nils-herrmann/clean_scirepeval huggingface dataset:
To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation

It is important for robots to be able to decide whether they can go through a space or not, as they navigate through a dynamic environment. This capability can help them avoid injury or serious damage, e.g., as a result of running into people and obstacles, getting stuck, or falling off an edge. To this end, we propose an unsupervised and a near-unsupervised method based on Generative Adversarial Networks (GAN) to classify scenarios as traversable or not based on visual data. Our method is inspired by the recent success of data-driven approaches on computer vision problems and anomaly detection, and reduces the need for vast amounts of negative examples at training time. Collecting negative data indicating that a robot should not go through a space is typically hard and dangerous because of collisions; whereas collecting positive data can be automated and done safely based on the robot’s own traveling experience. We verify the generality and effectiveness of the proposed approach on a test dataset collected in a previously unseen environment with a mobile robot. Furthermore, we show that our method can be used to build costmaps (we call as ”GoNoGo” costmaps) for robot path planning using visual data only.
Machine Learning Algorithms and Predictive Models for Undergraduate Student Retention

--In this paper, we have presented some results of undergraduate student retention using machine learning algorithms classifying the student data. We have also made some improvements to the classification algorithms such as Decision tree, Support Vector Machines (SVM), and neural networks supported by Weka software toolkit. The experiments revealed that the main factors that influence student retention in the Historically Black Colleges and Universities (HBCU) are the cumulative grade point average (GPA) and total credit hours (TCH) taken. The target functions derived from the bare minimum decision tree and SVM algorithms were further revised to create a two-layer neural network and a regression to predict the retention. These new models improved the classification accuracy.
An Exploration on Brain Computer Interface and Its Recent Trends

Detailed exploration on Brain Computer Interface (BCI) and its recent trends has been done in this paper. Work is being done to identify objects, images, videos and their color compositions. Efforts are on the way in understanding speech, words, emotions, feelings and moods. When humans watch the surrounding environment, visual data is processed by the brain, and it is possible to reconstruct the same on the screen with some appreciable accuracy by analyzing the physiological data. This data is acquired by using one of the non-invasive techniques like electroencephalography (EEG) in BCI. The acquired signal is to be translated to produce the image on to the screen. This paper also lays suitable directions for future work. KeywordsBCI; EEG; brain image reconstruction.
Towards Modernizing the Future of American Voting

With the proliferation of computing and information technologies, we have an opportunity to envision a fully participatory democracy in the country through a fully digitized voting platform. However, the growing interconnectivity of systems and people across the globe, and the proliferation of cybersecurity issues pose a significant bottleneck towards achieving such a vision. In this paper, we discuss a vision to modernize our voting processes and discuss the challenges for creating a national e-voting framework that incorporates policies, standards and technological infrastructure that is secure, privacy-preserving, resilient and transparent. Through partnerships among private industry, academia, and State and Federal Government, technology must be the catalyst to develop a national platform for American voters. Along with integrating biometrics to authenticate each registered voter for transparency and accountability, the platform provides depth in the e-voting infrastructure with emerging blockchain technologies. We outline the way voting process runs today with the challenges; states are having from funding to software development concerns. Additionally, we highlight attacks from malware infiltrations from off the shelf products made from factories from countries such as China. This paper illustrates a strategic level of voting challenges and modernizing processes that will enhance the voter’s trust in America democracy.
Ecophysiology of mangroves

Mangrove forests are of major ecological and commercial importance, yet the future of these resources is threatened by pollution, development and over-exploitation. There is an urgent need to develop sound management practices based on a functional understanding of the physical and biological processes underlying mangrove ecosystem dynamics. Such biological processes include dispersal (Rabinowitz 1978), herbivory (Smith 1987) and the physiological bases of species interactions and responses to environmental factors. Understanding these processes is essential for the development of more comprehensive and predictive modelling of mangrove ecosystem dynamics than has previously been possible.
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled `seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.
Adaptive Window Control (AWC) Technique for Hysteresis DC–DC Buck Converters With Improved Light and Heavy Load Performance

