nils-herrmann/clean_scirepeval huggingface dataset:
A probabilistic model for item-based recommender systems

Recommender systems estimate the conditional probability P(χj|χi) of item χj being bought, given that a customer has already purchased item χi. While there are different ways of approximating this conditional probability, the expression is generally taken to refer to the frequency of co-occurrence of items in the same basket, or other user-specific item lists, rather than being seen as the co-occurrence of χj with χi as a proportion of all other items bought alongside χi. This paper proposes a probabilistic calculus for the calculation of conditionals based on item rather than basket counts. The proposed method has the consequence that items bough together as part of small baskets are more predictive of each other than if they co-occur in large baskets. Empirical results suggests that this may result in better take-up of personalised recommendations.
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling.

State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communication-heavy systems depends on the computation time, communication time and the extent of overlap of computation and communication. In this work, we identify a shortcoming in systems with graph representation for computation, such as TensorFlow and PyTorch, that result in high variance in iteration time — random order of received parameters across workers. We develop a system, TicTac, to improve the iteration time by fixing this issue in distributed deep learning with Parameter Servers while guaranteeing near-optimal overlap of communication and computation. TicTac identifies and enforces an order of network transfers which improves the iteration time using prioritization. Our system is implemented over TensorFlow and requires no changes to the model or developer inputs. TicTac improves the throughput by up to 37.7% in inference and 19.2% in training, while also reducing straggler effect by up to 2.3×. Our code is publicly available.
Attacks and Countermeasures in Fingerprint Based Biometric Cryptosystems

We investigate implementations of biometric cryptosystems protecting fingerprint templates (which are mostly based on the fuzzy vault scheme by Juels and Sudan in 2002) with respect to the security they provide. We show that attacks taking advantage of the system’s false acceptance rate, i.e. false-accept attacks, pose a very serious risk — even if brute-force attacks are impractical to perform. Our observations lead to the clear conclusion that currently a single fingerprint is not sufficient to provide a secure biometric cryptosystem. But there remain other problems that can not be resolved by merely switching to multi-finger: Kholmatov and Yanikoglu in 2007 demonstrated that it is possible to break two matching vault records at quite a high rate via the correlation attack. We propose an implementation of a minutiae fuzzy vault that is inherently resistant against cross-matching and the correlation attack. Surprisingly, achieving cross-matching resistance is not at the cost of authentication performance. In particular, we propose to use a randomized decoding procedure and find that it is possible to achieve a GAR = 91% at which no false accepts are observed on a database generally used. Our ideas can be adopted into an implementation of a multibiometric cryptosystem. All experiments described in this paper can fully be reproduced using software available for download.
Dynamic mode decomposition of numerical and experimental data

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Dynamic mode decomposition of numerical and experimental data Peter Schmid
Facilitating Optimal Motivation and Psychological Well-Being Across Life ’ s Domains

Self-determination theory (SDT) differentiates motivation, with autonomous and controlled motivations constituting the key, broad distinction. Research has shown that autonomous motivation predicts persistence and adherence and is advantageous for effective performance, especially on complex or heuristic tasks that involve deep information processing or creativity. Autonomous motivation is also reliably related to psychological health. Considerable research has found interpersonal contexts that facilitate satisfaction of the basic psychological needs for competence, autonomy, and relatedness to enhance autonomous motivation, which comprises intrinsic motivation and well-internalized extrinsic motivation. SDT has been applied in varied cultures and in many life domains, and research is reviewed that has related autonomous and controlled motivation to education, parenting, work, health care, sport, and close relationships.
Probability Product Kernels

The advantages of discriminative learning algorithms and kernel machines are combined with generative modeling using a novel kernel between distributions. In the probability product kernel, data points in the input space are mapped to distributions over the sample space and a general inner product is then evaluated as the integral of the product of pairs of distributions. The kernel is straightforward to evaluate for all exponential family models such as multinomials and Gaussians and yields interesting nonlinear kernels. Furthermore, the kernel is computable in closed form for latent distributions such as mixture models, hidden Markov models and linear dynamical systems. For intractable models, such as switching linear dynamical systems, structured mean-field approximations can be brought to bear on the kernel evaluation. For general distributions, even if an analytic expression for the kernel is not feasible, we show a straightforward sampling method to evaluate it. Thus, the kernel permits discriminative learning methods, including support vector machines, to exploit the properties, metrics and invariances of the generative models we infer from each datum. Experiments are shown using multinomial models for text, hidden Markov models for biological data sets and linear dynamical systems for time series data.
A compact cavity filter with novel TM mode dielectric resonator structure

