nils-herrmann/clean_scirepeval huggingface dataset:
Towards gateway to Cloud offloading in IoT publish/subscribe systems

It is not uncommon today that sensor devices connected to the Internet solely send their data to Cloud-based servers for storage and processing. This does not only mean clients requesting data have to contact the Cloud-based service, even if the data is available in the local network, but also that data is sent to external services with unknown or ambiguous privacy policies. The great potential in using closer to the edge fog computing instead of Cloud computing to both enable faster and more privacy-aware processing locally has been recognized in the research community. In particular, on premise smart gateways can provide local low-latency storage and processing capabilities that are controlled locally and can be trusted. In this work, we outline a combined fog Cloud system that automatically selects a suitable execution location for processing tasks. We emphasize on the design challenges of such a system and further demonstrate a solution for the interplay between Fog and Cloud by showing how processing task can be migrated from one system to another on the fly without service interruption.
Brushstroke rendering algorithm for a painting robot

A painting robot ARTCYBE is a machine developed to imitate an ability of a human painter to create realistic images with acrylic paints and a brush. In this paper a brushstroke rendering algorithm for this robot is presented. The algorithm converts a source bitmap image into a set of brushstrokes which we call a brushstroke map. The brushstroke map can be unambiguously transformed into a list of commands driving the painting robot. Simulated paintings obtained by proposed algorithm are represented. Advantages of the described algorithm in comparison to other known brushstroke rendering algorithms are discussed.
Impacts of Bt transgenic cotton on integrated pest management.

Transgenic cotton that produced one or more insecticidal proteins of Bacillus thuringiensis (Bt) was planted on over 15 million hectares in 11 countries in 2009 and has contributed to a reduction of over 140 million kilograms of insecticide active ingredient between 1996 and 2008. As a highly selective form of host plant resistance, Bt cotton effectively controls a number of key lepidopteran pests and has become a cornerstone in overall integrated pest management (IPM). Bt cotton has led to large reductions in the abundance of targeted pests and benefited non-Bt cotton adopters and even producers of other crops affected by polyphagous target pests. Reductions in insecticide use have enhanced biological control, which has contributed to significant suppression of other key and sporadic pests in cotton. Although reductions in insecticide use in some regions have elevated the importance of several pest groups, most of these emerging problems can be effectively solved through an IPM approach.
The Mirai botnet and the IoT Zombie Armies

The rapidly growing presence of Internet of Things (IoT) devices is becoming a continuously alluring playground for malicious actors who try to harness their vast numbers and diverse locations. One of their primary goals is to assemble botnets that can serve their nefarious purposes, ranging from Denial of Service (DoS) to spam and advertisement fraud. The most recent example that highlights the severity of the problem is the Mirai family of malware, which is accountable for a plethora of massive DDoS attacks of unprecedented volume and diversity. The aim of this paper is to offer a comprehensive state-of-the-art review of the IoT botnet landscape and the underlying reasons of its success with a particular focus on Mirai and major similar worms. To this end, we provide extensive details on the internal workings of IoT malware, examine their interrelationships, and elaborate on the possible strategies for defending against them.
A Smart-Home System to Unobtrusively and Continuously Assess Loneliness in Older Adults

Loneliness is a common condition in older adults and is associated with increased morbidity and mortality, decreased sleep quality, and increased risk of cognitive decline. Assessing loneliness in older adults is challenging due to the negative desirability biases associated with being lonely. Thus, it is necessary to develop more objective techniques to assess loneliness in older adults. In this paper, we describe a system to measure loneliness by assessing in-home behavior using wireless motion and contact sensors, phone monitors, and computer software as well as algorithms developed to assess key behaviors of interest. We then present results showing the accuracy of the system in detecting loneliness in a longitudinal study of 16 older adults who agreed to have the sensor platform installed in their own homes for up to 8 months. We show that loneliness is significantly associated with both time out-of-home (β = -0.88 andp <; 0.01) and number of computer sessions (β = 0.78 and p <; 0.05). R2 for the model was 0.35. We also show the model's ability to predict out-of-sample loneliness, demonstrating that the correlation between true loneliness and predicted out-of-sample loneliness is 0.48. When compared with the University of California at Los Angeles loneliness score, the normalized mean absolute error of the predicted loneliness scores was 0.81 and the normalized root mean squared error was 0.91. These results represent first steps toward an unobtrusive, objective method for the prediction of loneliness among older adults, and mark the first time multiple objective behavioral measures that have been related to this key health outcome.
The impacts of climate change and human activities on biogeochemical cycles on the Qinghai-Tibetan Plateau.

