nils-herrmann/clean_scirepeval huggingface dataset:
Multi-view Domain Generalization for Visual Recognition

In this paper, we propose a new multi-view domain generalization (MVDG) approach for visual recognition, in which we aim to use the source domain samples with multiple types of features (i.e., multi-view features) to learn robust classifiers that can generalize well to any unseen target domain. Considering the recent works show the domain generalization capability can be enhanced by fusing multiple SVM classifiers, we build upon exemplar SVMs to learn a set of SVM classifiers by using one positive sample and all negative samples in the source domain each time. When the source domain samples come from multiple latent domains, we expect the weight vectors of exemplar SVM classifiers can be organized into multiple hidden clusters. To exploit such cluster structure, we organize the weight vectors learnt on each view as a weight matrix and seek the low-rank representation by reconstructing this weight matrix using itself as the dictionary. To enforce the consistency of inherent cluster structures discovered from the weight matrices learnt on different views, we introduce a new regularizer to minimize the mismatch between any two representation matrices on different views. We also develop an efficient alternating optimization algorithm and further extend our MVDG approach for domain adaptation by exploiting the manifold structure of unlabeled target domain samples. Comprehensive experiments for visual recognition clearly demonstrate the effectiveness of our approaches for domain generalization and domain adaptation.
A combinatorial Artificial Bee Colony algorithm for traveling salesman problem

Traveling Salesman Problem is an important optimization issue of many fields such as transportation, logistics and semiconductor industries and it is about finding a Hamiltonian path with minimum cost. To solve this problem, many researchers have proposed different approaches including metaheuristic methods. Artificial Bee Colony algorithm is a well known swarm based optimization technique. In this paper we propose a new Artificial Bee Colony algorithm called Combinatorial ABC for Traveling Salesman Problem. Simulation results show that this Artificial Bee Colony algorithm can be used for combinatorial optimization problems.
Load-Adaptive Control Algorithm of Half-Bridge Series Resonant Inverter for Domestic Induction Heating

Domestic induction cookers operation is based on a resonant inverter which supplies medium-frequency currents (20-100 kHz) to an inductor, which heats up the pan. The variable load that is inherent to this application requires the use of a reliable and load-adaptive control algorithm. In addition, a wide output power range is required to get a satisfactory user performance. In this paper, a control algorithm to cover the variety of loads and the output power range is proposed. The main design criteria are efficiency, power balance, acoustic noise, flicker emissions, and user performance. As a result of the analysis, frequency limit and power level limit algorithms are proposed based on square wave and pulse density modulations. These have been implemented in a field-programmable gate array, including output power feedback and mains-voltage zero-cross-detection circuitry. An experimental verification has been performed using a commercial induction heating inverter. This provides a convenient experimental test bench to analyze the viability of the proposed algorithm.
UBIRIS: A Noisy Iris Image Database

This paper presents a new iris database that contains images with noise. This is in contrast with the existing databases, that are noise free. UBIRIS is a tool for the development of robust iris recognition algorithms for biometric proposes. We present a detailed description of the many characteristics of UBIRIS and a comparison of several image segmentation approaches used in the current iris segmentation methods where it is evident their small tolerance to noisy images.
Environmental performance of bio-based and biodegradable plastics: the road ahead.

Future plastic materials will be very different from those that are used today. The increasing importance of sustainability promotes the development of bio-based and biodegradable polymers, sometimes misleadingly referred to as 'bioplastics'. Because both terms imply "green" sources and "clean" removal, this paper aims at critically discussing the sometimes-conflicting terminology as well as renewable sources with a special focus on the degradation of these polymers in natural environments. With regard to the former we review innovations in feedstock development (e.g. microalgae and food wastes). In terms of the latter, we highlight the effects that polymer structure, additives, and environmental variables have on plastic biodegradability. We argue that the 'biodegradable' end-product does not necessarily degrade once emitted to the environment because chemical additives used to make them fit for purpose will increase the longevity. In the future, this trend may continue as the plastics industry also is expected to be a major user of nanocomposites. Overall, there is a need to assess the performance of polymer innovations in terms of their biodegradability especially under realistic waste management and environmental conditions, to avoid the unwanted release of plastic degradation products in receiving environments.
Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review