A novel transverse magnetic (TM) mode dielectric resonator and its filter realization are proposed for mobile communication system miniaturization applications. Single dielectric resonator and an eight-pole filter with three-transmission-zeros are modeled and designed for desired specification by using efficient optimization technique. About 30% horizontal dimensions are saved comparing with the common coaxial filter. Excellent filter responses are obtained and measured.
Culture and Conformity : A Meta-Analysis of Studies Using Asch ' s ( 1952 b , 1956 ) Line Judgment Task

A meta-analysis of conformity studies using an Asch-type line judgment task (1952b, 1956) was conducted to investigate whether the level of conformity has changed over time and whether it is related crogs-culturally to individualism-collectivism. The fiterature search produced 133 studies drawn from 17 countries. An analysis of U.S. studies found that conformity has declined since the 1950s. Results from 3 surveys were used to assess a country's individualism-collectivism, and for each survey the measures were found to be significantly related to conformity. Collectivist countries tended to show higher levels of conformity than individualist countries. Conformity research must attend more to cultural variables and to their role in the processes involved in social influence.
Classification of social networking skills for promoting personalized learning of Thai seniors

Some research proposes that age changing effects on learning performance. Although, age-related degraded perception cannot be rolled-back, it can be improved by training and learning. Since seniors can reach new technology slowly, social media is a way to support their participation in long term learning. However, the level of learning is limited by each personal background knowledge, that each senior supposes to have the learning path which mostly match with their social media knowledge. Therefore, this study proposes the method to classify groups of senior by using useful data mining algorithms. In this case, Random Forest and K-nearest neighbors algorithm (k-NN) model are used. The study conducted with 60 senior between 60–75 years old from seniors school in chiangrai province. Upon participants have done the pretest, these algorithms classifies an senior test results, and assign them into 4 appropriate groups, including Professional, Medium, Less Knowledge, and No Experience. The results show that both algorithms, Random Forest and K-nearest neighbors algorithm (k-NN) provide 95.56% and 93.33% accuracy respectively.
Air Traffic Security : Aircraft Classification Using ADS-B Message ’ s Phase-Pattern

Automatic Dependent Surveillance-Broadcast (ADS-B) is a surveillance system used in Air Traffic Control. With this system, the aircraft transmits their own information (identity, position, velocity, etc.) to any equipped listener for surveillance scope. The ADS-B is based on a very simple protocol and does not provide any kind of authentication and encryption, making it vulnerable to many types of cyber-attacks. In the paper, the use of the airplane/transmitter carrier phase is proposed as a feature to perform a classification of the aircraft and, therefore, distinguish legitimate messages from fake ones. The feature extraction process is described and a classification method is selected. Finally, a complete intruder detection algorithm is proposed and evaluated with real data.
A note on the maximum flow through a network

-This note discusses the problem of maximizing the rate of flow from one terminal to another, through a network which consists of a number of branches, each of which has a !imited capacity. The main result is a theorem: The maximum possible flow from left to right through a network is equal to the minimum value among all simple cut-sets. This theorem is applied to solve a more general problem, in which a number of input nodes and a number of output nodes are used.
Overcoming the challenge for text classification in the open world

Classification is often referred to as the task of discriminating one class from others in a given set of classes. Traditionally, classifiers work well assuming that a priori knowledge of all classes are given. Unfortunately, a presenting of unknown class during testing can lead to poor performance of even state-of-the-art classifiers due to observed classes being incorrectly identified to other classes. Recent proposed open world recognition framework provides a promising venue for tackling this challenge. While the majority of works in this relative new field is in computer vision, the rare work in Natural Language Processing shows its instability in its performance and is not based on the open world recognition framework. To tackle this problem, we represent our Nearest Centroid Class (NCC) which is incremental learning and able to detect unknown class during testing. Our model yields promising results in a document classification on text classification domains among current state-of-the-art models.
A conditional approach to dispositional constructs: the local predictability of social behavior.

