nils-herrmann/clean_scirepeval huggingface dataset:
Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications

In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, are investigated. In particular, to enable reliable uplink communications for the IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the 3D placement and the mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs’ locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total-transmit power of the IoT devices is reduced by 45% compared with a case, in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28% enhanced system reliability compared with the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs.
Exact Soft Confidence-Weighted Learning

In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-theart algorithms (including AROW, NAROW and NHERD), we found that SCW generally achieves better or at least comparable predictive accuracy, but enjoys significant advantage of computational efficiency (i.e., smaller number of updates and lower time cost).
Robust MIL-Based Feature Template Learning for Object Tracking

Because of appearance variations, training samples of the tracked targets collected by the online tracker are required for updating the tracking model. However, this often leads to tracking drift problem because of potentially corrupted samples: 1) contaminated/outlier samples resulting from large variations (e.g. occlusion, illumination), and 2) misaligned samples caused by tracking inaccuracy. Therefore, in order to reduce the tracking drift while maintaining the adaptability of a visual tracker, how to alleviate these two issues via an effective model learning (updating) strategy is a key problem to be solved. To address these issues, this paper proposes a novel and optimal model learning (updating) scheme which aims to simultaneously eliminate the negative effects from these two issues mentioned above in a unified robust feature template learning framework. Particularly, the proposed feature template learning framework is capable of: 1) adaptively learning uncontaminated feature templates by separating out contaminated samples, and 2) resolving label ambiguities caused by misaligned samples via a probabilistic multiple instance learning (MIL) model. Experiments on challenging video sequences show that the proposed tracker performs favourably against several state-of-the-art trackers.
A Comparative Study of Users' Microblogging Behavior on Sina Weibo and Twitter

In this article, we analyze and compare user behavior on two different microblogging platforms: (1) Sina Weibo which is the most popular microblogging service in China and (2) Twitter. Such a comparison has not been done before at this scale and is therefore essential for understanding user behavior on microblogging services. In our study, we analyze more than 40 million microblogging activities and investigate microblogging behavior from different angles. We (i) analyze how people access microblogs and (ii) compare the writing style of Sina Weibo and Twitter users by analyzing textual features of microposts. Based on semantics and sentiments that our user modeling framework extracts from English and Chinese posts, we study and compare (iii) the topics and (iv) sentiment polarities of posts on Sina Weibo and Twitter. Furthermore, (v) we investigate the temporal dynamics of the microblogging behavior such as the drift of user interests over time.
 Our results reveal significant differences in the microblogging behavior on Sina Weibo and Twitter and deliver valuable insights for multilingual and culture-aware user modeling based on microblogging data. We also explore the correlation between some of these differences and cultural models from social science research.
Breast Cancer Histopathological Image Classification: Is Magnification Important?

Breast cancer is one of the most common cancer in women worldwide. It is typically diagnosed via histopathological microscopy imaging, for which image analysis can aid physicians for more effective diagnosis. Given a large variability in tissue appearance, to better capture discriminative traits, images can be acquired at different optical magnifications. In this paper, we propose an approach which utilizes joint colour-texture features and a classifier ensemble for classifying breast histopathology images. While we demonstrate the effectiveness of the proposed framework, an important objective of this work is to study the image classification across different optical magnification levels. We provide interesting experimental results and related discussions, demonstrating a visible classification invariance with cross-magnification training-testing. Along with magnification-specific model, we also evaluate the magnification independent model, and compare the two to gain some insights.
Reliability characterization of Chip-on-Wafer-on-Substrate (CoWoS) 3D IC integration technology

With the size of transistors scaling down, 3D IC packaging emerged as one of the most promising solutions to achieve system integration on the track of Moore's Law. In this article, we demonstrated a sub-system with one 28nm logic device and two 40nm chips on a 600mm2 silicon interposer with Through-Silicon-Via (TSV) integrating 4 layers of high density interconnects. The packages were assembled using our proprietary CoWoS (Chip on Wafer on Substrate) technology that incorporated 270,000 micro-bump (μBump) and 8,700 C4 bumps. Comprehensive reliability characterization and test methods will be presented. It includes copper interconnect reliability of silicon interposer on EM, SM and IMD TDDB with the presence of TSV. μBump EM and TSV EM are characterized to provide a design guideline for maximum current carrying capability; the EM test methodology was also used to optimize the integrated process, e.g. TSV copper plating, μBump joint, and interface treatment of TSV revealing. Package process optimization, bill of material selectio n and qualificatio n we r e conducted on a 28nm Cu/ELK (Extreme low-K) test vehicle taking Chip-Package-Interaction into consideration. Not only component level package reliability tests were performed, board level Thermal Cycling, Power Cycling and Mechanical Shock tests were also executed to understand the reliability margin and potential failure modes in field use condition. The typical failure modes and mechanisms will be discussed. CoWoS technology is eventually successfully developed with enhanced reliability. The results highlight the importance of a highly integrated 3D IC technology from silicon wafer process to assembly packaging. This work shows that in the new paradigm of 3D IC integration, Si Foundry is positioned at a unique leadership to manage the innovation in Si processes and the improvement of Si assembly operating life.
Coarse-grained Argumentation Features for Scoring Persuasive Essays

Scoring the quality of persuasive essays is an important goal of discourse analysis, addressed most recently with highlevel persuasion-related features such as thesis clarity, or opinions and their targets. We investigate whether argumentation features derived from a coarse-grained argumentative structure of essays can help predict essays scores. We introduce a set of argumentation features related to argument components (e.g., the number of claims and premises), argument relations (e.g., the number of supported claims) and typology of argumentative structure (chains, trees). We show that these features are good predictors of human scores for TOEFL essays, both when the coarsegrained argumentative structure is manually annotated and automatically predicted.
Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.
Are we ready for sex robots?