With a pace of about twice the observed rate of global warming, the temperature on the Qinghai-Tibetan Plateau (Earth's 'third pole') has increased by 0.2 °C per decade over the past 50 years, which results in significant permafrost thawing and glacier retreat. Our review suggested that warming enhanced net primary production and soil respiration, decreased methane (CH(4)) emissions from wetlands and increased CH(4) consumption of meadows, but might increase CH(4) emissions from lakes. Warming-induced permafrost thawing and glaciers melting would also result in substantial emission of old carbon dioxide (CO(2)) and CH(4). Nitrous oxide (N(2)O) emission was not stimulated by warming itself, but might be slightly enhanced by wetting. However, there are many uncertainties in such biogeochemical cycles under climate change. Human activities (e.g. grazing, land cover changes) further modified the biogeochemical cycles and amplified such uncertainties on the plateau. If the projected warming and wetting continues, the future biogeochemical cycles will be more complicated. So facing research in this field is an ongoing challenge of integrating field observations with process-based ecosystem models to predict the impacts of future climate change and human activities at various temporal and spatial scales. To reduce the uncertainties and to improve the precision of the predictions of the impacts of climate change and human activities on biogeochemical cycles, efforts should focus on conducting more field observation studies, integrating data within improved models, and developing new knowledge about coupling among carbon, nitrogen, and phosphorus biogeochemical cycles as well as about the role of microbes in these cycles.
Design, modelling and control of a Single Rotor UAV

In this article, a novel Vertical Take-Off and Landing (VTOL) Single Rotor Unmanned Aerial Vehicle (SR-UAV) will be presented. The SRUAV's design properties will be analysed in detail, with respect to technical novelties outlining the merits of such a conceptual approach. The system's model will be mathematically formulated, while a cascaded P-PI and PID-based control structure will be utilized in extensive simulation trials for the preliminary evaluation of the SR-UAV's attitude and translational performance.
Toward automating a human behavioral coding system for married couples' interactions using speech acoustic features

Observational methods are fundamental to the study of human behavior in the behavioral sciences. For example, in the context of research on intimate relationships, psychologists’ hypotheses are often empirically tested by video recording interactions of couples and manually coding relevant behaviors using standardized coding systems. This coding process can be time-consuming, and the resulting coded data may have a high degree of variability because of a number of factors (e.g., inter-evaluator differences). These challenges provide an opportunity to employ engineering methods to aid in automatically coding human behavioral data. In this work, we analyzed a large corpus of married couples’ problem-solving interactions. Each spouse was manually coded with multiple session-level behavioral observations (e.g., level of blame toward other spouse), and we used acoustic speech features to automatically classify extreme instances for six selected codes (e.g., “low” vs. “high” blame). Specifically, we extracted prosodic, spectral, and voice quality features to capture global acoustic properties for each spouse and trained gender-specific and gender-independent classifiers. The best overall automatic system correctly classified 74.1% of the instances, an improvement of 3.95% absolute (5.63% relative) over our previously reported best results. We compare performance for the various factors: across codes, gender, classifier type, and feature type.
LPCNet: Improving Neural Speech Synthesis Through Linear Prediction

Neural speech synthesis models have recently demonstrated the ability to synthesize high quality speech for text-to-speech and compression applications. These new models often require powerful GPUs to achieve real-time operation, so being able to reduce their complexity would open the way for many new applications. We propose LPCNet, a WaveRNN variant that combines linear prediction with recurrent neural networks to significantly improve the efficiency of speech synthesis. We demonstrate that LPCNet can achieve significantly higher quality than WaveRNN for the same network size and that high quality LPCNet speech synthesis is achievable with a complexity under 3 GFLOPS. This makes it easier to deploy neural synthesis applications on lower-power devices, such as embedded systems and mobile phones.
Legal Document Retrieval using Document Vector Embeddings and Deep Learning

Domain specific information retrieval process has been a prominent and ongoing research in the field of natural language processing. Many researchers have incorporated different techniques to overcome the technical and domain specificity and provide a mature model for various domains of interest. The main bottleneck in these studies is the heavy coupling of domain experts, that makes the entire process to be time consuming and cumbersome. In this study, we have developed three novel models which are compared against a golden standard generated via the on line repositories provided, specifically for the legal domain. The three different models incorporated vector space representations of the legal domain, where document vector generation was done in two different mechanisms and as an ensemble of the above two. This study contains the research being carried out in the process of representing legal case documents into different vector spaces, whilst incorporating semantic word measures and natural language processing techniques. The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semantic similarity measures into the information retrieval process. This study also shows, the impact of varying distribution of the word similarity measures, against varying document vector dimensions, which can lead to improvements in the process of legal information retrieval.  Document Embedding, Deep Learning, Information Retrieval
Design and Modeling of a Six-Degree-of-Freedom Magnetically Levitated Positioner Using Square Coils and 1-D Halbach Arrays

