nils-herrmann/clean_scirepeval huggingface dataset:
Noncontact Proximity Vital Sign Sensor Based on PLL for Sensitivity Enhancement

In this paper, a noncontact proximity vital sign sensor, using a phase locked loop (PLL) incorporated with voltage controlled oscillator (VCO) built-in planar type circular resonator, is proposed to enhance sensitivity in severe environments. The planar type circular resonator acts as a series feedback element of the VCO as well as a near-field receiving antenna. The frequency deviation of the VCO related to the body proximity effect ranges from 0.07 MHz/mm to 1.8 MHz/mm (6.8 mV/mm to 205 mV/mm in sensitivity) up to a distance of 50 mm, while the amount of VCO drift is about 21 MHz in the condition of 60 °C temperature range and discrete component tolerance of ±5%. Total frequency variation occurs in the capture range of the PLL which is 60 MHz. Thus, its loop control voltage converts the amount of frequency deviation into a difference of direct current (DC) voltage, which is utilized to extract vital signs regardless of the ambient temperature. The experimental results reveal that the proposed sensor placed 50 mm away from a subject can reliably detect respiration and heartbeat signals without the ambiguity of harmonic signals caused by respiration signal at an operating frequency of 2.4 GHz.
Automatic natural language style classification and transformation

Style is an integral part of natural language in written, spoken or machine generated forms. Humans have been dealing with style in language since the beginnings of language itself, but computers and machine processes have only recently begun to process natural language styles. Automatic processing of styles poses two interrelated challenges: classification and transformation. There have been recent advances in corpus classification, automatic clustering and authorship attribution along many dimensions but little work directly related to writing styles directly and even less in transformation. In this paper we examine relevant literature to define and operationalize a notion of “style” which we employ to designate style markers usable in classification machines. A measurable reading of these markers also helps guide style transformation algorithms. We demonstrate the concept by showing a detectable stylistic shift in a sample piece of text relative to a target corpus. We present ongoing work in building a comprehensive style recognition and transformation system and discuss our results.
A smart watch for women security based on iot concept ‘watch me’

Today, in the current global scenario, Women were facing lot of challenges. We can hear the news of women harassments than their achievements. There are many existing apps and devices for women security via smart phones. Though the smart phones have increased rapidly, it is not possible to have the phone all the time in our hand to make a call or click on it, so here we introduced a new technique via smart watches. When a women or child wearing this ‘watch me’ is exposed to sexual or vulnerable attack, the sensor present in it detects the heart beat rate of a person which will be high at the moment by the secretion of epinephrine hormone from hpa axis and gets activated, this will not only provide a alarm sound to the attention of nearby people, it will automatically make an call to our registered contact and also through GPS/GSM it will detect the nearby police station and make an ring there so it will be helpful for police to arrive soon at the spot by tracking the GPS, such a system will lead to safer and better environment.
Deep transformation learning for face recognition in the unconstrained scene

Because human pose variations cannot be controlled in unconstrained scene, it is frequently hard to capture frontal face image. This is why either face recognition rate is low, or face image cannot be recognized at all. To tackle the problem, this paper proposes deep transformation learning to extract the pose-robust feature within one model; it includes feature transformation and joint supervision of softmax loss and pose loss. Specifically, the feature transformation is designed to learn the transformation from different poses. The pose loss is designed to simultaneously learn the feature center of different poses and keep intra-pose relationships. The extracted deep features tend to be more pose-robust and discriminative. Experimental results also confirm the performances to be valid on several important face recognition benchmarks, including Labeled Faces in the Wild, IARPA Janus Benchmark A.
gpdsHMM : A HIDDEN MARKOV MODEL TOOLBOX IN THE MATLAB ENVIRONMENT

A Hidden Markov Model (HMM) Toolbox within the Matlab environment is presented. In this toolbox, the conventional techniques for the continuous and discrete HMM are developed for the training as well as for the test phases. The ability to make different groups of components for the vector pattern is provided. Multilabeling techniques for the discrete HMM is also provided. The toolbox includes procedures suitable for the classical applications based on the HMM, as pattern recognition, speech recognition and DNA sequence analysis.
Sensory integration dysfunction affects efficacy of speech therapy on children with functional articulation disorders

Articulation disorders in young children are due to defects occurring at a certain stage in sensory and motor development. Some children with functional articulation disorders may also have sensory integration dysfunction (SID). We hypothesized that speech therapy would be less efficacious in children with SID than in those without SID. Hence, the purpose of this study was to compare the efficacy of speech therapy in two groups of children with functional articulation disorders: those without and those with SID.



