nils-herrmann/clean_scirepeval huggingface dataset:
An Architectural Approach Towards the Future Internet of Things

Many of the initial developments towards the Internet of Things have focused on the combination of Auto-ID and networked infrastructures in businessto-business logistics and product life cycle applications. However, a future Internet of Things can provide a broader vision and also enable everyone to access and contribute rich information about things and locations. The success of social networks to share experience and personalised insights shows also great potential for integration with business-centric applications. The integration and interoperability with mainstream business software platforms can be enhanced and extended by real-time analytics, business intelligence and agent-based autonomous services. Information sharing may be rewarded through incentives, thus transforming the Internet of Things from a cost-focused experiment to a revenue-generating infrastructure to enable trading of enriched information and accelerate business innovation. Mash-ups and end-user programming will enable people to contribute to the Internet of Things with data, presentation and functionality. Things-generated physical world content and events from Auto-ID, sensors, actuators or meshed networks will be aggregated and combined with information from virtual worlds, such as business databases and Web 2.0 applications, and processed based on new business intelligence concepts. Direct action on the physical world will be supported through machine-interfaces and introduction of agile strategies. This chapter aims to provide a concept for a future architecture of the Internet of Things, including a definition, a review of developments, a list of key requirements and a technical design for possible implementation of the future Internet of Things. As open issues, the evaluation of usability by stakeholders in user-centric as well as business-centric scenarios is discussed and the need for quantifying costs and benefits for businesses, consumers, society and the environment is emphasised. Finally, guidelines are derived, for use by researchers as well as practitioners. Information Management, ETH Zurich, Switzerland 1 D. Uckelmann et al. (eds.), Architecting the Internet of Things, DOI 10.1007/978-3-642-19157-2_1,
Translating Learning into Numbers: A Generic Framework for Learning Analytics

With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples.
Graph based E-Government web service composition

Nowadays, e-government has emerged as a government policy to improve the quality and efficiency of public administrations. By exploiting the potential of new information and communication technologies, government agencies are providing a wide spectrum of online services. These services are composed of several web services that comply with well defined processes. One of the big challenges is the need to optimize the composition of the elementary web services. In this paper, we present a solution for optimizing the computation effort in web service composition. Our method is based on Graph Theory. We model the semantic relationship between the involved web services through a directed graph. Then, we compute all shortest paths using for the first time, an extended version of the FloydWarshall algorithm.
Miniaturized UWB Log-Periodic Square Fractal Antenna

In this letter, the log-periodic square fractal geometry is presented for the design of a miniaturized patch antenna for the ultra-wideband (UWB) services (3.1-10.6 GHz). A miniaturization factor of 23% is achieved with a constant and stable gain in the desired band. The radiation pattern is broadside, which finds suitable applications in the UWB radars and medical imaging. Furthermore, the time-domain performance of the proposed antenna is investigated. A prototype model of the proposed antenna is fabricated and measured as a proof of concept.
Path tracking Model Predictive Control of a Tilt-rotor UAV carrying a suspended load

This paper proposes a constrained Model Predictive Controller (MPC) to solve the path tracking problem of a Tilt-rotor UAV carrying a suspended load. Initially, a nonlinear model for the multi-body system is obtained via Euler-Lagrange formulation. Then, to design the constrained linear MPC, the equations of motion of the Tilt-rotor UAV with suspended load are linearized around the reference trajectory. Furthermore, to achieve null steady state error throughout the trajectory in presence of constant external disturbances and parametric uncertainties, the state vector is augmented with the integral action of some selected states. In addition, since the system is underactuated, the prediction model is also formulated in the incremental form to cope with disturbances affecting the system. Simulation results are carried out to corroborate the good performance of the proposed MPC when performing path tracking of the UAV with reduced load's swing.
A Bayesian Approach to Predict Performance of a Student (BAPPS): A Case with Ethiopian Students

The importance of accurate estimation of student’s future performance is essential in order to provide the student with adequate assistance in the learning process. To this end, this research aimed at investigating the use of Bayesian networks for predicting performance of a student, based on values of some identified attributes. We presented empirical experiments on the prediction of performance with a data set of high school students containing 8 attributes. The paper demonstrates an application of the Bayesian approach in the field of education and shows that the Bayesian network classifier has a potential to be used as a tool for prediction of student performance.
A Topic-based Reviewer Assignment System

