nils-herrmann/clean_scirepeval huggingface dataset:
Face recognition for web-scale datasets

With millions of users and billions of photos, web-scale face recognition is a challenging task that demands speed, accuracy, and scalability. Most current approaches do not address and do not scale well to Internet-sized scenarios such as tagging friends or finding celebrities. Focusing on web-scale face identification, we gather an 800,000 face dataset from the Facebook social network that models real-world situations where specific faces must be recognized and unknown identities rejected. We propose a novel Linearly Approximated Sparse Representation-based Classification (LASRC) algorithm that uses linear regression to perform sample selection for ‘-minimization, thus harnessing the speed of least-squares and the robustness of sparse solutions such as SRC. Our efficient LASRC algorithm achieves comparable performance to SRC with a 100–250 times speedup and exhibits similar recall to SVMs with much faster training. Extensive tests demonstrate our proposed approach is competitive on pair-matching verification tasks and outperforms current state-of-the-art algorithms on open-universe identification in uncontrolled, web-scale scenarios.
Retrieval-Based Learning : A Perspective for Enhancing Meaningful Learning

Learning is often identified with the acquisition, encoding, or construction of new knowledge, while retrieval is often considered only a means of assessing knowledge, not a process that contributes to learning. Here, we make the case that retrieval is the key process for understanding and for promoting learning. We provide an overview of recent research showing that active retrieval enhances learning, and we highlight ways researchers have sought to extend research on active retrieval to meaningful learning—the learning of complex educational materials as assessed on measures of inference making and knowledge application. However, many students lack metacognitive awareness of the benefits of practicing active retrieval. We describe two approaches to addressing this problem: classroom quizzing and a computer-based learning program that guides students to practice retrieval. Retrieval processes must be considered in any analysis of learning, and incorporating retrieval into educational activities represents a powerful way to enhance learning.
A non-parametric approach to web log analysis

Clustering data generally involves some input parameters or heuristics that are usually unknown at the time they are needed. We discuss the general problem of parameters in clustering and present a new approach, TURN, based on boundary detection and apply it to the clustering of web log data. We also present the use of di erent lters on the web log data to focus the clustering results and discuss di erent coeÆcients for de ning similarity in a non-Euclidean space.
MSR-net: Low-light Image Enhancement Using Deep Convolutional Network

Images captured in low-light conditions usually suffer from very low contrast, which increases the difficulty of subsequent computer vision tasks in a great extent. In this paper, a low-light image enhancement model based on convolutional neural network and Retinex theory is proposed. Firstly, we show that multi-scale Retinex is equivalent to a feedforward convolutional neural network with different Gaussian convolution kernels. Motivated by this fact, we consider a Convolutional Neural Network(MSR-net) that directly learns an end-to-end mapping between dark and bright images. Different fundamentally from existing approaches, low-light image enhancement in this paper is regarded as a machine learning problem. In this model, most of the parameters are optimized by back-propagation, while the parameters of traditional models depend on the artificial setting. Experiments on a number of challenging images reveal the advantages of our method in comparison with other state-of-the-art methods from the qualitative and quantitative perspective.
LOGISTICS-PRODUCTION , LOGISTICS-MARKETING AND EXTERNAL INTEGRATION : THEIR IMPACT ON PERFORMANCE

Highly competitive environments are leading companies to implement Supply Chain Management (SCM) to improve performance and gain a competitive advantage. SCM involves integration, co-ordination and collaboration across organisations and throughout the supply chain. It means that SCM requires internal (intraorganisational) and external (interorganisational) integration. This paper examines the Logistics-Production and Logistics-Marketing interfaces and their relation with the external integration process. The study also investigates the causal impact of these internal and external relationships on the company’s logistical service performance. To analyse this, an empirical study was conducted in the Spanish Fast Moving Consumer Goods (FMCG) sector.
Lateral Stability Control of In-Wheel-Motor-Driven Electric Vehicles Based on Sideslip Angle Estimation Using Lateral Tire Force Sensors