A conditional approach to dispositions is developed in which dispositional constructs are viewed as clusters of if-then propositions. These propositions summarize contingencies between categories of conditions and categories of behavior rather than generalized response tendencies. A fundamental unit for investigating dispositions is therefore the conditional frequency of acts that are central to a given behavior category in circumscribed situations, not the overall frequency of behaviors. In an empirical application of the model, we examine how people's dispositional judgments are linked to extensive observations of targets' behavior in a range of natural social situations. We identify categories of these social situations in which targets' behavior may be best predicted from observers' dispositional judgements, focusing on the domains of aggression and withdrawal. One such category consists of subjectively demanding or stressful situations that tax people's performance competencies. As expected, children judged to be aggressive or withdrawn were variable across situations in dispositionally relevant behaviors, but they diverged into relatively predictable aggressive and withdrawn actions in situations that required the social, self-regulatory, and cognitive competencies they lacked. Implications of the conditional approach for personality assessment and person perception research are considered.
Path-tracking of a tractor-trailer vehicle along rectilinear and circular paths: a Lyapunov-based approach

The problem of asymptotic stabilization for straight and circular forward/backward motions of a tractor-trailer system is addressed using Lyapunov techniques. Smooth, bounded, nonlinear control laws achieving asymptotic stability along the desired path are designed, and explicit bounds on the region of attraction are provided. The problem of asymptotic controllability with bounded control is also addressed.
Detecting Curve Text in the Wild: New Dataset and New Solution

Scene text detection has been made great progress in recent years. The detection manners are evolving from axisaligned rectangle to rotated rectangle and further to quadrangle. However, current datasets contain very little curve text, which can be widely observed in scene images such as signboard, product name and so on. To raise the concerns of reading curve text in the wild, in this paper, we construct a curve text dataset named CTW1500, which includes over 10k text annotations in 1,500 images (1000 for training and 500 for testing). Based on this dataset, we pioneering propose a polygon based curve text detector (CTD) which can directly detect curve text without empirical combination. Moreover, by seamlessly integrating the recurrent transverse and longitudinal offset connection (TLOC), the proposed method can be end-to-end trainable to learn the inherent connection among the position offsets. This allows the CTD to explore context information instead of predicting points independently, resulting in more smooth and accurate detection. We also propose two simple but effective post-processing methods named nonpolygon suppress (NPS) and polygonal non-maximum suppression (PNMS) to further improve the detection accuracy. Furthermore, the proposed approach in this paper is designed in an universal manner, which can also be trained with rectangular or quadrilateral bounding boxes without extra efforts. Experimental results on CTW-1500 demonstrate our method with only a light backbone can outperform state-of-the-art methods with a large margin. By evaluating only in the curve or non-curve subset, the CTD + TLOC can still achieve the best results. Code is available at https://github.com/Yuliang-Liu/Curve-Text-Detector.
SWAM: Stuxnet Worm Analysis in Metasploit

Nowadays cyber security is becoming a great challenge. Attacker's community is progressing towards making smart and intelligent malwares (viruses, worms and Root kits). They stealth their existence and also use administrator rights without knowing legal user. Stuxnet worm is an example of a recent malware first detected in July 2010. Its variants were also detected earlier. It is the first type of worm that affects the normal functionality of industrial control systems (ICS) having programmable logic controllers (PLC) through PLC Root kit. Its main goal is to modify ICS behavior by changing the code of PLC and make it to behave in a way that attacker wants. It is a complex piece of malware having different operations and functionalities which are achieved by exploiting zero day vulnerabilities. Stuxnet exploits various vulnerable services in Microsoft Windows. In this paper we will show real time simulation of first three vulnerabilities of these through Metasploit Framework 3.2 and analyze results. A real time scenario is established based on some assumptions. We assumed Proteus design (pressure sensor) as PLC and showed after exploitation that the pressure value drops to an unacceptable level by changing Keil code of this design.
One for All: Towards Language Independent Named Entity Linking

Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc). Most previous EL research has focused mainly on one language, English, with less attention being paid to other languages, such as Spanish or Chinese. In this paper, we introduce LIEL, a Language Independent Entity Linking system, which provides an EL framework which, once trained on one language, works remarkably well on a number of different languages without change. LIEL makes a joint global prediction over the entire document, employing a discriminative reranking framework with many domain and language-independent feature functions. Experiments on numerous benchmark datasets, show that the proposed system, once trained on one language, English, outperforms several state-of-the-art systems in English (by 4 points) and the trained model also works very well on Spanish (14 points better than a competitor system), demonstrating the viability of the approach.
An Analytical Method to Evaluate and Design Hybrid Switched-Capacitor and Multilevel Converters

This paper investigates the use of multilevel conversion in dc–dc applications that require a large voltage conversion ratio. A quantitative method that can serve as a guide to compare and design multilevel topologies for large conversion ratio applications is presented. The proposed method keeps the conduction loss and switching loss constant across the different converters and employs the passive component volume as the single performance metric. As examples, flying capacitor multilevel converters and hybrid switched-capacitor (SC) converters are compared to conventional two-level buck converters, and are shown analytically to have significantly reduced passive component size. Three converter prototypes are implemented, based on the presented methodology to experimentally validate the method as well as demonstrate the advantages of multilevel and hybrid SC converters.
User preferences of software documentation genres

Today's technical software users find a wide variety of content online, from videos to forum posts to online articles. This study examines the extent to which the genre of this content matters to the users searching for it. It presents the findings of a new exploratory study that addresses the significance of genre preferences to the users, given that genre is secondary to their actual business goals. It looks at documentation users' usage of, and attitudes towards, documentation genres, and how factors such as work role and past experience come into play. Our conclusions can help individuals who formulate content strategies to understand the extent and strength of genre usage. This in turn can help them determine a strategic mix of documentation genres.
An Automatic Collocation Extraction from Arabic Corpus

The identification of collocations is very importa n part in natural language processing applications that require some degree of semantic interpretation such as, machine translation, information retrieval and text summari z tion. Because of the complexities of Arabic, the collocations undergo some variations such as, morph ological, graphical, syntactic variation that constitutes the difficulties of identifying the col location.  We used the hybrid method for extracting the collocations from Arabic corpus that is based on linguistic information and association measures.  This method extracted the bi-gram candidates of Ar abic collocation from corpus and evaluated the association measures by using the n-best evaluation method. We reported the precision values for each association measure in ea ch n-best list.  The experimental results showed that the log-likelihood ratio is the best as ociation measure that achieved highest precision.
Fast scan algorithms on graphics processors

Scan and segmented scan are important data-parallel primitives for a wide range of applications. We present fast, work-efficient algorithms for these primitives on graphics processing units (GPUs). We use novel data representations that map well to the GPU architecture. Our algorithms exploit shared memory to improve memory performance. We further improve the performance of our algorithms by eliminating shared-memory bank conflicts and reducing the overheads in prior shared-memory GPU algorithms. Furthermore, our algorithms are designed to work well on general data sets, including segmented arrays with arbitrary segment lengths. We also present optimizations to improve the performance of segmented scans based on the segment lengths. We implemented our algorithms on a PC with an NVIDIA GeForce 8800 GPU and compared our results with prior GPU-based algorithms. Our results indicate up to 10x higher performance over prior algorithms on input sequences with millions of elements.
AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security

Benefits of Internet of Things and cloud–fog-edge computing are associated with the risks of confidentiality, integrity, and availability related with the loss of information, denial of access for a long time, information leakage, conspiracy and technical failures. In this article, we propose a configurable, reliable, and confidential distributed data storage scheme with the ability to process encrypted data and control results of computations. Our systemutilizes Redundant ResidueNumber System (RRNS)with newmethod of error correction codes and secret sharing schemes. We introduce the concept of an approximate value of a rank of a number (AR), which allows us to reduce the computational complexity of the decoding from RNS to binary representation, and size of the coefficients. Based on the properties of the approximate value and arithmetic properties of RNS,we introduce AR-RRNSmethod for error detection, correction, and controlling computational results. We provide a theoretical basis to configure probability of information loss, data redundancy, speed of encoding and decoding to cope with different objective preferences, workloads, and storage properties. Theoretical analysis shows that by appropriate selection of RRNS parameters, the proposed scheme allows not only increasing safety, reliability, and reducing an overhead of data storage, but also processing of encrypted data.