nils-herrmann/clean_scirepeval huggingface dataset:
A survey of intrusion detection techniques for cyber-physical systems

Pervasive healthcare systems, smart grids, and unmanned aircraft systems are examples of Cyber-Physical Systems (CPSs) that have become highly integrated in the modern world. As this integration deepens, the importance of securing these systems increases. In order to identify gaps and propose research directions in CPS intrusion detection research, we survey the literature of this area. Our approach is to classify modern CPS Intrusion Detection System (IDS) techniques based on two design dimensions: detection technique and audit material. We summarize advantages and drawbacks of each dimension’s options. We also summarize the most and least studied CPS IDS techniques in the literature and provide insight on the effectiveness of IDS techniques as they apply to CPSs. Finally, we identify gaps in CPS IDS research and suggest future research areas.
Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions

The main question we address in this paper is how to use purely textual description of categories with no training images to learn visual classifiers for these categories. We propose an approach for zero-shot learning of object categories where the description of unseen categories comes in the form of typical text such as an encyclopedia entry, without the need to explicitly defined attributes. We propose and investigate two baseline formulations, based on regression and domain adaptation. Then, we propose a new constrained optimization formulation that combines a regression function and a knowledge transfer function with additional constraints to predict the classifier parameters for new classes. We applied the proposed approach on two fine-grained categorization datasets, and the results indicate successful classifier prediction.
Image Pivoting for Learning Multilingual Multimodal Representations

In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.
Evaluation of bidirectional LSTM for short-and long-term stock market prediction

Recently, there has been a rapidly growing interest in deep learning research and their applications to real-world problems. In this paper, we aim at evaluating and comparing LSTM deep learning architectures for short-and long-term prediction of financial time series. This problem is often considered as one of the most challenging real-world applications for time-series prediction. Unlike traditional recurrent neural networks, LSTM supports time steps of arbitrary sizes and without the vanishing gradient problem. We consider both bidirectional and stacked LSTM predictive models in our experiments and also benchmark them with shallow neural networks and simple forms of LSTM networks. The evaluations are conducted using a publicly available dataset for stock market closing prices.
A Recurrent Variational Autoencoder for Human Motion Synthesis

We propose a novel generative model of human motion that can be trained using a large motion capture dataset, and allows users to produce animations from high-level control signals. As previous architectures struggle to predict motions far into the future due to the inherent ambiguity, we argue that a user-provided control signal is desirable for animators and greatly reduces the predictive error for long sequences. Thus, we formulate a framework which explicitly introduces an encoding of control signals into a variational inference framework trained to learn the manifold of human motion. As part of this framework, we formulate a prior on the latent space, which allows us to generate high-quality motion without providing frames from an existing sequence. We further model the sequential nature of the task by combining samples from a variational approximation to the intractable posterior with the control signal through a recurrent neural network (RNN) that synthesizes the motion. We show that our system can predict the movements of the human body over long horizons more accurately than state-of-theart methods. Finally, the design of our system considers practical use cases and thus provides a competitive approach to motion synthesis.
Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation

With growing interest in adversarial machine learning, it is important for practitioners and users of machine learning to understand how their models may be attacked. We present a web-based visualization tool, ADVERSARIALPLAYGROUND, to demonstrate the efficacy of common adversarial methods against a convolutional neural network. ADVERSARIAL-PLAYGROUND provides users an efficient and effective experience in exploring algorithms for generating adversarial examples — samples crafted by an adversary to fool a machine learning system. To enable fast and accurate responses to users, our webapp employs two key features: (1) We split the visualization and evasive sample generation duties between client and server while minimizing the transferred data. (2) We introduce a variant of the Jacobian Saliency Map Approach that is faster and yet maintains a comparable evasion rate 1.
Measurement Requirements Biometric Systems Biometric Recognition: Security and Privacy Concerns

