introduced. A new method based on BPDL was proposed for wavelet image inpainting. Unlike conventional methods which mostly based on diffusion theory in physics, this method is based on sparse image representation and considers an image as a combination of different structural patterns to achieve inpainting. The image simulation experiments were designed to test the algorithm. The results demonstrated that the connectivity principle of human perception was well realized with good vision effect. The PSNR of the wavelet coefficients partly damaged images was improved significantly after processed by the new method. It's also available for NIR images. It's concluded that the presented method based on BPDL was an effective method for wavelet image inpainting.
Loss of the Y chromosome in Philadelphia-positive cells predicts a poor response of chronic myeloid leukemia patients to imatinib mesylate therapy

In chronic myeloid leukemia (CML), cytogenetic abnormalities found in addition to the t(9;22) translocation may impact the response to therapy. Loss of the Y chromosome is generally overlooked in this context, owing to its relatively frequent occurrence in healthy elderly patients. In this multicenter retrospective study, the outcome after imatinib treatment of 30 CML patients with karyotype showing Y chromosome loss (Y−) was compared to 30 Y+ control males diagnosed and treated at the same time in the same institutions. Y− patients had significantly delayed cytogenetic and molecular responses, lower event-free survival and shorter overall survival than Y+ patients. The negative impact of this abnormality was particularly marked when it occurred in a sub-clone (clonal evolution) rather than in all mitoses. These data indicate that loss of the Y chromosome should be taken into account in the prognostic evaluation of chronic myelogenous leukemia patients.
Global task space manipulability ellipsoids for multiple-arm systems

New definitions of force and velocity manipulability ellipsoids for multiple-arm systems are given. A suitable kinetostatic formulation for multiple cooperating arms is adopted that allows a global task space description of external and internal forces as well as absolute and relative velocities at the object level. The concept of a force manipulability ellipsoid for a single arm is formally extended to the multi-arm case by regarding the whole system as a mechanical transformer from the extended joint space to the global task space. Kinetostatic duality properties are then exploited to derive velocity manipulability ellipsoids for the multiple-arm system. The proposed method is compared with other approaches using numerical examples. >
Distance-based tuning of the EKF for indoor positioning in WSNs

This work proposes a filtering method for indoor positioning and tracking applications which combines position, speed and heading measurements with the aim of achieving more accurate position estimates both in the short and the long term. We combine all this data using the well-known Extended Kalman Filter (EKF). The particularity in our proposal is that the EKF is configured using the designed statistical covariance matrix tuning method (SCMT), which is based on the the statistical characteristics of the position measurements. Thanks to the SCMT, the EKF is able to efficiently cope with measurements that have different degrees of uncertainty and, therefore, it achieves high accuracy also in the long-term. The system has been validated in a real environment and the results show a reduction in the positioning error of more than 48% when compared to a regular EKF in the tested scenarios.
Context and Implications Document for: How to guide effective student questioning: a review of teacher guidance in primary education

In recent decades, teachers, scholars and policy-makers have a growing interest in the potential of student questioning for learning and teaching. Research shows that stu- dent questioning elicits intrinsic motivation for learning, supports the development of cognitive and metacognitive skills and allows for differentiated and self- regulated learning (Chin & Osborne, 2008). Therefore, many countries have inte- grated inquiry-based pedagogy in domains such as science, literacy and numeracy (e.g. NCR, 2012). Unfortunately, student questioning remains rare in many class- rooms, in spite of its acclaimed benefits and the positive stance many teachers have towards it (Reinsvold & Cochran, 2012). Arguably, one of the main causes for its absence, is that teachers find it difficult to allow for student questioning while facing curricular pressures (Wells, 2001). Teachers seem to need support to align student questioning to curriculum objectives, or in other words, to guide effective student questioning. This review explores the literature on student questioning in primary education since the 1990s to identify emergent themes that might support teacher guidance of effective student questioning. The intended audiences for this article are policy-makers, teacher-educators, educational designers, scholars and, last but not least, teachers. The four emergent themes that were identified in this study might have various implications for these audiences.