Miracle-dz/UniFAR-FTUs huggingface dataset:
Fc g -receptor – mediated trogocytosis impacts mAb-based therapies: historical precedence and recent developments

A specialized form of trogocytosis occurs whenFc g receptorsonacceptorcellstake up and internalize donor cell-associated immune complexes composed of specific monoclonal antibodies (mAbs) bound to target antigens on donor cells. This trogocytosis reaction, an example of antigenic modulation, has been described in recent clinical correlative studies and in vitro investigations for several mAbs used in cancer immunotherapy, including rituximab and ofatumumab. We discuss the impact of Fc g -receptor–mediated trogocytosis on the efficacy of cancer immunotherapy and other mAb-based therapies.
Fear of Hazards in Commodity Futures Markets

We examine the commodity futures pricing role of ac tive attention to weather, disease, geopolitical or economic threats or “hazard fear” a s proxied by the volume of internet searches by 149 query terms. A long-short portfolio strategy that sorts the cross-section of commodity futures contracts according to a hazard fear signal captures a significant premium. This commodity hazard fear premium reflects compensation for extant fundamental, tail, volatility and liquidity risks factors, but it is not subsumed by them. Exposure to hazard-fear is strongly priced in the cross-section of commodity portfolios . The hazard fear premium exacerbates during periods of adverse sentiment or pessimism in financial mark ets.
An invariant manifold of slowly oscillating solutions for

The equation x˙(t)=−μx(t)+f(x(t−1)) with μ>0 and with a smooth real function f satisfying f(0)=0 and f'(0)<0, models delayed negative feedback: Sufficiently small deviations at times t−1 and t from equilibrium x=0, are followed by a motion into the opposite direction x˙(t)<0
MAXIMUM-LIKELIHOOD ESTIMATION FOR DIFFUSION PROCESSES VIA CLOSED-FORM DENSITY EXPANSIONS

This paper proposes a widely applicable method of approximate maxi-mum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for transition density is proposed and accompanied by an algorithm containing only basic and explicit calculations for delivering any arbitrary order of the expansion. The likelihood function is thus approximated explicitly and employed in statistical estimation. The performance of our method is demonstrated by Monte Carlo simulations from implementing several examples, which represent a wide range of commonly used diffusion models. The convergence related to the expansion and the estimation method are theoretically justiﬁed using the theory of Watanabe [ Ann. Probab. 15 (1987) 1–39] and Yoshida [ J. Japan Statist. Soc. 22 (1992) 139–159] on analysis of the generalized random variables under some standard sufﬁcient conditions. in valuation. various methods of and the approximate MLE. to a widely closed-form asymptotic expansion for transition density and thus to apply it in approximate MLE for multivariate diffusion process.
Numerical investigation of the smallest eigenvalues of the p-Laplace operator on planar domains

The eigenvalue problem for the p-Laplace operator with p>1 on planar domains with the zero Dirichlet boundary condition is considered. The Constrained Descent Method and the Constrained Mountain Pass Algorithm are used in the Sobolev space setting to numerically investigate the dependence of the two smallest eigenvalues on p. Computations are conducted for values of p between 1.1 and 10. Symmetry properties of the second eigenfunction are also examined numerically. While for the disk an odd symmetry about the nodal line dividing the disk in halves is maintained for all the considered values of p, for rectangles and triangles symmetry changes as p varies. Based on the numerical evidence the change of symmetry in this case occurs at a certain value p_0 which depends on the domain.
Automatic generation of CityGML LoD3 building models from IFC models

CityGML is a standardized data format used to store the semantic information and geometries of buildings and other object classes of 3D city models. The Level of Detail of current state of the art city models (LoD2) is not sufficient for accurate environmental simulations like noise, the solar potential of windows and other types of analyses. An LoD3 building model represents the full architectural exterior of a building with balconies, windows and so forth. The generation of these models needs to be automated as it is otherwise infeasible due to the required high amount of manual labour. In the architectural world, detailed building models are created in IFC format. This thesis shows that it is possible to automatically generate valid and semantically rich CityGML LoD3 building models directly from IFC models. Also an initial investigation is done on the possibilities for the conversion of IFC models to CityGML LoD4. For the conversion the semantic and geometric validity requirements are determined for CityGML. A methodology for the conversion is developed and a prototype implementation is made to prove the effectiveness of the conversion. The conversion consists of three parts: 1) The extraction and mapping of IFC semantics to CityGML semantics; 2) A geometric generalization which extracts the exterior shell using a transformation based on Boolean and morphological operations; 3) Semantic and geometric refinements which optimize the model for analyses. The developed prototype is able to successfully convert IFC models to CityGML LoD3. All the resulting models were geometrically validated according to the ISO19107 standard, and semantics were checked manually. Few improper semantics occur in the output due to missing semantics in IFC. For example, there are no semantics for balconies or dormers in IFC. Recommendations are given to improve the alignment between the two formats. For IFC additional semantics are recommended whereas it is important for CityGML to specify how certain aspects are to be modelled. The research presented in this thesis can be used as the foundation for future work on the interoperability between Architecture and Geomatics. The software package is open source and freely available at https://github.com/tudelft-gist/ifc2citygml.
Big N auditors and the quality of earnings: Evidence from Middle Eastern and North African countries

