nils-herrmann/clean_scirepeval huggingface dataset:
63.5–65.5-GHz Transmit/Receive Phased-Array Communication Link With 0.5–2 Gb/s at 100–800 m and ± 50° Scan Angles

This paper presents a 32-element phased array centered at 64 GHz using multiple SiGe chips on a single printed-circuit board. The antenna element is a series-fed patch array, which provides directivity in the elevation plane. The transmit array results in an effective isotropic radiated power of 42± 2 dBm at 63–65.5 GHz, while the receive array provides an electronic gain of 24 dB and a system noise figure <7.7 dB, including antenna loss, T/R switch, beamformer, and transceiver. The arrays can be scanned to ±50° in the azimuth using a 5-bit phase shifter on the SiGe chip without degradation in sidelobes and maintaining a near-ideal pattern. A communication link between two phased arrays at 100, 300, and 800 m is also demonstrated, and employs one array on the transmit side and another array on the receive side, together with external mixers and IF amplifiers. The link performance was measured for different scan angles and modulation formats. Data rates of 0.5–2 Gb/s using 16-QAM and QPSK waveforms are demonstrated at 100–800 m, with 2-Gb/s data rate at 300 m and ~500-Mb/s data rate at 800 m. To the best of our knowledge, this is the first system-level demonstration of a silicon-based Gb/s 60-GHz phased array over hundreds of meters. Application areas are in point-to-multipoint links, back-haul and front-haul links, and reconfigurable mesh networks for advanced communication systems.
Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges

Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction. As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans. Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process. Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation’s recommendations to select an appropriate level of autonomy at a certain point of time. However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are. We explore open challenges that hamper the process of developing effective flexible autonomy. We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.
Optimal Processing of Polarimetric Synthetic-Aperture Radar Imagery

The Advanced Detection Technology Sensor can detect, discriminate, and classify stationary ground targets-during the day or night-even through cloud cover, fog, smoke, dust, or rain. The sensor is a coherent, fully polarimetric, 35-GHz synthetic-aperture radar (SAR) with a resolution of 1 ft x 1 ft. And, to minimize SARspeckle while preserving image resolution, it uses the polarimetric whitening filter, our recently developed method for processing fully polarimetric data into SAR imagery.
Intelligent Video Systems and Analytics: A Survey

Recent technology and market trends have demanded the significant need for feasible solutions to video/camera systems and analytics. This paper provides a comprehensive account on theory and application of intelligent video systems and analytics. It highlights the video system architectures, tasks, and related analytic methods. It clearly demonstrates that the importance of the role that intelligent video systems and analytics play can be found in a variety of domains such as transportation and surveillance. Research directions are outlined with a focus on what is essential to achieve the goals of intelligent video systems and analytics.
Two-tier network anomaly detection model: a machine learning approach

Network anomaly detection is one of the most challenging fields in cyber security. Most of the proposed techniques have high computation complexity or based on heuristic approaches. This paper proposes a novel two-tier classification models based on machine learning approaches Naïve Bayes, certainty factor voting version of KNN classifiers and also Linear Discriminant Analysis for dimension reduction. Experimental results show a desirable and promising gain in detection rate and false alarm compared with other existing models. The model also trained by two generated balance training sets using SMOTE method to evaluate the chosen similarity measure for dealing with imbalanced network anomaly data sets. The two-tier model provides low computation time due to optimal dimension reduction and feature selection, as well as good detection rate against rare and complex attack types which are so dangerous because of their close similarity to normal behaviors like User to Root and Remote to Local. All evaluation processes experimented by NSL-KDD data set.
Leakproofing the Singularity Artificial Intelligence Confinement Problem

This paper attempts to formalize and to address the ‘leakproofing’ of the Singularity problem presented by David Chalmers. The paper begins with the definition of the Artificial Intelligence Confinement Problem. After analysis of existing solutions and their shortcomings, a protocol is proposed aimed at making a more secure confinement environment which might delay potential negative effect from the technological singularity while allowing humanity to benefit from the superintelligence.
A Survey of Definitions and Models of Exploratory Search