This paper presents a method for using lateral tire force sensors to estimate vehicle sideslip angle and to improve vehicle stability of in-wheel-motor-driven electric vehicles (IWM-EVs). Considering that the vehicle motion is governed by tire forces, lateral tire force measurements give practical benefits in estimation and motion control. To estimate the vehicle sideslip angle, a state observer derived from the extended-Kalman-filtering (EKF) method is proposed and evaluated through field tests on an experimental IWM-EV. Experimental results show the ability of a proposed observer to provide accurate estimation. Moreover, using the estimated sideslip angle and tire cornering stiffness, the vehicle stability control system, making best use of the advantages of IMW-EVs with a steer-by-wire system, is proposed. Computer simulation using Matlab/Simulink-Carsim and experiments are carried out to demonstrate the effectiveness of the proposed stability control system. Practical application of lateral tire force sensors to vehicle control systems is discussed for future personal electric vehicles.
Compression Format for IPv6 Datagrams over IEEE 802.15.4-Based Networks

This document updates RFC 4944, "Transmission of IPv6 Packets over IEEE 802.15.4 Networks". This document specifies an IPv6 header compression format for IPv6 packet delivery in Low Power Wireless Personal Area Networks (6LoWPANs). The compression format relies on shared context to allow compression of arbitrary prefixes. How the information is maintained in that shared context is out of scope. This document specifies compression of multicast addresses and a framework for compressing next headers. UDP header compression is specified within this framework. Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License.
Wideband Single-Layer 90 $^{\circ}$ Phase Shifter Using Stepped Impedance Open Stub and Coupled-Line With Weak Coupling

This letter proposes a novel design approach of wideband 90 ° phase shifter, which comprises a stepped impedance open stub (SIOS) and a coupled-line with weak coupling. The result of analyses demonstrates that the bandwidths of return loss (RL) and phase deviation (PD) can be expanded by increasing the impedance ratio of the SIOS and the coupling strength of the coupled-line. For RL > 10 dB, insertion loss (IL) 1.1 dB, and PD of ±5°, the fabricated microstrip single-layer phase shifter exhibits bandwidth of 105% from 0.75 to 2.4 GHz.
Deep Reinforcement Learning with a Natural Language Action Space

This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games. Termed a deep reinforcement relevance network (DRRN), the architecture represents action and state spaces with separate embedding vectors, which are combined with an interaction function to approximate the Q-function in reinforcement learning. We evaluate the DRRN on two popular text games, showing superior performance over other deep Qlearning architectures. Experiments with paraphrased action descriptions show that the model is extracting meaning rather than simply memorizing strings of text.
The VMware mobile virtualization platform: is that a hypervisor in your pocket?

The virtualization of mobile devices such as smartphones, tablets, netbooks, and MIDs offers significant potential in addressing the mobile manageability, security, cost, compliance, application development and deployment challenges that exist in the enterprise today. Advances in mobile processor performance, memory and storage capacities have led to the availability of many of the virtualization techniques that have previously been applied in the desktop and server domains. Leveraging these opportunities, VMware's Mobile Virtualization Platform (MVP) makes use of system virtualization to deliver an end-to-end solution for facilitating employee-owned mobile phones in the enterprise. In this paper we describe the use case behind MVP, and provide an overview of the hypervisor's design and implementation. We present a novel system architecture for mobile virtualization and describe key aspects of both core and platform virtualization on mobile devices
Possible and Certain SQL Key

Driven by the dominance of the relational model, the requirements of modern applications, and the veracity of data, we revisit the fundamental notion of a key in relational databases with NULLs. In SQL database systems primary key columns are NOT NULL by default. NULL columns may occur in unique constraints which only guarantee uniqueness for tuples which do not feature null markers in any of the columns involved, and therefore serve a different function than primary keys. We investigate the notions of possible and certain keys, which are keys that hold in some or all possible worlds that can originate from an SQL table, respectively. Possible keys coincide with the unique constraint of SQL, and thus provide a semantics for their syntactic definition in the SQL standard. Certain keys extend primary keys to include NULL columns, and thus form a sufficient and necessary condition to identify tuples uniquely, while primary keys are only sufficient for that purpose. In addition to basic characterization, axiomatization, and simple discovery approaches for possible and certain keys, we investigate the existence and construction of Armstrong tables, and describe an indexing scheme for enforcing certain keys. Our experiments show that certain keys with NULLs do occur in real-world databases, and that related computational problems can be solved efficiently. Certain keys are therefore semantically well-founded and able to maintain data quality in the form of Codd’s entity integrity rule while handling the requirements of modern applications, that is, higher volumes of incomplete data from different formats.
ADAPTIVE GOVERNANCE OF SOCIAL-ECOLOGICAL SYSTEMS

