nils-herrmann/clean_scirepeval huggingface dataset:
A Beginner ’ s Guide To The General Number Field Sieve

RSA is a very popular public key cryptosystem. This algorithm is known to be secure, but this fact relies on the difficulty of factoring large numbers. Because of the popularity of the algorithm, much research has gone into this problem of factoring a large number. The size of the number that we are able to factor increases exponentially year by year. This fact is partly due to advancements in computing hardware, but it is largely due to advancements in factoring algorithms. The General Number Field Sieve is an example of just such an advanced factoring algorithm. This is currently the best known method for factoring large numbers. This paper is a presentation of the General Number Field Sieve. It begins with a discussion of the algorithm in general and covers the theory that is responsible for its success. Because often the best way to learn an algorithm is by applying it, an extensive numerical example is included as well. I. I NTRODUCTION The General Number Field Sieve is an algorithm for factoring very large numbers. Factoring is very important in the field of cryptography, specifically in the RSA cryptosystem. The Rivest, Shamir, Adleman (RSA) cryptosystem is a scheme for encrypting and decrypting messages, and its security relies on the fact that factoring large composite numbers is a very hard, computationally intensive task. The RSA algorithm works in the following way: • Choose two large primes p andq. Setn = pq. • Choose a randome satisfying1 ≤ e < n. • Setd = e−1 (mod (p− 1)(q − 1)). • A messagem is encrypted toc ≡ me (mod n). Note that onlye andn were needed to compute c. e andn are known as the public key and are public information.
Antioxidant Phytochemicals for the Prevention and Treatment of Chronic Diseases

Overproduction of oxidants (reactive oxygen species and reactive nitrogen species) in the human body is responsible for the pathogenesis of some diseases. The scavenging of these oxidants is thought to be an effective measure to depress the level of oxidative stress of organisms. It has been reported that intake of vegetables and fruits is inversely associated with the risk of many chronic diseases, and antioxidant phytochemicals in vegetables and fruits are considered to be responsible for these health benefits. Antioxidant phytochemicals can be found in many foods and medicinal plants, and play an important role in the prevention and treatment of chronic diseases caused by oxidative stress. They often possess strong antioxidant and free radical scavenging abilities, as well as anti-inflammatory action, which are also the basis of other bioactivities and health benefits, such as anticancer, anti-aging, and protective action for cardiovascular diseases, diabetes mellitus, obesity and neurodegenerative diseases. This review summarizes recent progress on the health benefits of antioxidant phytochemicals, and discusses their potential mechanisms in the prevention and treatment of chronic diseases.
Augmenting End-to-End Dialogue Systems With Commonsense Knowledge

Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human utterances in an interesting and engaging way, commonsense knowledge has to be integrated into the model effectively. In this paper, we investigate the impact of providing commonsense knowledge about the concepts covered in the dialogue. Our model represents the first attempt to integrating a large commonsense knowledge base into end-toend conversational models. In the retrieval-based scenario, we propose a model to jointly take into account message content and related commonsense for selecting an appropriate response. Our experiments suggest that the knowledgeaugmented models are superior to their knowledge-free counterparts.
Deep Convolution Networks for Compression Artifacts Reduction

Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restore sharpened images that are accompanied with ringing effects. Inspired by the success of deep convolutional networks (DCN) on superresolution [6], we formulate a compact and efficient network for seamless attenuation of different compression artifacts. To meet the speed requirement of real-world applications, we further accelerate the proposed baseline model by layer decomposition and joint use of large-stride convolutional and deconvolutional layers. This also leads to a more general CNN framework that has a close relationship with the conventional Multi-Layer Perceptron (MLP). Finally, the modified network achieves a speed up of 7.5× with almost no performance loss compared to the baseline model. We also demonstrate that a deeper model can be effectively trained with features learned in a shallow network. Following a similar “easy to hard” idea, we systematically investigate three practical transfer settings and show the effectiveness of transfer learning in low-level vision problems. Our method shows superior performance than the state-of-the-art methods both on benchmark datasets and a real-world use case.
Analysis of sustained-release tablet film coats using terahertz pulsed imaging.

