nils-herrmann/clean_scirepeval huggingface dataset:
Dynamics of bidding in a P2P lending service: effects of herding and predicting loan success

Online peer-to-peer (P2P) lending services are a new type of social platform that enables individuals borrow and lend money directly from one to another. In this paper, we study the dynamics of bidding behavior in a P2P loan auction website, Prosper.com. We investigate the change of various attributes of loan requesting listings over time, such as the interest rate and the number of bids. We observe that there is herding behavior during bidding, and for most of the listings, the numbers of bids they receive reach spikes at very similar time points. We explain these phenomena by showing that there are economic and social factors that lenders take into account when deciding to bid on a listing. We also observe that the profits the lenders make are tied with their bidding preferences. Finally, we build a model based on the temporal progression of the bidding, that reliably predicts the success of a loan request listing, as well as whether a loan will be paid back or not.
Design of Power and Area Efficient Approximate Multipliers

Approximate computing can decrease the design complexity with an increase in performance and power efficiency for error resilient applications. This brief deals with a new design approach for approximation of multipliers. The partial products of the multiplier are altered to introduce varying probability terms. Logic complexity of approximation is varied for the accumulation of altered partial products based on their probability. The proposed approximation is utilized in two variants of 16-bit multipliers. Synthesis results reveal that two proposed multipliers achieve power savings of 72% and 38%, respectively, compared to an exact multiplier. They have better precision when compared to existing approximate multipliers. Mean relative error figures are as low as 7.6% and 0.02% for the proposed approximate multipliers, which are better than the previous works. Performance of the proposed multipliers is evaluated with an image processing application, where one of the proposed models achieves the highest peak signal to noise ratio.
Current and New Developments in Spam Filtering

This paper provides an overview of current and potential future spam filtering techniques. We examine the problems spam introduces, what spam is and how we can measure it. The paper primarily focuses on automated, non-interactive filters, with a broad review ranging from commercial implementations to ideas confined to current research papers. Both machine learning and non-machine learning based filters are reviewed as potential solutions and a taxonomy of known approaches presented. While a range of different techniques have and continue to be evaluated in academic research, heuristic and Bayesian filtering - along with its variants - provide the greatest potential for future spam prevention
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Building such a large-scale dataset, however, is highly challenging; existing datasets either contain only synthetic data, or lack precise alignment between 2D images and 3D shapes, or only have a small number of images. Second, we calibrate the evaluation criteria for 3D shape reconstruction through behavioral studies, and use them to objectively and systematically benchmark cutting-edge reconstruction algorithms on Pix3D. Third, we design a novel model that simultaneously performs 3D reconstruction and pose estimation; our multi-task learning approach achieves state-of-the-art performance on both tasks.
Effective deep learning-based multi-modal retrieval

Multi-modal retrieval is emerging as a new search paradigm that enables seamless information retrieval from various types of media. For example, users can simply snap a movie poster to search for relevant reviews and trailers. The mainstream solution to the problem is to learn a set of mapping functions that project data from different modalities into a common metric space in which conventional indexing schemes for high-dimensional space can be applied. Since the effectiveness of the mapping functions plays an essential role in improving search quality, in this paper, we exploit deep learning techniques to learn effective mapping functions. In particular, we first propose a general learning objective that effectively captures both intramodal and intermodal semantic relationships of data from heterogeneous sources. Given the general objective, we propose two learning algorithms to realize it: (1) an unsupervised approach that uses stacked auto-encoders and requires minimum prior knowledge on the training data and (2) a supervised approach using deep convolutional neural network and neural language model. Our training algorithms are memory efficient with respect to the data volume. Given a large training dataset, we split it into mini-batches and adjust the mapping functions continuously for each batch. Experimental results on three real datasets demonstrate that our proposed methods achieve significant improvement in search accuracy over the state-of-the-art solutions.
Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning

Collecting training images for all visual categories is not only expensive but also impractical. Zero-shot learning (ZSL), especially using attributes, offers a pragmatic solution to this problem. However, at test time most attribute-based methods require a full description of attribute associations for each unseen class. Providing these associations is time consuming and often requires domain specific knowledge. In this work, we aim to carry out attribute-based zero-shot classification in an unsupervised manner. We propose an approach to learn relations that couples class embeddings with their corresponding attributes. Given only the name of an unseen class, the learned relationship model is used to automatically predict the class-attribute associations. Furthermore, our model facilitates transferring attributes across data sets without additional effort. Integrating knowledge from multiple sources results in a significant additional improvement in performance. We evaluate on two public data sets: Animals with Attributes and aPascal/aYahoo. Our approach outperforms state-of the-art methods in both predicting class-attribute associations and unsupervised ZSL by a large margin.
Population growth of human Y chromosomes: a study of Y chromosome microsatellites.

