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

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: (1) 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 intented recipient. Only he can decipher the message, since only he knows the corresponding decryption key. (2) 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 primer 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.
A Computational Framework for Determining Stereo Correspondence from a Set of Linear Spatial Filters

We present a computational framework for stereopsis based on the outputs of linear spatial lters tuned to a range of orientations and scales This approach goes beyond edge based and area based approaches by using a richer image description and incorporating several stereo cues that have previously been neglected in the computer vision literature A technique based on using the pseudo inverse is presented for charac terizing the information present in a vector of lter responses We show how in our framework viewing geometry can be recovered to determine the locations of epipolar lines An assumption that visible surfaces in the scene are piecewise smooth leads to di erential treatment of image regions corre sponding to binocularly visible surfaces surface boundaries and occluded regions that are only monocularly visible The constraints imposed by view ing geometry and piecewise smoothness are incorporated into an iterative algorithm that gives good results on random dot stereograms arti cially generated scenes and natural grey level images
E-Commerce and E-Government: How Do They Compare? What Can They Learn from Each Other?

For more than a decade e-Commerce and e-Government applications have made major impacts in their respective sectors, private and public. Some time ago, we presented early insights from a comparative study of the two phenomena. This paper reports on more robust findings from an ongoing empirical investigation and deepens our understanding of similarities and differences between e-Commerce and e-Government. The findings show that despite major similarities the two phenomena follow quite separate and distinct trajectories.
Happiness Runs in a Circular Motion : Evidence for a Positive Feedback Loop between Prosocial Spending and Happiness

We examine whether a positive feedback loop exists between spending money on others (i.e. prosocial spending) and happiness. Participants recalled a previous purchase made for either themselves or someone else and then reported their happiness. Afterward, participants chose whether to spend a monetary windfall on themselves or someone else. Participants assigned to recall a purchase made for someone else reported feeling significantly happier immediately after this recollection; most importantly, the happier participants felt, the more likely they were to choose to spend a windfall on someone else in the near future. Thus, by providing initial evidence for a positive feedback loop between prosocial spending and well-being, these data offer one potential path to sustainable happiness: prosocial spending increases happiness which in turn encourages prosocial spending.
A 64 GHz 2 Gbps transmit/receive phased-array communication link in SiGe with 300 meter coverage

This paper presents a 64 GHz transmit/receive communication link between two 32-element SiGe-based phased arrays. The antenna element is a series-fed patch array, which provides directivity in the elevation plane. The transmit array results in an EIRP of 42 dBm, while the receive array provides an electronic gain of 33 dB and a system NF < 8 dB including the T/R switch and antenna losses. The arrays can be scanned to +/−50° in the azimuth using a 5-bit phase shifter on the SiGe chip, while keeping very low sidelobes and a near-ideal pattern. The communication link uses one array on the transmit side and another array on the receive side, together with external mixers and IF amplifiers. A Keysight M8195A arbitrary waveform generator is used to generate the modulated waveforms on the transmit side and a Keysight DSO804A oscilloscope is used to demodulate the received IF signal. The link performance was measured for different scan angles and modulation formats. Data rates of 1 Gbps using 16-QAM and 2 Gbps using QPSK are demonstrated at 300 m. The system also results in > 4 Gbps data rate at 100 meters, and ∼ 500 Mbps data rate at 800 meters.
Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application

Data-driven soft sensors have been widely utilized in industrial processes to estimate the critical quality variables which are intractable to directly measure online through physical devices. Due to the low sampling rate of quality variables, most of the soft sensors are developed on small number of labeled samples and the large number of unlabeled process data is discarded. The loss of information greatly limits the improvement of quality prediction accuracy. One of the main issues of data-driven soft sensor is to furthest exploit the information contained in all available process data. This paper proposes a semisupervised deep learning model for soft sensor development based on the hierarchical extreme learning machine (HELM). First, the deep network structure of autoencoders is implemented for unsupervised feature extraction with all the process samples. Then, extreme learning machine is utilized for regression through appending the quality variable. Meanwhile, the manifold regularization method is introduced for semisupervised model training. The new method can not only deeply extract the information that the data contains, but learn more from the extra unlabeled samples as well. The proposed semisupervised HELM method is applied in a high–low transformer to estimate the carbon monoxide content, which shows a significant improvement of the prediction accuracy, compared to traditional methods.
Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks

In the developing technology, crime reduction is one of the major and complex processes due to the various techniques and minimum amount of crime-related data. The traditional method is difficult to identify the crime activities with effective manner due to the minimum data. So, this paper introduces the novel big data and soft computing techniques for recognizing the crime activities with effective manner. Initially, the crime activities-related data have been collected from the various resources present in the big data. From the collected data, the inconsistent data and missing values are eliminated by applying the incremental mean normalization method. After that, the similar crime data have been clustered with the help of the fireflies-based fuzzy cognitive map neural networks which help to predict the crime activity-related features with effective manner. Finally, the prediction process is done by using the enhanced associative neural networks approach. The efficiency of the system is evaluated with the help of the experimental results and discussions in terms of the precision, recall, accuracy.
CHARM: An Efficient Algorithm for Closed Itemset Mining

The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper we present CHARM, an efficient algorithm for mining all frequent closed itemsets. It enumerates closed sets using a dual itemset-tidset search tree, using an efficient hybrid search that skips many levels. It also uses a technique called diffsets to reduce the memory footprint of intermediate computations. Finally it uses a fast hash-based approach to remove any “non-closed” sets found during computation. An extensive experimental evaluation on a number of real and synthetic databases shows that CHARM significantly outperforms previous methods. It is also linearly scalable in the number of transactions.
Traffic Light Control System for Emergency Vehicles Using Radio Frequency

Traffic congestion problem is a phenomena which contributed huge impact to the transportation system in country. This causes many problems especially when there are emergency cases at traffic light intersections which are always busy with many vehicles. A traffic light controller system is designed in order to solve these problems. This system was designed to be operated when it received signal from emergency vehicles based on radio frequency (RF) transmission and used the Programmable Integrated Circuit (PIC) 16F877A microcontroller to change the sequence back to the normal sequence before the emergency mode was triggered. This system will reduce accidents which often happen at the traffic light intersections because of other vehicle had to huddle for given a special route to emergency vehicle. As the result, this project successful analyzing and implementing the wireless communication; the radio frequency (RF) transmission in the traffic light control system for emergency vehicles. The prototype of this project is using the frequency of 434 MHz and function with the sequence mode of traffic light when emergency vehicles passing by an intersection and changing the sequence back to the normal sequence before the emergency mode was triggered. In future, this prototype system can be improved by controlling the real traffic situation, in fact improving present traffic light system technology.
Towards a Fully Automated Diagnostic System for Orthodontic Treatment in Dentistry

A deep learning technique has emerged as a successful approach for diagnostic imaging. Along with the increasing demands for dental healthcare, the automation of diagnostic imaging is increasingly desired in the field of orthodontics for many reasons (e.g., remote assessment, cost reduction, etc.). However, orthodontic diagnoses generally require dental and medical scientists to diagnose a patient from a comprehensive perspective, by looking at the mouth and face from different angles and assessing various features. This assessment process takes a great deal of time even for a single patient, and tends to generate variation in the diagnosis among dental and medical scientists. In this paper, the authors propose a deep learning model to automate diagnostic imaging, which provides an objective morphological assessment of facial features for orthodontic treatment. The automated diagnostic imaging system dramatically reduces the time needed for the assessment process. It also helps provide objective diagnosis that is important for dental and medical scientists as well as their patients because the diagnosis directly affects to the treatment plan, treatment priorities, and even insurance coverage. The proposed deep learning model outperforms a conventional convolutional neural network model in its assessment accuracy. Additionally, the authors present a work-in-progress development of a data science platform with a secure data staging mechanism, which supports computation for training our proposed deep learning model. The platform is expected to allow users (e.g., dental and medical scientists) to securely share data and flexibly conduct their data analytics by running advanced machine learning algorithms (e.g., deep learning) on high performance computing resources (e.g., a GPU cluster).
Learning both Weights and Connections for Efficient Neural Networks