The coating quality of a batch of lab-scale, sustained-release coated tablets was analysed by terahertz pulsed imaging (TPI). Terahertz radiation (2 to 120 cm(-1)) is particularly interesting for coating analysis as it has the capability to penetrate through most pharmaceutical excipients, and hence allows non-destructive coating analysis. Terahertz pulsed spectroscopy (TPS) was employed for the determination of the terahertz refractive indices (RI) on the respective sustained-release excipients used in this study. The whole surface of ten tablets with 10 mg/cm(2) coating was imaged using the fully-automated TPI imaga2000 system. Multidimensional coating thickness or signal intensity maps were reconstructed for the analysis of coating layer thickness, reproducibility, and uniformity. The results from the TPI measurements were validated with optical microscopy imaging and were found to be in good agreement with this destructive analytical technique. The coating thickness around the central band was generally 33% thinner than that on the tablet surfaces. Bimodal coating thickness distribution was detected in some tablets, with thicker coatings around the edges relative to the centre. Aspects of coating defects along with their site, depth and size were identified with virtual terahertz cross-sections. The inter-day precision of the TPI measurement was found to be within 0.5%.
Simulation of Vision-Based For Automatic Takeoff and Landing Hexacopter on a Moving Ground Vehicle

In this paper presents Software In the Loop Simulation (SILS) and explain about the existing systems such as frame hexacopter, flight dynamics, on-board sensors, such as the IMU, external imaging sensor and the environment presented integrated with ROS and Gazebo simulator. After this simulation is done in the Gazebo simulator, the author will be implemented into the real hardware. The choice of platform for UAV is tarot t-680 hexacopter, it have six rotor aerial vehicle. To perform a precise landing add an external imaging sensor camera 3D intel realsense R200 static mounted facing downwards. Where the hexacopter will estimate pose and then proceed to altitude control its for autonomous landing on a moving UGV.
PREDICTION SYSTEM FOR HEART DISEASE USING NAIVE BAYES

The main objective of this research is to develop a n Intelligent System using data mining modeling tec hnique, namely, Naive Bayes. It is implemented as web based applica tion in this user answers the predefined questions. It retrieves hidden data from stored database and compares the u er values with trained data set. It can answer com plex queries for diagnosing heart disease and thus assist healthcare practitioners to make intelligent clinical decisio ns which traditional decision support systems cannot. By providing effec tiv treatments, it also helps to reduce treatment cos s. Keyword: Data mining Naive bayes, heart disease, prediction
Here's What I Can Do: Chatbots' Strategies to Convey Their Features to Users

Chatbots have been around since the 1960's, but recently they have risen in popularity especially due to new compatibility with social networks and messenger applications. Chatbots are different from traditional user interfaces, for they unveil themselves to the user one sentence at a time. Because of that, users may struggle to interact with them and to understand what they can do. Hence, it is important to support designers in deciding how to convey chatbots' features to users, as this might determine whether the user continues to chat or not. As a first step in this direction, in this paper our goal is to analyze the communicative strategies that have been used by popular chatbots to convey their features to users. To perform this analysis we use the Semiotic Inspection Method (SIM). As a result we identify and discuss the different strategies used by the analyzed chatbots to present their features to users. We also discuss the challenges and limitations of using SIM on such interfaces.
Accuracy of Assessment of Gonial Angle by Both Hemispheres of Panoramic Images and Its Comparison with Lateral Cephalometric Radiographic Measurements

Professor Yrjo Paatero, in 1961, first introduced the Orthopantomography (OPG) . It has been extensively used in dentistry for analysing the number and type of teeth present, caries, impacted teeth, root resorption, ankylosis, shape of the condyles , temporomandibular joints, sinuses, fractures, cysts, tumours and alveolar bone level [3,4]. Panoramic radiography is advised to all patients seeking orthodontic treatment; including Class I malocclusions .
Distral: Robust Multitask Reinforcement Learning

Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model. We propose a new approach for joint training of multiple tasks, which we refer to as Distral (distill & transfer learning). Instead of sharing parameters between the different workers, we propose to share a “distilled” policy that captures common behaviour across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies. Both aspects of the learning process are derived by optimizing a joint objective function. We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust to hyperparameter settings and more stable—attributes that are critical in deep reinforcement learning.
Internet of Things : Perspectives , Challenges and Opportunities

