nils-herrmann/clean_scirepeval huggingface dataset:
Non-Technical Loss analysis for detection of electricity theft using support vector machines

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards Non-Technical Loss (NTL) analysis for electric utilities using a novel intelligence-based technique, Support Vector Machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector due to electricity theft. The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behavior. This approach provides a method of data mining and involves feature extraction from historical customer consumption data. The SVM based approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behavior that emerges due to irregularities in consumption. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities.
New Privacy Threat on 3G, 4G, and Upcoming 5G AKA Protocols

Mobile communications are used by more than two thirds of the world population who expect security and privacy guarantees. The 3rd Generation Partnership Project (3GPP) responsible for the worldwide standardization of mobile communication has designed and mandated the use of the AKA protocol to protect the subscribers’ mobile services. Even though privacy was a requirement, numerous subscriber location attacks have been demonstrated against AKA, some of which have been fixed or mitigated in the enhanced AKA protocol designed for 5G. In this paper, we reveal a new privacy attack against all variants of the AKA protocol, including 5G AKA, that breaches subscriber privacy more severely than known location privacy attacks do. Our attack exploits a new logical vulnerability we uncovered that would require dedicated fixes. We demonstrate the practical feasibility of our attack using low cost and widely available setups. Finally we conduct a security analysis of the vulnerability and discuss countermeasures to remedy our attack.
Comparative PSCAD and Matlab/Simulink simulation models of power losses for SiC MOSFET and Si IGBT devices

Silicon carbide devices have several advantages, including high blocking voltage, lower conduction losses, and lower switching losses, when compared to silicon-based devices. This paper demonstrates high power and high frequency operation of the SiC MOSFETs compared to conventional Si IGBT with a similar power rating. The commercial SiC MOSFET QJD1210007 (1200V/100A) from POWEREX and Si IGBT CM100TF-24H (1200V/100A) from Mitsubishi were used in this study. PSCAD and Matlab/Simulink models were used to analyze the power losses of the devices. The following studies were carried out: (i) a comparison between PSCAD and Matlab/Simulink simulation results for a push-pull converter and boost converter, (ii) switching power loss calculations via Matlab/Simulink and accurate loss measurements, and (iii) finally a prototype was built to gather physical measurements to compare with the simulation results. The SiC MOSFET model has been verified by comparing the simulation results with experimental switching waveforms. Based upon the experimental and analytical results, our results show that Matlab/Simulink provides better simulation capability for computing switching losses of the semiconductor devices under investigation. The characterization and modeling processes are generic enough so that these methods can be applied to study new SiC devices.
Biomechanical Evaluation of the Medial Stabilizers of the Patella.

Quantification of the biomechanical properties of each individual medial patellar ligament will facilitate an understanding of injury patterns and enhance anatomic reconstruction techniques by improving the selection of grafts possessing appropriate biomechanical properties for each ligament.



To determine the ultimate failure load, stiffness, and mechanism of failure of the medial patellofemoral ligament (MPFL), medial patellotibial ligament (MPTL), and medial patellomeniscal ligament (MPML) to assist with selection of graft tissue for anatomic reconstructions.



Descriptive laboratory study.



Twenty-two nonpaired, fresh-frozen cadaveric knees were dissected free of all soft tissue structures except for the MPFL, MPTL, and MPML. Two specimens were ultimately excluded because their medial structure fibers were lacerated during dissection. The patella was obliquely cut to test the MPFL and the MPTL-MPML complex separately. To ensure that the common patellar insertion of the MPTL and MPML was not compromised during testing, only one each of the MPML and MPTL were tested per specimen (n = 10 each). Specimens were secured in a dynamic tensile testing machine, and the ultimate load, stiffness, and mechanism of failure of each ligament (MPFL = 20, MPML = 10, and MPTL = 10) were recorded.



The mean ± SD ultimate load of the MPFL (178 ± 46 N) was not significantly greater than that of the MPTL (147 ± 80 N; P = .706) but was significantly greater than that of the MPML (105 ± 62 N; P = .001). The mean ultimate load of the MPTL was not significantly different from that of the MPML ( P = .210). Of the 20 MPFLs tested, 16 failed by midsubstance rupture and 4 by bony avulsion on the femur. Of the 10 MPTLs tested, 9 failed by midsubstance rupture and 1 by bony avulsion on the patella. Finally, of the 10 MPMLs tested, all 10 failed by midsubstance rupture. No significant difference was found in mean stiffness between the MPFL (23 ± 6 N/mm2) and the MPTL (31 ± 21 N/mm2; P = .169), but a significant difference was found between the MPFL and the MPML (14 ± 8 N/mm2; P = .003) and between the MPTL and MPML ( P = .028).



