nils-herrmann/clean_scirepeval huggingface dataset:
A Dynamic Voltage Scaling Algorithm for Dynamic-Priority Hard Real-Time Systems Using Slack Time Analysis

Dynamic voltage scaling (DVS), which adjusts the clockspeed and supply voltage dynamically, is an effective techniquein reducing the energy consumption of embedded real-timesystems. The energy efficiency of a DVS algorithm largelydepends on the performance of the slack estimation methodused in it. In this paper, we propose a novel DVS algorithmfor periodic hard real-time tasks based on an improved slackestimation algorithm. Unlike the existing techniques, the proposedmethod takes full advantage of the periodic characteristicsof the real-time tasks under priority-driven schedulingsuch as EDF. Experimental results show that the proposed algorithmreduces the energy consumption by 20~40% over theexisting DVS algorithm. The experiment results also show thatour algorithm based on the improved slack estimation methodgives comparable energy savings to the DVS algorithm basedon the theoretically optimal (but impractical) slack estimationmethod.
Mathematical programming for piecewise linear regression analysis

In data mining, regression analysis is a computational tool that predicts continuous output variables from a number of independent input variables, by approximating their complex inner relationship. A large number of methods have been successfully proposed, based on various methodologies, including linear regression, support vector regression, neural network, piece-wise regression etc. In terms of piece-wise regression, the existing methods in literature are usually restricted to problems of very small scale, due to their inherent non-linear nature. In this work, a more efficient piece-wise regression method is introduced based on a novel integer linear programming formulation. The proposed method partitions one input variable into multiple mutually exclusive segments, and fits one multivariate linear regression function per segment to minimise the total absolute error. Assuming both the single partition feature and the number of regions are known, the mixed integer linear model is proposed to simultaneously determine the locations of multiple break-points and regression coefficients for each segment. Furthermore, an efficient heuristic procedure is presented to identify the key partition feature and final number of break-points. 7 real world problems covering several application domains have been used to demon∗Corresponding author:  +442073882348 Email addresses: lingjian.yang.10@ucl.ac.uk (Lingjian Yang), s.liu@ucl.ac.uk (Songsong Liu), sophia.tsoka@kcl.ac.uk (Sophia Tsoka), l.papageorgiou@ucl.ac.uk (Lazaros G. Papageorgiou)  2015 Accepted Manuscript of a publised work apears its final form in Expert Systems with Applications. To access the final edited and published work see http://dx.doi.org/10.1016/j.eswa.2015.08.034. Open Access under CC-BY-NC-ND user license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Multicamera fusion for shape estimation and visibility analysis of unknown deforming objects

A method is proposed for fused three-dimensional (3-D) shape estimation and visibility analysis of an unknown, markerless, deforming object through a multicamera vision system. Complete shape estimation is defined herein as the process of 3-D reconstruction of a model through fusion of stereo triangulation data and a visual hull. The differing accuracies of both methods rely on the number and placement of the cameras. Stereo triangulation yields a high-density, high-accuracy reconstruction of a surface patch from a small surface area, while a visual hull yields a complete, low-detail volumetric approximation of the object. The resultant complete 3-D model is, then, temporally projected based on the tracked object’s deformation, yielding a robust deformed shape prediction. Visibility and uncertainty analyses, on the projected model, estimate the expected accuracy of reconstruction at the next sampling instant. In contrast to common techniques that rely on a priori known models and identities of static objects, our method is distinct in its direct application to unknown, markerless, deforming objects, where the object model and identity are unknown to the system. Extensive simulations and comparisons, some of which are presented herein, thoroughly demonstrate the proposed method and its benefits over individual reconstruction techniques.
GarNet: A Two-stream Network for Fast and Accurate 3D Cloth Draping

While Physics-Based Simulation (PBS) can highly accurately drape a 3D garment model on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inference in a deep network, that is, a single forward pass, is typically quite fast. In this paper, we leverage this property and introduce a novel architecture to fit a 3D garment template to a 3D body model. Specifically, we build upon the recent progress in 3D point-cloud processing with deep networks to extract garment features at varying levels of detail, including point-wise, patch-wise and global features. We then fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions. The resulting two-stream architecture is trained with a loss function inspired by physics-based modeling, and delivers realistic garment shapes whose 3D points are, on average, less than 1.5cm away from those of a PBS method, while running 40 times faster.
BACON.5: The Discovery of Conservation Laws

BACON.5 is a program that discovers empirical laws. The program represents information at varying levels of description, with higher levels summarizing the levels below them. The system applies a small set of data-driven heuristics to detect regularities in numeric and nominal data. These heuristics note constancies and trends, leading BACONS to formulate hypotheses, define theoretical terms, and postulate intrinsic properties. Once the program has formulated an hypothesis, it' uses this to reduce the amount of data it must consider at later times. A simple type of reasoning by analogy also simplifies the discovery of laws containing symmetric forms. These techniques have allowed the system to rediscover Snail's law of refraction, conservation of momentum, Black's specific heat law, and Joule's formulation of conservation of energy. Thus, BACON.S'S heuristics appear to be general mechanisms applicable to discovery in diverse domains.
Business-to-Business E-Marketplaces: A Strategic Archetypes Approach

