nils-herrmann/clean_scirepeval huggingface dataset:
Combining static analysis and targeted symbolic execution for scalable bug finding in application binaries
Venue: CASCON

Symbolic execution is an automated technique for program analysis that has recently become practical due to advances in constraint solvers. Symbolic execution eventually enumerates all feasible program executions, check assertions on all values of varaibles in a program path, and can prioritize executions of interest. However, path explosion, the fact that the number of program executions is typically at least exponential in the size of the program, hinders the adoption of symbolic execution in the real world. In this paper, we present a method for generating test cases using symbolic execution which reach a given potentially buggy target statement. Such a potentially buggy program statement can be found by static program analysis or from crash reports given by the users. The test case generated by our technique serves as a proof of the bug. Generating crashes at the target statement have many applications including re producing crashes, checking warnings generated by static program analysis tools, or analysis of source code patches in code review process. By constantly steering the symbolic execution along the branches that are most likely to lead to the target program statement and pruning the search space that are unlikely to reach the target, we were able to detect deep bugs in real programs. To tackle the memory requirement due to the exponential growth of program paths, we propose a new scheme to manage program execution paths without exhausting system memory. Experiments on real life programs demonstrate that our tool WatSym, built on selective symbolic execution engine S2E, can generate crashing inputs in feasible time and order of magnitude better than symbolic approaches (as embodied by S2E) failed.
Family ownership, control and corporate capital structure: An examination of small capitalization public firms

The purpose of this paper is to explain how family firm ownership and management control affect corporate capital structure strategy after controlling for other significant variables. The authors argue that, although family ownership has a positive effect on a firm's leverage, family control through the CEO position and equity performance moderate its impact.  Using a stratified random sample of 200 US public firms in the S P Small Cap 600 index from 1999 to 2007, this study uses random effect panel regressions to test the impact of family ownership on market value and book value debt ratios and the moderating effects of family control and equity performance after controlling for firm, industry, and macroeconomic variables.  The initial panel regression suggests that family ownership is not related to debt ratios. However, further examination with controls for family CEO and equity performance shows that family ownership is positively related to market an.
Liquid sensing in aquatic environment using high quality planar microwave resonator

This paper describes a non contact liquid sensor operating in an aquatic environment using an active, feedback loop assisted, planar, micro strip microwave resonator. The proposed sensor has the ability to operate in noncontact fashion within a distance of 0 to 8 cm. The active loop technique is shown to increase the primary quality factor from 210 to 500,000 in air when measured at a resonant frequency of 1.52 GHz. The quality factor of the proposed resonator is adjustable with the direct current (DC) bias voltage and is set to 450,000 in the presence of an aquatic medium. The ability to adjust the quality factor of the reported microwave sensor allows for larger dynamic range and permittivity variation monitoring. Different shifts in resonant frequency are reported in the presence of various liquid samples in tubes which are submerged in deionized (DI) water. The proposed device is used to distinguish between Water, Ethanol, Methanol, Isopropanol, and Acetone in a submerged tube inside a water filled container.
Memory Guided Attention: Independent Contributions of the Hippocampus and Striatum
Venue: Neuron

Memory can strongly influence how attention is deployed in future encounters. Though memory dependent on the medial temporal lobes has been shown to drive attention, how other memory systems could concurrently and comparably enhance attention is less clear. Here, we demonstrate that both reinforcement learning and context memory facilitate attention in a visual search task. Using functional magnetic resonance imaging, we dissociate the mechanisms by which these memories guide attention: trial by trial, the hippocampus (not the striatum) predicted attention benefits from context memory, while the striatum (not the hippocampus) predicted facilitation from rewarded stimulus response associations. Responses in these regions were also distinctly correlated with individual differences in each type of memory guided attention. This study provides novel evidence for the role of the striatum in guiding attention, dissociable from hippocampus dependent context memory.
Biomedical event extraction via Long Short Term Memory networks along dynamic extended tree
Venue: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Extracting knowledge from unstructured text is one of the most important goals of Natural Language Processing, especially in biomedical event extraction domain. In this paper, we describe a system for extracting biomedical events among biotope and bacteria from biomedical literature, using the corpus from the BioNLP'16 Shared Task on Bacteria Biotope task. The current mainstream methods for event extraction are based on shallow machine learning methods. However, these methods mainly rely on domain experience and need enormous manual efforts to select features. Therefore, we propose a novel Long Short Term Memory (LSTM) Networks framework DETBLSTM for event extraction. In our framework, a dynamic extended tree is introduced as the input instead of the original sentences, which utilizes the syntactic information. Furthermore, the POS and distance embeddings are added to enrich input information and thus the complex feature extraction can be skipped. In final, we construct a bidirectional LSTM model to extract biomedical events and achieve 57.14% F score in the test set. Our model obtains a better F score than all official submissions to BioNLP ST 2016, which is 1.34% higher than the best system.
Secure Logging Schemes and Certificate Transparency
Venue: ESORICS

