nils-herrmann/clean_scirepeval huggingface dataset:
Building multi-domain conversational systems from single domain resources

Current advances in the development of mobile and smart devices have generated a growing demand for natural human-machine interaction and favored the intelligent assistant metaphor, in which a single interface gives access to a wide range of functionalities and services. Conversational systems constitute an important enabling technology in this paradigm. However, they are usually defined to interact in semantic-restricted domains in which users are offered a limited number of options and functionalities. The design of multi-domain systems implies that a single conversational system is able to assist the user in a variety of tasks. In this paper we propose an architecture for the development of multi-domain conversational systems that allows: (1) integrating available multi and single domain speech recognition and understanding modules, (2) combining available system in the different domains implied so that it is not necessary to generate new expensive resources for the multi-domain system, (3) achieving better domain recognition rates to select the appropriate interaction management strategies. We have evaluated our proposal combining three systems in different domains to show that the proposed architecture can satisfactory deal with multi-domain dialogs.
A survey of in-vehicle communications: Requirements, solutions and opportunities in IoT

As the automobile industry evolves, a number of in-vehicle communication protocols are developed for different in-vehicle applications. With the emerging new applications towards Internet of Things (IoT), a more integral solution is needed to enable the pervasiveness of intra- and inter-vehicle communications. In this survey, we first introduce different classifications of automobile applications with focus on their bandwidth and latency. Then we survey different in-vehicle communication bus protocols including both legacy protocols and emerging Ethernet. In addition, we highlight our contribution in the field to employ power line as the in-vehicle communication medium. We believe power line communication will play an important part in future automobile which can potentially reduce the amount of wiring, simplify design and reduce cost. Based on these technologies, we also introduce some promising applications in future automobile enabled by the development of in-vehicle network. Finally, We will share our view on how the in-vehicle network can be merged into the future IoT.
Fuzzy-Rough Sets Assisted Attribute Selection

Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, attribute selectors preserve the original meaning of the attributes after reduction. This has found application in tasks that involve datasets containing huge numbers of attributes (in the order of tens of thousands) which, for some learning algorithms, might be impossible to process further. Recent examples include text processing and web content classification. AS techniques have also been applied to small and medium-sized datasets in order to locate the most informative attributes for later use. One of the many successful applications of rough set theory has been to this area. The rough set ideology of using only the supplied data and no other information has many benefits in AS, where most other methods require supplementary knowledge. However, the main limitation of rough set-based attribute selection in the literature is the restrictive requirement that all data is discrete. In classical rough set theory, it is not possible to consider real-valued or noisy data. This paper investigates a novel approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics. FRFS is applied to two challenging domains where a feature reducing step is important; namely, web content classification and complex systems monitoring. The utility of this approach is demonstrated and is compared empirically with several dimensionality reducers. In the experimental studies, FRFS is shown to equal or improve classification accuracy when compared to the results from unreduced data. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. In addition, it is shown that FRFS is more powerful than the other AS techniques in the comparative study
Open source as enabler for OPC UA in industrial automation

As a standardized communication protocol, OPC UA is the main focal point with regard to information exchange in the ongoing initiative Industrie 4.0. But there are also considerations to use it within the Internet of Things. The fact that currently no open reference implementation can be used in research for free represents a major problem in this context. The authors have the opinion that open source software can stabilize the ongoing theoretical work. Recent efforts to develop an open implementation for OPC UA were not able to meet the requirements of practical and industrial automation technology. This issue is addressed by the open62541 project which is presented in this article including an overview of its application fields and main research issues.
Pelvic Organ Prolapse Quantification System 
(POP–Q) – a new era in pelvic prolapse staging


The prolapse of one or several pelvic organs is a condition that has been known by medicine since its early days, and different therapeutic approaches have been proposed and accepted. But one of the main problems concerning the prolapse of pelvic organs is the need for a universal, clear and reliable staging method.Because the prolapse has been known and recognized as a disease for more than one hundred years, so are different systems proposed for its staging. But none has proved itself to respond to all the requirements of the medical community, so the vast majority were seen coming and going, failing to become the single most useful system for staging in pelvic organ prolapse (POP).The latest addition to the group of staging systems is the POP-Q system, which is becoming increasingly popular with specialists all over the world, because, although is not very simple as a concept, it helps defining the features of a prolapse at a level of completeness not reached by any other system to date. In this vision, the POP-Q system may reach the importance and recognition of the TNM system use in oncology.This paper briefly describes the POP-Q system, by comparison with other staging systems, analyzing its main features and the concept behind it.
Phenylalanine hydroxylase deficiency