Peer reviewing is a widely accepted mechanism for assessing the quality of submitted articles to scientific conferences or journals. Conference management systems (CMS) are used by conference organizers to invite appropriate reviewers and assign them to submitted papers. Typical CMS rely on paper bids entered by the reviewers and apply simple matching algorithms to compute the paper assignment. In this paper, we demonstrate our Reviewer Assignment System (RAS), which has advanced features compared to broadly used CMSs. First, RAS automatically extracts the profiles of reviewers and submissions in the form of topic vectors. These profiles can be used to automatically assign reviewers to papers without relying on a bidding process, which can be tedious and error-prone. Second, besides supporting classic assignment models (e.g., stable marriage and optimal assignment), RAS includes a recently published assignment model by our research group, which maximizes, for each paper, the coverage of its topics by the profiles of its reviewers. The features of the demonstration include (1) automatic extraction of paper and reviewer profiles, (2) assignment computation by different models, and (3) visualization of the results by different models, in order to assess their effectiveness.
Improved pulse detection from head motions using DCT

The heart pulsation sends out the blood throughout the body. The rate in which the heart performs this vital task, heartbeat rate, is of curial importance to the body. Therefore, measuring heartbeat rate, a.k.a. pulse detection, is very important in many applications, especially the medical ones. To measure it, physicians traditionally, either sense the pulsations of some blood vessels or install some sensors on the body. In either case, there is a need for a physical contact between the sensor and the body to obtain the heartbeat rate. This might not be always feasible, for example, for applications like remote patient monitoring. In such cases, contactless sensors, mostly based on computer vision techniques, are emerging as interesting alternatives. This paper proposes such a system, in which the heartbeats (pulses) are detected by subtle motions that appear on the face due to blood circulation. The proposed system has been tested in different facial expressions. The experimental results show that the proposed system is correct and robust and outperforms state-of-the-art.
Recovery of Interdependent Networks.

Recent network research has focused on the cascading failures in a system of interdependent networks and the necessary preconditions for system collapse. An important question that has not been addressed is how to repair a failing system before it suffers total breakdown. Here we introduce a recovery strategy for nodes and develop an analytic and numerical framework for studying the concurrent failure and recovery of a system of interdependent networks based on an efficient and practically reasonable strategy. Our strategy consists of repairing a fraction of failed nodes, with probability of recovery γ, that are neighbors of the largest connected component of each constituent network. We find that, for a given initial failure of a fraction 1 - p of nodes, there is a critical probability of recovery above which the cascade is halted and the system fully restores to its initial state and below which the system abruptly collapses. As a consequence we find in the plane γ - p of the phase diagram three distinct phases. A phase in which the system never collapses without being restored, another phase in which the recovery strategy avoids the breakdown, and a phase in which even the repairing process cannot prevent system collapse.
Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage

Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports. We observe that when the type set spans several domains, detection of entity mention becomes a limitation for supervised learning models. The primary reason being lack of dataset where entity boundaries are properly annotated while covering a large spectrum of entity types. Our work directly addresses this issue. We propose Heuristics Allied with Distant Supervision (HAnDS) framework to automatically construct a quality dataset suitable for the FgER task. HAnDS framework exploits the high interlink among Wikipedia and Freebase in a pipelined manner, reducing annotation errors introduced by naively using distant supervision approach. Using HAnDS framework, we create two datasets, one suitable for building FgER systems recognizing up to 118 entity types based on the FIGER type hierarchy and another for up to 1115 entity types based on the TypeNet hierarchy. Our extensive empirical experimentation warrants the quality of the generated datasets. Along with this, we also provide a manually annotated dataset for benchmarking FgER systems.
Automated Human Behavior Prediction through Handwriting Analysis

Handwriting Analysis or Graphology is a scientific method of identifying, evaluating and understanding personality through the strokes and patterns revealed by handwriting. Handwriting reveals the true personality including emotional outlay, fears, honesty, defenses and many others. Professional handwriting examiners called graphologist often identify the writer with a piece of handwriting. Accuracy of handwriting analysis depends on how skilled the analyst is. Although human intervention in handwriting analysis has been effective, it is costly and prone to fatigue. Hence the proposed methodology focuses on developing a tool for behavioral analysis which can predict the personality traits automatically with the aid of a computer without the human intervention. In this paper a method has been proposed to predict the personality of a person from the baseline, the pen pressure, the letter‘t’, the lower loop of letter ‘y’ and the slant of the writing as found in an individual’s handwriting. These parameters are the inputs to a Rule-Base which outputs the personality trait of the writer.
Community Detection in Temporal Networks

