nils-herrmann/clean_scirepeval huggingface dataset:
Unseen Class Discovery in Open-world Classification

This paper concerns open-world classification, where the classifier not only needs to classify test examples into seen classes that have appeared in training but also reject examples from unseen or novel classes that have not appeared in training. Specifically, this paper focuses on discovering the hidden unseen classes of the rejected examples. Clearly, without prior knowledge this is difficult. However, we do have the data from the seen training classes, which can tell us what kind of similarity/difference is expected for examples from the same class or from different classes. It is reasonable to assume that this knowledge can be transferred to the rejected examples and used to discover the hidden unseen classes in them. This paper aims to solve this problem. It first proposes a joint open classification model with a sub-model for classifying whether a pair of examples belongs to the same or different classes. This sub-model can serve as a distance function for clustering to discover the hidden classes of the rejected examples. Experimental results show that the proposed model is highly promising.
Land Surface Temperature Retrieval from LANDSAT data using Emissivity Estimation

Land surface temperature (LST) is an essential factor in many areas like global climate change studies, urban land use/land cover, geo-/biophysical and also a key input for climate models. LANDSAT 8, the latest satellite from LANDSAT series, has given lot of possibilities to study the land processes using remote sensing. In this study an attempt has been made to estimate LST over Chittoor district, Andhra Pradesh, India, using LANDSAT 8 – Operational Line Imager & Thermal Infrared Sensor (OLI & TIRS) satellite data. The variability of retrieved LSTs has been investigated with respect to Normalized Difference Vegetation Index (NDVI) values for different land use/land cover (LU/LC) types determined from the Landsat visible and NIR channels. The Land Surface Emissivity (LSE) values needed in order to apply the method have been estimated from a procedure that uses the visible and near infrared bands. The present study focuses on developing an ERDAS IMAGINE image processing method using the LANDSAT 8 thermal imagery of band 10 data. The difference between retrieved LST and Automatic Weather Station (AWS) data indicates that the technique works by giving an error of ±3C.
Catastrophic Forgetting, Rehearsal and Pseudorehearsal

This paper reviews the problem of catastrophic forgetting (the loss or disruption of previously learned information when new information is learned) in neural networks, and explores rehearsal mechanisms (the retraining of some of the previously learned information as the new information is added) as a potential solution. We replicate some of the experiments described by Ratcliff (1990), including those relating to a simple “recency” based rehearsal regime. We then develop further rehearsal regimes which are more effective than recency rehearsal. In particular “sweep rehearsal” is very successful at minimising catastrophic forgetting. One possible limitation of rehearsal in general, however, is that previously learned information may not be available for retraining. We describe a solution to this problem, “pseudorehearsal”, a method which provides the advantages of rehearsal without actually requiring any access to the previously learned information (the original training population) itself. We then suggest an interpretation of these rehearsal mechanisms in the context of a function approximation based account of neural network learning. Both rehearsal and pseudorehearsal may have practical applications, allowing new information to be integrated into an existing network with minimum disruption of old information.
Acetaminophen reduces social pain: behavioral and neural evidence.

Pain, whether caused by physical injury or social rejection, is an inevitable part of life. These two types of pain-physical and social-may rely on some of the same behavioral and neural mechanisms that register pain-related affect. To the extent that these pain processes overlap, acetaminophen, a physical pain suppressant that acts through central (rather than peripheral) neural mechanisms, may also reduce behavioral and neural responses to social rejection. In two experiments, participants took acetaminophen or placebo daily for 3 weeks. Doses of acetaminophen reduced reports of social pain on a daily basis (Experiment 1). We used functional magnetic resonance imaging to measure participants' brain activity (Experiment 2), and found that acetaminophen reduced neural responses to social rejection in brain regions previously associated with distress caused by social pain and the affective component of physical pain (dorsal anterior cingulate cortex, anterior insula). Thus, acetaminophen reduces behavioral and neural responses associated with the pain of social rejection, demonstrating substantial overlap between social and physical pain.
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights

We propose BinaryRelax, a simple two-phase algorithm, for training deep neural networks with quantized weights. The set constraint that characterizes the quantization of weights is not imposed until the late stage of training, and a sequence of pseudo quantized weights is maintained. Specifically, we relax the hard constraint into a continuous regularizer via Moreau envelope, which turns out to be the squared Euclidean distance to the set of quantized weights. The pseudo quantized weights are obtained by linearly interpolating between the float weights and their quantizations. A continuation strategy is adopted to push the weights towards the quantized state by gradually increasing the regularization parameter. In the second phase, exact quantization scheme with a small learning rate is invoked to guarantee fully quantized weights. We test BinaryRelax on the benchmark CIFAR and ImageNet color image datasets to demonstrate the superiority of the relaxed quantization approach and the improved accuracy over the state-of-the-art training methods. Finally, we prove the convergence of BinaryRelax under an approximate orthogonality condition.
Subduction styles in the Precambrian : Insight from numerical experiments