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unimportant connections. Finally, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9×, from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the total number of parameters can be reduced by 13×, from 138 million to 10.3 million, again with no loss of accuracy.
Residual Reconstruction for Block-Based Compressed Sensing of Video

A simple block-based compressed-sensing reconstruction for still images is adapted to video. Incorporating reconstruction from a residual arising from motion estimation and compensation, the proposed technique alternatively reconstructs frames of the video sequence and their corresponding motion fields in an iterative fashion. Experimental results reveal that the proposed technique achieves significantly higher quality than a straightforward reconstruction that applies a still-image reconstruction independently frame by frame, a 3D reconstruction that exploits temporal correlation between frames merely in the form of a motion-agnostic 3D transform, and a similar, yet non-iterative, motion-compensated residual reconstruction.
Reflectance Capture Using Univariate Sampling of BRDFs

We propose the use of a light-weight setup consisting of a collocated camera and light source – commonly found on mobile devices – to reconstruct surface normals and spatially-varying BRDFs of near-planar material samples. A collocated setup provides only a 1-D “univariate” sampling of a 3-D isotropic BRDF. We show that a univariate sampling is sufficient to estimate parameters of commonly used analytical BRDF models. Subsequently, we use a dictionary-based reflectance prior to derive a robust technique for per-pixel normal and BRDF estimation. We demonstrate real-world shape and capture, and its application to material editing and classification, using real data acquired using a mobile phone.
A Complete Mispronunciation Detection System for Arabic Phonemes using SVM

Computer Assisted Language Learning Systems have gained a lot of attention in recent decades. Mispronunciation detection is probably the most important feature of these systems. It helps user to find out their pronunciation mistakes and provide useful feedback related to that mistake. Mispronunciation detection systems can be categorized in two classes; Posterior Probability based and Classifier based systems. In this paper pronunciation assessment problem is formulated as a classification problem. This research paper explores the Acoustic Phonetic Features (APF) rather than traditional Confidence Measure based scores for mispronunciation detection. Support Vector Machines (SVM) is used as a classifier to detect pronunciation mistakes. As a test case five Arabic phoneme are tested for mispronunciation detection. APF based classifier produced excellent results and give average accuracy of 97.5%. The proposed system outperforms the existing systems that have been developed for Arabic phonemes.
Software Cost Estimation Approaches : A Survey

The software cost estimation aims to predict the most realistic effort that is required to finish a software project and so it is critical to the success of a software project management. A Software Cost Estimation affects nearly all management activities, including project bidding, resource allocation and project planning. It is affected by a number of factors, such as implementation efficiency, as well as how much the various reviews and studies completed prior to the software development stage cost. Accurate cost estimation will help us to complete the project on time and within budget. Accurate estimation is important because it has led to extensive research into the methods of software cost estimation. Some important software cost estimation methods have been studied in this research work. In addition, we have set out own criteria, which has been used to compare all the different selected methods. We have also given a score for each evaluation criteria, so that we can compare the different methods numerically for cost estimation. Our observations have shown that it is best to use a number of different estimating techniques or cost models, and then compare the results before determining the reasons for any of the large variations. None of the methods are necessarily better or worse than the others. We found, in fact, that their strengths and weaknesses often complement each other. Therefore, the main conclusion is that there is no one single technique that is best for every situation, and the results of a number of different approaches need to be carefully considered to discover what is the most likely to produce estimates that are realistic.
Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding

One of the key problems in spoken language understanding (SLU) is the task of slot filling. In light of the recent success of applying deep neural network technologies in domain detection and intent identification, we carried out an in-depth investigation on the use of recurrent neural networks for the more difficult task of slot filling involving sequence discrimination. In this work, we implemented and compared several important recurrent-neural-network architectures, including the Elman-type and Jordan-type recurrent networks and their variants. To make the results easy to reproduce and compare, we implemented these networks on the common Theano neural network toolkit, and evaluated them on the ATIS benchmark. We also compared our results to a conditional random fields (CRF) baseline. Our results show that on this task, both types of recurrent networks outperform the CRF baseline substantially, and a bi-directional Jordantype network that takes into account both past and future dependencies among slots works best, outperforming a CRFbased baseline by 14% in relative error reduction.
A soft hand model for physically-based manipulation of virtual objects

We developed a new hand model for increasing the robustness of finger-based manipulations of virtual objects. Each phalanx of our hand model consists of a number of deformable soft bodies, which dynamically adapt to the shape of grasped objects based on the applied forces. Stronger forces directly result in larger contact areas, which increase the friction between hand and object as would occur in reality. For a robust collision-based soft body simulation, we extended the lattice-shape matching algorithm to work with adaptive stiffness values, which are dynamically derived from force and velocity thresholds. Our implementation demonstrates that this approach allows very precise and robust grasping, manipulation and releasing of virtual objects and performs in real-time for a variety of complex scenarios. Additionally, laborious tuning of object and friction parameters is not necessary for the wide range of objects that we typically grasp with our hands.
Ego-centric Graph Pattern Census

There is increasing interest in analyzing networks of all types including social, biological, sensor, computer, and transportation networks. Broadly speaking, we may be interested in global network-wide analysis (e.g., centrality analysis, community detection) where the properties of the entire network are of interest, or local ego-centric analysis where the focus is on studying the properties of nodes (egos) by analyzing their neighborhood sub graphs. In this paper we propose and study ego-centric pattern census queries, a new type of graph analysis query, where a given structural pattern is searched for in every node's neighborhood and the counts are reported or used in further analysis. This kind of analysis is useful in many domains in social network analysis including opinion leader identification, node classification, link prediction, and role identification. We propose an SQL-based declarative language to support this class of queries, and develop a series of efficient query evaluation algorithms for it. We evaluate our algorithms on a variety of synthetically generated graphs. We also show an application of our language in a real-world scenario for predicting future collaborations from DBLP data.
A New Control Strategy of an Electric-Power-Assisted Steering System

The control of electric-power-assisted steering (EPAS) systems is a challenging problem due to multiple objectives and the need for several pieces of information to implement the control. The control objectives are to generate assist torque with fast responses to driver's torque commands, ensure system stability, attenuate vibrations, transmit the road information to the driver, and improve the steering wheel returnability and free-control performance. The control must also be robust to modeling errors and parameter uncertainties. To achieve these objectives, a new control strategy is introduced in this paper. A reference model is used to generate an ideal motor angle that can guarantee the desired performance, and then, a sliding-mode control is used to track the desired motor angle. This reference model is built using a dynamic mechanical EPAS model, which is driven by the driver torque, the road reaction torque, and the desired assist torque. To implement the reference model with a minimum of sensors, a sliding-mode observer with unknown inputs and robust differentiators are employed to estimate the driver torque, the road reaction torque, and the system's states. With the proposed control strategy, there is no need for different algorithms, rules for switching between these algorithms, or fine-tuning of several parameters. In addition, our strategy improves system performance and robustness and reduces costs. The simulation results show that the proposed control structure can satisfy the desired performance.
Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being.

Human beings can be proactive and engaged or, alternatively, passive and alienated, largely as a function of the social conditions in which they develop and function. Accordingly, research guided by self-determination theory has focused on the social-contextual conditions that facilitate versus forestall the natural processes of self-motivation and healthy psychological development. Specifically, factors have been examined that enhance versus undermine intrinsic motivation, self-regulation, and well-being. The findings have led to the postulate of three innate psychological needs--competence, autonomy, and relatedness--which when satisfied yield enhanced self-motivation and mental health and when thwarted lead to diminished motivation and well-being. Also considered is the significance of these psychological needs and processes within domains such as health care, education, work, sport, religion, and psychotherapy.