Many complex systems in nature, society and technologyfrom the online social networks to the internet from the nervous system to power gridscan be represented as a graph of vertices interconnected by edges. Small world network, Scale free network, Community detection are fundamental properties of complex networks. Community is a sub graph with densely connected nodes typically reveals topological relations, network structure, organizational and functional characteristics of the underlying network, e.g., friendships on Facebook, Followers of a VIP account on Twitter, and interaction with business professionals on LinkedIn. Online social networks are dynamic in nature, they evolve in size and space rapidly. In this paper we are detecting incremental disjoint communities using dyanamic multi label propagation algorithm. It tackles the temporal event changes, i.e., addition, deletion of an edge or vertex in a sub graph for every timestamp. The experimental results on Enron real network dataset shows that the proposed method is quite well in identifying communities.
Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving

Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develop robust sensors and algorithms, tests with current sensors in defined weather conditions are crucial for determining the impact of bad weather for each sensor. This work describes a testing and evaluation methodology that helps to benchmark novel sensor technologies and compare them to state-of-the-art sensors. As an example, gated imaging is compared to standard imaging under foggy conditions. It is shown that gated imaging outperforms state-of-the-art standard passive imaging due to time-synchronized active illumination.
Performance Analysis of IEEE 802.11ac DCF with Hidden Nodes

Recently, the IEEE 802.11 standard based Wireless Local Area Networks (WLAN) have become more popular and are widely deployed. It is anticipated that WLAN will play an important rule in the future wireless communication systems in order to provide several gigabits data rate. IEEE 802.11ac is one of the ongoing WLAN standard aiming to support very high throughput (VHT) with data rate of up to 6 Gbps below the 6 GHz band. In the development of IEEE 802.11ac standard, several new physical layer (PHY) and medium access control layer (MAC) features are taken into consideration, such as employing wider bandwidth in PHY and incrementing the limits of frame aggregation in MAC. However, due to the newly introduced features, some traditional techniques used in previous standards could face some problems. This paper presents a performance analysis of 802.11ac Distributed Coordination Function (DCF) in presence of hidden nodes in overlapping BSS (OBSS) environment. The effectiveness of DCF in IEEE 802.11ac WLAN when using different primary channels and different frequency bandwidth has also been discussed. Our results indicate that the traditional RTS/CTS handshake mechanism faces shortcomings and needs to be modified in order to support the newly defined 802.11ac amendment.
Using Data to Understand How to Better Design Adaptive Learning

There is much enthusiasm in higher education about the benefits of adaptive learning and using big data to investigate learning processes to make data-informed educational decisions. The benefits of adaptive learning to achieve personalized learning are obvious. Yet, there lacks evidence-based research to understand how data such as user behavior patterns can be used to design effective adaptive learning systems. The purpose of this study, therefore, is to investigate what behavior patterns learners with different characteristics demonstrate when they interact with an adaptive learning environment. Incoming 1st-year students in a pharmacy professional degree program engaged in an adaptive learning intervention that aimed to provide remedial instruction to better prepare these professional students before they began their formal degree program. We analyzed the participants’ behavior patterns through the usage data to understand how they used the adaptive system based upon their needs and interests. Using both statistical analyses and data visualization techniques, this study found: (1) apart from learners’ cognitive ability, it is important to consider affective factors such as motivation in adaptive learning, (2) lack of alignment among various components in an adaptive system can impact how learners accessed the system and, more importantly, their performance, and (3) visualizations can reveal interesting findings that can be missed otherwise. Such research should provide much needed empirical evidences and useful insights about how the analytics can inform the effective designs of adaptive learning.
Reusing stanford POS tagger for tagging Urdu sentences