A total of 30 young children with functional articulation disorders were divided into two groups, the no-SID group (15 children) and the SID group (15 children). The number of pronunciation mistakes was evaluated before and after speech therapy.



There were no statistically significant differences in age, sex, sibling order, education of parents, and pretest number of mistakes in pronunciation between the two groups (P > 0.05). The mean and standard deviation in the pre- and post-test number of mistakes in pronunciation were 10.5 ± 3.2 and 3.3 ± 3.3 in the no-SID group, and 10.1 ± 2.9 and 6.9 ± 3.5 in the SID group, respectively. Results showed great changes after speech therapy treatment (F = 70.393; P < 0.001) and interaction between the pre/post speech therapy treatment and groups (F = 11.119; P = 0.002).



Speech therapy can improve the articulation performance of children who have functional articulation disorders whether or not they have SID, but it results in significantly greater improvement in children without SID. SID may affect the treatment efficiency of speech therapy in young children with articulation disorders.
Semantic Reasoning for Context-Aware Internet of Things Applications

Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for Internet of Things (IoT) applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.
A realist review of mobile phone-based health interventions for non-communicable disease management in sub-Saharan Africa

The prevalence of non-communicable diseases (NCDs) is increasing in sub-Saharan Africa. At the same time, the use of mobile phones is rising, expanding the opportunities for the implementation of mobile phone-based health (mHealth) interventions. This review aims to understand how, why, for whom, and in what circumstances mHealth interventions against NCDs improve treatment and care in sub-Saharan Africa.



Four main databases (PubMed, Cochrane Library, Web of Science, and Google Scholar) and references of included articles were searched for studies reporting effects of mHealth interventions on patients with NCDs in sub-Saharan Africa. All studies published up until May 2015 were included in the review. Following a realist review approach, middle-range theories were identified and integrated into a Framework for Understanding the Contribution of mHealth Interventions to Improved Access to Care for patients with NCDs in sub-Saharan Africa. The main indicators of the framework consist of predisposing characteristics, needs, enabling resources, perceived usefulness, and perceived ease of use. Studies were analyzed in depth to populate the framework.



The search identified 6137 titles for screening, of which 20 were retained for the realist synthesis. The contribution of mHealth interventions to improved treatment and care is that they facilitate (remote) access to previously unavailable (specialized) services. Three contextual factors (predisposing characteristics, needs, and enabling resources) influence if patients and providers believe that mHealth interventions are useful and easy to use. Only if they believe mHealth to be useful and easy to use, will mHealth ultimately contribute to improved access to care. The analysis of included studies showed that the most important predisposing characteristics are a positive attitude and a common language of communication. The most relevant needs are a high burden of disease and a lack of capacity of first-contact providers. Essential enabling resources are the availability of a stable communications network, accessible maintenance services, and regulatory policies.



Policy makers and program managers should consider predisposing characteristics and needs of patients and providers as well as the necessary enabling resources prior to the introduction of an mHealth intervention. Researchers would benefit from placing greater attention on the context in which mHealth interventions are being implemented instead of focusing (too strongly) on the technical aspects of these interventions.
Detecting Ironic Intent in Creative Comparisons

Irony is an effective but challenging mode of communication that allows a speaker to express sentiment-rich viewpoints with concision, sharpness and humour. Irony is especially common in online documents that express subjective and deeply-felt opinions, and thus represents a significant obstacle to the accurate analysis of sentiment in web texts. In this paper we look at one commonly used framing device for linguistic irony – the simile – to show how irony is often marked in ways that make it computationally feasible to detect. We conduct a very large corpus analysis of web-harvested similes to identify the most interesting characteristics of ironic comparisons, and provide an empirical evaluation of a new algorithm for separating ironic from non-ironic similes.
Design and implementation of full bridge bidirectional isolated DC-DC converter for high power applications

