nils-herrmann/clean_scirepeval huggingface dataset:
Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing

New paradigms such as Mobile Edge Computing (MEC) are becoming feasible for use in, e.g., real-time decision-making during disaster incident response to handle the data deluge occurring in the network edge. However, MEC deployments today lack flexible IoT device data handling such as handling user preferences for real-time versus energy-efficient processing. Moreover, MEC can also benefit from a policy-based edge routing to handle sustained performance levels with efficient energy consumption. In this paper, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a facial recognition application that is important in disaster incident response scenarios, we propose a novel “offload decision-making” algorithm that analyzes the tradeoffs in computing policies to offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads. This algorithm also analyzes the impact on energy consumption in the decision-making under different visual data consumption requirements (i.e., users with thick clients or thin clients). To address the processing-throughput versus energy-efficiency tradeoffs, we propose a “Sustainable Policy-based Intelligence-Driven Edge Routing” algorithm that uses machine learning within Mobile Ad hoc Networks. This algorithm is energy aware and improves the geographic routing baseline performance (i.e., minimizes impact of local minima) for throughput performance sustainability, while also enabling flexible policy specification. We evaluate our proposed algorithms by conducting experiments on a realistic edge and core cloud testbed in the GENI Cloud infrastructure, and recreate disaster scenes of tornado damages within simulations. Our empirical results show how MEC can provide flexibility to users who desire energy conservation over low latency or vice versa in the visual data processing with a facial recognition application. In addition, our simulation results show that our routing approach outperforms existing solutions under diverse user preferences, node mobility, and severe node failure conditions.
Cognitive control and lexical access in younger and older bilinguals.

Ninety-six participants, who were younger (20 years) or older (68 years) adults and either monolingual or bilingual, completed tasks assessing working memory, lexical retrieval, and executive control. Younger participants performed most of the tasks better than older participants, confirming the effect of aging on these processes. The effect of language group was different for each type of task: Monolinguals and bilinguals performed similarly on working memory tasks, monolinguals performed better on lexical retrieval tasks, and bilinguals performed better on executive control tasks, with some evidence for larger language group differences in older participants on the executive control tasks. These results replicate findings from individual studies obtained using only 1 type of task and different participants. The confirmation of this pattern in the same participants is discussed in terms of a suggested explanation of how the need to manage 2 language systems leads to these different outcomes for cognitive and linguistic functions.
Broadband Bent Triangular Omnidirectional Antenna for RF Energy Harvesting

In this letter, a broadband bent triangular omnidirectional antenna is presented for RF energy harvesting. The antenna has a bandwidth for VSWR ≤ 2 from 850 MHz to 1.94 GHz. The antenna is designed to receive both horizontal and vertical polarized waves and has a stable radiation pattern over the entire bandwidth. Antenna has also been optimized for energy harvesting application and it is designed for 100 Ω input impedance to provide a passive voltage amplification and impedance matching to the rectifier. A peak efficiency of 60% and 17% is obtained for a load of 500 Ω at 980 and 1800 MHz, respectively. At a cell site while harvesting all bands simultaneously a voltage of 3.76 V for open circuit and 1.38 V across a load of 4.3 k Ω is obtained at a distance of 25 m using an array of two elements of the rectenna.
Drive-by-wire vehicle stabilization and yaw regulation: a hybrid Model Predictive Control design

Electronic Stability Control (ESC) and Active Front Steering (AFS) have been introduced in production vehicles in recent years, due to improved vehicle maneuverability and the effects in reducing single vehicle accident. We propose a hybrid Model Predictive Control (MPC) design for coordinated control of AFS and ESC. By formulating the vehicle dynamics with respect to the front and rear tire slip angles and by approximating the tire-force characteristics by piecewise affine functions, the vehicle dynamics are formulated as a linear hybrid dynamical system. This model is used to design a hybrid model predictive controller. The proposed model formulation allows one to visually analyze the stability region of the closed-loop system and to assess the stabilizing capability of the hybrid MPC controller. Simulations of the controller in closed-loop with an accurate nonlinear model are presented.
Floor Detection with Indoor Vertical Positioning in LTE Femtocell Networks

Vertical positioning is nowadays a topic of high interest in indoor environments, due to the stringent requirements on indoor location accuracy for emergency calls, such as E911. Thus, indoor positioning techniques are investigated to achieve floor detection in order to fulfill these legal mandates. The use of Long Term Evolution (LTE) heterogeneous networks is an attractive solution due to the combination of communications and positioning capabilities. This paper provides an overview of the existing floor detection techniques. Practical results using Global Navigation Satellite Systems (GNSS), inertial sensors, barometer, and LTE signals are obtained in an experimental LTE femtocell network, within a deployment in a two-story building. Probabilities of floor detection above 67% of the cases are found for the positioning solutions assessed.
A unified neural model for review-based rating prediction by leveraging multi-criteria ratings and review text