Several Natural Language Processing applications in a particular language consider POS tagging a necessary component. To develop a new language specific POS tagger targeting such particular language is a tedious job for unstructured data due to the variation in text, type and complexity of text. For that reason, it impacts the precision of tagging as a result of the variety of a certain language. Current research focused on the thought of reusability of a popular language specific Part of speech tagger, for example, Stanford Part of speech Tagger can be employed for tagging non-Engish phrases. For generalizeability, any translator can be used to translate the sentences, however, a well-known translator, named “Google translator” is considered for sentence translation purpose across the languages. For evaluation perspective, Urdu tweets of a hot political issue “Panama leaks” from twitter.com are extracted. To measure the accuracy, the kappa statistic along with confusion matrix is deliberated. The precision of tagging the Urdu sentences by reusing Stanford Part of speech tagger is 96.05 percent. The respected approach can be globally applied to tagging the sentences of several different languages.
Reproducible network experiments using container-based emulation

In an ideal world, all research papers would be runnable: simply click to replicate all results, using the same setup as the authors. One approach to enable runnable network systems papers is Container-Based Emulation (CBE), where an environment of virtual hosts, switches, and links runs on a modern multicore server, using real application and kernel code with software-emulated network elements. CBE combines many of the best features of software simulators and hardware testbeds, but its performance fidelity is unproven.
 In this paper, we put CBE to the test, using our prototype, Mininet-HiFi, to reproduce key results from published network experiments such as DCTCP, Hedera, and router buffer sizing. We report lessons learned from a graduate networking class at Stanford, where 37 students used our platform to replicate 18 published results of their own choosing. Our experiences suggest that CBE makes research results easier to reproduce and build upon.
Novel Bow-tie–AMC Combination for 5.8-GHz RFID Tags Usable With Metallic Objects

A novel combination of coplanar waveguide (CPW)-fed double bow-tie slot antenna and artificial magnetic conductor (AMC), which meets the requirements of a 5.8-GHz SHF RFID tag antenna usable on metallic objects, is presented. The manufactured prototype is characterized in terms of return loss, gain, and radiation pattern measurements in an anechoic chamber, both alone and on a metallic plate.
Rapid solution of problems by nuclear-magnetic resonance quantum computation

We offer an improved method for using a nuclear-magnetic-resonance quantum computer (NMRQC) to solve the Deutsch-Jozsa problem. Two known obstacles to the application of the NMRQC are exponential diminishment of density-matrix elements with the number of bits, threatening weak signal levels, and the high cost of preparing a suitable starting state. A third obstacle is a heretofore unnoticed restriction on measurement operators available for use by an NMRQC. Variations on the function classes of the Deutsch-Jozsa problem are introduced, both to extend the range of problems advantageous for quantum computation and to escape all three obstacles to use of an NMRQC. By adapting it to one such function class, the Deutsch-Jozsa problem is made solvable without exponential loss of signal. The method involves an extra work bit and a polynomially more involved Oracle; it uses the thermal-equilibrium density matrix systematically for an arbitrary number of spins, thereby avoiding both the preparation of a pseudopure state and temporal averaging.
A stable-isotope dilution GC-MS approach for the analysis of DFRC (derivatization followed by reductive cleavage) monomers from low-lignin plant materials.

The derivatization followed by reductive cleavage (DFRC) method is a well-established tool to characterize the lignin composition of plant materials. However, the application of the original procedure, especially the chromatographic determination of the DFRC monomers, is problematic for low-lignin foods. To overcome these problems a modified sample cleanup and a stable-isotope dilution approach were developed and validated. To quantitate the diacetylated DFRC monomers, their corresponding hexadeuterated analogs were synthesized and used as internal standards. By using the selected-ion monitoring mode, matrix-associated interferences can be minimized resulting in higher selectivity and sensitivity. The modified method was applied to four low-lignin samples. Lignin from carrot fibers was classified as guaiacyl-rich whereas the lignins from radish, pear, and asparagus fibers where classified as balanced lignins (guaiacyl/syringyl ratio=1-2).
Synthesis of graphene-based nanosheets via chemical reduction of exfoliated graphite oxide

Reduction of a colloidal suspension of exfoliated graphene oxide sheets in water with hydrazine hydrate results in their aggregation and subsequent formation of a high-surface-area carbon material which consists of thin graphene-based sheets. The reduced material was characterized by elemental analysis, thermo-gravimetric analysis, scanning electron microscopy, X-ray photoelectron spectroscopy, NMR spectroscopy, Raman spectroscopy, and by electrical conductivity measurements.