The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. A class of deep convolutional networks represent an important special case of these conditions, though weight sharing is not the main reason for their exponential advantage. Implications of a few key theorems are discussed, together with new results, open problems and conjectures.
Improving RSS-Based Indoor Positioning Algorithm via K-Means Clustering

Advances in mobile technologies and devices has changed the way users interact with devices and other users. These new interaction methods and services are offered by the help of intelligent sensing capabilities, using context, location and motion sensors. However, indoor location sensing is mostly achieved by utilizing radio signal (Wi-Fi, Bluetooth, GSM etc.) and nearest neighbor identification. The most common algorithm adopted for Received Signal Strength (RSS)-based location sensing is K Nearest Neighbor (KNN), which calculates K nearest neighboring points to mobile users (MUs). Accordingly, in this paper, we aim to improve the KNN algorithm by enhancing the neighboring point selection by applying k-means clustering approach. In the proposed method, k-means clustering algorithm groups nearest neighbors according to their distance to mobile user. Then the closest group to the mobile user is used to calculate the MU's location. The evaluation results indicate that the performance of clustered KNN is closely tied to the number of clusters, number of neighbors to be clustered and the initiation of the center points in k-mean algorithm. Keywords-component; Received signal strength, k-Means, clustering, location estimation, personal digital assistant (PDA), wireless, indoor positioning
Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.
Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out.

Social networking sites (SNS) are especially attractive for adolescents, but it has also been shown that these users can suffer from negative psychological consequences when using these sites excessively. We analyze the role of fear of missing out (FOMO) and intensity of SNS use for explaining the link between psychopathological symptoms and negative consequences of SNS use via mobile devices. In an online survey, 1468 Spanish-speaking Latin-American social media users between 16 and 18 years old completed the Hospital Anxiety and Depression Scale (HADS), the Social Networking Intensity scale (SNI), the FOMO scale (FOMOs), and a questionnaire on negative consequences of using SNS via mobile device (CERM). Using structural equation modeling, it was found that both FOMO and SNI mediate the link between psychopathology and CERM, but by different mechanisms. Additionally, for girls, feeling depressed seems to trigger higher SNS involvement. For boys, anxiety triggers higher SNS involvement.
Scalable DC Microgrids for Rural Electrification in Emerging Regions

We present the design and experimental validation of a scalable dc microgrid for rural electrification in emerging regions. A salient property of the dc microgrid architecture is the distributed control of the grid voltage, which enables both instantaneous power sharing and a metric for determining the available grid power. A droop-voltage power-sharing scheme is implemented wherein the bus voltage droops in response to low supply/high demand. In addition, the architecture of the dc microgrid aims to minimize the losses associated with stored energy by distributing storage to individual households. In this way, the number of conversion steps and line losses are reduced. We calculate that the levelized cost of electricity of the proposed dc microgrid over a 15-year time horizon is $0.35/kWh. We also present the experimental results from a scaled-down experimental prototype that demonstrates the steady-state behavior, the perturbation response, and the overall efficiency of the system. Moreover, we present fault mitigation strategies for various faults that can be expected to occur in a microgrid distribution system. The experimental results demonstrate the suitability of the presented dc microgrid architecture as a technically advantageous and cost-effective method for electrifying emerging regions.
Deep Extreme Multi-label Learning

Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves 2L possible label sets especially when the label dimension L is huge, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by originally establishing an explicit label graph. In the meanwhile, deep learning has been widely studied and used in various classification problems including multi-label classification, however it has not been properly introduced to XML, where the label space can be as large as in millions. In this paper, we propose a practical deep embedding method for extreme multi-label classification, which harvests the ideas of non-linear embedding and graph priors-based label space modeling simultaneously. Extensive experiments on public datasets for XML show that our method performs competitive against state-of-the-art result.
Mining data records in Web pages

A large amount of information on the Web is contained in regularly structured objects, which we call data records. Such data records are important because they often present the essential information of their host pages, e.g., lists of products or services. It is useful to mine such data records in order to extract information from them to provide value-added services. Existing automatic techniques are not satisfactory because of their poor accuracies. In this paper, we propose a more effective technique to perform the task. The technique is based on two observations about data records on the Web and a string matching algorithm. The proposed technique is able to mine both contiguous and non-contiguous data records. Our experimental results show that the proposed technique outperforms existing techniques substantially.
IT service management automation and its impact to IT industry