a r t i c l e i n f o Plate tectonics is a self-organizing global system driven by the negative buoyancy of the thermal boundary layer resulting in subduction. Although the signature of plate tectonics is recognized with some confidence in the Phanerozoic geological record of the continents, evidence for plate tectonics becomes less certain further back in time. To improve our understanding of plate tectonics on the Earth in the Precambrian we have to combine knowledge derived from the geological record with results from well-constrained numerical modeling. In a series of experiments using a 2D petrological–thermomechanical numerical model of oceanic subduction we have systematically investigated the dependence of tectono-metamorphic and magmatic regimes at an active plate margin on upper-mantle temperature, crustal radiogenic heat production, degree of lithospheric weakening and other parameters. We have identified a first-order transition from a " no-subduction " tectonic regime through a " pre-subduction " tectonic regime to the modern style of subduction. The first transition is gradual and occurs at upper-mantle temperatures between 250 and 200 K above the present-day values, whereas the second transition is more abrupt and occurs at 175–160 K. The link between geological observations and model results suggests that the transition to the modern plate tectonic regime might have occurred during the Mesoarchean–Neoarchean time (ca. 3.2–2.5 Ga). In the case of the " pre-subduction " tectonic regime (upper-mantle temperature 175–250 K above the present) the plates are weakened by intense percolation of melts derived from the underlying hot melt-bearing sub-lithospheric mantle. In such cases, convergence does not produce self-sustaining one-sided subduction, but rather results in shallow underthrusting of the oceanic plate under the continental plate. Further increase in the upper-mantle temperature (N 250 K above the present) causes a transition to a " no-subduction " regime where horizontal movements of small deformable plate fragments are accommodated by internal strain and even shallow underthrusts do not form under the imposed convergence. Thus, based on the results of the numerical modeling, we suggest that the crucial parameter controlling the tectonic regime is the degree of lithospheric weakening induced by emplacement of sub-lithospheric melts into the lithosphere. A lower melt flux at upper-mantle temperatures b 175–160 K results in a lesser degree of melt-related weakening leading to stronger plates, which stabilizes modern style subduction even at high mantle temperatures.
A Blockchain-Based Authentication and Security Mechanism for IoT

The existing identity authentication of IoT devices mostly depends on an intermediary institution, i.e., a CA server, which suffers from the single-point-failure attack. Even worse, the critical data of authenticated devices can be tampered by inner attacks without being identified. To address these issues, we utilize blockchain technology, which serves as a secure tamper-proof distributed ledger to IoT devices. In the proposed method, we assign a unique ID for each individual device and record them into the blockchain, so that they can authenticate each other without a central authority. We also design a data protection mechanism by hashing significant data (i.e. firmware) into the blockchain where any state changes of the data can be detected immediately. Finally, we implement a prototype based on an open source blockchain platform Hyperledger Fabric to verify the proposed system.
5G multi-antenna technology and experimental trials

In order to provide a wide variety of mobile services and applications, the fifth-generation (5G) mobile communication system has attracted much attention to improve system capacity much more than the 4G system. The drastic improvement is mainly realized by small/semi-macro cell deployment with much wider bandwidth in higher frequency bands. To cope with larger pathloss in the higher frequency bands, Massive MIMO is one of key technologies to acquire beamforming (BF) in addition to spatial multiplexing. This paper introduces 5G Massive MIMO technologies including high-performance hybrid BF and novel digital BF schemes in addition to distributed Massive MIMO concept with flexible antenna deployment. The latest 5G experimental trials using the Massive MIMO technologies are also shown briefly.
Data Science and its Relationship to Big Data and Data-Driven Decision Making.

Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.
Mutual Reinforcement of Academic Performance Prediction and Library Book Recommendation

The prediction of academic performance is one of the most important tasks in educational data mining, and has been widely studied in MOOCs and intelligent tutoring systems. Academic performance could be affected with factors like personality, skills, social environment, the use of library books and so on. However, it is still less investigated that how could the use of library books affect academic performance of college students and even leverage book-loan history for predicting academic performance. To this end, we propose a supervised content-aware matrix factorization for mutual reinforcement of academic performance prediction and library book recommendation. This model not only addresses the sparsity challenge by explainable dimension reduction techniques, but also promotes library book recommendation by recommending "right" books for students based on their performance levels and book meta information. Finally, we evaluate the proposed model on three years of the book-loan history and cumulative grade point average of 13,047 undergraduate students in one university. The results show that the proposed model outperforms the competing baselines on both tasks, and that academic performance is not only predictable from the book-loan history but also improves the recommendation of library books for students.
A survey on motion prediction and risk assessment for intelligent vehicles

With the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model.
Towards Real-Time Physical Human-Robot Interaction Using Skeleton Information and Hand Gestures

