nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements

Classification of rare events has many important data mining applications. Boosting is a promising metatechnique that improves the classification performance of any weak classifier. So far, no systematic study has been conducted to evaluate how boosting performs for the task of mining rare classes. In this paper, we evaluate three existing categories of boosting algorithms from the single viewpoint of how they update the example weights in each iteration, and discuss their possible effect on recall and precision of the rare class. We propose enhanced algorithms in two of the categories, and justify their choice of weight updating parameters theoretically. Using some specially designed synthetic datasets, we compare the capability of all the algorithms from the rare class perspective. The results support our qualitative analysis, and also indicate that our enhancements bring an extra capability for achieving better balance between recall and precision in mining rare classes.
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video

In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017.We propose an ensemble of several models, which capture spatial and audio features from videos. Spatial features are captured by convolutional neural networks, pretrained on large face recognition datasets. We show that usage of strong industry-level face recognition networks increases the accuracy of emotion recognition. Using our ensemble we improve on the previous best result on the test set by about 1 %, achieving a 60.03 % classification accuracy without any use of visual temporal information.
Defeating DDoS attacks by fixing the incentive chain

Cooperative technological solutions for Distributed Denial-of-Service (DDoS) attacks are already available, yet organizations in the best position to implement them lack incentive to do so, and the victims of DDoS attacks cannot find effective methods to motivate them. In this article we discuss two components of the technological solutions to DDoS attacks: cooperative filtering and cooperative traffic smoothing by caching. We then analyze the broken incentive chain in each of these technological solutions. As a remedy, we propose usage-based pricing and Capacity Provision Networks, which enable victims to disseminate enough incentive along attack paths to stimulate cooperation against DDoS attacks.
Recovering Shading from Color Images

Existing shape-from-shading algorithms assume constant reflectance across the shaded surface. Multi-colored surfaces are excluded because both shading and reflectance affect the measured image intensity. Given a standard RGB color image, we describe a method of eliminating the reflectance effects in order to calculate a shading field that depends only on the relative positions of the illuminant and surface. Of course, shading recovery is closely tied to lightness recovery and our method follows from the work of Land [10, 9], Horn [7] and Blake . In the luminance image, R+G+B, shading and reflectance are confounded. Reflectance changes are located and removed from the luminance image by thresholding the gradient of its logarithm at locations of abrupt chromaticity change. Thresholding can lead to gradient fields which are not conservative (do not have zero curl everywhere and are not integrable) and therefore do not represent realizable shading fields. By applying a new curl-correction technique at the thresholded locations, the thresholding is improved and the gradient fields are forced to be conservative. The resulting Poisson equation is solved directly by the Fourier transform method. Experiments with real images are presented.
Using LSTM encoder-decoder algorithm for detecting anomalous ADS-B messages

Although the ADS-B system is going to play a major role in the safe navigation of airplanes and air traffic control (ATC) management, it is also well known for its lack of security mechanisms. Previous research has proposed various methods for improving the security of the ADS-B system and mitigating associated risks. However, these solutions typically require the use of additional participating nodes (or sensors) (e.g., to verify the location of the airplane by analyzing the physical signal) or modification of the current protocol architecture (e.g., adding encryption or authentication mechanisms.) Due to the regulation process regarding avionic systems and the fact that the ADS-B system is already deployed in most airplanes, applying such modifications to the current protocol at this stage is impractical. In this paper we propose an alternative security solution for detecting anomalous ADS-B messages aimed at the detection of spoofed or manipulated ADSB messages sent by an attacker or compromised airplane. The proposed approach utilizes an LSTM encoder-decoder algorithm for modeling flight routes by analyzing sequences of legitimate ADS-B messages. Using these models, aircraft can autonomously evaluate received ADS-B messages and identify deviations from the legitimate flight path (i.e., anomalies). We examined our approach on six different flight route datasets to which we injected different types of anomalies. Using our approach we were able to detect all of the injected attacks with an average false alarm rate of 4.3% for all of datasets. ADS-B; Security; LSTM; Anomaly Detection; Aviation.
Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied. 1 Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire, UK,
A High-Density Metal-Fuse Technology Featuring a 1.6 V Programmable Low-Voltage Bit Cell With Integrated 1 V Charge Pumps in 22 nm Tri-Gate CMOS