The problem of personalized review-based rating prediction aims at inferring users’ ratings over their unrated items using historical ratings and reviews. Most of existing methods solve this problem by integrating latent factor model and topic model to learn interpretable user and items factors. However, these methods ignore the word order in reviews and the learned topic factors are limited to review text, which cannot fully reveal the complicated interaction relations between reviews and ratings. Moreover, they only utilize overall ratings instead of multi-criteria ratings which can represent more detailed preferences of users. In this paper, we propose a deep learning framework named NRPMT in which multi-criteria ratings and user reviews can complement each other to improve recommendation accuracy. The proposed model can simultaneously predict accurate ratings and generate review content expressing user experience and feelings. For rating prediction, a neural factorization machines-based regression model are used to project the feature interactions between user, item and criteria into rating. For review generation, gated recurrent neural networks are employed to “translate” the feature representation of user, item and criteria into a review. Extensive experiments on three real-world datasets demonstrate that NRPMT achieves significant improvement over the several competitive baselines.
Hawkes Processes with Stochastic Excitations

We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm for simulating draws from Hawkes processes whose levels of excitation are stochastic processes, and propose a hybrid Markov chain Monte Carlo approach for model fitting. Our sampling procedure scales linearly with the number of required events and does not require stationarity of the point process. A modular inference procedure consisting of a combination between Gibbs and Metropolis Hastings steps is put forward. We recover expectation maximization as a special case. Our general approach is illustrated for contagion following geometric Brownian motion and exponential Langevin dynamics.
Designing a Programming Language for Home Automation

The AutoHAN project at the Cambridge Computer Laboratory is developing a range of technologies related to next-generation home networking and automation. Several aspects of the project involve the development of programming languages suitable for use in the home. Languages of this sort will clearly have a significant impact on the usability of domestic electronic devices, and greatly broaden the range of users who might benefit from psychology of programming research. As yet, the design of the AutoHAN languages is not complete. This paper therefore describes the design process and design criteria that have been applied so far. This is as a basis for discussion at this workshop, and for planning of further psychology of programming research related to the AutoHAN programme. Home Automation Background Home networking technologies are rapidly being deployed, if not in the average person’s house, then at least in many research environments. Large companies are experimenting with home networking. Nascent standardisation bodies are competing to define networking and communications protocols (e.g. Waldo/Sun Microsystem 1999, Microsoft Corporation 2000) by which appliances can communicate with control devices and with each other. These trends will soon bring significant challenges to the psychology of programming community. PPIG researchers have occasionally wondered in the past whether the proverbial impossibility of programming a video cassette recorder should be a concern of ours. A few people have investigated the area, but not many. Thomas Green, Alan Blackwell and Rachel Hewson have given some thought to the programming of central heating systems (Blackwell, Green & Hewson submitted), as have Anna Cox and Richard Young (2000). Harold Thimbleby (1993) has analysed the programming interfaces of microwave ovens, and even VCRs (Thimbleby 1991). But even though these results might bring psychological or usability insights, they are seldom considered to be directly relevant to programming language design. “Programming” a VCR is not real programming in the eyes of most computer scientists. But if you want to instruct your VCR to start recording from the front door security camera for 5 minutes after the time that someone presses the front doorbell, this seems a lot more like real programming. So the significance of home networking is that programming in your house may suddenly become a great deal more complicated. If your VCR can talk to your home security system over the home network, how will it know what to say? Perhaps the manufacturer will build in all the required functionality to both devices. But would you trust your VCR manufacturer to do this? Perhaps manufacturers will just keep themselves to themselves, and won’t be tempted to mess with other devices in your house. Would you trust them to do that? On balance, it does seem that homeowners will find themselves with the potential, and maybe the inclination, to define complicated behaviour in their own homes.
Two-Dimensional Materials for Sensing : Graphene and Beyond

Two-dimensional materials have attracted great scientific attention due to their unusual and fascinating properties for use in electronics, spintronics, photovoltaics, medicine, composites, etc. Graphene, transition metal dichalcogenides such as MoS2, phosphorene, etc., which belong to the family of two-dimensional materials, have shown great promise for gas sensing applications due to their high surface-to-volume ratio, low noise and sensitivity of electronic properties to the changes in the surroundings. Two-dimensional nanostructured semiconducting metal oxide based gas sensors have also been recognized as successful gas detection devices. This review aims to provide the latest advancements in the field of gas sensors based on various two-dimensional materials with the main focus on sensor performance metrics such as sensitivity, specificity, detection limit, response time, and reversibility. Both experimental and theoretical studies on the gas sensing properties of graphene and other two-dimensional materials beyond graphene are also discussed. The article concludes with the current challenges and future prospects for two-dimensional materials in gas sensor applications.
Bullying and victimization in adolescence: concurrent and stable roles and psychological health symptoms.