In recent years, the importance of audit quality has garnered significant attention by managements, academics and the general public. In this study, we examine the relationship between Big N auditors and earnings quality in the Middle Eastern and North African (MENA) countries, where cultural and institutional contexts are quite different from those in Western countries. In addressing this issue, we employ three audit quality metrics – value relevance, abnormal accruals and earnings persistence – and use the propensity score matching technique to deal with the potential endogeneity arising from the nature of external auditor choices. Contrary to the view that Big N auditors supply quality-differentiated audits, the results obtained employing a cross-country sample of publicly listed non-financial firms of 10 MENA countries during 2009–2019 provide evidence that Big N auditors are insignificantly associated with the three measures of audit quality. Furthermore, we find that the differences in audit quality between the two auditor groups are mainly due to the clients’ innate characteristics, rather than the competency of Big N auditors. Our results hold after performing different sensitivity analyses. Overall, our findings challenge the conventional notion that Big N auditors deliver quality-differentiated audits.
Organizational structure and organizational learning: The moderating role of organizational defensive routines

Literature on organizational learning (OL) shows that even firms who self-perceive themselves to have appropriate organizational structures still fail to generate appropriate learning outcomes. In this article, we claim that one of the neglected key factors explaining learning failures are organizational defensive routines (ODRs). Drawing on the literature, our theoretical inference is that high levels of ODRs can negatively moderate the relationship between organizational structures and OL. To test this moderation hypothesis, the study employs hierarchical regression analysis on a sample of 358 respondents from various industries in the United Kingdom. The result confirms that formalization negatively affects OL, and the effect of formalization on OL is negatively moderated by ODRs regardless of organizational age, size, and sector.
Profile of Teachers in Carrying out Political Education in Elementary Schools

Political education plays an important role in educating the younger generation to gain a clear understanding of various political concepts and symbols, especially in shaping their political awareness. Democracy as a central theme in the political life of people in Indonesia, its position is very urgent to be taught to children from an early age. Ideally, the theme that should be taught, apart from achieving learning competence, is also directed at the holistic political education of students in accordance with the school environment. The purpose of this study was to obtain an overview of the implementation of political education in elementary schools. This study uses a survey method to obtain data from certain natural places (not artificial) but the researchers treat the data collection by distributing questionnaires and structured interviews with data analysis techniques using descriptive statistical methods. The results showed that the implementation of elementary school political education in Langsa City was in the sufficient category with the highest percentage of 73.08% and the lowest percentage of 65.45%.
Patch correctness assessment in automated program repair based on the impact of patches on production and test code

Test-based generate-and-validate automated program repair (APR) systems often generate many patches that pass the test suite without fixing the bug. The generated patches must be manually inspected by the developers, so previous research proposed various techniques for automatic correctness assessment of APR-generated patches. Among them, dynamic patch correctness assessment techniques rely on the assumption that, when running the originally passing test cases, the correct patches will not alter the program behavior in a significant way, e.g., removing the code implementing correct functionality of the program. In this paper, we propose and evaluate a novel technique, named Shibboleth, for automatic correctness assessment of the patches generated by test-based generate-and-validate APR systems. Unlike existing works, the impact of the patches is captured along three complementary facets, allowing more effective patch correctness assessment. Specifically, we measure the impact of patches on both production code (via syntactic and semantic similarity) and test code (via code coverage of passing tests) to separate the patches that result in similar programs and that do not delete desired program elements. Shibboleth assesses the correctness of patches via both ranking and classification. We evaluated Shibboleth on 1,871 patches, generated by 29 Java-based APR systems for Defects4J programs. The technique outperforms state-of-the-art ranking and classification techniques. Specifically, in our ranking data set, in 43% (66%) of the cases, Shibboleth ranks the correct patch in top-1 (top-2) positions, and in classification mode applied on our classification data set, it achieves an accuracy and F1-score of 0.887 and 0.852, respectively.
Improving cross-platform binary analysis using representation learning via graph alignment