Since hundreds of certificate authorities (CAs) can issue browser trusted certificates, it can be difficult for domain owners to detect certificates that have been fraudulently issued for their domain. Certificate Transparency (CT) is a recent standard by the Internet Engineering Task Force (IETF) that aims to construct public logs of all certificates issued by CAs, making it easier for domain owners to monitor for fraudulently issued certificates. To avoid relying on trusted log servers, CT includes mechanisms by which monitors and auditors can check whether logs are behaving honestly or not; these mechanisms are primarily based on Merkle tree hashing and authentication proofs. Given that CT is now being deployed, it is important to verify that it achieves its security goals. In this work, we define four security properties of logging schemes such as CT that can be assured via cryptographic means, and show that CT does achieve these security properties. We consider two classes of security goals: those involving security against a malicious logger attempting to present different views of the log to different parties or at different points in time, and those involving security against malicious monitors who attempt to frame an honest log for failing to include a certificate in the log. We show that Certificate Transparency satisfies these security properties under various assumptions on Merkle trees all of which reduce to collision resistance of the underlying hash function (and in one case with the additional assumption of unforgeable signatures)
Enhanced keystroke dynamics authentication utilizing pressure detection
Venue: 2016 International Conference on Applied System Innovation (ICASI)

Current computer and information systems are now used in almost all aspects of our lives, especially in banking systems and network systems. Most of the times, many computer systems use the simple and common username/password or keystroke biometric scheme via a keyboard for authentication. However, those fixed secret information can be guessed easily using different methods such as network sniffer, social engineering, spyware, dictionary attack and brute force attacks, etc. Meanwhile, using the password based solution suffers from many security flaws and usability limitations. Although the user adopts extreme measures such as changing of the password and using long and complex passwords to be efficient and secure, these are unfriendly and hard to memorize for the user. Therefore, a solution to this problem is the use of an alternative biometric authentication method called Keystroke Dynamics. It is one of the famous biometric technologies and can be yet provide ease of use and transparency to the user in addition to security robustness. Keystroke Dynamics allows to secure the authentication process by verifying the way of typing the static credentials such as typing behavior, typing period of time, etc. But the static behavior can be easy continuously monitoring the user's activities. There is always a golden opportunity for attacker who is physically close to the machine to have access to it. This paper investigates the use of keystroke dynamics that is combined user's typing pressure activities on keyboard. The proposed scheme utilizes the key press and release pressure to build random user's typing profile. The research done on Dynamic Keystroke Pressure Based is ability to provide continual identity verification during the whole time and has a high accuracy level which was obtained under strictly controlled conditions.
Assessment of Urban Heat Island based on the relationship between land surface temperature and Land Use/ Land Cover in Tehran

In this study, the relationship between land surface temperature (LST) and Land Use/Land Cover (LULC) in Tehran Metropolitan City was studied using Landsat TM satellite image. For this, LST was calculated in accordance with the algorithm (Jimenez Munoz Sobrino, 2003) and the LULC map was prepared based on supervised classification method. According to the LST map obtained by processing the thermal band of the satellite image, areas affected by Urban Heat Island (UHI) were detected to examine their status in relation to the existing LULC classes and the population density. The results showed that UHI created in Tehran are different in terms of the causative agent. This difference is primarily due to the status of LULC in the region and reflects the close relationship between land cover and land surface temperature. Also, the distribution of vegetation and green spaces in different areas of Tehran City was studied by normalized vegetation index (NDVI) using remotely sensed data. Correlation study between the land cover and land surface temperature showed a negative correlation between these two parameters. Study of the average surface temperature in six LULC classes indicated full compliance of heat islands with LULC classes.
Experimenting with Machine Learning in Automatic Theorem Proving

First the notion of the heuristic and its importance for automatic theorem proving is explained. The question of how to construct a useful system of heuristics and notions is posed, and it is argued for the importance of learning. A simple example of learning a lemma is shown using the Inductive Logic Programming system Progol. Second a general framework for learning heuristics is discussed and the proof checking system Mizar providing a large body of mathematics is found suitable to learn upon. The process of modifying Mizar for use with Progol is shortly described, e.g. turning the mathematical texts to Prolog structures, creating initial notions, creating the initial base of rules from theorems and definitions, preparing proofs for learning, and problems with polymorphism. Finally first experiments in this environment are described, which include learning the notion of atomic formula and learning how to use initial rules in the proof of Rolle's theorem. This work was supported by Esprit Project 20237 Inductive Logic Programming II This article is an abridged version of the master thesis done under the guidence of doc. RNDr. Petr Stepanek, DrSc. at the Faculty of Mathematics and Physics of the Charles University
Jaw lesions associated with impacted tooth: A radiographic diagnostic guide
Venue: Imaging science in dentistry