The MPFL and MPTL had comparable ultimate loads and stiffness, while the MPML had lower failure loads and stiffness. Midsubstance failure was the most common type of failure; therefore, reconstruction grafts should meet or exceed the values reported herein.



For an anatomic medial-sided knee reconstruction, the individual biomechanical contributions of the medial patellar ligamentous structures (MPFL, MPTL, and MPML) need to be characterized to facilitate an optimal reconstruction design.
MPPT Scheme for a PV-Fed Single-Phase Single-Stage Grid-Connected Inverter Operating in CCM With Only One Current Sensor

The cost and efficiency of a photovoltaic (PV)-based grid-connected system depends upon the number of components and stages involved in the power conversion. This has led to the development of several single-stage configurations that can perform voltage transformation, maximum power point tracking (MPPT), inversion, and current shaping-all in one stage. Such configurations would usually require at least a couple of current and voltage sensors and a relatively complex control strategy. With a view to minimize the overall cost and control complexity, this paper presents a novel MPPT scheme with reduced number of sensors. The proposed scheme is applicable to any single-stage, single-phase grid-connected inverter operating in continuous conduction mode (CCM). The operation in CCM is desirable as it drastically reduces the stress on the components. Unlike other MPPT methods, which sense both PV array's output current and voltage, only PV array's output voltage is required to be sensed to implement MPPT. Only one current sensor is used for shaping the buck-boost inductor current as well as for MPPT. The information about power output of the array is obtained indirectly from array's voltage and the inductor current amplitude. Detailed analysis and the flowchart of the algorithm for the proposed scheme are included. Simulation and experimental results are also presented to highlight the usefulness of the scheme.
Mobile Manipulation and Mobility as Manipulation - Design and Algorithms of RoboSimian

This article presents the hardware design and software algorithms of RoboSimian, a statically stable quadrupedal robot capable of both dexterous manipulation and versatile mobility in difficult terrain. The robot has generalized limbs and hands capable of mobility and manipulation, along with almost fully hemispherical 3D sensing with passive stereo cameras. The system is semi-autonomous, enabling low-bandwidth, high latency control operated from a standard laptop. Because limbs are used for mobility and manipulation, a single unified mobile manipulation planner is used to generate autonomous behaviors, including walking, sitting, climbing, grasping, and manipulating. The remote operator interface is optimized to designate, parameterize, sequence, and preview behaviors, which are then executed by the robot. RoboSimian placed fifth in the DARPA Robotics Challenge (DRC) Trials, demonstrating its ability to perform disaster recovery tasks in degraded human environments.
On Graph Problems in a Semi-streaming Model

Massive graphs arise naturally in a lot of applications, especially in communication networks like the internet. The size of these graphs makes it very hard or even impossible to store set of edges in the main memory. Thus, random access to the edges can't be realized, which makes most o ine algorithms unusable. This essay investigates e cient algorithms that read the edges only in a xed sequential order. Since even basic graph problems often need at least linear space in the number of vetices to be solved, the storage space bounds are relaxed compared to the classic streaming model, such that the bound is O(n · polylog n). The essay describes algorithms for approximations of the unweighted and weighted matching problem and gives a o(log1− n) lower bound for approximations of the diameter. Finally, some results for further graph problems are discussed.
The Discrete Cosine Transform

Each discrete cosine transform (DCT) uses N real basis vectors whose components are cosines. In the DCT-4, for example, the jth component of vk is cos(j+ 2 )(k+ 1 2 ) π N . These basis vectors are orthogonal and the transform is extremely useful in image processing. If the vector x gives the intensities along a row of pixels, its cosine series ∑ ckvk has the coefficients ck = (x,vk)/N . They are quickly computed from a Fast Fourier Transform. But a direct proof of orthogonality, by calculating inner products, does not reveal how natural these cosine vectors are. We prove orthogonality in a different way. Each DCT basis contains the eigenvectors of a symmetric “second difference” matrix. By varying the boundary conditions we get the established transforms DCT-1 through DCT-4. Other combinations lead to four additional cosine transforms. The type of boundary condition (Dirichlet or Neumann, centered at a meshpoint or a midpoint) determines the applications that are appropriate for each transform. The centering also determines the period: N − 1 or N in the established transforms, N− 2 or N+ 1 2 in the other four. The key point is that all these “eigenvectors of cosines” come from simple and familiar matrices.
Community-based natural resource use and management of Bigodi Wetland Sanctuary, Uganda, for livelihood benefits