The first metal-fuse technology in 22 nm tri-gate high-k metal-gate CMOS technology is presented. The memory technology offerings in high-volume manufacturing include a 2.05 μm2 2.2 V programmable high-density and a 16.4 μm2 1.6 V programmable low-voltage (LV) 1T1R bit cell. The LV operability of the technology allows the fuse arrays to be coupled with power delivery circuits operating at standard logic voltage levels. A charge pump voltage doubler operating on a 1 V voltage rail is demonstrated in this paper with healthy fusing yield.
A Temperature Compensated Fully Trimmable On-Chip IC Oscillator

The design of an IC on-chip oscillator including a temperature compensation circuitry, a robust spread reduction technique and digital trimming is described. The IC oscillator provides a 12.8MHz clock signal with a frequency spread of ±25% before the 8-bits digital trimming. After centering the oscillator at the target frequency, a temperature compensated voltage and current reference circuit allows for less than ±5% frequency variation when operating from 3 to 5V of power supply and from -40 to 125°C of temperature range. The oscillator is implemented in a 0.5 µm CMOS technology, occupies an area of 420x440 µm2 and dissipates less than 400 µW at 3V of supply without requiring any external reference or components.
DLANet: A manifold-learning-based discriminative feature learning network for scene classification

This paper presents Discriminative Locality Alignment Network (DLANet), a novel manifold-learningbased discriminative learnable feature, for wild scene classification. Based on a convolutional structure, DLANet learns the filters of multiple layers by applying DLA and exploits the block-wise histograms of the binary codes of feature maps to generate the local descriptors. A DLA layer maximizes the margin between the inter-class patches and minimizes the distance of the intra-class patches in the local region. In particular, we construct a two-layer DLANet by stacking two DLA layers and a feature layer. It is followed by a popular framework of scene classification, which combines Locality-constrained Linear Coding–Spatial Pyramid Matching (LLC–SPM) and linear Support Vector Machine (SVM). We evaluate DLANet on NYU Depth V1, Scene-15 and MIT Indoor-67. Experiments show that DLANet performs well on depth image. It outperforms the carefully tuned features, including SIFT and is also competitive to the other reported methods. & 2015 Elsevier B.V. All rights reserved.
Conception, Evolution, and Application of Functional Programming Languages

The foundations of functional programming languages are examined from both historical and technical perspectives. Their evolution is traced through several critical periods: early work on lambda calculus and combinatory calculus, Lisp, Iswim, FP, ML, and modern functional languages such as Miranda1 and Haskell. The fundamental premises on which the functional programming methodology stands are critically analyzed with respect to philosophical, theoretical, and pragmatic concerns. Particular attention is paid to the main features that characterize modern functional languages: higher-order functions, lazy evaluation, equations and pattern matching, strong static typing and type inference, and data abstraction. In addition, current research areas—such as parallelism, nondeterminism, input/output, and state-oriented computations—are examined with the goal of predicting the future development and application of functional languages.
Learning from Medical Summaries: The University of Michigan at TREC 2015 Clinical Decision Support Track

The goal of TREC 2015 Clinical Decision Support Track was to retrieve biomedical articles relevant for answering three kinds of generic clinical questions, namely diagnosis, test, and treatment. In order to achieve this purpose, we investigated three approaches to improve the retrieval of relevant articles: modifying queries, improving indexes, and ranking with ensembles. Our final submissions were a combination of several different configurations of these approaches. Our system mainly focused on the summary fields of medical reports. We built two different kinds of indexes – an inverted index on the free text and a second kind of indexes on the Unified Medical Language System (UMLS) concepts within the entire articles that were recognized by MetaMap. We studied the variations of including UMLS concepts at paragraph and sentence level and experimented with different thresholds of MetaMap matching scores to filter UMLS concepts. The query modification process in our system involved automatic query construction, pseudo relevance feedback, and manual inputs from domain experts. Furthermore, we trained a re-ranking sub-system based on the results of TREC 2014 Clinical Decision Support track using Indri’s Learning to Rank package, RankLib. Our experiments showed that the ensemble approach could improve the overall results by boosting the ranking of articles that are near the top of several single ranked lists.
Model-based influences on humans’ choices and striatal
prediction errors

