nils-herrmann/clean_scirepeval huggingface dataset:
Combining UML, ASTD and B for the formal specification of an access control filter

Combination of formal and semi-formal methods is more and more required to produce specifications that can be, on the one hand, understood and thus validated by both designers and users and, on the other hand, precise enough to be verified by formal methods. This motivates our aim to use these complementary paradigms in order to deal with security aspects of information systems. This paper presents a methodology to specify access control policies starting with a set of graphical diagrams: UML for the functional model, SecureUML for static access control and ASTD for dynamic access control. These diagrams are then translated into a set of B machines. Finally, we present the formal specification of an access control filter that coordinates the different kinds of access control rules and the specification of functional operations. The goal of such B specifications is to rigorously check the access control policy of an information system taking advantage of tools from the B method.
Screening for Psychosocial Risk in Dutch Families of a Child With Cancer: Reliability, Validity, and Usability of the Psychosocial Assessment Tool.

The Psychosocial Assessment Tool (PAT) was developed to screen for psychosocial risk in families of a child diagnosed with cancer. The current study is the first describing the cross-cultural adaptation, reliability, validity, and usability of the PAT in an European country (Dutch translation).



A total of 117 families (response rate 59%) of newly diagnosed children with cancer completed the PAT2.0 and validation measures.



Acceptable reliability was obtained for the PAT total score (α = .72) and majority of subscales (0.50-0.82). Two subscales showed inadequate internal consistency (Social Support α = .19; Family Beliefs α = .20). Validity and usability were adequate. Of the families, 66% scored low (Universal), 29% medium (Targeted), and 5% high (Clinical) risk.



This study confirms the cross-cultural applicability, reliability, and validity of the PAT total score. Reliability left room for improvement on subscale level. Future research should indicate whether the PAT can be used to provide cost-effective care.
Kernel-based methods for bandit convex optimization

We consider the adversarial convex bandit problem and we build the first <i>poly</i>(<i>T</i>)-time algorithm with <i>poly</i>(<i>n</i>) â<i>T</i>-regret for this problem. To do so we introduce three new ideas in the derivative-free optimization 
literature: (i) kernel methods, (ii) a generalization of Bernoulli convolutions, and (iii) a new annealing schedule for exponential weights (with increasing learning rate). The basic version of our algorithm achieves Ã(<i>n</i><sup>9.5</sup> â<i>T</i>)-regret, and we show that a simple variant of this algorithm can be run in <i>poly</i>(<i>n</i> log(<i>T</i>))-time per step at the cost of an additional <i>poly</i>(<i>n</i>) <i>T</i><sup><i>o</i>(1)</sup> factor in the regret. These results improve upon the Ã(<i>n</i><sup>11</sup> â<i>T</i>)-regret and exp(<i>poly</i>(<i>T</i>))-time result of the first two authors, and the log(<i>T</i>)<sup><i>poly</i>(<i>n</i>)</sup> â<i>T</i>-regret and log(<i>T</i>)<sup><i>poly</i>(<i>n</i>)</sup>-time result of Hazan and Li. Furthermore we conjecture that another variant of the algorithm could achieve Ã(<i>n</i><sup>1.5</sup> â<i>T</i>)-regret, and moreover that this regret is unimprovable (the current best lower bound being Î©(<i>n</i> â<i>T</i>) and it is achieved with linear functions). For the simpler situation of zeroth order stochastic convex optimization this corresponds to the conjecture that the optimal query complexity is of order <i>n</i><sup>3</sup> / Ñ<sup>2</sup>.
The Suggested Upper Merged Ontology: A Large Ontology for the Semantic Web and its Applic ations

In this paper we discuss the development and application of a large formal ontology to the semantic web. The Suggested Upper Merged Ontology (SUMO) (Niles & Pease, 2001) (SUMO, 2002) is a “starter document” in the IEEE Standard Upper Ontology effort. This upper ontology is extremely broad in scope and can serve as a semantic foundation for search, interoperation, and communication on the semantic web.
Applying Deep Learning To Airbnb Search

e application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. e gains, however, plateaued over time. is paper discusses the work done in applying neural networks in an aempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!
A case for ICN usage in IoT environments