Exploratory search has an unclear and open-ended definition. The complexity of the task and the difficulty of defining this activity are reflected in the limits of existing evaluation methods for exploratory search systems. In order to improve them, we intend to design an evaluation method based on a user-centered model of exploratory search. In this work, we identified and defined the characteristics of exploratory search and used them as an information seeking model evaluation grid. We tested this analytic grid on two information seeking models: Ellis' and Marchionini's models. The results show that Marchonini's model does not match our evaluation method's requirements whereas on the other hand Ellis' model could be adapted to better suit exploratory search.
A Slot Loaded Circularly Polarized Patch Antenna for UHF RFID Reader

The design of a simple UHF (ultrahigh frequency) RFID (radio frequency identification) reader antenna that operates within the 900 MHz band (902-928 MHz) is studied. To generate circular polarization (CP) radiation, a novel method of loading a semicircular slot into the main circular radiating patch is proposed. To allow optimum impedance matching with enhanced CP bandwidth, the L-shaped probe-feed technique is employed in this design. From the experimental results, besides achieving a gain level of more than 7 dBic, the proposed antenna can also yield an impedance bandwidth (10-dB return loss) from 880 to 1100 MHz, while good CP performances between 901 to 930 MHz are exhibited. To further validate the experimental results, parametric studies are also carried out via simulation.
Inverse procedural modeling of 3D models for virtual worlds

This course presents a collection of state-of-the-art approaches for modeling and editing of 3D models for virtual worlds, simulations, and entertainment, in addition to real-world applications. The first contribution of this course is a coherent review of inverse procedural modeling (IPM) (i.e., proceduralization of provided 3D content). We describe different formulations of the problem as well as solutions based on those formulations. We show that although the IPM framework seems under-constrained, the state-of-the-art solutions actually use simple analogies to convert the problem into a set of fundamental computer science problems, which are then solved by corresponding algorithms or optimizations. The second contribution includes a description and categorization of results and applications of the IPM frameworks. Moreover, a substantial part of the course is devoted to summarizing different domain IPM frameworks for practical content generation in modeling and animation.
Using a Programmable Toy at Preschool Age: Why and How?

Robotic toys bring new dimension to role-play activities in kindergarten. Some preschool curricula clearly identify reasons for their inclusion. However, preschool teacher needs to revise usual teaching methods in order to use them. Offering a programmable toy or robotic-related activity doesn't mean immediate success in work with children. We document our research with concrete programmable device in a preschool classroom. Details of robotic-related sessions can help reader to design the quality game for preschool-age based on using a programmable toy.
Effective Feature Integration for Automated Short Answer Scoring

A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.
Visualization of movie features in collaborative filtering

In this paper we will describe a modification of the matrix factorization (MF) algorithm which allows visualizing the user and item characteristics. When applying MF for collaborative filtering, we get a model that represents the attributes of users and items by feature vectors. Some elements of these vectors may have understandable meaning for humans but due to the lack of internal connections between the feature vectors, these are difficult to visualize. In this paper we give a detailed description of a MF method enabling better visualization of features by arranging them into a 2D map, where via the calculation of the feature values we try to position features with similar “meaning” close to each other. To achieve this first we define a neighborhood relation on features, then we modify the MF so that we introduce a new term in the error function which penalize the difference between the neighbor features. We show that this modification slightly decrease the accuracy of the model but we get well visualized feature maps. On the feature maps meanings can be associated with regions, and so we can provide an interesting explanation for the user why he/she was recommended the movie. Such plausible explanations may result in that users will better understand how the system works, which can also increase customer loyalty towards the service provider.
Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends

Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. We utilized PubMed for the testing. We investigated gene–gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene–gene relations and gene functions.
A survey of data mining and knowledge discovery process models and methodologies

Up to now, many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success. In this paper, we describe the most used (in industrial and academic projects) and cited (in scientific literature) data mining and knowledge discovery methodologies and process models, providing an overview of its evolution along data mining and knowledge discovery history and setting down the state of the art in this topic. For every approach, we have provided a brief description of the proposed knowledge discovery in databases (KDD) process, discussing about special features, outstanding advantages and disadvantages of every approach. Apart from that, a global comparative of all presented data mining approaches is provided, focusing on the different steps and tasks in which every approach interprets the whole KDD process. As a result of the comparison, we propose a new data mining and knowledge discovery process named refined data mining process for developing any kind of data mining and knowledge discovery project. The refined data mining process is built on specific steps taken from analyzed approaches.
A rule based bengali stemmer

