nils-herrmann/clean_scirepeval huggingface dataset:
Intrusion Detection Using Data Mining Techniques Intrusion Detection Using Data Mining Techniques

In these days an increasing number of public and commercial services are used through the Internet, so that security of information becomes more important issue in the society information Intrusion Detection System (IDS) used against attacks for protected to the Computer networks. On another way, some data mining techniques also contribute to intrusion detection. Some data mining techniques used for intrusion detection can be classified into two classes: misuse intrusion detection and anomaly intrusion detection. Misuse always refers to known attacks and harmful activities that exploit the known sensitivity of the system. Anomaly generally means a generally activity that is able to indicate an intrusion. In this paper, comparison made between 23 related papers of using data mining techniques for intrusion detection. Our work provide an overview on data mining and soft computing techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS), etc. In this paper comparison shown between IDS data mining techniques and tuples used for intrusion detection. In those 23 related papers, 7 research papers use ANN and 4 ones use SVM, because of ANN and SVM are more reliable than other models and structures. In addition, 8 researches use the DARPA1998 tuples and 13 researches use the KDDCup1999, because the standard tuples are much more credible than others. There is no best intrusion detection model in present time. However, future research directions for intrusion detection should be explored in this paper.  intrusion detection, data mining,
Progress in natural language understanding: an application to lunar geology

The advent of computer networks such as the ARPA net (see e.g., Ornstein et al.) has significantly increased the opportunity for access by a single researcher to a variety of different computer facilities and data bases, thus raising expectations of a day when it will be a common occurrence rather than an exception that a scientist will casually undertake to use a computer facility located 3000 miles away and whose languages, formats, and conventions are unknown to him. In this foreseeable future, learning and remembering the number of different languages and conventions that such a scientist would have to know will require significant effort---much greater than that now required to learn the conventions of his local computing center (where other users and knowledgeable assistance is readily available). The Lunar Sciences Natural Language Information System (which we will hereafter refer to as LUNAR) is a research prototype of a system to deal with this and other man-machine communication problems by adapting the machine to the conventions of ordinary natural English rather than requiring the man to adapt to the machine.
Efficient Indexing of Billion-Scale Datasets of Deep Descriptors

Existing billion-scale nearest neighbor search systems have mostly been compared on a single dataset of a billion of SIFT vectors, where systems based on the Inverted Multi-Index (IMI) have been performing very well, achieving state-of-the-art recall in several milliseconds. SIFT-like descriptors, however, are quickly being replaced with descriptors based on deep neural networks (DNN) that provide better performance for many computer vision tasks. In this paper, we introduce a new dataset of one billion descriptors based on DNNs and reveal the relative inefficiency of IMI-based indexing for such descriptors compared to SIFT data. We then introduce two new indexing structures, the Non-Orthogonal Inverted Multi-Index (NO-IMI) and the Generalized Non-Orthogonal Inverted Multi-Index (GNO-IMI). We show that due to additional flexibility, the new structures are able to adapt to DNN descriptor distribution in a better way. In particular, extensive experiments on the new dataset demonstrate that these data structures provide considerably better trade-off between the speed of retrieval and recall, given similar amount of memory, as compared to the standard Inverted Multi-Index.
Clothing identification via deep learning: forensic applications

Attribute-based identification systems are essential for forensic investigations because they help in identifying individuals. An item such as clothing is a visual attribute because it can usually be used to describe people. The method proposed in this article aims to identify people based on the visual information derived from their attire. Deep learning is used to train the computer to classify images based on clothing content. We first demonstrate clothing classification using a large scale dataset, where the proposed model performs relatively poorly. Then, we use clothing classification on a dataset containing popular logos and famous brand images. The results show that the model correctly classifies most of the test images with a success rate that is higher than 70%. Finally, we evaluate clothing classification using footage from surveillance cameras. The system performs well on this dataset, labelling 70% of the test images correctly.
Nonlinear connectivity by Granger causality