From an initial sample of 1278 Italian students, the authors selected 537 on the basis of their responses to a self-report bully and victim questionnaire. Participants' ages ranged from 13 to 20 years (M = 15.12 years, SD = 1.08 years). The authors compared the concurrent psychological symptoms of 4 participant groups (bullies, victims, bully/victims [i.e., bullies who were also victims of bullying], and uninvolved students). Of participants, 157 were in the bullies group, 140 were in the victims group, 81 were in the bully/victims group, and 159 were in the uninvolved students group. The results show that bullies reported a higher level of externalizing problems, victims reported more internalizing symptoms, and bully/victims reported both a higher level of externalizing problems and more internalizing symptoms. The authors divided the sample into 8 groups on the basis of the students' recollection of their earlier school experiences and of their present role. The authors classified the participants as stable versus late bullies, victims, bully/victims, or uninvolved students. The authors compared each stable group with its corresponding late group and found that stable victims and stable bully/victims reported higher degrees of anxiety, depression, and withdrawal than did the other groups. The authors focus their discussion on the role of chronic peer difficulties in relation to adolescents' symptoms and well-being.
Challenge Benchmarks That Must be Conquered to Sustain the GPU Revolution Emily Blem

The shift from GPUs to GPGPUs has brought with it many changes to the GPU architecture (e.g. more caches, more concurrent kernels, better synchronization). As GPUs press further into the general-purpose domain, architects must continue to address the performance of challenging workloads. This paper presents a set of challenge benchmarks and their key performance limitations to help direct future GPU architecture research. Our study shows GPUs must develop multiple innovative architectural techniques to efficiently execute these applications to continue making inroads into general purpose computing.
iPhysioMeter: a new approach for measuring heart rate and normalized pulse volume using only a smartphone.

Heart rate (HR) and normalized pulse volume (NPV) are physiological indices that have been used in a diversity of psychological studies. However, measuring these indices often requires laborious processes. We therefore developed a new smartphone program, named iPhysioMeter, that makes it possible to measure beat-by-beat HR and ln NPV using only a smartphone. We examined its accuracy against conventional laboratory measures. Mental stress tasks were used to alter HR and ln NPV in 12 participants. Bland-Altman analyses revealed negligible proportional bias for HR and ln NPV or for their change values, expressed as ΔHR and Δln NPV. However, a relatively large fixed bias did emerge for ln NPV, as well as a small one for Δln NPV, although both were within the limits of agreement. These findings suggest that iPhysioMeter can yield valid measures of the absolute level of HR and of relative changes in ln NPV.
Motivations and Methods for Text Simplification

Long and complicated sentences prove to be a stumbling block for current systems relying on NL input. These systems stand to gain from methods that syntactically simplify such sentences. To simplify a sentence, we need an idea of the structure of the sentence, to identify the components to be separated out. Obviously a parser could be used to obtain the complete structure of the sentence. However, full parsing is slow and prone to failure, especially on complex sentences. In this paper, we consider two alternatives to full parsing which could be used for simpli cation. The rst approach uses a Finite State Grammar (FSG) to produce noun and verb groups while the second uses a Supertagging model to produce dependency linkages. We discuss the impact of these two input representations on the simpli cation process. 1 Reasons for Text Simpli cation Long and complicated sentences prove to be a stumbling block for current systems which rely on natural language input. These systems stand to gain from methods that preprocess such sentences so as to make them simpler. Consider, for example, the following sentence: (1) The embattled Major government survived a crucial vote on coal pits closure as its last-minute concessions curbed the extent of Tory revolt over an issue that generated unusual heat in the House of Commons and brought the miners to London streets. Such sentences are not uncommon in newswire texts. Compare this with the multi-sentence version which has been manually simpli ed: (2) The embattled Major government survived a crucial vote on coal pits closure. Its last-minute concessions curbed the extent of On leave from the National Centre for Software Technology, Gulmohar Cross Road No. 9, Juhu, Bombay 400 049, India Tory revolt over the coal-mine issue. This issue generated unusual heat in the House of Commons. It also brought the miners to
User Traffic Profile for Traffic Reduction and Effective Bot C&C Detection

