nils-herrmann/clean_scirepeval huggingface dataset:
MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis

Model diagnosis is the process of analyzing machine learning (ML) model performance to identify where the model works well and where it doesn't. It is a key part of the modeling process and helps ML developers iteratively improve model accuracy. Often, model diagnosis is performed by analyzing different datasets or intermediates associated with the model such as the input data and hidden representations learned by the model (e.g., [4, 24, 39,]). The bottleneck in fast model diagnosis is the creation and storage of model intermediates. Storing these intermediates requires tens to hundreds of GB of storage whereas re-running the model for each diagnostic query slows down model diagnosis. To address this bottleneck, we propose a system called MISTIQUE that can work with traditional ML pipelines as well as deep neural networks to efficiently capture, store, and query model intermediates for diagnosis. For each diagnostic query, MISTIQUE intelligently chooses whether to re-run the model or read a previously stored intermediate. For intermediates that are stored in MISTIQUE, we propose a range of optimizations to reduce storage footprint including quantization, summarization, and data de-duplication. We evaluate our techniques on a range of real-world ML models in scikit-learn and Tensorflow. We demonstrate that our optimizations reduce storage by up to 110X for traditional ML pipelines and up to 6X for deep neural networks. Furthermore, by using MISTIQUE, we can speed up diagnostic queries on traditional ML pipelines by up to 390X and 210X on deep neural networks.
STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation

Predicting users' actions based on anonymous sessions is a challenging problem in web-based behavioral modeling research, mainly due to the uncertainty of user behavior and the limited information. Recent advances in recurrent neural networks have led to promising approaches to solving this problem, with long short-term memory model proving effective in capturing users' general interests from previous clicks. However, none of the existing approaches explicitly take the effects of users' current actions on their next moves into account. In this study, we argue that a long-term memory model may be insufficient for modeling long sessions that usually contain user interests drift caused by unintended clicks. A novel short-term attention/memory priority model is proposed as a remedy, which is capable of capturing users' general interests from the long-term memory of a session context, whilst taking into account users' current interests from the short-term memory of the last-clicks. The validity and efficacy of the proposed attention mechanism is extensively evaluated on three benchmark data sets from the RecSys Challenge 2015 and CIKM Cup 2016. The numerical results show that our model achieves state-of-the-art performance in all the tests.
Vehicle Number Plate Detection and Recognition

The study of this paper presents a new technique to use automatic number plate detection and recognition. This system plays a significant role throughout this busy world, owing to rise in use of vehicles day-by-day. Some of the applications of this software are automatic toll tax collection, unmanned parking slots, safety, and security. The current scenario happening in India is, people, break the rules of the toll and move away which can cause many serious issues like accidents. This system uses efficient algorithms to detect the vehicle number from real-time images. The system detects the license plate on the vehicle first and then captures the image of it. Vehicle number plate is localized and characters are segmented and further recognized with help of neural network. The system is designed for grayscale images so it detects the number plate regardless of its color. The resulting vehicle number plate is then compared with the available database of all vehicles which have been already registered by the users so as to come up with information about vehicle type and charge accordingly. The vehicle information such as date, toll amount is stored in the database to maintain the record.
Local Rule-Based Explanations of Black Box Decision Systems

The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. In this paper we focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance’s features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box.
Web-Server based Student Attendance System using RFIDTechnology

-Most educational institutions’ administrators are concerned about student irregular attendance. Truancies can affect student overall academic performance. The conventional method of taking attendance by calling names or signing on paper is very time consuming and insecure, hence inefficient. Therefore, computer based student attendance management system is required to assist the faculty and the lecturer for this time-provide much convenient method to take attendance, but some prerequisites has to be done before start using the program. Although the use of RFID systems in educational institutions is not new, it is intended to show how the use of it came to solve daily problems in our university. The system has been built using the web-based applications such as ASP.NET and IIS server to cater the recording and reporting of the students’ attendances The system can be easily accessed by the lecturers via the web and most importantly, the reports can be generated in real-time processing, thus, providing valuable information about the students’.
A design methodology for LLC resonant converters based on inspection of resonant tank currents.