The communication among neuronal populations, reflected by transient synchronous activity, is the mechanism underlying the information processing in the brain. Although it is widely assumed that the interactions among those populations (i.e. functional connectivity) are highly nonlinear, the amount of nonlinear information transmission and its functional roles are not clear. The state of the art to understand the communication between brain systems are dynamic causal modeling (DCM) and Granger causality. While DCM models nonlinear couplings, Granger causality, which constitutes a major tool to reveal effective connectivity, and is widely used to analyze EEG/MEG data as well as fMRI signals, is usually applied in its linear version. In order to capture nonlinear interactions between even short and noisy time series, a few approaches have been proposed. We review them and focus on a recently proposed flexible approach has been recently proposed, consisting in the kernel version of Granger causality. We show the application of the proposed approach on EEG signals and fMRI data.
Detecting Insider Threats Using RADISH: A System for Real-Time Anomaly Detection in Heterogeneous Data Streams

We present a scalable system for high-throughput real-time analysis of heterogeneous data streams. Our architecture enables incremental development of models for predictive analytics and anomaly detection as data arrives into the system. In contrast with batch data-processing systems, such as Hadoop, that can have high latency, our architecture allows for ingest and analysis of data on the fly, thereby detecting and responding to anomalous behavior in near real time. This timeliness is important for applications such as insider threat, financial fraud, and network intrusions. We demonstrate an application of this system to the problem of detecting insider threats, namely, the misuse of an organization's resources by users of the system and present results of our experiments on a publicly available insider threat dataset.
CENTURION: Incentivizing multi-requester mobile crowd sensing

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. 's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations.
De-Conflated Semantic Representations

One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years. However, the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses. We propose a technique that tackles these problems by de-conflating the representations of words based on the deep knowledge that can be derived from a semantic network. Our approach provides multiple advantages in comparison to the previous approaches, including its high coverage and the ability to generate accurate representations even for infrequent word senses. We carry out evaluations on six datasets across two semantic similarity tasks and report state-of-the-art results on most of them.
sec-cs: Getting the Most out of Untrusted Cloud Storage

We present sec-cs, a hash-table-like data structure for contents on untrusted storage that is provably secure and storage-efficient. We achieve authenticity and confidentiality with zero storage overhead using deterministic authenticated encryption. State-of-the-art data deduplication approaches prevent redundant storage of shared parts of different contents irrespective of whether relationships between contents are known a priori.,,,,Instead of just adapting existing approaches, we introduce novel (multi-level) chunking strategies, ML-SC and ML-CDC, which are significantly more storage-efficient than existing approaches in presence of high redundancy.,,,,We prove sec-cs's security, publish an implementation, and present evaluation results indicating suitability for, e.g., future backup systems that should preserve many versions of files on little available storage.
Dual broadband high gain SIW slot array antenna

A novel design of dual broadband high gain substrate integrated waveguide (SIW) cavity backed slot array antenna is described. The SIW cavity is design in dominant mode TE10. The proposed antenna is design by two pair of λg/2-resonators cavity backed slot on broad wall of single layer substrate integrated waveguide. To place two pair unequal slot length in stair nature on broad-wall of SIW cavity are responsible to broadening the bandwidth at two different frequency band and also due to arraying nature of slot enhanced the gain. The proposed antenna have Lower frequency bandwidth 4.4% (9.58 GHz–10.02 GHz) and higher frequency bandwidth is 9.49% (10.84 GHz–11.92 GHz) with 10 dB gain at lower band and 9.4 dB gain at higher band. The proposed antenna has high gain, directive radiation field with high FTBR.
Simple and Effective Approach for Consistent Training of Hierarchical Phrase-based Translation Models

In this paper, we present a simple approach for consistent training of hierarchical phrase-based translation models. In order to consistently train a translation model, we perform hierarchical phrasebased decoding on training data to find derivations between the source and target sentences. This is done by synchronous parsing the given sentence pairs. After extracting k-best derivations, we reestimate the translation model probabilities based on collected rule counts. We show the effectiveness of our procedure on the IWSLT German→English and English→French translation tasks. Our results show improvements of up to 1.6 points BLEU.
Semi-Supervised Kernel Mean Shift Clustering

Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function. We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms.
A Business Intelligence System

ระบบ Business Intelligence เป็นซอฟแวร์ที่เข้าไปช่วยอ านวยความสะดวกในด้านการวเิคราะห์ข้อมลู และการตัดสินใจใน ข้อมูลทางธุรกิจทั้งหมดของในหนว่ยงาน ซึ่งช่วยให้ผู้ใช้งานระบบงานในองค์กรท าการตัดสินใจทางธุรกิจที่ดียิ่งข้ึน มีฟังก์ช่ันการใช้ งานต่างๆครบถ้วนไม่ว่าจะเป็น Data Integration, Reporting, ad-hoc Query, Analysis Dashboard, Data Mining ทั้งนี้เพื่อลดการท างานท่ี ซ้ าซ้อน ลดความผิดพลาดและความขัดแย้งท่ีอาจจะเกิดขึ้นเนื่องจากการสื่อสารระหว่างหน่วยงานภายในกันเอง หรือกับหน่วยงาน ภายนอก
Deep neural network architecture for sentiment analysis and emotion identification of Twitter messages

In the work presented in this paper, we showcase a deep learning system for sentiment analysis and emotion identification in Twitter messages. The system consists of a convolutional neural network used for extracting features from textual data and a classifier for which we experiment with several different classifying algorithms. We train the network using pre-trained word embeddings obtained by unsupervised learning on large text corpora and compare the effectiveness of the different word vectors for this task. We evaluate our system on 3-class sentiment analysis with datasets provided by the Sentiment analysis in Twitter task from the SemEval competition. Additionally, we explore the effectiveness of our approach for emotion identification, by using an automatically annotated dataset with 7 distinct emotions. Our architecture achieves comparable performances to state-of-the-art techniques in the field of sentiment analysis and improves results in the field of emotion identification on the test we use in our evaluation. Moreover, the paper presents several use case scenarios, depicting real-world usage of our architecture.
A Polymer-Based Capacitive Sensing Array for Normal and Shear Force Measurement

In this work, we present the development of a polymer-based capacitive sensing array. The proposed device is capable of measuring normal and shear forces, and can be easily realized by using micromachining techniques and flexible printed circuit board (FPCB) technologies. The sensing array consists of a polydimethlysiloxane (PDMS) structure and a FPCB. Each shear sensing element comprises four capacitive sensing cells arranged in a 2 × 2 array, and each capacitive sensing cell has two sensing electrodes and a common floating electrode. The sensing electrodes as well as the metal interconnect for signal scanning are implemented on the FPCB, while the floating electrodes are patterned on the PDMS structure. This design can effectively reduce the complexity of the capacitive structures, and thus makes the device highly manufacturable. The characteristics of the devices with different dimensions were measured and discussed. A scanning circuit was also designed and implemented. The measured maximum sensitivity is 1.67%/mN. The minimum resolvable force is 26 mN measured by the scanning circuit. The capacitance distributions induced by normal and shear forces were also successfully captured by the sensing array.
Principles of spatial database analysis and design

This chapter covers the fundamentals of spatial database analysis and design, It begins by defining the most important concepts: ‘spatial database’, ‘analysis’, ‘design’, and ‘model’; and continues with a presentation of the rationale supporting the use of formal methods for analysis and design. The basic elements and approaches of such methods are described, in addition to the processes used. Emphasis is placed on the particularities of spatial databases and the improvements needed for non-spatial methods and tools in order to enhance their efficiency. Finally, the chapter presents a set of tools, called CASE (computer-assisted software engineering), which are built to support the formal analysis and design methods.
A New Edge Termination Technique for High-Voltage Devices in 4H-SiC–Multiple-Floating-Zone Junction Termination Extension

A new edge termination method, referred to as multiple-floating-zone junction termination extension (MFZ-JTE), is presented for high-voltage devices in 4H-SiC. 4H-SiC PiN rectifiers with a breakdown voltage of 10 kV (about 88% of the theoretical value) were fabricated using MFZ-JTEs. The MFZ-JTE technique only requires a single pattern-and-implant step while providing significant process latitude for parameter variations such as implantation dose and activation anneal condition.
Privacy by BlockChain Design: A BlockChain-enabled GDPR-compliant Approach for Handling Personal Data

