nils-herrmann/clean_scirepeval huggingface dataset:
End-to-End Incremental Learning

Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall performance when training with new classes added incrementally. This is due to current neural network architectures requiring the entire dataset, consisting of all the samples from the old as well as the new classes, to update the model—a requirement that becomes easily unsustainable as the number of classes grows. We address this issue with our approach to learn deep neural networks incrementally, using new data and only a small exemplar set corresponding to samples from the old classes. This is based on a loss composed of a distillation measure to retain the knowledge acquired from the old classes, and a cross-entropy loss to learn the new classes. Our incremental training is achieved while keeping the entire framework end-to-end, i.e., learning the data representation and the classifier jointly, unlike recent methods with no such guarantees. We evaluate our method extensively on the CIFAR-100 and ImageNet (ILSVRC 2012) image classification datasets, and show state-of-the-art performance.
XSS-Dec: A Hybrid Solution to Mitigate Cross-Site Scripting Attacks

Cross-site scripting attacks represent one of the major security threats in today’s Web applications. Current approaches to mitigate cross-site scripting vulnerabilities rely on either server-based or client-based defense mechanisms. Although effective for many attacks, server-side protection mechanisms may leave the client vulnerable if the server is not well patched. On the other hand, client-based mechanisms may incur a significant overhead on the client system. In this work, we present a hybrid client-server solution that combines the benefits of both architectures. Our Proxy-based solution leverages the strengths of both anomaly detection and control flow analysis to provide accurate detection. We demonstrate the feasibility and accuracy of our approach through extended testing using real-world cross-site scripting exploits.
Foot trajectory in human gait: a precise and multifactorial motor control task.

The trajectory of the heel and toe during the swing phase of human gait were analyzed on young adults. The magnitude and variability of minimum toe clearance and heel-contact velocity were documented on 10 repeat walking trials on 11 subjects. The energetics that controlled step length resulted from a separate study of 55 walking trials conducted on subjects walking at slow, natural, and fast cadences. A sensitivity analysis of the toe clearance and heel-contact velocity measures revealed the individual changes at each joint in the link-segment chain that could be responsible for changes in those measures. Toe clearance was very small (1.29 cm) and had low variability (about 4 mm). Heel-contact velocity was negligible vertically and small (0.87 m/s) horizontally. Six joints in the link-segment chain could, with very small changes (+/- 0.86 degrees - +/- 3.3 degrees), independently account for toe clearance variability. Only one muscle group in the chain (swing-phase hamstring muscles) could be responsible for altering the heel-contact velocity prior to heel contact. Four mechanical power phases in gait (ankle push-off, hip pull-off, knee extensor eccentric power at push-off, and knee flexor eccentric power prior to heel contact) could alter step length and cadence. These analyses demonstrate that the safe trajectory of the foot during swing is a precise endpoint control task that is under the multisegment motor control of both the stance and swing limbs.
Heterogeneous Bitwidth Binarization in Convolutional Neural Networks

To address this shortcoming, we propose a method for training binary neural networks with a mixture of bits, yielding effectively fractional bitwidths. We demonstrate that our method is not only effective in allowing finer tuning of the speed to accuracy trade-off, but also has inherent representational advantages. Middle-Out Algorithm Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
Kernel-Based Positioning in Wireless Local Area Networks

The recent proliferation of location-based services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, wireless local area network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods
Effect of geometry on drug release from 3D printed tablets.

The aim of this work was to explore the feasibility of combining hot melt extrusion (HME) with 3D printing (3DP) technology, with a view to producing different shaped tablets which would be otherwise difficult to produce using traditional methods. A filament extruder was used to obtain approx. 4% paracetamol loaded filaments of polyvinyl alcohol with characteristics suitable for use in fused-deposition modelling 3DP. Five different tablet geometries were successfully 3D-printed-cube, pyramid, cylinder, sphere and torus. The printing process did not affect the stability of the drug. Drug release from the tablets was not dependent on the surface area but instead on surface area to volume ratio, indicating the influence that geometrical shape has on drug release. An erosion-mediated process controlled drug release. This work has demonstrated the potential of 3DP to manufacture tablet shapes of different geometries, many of which would be challenging to manufacture by powder compaction.
A Smart Antenna Module Using OMNeT++ for Wireless Sensor Network Simulation