Sex robots are gaining a remarkable amount of attention in current discussions about technology and the future of human relationships. To help understand what kinds of relationships people will have with these robots, empirical data about people's views of sex robots is needed. We report the results of the first systematic survey that asks about the appropriateness and value of sex robots, acceptable forms they can take, and the degree to which using them counts as sex. The results show a consistent difference in the uses for which women and men found sex robots to be appropriate, with women less and men more inclined to consider them socially useful. We also found convergences on what sex robots are like and how sex with them is to be classified, suggesting that larger views about relationships and society, not just understandings of sex robots themselves, should be a matter for more research and thus frame future work on the ethics of sex robots.
Context-Aware Representations for Knowledge Base Relation Extraction

We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction. We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24% on a held-out set of relations. The code and the dataset to replicate the experiments are made available at https://github.com/ukplab.
Collaborative visualization: Definition, challenges, and research agenda

The conflux of two growing areas of technology— collaboration and visualization—into a new research direction, collaborative visualization, provides new research challenges. Technology now allows us to easily connect and collaborate with one another—in settings as diverse as over networked computers, across mobile devices, or using shared displays such as interactive walls and tabletop surfaces. Digital information is now regularly accessed by multiple people in order to share information, to view it together, to analyze it, or to form decisions. Visualizations are used to deal more effectively with large amounts of information while interactive visualizations allow users to explore the underlying data. While researchers face many challenges in collaboration and in visualization, the emergence of collaborative visualization poses additional challenges but is also an exciting opportunity to reach new audiences and applications for visualization tools and techniques. The purpose of this article is (1) to provide a definition, clear scope, and overview of the evolving field of collaborative visualization, (2) to help pinpoint the unique focus of collaborative visualization with its specific aspects, challenges, and requirements within the intersection of general computer-supported cooperative work (CSCW) and visualization research, and (3) to draw attention to important future research questions to be addressed by the community. We conclude by discussing a research agenda for future work on collaborative visualization and urge for a new generation of visualization tools that are designed with collaboration in mind from their very inception.
Compact Crossed-Dipole Antennas Loaded With Near-Field Resonant Parasitic Elements

Two compact planar crossed-dipole antennas loaded with near-field resonant parasitic (NFRP) elements are reported. The NFRP and crossed-dipole elements are designed for the desired circularly polarized (CP) radiation. By placing the NFRP element over the driven element at angles of 0° and 45°, respectively, dual-band and broadband CP antennas are realized. All radiating elements of antennas are 35 mm <inline-formula> <tex-math notation="LaTeX">$\times35$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times0.508$ </tex-math></inline-formula> mm (<inline-formula> <tex-math notation="LaTeX">$0.187~\lambda _{0} \times 0.187 \lambda _{0} \times 0.0027 \lambda _{0}$ </tex-math></inline-formula> at 1.6 GHz) in size. The dual-band CP antenna has a measured <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -10$ </tex-math></inline-formula>-dB bandwidth of 226 MHz (1.473–1.699 GHz) and measured 3-dB axial ratio (AR) bandwidths of 12 MHz (1.530–1.542 GHz) and 35 MHz (1.580–1.615 GHz) with minimum AR CP frequencies of 1.535 GHz (AR = 0.26 dB) and 1.595 GHz (AR = 2.08 dB), respectively. The broadband CP antenna has a measured <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -10$ </tex-math></inline-formula>-dB bandwidth of 218 MHz (1.491–1.709 GHz) and a 3-dB AR bandwidth of 145 MHz (1.490–1.635 GHz). These compact antennas yield bidirectional electromagnetic fields with high radiation efficiency across their operational bandwidths.
An improved bee colony optimization algorithm with an application to document clustering