Phenylalanine hydroxylase deficiency is an autosomal recessive disorder that results in intolerance to the dietary intake of the essential amino acid phenylalanine. It occurs in approximately 1:15,000 individuals. Deficiency of this enzyme produces a spectrum of disorders including classic phenylketonuria, mild phenylketonuria, and mild hyperphenylalaninemia. Classic phenylketonuria is caused by a complete or near-complete deficiency of phenylalanine hydroxylase activity and without dietary restriction of phenylalanine most children will develop profound and irreversible intellectual disability. Mild phenylketonuria and mild hyperphenylalaninemia are associated with lower risk of impaired cognitive development in the absence of treatment. Phenylalanine hydroxylase deficiency can be diagnosed by newborn screening based on detection of the presence of hyperphenylalaninemia using the Guthrie microbial inhibition assay or other assays on a blood spot obtained from a heel prick. Since the introduction of newborn screening, the major neurologic consequences of hyperphenylalaninemia have been largely eradicated. Affected individuals can lead normal lives. However, recent data suggest that homeostasis is not fully restored with current therapy. Treated individuals have a higher incidence of neuropsychological problems. The mainstay of treatment for hyperphenylalaninemia involves a low-protein diet and use of a phenylalanine-free medical formula. This treatment must commence as soon as possible after birth and should continue for life. Regular monitoring of plasma phenylalanine and tyrosine concentrations is necessary. Targets of plasma phenylalanine of 120–360 μmol/L (2–6 mg/dL) in the first decade of life are essential for optimal outcome. Phenylalanine targets in adolescence and adulthood are less clear. A significant proportion of patients with phenylketonuria may benefit from adjuvant therapy with 6R-tetrahydrobiopterin stereoisomer. Special consideration must be given to adult women with hyperphenylalaninemia because of the teratogenic effects of phenylalanine. Women with phenylalanine hydroxylase deficiency considering pregnancy should follow special guidelines and assure adequate energy intake with the proper proportion of protein, fat, and carbohydrates to minimize risks to the developing fetus. Molecular genetic testing of the phenylalanine hydroxylase gene is available for genetic counseling purposes to determine carrier status of at-risk relatives and for prenatal testing.
[Cannibalism and psychopathology].

Eating members of one's own species is one of the few remaining taboos in modern human societies. In humans, aggression cannibalism has been associated with mental illness. The objective of this report is to examine the unique set of circumstances and characteristics revealing the underlying etiology leading to such an act and the type of psychological effect it has for the perpetrator. A case report of a patient with paranoid schizophrenia who committed patricide and cannibalism is presented. The psychosocial implications of anthropophagy on the particular patient management are outlined.
Student-teacher network learning with enhanced features

Recent advances in distant-talking ASR research have confirmed that speech enhancement is an essential technique for improving the ASR performance, especially in the multichannel scenario. However, speech enhancement inevitably distorts speech signals, which can cause significant degradation when enhanced signals are used as training data. Thus, distant-talking ASR systems often resort to using the original noisy signals as training data and the enhanced signals only at test time, and give up on taking advantage of enhancement techniques in the training stage. This paper proposes to make use of enhanced features in the student-teacher learning paradigm. The enhanced features are used as input to a teacher network to obtain soft targets, while a student network tries to mimic the teacher network's outputs using the original noisy features as input, so that speech enhancement is implicitly performed within the student network. Compared with conventional student-teacher learning, which uses a better network as teacher, the proposed self-supervised method uses better (enhanced) inputs to a teacher. This setup matches the above scenario of making use of enhanced features in network training. Experiments with the CHiME-4 challenge real dataset show significant ASR improvements with an error reduction rate of 12% in the single-channel track and 15% in the 2-channel track, respectively, by using 6-channel beamformed features for the teacher model.
On the computation of the minimal siphons of S4PR nets from a generating family of siphons

Minimal siphons in the class of S4PR nets have become a conceptual and practical central tool to deal with deadlocks caused by the sharing of resources in Flexible Manufacturing Systems. The availability of efficient algorithms to compute these structural objects is very important. In this paper we take advantage from the particular properties of the siphons in S4PR to obtain an efficient algorithm. These properties allow to compute the minimal siphons from a generating family of minimal siphons. This family is composed by the minimal siphons containing only one resource. The computation of the minimal siphons is based in the maximal strongly connected components of a graph representing the relations between the siphons of the generating family. The algorithm is very economic in memory in all intermediate steps with respect to the classical algorithms.
Finger Vein Identification Based on Minutiae Feature Extraction with Spurious Minutiae Removal