The Internet of things can be defined as to make the “things” belong to the Internet. However, many wonder if the current Internet can support such a challenge. For this and other reasons, hundreds of worldwide initiatives to redesign the Internet are underway. This article discusses the perspectives, challenges and opportunities behind a future Internet that fully supports the “things”, as well as how the “things” can help in the design of a more synergistic future Internet. Internet of things, smart things, future Internet, software-defined networking, service-centrism, informationcentrism, ID/Loc splitting, security, privacy, trust.
Diverse Beam Search for Increased Novelty in Abstractive Summarization

Text summarization condenses a text to a shorter version while retaining the important informations. Abstractive summarization is a recent development that generates new phrases, rather than simply copying or rephrasing sentences within the original text. Recently neural sequence-to-sequence models have achieved good results in the field of abstractive summarization, which opens new possibilities and applications for industrial purposes. However, most practitioners observe that these models still use large parts of the original text in the output summaries, making them often similar to extractive frameworks. To address this drawback, we first introduce a new metric to measure how much of a summary is extracted from the input text. Secondly, we present a novel method, that relies on a diversity factor in computing the neural network loss, to improve the diversity of the summaries generated by any neural abstractive model implementing beam search. Finally, we show that this method not only makes the system less extractive, but also improves the overall rouge score of state-of-the-art methods by at least 2 points.
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way. Point2Sequence employs a novel sequence learning model for point clouds to capture the correlations by aggregating multi-scale areas of each local region with attention. Specifically, Point2Sequence first learns the feature of each area scale in a local region. Then, it captures the correlation between area scales in the process of aggregating all area scales using a recurrent neural network (RNN) based encoder-decoder structure, where an attention mechanism is proposed to highlight the importance of different area scales. Experimental results show that Point2Sequence achieves state-of-the-art performance in shape classification and segmentation tasks.
A Survey on Credit Card Fraud Detection Using Machine Learning

Nowadays digitalization gaining popularity because of seamless, easy and convenience use of e-commerce. It became very rampant and easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, etc. As the result of huge amount of e-commerce use, there is a vast increment in credit card fraud also. Fraudsters try to misuse the card and transparency of online payments. Thus to overcome with the fraudsters activity become very essential. The main aim is to secure credit card transactions; so people can use e-banking safely and easily. To detecting the credit card fraud there are various techniques which are based on Deep learning, Logistic Regression, Naive Bayesian, Support Vector Machine (SVM), Neural Network, Artificial Immune System, K Nearest Neighbor, Data Mining, Decision Tree, Fuzzy logic based System, Genetic Algorithm etc.
Meta-SGD: Learning to Learn Quickly for Few Shot Learning

Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial. In this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner that can initialize and adapt any differentiable learner in just one step, on both supervised learning and reinforcement learning. Compared to the popular meta-learner LSTM, Meta-SGD is conceptually simpler, easier to implement, and can be learned more efficiently. Compared to the latest meta-learner MAML, Meta-SGD has a much higher capacity by learning to learn not just the learner initialization, but also the learner update direction and learning rate, all in a single meta-learning process. Meta-SGD shows highly competitive performance for few-shot learning on regression, classification, and reinforcement learning.
Dental erosion: understanding this pervasive condition.

Dental erosion is a contemporary disease, mostly because of the change of the eating patterns that currently exist in society. It is a "silent" and multifactorial disease, and is highly influenced by habits and lifestyles. The prevalence of dental erosion has considerably increased, with this condition currently standing as a great challenge for the clinician, regarding the diagnosis, identification of the etiological factors, prevention, and execution of an adequate treatment. This article presents a dental erosion review and a case report of a restorative treatment of dental erosion lesions using a combination of bonded ceramic overlays to reestablish vertical dimension and composite resin to restore the worn palatal and incisal surfaces of the anterior upper teeth. Adequate function and esthetics can be achieved with this approach.
Leveraging Context Information for Natural Question Generation

The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequence-to-sequence models that take a passage with an additional bit to indicate answer position as input. However, they do not explicitly model the information between answer and other context within the passage. We propose a model that matches the answer with the passage before generating the question. Experiments show that our model outperforms the existing state of the art using rich features.
Long-term follow-up study of osseointegrated implants in the treatment of totally edentulous jaws.

