nils-herrmann/clean_scirepeval huggingface dataset:
Resonator-Loaded Broadband Antenna for LTE700/GSM850/GSM900 Base Stations

A resonator-loaded broadband antenna is proposed, which is composed of a resonator-loaded dipole, a balun, a U-shaped strip feed, and a box-shaped reflector. The resonator beside the dipole is adopted for impedance bandwidth enhancement. The higher resonate modes can be moved lower by using a resonator, and wide bandwidth can be achieved by moving this mode close to the main resonating mode of the dipole. A prototype of the proposed antenna is designed, fabricated, and tested. Measured results show that the antenna achieves an impedance bandwidth of 37.5% (0.67–0.98 GHz) for |<inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula><sub> 11</sub>| < –15 dB.
Discriminative Multi-View Interactive Image Re-Ranking

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users’ intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.
Hidden attribute-based signatures without anonymity revocation

We propose a new notion called hidden attribute-based signature, which is inspired by the recent developments in attribute-based cryptosystem. With this technique, users are able to sign messages with any subset of their attributes issued from an attribute center. In this notion, a signature attests not to the identity of the individual who endorsed a message, but instead to a claim regarding the attributes the underlying signer possesses. Users cannot forge signature with attributes which they have not been issued. Furthermore, signer remains anonymous without the fear of revocation, among all users with the attributes purported in the signature. After formalizing the security model, we propose two constructions of hidden attributebased signature from pairings. The first construction supports a large universe of attributes and its security proof relies on the random oracle assumption, which can be removed in the second construction. Both constructions have proven to be secure under the standard computational Diffie–Hellman assumption.
Detection of User Demographics on Social Media: A Review of Methods and Recommendations for Best Practices

Researchers in fields such as sociology, demography and public health, have used data from social media to explore a diversity of questions. In public health, researchers use data from social media to monitor disease spread, assess population attitudes toward health-related issues, and to better understand the relationship between behavioral changes and population health. However, a major limitation of the use of these data for population health research is a lack of key demographic indicators such as, age, race and gender. Several studies have proposed methods for automated detection of social media users’ demographic characteristics. These range from facial recognition to classic supervised and unsupervised machine learning methods. We seek to provide a review of existing approaches to automated detection of demographic characteristics of social media users. We also address the applicability of these methods to public health research; focusing on the challenge of working with highly dynamical, large scale data to study health trends. Furthermore, we provide an overview of work that emphasizes scalability and efficiency in data acquisition and processing, and make best practice recommendations.
A Particle Swarm Optimization ( PSO )-based Heuristic for Scheduling Work fl ow Applications in Cloud Computing Environments

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the ‘execution time’. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based scheduling heuristic for data intensive applications that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We analyze the cost savings when using PSO as compared to using existing ‘Best Resource Selection’ (BRS) algorithm. Our results show that we can achieve: a) as much as 3 times cost savings as compared to BRS, b) good distribution of workload onto resources, when using PSO based scheduling heuristic.
Novelty and interestingness measures for design-space exploration

Measures of novelty and interestingness are frequently encountered in the context of developmental robotics, being derived from human psychology. This work addresses these measures from the viewpoint of enhancing design-space exploration in black-box optimization. We provide a unifying notational and naming scheme with the intent of facilitating comparison, implementation, and application in the domain of design optimization. Initial analysis shows a promising interestingness measure for being tried on real-world design problems.
Spare parts inventory control : a literature review

Spare parts inventory are needed for maintenance and repair of final products, vehicles, industrial machines and equipments, frequently requiring high investments and significantly affecting customer satisfaction. Inventory management is complex due to the large number of different items and low demands. This article presents a literature review on single location spare parts inventory control, embracing both demand forecasting techniques and inventory control decisions on the different life cycle stages. Overall, the literature review identified the following research opportunities on inventory management: criteria to decide to stock or not an item, how much to order in the first and the last batch, demand forecasting and inventory control models integration and case studies on real applications.
Flight Dynamics-Based Recovery of a UAV Trajectory Using Ground Cameras