Cross-platform binary analysis requires a common representation of binaries across platforms, on which a specific analysis can be performed. Recent work proposed to learn low-dimensional, numeric vector representations (i.e., embeddings) of disassembled binary code, and perform binary analysis in the embedding space. Unfortunately, however, existing techniques fall short in that they are either (i) specific to a single platform producing embeddings not aligned across platforms, or (ii) not designed to capture the rich contextual information available in a disassembled binary. We present a novel deep learning-based method, XBA, which addresses the aforementioned problems. To this end, we first abstract binaries as typed graphs, dubbed binary disassembly graphs (BDGs), which encode control-flow and other rich contextual information of different entities found in a disassembled binary, including basic blocks, external functions called, and string literals referenced. We then formulate binary code representation learning as a graph alignment problem, i.e., finding the node correspondences between BDGs extracted from two binaries compiled for different platforms. XBA uses graph convolutional networks to learn the semantics of each node, (i) using its rich contextual information encoded in the BDG, and (ii) aligning its embeddings across platforms. Our formulation allows XBA to learn semantic alignments between two BDGs in a semi-supervised manner, requiring only a limited number of node pairs be aligned across platforms for training. Our evaluation shows that XBA can learn semantically-rich embeddings of binaries aligned across platforms without apriori platform-specific knowledge. By training our model only with 50% of the oracle alignments, XBA was able to predict, on average, 75% of the rest. Our case studies further show that the learned embeddings encode knowledge useful for cross-platform binary analysis.
Communication Requirements of VCG-Like Mechanisms in Convex Environments

We develop VCG-like mechanisms for resource allocation environments where the players have concave utility functions, and the resource constraints can be represented through convex inequality constraints; multicommodity flow problems are a prime example. Unlike VCG mechanisms that require each player to communicate an entire utility function, our mechanisms only require each player to communicate a single scalar quantity. Despite the limited communication, we establish the existence of an efficient Nash equilibrium. Under some further assumptions, we also establish that all Nash equilibria are efficient. Our approach defines an entire family of resource allocation mechanisms; as a special case, we recover the class of mechanisms recently introduced by Yang and Hajek for a single resource.
Understanding device integration bugs in smart home system

Smart devices have been widely adopted in our daily life. A smart home system, e.g., Home Assistant and openHAB, can be equipped with hundreds and even thousands of smart devices. A smart home system communicates with smart devices through various device integrations, each of which is responsible for a specific kind of devices. Developing high-quality device integrations is a challenging task, in which developers have to properly handle the heterogeneity of different devices, unexpected exceptions, etc. We find that device integration bugs, i.e., iBugs, are prevalent and have caused various consequences, e.g., causing devices unavailable, unexpected device behaviors. In this paper, we conduct the first empirical study on 330 iBugs in Home Assistant, the most popular open source smart home system. We investigate their root causes, trigger conditions, impacts, and fixes. From our study, we obtain many interesting findings and lessons that are helpful for device integration developers and smart home system designers. Our study can open up new research directions for combating iBugs in smart home systems.
The Effect of Self-Management Techniques on Reducing Academic Procrastination Behavior in Facing The Era of Society 5.0

This study aimed to determine the level of student academic procrastination behavior before and after the application of self-management techniques and the effect of self-management techniques on reducing students’ academic procrastination behavior at SMP Negeri 1 Mattiro Bulu. This research was quantitative research with a quasi-experimental design. Data collection tools used to scale and observe. The population in this study amounted to 53 students with a sample of 12 students. The sampling method used proportional random sampling. The results show that the level of students’ academic procrastination before and after the application of self-management techniques showed a significant change in the experimental group, and there is no significant change in the control group. The results of the Wilcoxon signed ranks test show that the Z value is -2.201 and the Asymp Sig value is 0.028, with an error rate of 5% or 0.05. Thus the value of Asymp Sig 0.028 < 0.05. So it can be concluded that self-management techniques are effective in reducing students' academic procrastination behavior.
On the use of mutation analysis for evaluating student test suite quality

