nils-herrmann/clean_scirepeval huggingface dataset:
Digital Beamforming on Receive: Techniques and Optimization Strategies for High-Resolution Wide-Swath SAR Imaging

Synthetic Aperture Radar (SAR) is a well-proven imaging technique for remote sensing of the Earth. However, conventional SAR systems are not capable of fulfilling the increasing demands for improved spatial resolution and wider swath coverage. To overcome these inherent limitations, several innovative techniques have been suggested which employ multiple receive-apertures to gather additional information along the synthetic aperture. These digital beamforming (DBF) on receive techniques are reviewed with particular emphasis on the multi-aperture signal processing in azimuth and a multi-aperture reconstruction algorithm is presented that allows for the unambiguous recovery of the Doppler spectrum. The impact of Doppler aliasing is investigated and an analytic expression for the residual azimuth ambiguities is derived. Further, the influence of the processing on the signal-to-noise ratio (SNR) is analyzed, resulting in a pulse repetition frequency (PRF) dependent factor describing the SNR scaling of the multi-aperture beamforming network. The focus is then turned to a complete high-resolution wide-swath SAR system design example which demonstrates the intricate connection between multi-aperture azimuth processing and the system architecture. In this regard, alternative processing approaches are compared with the multi-aperture reconstruction algorithm. In a next step, optimization strategies are discussed as pattern tapering, prebeamshaping-on-receive, and modified processing algorithms. In this context, the analytic expressions for both the residual ambiguities and the SNR scaling factor are generalized to cascaded beamforming networks. The suggested techniques can moreover be extended in many ways. Examples discussed are a combination with ScanSAR burst mode operation and the transfer to multistatic sparse array configurations.
Efficient and secure data storage operations for mobile cloud computing

In a mobile cloud computing system, lightweight wireless communication devices extend cloud services into the sensing domain. A common mobile cloud secure data service is to inquiry the data from sensing devices. The data can be collected from multiple requesters, which may drain out the power of sensing devices quickly. Thus, an efficient data access control model is desired. To this end, we present a comprehensive security data inquiry framework for mobile cloud computing. Our solution focuses on the following two research directions: First, we present a novel Privacy Preserving Cipher Policy Attribute-Based Encryption (PP-CP-ABE) to protect sensing data. Using PP-CP-ABE, light-weight devices can securely outsource heavy encryption and decryption operations to cloud service providers, without revealing the data content. Second, we propose an Attribute Based Data Storage (ABDS) system as a cryptographic group-based access control mechanism. Our performance assessments demonstrate the security strength and efficiency of the presented solution in terms of computation, communication, and storage.
A Comparison of Common Users across Instagram and Ask.fm to Better Understand Cyberbullying

This paper examines users who are common to two popular online social networks, Instagram and Ask.fm, That are often used for cyber bullying. An analysis of the negativity and positivity of word usage in posts by common users of these two social networks is performed. These results are normalized in comparison to a sample of typical users in both networks. We also examine the posting activity of common user profiles and consider its correlation with negativity. Within the Ask.fm social network, which allows anonymous posts, the relationship between anonymity and negativity is further explored.
Radio Link Frequency Assignment

The problem of radio frequency assignment is to provide communication channels from limited spectral resources whilst keeping to a minimum the interference suffered by those whishing to communicate in a given radio communication network. This problem is a combinatorial (NP-hard) optimization problem. In 1993, the CELAR (the French “Centre d'Electronique de l'Armement”) built a suite of simplified versions of Radio Link Frequency Assignment Problems (RLFAP) starting from data on a real network Roisnel93. Initially designed for assessing the performances of several Constraint Logic Programming languages, these benchmarks have been made available to the public in the framework of the European EUCLID project CALMA (Combinatorial Algorithms for Military Applications). These problems should look very attractive to the CSP community: the problem is simple to represent, all constraints are binary and involve finite domain variables. They nevertheless have some of the flavors of real problems (including large size and several optimization criteria). This paper gives essential facts about the CELAR instances and also introduces the GRAPH instances which were generated during the CALMA project.
Observations and considerations on a case of Mondor's syndrome associated with gigantomastia.

The study of the Mondor's syndrome as a possible complication of gigantomastia.



A 49 year old woman, with gigantomastia, came to our observation for the presence of a large superficial vein of the left breast affected by thrombophlebitis, compatible with Mondor's syndrome.