The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.
Inventory Models for Substitutable Products: Optimal Policies and Heuristics

In this paper, we examine the nature of optimal inventory policies in a system where a retailer manages substitutable products. We first consider a system with two products 1 and 2 whose total demand is D and individual demand proportions are p and (1-p). A fixed proportion of the unsatisfied customers for 1(2) will purchase item 2 (1), if it is available in inventory. For the single period case, we show that the optimal inventory levels of the two items can be computed easily and follow what we refer to as "partially decoupled" policies, i.e. base stock policies that are not state dependent, in certain critical regions of interest both when D is known and random. Furthermore, we show that such a partially decoupled base-stock policy is optimal even in a multi-period version of the problem for known D for a wide range of parameter values. Using a numerical study, we show that heuristics based on the de-coupled inventory policies perform well in conditions more general than the ones assumed to obtain the analytical results. The analytical and numerical results suggest that the approach presented here is most valuable in retail settings for product categories where there is a moderate level of substitution between items in the category, demand variation at the category level is not too high and service levels are high.
Genome editing with engineered zinc finger nucleases

Reverse genetics in model organisms such as Drosophila melanogaster, Arabidopsis thaliana, zebrafish and rats, efficient genome engineering in human embryonic stem and induced pluripotent stem cells, targeted integration in crop plants, and HIV resistance in immune cells — this broad range of outcomes has resulted from the application of the same core technology: targeted genome cleavage by engineered, sequence-specific zinc finger nucleases followed by gene modification during subsequent repair. Such 'genome editing' is now established in human cells and a number of model organisms, thus opening the door to a range of new experimental and therapeutic possibilities.
A Shoulder Surfing Resistant Graphical Authentication System

Authentication based on passwords is used largely in applications for computer security and privacy. However, human actions such as choosing bad passwords and inputting passwords in an insecure way are regarded as “the weakest link” in the authentication chain. Rather than arbitrary alphanumeric strings, users tend to choose passwords either short or meaningful for easy memorization. With web applications and mobile apps piling up, people can access these applications anytime and anywhere with various devices. This evolution brings great convenience but also increases the probability of exposing passwords to shoulder surfing attacks. Attackers can observe directly or use external recording devices to collect users’ credentials. To overcome this problem, we proposed a novel authentication system PassMatrix, based on graphical passwords to resist shoulder surfing attacks. With a one-time valid login indicator and circulative horizontal and vertical bars covering the entire scope of pass-images, PassMatrix offers no hint for attackers to figure out or narrow down the password even they conduct multiple camera-based attacks. We also implemented a PassMatrix prototype on Android and carried out real user experiments to evaluate its memorability and usability. From the experimental result, the proposed system achieves better resistance to shoulder surfing attacks while maintaining usability.
Sentiment Analysis in Twitter: From Classification to Quantification of Sentiments within Tweets

Twitter is attracting significant interests from the research community in the last few years. Sentiment analysis of tweets is among the hottest topics of research nowadays. State of the art approaches of sentiment analysis present many shortcomings when classifying tweets, in particular when the classification goes beyond the binary or ternary classification. Multi-class sentiment analysis has proven to be a very challenging task. This is mainly for the simple reason that a tweet usually does not contain a single sentiment, but many ones. In this paper, we propose a pattern-based approach for sentiment quantification in Twitter. By quantification, we refer to the detection of the existing sentiments within a tweet and the detection of the weight of these sentiments. In a first step, we classify tweets into positive, negative, or neutral. Our approach reaches an accuracy of 81%. We then perform the sentiment quantification on the sentimental tweets (i.e., positive and negative ones) to extract the sentiments within them: we define 5 positive sentiment sub-classes 5 negative ones and detect which exist in each tweet. We define 2 metrics to measure the correctness of sentiment detection, and prove that sentiment quantification can be a more meaningful task than the regular multi-class classification.
Automated identification of security issues from commit messages and bug reports

