nils-herrmann/clean_scirepeval huggingface dataset:
Machine learning in cardiovascular medicine: are we there yet?

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.
Space-Time Alignment for Channel Estimation in Millimeter Wave Communication with Beam Sweeping

In millimeter wave (mmWave) communication systems with hybrid multiple-input multiple-output (MIMO) processors, it is often necessary to employ analog beam sweeping during pilot signal transmission/reception to increase the signal to noise ratio (SNR). In this paper, we develop a process, called the space-time (ST) alignment, for receiving pilot sequences for mmWave channel estimation from the signal received during beam sweeping. The spatial alignment removes the effect of beam sweeping on pilot arrival time (PATs). The process for locating the temporal windows on the received signal to obtain pilot-containing sequences for channel estimation is called the temporal alignment. Simulation results demonstrate that mmWave channels can be estimated successfully using the sequences obtained via the ST alignment.
A novel methodology for mapping objective video quality metrics to the subjective MOS scale

With the rapid growth in video-based services, and as users are becoming increasingly quality-aware, the reliable estimation of video quality has become extremely important. While a multitude of objective Video Quality Assessment (VQA) metrics with various performance and complexity have been proposed, the nonlinearity of video quality and the lack of clear interpretations of the metrics make difficult to understand how the objective metric values reflect the video quality as perceived subjectively in terms of Mean Opinion Scores (MOS). This paper proposes and evaluates a methodology for mapping objective VQA metric values to subjective MOS scores based on publicly available VQA databases. Three different databases were used for comparing the performance of various objective metrics and evaluating the proposed methodology.
As Cool as a Cucumber: Towards a Corpus of Contemporary Similes in Serbian

Similes are natural language expressions used to compare unlikely things, where the comparison is not taken literally. They are often used in everyday communication and are an important part of cultural heritage. Having an up-to-date corpus of similes is challenging, as they are constantly coined and/or adapted to the contemporary times. In this paper we present a methodology for semi-automated collection of similes from the world wide web using text mining techniques. We expanded an existing corpus of traditional similes (containing 333 similes) by collecting 446 additional expressions. We, also, explore how crowdsourcing can be used to extract and curate new similes.
AIDEA: a methodology for supplier evaluation and selection in a supplier-based manufacturing environment

Supplier evaluation and selection, an important element in supplier-based manufacturing and supply chain management has been gaining attention in both academic literature and industrial practice. In this paper, we presented a modified data envelopment analysis (DEA) method for supplier selection which can operate under conditions of imprecise information. A brief description of the importance of supplier evaluation and selection is given, followed by an overview of DEA, along with several extensions of DEA applied specifically to supplier evaluation and selection. Contributions of our proposed approach include the elimination of the poor discriminatory power and inability of traditional DEA to rank the efficient suppliers. In addition, our method can be used in situations with imprecise data.
False Positives Reduction Techniques in Intrusion Detection Systems-A Review

During the last decade with the growth of cyber attacks, information safety has become an important issue all over the world. Intrusion detection systems (IDSs) are an essential element for network security infrastructure and play a very important role in detecting large number of attacks. Although there are different types of intrusion detection systems, all these systems suffer a common problem which is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. The aim of conducted research in the field is to propose different techniques to handle the alerts, reduce them and distinguish real attacks from false positives and low importance events. This manuscript is a survey paper that represents a review of the current research related to the false positives problem. The focus will be on data mining techniques of alert reduction. This paper reviews more than 30 related studies during the last decade with the hope of providing a reference for further research in this area. Several open issues have also been addressed in this paper.
Survey of review spam detection using machine learning techniques