Information-Centric Networking (ICN) has recently emerged as a promising Future Internet architecture that aims to cope with the increasing demand for highly scalable and efficient distribution of content. Moving away from the Internet communication model based in addressable hosts, ICN leverages in-network storage for caching, multi-party communication through replication, and interaction models that decouple senders and receivers. This novel networking approach has the potential to outperform IP in several dimensions, besides just content dissemination. Concretely, the rise of the Internet of Things (IoT), with its rich set of challenges and requirements placed over the current Internet, provide an interesting ground for showcasing the contribution and performance of ICN mechanisms. This work analyses how the in-network caching mechanisms associated to ICN, particularly those implemented in the Content-Centric Networking (CCN) architecture, contribute in IoT environments, particularly in terms of energy consumption and bandwidth usage. A simulation comparing IP and the CCN architecture (an instantiation of ICN) in IoT environments demonstrated that CCN leads to a considerable reduction of the energy consumed by the information producers and to a reduction of bandwidth requirements, as well as highlighted the flexibility for adapting current ICN caching mechanisms to target specific requirements of IoT.
Towards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging

Automatic multi-document summarization is a process of generating a summary that contains the most important information from multiple documents. In this thesis, we design an automatic multi-document summarization system using different abstraction-based methods and submodularity. Our proposed model considers summarization as a budgeted submodular function maximization problem. The model integrates three important measures of a summary namely importance, coverage, and non-redundancy, and we design a submodular function for each of them. In addition, we integrate sentence compression and sentence merging. When evaluated on the DUC 2004 data set, our generic summarizer has outperformed the state-of-the-art summarization systems in terms of ROUGE-1 recall and f1-measure. For query-focused summarization, we used the DUC 2007 data set where our system achieves statistically similar results to several well-established methods in terms of the ROUGE-2 measure.
Subsea solution for anti-slug control of multiphase risers

A top-side choke valve is usually used as the manipulated variable for anti-slug control of multi-phase risers at offshore oil-fields. With new advances in the subsea technology, it is now possible to move top-side facilities to the sea floor. The two main contributions in this paper are to consider an alternative location for the control valve and to consider how to deal with nonlinearity. This research involved controllability analysis based on a simplified model fitted to experiments, simulations using the OLGA simulator, as well as an experimental study. It was concluded that a control valve close to the riser-base is very suitable for anti-slug control, and its operation range is the same as the top-side valve. However, a subsea choke valve placed at the well-head can not be used for preventing the riser-slugging.
Comparison of semantic segmentation approaches for horizon/sky line detection

Horizon or skyline detection plays a vital role towards mountainous visual geo-localization, however most of the recently proposed visual geo-localization approaches rely on user-in-the-loop skyline detection methods. Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. This paper provides a quantitative comparison of four such methods for autonomous horizon/sky line detection on an extensive data set. Specifically, we provide the comparison between four recently proposed segmentation methods; one explicitly targeting the problem of horizon detection[2], second focused on visual geo-localization but relying on accurate detection of skyline [15] and other two proposed for general semantic segmentation — Fully Convolutional Networks (FCN)  and SegNet[22]. Each of the first two methods is trained on a common training set  comprised of about 200 images while models for the third and fourth method are fine tuned for sky segmentation problem through transfer learning using the same data set. Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions. We report average accuracy and average absolute pixel error for each of the presented formulation.
Practical filtering for efficient ray-traced directional occlusion

Ambient occlusion and directional (spherical harmonic) occlusion have become a staple of production rendering because they capture many visually important qualities of global illumination while being reusable across multiple artistic lighting iterations. However, ray-traced solutions for hemispherical occlusion require many rays per shading point (typically 256-1024) due to the full hemispherical angular domain. Moreover, each ray can be expensive in scenes with moderate to high geometric complexity. However, many nearby rays sample similar areas, and the final occlusion result is often low frequency. We give a frequency analysis of shadow light fields using distant illumination with a general BRDF and normal mapping, allowing us to share ray information even among complex receivers. We also present a new rotationally-invariant filter that easily handles samples spread over a large angular domain. Our method can deliver 4x speed up for scenes that are computationally bound by ray tracing costs.
Sensing Technologies for Monitoring Serious Mental Illnesses

Mental health is an urgent global issue. Around 450 million people suffer from serious mental illnesses worldwide, which results in devastating personal outcomes and huge societal burden. Effective symptom monitoring and personalized interventions can significantly improve mental health care across different populations. However, traditional clinical methods often fall short when it comes to real-time monitoring of symptoms. Sensing technologies can address these issues by enabling granular tracking of behavioral, physiological, and social signals relevant to mental health. In this article, we describe how sensing technologies can be used to diagnose and monitor patient states for numerous serious mental illnesses. We also identify current limitations and potential future directions. We believe that the multimedia community can build on sensing technologies to enable efficient clinical decision-making in mental health care. Specifically, innovative multimedia systems can help identify and visualize personalized early-warning signs from complex multimodal signals, which could lead to effective intervention strategies and better preemptive care.
Entity Extraction via Ensemble Semantics