This paper proposes the design and implementation of a high power full bridge bidirectional isolated DC-DC converter (BIDC) which comprises of two symmetrical voltage source converters and a high frequency transformer. In the proposed BIDC, a well-known PI controller based single phase-shift (SPS) modulation technique is used in order to achieve high power transfer. Besides, different phase shift methods such as extended phase-shift (EPS) and double phase-shift (DPS) are compared with SPS. Both simulation and experimental results are caried out to verify PI controller based simple phase-shift controlled BIDC prototype that is designed for 300-V 2.4-kW and operating at 20 kHz.
A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis

With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective and adaptable measurement device for monitoring wheelchair kinematics; however, the outcomes are dependent on the reliability of the processing algorithms. Though there are a variety of algorithms that have been proposed to monitor wheelchair propulsion in court sports, they all have limitations. Through experimental testing, we have shown the Attitude and Heading Reference System (AHRS)-based algorithm to be a suitable and reliable candidate algorithm for estimating velocity, distance, and approximating trajectory. The proposed algorithm is computationally inexpensive, agnostic of wheel camber, not sensitive to sensor placement, and can be embedded for real-time implementations. The research is conducted under Griffith University Ethics (GU Ref No: 2016/294).
EMBROIDERED FULLY TEXTILE WEARABLE AN- TENNA FOR MEDICAL MONITORING APPLICATIONS

Telecommunication systems integrated within garments and wearable products are such methods by which medical devices are making an impact on enhancing healthcare provisions around the clock. These garments when fully developed will be capable of alerting and demanding attention if and when required along with minimizing hospital resources and labour. Furthermore, they can play a major role in preventative ailments, health irregularities and unforeseen heart or brain disorders in apparently healthy individuals. This work presents the feasibility of investigating an Ultra-WideBand (UWB) antenna made from fully textile materials that were used for the substrate as well as the conducting parts of the designed antenna. Simulated and measured results show that the proposed antenna design meets the requirements of wide working bandwidth and provides 17GHz bandwidth with compact size, washable and flexible materials. Results in terms of return loss, bandwidth, radiation pattern, current distribution as well as gain and efficiency are presented to validate the usefulness of the current manuscript design. The work presented here has profound implications for future studies of a standalone suite that may one day help to provide wearer (patient) with such reliable and comfortable medical monitoring techniques.  May 2011, Scheduled 10 June 2011 *  Mai A. Rahman Osman (mai.rahman@fkegraduate.utm.my).
Memory-Size-Assisted Buffer Overflow Detection

-Since the first buffer overflow problem occurred, many detection techniques have been presented. These techniques are effective in detecting most attacks, but some attacks still remain undetected. In order to be more effective, a memory-size-assisted buffer overflow detection(MBOD) is presented. The key feature of buffer overflow is that the size of the source memory is bigger than the size of the destination memory when memory copying operation occurs. By capturing memory copying operation and comparing memory size at run time, MBOD detects buffer overflow. MBOD collects the information of memory size in both dynamic way and static way. An implementation shows that the technique is feasible.
Taxonomy Model for Cyber Threat Intelligence Information Exchange Technologies

The cyber threat intelligence information exchange ecosystem is a holistic approach to the automated sharing of threat intelligence. For automation to succeed, it must handle tomorrow's attacks, not just yesterday's. There are numerous ontologies that attempt to enable the sharing of cyber threats, such as OpenIOC, STIX, and IODEF. To date, most ontologies are based on various use cases. Ontology developers collect threat indicators that through experi-ence seem to be useful for exchange. This approach is pragmatic and offers a collection of useful threat indicators in real-world scenarios. However, such a selection method is episodic. What is useful today may not be useful tomorrow. What we consider to be chaff or too hard to share today might become a critically im-portant piece of information. Therefore, in addition to use case-based ontology, ontologies need to be based on first principles. In this document we propose taxonomy for classifying threat-sharing technologies. The purpose of this taxonomy is to classify existing technologies using an agnostic framework, identify gaps in existing technologies, and explain their differences from a sci-entific perspective. We are currently working on a thesaurus that will describe, compare, and classify detailed cyber security terms. This paper focuses on the classification of the ontologies them-selves.
DC 30-GHz DPDT Switch Matrix Design in High Resistivity Trap-Rich SOI