One of the biggest challenges in doing word lookups is to derive the appropriate base word for any given word in Bengali. The basic concept to the solution of the problem is to eliminate inflections from a given word to derive its stem word. Stemmers attempt to reduce a word to its root form using stemming process, which reduces an inflected or derived word to its stem or root form. Existing works in the literature use lookup tables either for stem words or suffixes, increasing the overheads in terms of memory and time. This paper develops a rule-based algorithm that eliminates inflections stepwise without continuously searching for the desired root in the dictionary. To the best of our knowledge, this paper first investigates that, in Bengali morphology, for a large set of inflections, the stems can be computed algorithmically cutting down the inflections step by step. The proposed algorithm is independent of inflected word lengths and our evaluation shows around 88% accuracy.
Learning Semantic Composition to Detect Non-compositionality of Multiword Expressions

Non-compositionality of multiword expressions is an intriguing problem that can be the source of error in a variety of NLP tasks such as language generation, machine translation and word sense disambiguation. We present methods of non-compositionality detection for English noun compounds using the unsupervised learning of a semantic composition function. Compounds which are not well modeled by the learned semantic composition function are considered noncompositional. We explore a range of distributional vector-space models for semantic composition, empirically evaluate these models, and propose additional methods which improve results further. We show that a complex function such as polynomial projection can learn semantic composition and identify non-compositionality in an unsupervised way, beating all other baselines ranging from simple to complex. We show that enforcing sparsity is a useful regularizer in learning complex composition functions. We show further improvements by training a decomposition function in addition to the composition function. Finally, we propose an EM algorithm over latent compositionality annotations that also improves the performance.
Exoskeleton inflatable robotic arm with thin McKibben muscle

To realize an extremely safe robot, an exoskeleton inflatable robotic arm with thin McKibben muscles and simple driving systems installed inside the arm, except for air-supply devices, was designed and manufactured using soft materials. McKibben muscles, which are considerably thin, lightweight, flexible, and can be mass produced, are suitable for this soft robotic mechanism. The arm is safe and useful for human-friendly robots owing to its softness, low weight, and compliance. The exoskeleton inflatable robotic arm was modeled, theoretical equations were derived for the joint angle and torque, and theoretical and experimental results obtained at various structural stiffness were compared. In the experiments, the developed arm could bend at 90° and 91° on each side. Furthermore, it was proposed that experimental values can be estimated using theoretical expressions with correction factors.
Load balancing system for IPTV web application virtualization

Web applications are easy to develop and maintain. It is desirable to provide Web application services to IPTV. However many legacy set-top boxes lack enough computing power to run Web applications. Desktop virtualization technologies can be used to provide Web application services to legacy set-top boxes. Multiple servers and a load balancing system are required to serve many clients. This paper describes a load balancing system for IPTV Web application virtualization. The load balancing system distributes requests based on server loads and supports high availability.
Normative Social Influence in Persuasive Technology: Intensity versus Effectiveness

Persuasion is a form of social influence that implies intentional but voluntary change of a person’s behaviours, feelings or thoughts about an issue, object or action. Successful persuasion by means of persuasive technology relies on a number of factors and motivators. Recently, the so called social acceptance or rejection motivator has formed the backbone of a new type of persuasion, called Mass Interpersonal Persuasion, or MIP for short. This type of persuasion uses the social influence that is generated on online social networks to persuade people. Though it has been established that normative social influence can be used effectively in persuasive technology, it is unknown if the application of more social pressure also makes it more effective. In order to test the hypothesis that the effectiveness of persuasion increases when the persuasion becomes more intense, a quantitative experiment was conducted on the online social network of Facebook. Although evidence to support the hypothesis was found, it cannot be concluded from this experiment that when utilizing normative social influence in persuasive technology more intense persuasion is also more effective.
A Simple and Effective Unsupervised Word Segmentation Approach