The bee colony optimization (BCO) algorithm is proved to be one of the fast, robust and efficient global search heuristics in tackling different practical problems. Considering BCO algorithm in this paper, we utilize it for the data clustering, a fundamental problem that frequently arises in many applications. However, we discovered some obstacles in directly applying the ancient BCO to address the clustering problem and managed to change some basic behaviors of this swarm algorithm. In particular, we present an improved bee colony optimization algorithm, dubbed IBCO, by introducing cloning and fairness concepts into the BCO algorithm and make it more efficient for data clustering. These features give BCO very powerful and balanced exploration and exploitation capabilities to effectively guide the search process toward the proximity of the high quality solutions. In particular, the cloning feature allows it to take advantage of experiences gained from previous generations when generating new solutions. The problem of getting stuck in a local optima still laid bare in the proposed improved version. As a result, to overcome the shortage of this swarm algorithm in searching locally, we hybridize it with the k-means algorithm to take advantage of fine tuning power of the widely used k-means algorithm which demonstrates good result in local searches. We propose four different hybridized algorithms based on IBCO and kmeans algorithms and investigate the clustering results and convergence behavior of them. We empirically demonstrate that our hybrid algorithms alleviate the problem of sticking in a local solution even for large and high dimensional data sets such as document clustering. The results show that proposed algorithms are robust enough to be used in many applications compared to k-means and other recently proposed evolutionary based clustering algorithms including genetic, particle swarm optimization, ant colony, and bee based algorithms.
A Survey on Data Quality: Classifying Poor Data

Data is part of our everyday life and an essential asset in numerous businesses and organizations. The quality of the data, i.e., the degree to which the data characteristics fulfill requirements, can have a tremendous impact on the businesses themselves, the companies, or even in human lives. In fact, research and industry reports show that huge amounts of capital are spent to improve the quality of the data being used in many systems, sometimes even only to understand the quality of the information in use. Considering the variety of dimensions, characteristics, business views, or simply the specificities of the systems being evaluated, understanding how to measure data quality can be an extremely difficult task. In this paper we survey the state of the art in classification of poor data, including the definition of dimensions and specific data problems, we identify frequently used dimensions and map data quality problems to the identified dimensions. The huge variety of terms and definitions found suggests that further standardization efforts are required. Also, data quality research on Big Data appears to be in its initial steps, leaving open space for further research.
Tutorial: introduction to the rational unified process®

The Rational Unified Process® (RUP®) is a software engineering process framework. It captures many of the best practices in modern software development in a form that is suitable for a wide range of projects and organizations. It embeds object-oriented techniques and uses the UML as the principal notation for the several models that are built during the development. The RUP is also an open process framework that allows software organizations to tailor the process to their specific need, and to capture their own specific process know-how in the form of process components. Many process components are now developed by various organizations to cover different domains, technologies, tools, or type of development, and these components can be assembled to rapidly compose a suitable process. This tutorial will introduce the basic concepts and principles, which lie under the RUP framework, and show concrete examples of its usage.
Neurobiology of emotion perception I: the neural basis of normal emotion perception

There is at present limited understanding of the neurobiological basis of the different processes underlying emotion perception. We have aimed to identify potential neural correlates of three processes suggested by appraisalist theories as important for emotion perception: 1) the identification of the emotional significance of a stimulus; 2) the production of an affective state in response to 1; and 3) the regulation of the affective state. In a critical review, we have examined findings from recent animal, human lesion, and functional neuroimaging studies. Findings from these studies indicate that these processes may be dependent upon the functioning of two neural systems: a ventral system, including the amygdala, insula, ventral striatum, and ventral regions of the anterior cingulate gyrus and prefrontal cortex, predominantly important for processes 1 and 2 and automatic regulation of emotional responses; and a dorsal system, including the hippocampus and dorsal regions of anterior cingulate gyrus and prefrontal cortex, predominantly important for process 3. We suggest that the extent to which a stimulus is identified as emotive and is associated with the production of an affective state may be dependent upon levels of activity within these two neural systems.
Fault tolerant permanent magnet motor drives for electric vehicles

This paper presents a novel five-phase fault tolerant interior permanent magnet (IPM) motor drive with higher performance and reliability for electric vehicles applications. A new machine design along with efficient control strategy is developed for fault tolerant operation of electric drive without severely compromising the drive performance. Fault tolerance is achieved by employing a five phase fractional-slot concentrated windings configuration IPM motor drive, with each phase electrically, magnetically, thermally and physically independent of all the others. The proposed electric drive system presents higher torque density, negligible cogging torque, and about ±0.5% torque ripple. Power converter requirements are discussed and control strategies to minimize the impact of machine or converter fault are developed. Besides, all the requirement of a fault tolerant operation, including high phase inductance and negligible mutual coupling between phases are met. Analytical and finite element analysis and comparison case studies are presented.
Analyzing modular CNN architectures for joint depth prediction and semantic segmentation

This paper addresses the task of designing a modular neural network architecture that jointly solves different tasks. As an example we use the tasks of depth estimation and semantic segmentation given a single RGB image. The main focus of this work is to analyze the cross-modality influence between depth and semantic prediction maps on their joint refinement. While most of the previous works solely focus on measuring improvements in accuracy, we propose a way to quantify the cross-modality influence. We show that there is a relationship between final accuracy and cross-modality influence, although not a simple linear one. Hence a larger cross-modality influence does not necessarily translate into an improved accuracy. We find that a beneficial balance between the cross-modality influences can be achieved by network architecture and conjecture that this relationship can be utilized to understand different network design choices. Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state-of-the-art results for depth estimation and semantic labeling. By balancing the cross-modality influences between depth and semantic prediction, we achieve improved results for both tasks using the NYU-Depth v2 benchmark.