33 I s Alice authorized to enter this facility? Is Bob entitled to access this Web site or privileged informa-tion? Are we administering our service exclusively to the enrolled users? Does Charlie have a criminal record? Every day, a variety of organizations pose questions such as these about personal recognition. One emerging technology that is becoming more widespread in such organizations is biometrics—automatic personal recognition based on physiological or behavioral characteristics. 1 The term comes from the Greek words bios (life) and metrikos (measure). To make a personal recognition, biometrics relies on who you are or what you do—as opposed to what you know (such as a password) or what you have (such as an ID card). Biometrics has several advantages compared with traditional recognition. In some applications, it can either replace or supplement existing technologies; in others, it is the only viable approach to personal recognition. With the increasing infrastructure for reliable automatic personal recognition and for associating an identity with other personal behavior, concern is naturally growing over whether this information might be abused to violate individuals' rights to anonymity. We argue here, however, that the accountable, responsible use of biometric systems can in fact protect individual privacy. What biological measurements qualify as biometrics? Any human physiological or behavioral trait can serve as a biometric characteristic as long as it satisfies the following requirements: • Universality. Each person should have the characteristic. • Distinctiveness. Any two persons should be different in terms of the characteristic. • Permanence. The characteristic should be sufficiently invariant (with respect to the matching criterion) over a period of time. • Collectibility. The characteristic should be quantitatively measurable. However, for a practical biometric system, we must also consider issues of performance, acceptability, and cir-cumvention. In other words, a practical system must meet accuracy, speed, and resource requirements, and it must be harmless to the users, accepted by the intended population, and sufficiently robust to various fraudulent methods and attacks. A biometric system is essentially a pattern-recognition system that recognizes a person based on a feature vector derived from a specific physiological or behavioral characteristic that the person possesses. Depending on the application context, a biometric system typically operates in one of two modes: verification or identification. (Throughout this article, we use the generic term " recognition " where we do not wish to distinguish between verification and identification.) In verification mode, the system validates …
A neural network with a case based dynamic window for stock trading prediction

Stock forecasting involves complex interactions between market-influencing factors and unknown random processes. In this study, an integrated system, CBDWNN by combining dynamic time windows, case based reasoning (CBR), and neural network for stock trading prediction is developed and it includes three different stages: (1) screening out potential stocks and the important influential factors; (2) using back propagation network (BPN) to predict the buy/sell points (wave peak and wave trough) of stock price and (3) adopting case based dynamic window (CBDW) to further improve the forecasting results from BPN. The system developed in this research is a first attempt in the literature to predict the sell/buy decision points instead of stock price itself. The empirical results show that the CBDW can assist the BPN to reduce the false alarm of buying or selling decisions. Nine different stocks with different trends, i.e., upward, downward and steady, are studied and one individual stock (AUO) will be studied as case example. The rates of return for upward, steady, and downward trend stocks are higher than 93.57%, 37.75%, and 46.62%, respectively. These results are all very promising and better than using CBR or BPN alone.
Ensemble of local and global information for finger-knuckle-print recognition

Biometrics authentication is an effective method for automatically recognizing a person’s identity. Recently, it has been found that the finger–knuckle-print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one’s finger, has high capability to discriminate different individuals, making it an emerging biometric identifier. In this paper, based on the results of psychophysics and neurophysiology studies that both local and global information is crucial for the image perception, we present an effective FKP recognition scheme by extracting and assembling local and global features of FKP images. Specifically, the orientation information extracted by the Gabor filters is coded as the local feature. By increasing the scale of Gabor filters to infinite, actually we can get the Fourier transform of the image, and hence the Fourier transform coefficients of the image can be taken as the global features. Such kinds of local and global features are naturally linked via the framework of time–frequency analysis. The proposed scheme exploits both local and global information for the FKP verification, where global information is also utilized to refine the alignment of FKP images in matching. The final matching distance of two FKPs is a weighted average of local and global matching distances. The experimental results conducted on our FKP database demonstrate that the proposed local–global information combination scheme could significantly improve the recognition accuracy obtained by either local or global information and lead to promising performance of an FKP-based personal authentication system. & 2010 Elsevier Ltd. All rights reserved.
Deep Deformation Network for Object Landmark Localization