We use variation at a set of eight human Y chromosome microsatellite loci to investigate the demographic history of the Y chromosome. Instead of assuming a population of constant size, as in most of the previous work on the Y chromosome, we consider a model which permits a period of recent population growth. We show that for most of the populations in our sample this model fits the data far better than a model with no growth. We estimate the demographic parameters of this model for each population and also the time to the most recent common ancestor. Since there is some uncertainty about the details of the microsatellite mutation process, we consider several plausible mutation schemes and estimate the variance in mutation size simultaneously with the demographic parameters of interest. Our finding of a recent common ancestor (probably in the last 120,000 years), coupled with a strong signal of demographic expansion in all populations, suggests either a recent human expansion from a small ancestral population, or natural selection acting on the Y chromosome.
On Horizontal Decomposition of the Operating System

As previous OS abstractions and structures fail to explicitly consider the separation between resource users an d providers, the shift toward server-side computing poses se rious challenges to OS structures, which is aggravated by the increasing many-core scale and workload diversity. This paper presents the horizontal OS model. We propose a new OS abstraction—subOS—an independent OS instance owning physical resources that can be created, destroyed, a nd resized swiftly. We horizontally decompose the OS into the s upervisor for the resource provider and several subOSes for r esource users. The supervisor discovers, monitors, and prov isions resources for subOSes, while each subOS independentl y runs applications. We confine state sharing among subOSes, but allow on-demand state sharing if necessary. We present the first implementation—RainForest, which supports unmodified Linux applications binaries. Our comprehensive evaluations using six benchmark suites quantit atively show RainForest outperforms Linux with three differ ent kernels, LXC, and XEN. The RainForest source code is soon available.
Phrasier: A System for Interactive Document Retrieval Using Keyphrases

information needs are often too complex to be effectively expressed in standard query interfaces to full-text retrieval systems. A typical need is to find documents that are similar to a given source document, yet describing the content of a document in a few terms is a difficult task. We describe Phrasier, an interactive system for browsing, querying and relating documents within a digital library. Phrasier exploits keyphrases that have been automatically extracted from source documents to create links to similar documents and to suggest appropriate query phrases to users. PhrasierÕs keyphrase-based retrieval engine returns ranked lists of documents that are similar to a given source text. Evaluation indicates that PhrasierÕs keyphrase-based retrieval performs as well as full-text retrieval when recall and relevance scores assigned by human assessors are considered.
A cooperative parallel metaheuristic for the capacitated vehicle routing problem

This paper introduces a cooperative parallel metaheuristic for solving the capacitated vehicle routing problem. The proposed metaheuristic consists of multiple parallel tabu search threads that cooperate by asynchronously exchanging best found solutions through a common solution pool. The solutions sent to the pool are clustered according to their similarities. The search history information identified from the solution clusters is applied to guide the intensification or diversification of the tabu search threads. Computational experiments on two sets of large scale benchmarks from the literature demonstrate that the suggested metaheuristic is highly competitive, providing new best solutions to ten of those well-studied instances.
A dynamic operating system for sensor nodes

Sensor network nodes exhibit characteristics of both embedded systems and general-purpose systems. They must use little energy and be robust to environmental conditions, while also providing common services that make it easy to write applications. In TinyOS, the current state of the art in sensor node operating systems, reusable components implement common services, but each node runs a single statically-linked system image, making it hard to run multiple applications or incrementally update applications. We present SOS, a new operating system for mote-class sensor nodes that takes a more dynamic point on the design spectrum. SOS consists of dynamically-loaded modules and a common kernel, which implements messaging, dynamic memory, and module loading and unloading, among other services. Modules are not processes: they are scheduled cooperatively and there is no memory protection. Nevertheless, the system protects against common module bugs using techniques such as typed entry points, watchdog timers, and primitive resource garbage collection. Individual modules can be added and removed with minimal system interruption. We describe SOS's design and implementation, discuss tradeoffs, and compare it with TinyOS and with the Maté virtual machine. Our evaluation shows that despite the dynamic nature of SOS and its higher-level kernel interface, its long term total usage nearly identical to that of systems such as Matè and TinyOS.
Prosocial learning agents solve generalized Stag Hunts better than selfish ones