Conservation and sustainable management of wetlands requires participation of local stakeholders, including communities. The Bigodi Wetland is unusual because it is situated in a common property landscape but the local community has been running a successful community-based natural resource management programme (CBNRM) for the wetland for over a decade. Whilst external visitors to the wetland provide ecotourism revenues we sought to quantify community benefits through the use of wetland goods such as firewood, plant fibres, and the like, and costs associated with wild animals damaging farming activities. We interviewed 68 households living close to the wetland and valued their cash and non-cash incomes from farming and collection of non-timber forest products (NTFPs) and water. The majority of households collected a wide variety of plant and fish resources and water from the wetland for household use and livestock. Overall, 53% of total household cash and non-cash income was from collected products, mostly the wetland, 28% from arable agriculture, 12% from livestock and 7% from employment and cash transfers. Female-headed households had lower incomes than male-headed ones, and with a greater reliance on NTFPs. Annual losses due to wildlife damage were estimated at 4.2% of total gross income. Most respondents felt that the wetland was important for their livelihoods, with more than 80% identifying health, education, craft materials and firewood as key benefits. Ninety-five percent felt that the wetland was in a good condition and that most residents observed the agreed CBNRM rules regarding use of the wetland. This study confirms the success of the locally run CBNRM processes underlying the significant role that the wetland plays in local livelihoods.
EMS maglev vehicles model reference adaptive control

Elastic deformation of the track effects on EMS maglev vehicles safety and comfort, brought challenges to the design of the suspension control system. In order to solve this problem, a nonlinear dynamic model of single magnet EMS maglev vehicles is built, discussed the model reference adaptive control method in the application of maglev vehicles suspension control; designed the controller based on Lyapunov stability theory. A numerical simulation is performed with MATLAB, and the effectiveness of the control method was verified.
Syntactic Dependency-Based N-grams: More Evidence of Usefulness in Classification

The paper introduces and discusses a concept of syntactic n-grams (sn-grams) that can be applied instead of traditional n-grams in many NLP tasks. Sn-grams are constructed by following paths in syntactic trees, so sngrams allow bringing syntactic knowledge into machine learning methods. Still, previous parsing is necessary for their construction. We applied sn-grams in the task of authorship attribution for corpora of three and seven authors with very promising results.
Work-related Mental Consequences: Implications of Burnout on Mental Health Status Among Health Care Providers

Burnout can create problems in every aspect of individual's' human life. It may have an adverse effect on interpersonal and family relations and can lead to a general negative attitude towards life.



The purpose of this study is to investigate whether burnout is associated with the mental health status of health care providers.



The sample in this study consisted of 240 health care employees. The Greek version of Maslach's Burnout Inventory (MBI) was used for measuring burnout levels and the Greek version of the Symptoms Rating Scale for Depression and Anxiety (SRSDA) questionnaire was used to evaluate health care providers' mental health status. Descriptive statistics were initially generated for sample characteristics. Normality was checked by the Kolmogorov-Smirnov test and data was processed with parametric tests. General linear models with MBI dimensions as independent variables and SRSDA subscales as dependent variables were used to determine the relation between burnout and mental health status. Statistics were processed with SPSS v. 17.0 (SPSS, Chicago, IL, USA). Statistical significance was set at p=0.05.



The average age of the sample is 40.00±7.95 years. Regarding gender the percentage of men is 21.40% (N=49) and of women is 78.60% (N=180). Overall the professional burnout of health care workers is moderate. The mean score for emotional exhaustion is 26.41, for personal accomplishment 36.70 and for depersonalization 9.81. The mean for each subscale of SRSDA is 8.23±6.79 for Depression Beck-21, 3.96±4.26 for Depression Beck-13, 4.91±4.44 for Melancholia, 6.32±4.35 for Asthenia and 6.36±4.72 for Anxiety. The results of general linear models with the MBI dimensions as independent variables and the SRSDA subscales as dependent variables are shown that emotional exhaustion and personal accomplishment are statistically correlated with all subscales of SRSDA, while depersonalization is not correlated with any SRSDA subscale.



Burnout appears to implicate mental health status of healthcare providers in work index. Emotional exhaustion is the burnout dimension that is correlated the most with employees' mental health.
Ribonuclease revisited: structural insights into ribonuclease III family enzymes.