Face Landmark Localization: Dataset ESR SDM ERT LBF cGPRT DDN (Ours) 300-W 7.58 7.52 6.40 6.32 5.71 5.65 Table 1: Mean relative error (%) on 300W. Human Body Part Localization: Method Head Shoulder Elbow Wrist Hip Knee Ankle Mean Pishchulin et al. 87.2 56.7 46.7 38.0 61.0 57.5 52.7 57.1 Tompson et al. 90.6 79.2 67.9 63.4 69.5 71.0 64.2 72.0 Chen & Yuille 91.8 78.2 71.8 65.5 73.3 70.2 63.4 73.4 DDN (Ours) 87.2 88.2 82.4 76.3 91.4 85.8 78.7 84.3 Table 2: PCK at 0.2 on LSP dataset.  PCK at 0.02 and 0.05 on CUB200-2011.
Distributed Network Forensics Framework: A Systematic Review

Network forensics is a branch of digital forensics, which applies to network security. It is used to relate monitoring and analysis of the computer network traffic, that helps us in collecting information and digital evidence, for the protection of network that can use as firewall and IDS. Firewalls and IDS can&apos;t always prevent and find out the unauthorized access within a network. This paper presents an extensive survey of several forensic frameworks. There is a demand of a system which not only detects the complex attack, but also it should be able to understand what had happened. Here it talks about the concept of the distributed network forensics. The concept of the Distributed network forensics is based on the distributed techniques, which are useful for providing an integrated platform for the automatic forensic evidence gathering and important data storage, valuable support and an attack attribution graph generation mechanism to depict hacking events.
A Deeper Look at Planning as Learning from Replay

In reinforcement learning, the notions of experience replay, and of planning as learning from replayed experience, have long been used to find good policies with minimal training data. Replay can be seen either as model-based reinforcement learning, where the store of past experiences serves as the model, or as a way to avoid a conventional model of the environment altogether. In this paper, we look more deeply at how replay blurs the line between model-based and model-free methods. First, we show for the first time an exact equivalence between the sequence of value functions found by a modelbased policy-evaluation method and by a modelfree method with replay. Second, we present a general replay method that can mimic a spectrum of methods ranging from the explicitly modelfree (TD(0)) to the explicitly model-based (linear Dyna). Finally, we use insights gained from these relationships to design a new model-based reinforcement learning algorithm for linear function approximation. This method, which we call forgetful LSTD(λ), improves upon regular LSTD(λ) because it extends more naturally to online control, and improves upon linear Dyna because it is a multi-step method, enabling it to perform well even in non-Markov problems or, equivalently, in problems with significant function approximation.
3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information

While there has been a success in 2D human pose estimation with convolutional neural networks (CNNs), 3D human pose estimation has not been thoroughly studied. In this paper, we tackle the 3D human pose estimation task with end-to-end learning using CNNs. Relative 3D positions between one joint and the other joints are learned via CNNs. The proposed method improves the performance of CNN with two novel ideas. First, we added 2D pose information to estimate a 3D pose from an image by concatenating 2D pose estimation result with the features from an image. Second, we have found that more accurate 3D poses are obtained by combining information on relative positions with respect to multiple joints, instead of just one root joint. Experimental results show that the proposed method achieves comparable performance to the state-of-the-art methods on Human 3.6m dataset.
A Survey on Digital Forensics Trends

1.  Digital forensic has evolved from addressing minor computer crimes to investigation of complex international cases with massive effect on the world. This paper studies the evolution of the digital forensic; its origins, its current position and its future directions. This paper sets the scene with exploring past literature on digital forensic approaches followed by the assessment and analysis of current state of art in both industrial and academic digital forensics research. The obtained results are compared and analyzed to provide a comprehensive view of the current digital forensics landscape. Furthermore, this paper highlights critical digital forensic issues that are being overlooked and not being addressed as deserved. The paper finally concludes with offering future research directions in this area.
Nominalization and Alternations in Biomedical Language

This paper presents data on alternations in the argument structure of common domain-specific verbs and their associated verbal nominalizations in the PennBioIE corpus. Alternation is the term in theoretical linguistics for variations in the surface syntactic form of verbs, e.g. the different forms of stimulate in FSH stimulates follicular development and follicular development is stimulated by FSH. The data is used to assess the implications of alternations for biomedical text mining systems and to test the fit of the sublanguage model to biomedical texts.