1 EXTENDED ABSTRACT Real world interactions are full of coordination problems [2, 3, 8, 14, 15] and thus constructing agents that can solve them is an important problem for artificial intelligence research. One of the simplest, most heavily studied coordination problems is the matrixform, two-player Stag Hunt. In the Stag Hunt, each player makes a choice between a risky action (hunt the stag) and a safe action (forage for mushrooms). Foraging for mushrooms always yields a safe payoff while hunting yields a high payoff if the other player also hunts but a very low payoff if one shows up to hunt alone. This game has two important Nash equilibria: either both players show up to hunt (this is called the payoff dominant equilibrium) or both players stay home and forage (this is called the risk-dominant equilibrium [7]). In the Stag Hunt, when the payoff to hunting alone is sufficiently low, dyads of learners as well as evolving populations converge to the risk-dominant (safe) equilibrium [6, 8, 10, 11]. The intuition here is that even a slight amount of doubt about whether one’s partner will show up causes an agent to choose the safe action. This in turn causes partners to be less likely to hunt in the future and the system trends to the inefficient equilibrium. We are interested in the problem of agent design: our task is to construct an agent that will go into an initially poorly understood environment and make decisions. Our agent must learn from its experiences to update its policy and maximize some scalar reward. However, there will also be other agents which we do not control. These agents will also learn from their experiences. We ask: if the environment has Stag Hunt-like properties, can we make changes to our agent’s learning to improve its outcomes? We focus on reinforcement learning (RL), however, many of our results should generalize to other learning algorithms.
An efficient filtering based approach improving LSB image steganography using status bit along with AES cryptography

In Steganography, the total message will be invisible into a cover media such as text, audio, video, and image in which attackers don't have any idea about the original message that the media contain and which algorithm use to embed or extract it. In this paper, the proposed technique has focused on Bitmap image as it is uncompressed and convenient than any other image format to implement LSB Steganography method. For better security AES cryptography technique has also been used in the proposed method. Before applying the Steganography technique, AES cryptography will change the secret message into cipher text to ensure two layer security of the message. In the proposed technique, a new Steganography technique is being developed to hide large data in Bitmap image using filtering based algorithm, which uses MSB bits for filtering purpose. This method uses the concept of status checking for insertion and retrieval of message. This method is an improvement of Least Significant Bit (LSB) method for hiding information in images. It is being predicted that the proposed method will able to hide large data in a single image retaining the advantages and discarding the disadvantages of the traditional LSB method. Various sizes of data are stored inside the images and the PSNR are also calculated for each of the images tested. Based on the PSNR value, the Stego image has higher PSNR value as compared to other method. Hence the proposed Steganography technique is very efficient to hide the secret information inside an image.
Routing Questions to the Right Users in Online Communities

Online forums contain huge amounts of valuable user-generated content. In current forum systems, users have to passively wait for other users to visit the forum systems and read/answer their questions. The user experience for question answering suffers from this arrangement. In this paper, we address the problem of "pushing" the right questions to the right persons, the objective being to obtain quick, high-quality answers, thus improving user satisfaction. We propose a framework for the efficient and effective routing of a given question to the top-k potential experts (users) in a forum, by utilizing both the content and structures of the forum system. First, we compute the expertise of users according to the content of the forum system—-this is to estimate the probability of a user being an expert for a given question based on the previous question answering of the user. Specifically, we design three models for this task, including a profile-based model, a thread-based model, and a cluster-based model. Second, we re-rank the user expertise measured in probability by utilizing the structural relations among users in a forum system. The results of the two steps can be integrated naturally in a probabilistic model that computes a final ranking score for each user. Experimental results show that the proposals are very promising.
A novel method for personalized music recommendation

With the development of digital music technologies, it is an interesting and useful issue to recommend the ‘favored music’ from large amounts of digital music. Some Web-based music stores can recommend popular music which has been rated by many people. However, three problems that need to be resolved in the current methods are: (a) how to recommend the ‘favored music’ which has not been rated by anyone, (b) how to avoid repeatedly recommending the ‘disfavored music’ for users, and (c) how to recommend more interesting music for users besides the ones users have been used to listen. To achieve these goals, we proposed a novel method called personalized hybrid music recommendation, which combines the content-based, collaboration-based and emotion-based methods by computing the weights of the methods according to users’ interests. Furthermore, to evaluate the recommendation accuracy, we constructed a system that can recommend the music to users after mining users’ logs on music listening records. By the feedback of the user’s options, the proposed methods accommodate the variations of the users’ musical interests and then promptly recommend the favored and more interesting music via consecutive recommendations. Experimental results show that the recommendation accuracy achieved by our method is as good as 90%. Hence, it is helpful for recommending the ‘favored music’ to users, provided that each music object is annotated with the related music emotions. The framework in this paper could serve as a useful basis for studies on music recommendation.
Dual-Band Dual-Polarized Compact Bowtie Antenna Array for Anti-Interference MIMO WLAN

