nils-herrmann/clean_scirepeval huggingface dataset:
DeepSense: A GPU-based Deep Convolutional Neural Network Framework on Commodity Mobile Devices

Recently, a branch of machine learning algorithms called deep learning gained huge attention to boost up accuracy of a variety of sensing applications. However, execution of deep learning algorithm such as convolutional neural network on mobile processor is non-trivial due to intensive computational requirements. In this paper, we present our early design of DeepSense - a mobile GPU-based deep convolutional neural network (CNN) framework. For its design, we first explored the differences between server-class and mobile-class GPUs, and studied effectiveness of various optimization strategies such as branch divergence elimination and memory vectorization. Our results show that DeepSense is able to execute a variety of CNN models for image recognition, object detection and face recognition in soft real time with no or marginal accuracy tradeoffs. Experiments also show that our framework is scalable across multiple devices with different GPU architectures (e.g. Adreno and Mali).
An Optimization Framework for QoS-Enabled Adaptive Video Streaming Over OpenFlow Networks

OpenFlow is a programmable network protocol and associated hardware designed to effectively manage and direct traffic by decoupling control and forwarding layers of routing. This paper presents an analytical framework for optimization of forwarding decisions at the control layer to enable dynamic Quality of Service (QoS) over OpenFlow networks and discusses application of this framework to QoS-enabled streaming of scalable encoded videos with two QoS levels. We pose and solve optimization of dynamic QoS routing as a constrained shortest path problem, where we treat the base layer of scalable encoded video as a level-1 QoS flow, while the enhancement layers can be treated as level-2 QoS or best-effort flows. We provide experimental results which show that the proposed dynamic QoS framework achieves significant improvement in overall quality of streaming of scalable encoded videos under various coding configurations and network congestion scenarios.
A Knowledge-Based Approach for Real-Time IoT Data Stream Annotation and Processing

Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average exchanged message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.
[Dermal fillers: types, indications, and complications].

There are many types of dermal fillers currently used for cosmetic and medical indications in routine clinical practice. Fillers can be classified as temporary, semipermanent, or permanent depending on the length of time the substance remains in tissue. They can also be classified by the composition of the product. Materials can be based on collagen (bovine, porcine, and human), hyaluronic acid, poly-L-lactic acid, calcium hydroxylapatite, polymethal methacrylates, and polyacrylamide gels, among others. Temporary fillers are the products most often used for cosmetic purposes, in particular hyaluronic acid. This is due to the ease of application of fillers based on this substance, the good results obtained, and their safety profile. This review presents an overview of the techniques used for the correct placement of dermal fillers and the most common clinical indications for these procedures. It also covers the nature, properties, and mechanisms of action of the principal temporary, semipermanent, and permanent dermal fillers as well as the indications for each type of material. Finally, we describe the most common complications encountered and their treatment.
OPTIMAL PRIORITY ASSIGNMENT AND FEASIBILITY OF STATIC PRIORITY TASKS WITH ARBITRARY START TIMES

Within the hard real-time community, static priority pre-emptive scheduling is receiving increased attention. Current optimal priority assignment schemes require that at some point in the system lifetime all tasks must be released simultaneously. Two main optimal priority assignment schemes have been proposed: rate-monotonic, where task period equals deadline, and deadlinemonotonic where task deadline maybe less than period. When tasks are permitted to have arbitrary start times, a common release time between all tasks in a task set may not occur. In this eventuality, both rate-monotonic and deadline-monotonic priority assignments cease to be optimal. This paper presents an method of determining if the tasks with arbitrary release times will ever share a common release time. This has complexity O(m loge m) in the longest task period. Also, an optimal priority assignment method is given, of complexity O(n 2 + n) in the number of tasks. Finally, an efficient feasibility test is presented, for those task sets whose tasks do not share a common release time.
Accurately detecting trolls in Slashdot Zoo via decluttering

Online social networks like Slashdot bring valuable information to millions of users - but their accuracy is based on the integrity of their user base. Unfortunately, there are many “trolls” on Slashdot who post misinformation and compromise system integrity. In this paper, we develop a general algorithm called TIA (short for Troll Identification Algorithm) to classify users of an online “signed” social network as malicious (e.g. trolls on Slashdot) or benign (i.e. normal honest users). Though applicable to many signed social networks, TIA has been tested on troll detection on Slashdot Zoo under a wide variety of parameter settings. Its running time is faster than many past algorithms and it is significantly more accurate than existing methods.
Magnetic-free non-reciprocity based on staggered commutation.