This paper presents a new adaptive window control (AWC) technique to adjust the hysteresis window to reduce the output ripple at heavy loads and keep the switching frequency higher than the acoustic frequency at light loads. Therefore, the driving capability and the power conversion efficiency of the hysteresis converter are improved at heavy loads. Besides, a new class AB current sensing circuit is also proposed to provide fast and accurate current sensing signal for achieving fast hysteresis window adjustment. Experimental results show that the proposed AWC technique not only reduces the output ripple smaller than 12 mV when load current is 800 mA to improve the power conversion efficiency but also keeps the switching frequency higher than acoustic frequency at light loads to avoid undesired noise.
Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci

The mammalian genome is transcribed into large numbers of long noncoding RNAs (lncRNAs), but the definition of functional lncRNA groups has proven difficult, partly due to their low sequence conservation and lack of identified shared properties. Here we consider promoter conservation and positional conservation as indicators of functional commonality. We identify 665 conserved lncRNA promoters in mouse and human that are preserved in genomic position relative to orthologous coding genes. These positionally conserved lncRNA genes are primarily associated with developmental transcription factor loci with which they are coexpressed in a tissue-specific manner. Over half of positionally conserved RNAs in this set are linked to chromatin organization structures, overlapping binding sites for the CTCF chromatin organiser and located at chromatin loop anchor points and borders of topologically associating domains (TADs). We define these RNAs as topological anchor point RNAs (tapRNAs). Characterization of these noncoding RNAs and their associated coding genes shows that they are functionally connected: they regulate each other’s expression and influence the metastatic phenotype of cancer cells in vitro in a similar fashion. Furthermore, we find that tapRNAs contain conserved sequence domains that are enriched in motifs for zinc finger domain-containing RNA-binding proteins and transcription factors, whose binding sites are found mutated in cancers. This work leverages positional conservation to identify lncRNAs with potential importance in genome organization, development and disease. The evidence that many developmental transcription factors are physically and functionally connected to lncRNAs represents an exciting stepping-stone to further our understanding of genome regulation.
Music-dependent memory in immediate and delayed word recall.

Undergraduate volunteers rated a series of words for pleasantness while hearing a particular background music. The subjects in Experiment 1 received, immediately or after a 48-h delay, an unexpected word-recall test in one of the following musical cue contexts: same cue (S), different cue (D), or no cue (N). For immediate recall, context dependency (S-D) was significant but same-cue facilitation (S-N) was not. No cue effects at all were found for delayed recall, and there was a significant interaction between cue and retention interval. A similar interaction was also found in Experiment 3, which was designed to rule out an alternative explanation with respect to distraction. When the different musical selection was changed specifically in either tempo or form (genre), only pieces having an altered tempo produced significantly lower immediate recall compared with the same pieces (Experiment 2). The results support a stimulus generalization view of music-dependent memory.
WorkloadCompactor: reducing datacenter cost while providing tail latency SLO guarantees

Service providers want to reduce datacenter costs by consolidating workloads onto fewer servers. At the same time, customers have performance goals, such as meeting tail latency Service Level Objectives (SLOs). Consolidating workloads while meeting tail latency goals is challenging, especially since workloads in production environments are often bursty. To limit the congestion when consolidating workloads, customers and service providers often agree upon rate limits. Ideally, rate limits are chosen to maximize the number of workloads that can be co-located while meeting each workload's SLO. In reality, neither the service provider nor customer knows how to choose rate limits. Customers end up selecting rate limits on their own in some ad hoc fashion, and service providers are left to optimize given the chosen rate limits.
 This paper describes WorkloadCompactor, a new system that uses workload traces to automatically choose rate limits simultaneously with selecting onto which server to place workloads. Our system meets customer tail latency SLOs while minimizing datacenter resource costs. Our experiments show that by optimizing the choice of rate limits, WorkloadCompactor reduces the number of required servers by 30--60% as compared to state-of-the-art approaches.
The fetal three-vessel and tracheal view revisited.

