nils-herrmann/clean_scirepeval huggingface dataset:
Software Defined Unified Device Management for Smart Environments

Applications such as street lights, parking meters, traffic light and congestion sensors, safety cameras, air quality sensors, weather sensors, remote personal healthcare monitors, and acoustic detection define the new world of &quot;Smart Environments&quot; that we live in. The technologies driving these applications are customized to the environment in which they run. The backhauling infrastructure to support each application is as diverse as the application itself. An abstraction which hides the complexities of such disparate systems presenting a unified view is desired. Integrated management platform aids in comprehensive management of the infrastructure. &quot;Smartness&quot; also means it is ready to use by just plug and play. A plug-in based implementation supports multiple vendor devices while a unified northbound controller ensures minimal vendor lock for the applications running on the top.
An airborne experimental test platform: From theory to flight

This paper provides an overview of the experimental flight test platform developed by the University of Minnesota Unmanned Aerial Vehicle Research Group. Key components of the current infrastructure are highlighted, including the flight test system, high-fidelity nonlinear simulations, software-and hardware-in-the-loop simulations, and the real-time flight software. Recent flight control research and educational applications are described to showcase the advanced capabilities of the platform. A view towards future expansion of the platform is given in the context of upcoming research projects.
Machine learning aided cognitive RAT selection for 5G heterogeneous networks

The starring role of the Heterogeneous Networks (HetNet) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current user association (cell selection) mechanisms used in cellular networks. The max-SINR algorithm, although historically effective for performing this function, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs that optimize the association and routing decisions in the context of single-RAT and multi-RAT connections, respectively. This paper proposes a framework under these guidelines that relies on Machine Learning techniques at the terminal device level for Cognitive RAT Selection and presents simulation results to suppport it.
Hierarchical Tree Long Short-Term Memory for Sentence Representations

A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they can’t capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and longterm dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.
A Compact Ka-Band Planar Three-Way Power Divider

A Ka-band planar three-way power divider which uses the coupled line instead of the transmission line is proposed to reduce chip size. The proposed planar topology, different from the conventional Wilkinson power divider, is analyzed and can provide not only compact but also dc block characteristics, which are very suitable for monolithic microwave integrated circuit applications. The divider implemented by a pHEMT process shows an insertion loss less than 5.1 dB and an output isolation better than 17 dB. A return loss less than 18 dB and a phase difference of 4.2deg at 30 GHz can be achieved. Finally, good agreements between the simulation and experimental results are shown.
A framework for designing closed domain virtual assistants

0957-4174/$ see front matter 2011  A doi:10.1016/j.eswa.2011.08.177 ⇑ Corresponding author.  +34 958 246163; fax: +34 958 243317. E-mail addresses: eisman@decsai.ugr.es (E.M. E (V. López), castro@decsai.ugr.es (J.L. Castro). Since its beginning in 1969, the Internet has grown rapidly, especially over the past few years. Companies and organizations store more and more information about themselves on the Internet. Sometimes, that information is not well organized. Other times, the huge volume of available data makes useful information difficult to be found. The result is that users have to waste their time looking for what they want to know using the traditional menu-driven navigation and keyword search that websites provide. This is a critical issue because it decreases users interest about companies. In order to avoid this problem, in this paper we propose a framework for designing virtual assistants, which are, considering first results, an ideal alternative to help users find, not only the information that they are looking for, but also some related information which could be of the highest interest.
Retaining ligaments of the face: review of anatomy and clinical applications.

