nils-herrmann/clean_scirepeval huggingface dataset:
A Method for Obtaining Digital Signatures and Public-Key Cryptosystems (Reprint)

An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences:Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intended recipient. Only he can decipher the message, since only he knows the corresponding decryption key.
A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret prime numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d = 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.
Energy-Efficient Multiple-Precision Floating-Point Multiplier for Embedded Applications

Floating-point (FP) multipliers are the main energy consumers in many modern embedded digital signal processing (DSP) and multimedia systems. For lossy applications, minimizing the precision of FP multiplication operations under the acceptable accuracy loss is a well-known approach for reducing the energy consumption of FP multipliers. This paper proposes a multiple-precision FP multiplier to efficiently trade the energy consumption with the output quality. The proposed FP multiplier can perform lowprecision multiplication that generates 8−, 14−, 20−, or 26bit mantissa product through an iterative and truncated modified Booth multiplier. Energy saving for lowprecision multiplication is achieved by partially suppressing the computation of mantissa multiplier. In addition, the proposed multiplier allows the bitwidth of mantissa in the multiplicand, multiplier, and output product to be dynamically changed when it performs different FP multiplication operations to further reduce energy consumption. Experimental results show that the proposed multiplier can achieve 59 %, 71 %, 73 %, and 82 % energy saving under 0.1 %, 1 %, 5 %, and 11 % accuracy loss, respectively, for the RGB-to-YUV & YUV-to-RGB conversion when compared to the conventional IEEE single-precision multiplier. In addition, the results also exhibit that the proposed multiplier can obtain more energy reduction than previous multipleprecision iterative FP multipliers.
Switched-Capacitor/Switched-Inductor Structures for Getting Transformerless Hybrid DC–DC PWM Converters

A few simple switching structures, formed by either two capacitors and two-three diodes (C-switching), or two inductors and two-three diodes (L-switching) are proposed. These structures can be of two types: ldquostep-downrdquo and ldquostep-up.rdquo These blocks are inserted in classical converters: buck, boost, buck-boost, Cuk, Zeta, Sepic. The ldquostep-downrdquo C- or L-switching structures can be combined with the buck, buck-boost, Cuk, Zeta, Sepic converters in order to get a step-down function. When the active switch of the converter is on, the inductors in the L-switching blocks are charged in series or the capacitors in the C-switching blocks are discharged in parallel. When the active switch is off, the inductors in the L-switching blocks are discharged in parallel or the capacitors in the C-switching blocks are charged in series. The ldquostep-uprdquo C- or L-switching structures are combined with the boost, buck-boost, Cuk, Zeta, Sepic converters, to get a step-up function. The steady-state analysis of the new hybrid converters allows for determing their DC line-to-output voltage ratio. The gain formula shows that the hybrid converters are able to reduce/increase the line voltage more times than the original, classical converters. The proposed hybrid converters contain the same number of elements as the quadratic converters. Their performances (DC gain, voltage and current stresses on the active switch and diodes, currents through the inductors) are compared to those of the available quadratic converters. The superiority of the new, hybrid converters is mainly based on less energy in the magnetic field, leading to saving in the size and cost of the inductors, and less current stresses in the switching elements, leading to smaller conduction losses. Experimental results confirm the theoretical analysis.
Worker types and personality traits in crowdsourcing relevance labels

Crowdsourcing platforms offer unprecedented opportunities for creating evaluation benchmarks, but suffer from varied output quality from crowd workers who possess different levels of competence and aspiration. This raises new challenges for quality control and requires an in-depth understanding of how workers' characteristics relate to the quality of their work.
 In this paper, we use behavioral observations (HIT completion time, fraction of useful labels, label accuracy) to define five worker types: Spammer, Sloppy, Incompetent, Competent, Diligent. Using data collected from workers engaged in the crowdsourced evaluation of the INEX 2010 Book Track Prove It task, we relate the worker types to label accuracy and personality trait information along the `Big Five' personality dimensions.
 We expect that these new insights about the types of crowd workers and the quality of their work will inform how to design HITs to attract the best workers to a task and explain why certain HIT designs are more effective than others.
Design of a novel variable stiffness gripper using permanent magnets