The routine use of four-chamber screening of the fetal heart was pioneered in the early 1980s and has been shown to detect reliably mainly univentricular hearts in the fetus. Many conotruncal anomalies and ductal-dependent lesions may, however, not be detected with the four-chamber view alone and additional planes are needed. The three-vessel and tracheal (3VT) view is a transverse plane in the upper mediastinum demonstrating simultaneously the course and the connection of both the aortic and ductal arches, their relationship to the trachea and the visualization of the superior vena cava. The purpose of the article is to review the two-dimensional anatomy of this plane and the contribution of colour Doppler and to present a checklist to be achieved on screening ultrasound. Typical suspicions include the detection of abnormal vessel number, abnormal vessel size, abnormal course and alignment and abnormal colour Doppler pattern. Anomalies such as pulmonary and aortic stenosis and atresia, aortic coarctation, interrupted arch, tetralogy of Fallot, common arterial trunk, transposition of the great arteries, right aortic arch, double aortic arch, aberrant right subclavian artery, left superior vena cava are some of the anomalies showing an abnormal 3VT image. Recent studies on the comprehensive evaluation of the 3VT view and adjacent planes have shown the potential of visualizing the thymus and the left brachiocephalic vein during fetal echocardiography and in detecting additional rare conditions. National and international societies are increasingly recommending the use of this plane during routine ultrasound in order to improve prenatal detection rates of critical cardiac defects.
Latent aspect rating analysis without aspect keyword supervision

Mining detailed opinions buried in the vast amount of review text data is an important, yet quite challenging task with widespread applications in multiple domains. Latent Aspect Rating Analysis (LARA) refers to the task of inferring both opinion ratings on topical aspects (e.g., location, service of a hotel) and the relative weights reviewers have placed on each aspect based on review content and the associated overall ratings. A major limitation of previous work on LARA is the assumption of pre-specified aspects by keywords. However, the aspect information is not always available, and it may be difficult to pre-define appropriate aspects without a good knowledge about what aspects are actually commented on in the reviews.
 In this paper, we propose a unified generative model for LARA, which does not need pre-specified aspect keywords and simultaneously mines 1) latent topical aspects, 2) ratings on each identified aspect, and 3) weights placed on different aspects by a reviewer. Experiment results on two different review data sets demonstrate that the proposed model can effectively perform the Latent Aspect Rating Analysis task without the supervision of aspect keywords. Because of its generality, the proposed model can be applied to explore all kinds of opinionated text data containing overall sentiment judgments and support a wide range of interesting application tasks, such as aspect-based opinion summarization, personalized entity ranking and recommendation, and reviewer behavior analysis.
Probiotics for the Control of Parasites: An Overview

Probiotics are defined as live organisms, which confer benefits to the host. Their efficiency was demonstrated for the treatment of gastrointestinal disorders, respiratory infections, and allergic symptoms, but their use is mostly limited to bacterial and viral diseases. During the last decade, probiotics as means for the control of parasite infections were reported covering mainly intestinal diseases but also some nongut infections, that are all of human and veterinary importance. In most cases, evidence for a beneficial effect was obtained by studies using animal models. In a few cases, cellular interactions between probiotics and pathogens or relevant host cells were also investigated using in vitro culture systems. However, molecular mechanisms mediating the beneficial effects are as yet poorly understood. These studies indicate that probiotics might indeed provide a strain-specific protection against parasites, probably through multiple mechanisms. But more unravelling studies are needed to justify probiotic utilisation in therapeutics.
Improving pattern tracking with a language-aware tree differencing algorithm

Tracking code fragments of interest is important in monitoring a software project over multiple versions. Various approaches, including our previous work on Herodotos, exploit the notion of Longest Common Subsequence, as computed by readily available tools such as GNU Diff, to map corresponding code fragments. Nevertheless, the efficient code differencing algorithms are typically line-based or word-based, and thus do not report changes at the level of language constructs. Furthermore, they identify only additions and removals, but not the moving of a block of code from one part of a file to another. Code fragments of interest that fall within the added and removed regions of code have to be manually correlated across versions, which is tedious and error-prone. When studying a very large code base over a long time, the number of manual correlations can become an obstacle to the success of a study. In this paper, we investigate the effect of replacing the current line-based algorithm used by Herodotos by tree-matching, as provided by the algorithm of the differencing tool GumTree. In contrast to the line-based approach, the tree-based approach does not generate any manual correlations, but it incurs a high execution time. To address the problem, we propose a hybrid strategy that gives the best of both approaches.
Design of a Hybrid Exploration Robot for Air and Land Deployment (H.E.R.A.L.D) for urban search and rescue applications