We introduce a smart antenna (SA) module to be used in OMNeT++ for wireless sensor networks (WSN). This module is a collection of tools in OMNeT++ for adding smart antenna capability to a central node in WSNs. It is based on sectoral sweeper (SS) scheme which was shown to provide efficient task management and easy localization scheme. The number of sensing nodes, energy consumption of nodes, and data traffic carried in the network are reduced with the SS. OMNeT++ is an open source object-oriented modular discrete event network simulator consisting of hierarchically nested modules. With the SA module, desired task region can be specified by a task beam with changing beam width and beam direction parameters in the OMNeT++ configuration file. In addition, each node in the network model that uses SA module has capability of Mobility framework which is intended to support wireless and mobile simulations within OMNeT++. The simulation results provide performance evaluation using the developed module in OMNeT++ for WSNs.
Accident or homicide--virtual crime scene reconstruction using 3D methods.

The analysis and reconstruction of forensically relevant events, such as traffic accidents, criminal assaults and homicides are based on external and internal morphological findings of the injured or deceased person. For this approach high-tech methods are gaining increasing importance in forensic investigations. The non-contact optical 3D digitising system GOM ATOS is applied as a suitable tool for whole body surface and wound documentation and analysis in order to identify injury-causing instruments and to reconstruct the course of event. In addition to the surface documentation, cross-sectional imaging methods deliver medical internal findings of the body. These 3D data are fused into a whole body model of the deceased. Additional to the findings of the bodies, the injury inflicting instruments and incident scene is documented in 3D. The 3D data of the incident scene, generated by 3D laser scanning and photogrammetry, is also included into the reconstruction. Two cases illustrate the methods. In the fist case a man was shot in his bedroom and the main question was, if the offender shot the man intentionally or accidentally, as he declared. In the second case a woman was hit by a car, driving backwards into a garage. It was unclear if the driver drove backwards once or twice, which would indicate that he willingly injured and killed the woman. With this work, we demonstrate how 3D documentation, data merging and animation enable to answer reconstructive questions regarding the dynamic development of patterned injuries, and how this leads to a real data based reconstruction of the course of event.
Spatio-temporal characteristics of bursty words in Twitter streams

Social networking and microblogging services such as Twitter provide a continuous source of data from which useful information can be extracted. The detection and characterization of bursty words play an important role in processing such data, as bursty words might hint to events or trending topics of social importance upon which actions can be triggered. While there are several approaches to extract bursty words from the content of messages, there is only little work that deals with the dynamics of continuous streams of messages, in particular messages that are geo-tagged.
 In this paper, we present a framework to identify bursty words from Twitter text streams and to describe such words in terms of their spatio-temporal characteristics. Using a time-aware word usage baseline, a sliding window approach over incoming tweets is proposed to identify words that satisfy some burstiness threshold. For these words then a time-varying, spatial signature is determined, which primarily relies on geo-tagged tweets. In order to deal with the noise and the sparsity of geo-tagged tweets, we propose a novel graph-based regularization procedure that uses spatial cooccurrences of bursty words and allows for computing sound spatial signatures. We evaluate the functionality of our online processing framework using two real-world Twitter datasets. The results show that our framework can efficiently and reliably extract bursty words and describe their spatio-temporal evolution over time.
Digital image analysis for automatic enumeration of malaria parasites using morphological operations