A "Thorek" breast reduction was performed; the patient was discharged on the second postoperative day and the follow up was smooth and fee from any complications.



In the case we observed, gigantomastia had a clear role in the pathogenesis of the Mondor's disease; in fact, the excessive weight of the breasts caused stretching of the mammary dorsal vein evolving in phlebitis. In literature, among the causes related to Mondor's disease the pendulous breast is described, probably with the same dynamic we have seen in this patient, but has never reported, so obviously, the correlation between the two events. It's considered as good practice in the pre-operative evaluation, to make differential diagnosis with any cancer, that, in a certain percentage, is associated with Mondor's syndrome, and once established the causes of the pathology, it is imperative to eliminate them to prevent any relapses.



Among the various causes recognized as predisposing to Mondor's syndrome, there has never been described gigantomastia so far. The observation and treatment of a patient in whom the gigantomastia certainly led to the onset of Mondor's disease, has led us to consider this as one of the possible complications of gigantomastia, providing an additional cue to the treatment of this condition even at an early stage.
Cable configuration analysis to increase the rotational range of suspended 6-DOF cable driven parallel robots

A cable-driven parallel robot (CDPR) is a special type of parallel robot. In the past two decades, CDPRs have been attracted a lot of attention since they have various advantages such as large workspace, low energy consumption, heavy payload capability and easy re-configurability. Especially, the greatest strength is that workspace can be expandable easily. CDPRs have enough large workspace inside an installed frame when the end-effector (EE) moves only translationally. However when the rotational motion of end-effector is used, the workspace is reduced substantially. To increase the workspace in the case that EE needs to rotate, there are two methods. One is to introduce a new mechanism of EE to change configuration parameters. Another is to change the cable configuration. In this paper, a cable configuration to increase the rotational workspace is proposed and analyzed. Using Wire Center software, workspaces were analyzed according to methods of cable connection. It is shown that the rotational workspace of the suspended 6-DOF CDPR is improved. This result is expected to influence the application expansion of CDPRs.
Understanding Representations Learned in Deep Architectures

Deep architectures have demonstrated state-of-the-art pe rformance in a variety of settings, especially with vision datasets. Deep learning a lgorithms are based on learning several levels of representationof the input. Beyond test-set performance, there is a need forqualitativecomparisons of the solutions learned by various deep architectures, focused on those learned representations. One of th goals of our research is to improve tools for finding good qualitative interpretat ions of high level features learned by such models. We also seek to gain insight into the i nvariances learned by deep networks. To this end, we contrast and compare several t echniques for finding such interpretations. We applied our techniques on Stacked Denoising Auto-Encoders and Deep Belief Networks, trained on several vision dataset s. We show that consistent filter-like interpretation is possible and simple to accomp lish at the unit level. The tools developed make it possible to analyze deep models in more dep th and accomplish the tracing ofinvariance manifoldsfor each of the hidden units. We hope that such techniques will allow researchers in deep architectures to unde rstand more of how and why deep architectures work.
Mutual Hypothesis Verification for 6D Pose Estimation of Natural Objects

Estimating the 6D pose of natural objects, such as vegetables and fruit, is a challenging problem due to the high variability of their shape. The shape variation limits the accuracy of previous pose estimation approaches because they assume that the training model and the object in the target scene have the exact same shape. To overcome this issue, we propose a novel framework that consists of a local and a global hypothesis generation pipeline with a mutual verification step. The new local descriptor is proposed to find critical parts of the natural object while the global estimator calculates object pose directly. To determine the best pose estimation result, a novel hypothesis verification step, Mutual Hypothesis Verification, is proposed. It interactively uses information from the local and the global pipelines. New hypotheses are generated by setting the initial pose using the global estimation and guiding an iterative closest point refinement using local shape correspondences. The confidence of a pose candidate is calculated by comparing with estimation results from both pipelines. We evaluate our framework with real fruit randomly piled in a box. The potential for estimating the pose of any natural object is proved by the experimental results that outperform global feature based approaches.
Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
A Unified Test Case Prioritization Approach