Online reviews are often the primary factor in a customer’s decision to purchase a product or service, and are a valuable source of information that can be used to determine public opinion on these products or services. Because of their impact, manufacturers and retailers are highly concerned with customer feedback and reviews. Reliance on online reviews gives rise to the potential concern that wrongdoers may create false reviews to artificially promote or devalue products and services. This practice is known as Opinion (Review) Spam, where spammers manipulate and poison reviews (i.e., making fake, untruthful, or deceptive reviews) for profit or gain. Since not all online reviews are truthful and trustworthy, it is important to develop techniques for detecting review spam. By extracting meaningful features from the text using Natural Language Processing (NLP), it is possible to conduct review spam detection using various machine learning techniques. Additionally, reviewer information, apart from the text itself, can be used to aid in this process. In this paper, we survey the prominent machine learning techniques that have been proposed to solve the problem of review spam detection and the performance of different approaches for classification and detection of review spam. The majority of current research has focused on supervised learning methods, which require labeled data, a scarcity when it comes to online review spam. Research on methods for Big Data are of interest, since there are millions of online reviews, with many more being generated daily. To date, we have not found any papers that study the effects of Big Data analytics for review spam detection. The primary goal of this paper is to provide a strong and comprehensive comparative study of current research on detecting review spam using various machine learning techniques and to devise methodology for conducting further investigation.
Process modeling for industry 4.0 applications: Towards an industry 4.0 process modeling language and method

The term Industry 4.0 derives from the new (fourth) industrial revolution enabling suppliers and manufacturers to leverage new technological concepts like Internet of Things, Big Data, and Cloud Computing: New or enhanced products and services can be created, cost be reduced and productivity be increased. Similar terms are Smart Factory or Smart Manufacturing. The ideas, concepts and technologies are not hype anymore - they are at least partly reality, but many software specification and development aspects are still not sufficiently covered, e.g. standardization, specification and modeling languages. This paper presents an Industry 4.0 process modeling language (I4PML) that is an extension (UML profile with stereotypes) of OMG's BPMN (Business Process Model and Notation) standard. We also describe a method for the specification of Industry 4.0 applications using UML and I4PML.
Indoors forensic entomology: colonization of human remains in closed environments by specific species of sarcosaprophagous flies.

Fly species that are commonly recovered on human corpses concealed in houses or other dwellings are often dependent on human created environments and might have special features in their biology that allow them to colonize indoor cadavers. In this study we describe nine typical cases involving forensically relevant flies on human remains found indoors in southern Finland. Eggs, larvae and puparia were reared to adult stage and determined to species. Of the five species found the most common were Lucilia sericata Meigen, Calliphora vicina Robineau-Desvoidy and Protophormia terraenovae Robineau-Desvoidy. The flesh fly Sarcophaga caerulescens Zetterstedt is reported for the first time to colonize human cadavers inside houses and a COI gene sequence based DNA barcode is provided for it to help facilitate identification in the future. Fly biology, colonization speed and the significance of indoors forensic entomological evidence are discussed.
YOLOv3: An Incremental Improvement

We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that’s pretty swell. It’s a little bigger than last time but more accurate. It’s still fast though, don’t worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared to 57.5 AP50 in 198 ms by RetinaNet, similar performance but 3.8× faster. As always, all the code is online at https://pjreddie.com/yolo/.
An FPGA-based AES-CCM Crypto Core For IEEE 802.11i Architecture

The widespread adoption of IEEE 802.11 wireless networks has brought its security paradigm under active research. One of the important research areas in this field is the realization of fast and secure implementations of cryptographic algorithms. Under this work, such an implementation has been done for Advanced Encryption Standard (AES) on fast, efficient and low power Field Programmable Gate Arrays (FPGAs) whereby computational intensive cryptographic processes are offloaded from the main processor thus results in achieving highspeed secure wireless connectivity. The dedicated resources of Spartan-3 FPGAs have been effectively utilized to develop wider logic function which minimizes the critical paths by confining logic to single Configurable Logic Block (CLB), thus improving the performance, density and power consumption of the design. The resultant design consumes only 4 Block RAMs and 487 Slices to fit both AES cores and its key scheduling.
BIM: An integrated model for planned and preventive maintenance of architectural heritage