■ Abstract We explore the social dimension that enables adaptive ecosystem-based management. The review concentrates on experiences of adaptive governance of socialecological systems during periods of abrupt change (crisis) and investigates social sources of renewal and reorganization. Such governance connects individuals, organizations, agencies, and institutions at multiple organizational levels. Key persons provide leadership, trust, vision, meaning, and they help transform management organizations toward a learning environment. Adaptive governance systems often self-organize as social networks with teams and actor groups that draw on various knowledge systems and experiences for the development of a common understanding and policies. The emergence of “bridging organizations” seem to lower the costs of collaboration and conflict resolution, and enabling legislation and governmental policies can support self-organization while framing creativity for adaptive comanagement efforts. A resilient social-ecological system may make use of crisis as an opportunity to transform into a more desired state.
A Cognitive Framework for the Analysis of Game Play : Tasks , Schemas and Attention Theory

A fully developed nomology for the study of games requires the development of explanatory theoretical constructs associated with validating observational techniques. Drawing from cognition sciences, a framework is proposed based upon the integration of schema theory with attention theory. Cognitive task analysis provides a foundation for preliminary schema descriptions, which can then be elaborated according to more detailed models of cognitive and attentional processes. The resulting theory provides a rich explanatory framework for the cognitive processes underlying game play, as well as detailed hypotheses for the hierarchical structure of pleasures and rewards motivating players. Game engagement is accounted for as a process of schema selection or development, while immersion is explained in terms of schema execution. This framework is being developed not only to explain the substructures of game play, but also to provide schema models that may inform game design processes and provide detailed criteria for the design of patterns of game features for entertainment, pedagogical and therapeutic purposes.
Socially Adaptive Path Planning in Human Environments Using Inverse Reinforcement Learning

A key skill for mobile robots is the ability to navigate e ciently through their environment. In the case of social or assistive robots, this involves navigating through human crowds. Typical performance criteria, such as reaching the goal using the shortest path, are not appropriate in such environments, where it is more important for the robot to move in a socially adaptive manner such as respecting comfort zones of the pedestrians. We propose a framework for socially adaptive path planning in dynamic environments, by generating human-like path trajectory. Our framework consists of three modules: a feature extraction module, Inverse Reinforcement Learning module, and a path planning module. The feature extraction module extracts features necessary to characterize the state information, such as density and velocity of surrounding obstacles, from a RGB-Depth sensor. The Inverse Reinforcement Learning module uses a set of demonstration trajectories generated by an expert to learn the expert’s behaviour when faced with di↵erent state features, and represent it as a cost function that respects social variables. Finally, the planning module integrates a threelayer architecture, where a global path is optimized according to a classical shortest-path objective using a global map known a priori, a local path is planned over a shorter distance using the features extracted from a RGB-D sensor and the cost function inferred from Inverse Reinforcement Learning module, and a low-level Beomjoon Kim E-mail: beomjoon.kim@mail.mcgill.ca Joelle Pineau School of Computer Science, McGill University, 3480 University, Canada Tel.: 514-398-5432 Fax: 514-398-3883 E-mail: jpineau@cs.mcgill.ca system handles avoidance of immediate obstacles. We evaluate our approach by deploying it on a real robotic wheelchair platform in various scenarios, and comparing the robot trajectories to human trajectories.
Getting by with a little help from self and others: self-esteem and social support as resources during early adolescence.