Ribonuclease III (RNase III) enzymes occur ubiquitously in biology and are responsible for processing RNA precursors into functional RNAs that participate in protein synthesis, RNA interference and a range of other cellular activities. Members of the RNase III enzyme family, including Escherichia coli RNase III, Rnt1, Dicer and Drosha, share the ability to recognize and cleave double-stranded RNA (dsRNA), typically at specific positions or sequences. Recent biochemical and structural data have shed new light on how RNase III enzymes catalyze dsRNA hydrolysis and how substrate specificity is achieved. A major theme emerging from these studies is that accessory domains present in different RNase III enzymes are the key determinants of substrate selectivity, which in turn dictates the specialized biological function of each type of RNase III protein.
Inferring Light Fields from Shadows

We present a method for inferring a 4D light field of a hidden scene from 2D shadows cast by a known occluder on a diffuse wall. We do this by determining how light naturally reflected off surfaces in the hidden scene interacts with the occluder. By modeling the light transport as a linear system, and incorporating prior knowledge about light field structures, we can invert the system to recover the hidden scene. We demonstrate results of our inference method across simulations and experiments with different types of occluders. For instance, using the shadow cast by a real house plant, we are able to recover low resolution light fields with different levels of texture and parallax complexity. We provide two experimental results: a human subject and two planar elements at different depths.
Rumor source detection with multiple observations: fundamental limits and algorithms

This paper addresses the problem of a single rumor source detection with multiple observations, from a statistical point of view of a spreading over a network, based on the susceptible-infectious model. For tree networks, multiple sequential observations for one single instance of rumor spreading cannot improve over the initial snapshot observation. The situation dramatically improves for multiple independent observations. We propose a unified inference framework based on the union rumor centrality, and provide explicit detection performance for degree-regular tree networks. Surprisingly, even with merely two observations, the detection probability at least doubles that of a single observation, and further approaches one, i.e., reliable detection, with increasing degree. This indicates that a richer diversity enhances detectability. For general graphs, a detection algorithm using a breadth-first search strategy is also proposed and evaluated. Besides rumor source detection, our results can be used in network forensics to combat recurring epidemic-like information spreading such as online anomaly and fraudulent email spams.
Building and scaling virtual clusters with residual resources from interactive clouds

The popularity of cloud-based interactive computing services (e.g., virtual desktops) brings new management challenges. Each interactive user leaves abundant but fluctuating residual resources while being intolerant to latency, precluding the use of aggressive VM consolidation. In this paper, we present the Resource Harvester for Interactive Clouds (RHIC), an autonomous management framework that harnesses dynamic residual resources aggressively without slowing the harvested interactive services. RHIC builds ad-hoc clusters for running throughput-oriented "background" workloads using a hybrid of residual and dedicated resources. For a given background job, RHIC intelligently discovers/maintains the ideal cluster size and composition, to meet user-specified goals such as cost/energy minimization or deadlines. RHIC employs black-box workload performance modeling, requiring only system-level metrics and incorporating techniques to improve modeling accuracy under bursty and heterogeneous residual resources. Our results show that RHIC finds near-ideal cluster sizes/compositions across a wide range of workload/goal combinations, significantly outperforms alternative approaches, tolerates high instability in the harvested interactive cloud, works with heterogeneous hardware and imposes minimal overhead.
Handwriting Analysis for Detection of Personality Traits using Machine Learning Approach

Among all the unique characteristics of a human being, handwriting carries the richest information to gain the insights into the physical, mental and emotional state of the writer. Graphology is the art of studying and analysing handwriting, a scientific method used to determine a person’s personality by evaluating various features from the handwriting. The prime features of handwriting such as the page margins, the slant of the alphabets, the baseline etc. can tell a lot about the individual. To make this method more efficient and reliable, introduction of machines to perform the feature extraction and mapping to various personality traits can be done. This compliments the graphologists, and also increases the speed of analysing handwritten samples. Various approaches can be used for this type of computer aided graphology. In this paper, a novel approach of machine learning technique to implement the automated handwriting analysis tool is discussed.
Mining Coherent Topics With Pre-Learned Interest Knowledge in Twitter

Discovering semantic coherent topics from the large amount of user-generated content (UGC) in social media would facilitate many downstream applications of intelligent computing. Topic models, as one of the most powerful algorithms, have been widely used to discover the latent semantic patterns in text collections. However, one key weakness of topic models is that they need documents with certain length to provide reliable statistics for generating coherent topics. In Twitter, the users’ tweets are mostly short and noisy. Observations of word co-occurrences are incomprehensible for topic models. To deal with this problem, previous work tried to incorporate prior knowledge to obtain better results. However, this strategy is not practical for the fast evolving UGC in Twitter. In this paper, we first cluster the users according to the retweet network, and the users’ interests are mined as the prior knowledge. Such data are then applied to improve the performance of topic learning. The potential cause for the effectiveness of this approach is that users in the same community usually share similar interests, which will result in less noisy sub-data sets. Our algorithm pre-learns two types of interest knowledge from the data set: the interest-word-sets and a tweet-interest preference matrix. Furthermore, a dedicated background model is introduced to judge whether a word is drawn from the background noise. Experiments on two real life twitter data sets show that our model achieves significant improvements over state-of-the-art baselines.