This paper presents low insertion loss, high isolation, ultra-wideband double-pole-double-throw (DPDT) switch matrix designed in a 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> commercial high resistivity trap-rich silicon-on-insulator (SOI) CMOS process for the first time. The switches are designed using series–shunt–series configuration in a ring-type structure with input and output matching networks. Transistor width and transistor channel length effects on the wideband DPDT switch performance are thoroughly investigated. The designed switches achieve widest bandwidth from dc to 30 GHz with a low insertion loss of 2.5 dB and a high isolation of 32 dB up to 30 GHz. The measured input P1dB of designed switches is higher than 18 dBm. It was found both second and third harmonics can be improved by widening switch transistor channel width, and third harmonic can be improved by shortening channel length. The active chip area of designed <inline-formula> <tex-math notation="LaTeX">$2 \times2$ </tex-math></inline-formula> switch matrix is very small size of only 0.28 mm <inline-formula> <tex-math notation="LaTeX">$\times0.21$ </tex-math></inline-formula> mm.
A geometric view of optimal transportation and generative model

In this work, we show the intrinsic relations between optimal transportation and convex geometry, especially the variational approach to solve Alexandrov problem: constructing a convex polytope with prescribed face normals and volumes. This leads to a geometric interpretation to generative models, and leads to a novel framework for generative models. By using the optimal transportation view of GAN model, we show that the discriminator computes the Kantorovich potential, the generator calculates the transportation map. For a large class of transportation costs, the Kantorovich potential can give the optimal transportation map by a close-form formula. Therefore, it is sufficient to solely optimize the discriminator. This shows the adversarial competition can be avoided, and the computational architecture can be simplified. Preliminary experimental results show the geometric method outperforms WGAN for approximating probability measures with multiple clusters in low dimensional space.
Utility-Preserving Differentially Private Data Releases Via Individual Ranking Microaggregation

Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted type and/or a limited number of queries. On the contrary, k-anonymity-like data releases make no assumptions on the uses of the protected data and, thus, do not restrict the number and type of doable analyses. Recently, some authors have proposed mechanisms to offer general-purpose differentially private data releases. This paper extends such works with a specific focus on the preservation of the utility of the protected data. Our proposal builds on microaggregation-based anonymization, which is more flexible and utility-preserving than alternative anonymization methods used in the literature, in order to reduce the amount of noise needed to satisfy differential privacy. In this way, we improve the utility of differentially private data releases. Moreover, the noise reduction we achieve does not depend on the size of the data set, but just on the number of attributes to be protected, which is a more desirable behavior for large data sets. The utility benefits brought by our proposal are empirically evaluated and compared with related works for several data sets and metrics.
Mitigating Crossfire Attacks Using SDN-Based Moving Target Defense

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.
Efficient Phrase-Based Document Similarity for Clustering

In this paper, we propose a phrase-based document similarity to compute the pair-wise similarities of documents based on the suffix tree document (STD) model. By mapping each node in the suffix tree of STD model into a unique feature term in the vector space document (VSD) model, the phrase-based document similarity naturally inherits the term tf-idf weighting scheme in computing the document similarity with phrases. We apply the phrase-based document similarity to the group-average Hierarchical Agglomerative Clustering (HAC) algorithm and develop a new document clustering approach. Our evaluation experiments indicate that, the new clustering approach is very effective on clustering the documents of two standard document benchmark corpora OHSUMED and RCV1. The quality of the clustering results significantly surpass the results of traditional single-word \textit{tf-idf} similarity measure in the same HAC algorithm, especially in large document data sets. Furthermore, by studying the property of STD model, we conclude that the feature vector of phrase terms in the STD model can be considered as an expanded feature vector of the traditional single-word terms in the VSD model. This conclusion sufficiently explains why the phrase-based document similarity works much better than the single-word tf-idf similarity measure.