Disaster scenarios involve a multitude of obstacles that are difficult to traverse for humans and robots alike. Most robotic search and rescue solutions to this problem involve large, tank-like robots that use brute force to cross difficult terrain; however, these large robots may cause secondary damage. H.E.R.A.L.D, the Hybrid Exploration Robot for Air and Land Deployment, is a novel integrated system of three nimble, lightweight robots which can travel over difficult obstacles by air, but also travel through rubble. We present the design methodology and optimization of each robot, as well as design and testing of the physical integration of the system as a whole, and compare the performance of the robots to the state of the art.
Current potential health benefits of sulforaphane

Sulforaphane [SFN: 1-isothiocyanato-4-(methylsulfinyl)butane] belongs to the isothiocyanate class of phytochemicals. Glucoraphanin, a glucosinolate precursor of SFN, is a glucosinolate found in cruciferous vegetables such as broccoli, cabbage, cauliflower, and kale. All glucosinolates are composed of a basic structure consisting of a β-D-thioglucose group, a sulfonated oxime group, and an amino acid-derived side chain. Glucosinolates are activated by enzyme-dependent hydrolysis to their respective isothiocyanates. SFN (molecular formula C6H11NOS2) is the biologically active isothiocyanate produced by the metabolism of glucoraphanin by the enzyme myrosinase (Fahey et al., 2015). SFN is one of the most frequently studied plant-derived isothiocyanate organosulfur compounds. It has been reported to exhibit a wide range of biological effects including antioxidant (Fahey and Talalay, 1999), antimicrobial (Johansson et al., 2008), anticancer (Amjad et al., 2015), anti-inflammatory (Greaney et al., 2016), anti-aging (Sikdar et al., 2016), neuroprotective (Tarozzi et al., 2013), and antidiabetic (Lee et al., 2012). SFN shows a range of biological activities and health benefits in humans, has been found to be a very promising chemopreventive agent against not only a variety of cancers such as breast, prostate, colon, skin, lung, stomach, and bladder but also against cardiovascular and neurodegenerative diseases and diabetes (Yang et al., 2016). In this present study, we reviewed the most recent studies on the biological and pharmacological activities of SFN (Table 1).
Context-Aware Tourist Trip Recommendations

Mobile and web-based services solving common tourist trip design problems are available, but only few solutions consider context for the recommendation of point of interest (POI) sequences. In this paper, we present a novel approach to incorporating context into a tourist trip recommendation algorithm. In addition to traditional context factors in tourism, such as location, weather or opening hours, we focus on two context factors that are highly relevant when recommending a sequence of POIs: time of the day and previously visited point of interest. We conducted an online questionnaire to determine the in uence of the context factors on the user’s decision of visiting a POI and the ratings of the POIs under these conditions. We integrated our approach into a web application recommending context-aware tourist trips across the world. In a user study, we veri ed the results of our novel approach as well as the application’s usability. The study proves a high usability of our system and shows that our context-aware approach outperforms a baseline algorithm.
Windows API based Malware Detection and Framework Analysis

Detection of zero day malware has been the great challenge for researchers from long time. Traditional signature based antimalware scanners detect malware based on their unique signatures. The major drawback of such traditional signatures based scanners is that it has no protection against zero-day or unseen malware. Further usage of packers and obfuscation techniques empowered the malware writers to recreate malware variants quickly with slight or no change in malcode. These new variants are undetectable by traditional signature based scanner until their signatures are not present in database. Therefore researchers are working towards finding patterns or features which have unchangeable characteristics of malware even though the malware mutates or obfuscates itself. To address the limitation of traditional signature based scanner, we propose the malware detection method based on extracting relevant application programming interface (API) calls from sub categories of malware. These malware are categorized based on their infection mechanism and actions performed. And because of their fundamental difference in infection mechanism, they do not share similar type of API calls in all malware categories. In this paper, we elucidate an automated framework for analyzing and classifying executables based on their relevant API calls. We explain all the software components used to make the framework fully automatic for extracting API calls. We further explain the Document Class wise Frequency feature selection measure (DCFS) to get the relevant API calls from the extracted API calls to increase the detection rate. We conclude the paper with our experimental result and discussion.