Smart antennas have received increasing interest for mitigating interference in the multiple-input-multiple-output (MIMO) wireless local area network (WLAN). In this paper, a dual-band dual-polarized compact bowtie dipole antenna array is proposed to support anti-interference MIMO WLAN applications. In the antenna array, there are 12 antennas, six for horizontal polarization and six for vertical polarization. In order to achieve dual linear polarizations and beam switching, six horizontal antennas are placed in a sequential, rotating arrangement on a horizontal substrate panel with an equal inclination angle of 60 ° to form a symmetrical structure, while the other six antennas for vertical polarization are inserted through slots made on the horizontal substrate panel. Furthermore, six pairs of meandered slits are introduced to reduce the mutual coupling between horizontal antennas in the lower band. A prototype of the array with a dimension of 150 × 150 × 60 mm3 is manufactured and exhibits the characteristics of high isolation, good front-to-back ratio, and average gains of 4.5 and 5 dBi over the 2.4- and 5-GHz band, respectively. The MIMO performance of the array is analyzed and evaluated by mutual coupling, the total active reflection coefficient (TARC) and the envelope correlation coefficient. The anti-interference capability of the array is also investigated by the experiment.
Eye Scrutinized Wheel Chair for People Affected with Tetraplegia

Nowadays the requirement for developing a wheel chair control which is useful for the physically disabled person with Tetraplegia. This system involves the control of the wheel chair with the eye moment of the affected person. Statistics suggest that there are 230,000 cases of Tetraplegia in India. Our system here is to develop a wheelchair which make the lives of these people easier and instigate confidence to live in them. We know that a person who is affected by Tetraplegia can move their eyes alone to a certain extent which paves the idea for the development of our system. Here we have proposed the method for a device where a patient placed on the wheel chair looking in a straight line at the camera which is permanently fixed in the optics, is capable to move in a track by gazing in that way. When we change the direction, the camera signals are given using the mat lab script to the microcontroller. Depends on the path of the eye, the microcontroller controls the wheel chair in all direction and stops the movement. If there is any obstacle to be found before the wheel chair the sensor mind that and it stop and move in right direction immediately. The benefit of this system is too easily travel anywhere in any direction which is handled by physically disabled person with Tetraplegia.
MahNMF: Manhattan Non-negative Matrix Factorization

Non-negative matrix factorization (NMF) approximates a non-negative matrix X by a product of two non-negative low-rank factor matrices W and H . NMF and its extensions minimize either the Kullback-Leibler divergence or the Euclidean distance between X and WH to model the Poisson noise or the Gaussian noise. In practice, when the noise distribution is heavy tailed, they cannot perform well. This paper presents Manhattan NMF (MahNMF) which minimizes the Manhattan distance between X and WH for modeling the heavy tailed Laplacian noise. Similar to sparse and low-rank matrix decompositions, e.g. robust principal component analysis (RPCA) and GoDec, MahNMF robustly estimates the low-rank part and the sparse part of a non-negative matrix and thus performs effectively when data are contaminated by outliers. We extend MahNMF for various practical applications by developing box-constrained MahNMF, manifold regularized MahNMF, group sparse MahNMF, elastic net inducing MahNMF, and symmetric MahNMF. The major contribution of this paper lies in two fast optimization algorithms for MahNMF and its extensions: the rank-one residual iteration (RRI) method and Nesterov’s smoothing method. In particular, by approximating the residual matrix by the outer product of one row of W and one row ofH in MahNMF, we develop an RRI method to iteratively update each variable of W and H in a closed form solution. Although RRI is efficient for small scale MahNMF and some of its extensions, it is neither scalable to large scale matrices nor flexible enough to optimize all MahNMF extensions. Since the objective functions of MahNMF and its extensions are neither convex nor smooth, we apply Nesterov’s smoothing  Guan, D. Tao, Z. Luo, and J. Shawe-Taylor method to recursively optimize one factor matrix with another matrix fixed. By setting the smoothing parameter inversely proportional to the iteration number, we improve the approximation accuracy iteratively for both MahNMF and its extensions. We conduct experiments on both synthetic and real-world datasets, such as face images, natural scene images, surveillance videos and multi-model datasets, to show the efficiency of the proposed Nesterov’s smoothing method-based algorithm for solving MahNMF and its variants, and the effectiveness of MahNMF and its variants, by comparing them with traditional NMF, RPCA, and GoDec.