Test case prioritization techniques attempt to reorder test cases in a manner that increases the rate at which faults are detected during regression testing. Coverage-based test case prioritization techniques typically use one of two overall strategies: a total strategy or an additional strategy. These strategies prioritize test cases based on the total number of code (or code-related) elements covered per test case and the number of additional (not yet covered) code (or code-related) elements covered per test case, respectively. In this article, we present a unified test case prioritization approach that encompasses both the total and additional strategies. Our unified test case prioritization approach includes two models (basic and extended) by which a spectrum of test case prioritization techniques ranging from a purely total to a purely additional technique can be defined by specifying the value of a parameter referred to as the fp value. To evaluate our approach, we performed an empirical study on 28 Java objects and 40 C objects, considering the impact of three internal factors (model type, choice offp value, and coverage type) and three external factors (coverage granularity, test case granularity, and programming/testing paradigm), all of which can be manipulated by our approach. Our results demonstrate that a wide range of techniques derived from our basic and extended models with uniform fp values can outperform purely total techniques and are competitive with purely additional techniques. Considering the influence of each internal and external factor studied, the results demonstrate that various values of each factor have nontrivial influence on test case prioritization techniques.
Almost sure stability of networked control systems under exponentially bounded bursts of dropouts

A wireless networked control systems (NCS) is a control system whose feedback path is realized over a wireless communication network. The stability of such systems can be problematic given the random way in which wireless channels drop feedback messages. This paper establishes sufficient conditions for the almost sure stability of NCS under random dropouts. These conditions relate the burstiness in the dropout process to the nominal response of the controlled system. In particular, this means that the burstiness of the dropout process provides a convenient quality-of-service (QoS) constraint on the wireless channel that can be used to adaptively reconfigure the control system in a manner that guarantees the almost sure stability of the NCS. We also show how a probabilistic extension of the network calculus can be used to reconfigure multi-hop communication networks so this paper's sufficient stability condition is not violated.
Speech and Language Processing

CHAPTER 18 Lexicons for Sentiment and Affect Extraction " [W]e write, not with the fingers, but with the whole person. The nerve which controls the pen winds itself about every fibre of our being, threads the heart, pierces the liver. " Virginia Woolf, Orlando " She runs the gamut of emotions from A to B. " Dorothy Parker, reviewing Hepburn's performance in Little Women In this chapter we turn to tools for interpreting affective meaning, extending our affective study of sentiment analysis in Chapter 7. We use the word 'affective', following the tradition in affective computing (Picard, 1995) to mean emotion, sentiment, personality , mood, and attitudes. Affective meaning is closely related to subjectivity, subjectivity the study of a speaker or writer's evaluations, opinions, emotions, and speculations (Wiebe et al., 1999). How should affective meaning be defined? One influential typology of affec-tive states comes from Scherer (2000), who defines each class of affective states by factors like its cognition realization and time course: Emotion: Relatively brief episode of response to the evaluation of an external or internal event as being of major significance.  Diffuse affect state, most pronounced as change in subjective feeling, of low intensity but relatively long duration, often without apparent cause. Interpersonal stance: Affective stance taken toward another person in a specific interaction, colouring the interpersonal exchange in that situation.  The Scherer typology of affective states, with descriptions from Scherer (2000).
Assessing automotive functional safety microprocessor with ISO 26262 hardware requirements

This paper provides a step-by-step guideline for the assessment of an automotive safety microprocessor with ISO 26262 hardware requirements. ISO 26262 part 5 - Product development at the hardware level - specifies the safety activities during the phase of the automotive hardware development. In this phase, hardware safety design is derived (from the results of ISO 26262 part 3 and 4), implemented, integrated, and tested. To prove the compliance with ISO 26262 hardware development process, quantitative evaluations on the hardware are indispensable. These quantitative evaluations are known as hardware architecture metrics and probabilistic hardware metrics. The assessment results qualify a design with an automotive safety integrity level (ASIL) which ranges from ASIL-A (lowest) to ASIL-D (highest). In this paper, we implemented an exemplary safety microprocessor to demonstrate the ISO 26262 hardware assessment process. The derivation procedures of the ASIL level from the hardware architecture metrics and probabilistic hardware metrics are fully discussed. Based on the evaluation results, we also provide design suggestions for the ISO 26262 safety hardware design.
A dual-layer estimator architecture for long-term localization