Finger vein technology is the newest biometric technology which utilizes the vein pattern which is hidden under the human finger for identification. As these patterns are hidden under the skin surface, they provide a huge privacy consideration, and are hence extremely difficult to forge. An approach to perform finger vein identification based on extracting minutiae features and the spurious minutiae removal is presented in this work. Minutiae feature extraction includes the extraction of end points and bifurcation points from the skeletal patterns of vein and the removal of spurious or false minutiae, makes the identification more accurate.
Active Plant Wall for Green Indoor Climate Based on Cloud and Internet of Things

An indoor climate is closely related to human health, well-being, and comfort. Thus, indoor climate monitoring and management are prevalent in many places, from public offices to residential houses. Our previous research has shown that an active plant wall system can effectively reduce the concentrations of particulate matter and volatile organic compounds and stabilize the carbon dioxide concentration in an indoor environment. However, regular plant care is restricted by geography and can be costly in terms of time and money, which poses a significant challenge to the widespread deployment of plant walls. In this paper, we propose a remote monitoring and control system that is specific to the plant walls. The system utilizes the Internet of Things technology and the Azure public cloud platform to automate the management procedure, improve the scalability, enhance user experiences of plant walls, and contribute to a green indoor climate.
Mirror-touch synesthesia is linked with empathy

Watching another person being touched activates a similar neural circuit to actual touch and, for some people with 'mirror-touch' synesthesia, can produce a felt tactile sensation on their own body. In this study, we provide evidence for the existence of this type of synesthesia and show that it correlates with heightened empathic ability. This is consistent with the notion that we empathize with others through a process of simulation.
Consumer perceptions of privacy, security and trust in ubiquitous commerce

Commerce is a rapidly emerging application area of ubiquitous computing. In this paper, we discuss the market forces that make the deployment of ubiquitous commerce infrastructures a priority for grocery retailing. We then proceed to report on a study on consumer perceptions of MyGrocer, a recently developed ubiquitous commerce system. The emphasis of the discussion is on aspects of security, privacy protection and the development of trust; we report on the findings of this study. We adopt the enacted view of technology adoption to interpret some of our findings based on three principles for the development of trust. We expect that this interpretation can help to guide the development of appropriate strategies for the successful deployment of ubiquitous commerce systems.
LPC and LPCC method of feature extraction in Speech Recognition System

Automatic speech recognition (ASR) has been under the scrutiny of researchers for many years. Speech Recognition System is the ability to listen what we speak, interpreter and perform actions according to spoken information. After so many detailed study and optimization of ASR and various techniques of features extraction, accuracy of the system is still a big challenge. The selection of feature extraction techniques is completely based on the area of study. In this paper, a detailed theory about features extraction techniques like LPC and LPCC is examined. The goal of this paper is to study the comparative analysis of features extraction techniques like LPC and LPCC.
Do race-specific definitions of short long bones improve the detection of down syndrome on second-trimester genetic sonograms?

The purpose of this study was to determine whether the use of race-specific definitions of short femur and humerus lengths improves Down syndrome detection.



This was a retrospective cohort study over 16 years. For each self-reported maternal race (white, African American, Hispanic, and Asian), we evaluated the efficiency of Down syndrome detection using published race-specific formulas compared with a standard formula for short femur and humerus lengths (observed versus expected lengths < or =0.91 and < or =0.89, respectively). The sensitivity, specificity, and 95% confidence intervals for each parameter were compared. Screening performance was compared by areas under the receiver operating characteristic curves.



Of 58,710 women, 209 (0.3%) had a diagnosis of a fetus with Down syndrome. Although the race-based formula increased sensitivity in each population, the increase was statistically significant only in the white population, whereas a decrease in specificity was statistically significant in all 4 populations, as denoted by nonoverlapping confidence intervals. The area under the receiver operating characteristic curve for the model using the race-specific definition of short femur length was 0.67 versus 0.65 compared with the standard definition, and for humerus length it was 0.70 versus 0.71.