Electronic marketplaces (EMs) are an important empirical phenomenon, because they are theoretically linked to significant economic and business effects. Different types of EMs have been identified; further, some researchers link different EM types with different impacts. Because the effects of EMs may vary with types, classifying and identifying the characteristics of EM types are fundamental to sound research. Some prior approaches to EM classification have been based on empirical observations, others have been theoretically motivated; each has strengths and limitations. This paper presents a third approach: surfacing strategic archetypes. The strategic archetypes approach has the empirical fidelity associated with the large numbers of attributes considered in the empirical classification approach, but the parsimony of types and the theoretical linkages associated with the theoretical classification approach. The strategic archetypes approach seeks a manageable number of EM configuration types in which the attributes are theoretically linked to each other and to hypothesized outcomes like performance and impacts. The strategic archetypes approach has the potential to inform future theoretical and empirical investigations of electronic marketplaces and to translate research findings into successful recommendations for practice.
A legal case OWL ontology with an instantiation of Popov v. Hayashi

The paper provides an OWL ontology for legal cases with an instantiation of the legal case Popov v. Hayashi. The ontology makes explicit the conceptual knowledge of the legal case domain, supports reasoning about the domain, and can be used to annotate the text of cases, which in turn can be used to populate the ontology. A populated ontology is a case base which can be used for information retrieval, information extraction, and case based reasoning. The ontology contains not only elements for indexing the case (e.g. the parties, jurisdiction, and date), but as well elements used to reason to a decision such as argument schemes and the components input to the schemes. We use the Protégé ontology editor and knowledge acquisition system, current guidelines for ontology development, and tools for visual and linguistic presentation of the ontology.
An Optimal and Progressive Approach to Online Search of Top-K Influential Communities

Community search over large graphs is a fundamental problem in graph analysis. Recent studies propose to compute top-k influential communities, where each reported community not only is a cohesive subgraph but also has a high influence value. The existing approaches to the problem of top-k influential community search can be categorized as index-based algorithms and online search algorithms without indexes. The index-based algorithms, although being very efficient in conducting community searches, need to pre-compute a specialpurpose index and only work for one built-in vertex weight vector. In this paper, we investigate online search approaches and propose an instance-optimal algorithm LocalSearch whose time complexity is linearly proportional to the size of the smallest subgraph that a correct algorithm needs to access without indexes. In addition, we also propose techniques to make LocalSearch progressively compute and report the communities in decreasing influence value order such that k does not need to be specified. Moreover, we extend our framework to the general case of top-k influential community search regarding other cohesiveness measures. Extensive empirical studies on real graphs demonstrate that our algorithms outperform the existing online search algorithms by several orders of magnitude.
Energy-Efficient Gait Planning and Control for Biped Robots Utilizing Vertical Body Motion and Allowable ZMP Region

An energy-efficient gait planning (EEGP) and control system is established for biped robots with three-mass inverted pendulum mode (3MIPM), which utilizes both vertical body motion (VBM) and allowable zero-moment-point (ZMP) region (AZR). Given a distance to be traveled, we newly designed an online gait synthesis algorithm to construct a complete walking cycle, i.e., a starting step, multiple cyclic steps, and a stopping step, in which: 1) ZMP was fully manipulated within AZR; and 2) vertical body movement was allowed to relieve knee bending. Moreover, gait parameter optimization is effectively performed to determine the optimal set of gait parameters, i.e., average body height and amplitude of VBM, number of steps, and average walking speed, which minimizes energy consumption of actuation motors for leg joints under practical constraints, i.e., geometrical constraints, friction force limit, and yawing moment limit. Various simulations were conducted to identify the effectiveness of the proposed method and verify energy-saving performance for various ZMP regions. Our control system was implemented and tested on the humanoid robot DARwIn-OP.
WAEM: A Web Accessibility Evaluation Metric Based on Partial User Experience Order