This paper takes an initial step forward in bringing to life the certification mechanisms according to Art. 42 of the General Data Protection Regulation (GDPR). These newly established methods of legal specification act not only as a central vehicle for overcoming widely articulated and discussed legal challenges, but also as a sandbox for the much needed close collaboration between computer sciences and legal studies. In order to illustrate, for example, what data protection seals could look like in the future, the authors propose a methodology for "translating" legal requirements into technical guidelines: architectural blueprints designed using legal requirements. The purpose of these blueprints is to show developers how their solutions might comply with the principle of Privacy by Design (Art. 25 GDPR). To demonstrate this methodology, the authors propose an architectural blueprint that embodies the legal concept of the data subject’s consent (Art. 6 sec. 1 lit. a GDPR) and elevates best practice to a high standard of Privacy by Design. Finally, the authors highlight further legal problems concerning blockchain technology under the GDPR that will have to be addressed in order to achieve a comprehensive certification mechanism for Privacy by Blockchain Design in the future.
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach

A main challenge in applying deep learning to music processing is the availability of training data. One potential solution is Multi-task Learning, in which the model also learns to solve related auxiliary tasks on additional datasets to exploit their correlation. While intuitive in principle, it can be challenging to identify related tasks and construct the model to optimally share information between tasks. In this paper, we explore vocal activity detection as an additional task to stabilise and improve the performance of vocal separation. Further, we identify problematic biases specific to each dataset that could limit the generalisation capability of separation and detection models, to which our proposed approach is robust. Experiments show improved performance in separation as well as vocal detection compared to single-task baselines. However, we find that the commonly used Signal-to-Distortion Ratio (SDR) metrics did not capture the improvement on non-vocal sections, indicating the need for improved evaluation methodologies.
Designing an Indonesian part of speech tagset and manually tagged Indonesian corpus

We describe our work on designing a linguistically principled part of speech (POS) tagset for the Indonesian language. The process involves a detailed study and analysis of existing tagsets and the manual tagging of an Indonesian corpus. The results of this work are an Indonesian POS tagset consisting of 23 tags and an Indonesian corpus of over 250.000 lexical tokens that have been manually tagged using this tagset.
Towards reducing the attack surface of software backdoors

Backdoors in software systems probably exist since the very first access control mechanisms were implemented and they are a well-known security problem. Despite a wave of public discoveries of such backdoors over the last few years, this threat has only rarely been tackled so far.
 In this paper, we present an approach to reduce the attack surface for this kind of attacks and we strive for an automated identification and elimination of backdoors in binary applications. We limit our focus on the examination of server applications within a client-server model. At the core, we apply variations of the delta debugging technique and introduce several novel heuristics for the identification of those regions in binary application that backdoors are typically installed in (i.e., authentication and command processing functions). We demonstrate the practical feasibility of our approach on several real-world backdoors found in modified versions of the popular software tools ProFTPD and OpenSSH. Furthermore, we evaluate our implementation not only on common instruction set architectures such as x86-64, but also on commercial off-the-shelf embedded devices powered by a MIPS32 processor.
New cardinality estimation algorithms for HyperLogLog sketches

This paper presents new methods to estimate the cardinalities of data sets recorded by HyperLogLog sketches. A theoretically motivated extension to the original estimator is presented that eliminates the bias for small and large cardinalities. Based on the maximum likelihood principle a second unbiased method is derived together with a robust and efficient numerical algorithm to calculate the estimate. The maximum likelihood approach can also be applied to more than a single HyperLogLog sketch. In particular, it is shown that it gives more precise cardinality estimates for union, intersection, or relative complements of two sets that are both represented by HyperLogLog sketches compared to the conventional technique using the inclusion-exclusion principle. All the new methods are demonstrated and verified by extensive simulations.