Combining information extraction systems yields significantly higher quality resources than each system in isolation. In this paper, we generalize such a mixing of sources and features in a framework called Ensemble Semantics. We show very large gains in entity extraction by combining state-of-the-art distributional and patternbased systems with a large set of features from a webcrawl, query logs, and Wikipedia. Experimental results on a webscale extraction of actors, athletes and musicians show significantly higher mean average precision scores (29% gain) compared with the current state of the art.
A fast heuristic for the train scheduling problem

The train scheduling problem is an integer programming problem known to be NP hard. In practice such a problem is often required to be solved in real time, hence a quick heuristic that allows a good feasible solution to be obtained in a predetermined and finite number of steps is most desired. We propose an algorithm which is based on local optimality criteria in the event of a potential crossing conflict. The suboptimal but feasible solution can be obtained very quickly in polynomial time. The model can also be generalized to cater for the possibility of overtaking when the trains have different speed. We also furnish a complexity analysis to show the NP-completeness of the problem. Simulation results for two non-trivial examples are presented to demonstrate the effectiveness of the proposed algorithm.
Are the Old Days Gone? A Survey on Actual Software Engineering Processes in Video Game Industry

In the past 10 years, several researches studied video game development process who proposed approaches to improve the way how games are developed. These approaches usually adopt agile methodologies because of claims that traditional practices and the waterfall process are gone. However, are the "old days" really gone in the game industry?
 In this paper, we present a survey of software engineering processes in video game industry from postmortem project analyses. We analyzed 20 postmortems from Gamasutra Portal. We extracted their processes and modelled them through using the Business Process Model and Notation (BPMN).
 This work presents three main contributions. First, a postmortem analysis methodology to identify and extract project processes. Second, the study main result: the "old days" are gone, but not completely. Iterative practices are increasing and are applied to at least 65% of projects in which 45% of this projects explicitly adopted Agile practices. However, waterfall process is still applied at least 30% of projects. Finally, we discuss some implications, directions and opportunities for video game development community.
Content-based recommender systems for spoken documents

Content-based recommender systems use preference ratings and features that characterize media to model users' interests or information needs for making future recommendations. While previously developed in the music and text domains, we present an initial exploration of content-based recommendation for spoken documents using a corpus of public domain internet audio. Unlike familiar speech technologies of topic identification and spoken document retrieval, our recommendation task requires a more comprehensive notion of document relevance than bags-of-words would supply. Inspired by music recommender systems, we automatically extract a wide variety of content-based features to characterize non-linguistic aspects of the audio such as speaker, language, gender, and environment. To combine these heterogeneous information sources into a single relevance judgement, we evaluate feature, score, and hybrid fusion techniques. Our study provides an essential first exploration of the task and clearly demonstrates the value of a multisource approach over a bag-of-words baseline.
Traumatic neuroma of the penis after circumcision - Case report*

Traumatic neuromas are tumors resulting from hyperplasia of axons and nerve sheath cells after section or injury to the nervous tissue. We present a case of this tumor, confirmed by anatomopathological examination, in a male patient with history of circumcision. Knowledge of this entity is very important in achieving the differential diagnosis with other lesions that affect the genital area such as condyloma acuminata, bowenoid papulosis, lichen nitidus, sebaceous gland hyperplasia, achrochordon and pearly penile papules.
Face recognition under illumination variations based on eight local directional patterns

Face recognition under varying illumination is a challenging task. This study proposes a modified version of local directional patterns (LDP), eight local directional patterns (ELDP), to produce an illumination insensitive representation of an input face image. The proposed ELDP code scheme uses Kirsch compass masks to compute the edge responses of a pixel’s neighbourhood. Then, ELDP uses all the directional numbers to produce an illumination invariant image. The authors’ extensive experiments show that the ELDP technique achieves an average recognition accuracy of 98.29% on the CMU-PIE face database and 100% on the Yale B face database, and clearly outperforms the state-of-the-art representative techniques.
Distal Proprioceptive Sensor for Motion Feedback in Endoscope-Based Modular Robotic Systems