This review article aimed to introduce a category of jaw lesions associated with impacted tooth. General search engines and specialized databases such as Google Scholar, PubMed, PubMed Central, MedLine Plus, Science Direct, Scopus, and well recognized textbooks were used to find relevant studies using keywords such as "jaw lesion" "jaw disease" "impacted tooth" and "unerupted tooth" More than 250 articles were found, of which approximately 80 were broadly relevant to the topic. We ultimately included 47 articles that were closely related to the topic of interest. When the relevant data were compiled, the following 10 lesions were identified as having a relationship with impacted tooth: dentigerous cysts, calcifying odontogenic cysts, unicystic (mural) ameloblastomas, ameloblastomas, ameloblastic fibromas, adenomatoid odontogenic tumors, keratocystic odontogenic tumors, calcifying epithelial odontogenic tumors, ameloblastic fibro odontomas, and odontomas. When clinicians encounter a lesion associated with an impacted tooth, they should first consider these entities in the differential diagnosis. This will help dental practitioners make more accurate diagnoses and develop better treatment plans based on patients' radiographs.
Breast cancer classification using deep belief networks
Venue: Expert Syst. Appl.

We present a CAD scheme using DBN unsupervised path followed by NN supervised path.Our two phase method 'DBN NN' classification accuracy is higher than using one phase.Overall accuracy of DBN NN reaches 99.68% with 100% sensitivity 99.47% specificity.DBN NN was tested on the Wisconsin Breast Cancer Dataset (WBCD).DBN NN results show classifier performance improvements over previous studies. Over the last decade, the ever increasing world wide demand for early detection of breast cancer at many screening sites and hospitals has resulted in the need of new research avenues. According to the World Health Organization (WHO) an early detection of cancer greatly increases the chances of taking the right decision on a successful treatment plan. The Computer Aided Diagnosis (CAD) systems are applied widely in the detection and differential diagnosis of many different kinds of abnormalities. Therefore, improving the accuracy of a CAD system has become one of the major research areas. In this paper, a CAD scheme for detection of breast cancer has been developed using deep belief network unsupervised path followed by back propagation supervised path. The construction is back propagation neural network with Liebenberg Marquardt learning function while weights are initialized from the deep belief network path (DBN NN) Our technique was tested on the Wisconsin Breast Cancer Dataset (WBCD) The classifier complex gives an accuracy of 99.68% indicating promising results over previously published studies. The proposed system provides an effective classification model for breast cancer. In addition, we examined the architecture at several train test partitions.
Social welfare benefits and their impacts on labour market participation among men and women in Mongolia

Aside from providing income support to individuals in dire situations, social welfare benefits may unintentionally influence labour decisions, such as whether or not to take up a job, how many hours to work and which type of work to opt for. This paper investigates the relationship between social welfare benefits and labour market outcomes, measured by labour market participation and work intensity for women and men in Mongolia. Mongolia has an extensive system of social welfare benefits, which are mainly allocated based on categorical criteria, and the country suffers from relatively low labour market participation. The empirical analysis uses data from the 2012 Mongolian Household Socio economic Survey and applies standard regression analysis and quasi experimental methods. The paper pays particular attention to women since in spite of the fact that their level of education is similar to that of men their labour market participation is considerably lower compared to men. The results of the analysis indicate that social welfare receipt does not affect the labour market participation of men, but it has a negative impact on women. In terms of hours worked, men in beneficiary households tend to work more hours, while women work fewer hours if they are social welfare recipients.
Integrating Scientific Publication into an Applied Gaming Ecosystem