This paper presents a simple and accurate design methodology for LLC resonant converters, based on a semi- empirical approach to model steady-state operation in the "be- low-resonance" region. This model is framed in a design strategy that aims to design a converter capable of operating with soft-switching in the specified input voltage range with a load ranging from zero up to the maximum specified level.
Emotional intelligence: new ability or eclectic traits?

Some individuals have a greater capacity than others to carry out sophisticated information processing about emotions and emotion-relevant stimuli and to use this information as a guide to thinking and behavior. The authors have termed this set of abilities emotional intelligence (EI). Since the introduction of the concept, however, a schism has developed in which some researchers focus on EI as a distinct group of mental abilities, and other researchers instead study an eclectic mix of positive traits such as happiness, self-esteem, and optimism. Clarifying what EI is and is not can help the field by better distinguishing research that is truly pertinent to EI from research that is not. EI--conceptualized as an ability--is an important variable both conceptually and empirically, and it shows incremental validity for predicting socially relevant outcomes.
Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection

With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds has become more direct, resulting in more and more “cyber” conflicts between these people. Consequently, hate speech is used more and more, to the point where it has become a serious problem invading these open spaces. Hate speech refers to the use of aggressive, violent or offensive language, targeting a specific group of people sharing a common property, whether this property is their gender (i.e., sexism), their ethnic group or race (i.e., racism) or their believes and religion. While most of the online social networks and microblogging websites forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content. Therefore, arises the necessity to detect such speech automatically and filter any content that presents hateful language or language inciting to hatred. In this paper, we propose an approach to detect hate expressions on Twitter. Our approach is based on unigrams and patterns that are automatically collected from the training set. These patterns and unigrams are later used, among others, as features to train a machine learning algorithm. Our experiments on a test set composed of 2010 tweets show that our approach reaches an accuracy equal to 87.4% on detecting whether a tweet is offensive or not (binary classification), and an accuracy equal to 78.4% on detecting whether a tweet is hateful, offensive, or clean (ternary classification).
A Fast and Simple Graph Kernel for RDF

In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster in terms of computation time. Prediction performance is worse than the state-of-the-art Weisfeiler Lehman RDF kernel, but our kernel is a factor 10 faster to compute. Thus, we consider this kernel a very suitable baseline for learning from RDF data. Furthermore, we extend this kernel to handle RDF literals as bag-ofwords feature vectors, which increases performance in two of the four experiments.
A Transaction Fee Market Exists Without a Block Size Limit

This paper shows how a rational Bitcoin miner should select transactions from his node’s mempool, when creating a new block, in order to maximize his profit in the absence of a block size limit. To show this, the paper introduces the block space supply curve and the mempool demand curve. The former describes the cost for a miner to supply block space by accounting for orphaning risk. The latter represents the fees offered by the transactions in mempool, and is expressed versus the minimum block size required to claim a given portion of the fees. The paper explains how the supply and demand curves from classical economics are related to the derivatives of these two curves, and proves that producing the quantity of block space indicated by their intersection point maximizes the miner’s profit. The paper then shows that an unhealthy fee market—where miners are incentivized to produce arbitrarily large blocks—cannot exist since it requires communicating information at an arbitrarily fast rate. The paper concludes by considering the conditions under which a rational miner would produce big, small or empty blocks, and by estimating the cost of a spam attack.
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.
Structure from motion for scenes with large duplicate structures

Most existing structure from motion (SFM) approaches for unordered images cannot handle multiple instances of the same structure in the scene. When image pairs containing different instances are matched based on visual similarity, the pairwise geometric relations as well as the correspondences inferred from such pairs are erroneous, which can lead to catastrophic failures in the reconstruction. In this paper, we investigate the geometric ambiguities caused by the presence of repeated or duplicate structures and show that to disambiguate between multiple hypotheses requires more than pure geometric reasoning. We couple an expectation maximization (EM)-based algorithm that estimates camera poses and identifies the false match-pairs with an efficient sampling method to discover plausible data association hypotheses. The sampling method is informed by geometric and image-based cues. Our algorithm usually recovers the correct data association, even in the presence of large numbers of false pairwise matches.
Real-Time Semantic Clothing Segmentation