The retaining ligaments of the face are important in understanding concepts of facial aging and rejuvenation. They are located in constant anatomic locations where they separate facial spaces and compartments. Their superficial extensions form subcutaneous septa that separate facial fat compartments. Their main significance relates to their surgical release in order to achieve the desired aesthetic outcome. Furthermore, they have a sentinel role in their anatomic relationship to facial nerve branches. When performing facial aesthetic surgery, plastic surgeons should select a plane of dissection, release the appropriate ligaments depending on the desired aesthetic goals, and avoid nerve injury by using the ligaments as anatomic landmarks. Descriptions of the retaining ligaments are variable in the literature; due to different interpretations of anatomy, several classifications, locations, and nomenclature systems have been proposed. This article will review and clarify the anatomy of the retaining ligaments of the face, including the cheek, mandible, temporal, and periorbital areas.
RecPlay: A Fully Integrated Practical Record/Replay System

This article presents a practical solution for the cyclic debugging of nondeterministic parallel programs. The solution consists of a combination of record/replay with automatic on-the-fly data race detection. This combination enables us to limit the record phase to the more efficient recording of the synchronization operations, while deferring the time-consuming data race detection to the replay phase. As the record phase is highly efficient, there is no need to switch it off, hereby eliminating the possibility of Heisenbugs because tracing can be left on all the time. This article describes an implementation of the tools needed to support RecPlay.
Anomaly Detection Algorithms in Business Process Logs

In some domains of application, like software development and health care processes, a normative business process system (e.g. workflow management system) is not appropriate because a flexible support is needed to the participants. On the other hand, while it is important to support flexibility of execution in these domains, security requirements can not be met whether these systems do not offer extra control, which characterizes a trade off between flexibility and security in such domains. This work presents and assesses a set of anomaly detection algorithms in logs of Process Aware Systems (PAS). The detection of an anomalous instance is based on the “noise” which an instance makes in a process model discovered by a process mining algorithm. As a result, a trace that is an anomaly for a discovered model will require more structural changes for this model fit it than a trace that is not an anomaly. Hence, when aggregated to PAS, these methods can support the coexistence of security and flexibility.
A Neural Model for User Geolocation and Lexical Dialectology

We propose a simple yet effective textbased user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.
Flowchart recognition for non-textual information retrieval in patent search

Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset.
Emerging Trends in Smart Home Security, Privacy, and Digital Forensics

Technology integration is becoming an impetus to everyday lives. This new interconnected world can be found from our most private spaces to the public ones. Smart homes, which is the use of Internet of Things (IoT) within a home, has become the utmost concern in the security and privacy domain. In this paper, we review the current literature from both research and practice and offer five research trends for security and privacy in smart homes: potential for remote security breaches, risks for smart home devices, privacy violations, infrastructure vulnerabilities, and digital forensics challenges. We integrated these five trends in a conceptual model showcasing their roles in the rapidly changing IoT landscape. Combining the trends and smart forensics, we elucidate the bimodal lenses in security and privacy: preventive and investigative. These discussions offer research directions and practitioners’ implications for further development of a safe and secured smart home.
A Nonisolated Single-Phase UPS Topology With 110-V/220-V Input–Output Voltage Ratings

A circuit configuration of a single-phase nonisolated online uninterruptible power supply (UPS) with 110-V/220-V input–output voltage ratings is proposed, allowing the bypass operation without a transformer even if the input voltage is different from the output voltage. The converter consists of an ac–dc/dc–dc three-level boost converter combined with a double half-bridge inverter. In this type of configuration size, cost and efficiency are improved due to the reduced number of switches and batteries, and also, no low-frequency isolation transformer is required to realize bypass operation because of the common neutral connection. Both stages of the proposed circuit operate at high frequency by using a passive nondissipative snubber circuit in the boost converter and insulated-gate bipolar-transistor switches in the double half-bridge inverter, with low conduction losses, low tail current, and low switching losses. Principle of operation and experimental results for a 2.6-kVA prototype are presented to demonstrate the UPS performance.
A Power Consumption Benchmark for Reasoners on Mobile Devices

We introduce a new methodology for benchmarking the performance per watt of semantic web reasoners and rule engines on smartphones to provide developers with information critical for deploying semantic web tools on power-constrained devices. We validate our methodology by applying it to three well-known reasoners and rule engines answering queries on two ontologies with expressivities in RDFS and OWL DL. While this validation was conducted on smartphones running Googles Android operating system, our methodology is general and may be applied to different hardware platforms, reasoners, ontologies, and entire applications to determine performance relevant to power consumption. We discuss the implications of our findings for balancing tradeoffs of local computation versus communication costs for semantic technologies on mobile platforms, sensor networks, the Internet of Things, and other power-constrained environments.
Anno : A Graphical Tool for the Transcription and Onthe-Fly Annotation of Handwritten Documents