This paper presents the design of a novel variable stiffness gripper with two parallel fingers (jaws). Compliance of the system is generated by using permanent magnets as the nonlinear springs. Based on the presented design, the position and stiffness level of the fingers can be adjusted simultaneously by changing the air gap between the magnets. The modeling of magnetic repulsion force and stiffness are presented and verified experimentally. An experiment is also conducted to demonstrate the functionality of the gripper to improve safety when a fragile object was grasped and the gripper collided with an obstacle.
A single-feeding circularly polarized microstrip antenna with the effect of hybrid feeding

A single series feed cross-aperture coupled microstrip antenna with the effect of hybrid feeding is proposed and demonstrated. To understand this antenna better, the characteristics according to the variation of parameters are shown. This proposed antenna has the following advantages of the effect of hybrid feeding: improved axial ratio bandwidth (4.6%); high gain (8 dBi); flat 3 dB gain bandwidth (above 16.7%). In measured radiation patterns, we have 3 dB beamwidth of /spl plusmn/30/spl deg/ and good F/B (front to back ratio) of 20 dB.
A PASSIVE MILLIMETER-WAVE IMAGER USED FOR CONCEALED WEAPON DETECTION

An 8mm-band passive millimeter-wave imager BHU-2D has been developed by Beihang University. This imager is designed for detecting concealed weapons on human body. The imager adopts two-dimensional synthetic aperture interferometric radiometer (SAIR) technique which avoids the radiation risk to human body. Compared with scanning technique, SAIR technique could obtain images of a larger field of view (FOV) while achieving high imaging rate, which are necessary for security monitoring. In this paper, the imaging principle of SAIR is stated firstly. Secondly, background cancellation method designed for BHU-2D is interpreted. The technique is used to reduce the complexity as well as the dimensional requirements of the receiving elements. Thirdly, system configuration is illustrated in detail. Then external point source calibration method is introduced and discussed specifically for security check applications. Finally, imaging experiments on a person with concealed weapon are conducted by which the design and image calibration algorithms are verified. To conclude, experimental results prove that BHU-2D could be employed in security check applications.
A New Scalable Parallel Algorithm for Fock Matrix Construction

Hartree-Fock (HF) or self-consistent field (SCF) calculations are widely used in quantum chemistry, and are the starting point for accurate electronic correlation methods. Existing algorithms and software, however, may fail to scale for large numbers of cores of a distributed machine, particularly in the simulation of moderately-sized molecules. In existing codes, HF calculations are divided into tasks. Fine-grained tasks are better for load balance, but coarse-grained tasks require less communication. In this paper, we present a new parallelization of HF calculations that addresses this trade-off: we use fine grained tasks to balance the computation among large numbers of cores, but we also use a scheme to assign tasks to processes to reduce communication. We specifically focus on the distributed construction of the Fock matrix arising in the HF algorithm, and describe the data access patterns in detail. For our test molecules, our implementation shows better scalability than NWChem for constructing the Fock matrix.
Making Sense of Word Embeddings

We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling of words in context with learned sense vectors, which gives rise to downstream applications. Experiments show that the performance of our method is comparable to state-of-the-art unsupervised WSD systems.
Information Security Policy Compliance in Higher Education: A Neo-Institutional Perspective

External pressures could be a powerful force that drives the institution of higher education to attain information security policy compliance. Drawing on the Neo-Institutional Theory (NIT), this study examined how the three external expectations: regulative, normative, and cognitive expectations, impel the higher education of the United States to reach information security policy compliance. The research findings suggest that regulatory and social normative pressures, but not cognitive pressure, have significant effects on information security policy compliance in higher education. Based on these results, this study unfolds both the practical and research implications.
Real-Time Barcode Detection and Classification using Deep Learning

Barcodes, in their different forms, can be found on almost any packages available in the market. Detecting and then decoding of barcodes have therefore great applications. We describe how to adapt the state-of-theart deep learning-based detector of You Only Look Once (YOLO) for the purpose of detecting barcodes in a fast and reliable way. The detector is capable of detecting both 1D and QR barcodes. The detector achieves state-of-the-art results on the benchmark dataset of Muenster BarcodeDB with a detection rate of 0.991. The developed system can also find the rotation of both the 1D and QR barcodes, which gives the opportunity of rotating the detection accordingly which is shown to benefit the decoding process in a positive way. Both the detection and the rotation prediction shows real-time performance.
The most effective exercise for strengthening the supraspinatus muscle: evaluation by magnetic resonance imaging.