Malaria parasites Image analysis Morphological operations Adaptive histogram equalization Adaptive thresholding a b s t r a c t Every year, malaria kills between 660,000 and 1.2 million people, many of whom are children in Africa. The World Health Organization (WHO) encourages the development of rapid and economical diagnostic tests that allow for the identification of proper treatment methods. In this paper a novel method to automatically enumerate malaria parasites is proposed and evaluated, using a database consisting of 475 images with varying densities of malaria parasites. This method will analyze data by utilizing standard operations of image processing such as histogram equalization, thresholding, morphological operations and connected components analysis for parasite density estimation. The application of the proposed method yields an average accuracy rate of 96.46% with a low processing time of two seconds per image on a custom computing platform. Malaria has infected humans for over 50,000 years (Day, 2010). Epidemiological data show that somewhere in the world a child dies every 30 s because of this disease. This suggests that both vaccine development and the proper administration of available drugs are absolutely essential to treat malaria patients (O'Meara, Hall, & McKenzie, 2007a). A wrong diagnosis of this disease may have adverse clinical and therapeutic implications for patients and for endpoints of clinical trials of anti-malarial vaccines or drugs. Nowadays , there are a lot of different techniques for malaria diagnosis Thus, the most important component of laboratory diagnosis is the quantification of parasite density. Accurate and replicable parasite counts are difficult to achieve because of inherent technical limitations and human inconsistency (Mitiku, Mengistu, & Gelaw, 2003). Digital image analysis provides an opportunity to improve the performance of parasite density quan-tification, decreasing the time required to count the parasites and avoiding bias from human error. A technique was proposed (Díaz, González, & Romero, 2009) for quantifying erythrocytes in stained thin blood films. The image was corrected from luminance differences and the normalized RGB color space was used for classifying pixels as erythrocyte or background followed by an Inclusion-Tree representation that structures the pixel information into objects. Finally, a two step classification process identifies infected erythrocytes, using a trained bank of classifiers. The main drawback of this approach is that it needs user intervention, which results in time consuming and large inter and intra-observer variabilities scheme based on HSV color space is presented. This method was focused on …
Functional Annotation of Genes Using Hierarchical Text Categorization

This paper addresses the task of functional annotation of genes from biomedical literature. We view this task as a hierarchical text categorization problem with Gene Ontology as a class hierarchy. We present a novel global hierarchical learning approach that takes into account the semantics of a class hierarchy. This algorithm with AdaBoost as the underlying learning procedure significantly outperforms the corresponding “flat” approach, i.e. the approach that does not consider any hierarchical information. In addition, we propose a novel hierarchical evaluation measure that gives credit to partially correct classification and discriminates errors by both distance and depth in a class hierarchy.
The situational factors that affect the software development process: Towards a comprehensive reference framework

An optimal software development process is regarded as being dependent on the situational characteristics of individual software development settings. Such characteristics include the nature of the application(s) under development, team size, requirements volatility and personnel experience. However, no comprehensive reference framework of the situational factors affecting the software development process is presently available.  The absence of such a comprehensive reference framework of the situational factors affecting the software development process is problematic not just because it inhibits our ability to optimise the software development process, but perhaps more importantly, because it potentially undermines our capacity to ascertain the key constraints and characteristics of a software development setting.  To address this deficiency, we have consolidated a substantial body of related research into an initial reference framework of the situational factors affecting the software development process. To support the data consolidation, we have applied rigorous data coding techniques from Grounded Theory and we believe that the resulting framework represents an important contribution to the software engineering field of knowledge.  The resulting reference framework of situational factors consists of 8 classifications and 44 factors that inform the software process. We believe that the situational factor reference framework presented herein represents a sound initial reference framework for the key situational elements affecting the software process definition.  In addition to providing a useful reference listing for the research community and for committees engaged in the development of standards, the reference framework also provides support for practitioners who are challenged with defining and maintaining software development processes. Furthermore, this framework can be used to develop a profile of the situational characteristics of a software development setting, which in turn provides a sound foundation for software development process definition and optimisation.
Psychometric Properties and Validation of the Arabic Social Media Addiction Scale

This study investigated the psychometric properties of the Arabic version of the SMAS. SMAS is a variant of IAT customized to measure addiction to social media instead of the Internet as a whole. Using a self-report instrument on a cross-sectional sample of undergraduate students, the results revealed the following. First, the exploratory factor analysis showed that a three-factor model fits the data well. Second, concurrent validity analysis showed the SMAS to be a valid measure of social media addiction. However, further studies and data should verify the hypothesized model. Finally, this study showed that the Arabic version of the SMAS is a valid and reliable instrument for use in measuring social media addiction in the Arab world.
Mining Chat-room Conversations for Social and Semantic Interactions