Organizations and businesses are rapidly shifting from goods-based economy to service-oriented economy . This has led to the increased focus to IT service innovation. IT service management is a framework for enterprises to define, deliver, operate and govern the IT services to meet the business objective. Information Technology Infrastructure Library (ITIL) is a process driven best practices framework for implementing Information Technology Service Management (ITSM). ITIL was originally developed by the UK Government has been adopted by many hundreds of organizations. ITIL has the proven track record of delivering services at the optimal service level. Digital revolution is rapidly transforming the organization and industries. Managing growth and reducing cost out at the same time is a requirement for digital business, but it's not easy for IT infrastructure and operations. Automation is the best solution for meeting the demands of the digital business. Large number of IT organizations have started automating the repeatable and recurring processes. IT organizations need to move from opportunistic to systematic automation of IT processes . In this study, we first systematically review the automation scope of ITSM from ITIL process perspective. Second, we identify the positive and negative impacts of automation to IT industry.
Internet of Things: Security vulnerabilities and challenges

Internet of Things (IoT) has been given a lot of emphasis since the 90s when it was first proposed as an idea of interconnecting different electronic devices through a variety of technologies. However, during the past decade IoT has rapidly been developed without appropriate consideration of the profound security goals and challenges involved. This study explores the security aims and goals of IoT and then provides a new classification of different types of attacks and countermeasures on security and privacy. It then discusses future security directions and challenges that need to be addressed to improve security concerns over such networks and aid in the wider adoption of IoT by masses.
Optimum Trajectory Control of the Current Vector of a Nonsalient-Pole PMSM in the Field-Weakening Region

Industry-proven field-weakening solutions for nonsalient-pole permanent-magnet synchronous motor drives are presented in this paper. The core algorithm relies on direct symbolic equations. The equations take into account the stator resistance and reveal its effect on overall algorithm quality. They establish a foundation for an offline calculated lookup table which secures effective d-axis current reference over entire field-weakening region. The table has been proven on its own and in combination with a PI compensator. Usage recommendations are given in this paper. Functionality of the proposed solutions has been investigated theoretically and in practice. The investigation has been carried out in the presence of motor magnetic saturation and parameter tolerance, taking into account the change of operating temperature. The results and analysis method are included in this paper.
Using assembly representations to enable evolutionary design of Lego structures

This paper presents an approach to the automatic generation of electromechanical engineering designs. We apply Messy Genetic Algorithm optimization techniques to the evolution of assemblies composed of the Lego structures. Each design is represented as a labeled assembly graph and is evaluated based on a set of behavior and structural equations. The initial populations are generated at random and design candidates for subsequent generations are produced by user-specified selection techniques. Crossovers are applied by using cut and splice operators at the random points of the chromosomes; random mutations are applied to modify the graph with a certain low probability. This cycle will continue until a suitable design is found. The research contributions in this work include the development of a new GA encoding scheme for mechanical assemblies (Legos), as well as the creation of selection criteria for this domain. Our eventual goal is to introduce a simulation of electromechanical devices into our evaluation functions. We believe that this research creates a foundation for future work and it will apply GA techniques to the evolution of more complex and realistic electromechanical structures.
Blackholing at IXPs: On the Effectiveness of DDoS Mitigation in the Wild

DDoS attacks remain a serious threat not only to the edge of the Internet but also to the core peering links at Internet Exchange Points (IXPs). Currently, the main mitigation technique is to blackhole traffic to a specific IP prefix at upstream providers. Blackholing is an operational technique that allows a peer to announce a prefix via BGP to another peer, which then discards traffic destined for this prefix. However, as far as we know there is only anecdotal evidence of the success of blackholing. Largely unnoticed by research communities, IXPs have deployed blackholing as a service for their members. In this first-of-its-kind study, we shed light on the extent to which blackholing is used by the IXP members and what effect it has on traffic. Within a 12 week period we found that traffic to more than 7, 864 distinct IP prefixes was blackholed by 75 ASes. The daily patterns emphasize that there are not only a highly variable number of new announcements every day but, surprisingly, there are a consistently high number of announcements (> 1000). Moreover, we highlight situations in which blackholing succeeds in reducing the DDoS attack traffic.
Linguistic Input Features Improve Neural Machine Translation