In this paper, we propose a new unsupervised approach for word segmentation. The core idea of our approach is a novel word induction criterion called WordRank, which estimates the goodness of word hypotheses (character or phoneme sequences). We devise a method to derive exterior word boundary information from the link structures of adjacent word hypotheses and incorporate interior word boundary information to complete the model. In light of WordRank, word segmentation can be modeled as an optimization problem. A Viterbistyled algorithm is developed for the search of the optimal segmentation. Extensive experiments conducted on phonetic transcripts as well as standard Chinese and Japanese data sets demonstrate the effectiveness of our approach. On the standard Brent version of BernsteinRatner corpora, our approach outperforms the state-ofthe-art Bayesian models by more than 3%. Plus, our approach is simpler and more efficient than the Bayesian methods. Consequently, our approach is more suitable for real-world applications.
3D printing for microwave: Materials characterization and application in the field of absorbers

In this study, the microwave properties of a wide range of materials, formed by two types of additive technologies (stereolithography apparatus and fused deposition modeling) were extracted in the X-band (8-12 GHz). ABS filled with carbon was chosen to realize a demonstrator. This high loss material was shaped in the form of a pyramid by 3D printing technology. By introducing it into a short-circuited waveguide, a matched load covering the whole X-band was realized (return loss <; -30 dB). The flexibility of these techniques and the availability of low cost commercial materials suggest many potential applications in the microwave range.
A gene module associated with dysregulated TCR signaling pathways in CD4+ T cell subsets in rheumatoid arthritis.

We analyzed the transcriptome of detailed CD4+ T cell subsets including them after abatacept treatment, and examined the difference among CD4+ T cell subsets and identified gene sets that are closely associated disease activity and abatacept treatment. Seven CD4+ T cell subsets (naive, Th1, Th17, Th1/17, nonTh1/17, Tfh and Treg) were sorted from PBMCs taken from 10 RA patients and 10 healthy controls, and three RA patients donated samples before and 6 months after abatacept treatment. Paired-end RNA sequencing was performed using HiSeq 2500. A total of 149 samples except for 12 outliers were analyzed. Overview of expression pattern of RA revealed that administration of abatacept exerts a large shift toward the expression pattern of HC. Most of differentially expressed gene (DEG) upregulated in RA (n = 1776) were downregulated with abatacept treatment (n = 1349). Inversely, most of DEG downregulated in RA (n = 1860) were upregulated with abatacept treatment (n = 1294). This DEG-based analysis revealed shared pathway changes in RA CD4+ T cell subsets. Knowledge-based pathway analysis revealed the upregulation of activation-related pathways in RA that was substantially ameliorated by abatacept. Weighted gene co-expression network analysis (WGCNA) evaluated CD4+ T cells collectively and identified a gene module that consisted of 227 genes and was correlated with DAS28-CRP (Spearman's rho = 0.46, p = 4 × 10-9) and abatacept administration (Spearman's rho = -0.91, p = 5 × 10-57). The most highly connected 30 genes of this module included ZAP70 and JAK3, and pathway analysis of this module revealed dysregulation of the TCR signaling pathway network, which was ameliorated by abatacept.
International Journal of Advanced Robotic Systems Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID Regular Paper

The  autonomous  vehicle  is  a  mobile  robot integrating  multi‐sensor  navigation  and  positioning, intelligent decision making and control  technology. This paper  presents  the  control  system  architecture  of  the autonomous vehicle, called “Intelligent Pioneer”, and the path  tracking  and  stability  of  motion  to  effectively navigate  in unknown  environments  is discussed.  In  this approach,  a  two  degree‐of‐freedom  dynamic  model  is developed to formulate the path‐tracking problem in state space format. For controlling the instantaneous path error, traditional  controllers  have  difficulty  in  guaranteeing performance and stability over a wide range of parameter changes and disturbances. Therefore, a newly developed adaptive‐PID  controller  will  be  used.  By  using  this approach the flexibility of the vehicle control system will be  increased  and  achieving  great  advantages. Throughout,  we  provide  examples  and  results  from Intelligent Pioneer and the autonomous vehicle using this approach  competed  in  the  2010  and  2011  Future Challenge of China. Intelligent Pioneer finished all of the competition programmes and won  first position  in 2010 and third position in 2011.