The ephemeral nature of human communication via networks today poses interesting and challenging problems for information technologists. The Intelink intelligence network, for example, has a need to monitor chat-room conversations to ensure the integrity of sensitive data being transmitted via the network. However, the sheer volume of communication in venues such as email, newsgroups, and chat precludes manual techniques of information management. It has been estimated that over 430 million instant messages, for example, are exchanged each day on the America Online network . Although a not insignificant fraction of such data may be temporarily archived (e.g., newsgroups), no systematic mechanisms exist for accumulating these artifacts of communication in a form that lends itself to the construction of models of semantics . In essence, dynamic techniques of analysis are needed if textual data of this nature is to be effectively mined. This article reports our progress in developing a text mining tool for analysis of chat-room conversations. Central to our efforts is the development of functionality to answer questions such as "What topics are being discussed in a chat-room?", "Who is discussing which topics?" and "Who is interacting with whom?" The objective of our research is to develop technology that can automatically identify such patterns of interaction in both social and semantic terms. In this article we report our preliminary findings in identifying threads of conversation in multi-topic, multi-person chat-rooms. We have achieved promising results in terms of precision and recall by employing pattern recognition techniques based on finite state automata. We also report the design of our approach to building models of social and semantic interactions based on our HDDI text mining infrastructure [13].
Hardware Software co-simulation for Image Processing Applications

We proposed the concept of hardware software co-simulation for image processing using Xilinx system generator. Recent advances in synthesis tools for SIMULINK suggest a feasible high-level approach to algorithm implementation for embedded DSP systems. An efficient FPGA based hardware design for enhancement of color and grey scale images in image and video processing. The top model – based visual development process of SIMULINK facilitates host side simulation and validation, as well as synthesis of target specific code, furthermore, legacy code written in MATLAB or ANCI C can be reuse in custom blocks. However, the code generated for DSP platforms is often not very efficient. We are implemented the Image processing applications on FPGA it can be easily design.
Video Object Segmentation with Re-identification

Conventional video segmentation methods often rely on temporal continuity to propagate masks. Such an assumption suffers from issues like drifting and inability to handle large displacement. To overcome these issues, we formulate an effective mechanism to prevent the target from being lost via adaptive object re-identification. Specifically, our Video Object Segmentation with Re-identification (VSReID) model includes a mask propagation module and a ReID module. The former module produces an initial probability map by flow warping while the latter module retrieves missing instances by adaptive matching. With these two modules iteratively applied, our VS-ReID records a global mean (Region Jaccard and Boundary F measure) of 0.699, the best performance in 2017 DAVIS Challenge.
Combining Long Short-Term Memory and Dynamic Bayesian Networks for Incremental Emotion-Sensitive Artificial Listening

The automatic estimation of human affect from the speech signal is an important step towards making virtual agents more natural and human-like. In this paper, we present a novel technique for incremental recognition of the user's emotional state as it is applied in a sensitive artificial listener (SAL) system designed for socially competent human-machine communication. Our method is capable of using acoustic, linguistic, as well as long-range contextual information in order to continuously predict the current quadrant in a two-dimensional emotional space spanned by the dimensions valence and activation. The main system components are a hierarchical dynamic Bayesian network (DBN) for detecting linguistic keyword features and long short-term memory (LSTM) recurrent neural networks which model phoneme context and emotional history to predict the affective state of the user. Experimental evaluations on the SAL corpus of non-prototypical real-life emotional speech data consider a number of variants of our recognition framework: continuous emotion estimation from low-level feature frames is evaluated as a new alternative to the common approach of computing statistical functionals of given speech turns. Further performance gains are achieved by discriminatively training LSTM networks and by using bidirectional context information, leading to a quadrant prediction F1-measure of up to 51.3 %, which is only 7.6 % below the average inter-labeler consistency.
An 800 MHz 2$\,\times\,$ 1 Compact MIMO Antenna System for LTE Handsets

A compact size 2 × 1 multiple-input-multiple-output (MIMO) antenna system operating in the 800 MHz band is proposed for long term evolution (LTE) handsets. The fabricated MIMO system has a typical isolation of 12 dB and a maximum gain of 2.2 dBi. Two isolation enhancement methods are investigated; one that looks into the depth of the ground split separating the two antennas, the other introduces extra splits within the arms of the GND plane. It is found that introducing extra splits within the arms of the GND plane of the proposed geometry enhances the isolation by at least 3 dB. The proposed MIMO antenna system is based on meander line antennas, and covers the band from 760-886 MHz. The size of the antenna system is 40 × 50 mm2.