Modern digital technologies give us great possibilities to organize knowledge about constructions, regarding multidisciplinary fields like preservation, conservation and valorization of our architectural heritage, in order to suggest restoration projects and related work, or to suppose a planned preventive maintenance. New procedures to archive, analyze and manage architectural information find a natural support in 3D model, thanks to the development of capacities of new modeling software. Moreover, if the model contains or interfaces with a heterogeneous archive of information, as it is for BIM, this model can be considered as the bases of critical studies, projects of restoration, heritage maintenance, integrated management, protection, and valorization, and evaluation of economic aspects, management and planning, that can flow into a planned preventive maintenance . The aspect that limit the use of BIM technology is the set up parametric object library inside programs: the standardized level of these objects deals difficulty with survey and restoration issues, where each single element has its own historical and architectural characterization . From this foreword, the goal of this research is more evident: the possibilities of using BIM modeling to the existing constructions and cultural heritage, as a support for the construction and management of a Plan for planned preventive maintenance.
Chapter 2 : Game Analytics – The Basics

Take Away Points:  Overview of important key terms in game analytics  Introduction to game telemetry as a source of business intelligence.  In-depth description and discussion of user-derived telemetry and metrics.  Introduction to feature selection in game analytics  Introduction to the knowledge discovery process in game analytics  References to essential further reading. 1 Analytics – a new industry paradigm Developing a profitable game in today's market is a challenging endeavor. Thousands of commercial titles are published yearly, across a number of hardware platforms and distribution channels, all competing for players' time and attention, and the game industry is decidedly competitive. In order to effectively develop games, a variety of tools and techniques from e.g. business practices, project management to user testing have been developed in the game industry, or adopted and adapted from other IT sectors. One of these methods is analytics, which in recent years has decidedly impacted the game industry and game research environment. Analytics is the process of discovering and communicating patterns in data, towards solving problems in business or conversely predictions for supporting enterprise decision management, driving action and/or improving performance. The meth-odological foundations for analytics are statistics, data mining, mathematics, programming and operations research, as well as data visualization in order to communicate insights learned to the relevant stakeholders. Analytics is not just the querying and reporting of BI data, but rests on actual analysis, e.g. statistical analysis, predic-tive modeling, optimization, forecasting, etc. (Davenport and Harris, 2007). Analytics typically relies on computational modeling. There are several branches or domains of analytics, e.g. marketing analytics, risk analytics, web analytics – and game analytics. Importantly, analytics is not the same thing as data analysis. Analytics is an umbrella term, covering the entire methodology of finding and communicating patterns in data, whereas analysis is used for individual applied instances, e.g. running
Comparative study of Antiplatelet Aggregation Activity of Bacopa monnieri extracted using Microwave and Ultrasonication Rency

Plant extracts having an antiplatelet aggregation activity may be a potential choice for development of antiinflammatory drugs because of their natural origin. The extraction of bioactive compounds being the most crucial step in the commercial development of natural products it is essential to have rapid and efficient phytochemical extraction methods. Extracts of B. monnieri were prepared using different methods such as the Conventional organic solvent extraction method, Microwave Assisted Extraction (MAE) and the Ultrasonication Assisted Extraction (UAE) method. The efficiency of the extraction methods were studied by determining antiinflammatory activity by the in vitro antiplatelet aggregation method. The study revealed a 48.41 % and 45.68 % increase in activity for the extracts prepared using ultrasonication and microwave assisted extraction establishing their superiority compared to the conventional organic solvent extraction method.
Smart home for elderly living using Wireless Sensor Networks and an Android application

A Smart Home (SH) is a house or an apartment equipped with advanced automation technologies to provide the occupants with intelligent monitoring and actionable information that can be situation specific. It allows for improvements in the way we live or work, and for improved energy efficiencies. The smart home is especially relevant for the elderly because the intelligent sensing systems would allow for remote monitoring and possibly control of health and environmental parameters according to their health status and living needs. The burgeoning elderly population and rising costs of elderly healthcare and home needs have led to a rapid evolution of the elderly smart living environment. However, the lack of proper eldercare smart home technologies has inspired us to develop Information and Communication Technologies (ICT) based smart home for the elderly using the Android platform. We have proposed a model for the elderly using Wireless Sensor Networks (WSNs) implemented as an Android application. Our smart home research potentially allows the elderly to continue to live in their own homes while being monitored non-invasively, seamlessly and economically according to their healthcare needs and status.
Enhancing computer vision to detect face spoofing attack utilizing a single frame from a replay video attack using deep learning