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure, which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAVs autopilot that determined its flight trajectory. Our method requires neither perfect single-view tracking nor appearance matching across views. For robustness, we allow the tracker to generate multiple detections per frame in each video. The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined in our bundle adjustment formulation. Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes shows that our technique is superior to existing methods.
Practical condition synchronization for transactional memory

Few transactional memory implementations allow for condition synchronization among transactions. The problems are many, most notably the lack of consensus about a single appropriate linguistic construct, and the lack of mechanisms that are compatible with hardware transactional memory. In this paper, we introduce a broadly useful mechanism for supporting condition synchronization among transactions. Our mechanism supports a number of linguistic constructs for coordinating transactions, and does so without introducing overhead on in-flight hardware transactions. Experiments show that our mechanisms work well, and that the diversity of linguistic constructs allows programmers to chose the technique that is best suited to a particular application.
Telling More Than We Can Know : Verbal Reports on Mental Processes

Evidence is reviewed which suggests that there may be little or no direct introspective access to higher order cognitive processes. Subjects are sometimes (a) unaware of the existence of a stimulus that importantly influenced a response, (b) unaware of the existence of the response, and (c) unaware that the stimulus has affected the response. It is proposed that when people attempt to report on their cognitive processes, that is, on the processes mediating the effects of a stimulus on a response, they do not do so on the basis of any true introspection. Instead, their reports are based on a priori, implicit causal theories, or judgments about the extent to which a particular stimulus is a plausible cause of a given response. This suggests that though people may not be able to observe directly their cognitive processes, they will sometimes be able to report accurately about them. Accurate reports will occur when influential stimuli are salient and are plausible causes of the responses they produce, and will not occur when stimuli are not salient or are not plausible causes.
Automated Linguistic Analysis of Deceptive and Truthful Synchronous Computer-Mediated Communication

The present study investigates changes in both the sender's and the target's linguistic style across truthful and deceptive dyadic communication in a synchronous text-based setting. A computer-based analysis of 242 transcripts revealed that senders produced more words overall, decreased their use of self-oriented pronouns but increased other-oriented pronouns, and used more sense-based descriptions (e.g., seeing, touching) when lying than when telling the truth. In addition, motivated senders avoided causal terms during deception, while unmotivated senders relied more heavily on simple negations. Receivers used more words when being deceived, but they also asked more questions and used shorter sentences when being lied to than when being told the truth, especially when the sender was unmotivated. These findings are discussed in terms of their implications for linguistic style matching and interpersonal deception theory.
Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

Multimedia content is dominating today's Web information. The nature of multimedia user-item interactions is 1/0 binary implicit feedback (e.g., photo likes, video views, song downloads, etc.), which can be collected at a larger scale with a much lower cost than explicit feedback (e.g., product ratings). However, the majority of existing collaborative filtering (CF) systems are not well-designed for multimedia recommendation, since they ignore the implicitness in users' interactions with multimedia content. We argue that, in multimedia recommendation, there exists item- and component-level implicitness which blurs the underlying users' preferences. The item-level implicitness means that users' preferences on items (e.g. photos, videos, songs, etc.) are unknown, while the component-level implicitness means that inside each item users' preferences on different components (e.g. regions in an image, frames of a video, etc.) are unknown. For example, a 'view'' on a video does not provide any specific information about how the user likes the video (i.e.item-level) and which parts of the video the user is interested in (i.e.component-level). In this paper, we introduce a novel attention mechanism in CF to address the challenging item- and component-level implicit feedback in multimedia recommendation, dubbed Attentive Collaborative Filtering (ACF). Specifically, our attention model is a neural network that consists of two attention modules: the component-level attention module, starting from any content feature extraction network (e.g. CNN for images/videos), which learns to select informative components of multimedia items, and the item-level attention module, which learns to score the item preferences. ACF can be seamlessly incorporated into classic CF models with implicit feedback, such as BPR and SVD++, and efficiently trained using SGD. Through extensive experiments on two real-world multimedia Web services: Vine and Pinterest, we show that ACF significantly outperforms state-of-the-art CF methods.
Oblivious Dynamic Searchable Encryption on Distributed Cloud Systems