The number of vulnerabilities in open source libraries is increasing rapidly. However, the majority of them do not go through public disclosure. These unidentified vulnerabilities put developers' products at risk of being hacked since they are increasingly relying on open source libraries to assemble and build software quickly. To find unidentified vulnerabilities in open source libraries and secure modern software development, we describe an efficient automatic vulnerability identification system geared towards tracking large-scale projects in real time using natural language processing and machine learning techniques. Built upon the latent information underlying commit messages and bug reports in open source projects using GitHub, JIRA, and Bugzilla, our K-fold stacking classifier achieves promising results on vulnerability identification. Compared to the state of the art SVM-based classifier in prior work on vulnerability identification in commit messages, we improve precision by 54.55% while maintaining the same recall rate. For bug reports, we achieve a much higher precision of 0.70 and recall rate of 0.71 compared to existing work. Moreover, observations from running the trained model at SourceClear in production for over 3 months has shown 0.83 precision, 0.74 recall rate, and detected 349 hidden vulnerabilities, proving the effectiveness and generality of the proposed approach.
Small form factor dual band (28/38 GHz) PIFA antenna for 5G applications

This paper presents for the first time, the design of a dual band PIFA antenna for 5G applications on a low-cost substrate with smallest form factor and widest bandwidth in both bands (28 GHz and 38 GHz). The proposed dual band PIFA antenna consists of a shorted patch and a modified U-shaped slot in the patch. The antenna shows good matching at and around both center frequencies. The antenna shows clean radiation pattern and bandwidth of 3.34 GHz and 1.395 GHz and gain of 3.75 dBi and 5.06 dBi at 28 and 38 GHz respectively. This antenna has ultra-small form factor of 1.3 mm × 1.2 mm. Patch is shorted at one end with a metallic cylindrical via. A CPW line and a feeding via are used on the bottom side of the substrate to excite the PIFA antenna patterned on the top side of the substrate which also facilitate the measurements of the antenna at mm-wave frequencies. The antenna was designed on low cost Isola FR406 substrate.
Customized Organization of Social Media Contents using Focused Topic Hierarchy

With the popularity of social media platforms such as Facebook and Twitter, the amount of useful data in these sources is rapidly increasing, making them promising places for information acquisition. This research aims at the customized organization of a social media corpus using focused topic hierarchy. It organizes the contents into different structures to meet with users' different information needs (e.g., "iPhone 5 problem" or "iPhone 5 camera"). To this end, we introduce a novel function to measure the likelihood of a topic hierarchy, by which the users' information need can be incorporated into the process of topic hierarchy construction. Using the structure information within the generated topic hierarchy, we then develop a probability based model to identify the representative contents for topics to assist users in document retrieval on the hierarchy. Experimental results on real world data illustrate the effectiveness of our method and its superiority over state-of-the-art methods for both information organization and retrieval tasks.
Efficient many-core query execution in main memory column-stores

We use the full query set of the TPC-H Benchmark as a case study for the efficient implementation of decision support queries on main memory column-store databases. Instead of splitting a query into separate independent operators, we consider the query as a whole and translate the execution plan into a single function performing the query. This allows highly efficient CPU utilization, minimal materialization, and execution in a single pass over the data for most queries. The single pass is performed in parallel and scales near-linearly with the number of cores. The resulting query plans for most of the 22 queries are remarkably simple and are suited for automatic generation and fast compilation. Using a data-parallel, NUMA-aware many-core implementation with block summaries, inverted index data structures, and efficient aggregation algorithms, we achieve one to two orders of magnitude better performance than the current record holders of the TPC-H Benchmark.
Information Theory and the IrisCode

Iris recognition has legendary resistance to false matches, and the tools of information theory can help to explain why. The concept of entropy is fundamental to understanding biometric collision avoidance. This paper analyses the bit sequences of IrisCodes computed both from real iris images and from synthetic white noise iris images, whose pixel values are random and uncorrelated. The capacity of the IrisCode as a channel is found to be 0.566 bits per bit encoded, of which 0.469 bits of entropy per bit is encoded from natural iris images. The difference between these two rates reflects the existence of anatomical correlations within a natural iris, and the remaining gap from one full bit of entropy per bit encoded reflects the correlations in both phase and amplitude introduced by the Gabor wavelets underlying the IrisCode. A simple two-state hidden Markov model is shown to emulate exactly the statistics of bit sequences generated both from natural and white noise iris images, including their imposter distributions, and may be useful for generating large synthetic IrisCode databases.