Quantitative accessibility metrics are widely used in accessibility evaluation, which synthesize a summative value to represent the accessibility level of a website. Many of these metrics are the results of a two-step process. The first step is the inspection with regard to potential barriers while different properties are reported, and the second step aggregates these fine-grained reports with varying weights for checkpoints. Existing studies indicate that finding appropriate weights for different checkpoint types is a challenging issue. Although some metrics derive the checkpoint weights from the WCAG priority levels, previous investigations reveal that the correlation between the WCAG priority levels and the user experience is not significant. Moreover, our website accessibility evaluation results also confirm the mismatches between the ranking of websites using existing metrics and the ranking based on user experience. To overcome this limitation, we propose a novel metric called the Web Accessibility Experience Metric (WAEM) that can better match the accessibility evaluation results with the user experience of people with disabilities by aligning the evaluation metric with the partial user experience order (PUEXO), i.e. pairwise comparisons between different websites. A machine learning model is developed to derive the optimal checkpoint weights from the PUEXO. Experiments on real-world web accessibility evaluation data sets validate the effectiveness of WAEM.
A 60 GHz integrated antenna array for high-speed digital beamforming applications

A 60 GHz integrated four-element planar antenna array is developed. Each antenna is integrated with a sub-harmonic I/Q mixer based on APDPs for the convenience of high-speed signal processing such as adaptive beamforming. An average conversion loss of four channels of less than 10.6 dB is achieved. Amplitude imbalance and phase deviation between I and Q outputs of each channel are less than 2.5 dB and 7/spl deg/, respectively. The array is used to construct a digital beamforming system along with an IF circuit block and DSP modules. Beam scanning results are successfully demonstrated.
Outcome of bonded vs all-ceramic and metal- ceramic fixed prostheses for single tooth replacement.

The conventional treatment of a single missing tooth is most frequently based on the provision of a fixed dental prosthesis (FDPs). A variety of designs and restorative materials are available which have an impact on the treatment outcome. Consequently, it was the aim of this review to compare resin-bonded, all-ceramic and metal-ceramic FDPs based on existing evidence.



An electronic literature search using "metal-ceramic" AND "fixed dental prosthesis" AND "clinical, all-ceramic" AND "fixed dental prosthesis" AND "clinical, resin-bonded" AND "fixed dental prosthesis" AND "clinical, fiber reinforced composite" AND "clinical, monolithic" AND "zirconia" AND "clinical" was conducted and supplemented by the manual searching of bibliographies from articles already included.



A total of 258 relevant articles were identified. Metal-ceramic FDPs still show the highest survival rates of all tooth-supported restorations. Depending on the ceramic system used, all-ceramic restorations may reach comparable survival rates while the technical complications, i.e. chipping fractures of veneering materials in particular, are more frequent. Resin-bonded FDPs can be seen as long-term provisional restorations with the survival rate being higher in anterior locations and when a cantilever design is applied. Inlay-retained FDPs and the use of fiber-reinforced composites overall results in a compromised long-term prognosis. Recently advocated monolithic zirconia restorations bear the risk of low temperature degradation.



Several variables affect treatment planning for a given patient situation, with survival and success rates of different restorative options representing only one factor. The broad variety of designs and materials available for conventional tooth-supported restorations should still be considered as a viable treatment option for single tooth replacement.
Enumeration of Total Heterotrophic Bacteria and Some Physico-Chemical Characteristics of Surface Water Used for Drinking Sources in Wilberforce Island , Nigeria

Surface water is a source of drinking water in most rural communities in Nigeria. This study evaluated the total heterotrophic bacteria (THB) counts and some physico-chemical characteristics of Rivers surrounding Wilberforce Island, Nigeria.Samples were collected in July 2007 and analyzed using standard procedures. The result of the THB ranged from 6.389 – 6.434Log cfu/ml. The physico-chemical parameters results ranged from 6.525 – 7.105 (pH), 56.075 – 64.950μS/cm (Conductivity), 0.010 – 0.050‰ (Salinity), 103.752 – 117.252 NTU (Turbidity), 27.250 – 27.325 oC (Temperature), 10.200 – 14.225 mg/l (Dissolved oxygen), 28.180 – 32.550 mg/l (Total dissolved solid), 0.330 – 0.813 mg/l (Nitrate), 0.378 – 0.530 mg/l (Ammonium). Analysis of variance showed that there were significant variation (P<0.05) in the physicochemical properties except for Salinity and temperature between the two rivers. Also no significant different (P>0.05) exist in the THB density of both rivers; upstream (Agudama-Ekpetiama) and downstream (Akaibiri) of River Nun with regard to ammonium and nitrate. Significant positive correlation (P<0.01) exist between dissolved oxygen with ammonium, Conductivity with salinity and total dissolved solid, salinity with total dissolved solid, turbidity with nitrate, and pH with nitrate. The positive correlation (P<0.05) also exist between pH with turbidity. High turbidity and bacteria density in the water samples is an indication of pollution and contamination respectively. Hence, the consumption of these surface water without treatment could cause health related effects.  Drinking water sources, microorganisms, physico-chemistry, surface water, Wilberforce Island
Coupled Attribute Similarity Learning on Categorical Data