This paper presents a novel design of six-degree-of-freedom (6-DOF) magnetically levitated (maglev) positioner, where its translator and stator are implemented by four groups of 1-D Halbach permanent-magnet (PM) arrays and a set of square coils, respectively. By controlling the eight-phase square coil array underneath the Halbach PM arrays, the translator can achieve 6-DOF motion. The merits of the proposed design are mainly threefold. First, this design is potential to deliver unlimited-stroke planar motion with high power efficiency if additional coil switching system is equipped. Second, multiple translators are allowed to operate simultaneously above the same square coil stator. Third, the proposed maglev system is less complex in regard to the commutation law and the phase number of coils. Furthermore, in this paper, an analytical modeling approach is established to accurately predict the Lorentz force generated by the square coil with the 1-D Halbach PM array by considering the corner region, and the proposed modeling approach can be extended easily to apply on other coil designs such as the circular coil, etc. The proposed force model is evaluated experimentally, and the results show that the approach is accurate in both single- and multiple-coil cases. Finally, a prototype of the proposed maglev positioner is fabricated to demonstrate its 6-DOF motion ability. Experimental results show that the root-mean-square error of the implemented maglev prototype is around 50 nm in planar motion, and its velocity can achieve up to 100 mm/s.
Improvement of Random Forest Classifier through Localization of Persian Handwritten OCR

The random forest (RF) classifier is an ensemble classifier derived from decision tree idea. However the parallel operations of several classifiers along with use of randomness in sample and feature selection has made the random forest a very strong classifier with accuracy rates comparable to most of currently used classifiers. Although, the use of random forest on handwritten digits has been considered before, in this paper RF is applied in recognizing Persian handwritten characters. Trying to improve the recognition rate, we suggest converting the structure of decision trees from a binary tree to a multi branch tree. The improvement gained this way proves the applicability of the idea.
Precision Landing System on H-Octocopter Drone Using Complementary Filter

An automatic landing system is required on a long-range drone because the position of the vehicle cannot be reached visually by the pilot. The autopilot system must be able to correct the drone movement dynamically in accordance with its flying altitude. The current article describes autopilot system on an H-Octocopter drone using image processing and complementary filter. This paper proposes a new approach to reduce oscillations during the landing phase on a big drone. The drone flies above 10 meters to a provided coordinate using GPS data, to check for the existence of the landing area. This process is done visually using the camera. PID controller is used to correct the movement by calculate error distance detected by camera. The controller also includes altitude parameters on its calculations through a complementary filter. The controller output is the PWM signals which control the movement and altitude of the vehicle. The signal then transferred to Flight Controller through serial communication, so that, the drone able to correct its movement. From the experiments, the accuracy is around 0.56 meters and it can be done in 18 seconds.
Institutionalizing enterprise resource planning in the Saudi steel industry: A punctuated socio-technical analysis

In this article, we analyze institutionalization as a process of transferring and stabilizing material artifacts and routines in the form of enterprise resource planning (ERP) systems. Although past studies have analyzed institutionalization as structuring around scripts or discourse moves, we emphasize the material role of artifacts and routines as carriers of institutional logics. In addition, insitutionalization is not linear and incremental, but goes through sudden, nonlinear disruptions. To this end, we apply punctuated socio-technical information system change (PSIC) model that draws upon Gersick’s model of change to identify and trace moves that are critical during the institutionalization. The model accounts for ERP institutionalization by chronicling complex interactions between socio-technical elements in the implementation system, the work system, and organizational and environmental context which together account for the institutionalization outcome. We use the model to analyze a longitudinal case covering 11 years (1993–2004) of ERP implementation processes in a large Saudi steel firm. Our analysis shows that the proposed material and punctuated lens toward institutionalization offers rich insights how and why ERP systems become institutions and why their institutionalization is difficult and unfolds in unpredictable ways. We conclude that the normally held assumptions of successful linear and incremental adaptation to new institutional patterns logics out by ERP systems do not hold. Journal of Information Technology (2009) 24, 286–304.
Supply chain network design under uncertainty: A comprehensive review and future research directions