Bots are malicious software components used for generating spams, launching denial of service attacks, phishing, identity theft and information exfiltration and such other illegal activities. Bot detection is an area of active research in recent times. Here we propose a bot detection mechanism for a single host. A user traffic profile is used to filter out normal traffic generated by the host. The remaining suspicious traffic is subject to detailed analysis. The nature of detailed analysis is derived from a characterization of bot traffic. The detection system is tested using two real world bots. Performance evaluation results show that the proposed system achieves a high detection rate (100%) and a low false positive rate (0–8%). The traffic filtering yielded an average reduction in traffic by 70%.
Development of 50-kV 100-kW Three-Phase Resonant Converter for 95-GHz Gyrotron

This paper describes the development of a 50-kV 100-kW cathode power supply (CPS) for the operation of a 30-kW 95-GHz gyrotron. For stable operation of the gyrotron, the requirements of CPS include low output voltage ripple and low arc energy less than 1% and 10 J, respectively. Depending on required specifications, a three-phase series-parallel resonant converter (SPRC) is proposed for designing CPS. In addition to high-efficiency performance of SPRC, three-phase operation provides the reduction of the output voltage ripple through a minimized output filter component that is closely related to the arc energy. For allowing symmetrical resonant current from three-phase resonant inverter, the high-voltage transformers are configured as star connection with floated neutral node. This facilitates balanced voltage on each secondary winding. In addition, distinctive design of the high-voltage rectifier is introduced, taking into consideration the effective series stacking of diodes by means of the parallel resonant capacitor. In particular, the implementation of the high-voltage part including transformer and rectifier is presented in detail. For providing high power density and high reliability, effective methods for winding the high-voltage transformer and stacking rectifier diodes are discussed. Finally, the developed CPS achieves 95.5% of maximum efficiency, 0.92 of maximum power factor, 500 W/liter of power density, 0.6% of output voltage ripple, with 8.3-J arc energy.
Competency based management: a review of systems and approaches

Aims to review the key concepts of competency management (CM) and to propose method for developing competency method.  Examines the CM features of 22 CM systems and 18 learning management systems.  Finds that the areas of open standard (XML, web services, RDF), semantic technologies (ontologies and the semantic web) and portals with self-service technologies are going to play a significant part in the evolution of CM systems.  Emphasizes the beneficial attributes of CM for private and public organizations.
The analyse of the various methods for location of Data Matrix codes in images

Data Matrix codes belong to the group of 2-D bar codes, which are widely used in marking the products in storing, production, distribution and sales processes. Their compact matrix structure enables to store big amount of information in a very small area compared to conventional 1-D bar codes. In following paper we compare several methods for detection of Data Matrix codes in images. When locating the position of the code we start from typical bordering of Data Matrix code — which forms “L” so called Finder Pattern and to it the parallel dotting so called Timing Pattern. On the first stage we try to locate Finder Pattern using the edge detection or adaptive thresholding, then continuing with connecting the points into the continuous regions, we are searching for the regions, which could represent the Finder Pattern. On the second stage we verify Timing Pattern where the number of crossing between the background and foreground must be even.
TMCoI-SIOT: A trust management system based on communities of interest for the social Internet of Things

We propose a trust management system (TMS) for the social Internet of things (SIOT) called TMCoI-SIOT. The proposed system integrates numerous factors such as direct and indirect trust; transaction factors and social modeling of trust. The novelty of our approach can be summed up in three aspects. The first aspect concerns the network architecture which is seen as a set of nodes groups called community of interest (CoI). Each community nodes shares the same interests and is headed by an administrator (admin. The second aspect is the trust management system that prevents On-Off attacks. The third aspect concerns the trust prediction method that uses the Kalman filter technique in order to estimate nodes behaviors and prevent possible attacks. We prove the effectiveness of the proposed system by simulation against TMS attacks.
Acquiring Hyponymy Relations from Web Documents

This paper describes an automatic method for acquiring hyponymy relations from HTML documents on the WWW. Hyponymy relations can play a crucial role in various natural language processing systems. Most existing acquisition methods for hyponymy relations rely on particular linguistic patterns, such as “ NP such as NP”. Our method, however, does not use such linguistic patterns, and we expect that our procedure can be applied to a wide range of expressions for which existing methods cannot be used. Our acquisition algorithm uses clues such as itemization or listing in HTML documents and statistical measures such as document frequencies and verb-noun co-occurrences.