Modular robotic systems that integrate distally with commercially available endoscopic equipment have the potential to improve the standard-of-care in therapeutic endoscopy by granting clinicians with capabilities not present in commercial tools, such as precision dexterity and feedback sensing. With the desire to integrate both sensing and actuation distally for closed-loop position control in fully deployable, endoscope-based robotic modules, commercial sensor and actuator options that acquiesce to the strict form-factor requirements are sparse or nonexistent. Herein, we describe a proprioceptive angle sensor for potential closed-loop position control applications in distal robotic modules. Fabricated monolithically using printed-circuit MEMS, the sensor employs a kinematic linkage and the principle of light intensity modulation to sense the angle of articulation with a high degree of fidelity. Onboard temperature and environmental irradiance measurements, coupled with linear regression techniques, provide robust angle measurements that are insensitive to environmental disturbances. The sensor is capable of measuring  $\pm$45 degrees of articulation with an RMS error of 0.98 degrees. An ex vivo demonstration shows that the sensor can give real-time proprioceptive feedback when coupled with an actuator module, opening up the possibility of fully distal closed-loop control.
A Novel Approach to Multiple Sequence Alignment Using Multiobjective Evolutionary Algorithm Based on Decomposition

Multiple sequence alignment (MSA) is a fundamental and key step for implementing other tasks in bioinformatics, such as phylogenetic analyses, identification of conserved motifs and domains, structure prediction, etc. Despite the fact that there are many methods to implement MSA, biologically perfect alignment approaches are not found hitherto. This paper proposes a novel idea to perform MSA, where MSA is treated as a multiobjective optimization problem. A famous multiobjective evolutionary algorithm framework based on decomposition is applied for solving MSA, named MOMSA. In the MOMSA algorithm, we develop a new population initialization method and a novel mutation operator. We compare the performance of MOMSA with several alignment methods based on evolutionary algorithms, including VDGA, GAPAM, and IMSA, and also with state-of-the-art progressive alignment approaches, such as MSAprobs, Probalign, MAFFT, Procons, Clustal omega, T-Coffee, Kalign2, MUSCLE, FSA, Dialign, PRANK, and CLUSTALW. These alignment algorithms are tested on benchmark datasets BAliBASE 2.0 and BAliBASE 3.0. Experimental results show that MOMSA can obtain the significantly better alignments than VDGA, GAPAM on the most of test cases by statistical analyses, produce better alignments than IMSA in terms of TC scores, and also indicate that MOMSA is comparable with the leading progressive alignment approaches in terms of quality of alignments.
A comparable study of modeling units for end-to-end Mandarin speech recognition

End-To-End speech recognition have become increasingly popular in mandarin speech recognition and achieved delightful performance. Mandarin is a tonal language which is different from English and requires special treatment for the acoustic modeling units. There have been several different kinds of modeling units for mandarin such as phoneme, syllable and Chinese character. In this work, we explore two major end-to-end models: connectionist temporal classification (CTC) model and attention based encoder-decoder model for mandarin speech recognition. We compare the performance of three different scaled modeling units: context dependent phoneme(CDP), syllable with tone and Chinese character. We find that all types of modeling units can achieve approximate character error rate (CER) in CTC model and the performance of Chinese character attention model is better than syllable attention model. Furthermore, we find that Chinese character is a reasonable unit for mandarin speech recognition. On DidiCallcenter task, Chinese character attention model achieves a CER of 5.68% and CTC model gets a CER of 7.29%, on the other DidiReading task, CER are 4.89% and 5.79%, respectively. Moreover, attention model achieves a better performance than CTC model on both datasets.
DexLego: Reassembleable Bytecode Extraction for Aiding Static Analysis

The scale of Android applications in the market is growing rapidly. To efficiently detect the malicious behavior in these applications, an array of static analysis tools are proposed. However, static analysis tools suffer from code hiding techniques like packing, dynamic loading, self modifying, and reflection. In this paper, we thus present DexLego, a novel system that performs a reassembleable bytecode extraction for aiding static analysis tools to reveal the malicious behavior of Android applications. DexLego leverages just-in-time collection to extract data and bytecode from an application at runtime, and reassembles them to a new Dalvik Executable (DEX) file offline. The experiments on DroidBench and real-world applications show that DexLego precisely reconstructs the behavior of an application in the reassembled DEX file, and significantly improves analysis result of the existing static analysis systems.
Prototype of group heart rate monitoring with NODEMCU ESP8266

Paper describes the development of prototype that enables monitoring of heart rate and inter beat interval for several subjects. The prototype was realized using ESP8266 hardware modules, WebSocket library, nodejs and JavaScript. System architecture is described where nodejs server acts as the signal processing and GUI code provider for clients. Signal processing algorithm was implemented in JavaScript. Application GUI is presented which can be used on mobile devices. Several important parts of the code are described which illustrate the communication between ESP8266 modules, server and clients. Developed prototype shows one of the possible realizations of group monitoring of biomedical data.