Recently, automatic face recognition has been applied in many web and mobile applications. Developers integrate and implement face recognition as an access control into these applications. However, face recognition authentication is vulnerable to several attacks especially when an attacker presents a 2-D printed image or recorded video frames in front of the face sensor system to gain access as a legitimate user. This paper introduces a non-intrusive method to detect face spoofing attacks that utilize a single frame of sequenced frames. We propose a specialized deep convolution neural network to extract complex and high features of the input diffused frame. We tested our method on the Replay Attack dataset which consists of 1200 short videos of both real-access and spoofing attacks. An extensive experimental analysis was conducted that demonstrated better results when compared to previous static algorithms results.
Fingerprint Matching by Genetic Algorithms

Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. In this paper, we propose a fingerprint-matching approach based on genetic algorithms (GA), which tries to find the optimal transformation between two different fingerprints. In order to deal with low-quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we design a fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.
Iterative unified clustering in big data

We propose a novel iterative unified clustering algorithm for data with both continuous and categorical variables, in the big data environment. Clustering is a well-studied problem and finds several applications. However, none of the big data clustering works discuss the challenge of mixed attribute datasets, with both categorical and continuous attributes. We study an application in the health care domain namely Case Based Reasoning (CBR), which refers to solving new problems based on solutions to similar past problems. This is particularly useful when there is a large set of clinical records with several types of attributes, from which similar patients need to be identified. We go one step further and include the genomic components of patient records to enhance the CBR discovery. Thus, our contributions in this paper spans across the big data algorithmic research and a key contribution to the domain of heath care information technology research. First, our clustering algorithm deals with both continuous and categorical variables in the data; second, our clustering algorithm is iterative where it finds the clusters which are not well formed and iteratively drills down to form well defined clusters at the end of the process; third we provide a novel approach to CBR across clinical and genomic data. Our research has implications for clinical trials and facilitating precision diagnostics in large and heterogeneous patient records. We present extensive experimental results to show the efficacy or our approach.
Antipodal Vivaldi antenna for phased array antenna applications

This paper describes an antipodal Vivaldi antenna on Rogers4350B substrate at the operating frequency band of 6~18GHz. Both simulation and measurement results show superior performance with high gain of 6-10dB and symmetrical H-plane characteristics. In addition, a 1×8 H-plane phased array with alterable spacing between adjacent elements is constructed with a stacked configuration, demonstrating a good performance in scanning area.
Tibetan-Chinese Cross Language Text Similarity Calculation Based onLDA Topic Model

Topic model building is the basis and the most critical module of cross-language topic detection and tracking. Topic model also can be applied to cross-language text similarity calculation. It can improve the efficiency and the speed of calculation by reducing the texts’ dimensionality. In this paper, we use the LDA model in cross-language text similarity computation to obtain Tibetan-Chinese comparable corpora: (1) Extending Tibetan-Chinese dictionary by extracting Tibetan-Chinese entities from Wikipedia. (2) Using topic model to make the texts mapped to the feature space of topics. (3) Calculating the similarity of two texts in different language according to the characteristics of the news text. The method for text similarity calculation based on LDA model reduces the dimensions of text space vector, and enhances the understanding of the text’s semantics. It also improves the speed and efficiency of calculation.
Let's stop trying to be "sexy" - preparing managers for the (big) data-driven business era

Let's stop trying to be 'sexy' – preparing managers for the (big) data-driven business era Kevin Daniel André Carillo, Article information: To cite this document: Kevin Daniel André Carillo, (2017) "Let's stop trying to be 'sexy' – preparing managers for the (big) data-driven business era", Business Process  Permanent link to this document: http://dx.doi.org/10.1108/BPMJ-09-2016-0188