In this paper, we present a localization algorithm for estimating the 3D position and orientation (pose) of a moving vehicle based on visual and inertial measurements. The main advantage of the proposed method is that it provides precise pose estimates at low computational cost. This is achieved by introducing a two-layer estimation architecture that processes measurements based on their information content. Inertial measurements and feature tracks between consecutive images are processed locally in the first layer (multi-state-constraint Kalman filter) providing estimates for the motion of the vehicle at a high rate. The second layer comprises a bundle adjustment iterative estimator that operates intermittently so as to (i) reduce the effect of the linearization errors, and (ii) update the state estimates every time an area is re-visited and features are re-detected (loop closure). Through this process reliable state estimates are available continuously, while the estimation errors remain bounded during long-term operation. The performance of the developed system is demonstrated in large-scale experiments, involving a vehicle localizing within an urban area.
Intellectual Capital (IC) Measurement in the Mass Media Context

Mass media is the key in°uencer of public opinion. The in°uence is not only limited to political and social, but also relates to organisational and economical reputation and brands. Within public opinion, organisations must manage how they are represented competitively within mass media so that they can develop their brand strategically to grow and compete in the current global knowledge economy. This is where the link to Intellectual Capital (IC) Measurement is signi ̄cant. IC, as the sum of all an organisation's intangible assets drives a company's presence and value within the media, albeit related to human, structural or relational capital attributes. The measurement, therefore, of IC in the mass media context is invaluable to understand how a company is placed strategically and competitively in the external space, and how this links to internal activities, goals and outcomes. This paper is an attempt to address some of the issues related to IC measurements in the mass media context by suggesting a framework that provides a multidisciplinary and holistic approach to the understanding and contextualising of the organisation's presence in the public space.
A New Framework for Distributed Submodular Maximization

A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. A lot of recent effort has been devoted to developing distributed algorithms for these problems. However, these results suffer from high number of rounds, suboptimal approximation ratios, or both. We develop a framework for bringing existing algorithms in the sequential setting to the distributed setting, achieving near optimal approximation ratios for many settings in only a constant number of MapReduce rounds. Our techniques also give a fast sequential algorithm for non-monotone maximization subject to a matroid constraint.
A 1.8 V 11.02 μW single-ended inverter-based OTA with 113.62 dB gain

This paper presents a low power 1.8 V single-ended operational transconductance amplifier (OTA) with a very high dc gain targeted for audio applications. Cascoding and gain boosting are employed to achieve the high gain. The amplifier is implemented in UMC 180 nm CMOS technology. For a typical process corner, the amplifier provides a dc gain of 113.62 dB and a unity gain bandwidth of 2.49 MHz at 62.9° phase margin with capacitive load of 2.5 pF. The amplifier consumes only 6.12 μA of quiescent current from 1.8 V supply. The amplifier exhibits good performance across all the process corners with the use of body bias technique.
A new variable stiffness design: Matching requirements of the next robot generation

Facing new tasks, the conventional rigid design of robotic joints has come to its limits. Operating in unknown environments current robots are prone to failure when hitting unforeseen rigid obstacles. Moreover, safety constraints are a major aspect for robots interacting with humans. In order to operate safely, existing robotic systems in this field are slow and have a lack of performance. To circumvent these limitations, a new robot joint with a variable stiffness approach (VS-Joint) is presented. It combines a compact and highly integrated design with high performance actuation. The VS- Joint features a highly dynamic stiffness adjustment along with a mechanically programmable system behavior. This allows an easy adaption to a big variety of tasks. A benefit of the joint is its intrinsic robustness against impacts and hard contacts, which permits faster trajectories and handling. Thus, it provides excellent attributes for the use in shoulder and elbow joints of an anthropomorphic robot arm.
Analysis and evaluation of Secos, a protocol for energy efficient and secure communication in sensor networks

Wireless sensor networks are increasingly being used in applications where the communication between nodes needs to be protected from eavesdropping and tampering. Such protection is typically provided using techniques from symmetric key cryptography. The protocols in this domain suffer from one or more of the following problems  weak security guarantees if some nodes are compromised, lack of scalability, high energy overhead for key management, and increased end-to-end data latency. In this paper, we propose a protocol called SECOS that mitigates these problems in static sensor networks. SECOS divides the sensor field into control groups each with a control node. Data exchange between nodes within a control group happens through the mediation of the control head which provides the common key. The keys are refreshed periodically and the control nodes are changed periodically to enhance security. SECOS enhances the survivability of the network by handling compromise and failures of control nodes. It provides the guarantee that the communication between any two sensor nodes remains secure despite the compromise of any number of other nodes in the network. The experiments based on a simulation model show a seven time reduction in energy overhead and a 50% reduction in latency compared to SPINS, which is one of the state-of-the-art protocols for key management in sensor networks.