Dynamic Searchable Symmetric Encryption (DSSE) allows search/update operations over encrypted data via an encrypted index. However, DSSE has been shown to be vulnerable to statistical inference attacks, which can extract a significant amount of information from access patterns on encrypted index and files. While generic Oblivious Random Access Machine (ORAM) can hide access patterns, it has been shown to be extremely costly to be directly used in DSSE setting. By exploiting the distributed cloud infrastructure, we develop a series of Oblivious Distributed DSSE schemes called ODSE, which enable oblivious access on the encrypted index with a high security and improved efficiency over the use of generic ORAM. Specifically, ODSE schemes are 3×-57× faster than applying the state-of-the-art generic ORAMs on encrypted dictionary index in real network settings. One of the proposed ODSE schemes offers desirable security guarantees such as informationtheoretic security with robustness against malicious servers. These properties are achieved by exploiting some of the unique characteristics of searchable encryption and encrypted index, which permits us to harness the computation and communication efficiency of multi-server PIR and Write-Only ORAM simultaneously. We fully implemented ODSE and have conducted extensive experiments to assess the performance of our proposed schemes in a real cloud environment.
Inference Over Distribution of Posterior Class Probabilities for Reliable Bayesian Classification and Object-Level Perception

State of the art Bayesian classification approaches typically maintain a posterior distribution over possible classes given available sensor observations (images). Yet, while these approaches fuse all classifier outputs thus far, they do not provide any indication regarding how reliable the posterior classification is, thus limiting its functionality in terms of autonomous systems and robotics. On the other hand, current deep learning based classifiers provide an uncertainty measure, thereby quantifying model uncertainty. However, they do so on a single frame basis and do not consider a sequential framework. In this letter, we develop a novel approach that infers a distribution over posterior class probabilities, while accounting for model uncertainty. This distribution enables reasoning about uncertainty in the posterior classification and, therefore, is of prime importance for robust classification, object-level perception in uncertain and ambiguous scenarios, and for safe autonomy in general. The distribution of the posterior class probability has no known analytical solution; thus, we propose to approximate this distribution via sampling. We evaluate our approach in simulation and using real images fed into a convolutional neural network classifier.
SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems

Despite their immense popularity, deep learningbased acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems to misbehave. In this paper, we present SIRENATTACK, a new class of attacks to generate adversarial audios. Compared with existing attacks, SIRENATTACK highlights with a set of significant features: (i) versatile – it is able to deceive a range of end-to-end acoustic systems under both white-box and black-box settings; (ii) effective – it is able to generate adversarial audios that can be recognized as specific phrases by target acoustic systems; and (iii) stealthy – it is able to generate adversarial audios indistinguishable from their benign counterparts to human perception. We empirically evaluate SIRENATTACK on a set of state-of-the-art deep learning-based acoustic systems (including speech command recognition, speaker recognition and sound event classification), with results showing the versatility, effectiveness, and stealthiness of SIRENATTACK. For instance, it achieves 99.45% attack success rate on the IEMOCAP dataset against the ResNet18 model, while the generated adversarial audios are also misinterpreted by multiple popular ASR platforms, including Google Cloud Speech, Microsoft Bing Voice, and IBM Speech-to-Text. We further evaluate three potential defense methods to mitigate such attacks, including adversarial training, audio downsampling, and moving average filtering, which leads to promising directions for further research.
A Taxonomy of Cyber Attacks on 3G Networks