Attribute independence has been taken as a major assumption in the limited research that has been conducted on similarity analysis for categorical data, especially unsupervised learning. However, in real-world data sources, attributes are more or less associated with each other in terms of certain coupling relationships. Accordingly, recent works on attribute dependency aggregation have introduced the co-occurrence of attribute values to explore attribute coupling, but they only present a local picture in analyzing categorical data similarity. This is inadequate for deep analysis, and the computational complexity grows exponentially when the data scale increases. This paper proposes an efficient data-driven similarity learning approach that generates a coupled attribute similarity measure for nominal objects with attribute couplings to capture a global picture of attribute similarity. It involves the frequency-based intra-coupled similarity within an attribute and the inter-coupled similarity upon value co-occurrences between attributes, as well as their integration on the object level. In particular, four measures are designed for the inter-coupled similarity to calculate the similarity between two categorical values by considering their relationships with other attributes in terms of power set, universal set, joint set, and intersection set. The theoretical analysis reveals the equivalent accuracy and superior efficiency of the measure based on the intersection set, particularly for large-scale data sets. Intensive experiments of data structure and clustering algorithms incorporating the coupled dissimilarity metric achieve a significant performance improvement on state-of-the-art measures and algorithms on 13 UCI data sets, which is confirmed by the statistical analysis. The experiment results show that the proposed coupled attribute similarity is generic, and can effectively and efficiently capture the intrinsic and global interactions within and between attributes for especially large-scale categorical data sets. In addition, two new coupled categorical clustering algorithms, i.e., CROCK and CLIMBO are proposed, and they both outperform the original ones in terms of clustering quality on UCI data sets and bibliographic data.
Robustifying EEG data analysis by removing outliers

Biomedical signals such as EEG are typically contaminated by measurement artifacts, outliers and non-standard noise sources. We propose to use techniques from robust statistics and machine learning to reduce the influence of such distortions. Two showcase application scenarios are studied: (a) Lateralized Readiness Potential (LRP) analysis, where we show that a robust treatment of the EEG allows to reduce the necessary number of trials for averaging and the detrimental influence of e.g. ocular artifacts and (b) single trial classification in the context of Brain Computer Interfacing, where outlier removal procedures can strongly enhance the classification performance.
Using Skin Melanin Layer for Facial Pore Identification in RGB Digital Images

Human face feature extraction using digital images is a vital element for several applications such as: identification and facial recognition, medical application, video games, cosmetology, etc. The skin pores are very important element of the structure of the skin. A novelty method is proposed allowing decomposing an photography of human face from digital image (RGB) in two layers, melanin and hemoglobin. From melanin layer, the main pores from the face can be obtained, as well as the centroids of each of them. It has been found that the pore configuration of the skin is invariant and unique for each individual. Therefore, from the localization of the pores of a human face, it is a possibility to use them for diverse application in the fields of pattern
A Design Procedure for High-Efficiency and Compact-Size 5–10-W MMIC Power Amplifiers in GaAs pHEMT Technology

This paper presents the design procedure of monolithic microwave integrated circuit (MMIC) high-power amplifiers (HPAs) as well as implementation of high-efficiency and compact-size HPAs in a 0.25- μm AlGaAs-InGaAs pHEMT technology. Presented design techniques used to extend bandwidth, improve efficiency, and reduce chip area of the HPAs are described in detail. The first HPA delivers 5 W of output power with 40% power-added efficiency (PAE) in the frequency band of 8.5-12.5 GHz, while providing 20 dB of small-signal gain. The second HPA delivers 8 W of output power with 35% PAE in the frequency band of 7.5-12 GHz, while maintaining a small-signal gain of 17.5 dB. The 8-W HPA chip area is 8.8 mm2, which leads to the maximum power/area ratio of 1.14 W/mm2. These are the lowest area and highest power/area ratio reported in GaAs HPAs operating within the same frequency band.
Commonsense computing (episode 5): algorithm efficiency and balloon testing

This paper investigates what students understand about algorithm efficiency before receiving any formal instruction on the topic. We gave students a challenging search problem and two solutions, then asked them to identify the more efficient solution and to justify their choice. Many students did not use the standard worst-case analysis of algorithms; rather they chose other metrics, including average-case, better for more cases, better in all cases, one algorithm being more correct, and better for real-world scenarios. Students were much more likely to choose the correct algorithm when they were asked to trace the algorithms on specific examples; this was true even if they traced the algorithms incorrectly.
Point-Based POMDP Algorithms: Improved Analysis and Implementation

Existing complexity bounds for point-based POMDP value iteration algorithms focus either on the curse of dimensionality or the curse of history. We derive a new bound that relies on both and uses the concept of discounted reachability; our conclusions may help guide future algorithm design. We also discuss recent improvements to our (point-based) heuristic search value iteration algorithm. Our new implementation calculates tighter initial bounds, avoids solving linear programs, and makes more effective use of sparsity. Empirical results show speedups of more than two orders of magnitude.