nils-herrmann/clean_scirepeval huggingface dataset:
Texture Classification by Wavelet Packet Signatures

This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity ( 0 2 0 ) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (Ds) . In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture.
TouchDevelop: app development on mobile devices

Mobile devices are becoming the prevalent computing platform for most people. TouchDevelop is a new mobile development environment that enables anyone with a Windows Phone to create new apps directly on the smartphone, without a PC or a traditional keyboard. At the core is a new mobile programming language and editor that was designed with the touchscreen as the only input device in mind. Programs written in TouchDevelop can leverage all phone sensors such as GPS, cameras, accelerometer, gyroscope, and stored personal data such as contacts, songs, pictures. Thousands of programs have already been written and published with TouchDevelop.
Social network analysis for routing in disconnected delay-tolerant MANETs

Message delivery in sparse Mobile Ad hoc Networks (MANETs) is difficult due to the fact that the network graph is rarely (if ever) connected. A key challenge is to find a route that can provide good delivery performance and low end-to-end delay in a disconnected network graph where nodes may move freely. This paper presents a multidisciplinary solution based on the consideration of the so-called small world dynamics which have been proposed for economy and social studies and have recently revealed to be a successful approach to be exploited for characterising information propagation in wireless networks. To this purpose, some bridge nodes are identified based on their centrality characteristics, i.e., on their capability to broker information exchange among otherwise disconnected nodes. Due to the complexity of the centrality metrics in populated networks the concept of ego networks is exploited where nodes are not required to exchange information about the entire network topology, but only locally available information is considered. Then SimBet Routing is proposed which exploits the exchange of pre-estimated "betweenness' centrality metrics and locally determined social "similarity' to the destination node. We present simulations using real trace data to demonstrate that SimBet Routing results in delivery performance close to Epidemic Routing but with significantly reduced overhead. Additionally, we show that SimBet Routing outperforms PRoPHET Routing, particularly when the sending and receiving nodes have low connectivity.
Understanding and Fighting Bullying With Machine Learning

Bullying, in both physical and cyber worlds, has been recognized as a serious health issue among adolescents. Given its significance, scholars are charged with identifying factors that influence bullying involvement in a timely fashion. However, previous social studies of bullying are handicapped by data scarcity. The standard psychological science approach to studying bullying is to conduct personal surveys in schools. The sample size is typically in the hundreds, and these surveys are often collected only once. On the other hand, the few computational studies narrowly restrict themselves to cyberbullying, which accounts for only a small fraction of all bullying episodes.
The RDFa Content Editor - From WYSIWYG to WYSIWYM

Recently practical approaches for managing and supporting the life-cycle of semantic content on the Web of Data made quite some progress. However, the currently least developed aspect of the semantic content life-cycle is the user-friendly manual and semi-automatic creation of rich semantic content. In this paper we present the RDFaCE approach for combining WYSIWYG text authoring with the creation of rich semantic annotations. Our approach is based on providing four different views to the content authors: a classical WYSIWYG view, a WYSIWYM (What You See Is What You Mean) view making the semantic annotations visible, a fact view and the respective HTML/RDFa source code view. The views are synchronized such that changes made in one of the views automatically update the others. They provide different means of semantic content authoring for the different personas involved in the content creation life-cycle. For bootstrapping the semantic annotation process we integrate five different text annotation services. We evaluate their accuracy and empirically show that a combination of them yields superior results.
Dual Local-Global Contextual Pathways for Recognition in Aerial Imagery

Visual context is important in object recognition and it is still an open problem in computer vision. Along with the advent of deep convolutional neural networks (CNN), using contextual information with such systems starts to receive attention in the literature. At the same time, aerial imagery is gaining momentum. While advances in deep learning make good progress in aerial image analysis, this problem still poses many great challenges. Aerial images are often taken under poor lighting conditions and contain low resolution objects, many times occluded by trees or taller buildings. In this domain, in particular, visual context could be of great help, but there are still very few papers that consider context in aerial image understanding. Here we introduce context as a complementary way of recognizing objects. We propose a dual-stream deep neural network model that processes information along two independent pathways, one for local and another for global visual reasoning. The two are later combined in the final layers of processing. Our model learns to combine local object appearance as well as information from the larger scene at the same time and in a complementary way, such that together they form a powerful classifier. We test our dual-stream network on the task of segmentation of buildings and roads in aerial images and obtain state-of-the-art results on the Massachusetts Buildings Dataset. We also introduce two new datasets, for buildings and road segmentation, respectively, and study the relative importance of local appearance vs. the larger scene, as well as their performance in combination. While our local-global model could also be useful in general recognition tasks, we clearly demonstrate the effectiveness of visual context in conjunction with deep nets for aerial image
Discriminative Deep Face Shape Model for Facial Point Detection