Electromyography has been used to determine the best exercise for strengthening the supraspinatus muscle, but conflicting results have been reported. Magnetic resonance imaging T2 relaxation time appears to be more accurate in determining muscle activation.



To determine the best exercises for strengthening the supraspinatus muscle.



Criterion standard.



Six male volunteers performed three exercises: the empty can, the full can, and horizontal abduction. Immediately before and after each exercise, magnetic resonance imaging examinations were performed and changes in relaxation time for the subscapularis, supraspinatus, infraspinatus, teres minor, and deltoid muscles were recorded.



The supraspinatus muscle had the greatest change among the studied muscles in relaxation time for the empty can (10.5 ms) and full can (10.5 ms) exercises. After the horizontal abduction exercise the change in relaxation time for the supraspinatus muscle (3.6 ms) was significantly smaller than that for the posterior deltoid muscle (11.5 ms) and not significantly different from that of the other muscles studied.



The empty can and full can exercises were most effective in activating the supraspinatus muscle.
GestureSleeve: using touch sensitive fabrics for gestural input on the forearm for controlling smartwatches

Smartwatches provide quick and easy access to information. Due to their wearable nature, users can perceive the information while being stationary or on the go. The main drawback of smartwatches, however, is the limited input possibility. They use similar input methods as smartphones but thereby suffer from a smaller form factor. To extend the input space of smartwatches, we present GestureSleeve, a sleeve made out of touch enabled textile. It is capable of detecting different gestures such as stroke based gestures or taps. With these gestures, the user can control various smartwatch applications. Exploring the performance of the GestureSleeve approach, we conducted a user study with a running application as use case. In this study, we show that input using the GestureSleeve outperforms touch input on the smartwatch. In the future the GestureSleeve can be integrated into regular clothing and be used for controlling various smart devices.
A Related-Key Rectangle Attack on the Full KASUMI

KASUMI is an 8-round Feistel block cipher used in the confidentiality and the integrity algorithms of the 3GPP mobile communications. As more and more 3GPP networks are being deployed, more and more users use KASUMI to protect their privacy. Previously known attacks on KASUMI can break up to 6 out of the 8 rounds faster than exhaustive key search, and no attacks on the full KASUMI have been published. In this paper we apply the recently introduced related-key boomerang and rectangle attacks to KASUMI, resulting in an attack that is faster than exhaustive search against the full cipher. We also present a relatedkey boomerang distinguisher for 6-round KASUMI using only 768 adaptively chosen plaintexts and ciphertexts encrypted or decrypted under
An incremental deployment algorithm for mobile robot teams

This paper describes an algorithm for deploying the members of a mobile robot team into an unknown environment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next deployment location. The deployment pattern is designed to maximize the area covered by the robots’ sensors, while simultaneously ensuring that the robots maintain line-of-sight contact with one another. This paper describes the basic algorithm and presents results obtained from a series of experiments conducted using both real and simulated
A Modular Grid-Connected Photovoltaic Generation System Based on DC Bus

In order to enhance the efficiency and reliability for distributed microgeneration, a modular grid-connected photovoltaic (PV) generation system is proposed. It consists of modular dc-dc converters and modular dc-ac inverters. The outputs of dc-dc converter and the inputs of dc-ac inverter share a dc bus. AC current coupling between the parallel-operated inverters, which is the key issue in this generation system, has been investigated. A current-decoupling method is proposed and implemented by regulating the currents of split-filter inductors, respectively. An optimal control strategy for the efficiency enhancement of PV generation system is proposed by utilizing the dispersion of control parameters. During power generation, only one modular dc-ac inverter is operating with nonfull load, and the other modular dc-ac inverters are operating with full load or at standby mode. A prototype of this modularized grid-connected PV generation system is implemented. The steady state and dynamic experimental results show that the fundamental components of two split-filter inductor currents in one inverter module are equivalent, which are decoupled completely, and only one inverter module operates with nonfull load among the parallel-operated inverter modules. The feasibility of the proposed system and the effectiveness of the control strategies have been verified by experimental results.
Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images