We examined 1,872 tokens of the ten most common domain-specific verbs or their zero-related nouns in the PennBioIE corpus and labelled them for the presence or absence of three alternations. We then annotated the arguments of 746 tokens of the nominalizations related to these verbs and counted alternations related to the presence or absence of arguments and to the syntactic position of non-absent arguments. We found that alternations are quite common both for verbs and for nominalizations. We also found a previously undescribed alternation involving an adjectival present participle.



We found that even in this semantically restricted domain, alternations are quite common, and alternations involving nominalizations are exceptionally diverse. Nonetheless, the sublanguage model applies to biomedical language. We also report on a previously undescribed alternation involving an adjectival present participle.
An Efficient Parallel Top-k Similarity Join for Massive Multidimensional Data Using Spark

Top-k similarity join has been used in a wide range of applications that require calculating the most top-k similar pairs of data records in a given database. However, the time performance will be a challenging problem, as an increasing trend of applications that need to process massive data. Obviously, finding the top-k pairs in such vast amounts of data with traditional methods is awkward. In this paper, we propose the RDD-based algorithm to perform the top-k similarity join for massive multidimensional data over a large cluster built with commodity machines using Spark. The RDD-based algorithm consists of four steps, which loads a set of multidimensional records stored in HDFS and finally output an ordered list of top-k closest pairs into HDFS. Firstly, we develop an efficient distance function based on LSH(Locality Sensitive Hashing) to improve the efficiency in pairwise similarity comparison. Secondly, to minimize the amount of data during the RDD running-time, we split conceptually all pairs of LSH signatures into partitions. Moreover, we exploit a serial computation strategy to calculate all top-k closest pairs in parallel. Finally, all the local top-k pairs sorted by their Hamming distances will contribute to the global top-k pairs. In this paper, the performance evaluation between Spark and Hadoop confirms the effectiveness and scalability of our RDD-based algorithm.
Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias has a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) Using the presented methodology we reveal that the VGG-16 architecture outperforms the AlexNet architecture at face recognition tasks because it can much better generalize to unseen face poses, although it has significantly more parameters. 4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation. 5) We demonstrate that our findings on synthetic data also apply when learning from real-world data. Our face image generator is publicly available to enable the community to benchmark other DCNN architectures.
Linguistic Regularities in Sparse and Explicit Word Representations

Recent work has shown that neuralembedded word representations capture many relational similarities, which can be recovered by means of vector arithmetic in the embedded space. We show that Mikolov et al.’s method of first adding and subtracting word vectors, and then searching for a word similar to the result, is equivalent to searching for a word that maximizes a linear combination of three pairwise word similarities. Based on this observation, we suggest an improved method of recovering relational similarities, improving the state-of-the-art results on two recent word-analogy datasets. Moreover, we demonstrate that analogy recovery is not restricted to neural word embeddings, and that a similar amount of relational similarities can be recovered from traditional distributional word representations.
The OpenCourseWare Metadiscourse (OCWMD) Corpus

This study describes a new corpus of over 60,000 hand-annotated metadiscourse acts from 106 OpenCourseWare lectures, from two different disciplines: Physics and Economics. Metadiscourse is a set of linguistic expressions that signal different functions in the discourse. This type of language is hypothesised to be helpful in finding a structure in unstructured text, such as lectures discourse. A brief summary is provided about the annotation scheme and labelling procedures, inter-annotator reliability statistics, overall distributional statistics, a description of auxiliary data that will be distributed with the corpus, and information relating to how to obtain the data. The results provide a deeper understanding of lecture structure and confirm the reliable coding of metadiscursive acts in academic lectures across different disciplines. The next stage of our research will be to build a classification model to automate the tagging process, instead of manual annotation, which take time and efforts. This is in addition to the use of these tags as indicators of the higher level structure of lecture discourse.