This study reviews the long-term outcome of prostheses and fixtures (implants) in 759 totally edentulous jaws of 700 patients. A total of 4,636 standard fixtures were placed and followed according to the osseointegration method for a maximum of 24 years by the original team at the University of Göteborg. Standardized annual clinical and radiographic examinations were conducted as far as possible. A lifetable approach was applied for statistical analysis. Sufficient numbers of fixtures and prostheses for a detailed statistical analysis were present for observation times up to 15 years. More than 95% of maxillae had continuous prosthesis stability at 5 and 10 years, and at least 92% at 15 years. The figure for mandibles was 99% at all time intervals. Calculated from the time of fixture placement, the estimated survival rates for individual fixtures in the maxilla were 84%, 89%, and 92% at 5 years; 81% and 82% at 10 years; and 78% at 15 years. In the mandible they were 91%, 98%, and 99% at 5 years; 89% and 98% at 10 years; and 86% at 15 years. (The different percentages at 5 and 10 years refer to results for different routine groups of fixtures with 5 to 10, 10 to 15, and 1 to 5 years of observation time, respectively.) The results of this study concur with multicenter and earlier results for the osseointegration method.
Image Processing Techniques for Detection of Leaf Disease

In agriculture research of automatic leaf disease detection is essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect symptoms of disease as soon as they appear on plant leaves. The term disease is usually used only for destruction of live plants. This paper provides various methods used to study of leaf disease detection using image processing. The methods studies are for increasing throughput and reduction subjectiveness arising from human experts in detecting the leaf disease[1].digital image processing is a technique used for enhancement of the image. To improve agricultural products automatic detection of symptoms is beneficial. Keyword— Leaf disease, Image processing.
A Human Mixed Strategy Approach to Deep Reinforcement Learning

In 2015, Google's Deepmind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is its lack of "exploration" during the training process, especially when working with high-dimensional problems. In this paper, we propose a mixed strategy approach that mimics behaviors of human when interacting with environment, and create a "thinking" agent that allows for more efficient exploration in the DRL training process. The simulation results based on the Breakout game show that our scheme achieves a higher probability of obtaining a maximum score than does the baseline DRL algorithm, i.e., the asynchronous advantage actor-critic method. The proposed scheme therefore can be applied effectively to solving a complicated task in a real-world application.
Toward Diverse Text Generation with Inverse Reinforcement Learning

Text generation is a crucial task in NLP. Recently, several adversarial generative models have been proposed to improve the exposure bias problem in text generation. Though these models gain great success, they still suffer from the problems of reward sparsity and mode collapse. In order to address these two problems, in this paper, we employ inverse reinforcement learning (IRL) for text generation. Specifically, the IRL framework learns a reward function on training data, and then an optimal policy to maximum the expected total reward. Similar to the adversarial models, the reward and policy function in IRL are optimized alternately. Our method has two advantages: (1) the reward function can produce more dense reward signals. (2) the generation policy, trained by “entropy regularized” policy gradient, encourages to generate more diversified texts. Experiment results demonstrate that our proposed method can generate higher quality texts than the previous methods.
Framing Analysis : An Approach to News Discourse

In the American political process, news discourse concerning public policy issues is carefully constructed. This occurs in part because both politicians and interest groups take an increasingly proactive approach to amplify their views of what an issue is about. However, news media also play an active role in framing public policy issues. Thus, in this article, news discourse is conceived as a sociocognitive process involving all three players: sources, journalists, and audience members operating in the universe of shared culture and on the basis of socially defined roles. Framing analysis is presented as a constructivist approach to examine news discourse with the primary focus on conceptualizing news texts into empirically operationalizable dimensions—syntactical, script, thematic, and rhetorical structures—so that evidence of the news media's framing of issues in news texts may be gathered. This is considered an initial step toward analyzing the news discourse process as a whole. Finally, an extended empirical example is provided to illustrate the applications of this conceptual framework of news texts.