Neural machine translation has recently achieved impressive results, while using little in the way of external linguistic information. In this paper we show that the strong learning capability of neural MT models does not make linguistic features redundant; they can be easily incorporated to provide further improvements in performance. We generalize the embedding layer of the encoder in the attentional encoder–decoder architecture to support the inclusion of arbitrary features, in addition to the baseline word feature. We add morphological features, part-ofspeech tags, and syntactic dependency labels as input features to English↔German and English→Romanian neural machine translation systems. In experiments on WMT16 training and test sets, we find that linguistic input features improve model quality according to three metrics: perplexity, BLEU and CHRF3. An opensource implementation of our neural MT system is available1, as are sample files and configurations2.
Dynamic Difficulty Adjustment through an Adaptive AI

Dynamic Difficulty Adjustment (DDA) consists in an alternative to the static game balancing performed in game design. DDA is done during execution, tracking the player's performance and adjusting the game to present proper challenges to the player. This approach seems appropriate to increase the player entertainment, since it provides balanced challenges, avoiding boredom or frustration during the gameplay. This paper presents a mechanism to perform the dynamic difficulty adjustment during a game match. The idea is to dynamically change the game AI, adapting it to the player skills. We implemented three different AIs to match player behaviors: beginner, regular and experienced in the game Defense of the Ancient (DotA), a modification (MOD) of the game Warcraft III. We performed a series of experiments and, after comparing all results, the presented mechanism was able to keep up with the player's abilities on 85% of all experiments. The remaining 15% failed to suit the player's need because the adjustment did not occur on the right moment.
Towards Real-Time Indoor Localization in Wireless Sensor Networks

Recently, Real Time Location Systems (RTLS) have been designed to provide location information of positioning target. The kernel of RTLS is localization algorithm, range-base localization algorithm is concerned as high precision. This paper introduces real-time range-based indoor localization algorithms, including Time of Arrival, Time Difference of Arrival, Received Signal Strength Indication, Time of Flight, and Symmetrical Double Sided Two Way Ranging. Evaluation criteria are proposed for assessing these algorithms, namely positioning accuracy, scale, cost, energy efficiency, and security. We also introduce the latest some solution, compare their Strengths and weaknesses. Finally, we give a recommendation about selecting algorithm from the viewpoint of the practical application need.
Vigilare: toward snoop-based kernel integrity monitor

In this paper, we present Vigilare system, a kernel integrity monitor that is architected to snoop the bus traffic of the host system from a separate independent hardware. This snoop-based monitoring enabled by the Vigilare system, overcomes the limitations of the snapshot-based monitoring employed in previous kernel integrity monitoring solutions. Being based on inspecting snapshots collected over a certain interval, the previous hardware-based monitoring solutions cannot detect transient attacks that can occur in between snapshots. We implemented a prototype of the Vigilare system on Gaisler's grlib-based system-on-a-chip (SoC) by adding Snooper hardware connections module to the host system for bus snooping. To evaluate the benefit of snoop-based monitoring, we also implemented similar SoC with a snapshot-based monitor to be compared with. The Vigilare system detected all the transient attacks without performance degradation while the snapshot-based monitor could not detect all the attacks and induced considerable performance degradation as much as 10% in our tuned STREAM benchmark test.
Network visualization and analysis of gene expression data using BioLayout Express3D

Network analysis has an increasing role in our effort to understand the complexity of biological systems. This is because of our ability to generate large data sets, where the interaction or distance between biological components can be either measured experimentally or calculated. Here we describe the use of BioLayout Express3D, an application that has been specifically designed for the integration, visualization and analysis of large network graphs derived from biological data. We describe the basic functionality of the program and its ability to display and cluster large graphs in two- and three-dimensional space, thereby rendering graphs in a highly interactive format. Although the program supports the import and display of various data formats, we provide a detailed protocol for one of its unique capabilities, the network analysis of gene expression data and a more general guide to the manipulation of graphs generated from various other data types.