Semantic segmentation has emerged as a mainstream method in very-high-resolution remote sensing land-use/land-cover applications. In this paper, we first review the state-of-the-art semantic segmentation models in both computer vision and remote sensing fields. Subsequently, we introduce two semantic segmentation frameworks: SNFCN and SDFCN, both of which contain deep fully convolutional networks with shortcut blocks. We adopt an overlay strategy as the postprocessing method. Based on our frameworks, we conducted experiments on two online ISPRS datasets: Vaihingen and Potsdam. The results indicate that our frameworks achieve higher overall accuracy than the classic FCN-8s and SegNet models. In addition, our postprocessing method can increase the overall accuracy by about 1%–2% and help to eliminate “salt and pepper” phenomena and block effects.
Unsupervised Feature Learning from Time Series

In this paper we study the problem of learning discriminative features (segments), often referred to as shapelets [Ye and Keogh, 2009] of time series, from unlabeled time series data. Discovering shapelets for time series classification has been widely studied, where many search-based algorithms are proposed to efficiently scan and select segments from a pool of candidates. However, such types of search-based algorithms may incur high time cost when the segment candidate pool is large. Alternatively, a recent work [Grabocka et al., 2014] uses regression learning to directly learn, instead of searching for, shapelets from time series. Motivated by the above observations, we propose a new Unsupervised Shapelet Learning Model (USLM) to efficiently learn shapelets from unlabeled time series data. The corresponding learning function integrates the strengths of pseudo-class label, spectral analysis, shapelets regularization term and regularized least-squares to auto-learn shapelets, pseudo-class labels and classification boundaries simultaneously. A coordinate descent algorithm is used to iteratively solve the learning function. Experiments show that USLM outperforms searchbased algorithms on real-world time series data.
Dynamic elicitation of user preferences in a chat-based group recommender system

Group recommender systems (GRSs) support users in a group when facing decision making tasks. Many GRSs simply perform a one shot aggregation of the individual preferences and then compute recommendations. In this paper, we propose a different approach; it allows users to iteratively express and revise their preferences during the decision making process and supports such a conversational process with a chat-based interface. The implemented system observes users' interactions with the discussed options and offers appropriate support as well as recommendations to facilitate group members to come up with an agreement and a final decision. We have evaluated the effectiveness of our approach by conducting a controlled live user study. The empirical results show that the mobile application that implements the proposed approach achieves usability as well as high recommendation quality and choice satisfaction.
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning

We combine three methods which significantly improve the OCR accuracy of OCR models trained on early printed books: (1) The pretraining method utilizes the information stored in already existing models trained on a variety of typesets (mixed models) instead of starting the training from scratch. (2) Performing cross fold training on a single set of ground truth data (line images and their transcriptions) with a single OCR engine (OCRopus) produces a committee whose members then vote for the best outcome by also taking the top-N alternatives and their intrinsic confidence values into account. (3) Following the principle of maximal disagreement we select additional training lines which the voters disagree most on, expecting them to offer the highest information gain for a subsequent training (active learning). Evaluations on six early printed books yielded the following results: On average the combination of pretraining and voting improved the character accuracy by 46% when training five folds starting from the same mixed model. This number rose to 53% when using different models for pretraining, underlining the importance of diverse voters. Incorporating active learning improved the obtained results by another 16% on average (evaluated on three of the six books). Overall, the proposed methods lead to an average error rate of 2.5% when training on only 60 lines. Using a substantial ground truth pool of 1,000 lines brought the error rate down even further to less than 1% on average.
A Malaysian Vehicle License Plate Localization and Recognition System

Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed license plate localization algorithm is based on a combination of morphological processes with a modified Hough Transform approach and the recognition of the license plates is achieved by the implementation of the feed-forward backpropagation artificial neural network. Experimental results show an average of 95% successful license plate localization and recognition in a total of 589 images captured from a complex outdoor environment.