A common practice in computer science courses is to evaluate student-written test suites against either a set of manually-seeded faults (handwritten by an instructor) or against all other student-written implementations (“all-pairs” grading). However, manually seeding faults is a time consuming and potentially error-prone process, and the all-pairs approach requires significant manual and computational effort to apply fairly and accurately. Mutation analysis, which automatically seeds potential faults in an implementation, is a possible alternative to these test suite evaluation approaches. Although there is evidence in the literature that mutants are a valid substitute for real faults in large open-source software projects, it is unclear whether mutants are representative of the kinds of faults that students make. If mutants are a valid substitute for faults found in student-written code, and if mutant detection is correlated with manually-seeded fault detection and faulty student implementation detection, then instructors can instead evaluate student test suites using mutants generated by open-source mutation analysis tools. Using a dataset of 2,711 student assignment submissions, we empirically evaluate whether mutation score is a good proxy for manually-seeded fault detection rate and faulty student implementation detection rate. Our results show a strong correlation between mutation score and manually-seeded fault detection rate and a moderately strong correlation between mutation score and faulty student implementation detection. We identify a handful of faults in student implementations that, to be coupled to a mutant, would require new or stronger mutation operators or applying mutation operators to an implementation with a different structure than the instructor-written implementation. We also find that this correlation is limited by the fact that faults are not distributed evenly throughout student code, a known drawback of all-pairs grading. Our results suggest that mutants produced by open-source mutation analysis tools are of equal or higher quality than manually-seeded faults and a reasonably good stand-in for real faults in student implementations. Our findings have implications for software testing researchers, educators, and tool builders alike.
On the use of evaluation measures for defect prediction studies

Software defect prediction research has adopted various evaluation measures to assess the performance of prediction models. In this paper, we further stress on the importance of the choice of appropriate measures in order to correctly assess strengths and weaknesses of a given defect prediction model, especially given that most of the defect prediction tasks suffer from data imbalance. Investigating 111 previous studies published between 2010 and 2020, we found out that over a half either use only one evaluation measure, which alone cannot express all the characteristics of model performance in presence of imbalanced data, or a set of binary measures which are prone to be biased when used to assess models especially when trained with imbalanced data. We also unveil the magnitude of the impact of assessing popular defect prediction models with several evaluation measures based, for the first time, on both statistical significance test and effect size analyses. Our results reveal that the evaluation measures produce a different ranking of the classification models in 82% and 85% of the cases studied according to the Wilcoxon statistical significance test and Â12 effect size, respectively. Further, we observe a very high rank disruption (between 64% to 92% on average) for each of the measures investigated. This signifies that, in the majority of the cases, a prediction technique that would be believed to be better than others when using a given evaluation measure becomes worse when using a different one. We conclude by providing some recommendations for the selection of appropriate evaluation measures based on factors which are specific to the problem at hand such as the class distribution of the training data, the way in which the model has been built and will be used. Moreover, we recommend to include in the set of evaluation measures, at least one able to capture the full picture of the confusion matrix, such as MCC. This will enable researchers to assess whether proposals made in previous work can be applied for purposes different than the ones they were originally intended for. Besides, we recommend to report, whenever possible, the raw confusion matrix to allow other researchers to compute any measure of interest thereby making it feasible to draw meaningful observations across different studies.
An empirical study on the effectiveness of static C code analyzers for vulnerability detection

Static code analysis is often used to scan source code for security vulnerabilities. Given the wide range of existing solutions implementing different analysis techniques, it is very challenging to perform an objective comparison between static analysis tools to determine which ones are most effective at detecting vulnerabilities. Existing studies are thereby limited in that (1) they use synthetic datasets, whose vulnerabilities do not reflect the complexity of security bugs that can be found in practice and/or (2) they do not provide differentiated analyses w.r. t. the types of vulnerabilities output by the static analyzers. Hence, their conclusions about an analyzer's capability to detect vulnerabilities may not generalize to real-world programs. In this paper, we propose a methodology for automatically evaluating the effectiveness of static code analyzers based on CVE reports. We evaluate five free and open-source and one commercial static C code analyzer(s) against 27 software projects containing a total of 1.15 million lines of code and 192 vulnerabilities (ground truth). While static C analyzers have been shown to perform well in benchmarks with synthetic bugs, our results indicate that state-of-the-art tools miss in-between 47% and 80% of the vulnerabilities in a benchmark set of real-world programs. Moreover, our study finds that this false negative rate can be reduced to 30% to 69% when combining the results of static analyzers, at the cost of 15 percentage points more functions flagged. Many vulnerabilities hence remain undetected, especially those beyond the classical memory-related security bugs.