For successful physical human-robot interaction, the capability of a robot to understand its environment is imperative. More importantly, the robot should extract from the human operator as much information as possible. A reliable 3D skeleton extraction is essential for a robot to predict the intentions of the operator while s/he moves toward the robot or performs a meaningful gesture. For this purpose, we have integrated a time-of-flight depth camera with a state-of-the-art 2D skeleton extraction library namely Openpose, to obtain 3D skeletal joint coordinates reliably. We have also developed a robust and rotation invariant (in the coronal plane)hand gesture detector using a convolutional neural network. At run time (after having been trained)the detector does not require any pre-processing of the hand images. A complete pipeline for skeleton extraction and hand gesture recognition is developed and employed for real-time physical human-robot interaction, demonstrating the promising capability of the designed framework. This work establishes a firm basis and will be extended for the development of intelligent human intention detection in physical human-robot interaction scenarios, to efficiently recognize a variety of static as well as dynamic gestures.
Prison Conditions , Capital Punishment , and Deterrence

Previous research has attempted to identify a deterrent effect of capital punishment. We argue that the quality of life in prison is likely to have a greater impact on criminal behavior than the death penalty. Using state-level panel data covering the period 1950±90, we demonstrate that the death rate among prisoners (the best available proxy for prison conditions) is negatively correlated with crime rates, consistent with deterrence. This finding is shown to be quite robust. In contrast, there is little systematic evidence that the execution rate influences crime rates in this time period.
A Model of Factors Influencing Electronic Commerce Adoption among Small and Medium Enterprises in Brunei Darussalam

This study aims to develop a model of factors motivating and inhibiting electronic commerce adoption among small and medium enterprises (SMEs) in Brunei Darussalam. Recent statistics show that total number of businesses employing web sites in Brunei Darussalam is very small compared to those in Singapore and Malaysia. Organizations are coming under increasing pressure to adopt electronic commerce as part of the need to be efficient and competitive and to play a greater role in driving the economy. With the new century, the continued prosperity of Brunei Darussalam can no longer be taken for granted. Qualitative research method, in the form of semi-structured interviews, was used to identify factors that are important and relevant to encourage willingness to adopt. Findings indicate that owner characteristics like lack of perceived benefits, lack of knowledge and skill, perceived lack of trust are significant inhibitors while environment characteristics like competitive pressure, government support and infrastructure are significant motivators of electronic commerce in Brunei Darussalam. The model forms a basis for further research as further quantitative study, in the form of survey, can be carried out to assess the model’s validity and provide further insights into the relationships among the factors.  Electronic Commerce, Diffusion of Innovation, Adoption, Small and Medium Enterprises
Using Instagram Picture Features to Predict Users' Personality

Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying these filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users manipulate the appearance of their pictures by applying filters to them. To investigate this relationship, we studied the relationship between picture features and personality traits. To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features (e.g., relevant to hue, brightness, saturation) that are related to personality traits. Our findings suggest a relationship between personality traits and these picture features. Based on our findings, we also show that personality traits can be accurately predicted. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.
Explorer Graph pattern matching revised for social network analysis

Graph pattern matching is fundamental to social network analysis. Traditional techniques are subgraph isomorphism and graph simulation. However, these notions often impose too strong a topological constraint on graphs to find meaningful matches. Worse still, graphs in the real world are typically large, with millions of nodes and billions of edges. It is often prohibitively expensive to compute matches in such graphs. With these comes the need for revising the notions of graph pattern matching and for developing techniques of querying large graphs, to effectively and efficiently identify social communities or groups. This paper aims to provide an overview of recent advances in the study of graph pattern matching in social networks. (1) We present several revisions of the traditional notions of graph pattern matching to find sensible matches in social networks. (2) We provide boundedness analyses of incremental graph pattern matching, in response to frequent updates to social networks. (3) To cope with large real-life graphs, we propose a framework of query preserving graph compression, which retains only information necessary for answering a certain class of queries of users’ choice. (4) We also address pattern matching in distributed graphs, and in particular, advocate the use of partial evaluation techniques. Finally, we identify directions for future research.
Multi-Label Classification Methods for Multi-Target Regression

Real world prediction problems often involve the simultaneous prediction of multiple target variables using the same set of predictive variables. When the target variables are binary, the prediction task is called multi-label classification while when the target variables are realvalued the task is called multi-target regression. Although multi-target regression attracted the attention of the research community prior to multi-label classification, the recent advances in this field motivate a study of whether newer state-of-the-art algorithms developed for multilabel classification are applicable and equally successful in the domain of multi-target regression. In this paper we introduce two new multitarget regression algorithms: multi-target stacking (MTS) and ensemble of regressor chains (ERC), inspired by two popular multi-label classification approaches that are based on a single-target decomposition of the multi-target problem and the idea of treating the other prediction targets as additional input variables that augment the input space. Furthermore, we detect an important shortcoming on both methods related to the methodology used to create the additional input variables and develop modified versions of the algorithms (MTSC and ERCC) to tackle it. All methods are empirically evaluated on 12 real-world multi-target regression datasets, 8 of which are first introduced in this paper and are made publicly available for future benchmarks. The experimental results show that ERCC performs significantly better than both a strong baseline that learns a single model for each target using bagging of regression trees and the state-of-the-art multi-objective random forest approach. Also, the proposed modification results in significant performance gains for both MTS and ERC.