The use of race-based formulas for the determination of short femur and humerus lengths did not significantly improve the detection rates for Down syndrome.
Web-based learning object selection software using analytical hierarchy process

The concepts of sustainability and reusability have great importance in engineering education. In this context, metadata provides reusability and the effective use of Learning Objects (LOs). In addition, searching the huge LO Repository with metadata requires too much time. If the selection criteria do not exactly match the metadata values, it is not possible to find the most appropriate LO. When this situation arises, the multi-criteria decision making (MCDM) method can meet the requirements. In this study, the SDUNESA software was developed and this software allows for the selection of a suitable LO from the repository by using an analytical hierarchy process MCDM method. This web-based SDUNESA software is also used to store, share and select a suitable LO in the repository. To meet these features, the SDUNESA software contains Web 2.0 technologies such as AJAX, XML and SOA Web Services. The SDUNESA software was especially developed for computer engineering education. Instructors can use this software to select LOs with defined criteria. The parameters of the web-based SDUNESA learning object selection software that use the AHP method are defined under the computer education priorities. The obtained results show that the AHP method selects the most reliable learning object that meets the criteria.
A Decomposition-Based Approach for Scalable Many-Field Packet Classification on Multi-core Processors

As a kernel function in network routers, packet classification requires the incoming packet headers to be checked against a set of predefined rules. There are two trends for packet classification: (1) to examine a large number of packet header fields, and (2) to use software-based solutions on multi-core general purpose processors and virtual machines. Although packet classification has been widely studied, most existing solutions on multi-core systems target the classic 5-field packet classification; it is not easy to scale up their performance with respect to the number of packet header fields. In this work, we present a decomposition-based packet classification approach; it supports large rule sets consisting of a large number of packet header fields. In our approach, range-tree and hashing are used to search the fields of the input packet header in parallel. The partial results from all the fields are represented in rule ID sets; they are merged efficiently to produce the final match result. We implement our approach and evaluate its performance with respect to overall throughput and processing latency for rule set size varying from 1 to 32 K. Experimental results on state-of-the-art 16-core platforms show that, an overall throughput of 48 million packets per second and a processing latency of 2,000 ns per packet can be achieved for a 32 K rule set.
Entropy loss in PUF-based key generation schemes: The repetition code pitfall

One of the promising usages of Physically Unclonable Functions (PUFs) is to generate cryptographic keys from PUFs for secure storage of key material. This usage has attractive properties such as physical unclonability and enhanced resistance against hardware attacks. In order to extract a reliable cryptographic key from a noisy PUF response a fuzzy extractor is used to convert non-uniform random PUF responses into nearly uniform randomness. Bösch et al. in 2008 proposed a fuzzy extractor suitable for efficient hardware implementation using two-stage concatenated codes, where the inner stage is a conventional error correcting code and the outer stage is a repetition code. In this paper we show that the combination of PUFs with repetition code approaches is not without risk and must be approached carefully. For example, PUFs with min-entropy lower than 66% may yield zero leftover entropy in the generated key for some repetition code configurations. In addition, we find that many of the fuzzy extractor designs in the literature are too optimistic with respect to entropy estimation. For high security applications, we recommend a conservative estimation of entropy loss based on the theoretical work of fuzzy extractors and present parameters for generating 128-bit keys from memory based PUFs.
Engineering multi-tenant software-as-a-service systems

Increasingly, Software-as-a-Service (SaaS) is becoming a dominant mechanism for the consumption of software by end users. From a vendor's perspective, the benefits of SaaS arise from leveraging economies of scale, by serving a large number of customers ("tenants") through a shared instance of a centrally hosted software service. Consequently, a SaaS provider would, in general, try to drive commonality amongst the requirements of different tenants, and at best, offer a fixed set of customization options. However, many tenants would also come with custom requirements, which may be a pre-requisite for them to adopt the SaaS system. These requirements should then be addressed by evolving the SaaS system in a controlled manner, while still supporting the needs of existing tenants. This need to balance tenant variability and commonality, and to optimize on development and testing effort, can make the evolution of multitenant SaaS systems an interesting engineering challenge; this has strong economic undertones as well, given the "pay-per-use" subscription model of SaaS, and the cost of incremental development and maintenance to cater to new tenant needs. In this paper, we outline a set of research issues in the design, testing and maintenance of multi-tenant SaaS systems, and highlight some of the interesting optimization questions that arise in the process. Presenting specific technical solutions is beyond the scope of this paper - instead, our goal is to help shape a research agenda for multi-tenant SaaS that can provide stimulus for further investigation into this area by the software and service engineering research community.