Supply chain network design (SCND) is one of the most crucial planning problems in supply chain management (SCM). Nowadays, design decisions should be viable enough to function well under complex and uncertain business environments for many years or decades. Therefore, it is essential to make these decisions in the presence of uncertainty, as over the last two decades, a large number of relevant publications have emphasized its importance. The aim of this paper is to provide a comprehensive review of studies in the fields of SCND and reverse logistics network design under uncertainty. The paper is organized in two main parts to investigate the basic features of these studies. In the first part, planning decisions, network structure, paradigms and aspects related to SCM are discussed. In the second part, existing optimization techniques for dealing with uncertainty such as recourse-based stochastic programming, risk-averse stochastic programming, robust optimization, and fuzzy mathematical programming are explored in terms of mathematical modeling and solution approaches. Finally, the drawbacks and missing aspects of the related literature are highlighted and a list of potential issues for future research directions is recommended.
A Content-Based Recommendation Approach Using Semantic User Profile in E-recruitment

In this paper, we propose a content-based recommendation approach in the domain of e-recruitment to recommend users with job offers that suit the most their profile and learned preferences. In order to present the best offers, we construct a semantic vocabulary of the domain from the job offers corpus and initialize a profile for each user based on his Curriculum Vitae. Our method is enriching the user profiles using triggers and statistical methods following his actions regarding the job offers. The approach we propose presents to the users job offers that are the closest to their learned needs and interests which also can be updated based on his daily actions regarding these offers.
Improved robustness to adversarial examples using Lipschitz regularization of the loss

We augment adversarial training (AT) with worst case adversarial training (WCAT) which improves adversarial robustness by 11% over the current stateof-the-art result in the `2 norm on CIFAR-10. We obtain verifiable average case and worst case robustness guarantees, based on the expected and maximum values of the norm of the gradient of the loss. We interpret adversarial training as Total Variation Regularization, which is a fundamental tool in mathematical image processing, and WCAT as Lipschitz regularization.
Gaussian Process Optimization with Mutual Information

In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the cumulative regret for this generic algorithm improve by an exponential factor the previously known bounds for algorithms like GP-UCB. We also introduce the novel Gaussian Process Mutual Information algorithm (GP-MI), which significantly improves further these upper bounds for the cumulative regret. We confirm the efficiency of this algorithm on synthetic and real tasks against the natural competitor, GP-UCB, and also the Expected Improvement heuristic. Preprint for the 31st International Conference on Machine Learning (ICML 2014) 1 ar X iv :1 31 1. 48 25 v3 [ st at .M L ] 8 J un 2 01 5 Erratum After the publication of our article, we found an error in the proof of Lemma 1 which invalidates the main theorem. It appears that the information given to the algorithm is not sufficient for the main theorem to hold true. The theoretical guarantees would remain valid in a setting where the algorithm observes the instantaneous regret instead of noisy samples of the unknown function. We describe in this page the mistake and its consequences.  X → R be the unknown function to be optimized, which is a sample from a Gaussian process. Let’s fix x, x1, . . . , xT ∈ X and the observations yt = f(xt)+ t where the noise variables t are independent Gaussian noise N (0, σ). We define the instantaneous regret rt = f(x?)− f(xt) and, MT = T ∑
Adaptive Energy-Control for In-Memory Database Systems

The ever-increasing demand for scalable database systems is limited by their energy consumption, which is one of the major challenges in research today. While existing approaches mainly focused on transaction-oriented disk-based database systems, we are investigating and optimizing the energy consumption and performance of data-oriented scale-up in-memory database systems that make heavy use of the main power consumers, which are processors and main memory. We give an in-depth energy analysis of a current mainstream server system and show that modern processors provide a rich set of energy-control features, but lack the capability of controlling them appropriately, because of missing application-specific knowledge. Thus, we propose the Energy-Control Loop (ECL) as an DBMS-integrated approach for adaptive energy-control on scale-up in-memory database systems that obeys a query latency limit as a soft constraint and actively optimizes energy efficiency and performance of the DBMS. The ECL relies on adaptive workload-dependent energy profiles that are continuously maintained at runtime. In our evaluation, we observed energy savings ranging from 20% to 40% for a real-world load profile.
Novel binary divider architecture for high speed VLSI applications

Vedic Mathematics is the ancient methodology of Indian mathematics which has a unique computational technique for calculations based on 16 Sutras (Formulae). Novel Binary divider architecture for high speed VLSI application using such ancient methodology is presented in this paper. Propagation delay and dynamic power consumption of a divider circuitry were minimized significantly by removing unnecessary recursion through Vedic division methodology. The functionality of these circuits was checked and performance parameters like propagation delay and dynamic power consumption were calculated by Xilinx ISE using 9Onm CMOS technology. The propagation delay of the resulting 8-bit binary dividend by an 4-bit divisor circuitry was only ~19.9ns and consumed ~34mW power for a LUT Utilization of 23/1536. By combining Boolean logic with ancient Vedic mathematics, substantial amount of iteration were eliminated that resulted in ~46% reduction in delay and ~27% reduction in power compared with the mostly used (Repetitive subtraction method) architecture.