The European (EU) based industry for non leisure games (so called Applied Games, AGs) is an emerging business. As such it is still fragmented and needs to achieve critical mass to compete globally. Nevertheless, its growth potential is widely recognized and even suggested to exceed the growth potential of the leisure games market. The European project Realizing an Applied Gaming Ecosystem (RAGE) is aiming at supporting this challenge. RAGE will help to seize these opportunities by making available an interoperable set of advanced Applied Game (AG) technology assets, as well as proven practices of using such AG assets in various real world contexts. As described in  RAGE will finally provide a centralized access to a wide range of applied gaming software modules, relevant information, knowledge and community services, and related scientific documents, taxonomies, media, and educational resources within an online community portal called the RAGE Ecosystem. Besides this, an integration between the RAGE Ecosystem and relevant social network interaction spaces that arranges and facilitates collaboration that underlie Research and Development (R&D) as well as marketoriented innovation and exploitation will be created in order to support community building, as well as collaborative asset exploitation of User Generated Contents (UGCs) of the RAGE Ecosystem. In this paper, we will describe the integration of the Scientific Publication Platform (SPP) Mendeley  into the RAGE Ecosystem. This will allow for automating repetitive tasks, reducing errors, and speeding up time consuming tasks. On the other hand it will support information, UGC, and knowledge sharing, as well as persistency of social interaction threads within Social Networking Sites (SNSs) and Groupware Systems (GWSs) that are connected to the RAGE Ecosystem. The paper reviews relevant use cases and scenarios, as well as related authentication, access, and information integration challenges. In this way, on the one hand a qualitative evaluation regarding an optimal technical integration is facilitated while on the other hand design approaches for supporting features of resulting user interfaces are initiated.
Participant, rater, and computer measures of coherence in posttraumatic stress disorder.
Venue: Journal of abnormal psychology

We examined the coherence of trauma memories in a trauma exposed community sample of 30 adults with and 30 without posttraumatic stress disorder. The groups had similar categories of traumas and were matched on multiple factors that could affect the coherence of memories. We compared the transcribed oral trauma memories of participants with their most important and most positive memories. A comprehensive set of 28 measures of coherence including 3 ratings by the participants, 7 ratings by outside raters, and 18 computer scored measures, provided a variety of approaches to defining and measuring coherence. A multivariate analysis of variance indicated differences in coherence among the trauma, important, and positive memories, but not between the diagnostic groups or their interaction with these memory types. Most differences were small in magnitude; in some cases, the trauma memories were more, rather than less, coherent than the control memories. Where differences existed, the results agreed with the existing literature, suggesting that factors other than the incoherence of trauma memories are most likely to be central to the maintenance of posttraumatic stress disorder and thus its treatment.
THE IMPACT OF RECASTS ON THE DEVELOPMENT OF PRIMARY STRESS IN A SYNCHRONOUS COMPUTER MEDIATED ENVIRONMENT
Venue: Studies in Second Language Acquisition

Although previous research has demonstrated the efficacy of recasts on second language (L2) morphology and lexis (e.g. Li, 2010; Mackey Goo, 2007) few studies have examined their effect on learners' phonological development (although see Saito, 2015; Saito Lyster, 2012) The current study investigates the impact of recasts on the development of lexical stress, defined as the placement of emphasis on a particular syllable within a word by making it louder and longer, in oral synchronous computer mediated communication (SCMC) and face to face (FTF) interaction. Using a pretest posttest design, intermediate learners of English were randomly assigned to one of four groups: FTF recast, SCMC recast, FTF control, or SCMC control. Pre and posttests consisted of sentence reading and information exchange tasks, while the treatment was an interactive role play task. Syllable duration, intensity, and pitch were used to analyze learners' development of stress placement. The statistical analyses of the acoustic correlates did not yield significant differences. However, the observed patterns suggest that there is need for further investigation to understand the relationship between recasts and development of lexical stress.
Beyond symptom self report: use of a computer "avatar" to assess post traumatic stress disorder (PTSD) symptoms
Venue: Stress

Post traumatic stress disorder (PTSD) can occur in the wake of exposure to a traumatic event. Currently, PTSD symptoms are assessed mainly through self report in the form of questionnaire or clinical interview. Self report has inherent limitations, particularly in psychiatric populations who may have limited awareness of deficit, reduced attention span, or poor vocabulary and/or literacy skills. Diagnosis and evaluation of treatment efficacy would be aided by behavioral measures. A viable alternative may be virtual environments, in which the participant guides an on screen "avatar" through a series of onscreen events meant to simulate real world situations. Here, a sample of 82 veterans, self assessed for PTSD symptoms was administered such a task, in which the avatar was confronted with situations that might evoke avoidant behavior, a core feature of PTSD. Results showed a strong correlation between PTSD symptom burden and task performance; in fact, the ability to predict PTSD symptom burden based on simple demographic variables (age, sex, combat exposure) was significantly improved by adding task score as a predictor variable. The results therefore suggest that virtual environments may provide a new way to assess PTSD symptoms, while avoiding at least some of the limitations associated with symptom self report, and thus might be a useful complement to questionnaire or clinical interview, potentially facilitating both diagnosis and evaluation of treatment efficacy.