We present Transc&Anno, a web-based collaboration tool allowing the transcription of text images and their shallow on-the-fly annotation. Transc&Anno was originally developed in order to address the needs of learner corpora research so as to facilitate digitisation of handwritten learner essays. However, the tool can be used for the creation of any type of corpora requiring transcription and shallow on-the-fly annotation resulting in inline XML. Transc&Anno provides an intuitive environment that is explicitly designed to facilitate the transcription and annotation process for linguists. Transc&Anno ensures a high transcription output quality by validating the XML and only allowing predefined tags. It was created on top of the FromThePage transcription tool developed entirely with standard web technologies – Ruby on Rails, Javascript, HTML, and CSS. We adapted this open-source web-based tool to linguistic research purposes by adding linguistic annotation functionalities to it. Thereby we united the convenience of a collaborative transcription tool with its advanced image visualisation, centralised data storage, version control and inter-collaborator communication facilities with the precision of a linguistic annotation tool with its well-developed tag definition possibilities, easy tagging process and tagged-text visualisation. Transc&Anno is easily customisable, open source, and available on Github.
A demonstration of the GUINNESS: A GUI based neural NEtwork SyntheSizer for an FPGA

The GUINNESS is a tool flow for the deep neural network toward FPGA implementation [3,4,5] based on the GUI (Graphical User Interface) including both the binarized deep neural network training on GPUs and the inference on an FPGA. It generates the trained the Binarized deep neural network [2] on the desktop PC, then, it generates the bitstream by using standard the FPGA CAD tool flow. All the operation is done on the GUI, thus, the designer is not necessary to write any scripts to descript the neural network structure, training behaviour, only specify the values for hyper parameters. After finished the training, it automatically generates C++ codes to synthesis the bitstream using the Xilinx SDSoC system design tool flow. Thus, our tool flow is suitable for the software programmers who are not familiar with the FPGA design.
Two decades of terror management theory: a meta-analysis of mortality salience research.

A meta-analysis was conducted on empirical trials investigating the mortality salience (MS) hypothesis of terror management theory (TMT). TMT postulates that investment in cultural worldviews and self-esteem serves to buffer the potential for death anxiety; the MS hypothesis states that, as a consequence, accessibility of death-related thought (MS) should instigate increased worldview and self-esteem defense and striving. Overall, 164 articles with 277 experiments were included. MS yielded moderate effects (r = .35) on a range of worldview- and self-esteem-related dependent variables (DVs), with effects increased for experiments using (a) American participants, (b) college students, (c) a longer delay between MS and the DV, and (d) people-related attitudes as the DV. Gender and self-esteem may moderate MS effects differently than previously thought. Results are compared to other reviews and examined with regard to alternative explanations of TMT. Finally, suggestions for future research are offered.
Intelligent Parking Management System Based on Image Processing

This paper aims to present an intelligent system for parking space detection based on image processing technique. The proposed system captures and processes the rounded image drawn at parking lot and produces the information of the empty car parking spaces. In this work, a camera is used as a sensor to take photos to show the occupancy of car parks. The reason why a camera is used is because with an image it can detect the presence of many cars at once. Also, the camera can be easily moved to detect different car parking lots. By having this image, the particular car parks vacant can be known and then the processed information was used to guide a driver to an available car park rather than wasting time to find one. The proposed system has been developed in both software and hardware platform. An automatic parking system is used to make the whole process of parking cars more efficient and less complex for both drivers and administrators.
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions

One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. To train a method to perform even one of these operations accurately from {image, question, answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best. Our method thus learns how to exploit a set of external off-the-shelf algorithms to achieve its goal, an approach that has something in common with the Neural Turing Machine [10]. The core of our proposed method is a new co-attention model. In addition, the proposed approach generates human-readable reasons for its decision, and can still be trained end-to-end without ground truth reasons being given. We demonstrate the effectiveness on two publicly available datasets, Visual Genome and VQA, and show that it produces the state-of-the-art results in both cases.
Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications

Fifth Generation (5G) wireless systems are expected to connect an avalanche of "smart" objects disseminated from the largest "Smart City" to the smallest "Smart Home". In this vision, Long Term Evolution-Advanced (LTE-A) is deemed to play a fundamental role in the Internet of Things (IoT) arena providing a large coherent infrastructure and a wide wireless connectivity to the devices. However, since LTE-A was originally designed to support high data rates and large data size, novel solutions are required to enable an efficient use of radio resources to convey small data packets typically exchanged by IoT applications in "smart" environments. On the other hand, the typically high energy consumption required by cellular communications is a serious obstacle to large scale IoT deployments under cellular connectivity as in the case of Smart City scenarios. Network-assisted Device-to-Device (D2D) communications are considered as a viable solution to reduce the energy consumption for the devices. The particular approach presented in this paper consists in appointing one of the IoT smart devices as a collector of all data from a cluster of objects using D2D links, thus acting as an aggregator toward the eNodeB. By smartly adapting the Modulation and Coding Scheme (MCS) on the communication links, we will show it is possible to maximize the radio resource utilization as a function of the total amount of data to be sent. A further benefit that we will highlight is the possibility to reduce the transmission power when a more robust MCS is adopted. A comprehensive performance evaluation in a wide set of scenarios will testify the achievable gains in terms of energy efficiency and resource utilization in the envisaged D2D-based IoT data collection.
1 Integration of Data Mining Clustering Approach with 2 the Personalized E-Learning System 3

Educational data-mining is an evolving discipline that focuses on the improvement of 10 self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of 11 educational data. In the arena of education, the heterogeneous data is involved and continuously 12 growing in the paradigm of big-data. To extract meaningful information adaptively from big 13 educational data, some specific data mining techniques are needed. This paper presents a 14 clustering approach to partition students into different groups or clusters based on their learning 15 behavior. Furthermore, personalized e-learning system architecture is also presented which detects 16 and responds teaching contents according to the students’ learning capabilities. The primary 17 objective includes the discovery of optimal settings, in which learners can improve their learning 18 capabilities. Moreover, the administration can find essential hidden patterns to bring the effective 19 reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based 20 Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and 21 Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP − HD) are 22 analyzed using educational data mining. It is observed that more robust results can be achieved by 23 the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally 24 effective to analyze the big data to make education systems vigorous.
Diffusion-on-Manifold Aggregation of Local Features for Shape-based 3D Model Retrieval

Aggregating a set of local features has become one of the most common approaches for representing a multi-media data such as 2D image and 3D model. The success of Bag-of-Features (BF) aggregation  prompted several extensions to BF, that are, VLAD , Fisher Vector (FV) coding [22] and Super Vector (SV) coding [34]. They all learn small number of codewords, or representative local features, by clustering a set of large number of local features. The set of local features extracted from a media data (e.g., an image) is encoded by considering distribution of features around the codewords; BF uses frequency, VLAD and FV uses displacement vector, and SV uses a combination of both. In doing so, these encoding algorithms assume linearity of feature space about a codeword. Consequently, even if the set of features form a non-linear manifold, its non-linearity would be ignored, potentially degrading quality of aggregated features. In this paper, we propose a novel feature aggregation algorithm called Diffusion-on-Manifold (DM) that tries to take into account, via diffusion distance, structure of non-linear manifold formed by the set of local features. In view of 3D shape retrieval, we also propose a local 3D shape feature defined for oriented point set. Experiments using shape-based 3D model retrieval scenario show that the DM aggregation results in better retrieval accuracy than the existing aggregation algorithms we've compared against, that are, VLAD, FV, and SV, etc..