Influences of social support and self-esteem on adjustment in early adolescence were investigated in a 2-year longitudinal study (N = 350). Multi-informant data (youth and parent) were used to assess both overall levels and balance in peer- versus adult-oriented sources for social support and self-esteem. Findings obtained using latent growth-curve modeling were consistent with self-esteem mediating effects of social support on both emotional and behavioral adjustment. Lack of balance in social support and self-esteem in the direction of stronger support and esteem from peer-oriented sources predicted greater levels and rates of growth in behavioral problems. Results indicate a need for process-oriented models of social support and self-esteem and sensitivity to patterning of sources for each resource relative to adaptive demands of early adolescence.
New dense dielectric patch array antenna for future 5G short-range communications

In this paper, new dense dielectric (DD) patch array antenna prototype operating at 28 GHz for the future fifth generation (5G) short-range wireless communications applications is presented. This array antenna is proposed and designed with a standard printed circuit board (PCB) process to be suitable for integration with radio-frequency/microwave circuitry. The proposed structure employs four circular shaped DD patch radiator antenna elements fed by a l-to-4 Wilkinson power divider surrounded by an electromagnetic bandgap (EBG) structure. The DD patch shows better radiation and total efficiencies compared with the metallic patch radiator. For further gain improvement, a dielectric layer of a superstrate is applied above the array antenna. The calculated impedance bandwidth of proposed array antenna ranges from 27.1 GHz to 29.5 GHz for reflection coefficient (Sn) less than -1OdB. The proposed design exhibits good stable radiation patterns over the whole frequency band of interest with a total realized gain more than 16 dBi. Due to the remarkable performance of the proposed array, it can be considered as a good candidate for 5G communication applications.
Multi-User Broadcast Authentication in Wireless Sensor Networks

Broadcast authentication is a critical security service in wireless sensor networks (WSNs), as it allows mobile users of WSNs to broadcast messages to multiple sensor nodes in a secure way. Although symmetric-key-based solutions such as muTESLA and multilevel muTESLA have been proposed, they all suffer from severe energy-depletion attacks resulting from the nature of delayed message authentication. This paper presents several efficient public-key-based schemes to achieve immediate broadcast authentication and thus avoid the security vulnerability that is intrinsic to muTESLA-like schemes. Our schemes are built upon the unique integration of several cryptographic techniques, including the Bloom filter, the partial message-recovery signature scheme, and the Merkle hash tree. We prove the effectiveness and efficiency of the proposed schemes by a comprehensive quantitative analysis of their energy consumption in both computation and communication.
What we talk about when we talk about context

The emergence of ubiquitous computing as a new design paradigm poses significant challenges for human-computer interaction (HCI) and interaction design. Traditionally, HCI has taken place within a constrained and well-understood domain of experience—single users sitting at desks and interacting with conventionally-designed computers employing screens, keyboards and mice for interaction. New opportunities have engendered considerable interest in “context-aware computing”—computational systems that can sense and respond to aspects of the settings in which they are used. However, considerable confusion surrounds the notion of “context”—what it means, what it includes and what role it plays in interactive systems. This paper suggests that the representational stance implied by conventional interpretations of “context” misinterprets the role of context in everyday human activity, and proposes an alternative model that suggests different directions for design.
Graph Theory and Cyber Security

One of the most important fields in discrete mathematics is graph theory. Graph theory is discrete structures, consisting of vertices and edges that connect these vertices. Problems in almost every conceivable discipline can be solved using graph models. The field graph theory started its journey from the problem of Konigsberg Bridges in 1735. This paper is a guide for the applied mathematician who would like to know more about network security, cryptography and cyber security based of graph theory. The paper gives a brief overview of the subject and the applications of graph theory in computer security, and provides pointers to key research and recent survey papers in the area.
Time series analysis of temporal networks

A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human faceto-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤ 20%) in prediction accuracy on an average across all datsets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. PACS. PACS 89.75.Hc,89.75.Fb
Non-Deterministic Policies in Markovian Decision Processes

Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problems in such environments. In recent years, attempts were made to apply methods from reinforcement learning to construct decision support systems for action selection in Markovian environments. Although conventional methods in reinforcement learning have proved to be useful in problems concerning sequential decision-making, they cannot be applied in their current form to decision support systems, such as those in medical domains, as they suggest policies that are often highly prescriptive and leave little room for the user’s input. Without the ability to provide flexible guidelines, it is unlikely that these methods can gain ground with users of such systems. This paper introduces the new concept of non-deterministic policies to allow more flexibility in the user’s decision-making process, while constraining decisions to remain near optimal solutions. We provide two algorithms to compute non-deterministic policies in discrete domains. We study the output and running time of these method on a set of synthetic and real-world problems. In an experiment with human subjects, we show that humans assisted by hints based on non-deterministic policies outperform both human-only and computer-only agents in a web navigation task.