Facial point detection is an active area in computer vision due to its relevance to many applications. It is a nontrivial task, since facial shapes vary significantly with facial expressions, poses or occlusion. In this paper, we address this problem by proposing a discriminative deep face shape model that is constructed based on an augmented factorized three-way Restricted Boltzmann Machines model. Specifically, the discriminative deep model combines the top-down information from the embedded face shape patterns and the bottom up measurements from local point detectors in a unified framework. In addition, along with the model, effective algorithms are proposed to perform model learning and to infer the true facial point locations from their measurements. Based on the discriminative deep face shape model, 68 facial points are detected on facial images in both controlled and “in-the-wild” conditions. Experiments on benchmark data sets show the effectiveness of the proposed facial point detection algorithm against state-of-the-art methods.
CuSha: vertex-centric graph processing on GPUs

Vertex-centric graph processing is employed by many popular algorithms (e.g., PageRank) due to its simplicity and efficient use of asynchronous parallelism. The high compute power provided by SIMT architecture presents an opportunity for accelerating these algorithms using GPUs. Prior works of graph processing on a GPU employ Compressed Sparse Row (CSR) form for its space-efficiency; however, CSR suffers from irregular memory accesses and GPU underutilization that limit its performance. In this paper, we present CuSha, a CUDA-based graph processing framework that overcomes the above obstacle via use of two novel graph representations: G-Shards and Concatenated Windows (CW). G-Shards uses a concept recently introduced for non-GPU systems that organizes a graph into autonomous sets of ordered edges called shards. CuSha's mapping of GPU hardware resources on to shards allows fully coalesced memory accesses. CW is a novel representation that enhances the use of shards to achieve higher GPU utilization for processing sparse graphs. Finally, CuSha fully utilizes the GPU power by processing multiple shards in parallel on GPU's streaming multiprocessors. For ease of programming, CuSha allows the user to define the vertex-centric computation and plug it into its framework for parallel processing of large graphs. Our experiments show that CuSha provides significant speedups over the state-of-the-art CSR-based virtual warp-centric method for processing graphs on GPUs.
SocialHelix: visual analysis of sentiment divergence in social media

Social media allow people to express and promote different opinions, on which people’s sentiments to a subject often diverge when their opinions conflict. An intuitive visualization that unfolds the process of sentiment divergence from the rich and massive social media data will have far-reaching impact on various domains including social science, politics and economics. In this paper, we propose a visual analysis system, SocialHelix, to achieve this goal. SocialHelix is a novel visual design which enables the users to detect and trace topics and events occurring in social media, and to understand when and why divergences occurred and how they evolved among different social groups. We demonstrate the effectiveness and usefulness of SocialHelix by conducting in-depth case studies on tweets related to the national political debates.
Ten Myths of Multimodal Interaction

Multimodal systems process combined natural input modes—such as speech, pen, touch, hand gestures, eye gaze, and head and body movements—in a coordinated manner with multimedia system output. These systems represent a new direction for computing that draws from novel input and output technologies currently becoming available. Since the appearance of Bolt's  " Put That There " demonstration system, which processed speech in parallel with manual pointing, a variety of multimodal systems has emerged. Some rudimentary ones process speech combined with mouse pointing, such as the early CUBRICON system . Others recognize speech while determining the location of pointing from users' manual gestures or gaze . Moving from traditional interfaces toward interfaces offering users greater expressive power, naturalness, and portability. Recent multimodal systems now recognize a broader range of signal integrations, which are no longer limited to the simple point-and-speak combinations handled by earlier systems. For example, the Quickset system integrates speech with pen input that includes drawn graphics, symbols, gestures, and pointing. It uses a semantic unification process to combine the meaningful multimodal information carried by two input signals, both of which are rich and multidimensional. Quickset also uses a multi-agent architecture and runs on a handheld PC .  1 illustrates Quickset's response to the multi-modal command " Airstrips... facing this way, facing this way, and facing this way, " which was spoken while the user drew arrows placing three airstrips in correct orientation on a map. Multimodal systems represent a research-level paradigm shift away from conventional windows-icons-menus-pointers (WIMP) interfaces toward providing users with greater expressive power, naturalness, flexibility , and portability. Well-designed multimodal systems integrate complementary modalities to yield a highly synergistic blend in which the strengths of each mode are capitalized upon and used to overcome weaknesses in the other. Such systems potentially can function more robustly than unimodal systems that involve a single recognition-based technology such as speech, pen, or vision.
PREPARATION OF CELLULOSE FROM OIL PALM EMPTY FRUIT BUNCHES VIA ETHANOL DIGESTION : EFFECT OF ACID AND ALKALI CATALYSTS