Clothing segmentation is a challenging field of research which is rapidly gaining attention. This paper presents a system for semantic segmentation of primarily monochromatic clothing and printed/stitched textures in single images or live video. This is especially appealing to emerging augmented reality applications such as retexturing sports players’ shirts with localized adverts or statistics in TV/internet broadcasting. We initialise points on the upper body clothing by body fiducials rather than by applying distance metrics to a detected face. This helps prevent segmentation of the skin rather than clothing. We take advantage of hue and intensity histograms incorporating spatial priors to develop an efficient segmentation method. Evaluated against ground truth on a dataset of 100 people, mostly in groups, the accuracy has an average F-score of 0.97 with an approach which can be over 88% more efficient than the state of the art.
Online free walking trajectory generation for biped humanoid robot KHR-3(HUBO)

This paper describes an algorithm about online gait trajectory generation method, controller for walking, brief introduction of humanoid robot platform KHR-3 (KAIST Humanoid Robot-3: HUBO) and experimental result. The gait trajectory has continuity, smoothness in varying walking period and stride, and it has simple mathematical form which can be implemented easily. It is tested on the robot with some control algorithms. The gait trajectory algorithm is composed of two kinds of function trajectory. The first one is cycloid function, which is used for ankle position in Cartesian coordinate space. Because this profile is made by superposition of linear and sinusoidal function, it has a property of slow start, fast moving, and slow stop. This characteristics can reduce the over burden at instantaneous high speed motion of the actuator. The second one is 3rd order polynomial function. It is continuous in the defined time interval, easy to use when the boundary condition is well defined, and has standard values of coefficients when the time scale is normalized. Position and velocity values are used for its boundary condition. Controllers mainly use F/T(Force/Torque) sensor at the ankle of the robot as a sensor data, and modify the input position profiles (in joint angle space and Cartesian coordinate space). They are to reduce unexpected external forces such as landing shock, and vibration induced by compliances of the sensors and reduction gears, because they can affect seriously on the walking stability. This trajectory and control algorithm is now on the implementing stage for the free-walking realization of KHR-3. As a first stage of realization, we realized the marking time and forward walking algorithm with variable frequency and stride
Michelangelo: art, anatomy, and the kidney.

Michelangelo (1475-1564) had a life-long interest in anatomy that began with his participation in public dissections in his early teens, when he joined the court of Lorenzo de' Medici and was exposed to its physician-philosopher members. By the age of 18, he began to perform his own dissections. His early anatomic interests were revived later in life when he aspired to publish a book on anatomy for artists and to collaborate in the illustration of a medical anatomy text that was being prepared by the Paduan anatomist Realdo Colombo (1516-1559). His relationship with Colombo likely began when Colombo diagnosed and treated him for nephrolithiasis in 1549. He seems to have developed gouty arthritis in 1555, making the possibility of uric acid stones a distinct probability. Recurrent urinary stones until the end of his life are well documented in his correspondence, and available documents imply that he may have suffered from nephrolithiasis earlier in life. His terminal illness with symptoms of fluid overload suggests that he may have sustained obstructive nephropathy. That this may account for his interest in kidney function is evident in his poetry and drawings. Most impressive in this regard is the mantle of the Creator in his painting of the Separation of Land and Water in the Sistine Ceiling, which is in the shape of a bisected right kidney. His use of the renal outline in a scene representing the separation of solids (Land) from liquid (Water) suggests that Michelangelo was likely familiar with the anatomy and function of the kidney as it was understood at the time.
Comparative Analysis of Different Single-Diode PV Modeling Methods