Early first and second generation (1G and 2G, respectively) wireless telecommunication networks were isolated in the sense that their signaling and control infrastructure was not directly accessible to end subscribers. The vision of the next generation 3G wireless telecommunication network is to use IP technologies for control and transport. The introduction of IP technologies has opened up a new generation of IP-based services that must interwork with traditional 3G wireless telecommunication networks. Cross Network Services will use a combination of Internet-based data and data from the wireless telecommunication network to provide services to the wireless subscriber. They will be multi-vendor, multi-domain, and will cater to a wide variety of needs. An example of such a Cross Network Service is the Email Based Call Forwarding Service (CFS), where the status of the subscriber’s email inbox is used to trigger call forwarding in the wireless telecommunication network. A security risk is introduced by providing Internet connectivity to the 3G networks in that certain attacks can be easily enforced on the wireless telecommunication network indirectly from the IP networks. These Cross Infrastructure Cyber Attacks are simple to execute and yet have serious effects. In this paper we present a unique attack taxonomy in which we consider the Cross Infrastructure Cyber attacks in addition to the standard Single Infrastructure attacks, some of which have already been studied. In presenting the taxonomy of attacks on the 3G Network, we classify the attacks into three dimensions summarized in Table 1.
The cognitive control of emotion

The capacity to control emotion is important for human adaptation. Questions about the neural bases of emotion regulation have recently taken on new importance, as functional imaging studies in humans have permitted direct investigation of control strategies that draw upon higher cognitive processes difficult to study in nonhumans. Such studies have examined (1) controlling attention to, and (2) cognitively changing the meaning of, emotionally evocative stimuli. These two forms of emotion regulation depend upon interactions between prefrontal and cingulate control systems and cortical and subcortical emotion-generative systems. Taken together, the results suggest a functional architecture for the cognitive control of emotion that dovetails with findings from other human and nonhuman research on emotion.
The general inefficiency of batch training for gradient descent learning

Gradient descent training of neural networks can be done in either a batch or on-line manner. A widely held myth in the neural network community is that batch training is as fast or faster and/or more 'correct' than on-line training because it supposedly uses a better approximation of the true gradient for its weight updates. This paper explains why batch training is almost always slower than on-line training-often orders of magnitude slower-especially on large training sets. The main reason is due to the ability of on-line training to follow curves in the error surface throughout each epoch, which allows it to safely use a larger learning rate and thus converge with less iterations through the training data. Empirical results on a large (20,000-instance) speech recognition task and on 26 other learning tasks demonstrate that convergence can be reached significantly faster using on-line training than batch training, with no apparent difference in accuracy.
Self-Autonomous Wireless Sensor Nodes With Wind Energy Harvesting for Remote Sensing of Wind-Driven Wildfire Spread

The satellite-based remote sensing technique has been widely used in monitoring wildfire spread. There are two prominent drawbacks with this approach of using satellites located in space: (1) very low sampling rate (temporal resolution problem) and (2) lack of accuracy (spatial and spectral resolution problem). To address these challenges, a wireless sensor network deployed at ground level with high-fidelity and low-altitude atmospheric sensing for wind speed of local wildfire spread has been used. An indirect approach in sensing wind speed has been proposed in this paper as an alternative to the bulky conventional wind anemometer to save cost and space. The wind speed is sensed by measuring the equivalent electrical output voltage of the wind turbine generator (WTG). The percentage error in the wind speed measurement using the proposed indirect method is measured to be well within the ±4% limit with respect to wind anemometer accuracy. The same WTG also functions as a wind energy harvesting (WEH) system to convert the available wind energy into electrical energy to sustain the operation of the wireless sensor node. The experimental results show that the designed WEH system is able to harvest an average electrical power of 7.7 mW at an average wind speed of 3.62 m/s for powering the operation of the wireless sensor node that consumes 3.5 mW for predicting the wildfire spread. Based on the sensed wind speed information, the fire control management system determines the spreading condition of the wildfire, and an adequate fire suppression action can be performed by the fire-fighting experts.
Lyapunov based model reference adaptive control for aerial manipulation

This paper presents a control scheme to achieve dynamic stability in an aerial vehicle with dual multi-degree of freedom manipulators using a lyapunov based model reference adaptive control. Our test flight results indicate that we can accurately model and control our aerial vehicle when both moving the manipulators and interacting with target objects. Using the Lyapunov stability theory, the controller is proven to be stable. The simulation results showed how the MRAC is capable of stabilizing the oscillations produced from the unstable PI-D attitude control loop. Finally a high level control system based on a switching automaton is proposed in order to ensure the safety of the aerial manipulation missions.