Ethanol digestion of oil palm empty fruit bunches (OPEFB) fibres at a temperature between 165oC 180oC for 2 hr and at a solid-to-liquid ratio of 10:1, ethanol-to-water ratio of 1:1, and with or without 10% 1 N HCl and 1.25 M NaOH as catalysts was studied in order to prepare cellulose via ethanol pulping. The pulp produced was studied for yield, moisture content, solubility in cold/hot water and 1% NaOH, lignin, holocellulose and α-cellulose content. The highest yield of pulp (57%, oven dried weight basis) was from OPEFB fibres digested at 170oC for 2 hr without addition of catalyst, whereas OPEFB fibres digested at 175oC for 2 hr with acid catalyst gave the lowest yield of 45% (oven dried weight basis) pulp. Higher cooking temperature gave lower yield of pulp since the reaction hydrolyzed out the hemicellulose, lignin and part of the cellulose. The reactions at 165oC, 170oC and 175oC with acid catalyst produced 56%, 50% and 45% of pulp yield, respectively. It was found that a temperature of 180oC with or without catalyst was too high for pulping because it totally digested the fibre into a viscous soluble pulp. On the effect of catalysts, acid catalyst was found to enhance the pulping of OPEFB fibres. Without the acid catalyst, at temperature of 165oC, the fibres could not be fully cooked and would still be in the fibrous form. Reactions at 170oC and 175oC without catalyst gave 57% and 55% yield of pulp, respectively whereas with acid catalyst gave 50% and 45% yield of pulp respectively. The base catalyst could only fully pulp the OPEFB fibres at a temperature of 175oC, but the fibres dissolved at temperature 180oC. Pulp produced at 175oC for 2 hr with 10% 1.25 M NaOH gave the best quality pulp, which contained lowest lignin and highest holocellulose at 8.2% and 91.8% (based on the dry weight of pulp), respectively. The maximum yield of α-cellulose (isolated from the pulp) also was obtained from OPEFB digested with alkali catalyst at 175oC for 2 hr (64.3% based from dry weight of pulp; 34.1% based on dry weight of OPEFB).
Sampling frequency, signal resolution and the accuracy of wearable context recognition systems

In this paper, we evaluate how the performance of a wearable context recognition system is affected by the sampling frequency and the resolution of the sensor signals used for the classification. We introduce our method for this evaluation and present the results for a widely studied activity recognition task: the classification of human modes of locomotion using body-worn acceleration sensors. With this example we show that both the sampling frequency and the resolution can be significantly reduced without much impact on the recognition performance. While many of the published approaches in this domain rely on higher sampling frequencies and signal resolutions, we show that good recognition performance can already be achieved with 20 Hz and 2 bit resolution.
Supporting collaborative software development through the visualization of socio-technical dependencies

One of the reasons large-scale software development is difficult is the number of dependencies that software engineers face. These dependencies create a need for communication and coordination that requires continuous effort by developers. Empirical studies, including our own, suggest that technical dependencies among software components create social dependencies among the software developers implementing those components. Based on this observation, we developed Ariadne, a plug-in for Eclipse. Ariadne analyzes software projects for dependencies and collects authorship information about projects relying on configuration management repositories. Ariadne can "translate" technical dependencies among components into social dependencies among developers. We have created visualizations to convey dependency information and the presence of coordination problems identified in our previous work. We believe the information conveyed in the visualizations will prove useful for software developers.
Dealing with feeling: a meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation.