Modeling of photovoltaic (PV) systems is essential for the designers of solar generation plants to do a yield analysis that accurately predicts the expected power output under changing environmental conditions. This paper presents a comparative analysis of PV module modeling methods based on the single-diode model with series and shunt resistances. Parameter estimation techniques within a modeling method are used to estimate the five unknown parameters in the single diode model. Two sets of estimated parameters were used to plot the I-V characteristics of two PV modules, i.e., SQ80 and KC200GT, for the different sets of modeling equations, which are classified into models 1 to 5 in this study. Each model is based on the different combinations of diode saturation current and photogenerated current plotted under varying irradiance and temperature. Modeling was done using MATLAB/Simulink software, and the results from each model were first verified for correctness against the results produced by their respective authors. Then, a comparison was made among the different models (models 1 to 5) with respect to experimentally measured and datasheet I-V curves. The resultant plots were used to draw conclusions on which combination of parameter estimation technique and modeling method best emulates the manufacturer specified characteristics.
FoodCam: A Real-Time Mobile Food Recognition System Employing Fisher Vector

In the demo, we demonstrate a mobile food recognition system with Fisher Vector and liner one-vs-rest SVMs which enable us to record our food habits easily. In the experiments with 100 kinds of food categories, we have achieved the 79.2% classification rate for the top 5 category candidates when the ground-truth bounding boxes are given. The prototype system is open to the public as an Android-based smart-
A Unified Query-based Generative Model for Question Generation and Question Answering

We propose a query-based generative model for solving both tasks of question generation (QG) and question answering (QA). The model follows the classic encoderdecoder framework. The encoder takes a passage and a query as input then performs query understanding by matching the query with the passage from multiple perspectives. The decoder is an attention-based Long Short Term Memory (LSTM) model with copy and coverage mechanisms. In the QG task, a question is generated from the system given the passage and the target answer, whereas in the QA task, the answer is generated given the question and the passage. During the training stage, we leverage a policy-gradient reinforcement learning algorithm to overcome exposure bias, a major problem resulted from sequence learning with cross-entropy loss. For the QG task, our experiments show higher performances than the state-of-the-art results. When used as additional training data, the automatically generated questions even improve the performance of a strong extractive QA system. In addition, our model shows better performance than the state-of-the-art baselines of the generative QA task.
Discriminative k-shot learning using probabilistic models

This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples. The new approach not only leverages the feature-based representation learned by a neural network from the initial task (representational transfer), but also information about the classes (concept transfer). The concept information is encapsulated in a probabilistic model for the final layer weights of the neural network which acts as a prior for probabilistic k-shot learning. We show that even a simple probabilistic model achieves state-ofthe-art on a standard k-shot learning dataset by a large margin. Moreover, it is able to accurately model uncertainty, leading to well calibrated classifiers, and is easily extensible and flexible, unlike many recent approaches to k-shot learning.
Cyber-physical systems clouds: A survey

Cyber-Physical Systems (CPSs) represent systems where computations are tightly coupled with the physical world, meaning that physical data is the core component that drives computation. Industrial automation systems, wireless sensor networks, mobile robots and vehicular networks are just a sample of cyber-physical systems. Typically, CPSs have limited computation and storage capabilities due to their tiny size and being embedded into larger systems. With the emergence of cloud computing and the Internetof-Things (IoT), there are several new opportunities for these CPSs to extend their capabilities by taking advantage of the cloud resources in different ways. In this survey paper, we present an overview of research effort s on the integration of cyber-physical systems with cloud computing and categorize them into three areas: (1) remote brain, (2) big data manipulation, (3) and virtualization. In particular, we focus on three major CPSs namely mobile robots, wireless sensor networks and vehicular networks.
Relative sub-image based features for leaf recognition using support vector machine

In this paper, we extract our proposed RSC features from leaf images and use SVM classifier to implement an automated leaf recognition system for plant leaf identification and classification. Automatic plant species identification and classification is helpful in biology, forest and agriculture to study and discover new species in plant in botanical gardens and is also used to recognize the medicinal plants to prepare herbal medicines. Here, 300 leaf features are extracted from a single leaf of 624 leaf dataset to classify 23 different kinds of plant species with an average accuracy of 95%. Compared with other approaches, our proposed algorithm has less time complexity and is easy to implementation with higher accuracy.