The present meta-analysis investigated the effectiveness of strategies derived from the process model of emotion regulation in modifying emotional outcomes as indexed by experiential, behavioral, and physiological measures. A systematic search of the literature identified 306 experimental comparisons of different emotion regulation (ER) strategies. ER instructions were coded according to a new taxonomy, and meta-analysis was used to evaluate the effectiveness of each strategy across studies. The findings revealed differences in effectiveness between ER processes: Attentional deployment had no effect on emotional outcomes (d(+) = 0.00), response modulation had a small effect (d(+) = 0.16), and cognitive change had a small-to-medium effect (d(+) = 0.36). There were also important within-process differences. We identified 7 types of attentional deployment, 4 types of cognitive change, and 4 types of response modulation, and these distinctions had a substantial influence on effectiveness. Whereas distraction was an effective way to regulate emotions (d(+) = 0.27), concentration was not (d(+) = -0.26). Similarly, suppressing the expression of emotion proved effective (d(+) = 0.32), but suppressing the experience of emotion or suppressing thoughts of the emotion-eliciting event did not (d(+) = -0.04 and -0.12, respectively). Finally, reappraising the emotional response proved less effective (d(+) = 0.23) than reappraising the emotional stimulus (d(+) = 0.36) or using perspective taking (d(+) = 0.45). The review also identified several moderators of strategy effectiveness including factors related to the (a) to-be-regulated emotion, (b) frequency of use and intended purpose of the ER strategy, (c) study design, and (d) study characteristics.
Dynamic Vehicle Routing for Robotic Systems

Recent years have witnessed great advancements in the science and technology of autonomy, robotics, and networking. This paper surveys recent concepts and algorithms for dynamic vehicle routing (DVR), that is, for the automatic planning of optimal multivehicle routes to perform tasks that are generated over time by an exogenous process. We consider a rich variety of scenarios relevant for robotic applications. We begin by reviewing the basic DVR problem: demands for service arrive at random locations at random times and a vehicle travels to provide on-site service while minimizing the expected wait time of the demands. Next, we treat different multivehicle scenarios based on different models for demands (e.g., demands with different priority levels and impatient demands), vehicles (e.g., motion constraints, communication, and sensing capabilities), and tasks. The performance criterion used in these scenarios is either the expected wait time of the demands or the fraction of demands serviced successfully. In each specific DVR scenario, we adopt a rigorous technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. First, we establish fundamental limits on the achievable performance, including limits on stability and quality of service. Second, we design algorithms, and provide provable guarantees on their performance with respect to the fundamental limits.
AUTOPROBE: Towards Automatic Active Malicious Server Probing Using Dynamic Binary Analysis

Malware continues to be one of the major threats to Internet security. In the battle against cybercriminals, accurately identifying the underlying malicious server infrastructure (e.g., C&C servers for botnet command and control) is of vital importance. Most existing passive monitoring approaches cannot keep up with the highly dynamic, ever-evolving malware server infrastructure. As an effective complementary technique, active probing has recently attracted attention due to its high accuracy, efficiency, and scalability (even to the Internet level). In this paper, we propose Autoprobe, a novel system to automatically generate effective and efficient fingerprints of remote malicious servers. Autoprobe addresses two fundamental limitations of existing active probing approaches: it supports pull-based C&C protocols, used by the majority of malware, and it generates fingerprints even in the common case when C&C servers are not alive during fingerprint generation. Using real-world malware samples we show that Autoprobe can successfully generate accurate C&C server fingerprints through novel applications of dynamic binary analysis techniques. By conducting Internet-scale active probing, we show that Autoprobe can successfully uncover hundreds of malicious servers on the Internet, many of them unknown to existing blacklists. We believe Autoprobe is a great complement to existing defenses, and can play a unique role in the battle against cybercriminals.
Lexical and Syntactic cues to identify Reference Scope of Citance

In this paper, we present our system addressing Task 1 of CL-SciSumm Shared Task at BIRNDL 2016. Our system makes use of lexical and syntactic dependency cues, and applies rule-based approach to extract text spans in the Reference Paper that accurately reflect the citances. Further, we make use of lexical cues to identify discourse facets of the paper to which cited text belongs. The lexical and syntactic cues are obtained on pre-processed text of the citances, and the reference paper. We report our results obtained for development set using our system for identifying reference scope of citances in this paper.
Tools to Support Systematic Literature Reviews in Software Engineering: A Mapping Study

Systematic literature reviews (SLRs) have become an established methodology in software engineering (SE) research however they can be very time consuming and error prone.  The aims of this study are to identify and classify tools that can help to automate part or all of the SLR process within the SE domain.  A mapping study was performed using an automated search strategy plus snowballing to locate relevant papers. A set of known papers was used to validate the search string.  14 papers were accepted into the final set. Eight presented text mining tools and six discussed the use of visualisation techniques. The stage most commonly targeted was study selection. Only two papers reported an independent evaluation of the tool presented. The majority were evaluated through small experiments and examples of their use.  A variety of tools are available to support the SLR process although many are in the early stages of development and usage.