Lorentz reciprocity is a fundamental characteristic of the vast majority of electronic and photonic structures. However, non-reciprocal components such as isolators, circulators and gyrators enable new applications ranging from radio frequencies to optical frequencies, including full-duplex wireless communication and on-chip all-optical information processing. Such components today dominantly rely on the phenomenon of Faraday rotation in magneto-optic materials. However, they are typically bulky, expensive and not suitable for insertion in a conventional integrated circuit. Here we demonstrate magnetic-free linear passive non-reciprocity based on the concept of staggered commutation. Commutation is a form of parametric modulation with very high modulation ratio. We observe that staggered commutation enables time-reversal symmetry breaking within very small dimensions (λ/1,250 × λ/1,250 in our device), resulting in a miniature radio-frequency circulator that exhibits reduced implementation complexity, very low loss, strong non-reciprocity, significantly enhanced linearity and real-time reconfigurability, and is integrated in a conventional complementary metal-oxide-semiconductor integrated circuit for the first time.
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art.
Discovering Overlapping Groups in Social Media

The increasing popularity of social media is shortening the distance between people. Social activities, e.g., tagging in Flickr, book marking in Delicious, twittering in Twitter, etc. are reshaping people’s social life and redefining their social roles. People with shared interests tend to form their groups in social media, and users within the same community likely exhibit similar social behavior (e.g., going for the same movies, having similar political viewpoints), which in turn reinforces the community structure. The multiple interactions in social activities entail that the community structures are often overlapping, i.e., one person is involved in several communities. We propose a novel co-clustering framework, which takes advantage of networking information between users and tags in social media, to discover these overlapping communities. In our method, users are connected via tags and tags are connected to users. This explicit representation of users and tags is useful for understanding group evolution by looking at who is interested in what. The efficacy of our method is supported by empirical evaluation in both synthetic and online social networking data.
Frontal fibrosing alopecia versus lichen planopilaris: a clinicopathological study.

Frontal fibrosing alopecia (FFA) is an acquired scarring alopecia currently considered a clinical variant of lichen planopilaris (LPP). Our purpose was to examine the clinicopathological features of FFA. In addition, we investigated the similarities and differences between FFA and LPP.



Biopsies from the scalp lesions of eight patients with FFA and eight patients with LPP were microscopically analyzed. Two cases of FFA and four cases of LPP were studied using direct immunofluorescence.



In spite of the completely different clinical characteristics of FFA and LPP patients, the histopathological findings for the two entities were similar. Common microscopic findings for both FFA and LPP included an inflammatory lymphocytic infiltrate involving the isthmus and infundibulum of the hair follicles, the presence of apoptotic cells in the external root sheath, and a concentric fibrosis surrounding the hair follicles that resulted in their destruction with subsequent scarring alopecia. Biopsies taken from FFA patients showed less follicular inflammation and more apoptotic cells than those from LPP patients. In some cases of LPP, the inflammatory infiltrate involved the interfollicular epidermis, a finding never present in our FFA cases. Direct immunofluorescence was negative in the two cases of FFA studied and showed deposits of immunoglobulins and/or complement in two of the four LPP cases examined.



The characteristic findings for FFA were more prominent apoptosis and less inflammation than found in LPP, along with spared interfollicular epidermis. FFA cases showed a rather characteristic histopathological pattern, although we could not find any clear-cut histological differences between FFA and LPP.
Audit Games

Effective enforcement of laws and policies requires expending resources to prevent and detect offenders, as well as appropriate punishment schemes to deter violators. In particular, enforcement of privacy laws and policies in modern organizations that hold large volumes of personal information (e.g., hospitals, banks) relies heavily on internal audit mechanisms. We study economic considerations in the design of these mechanisms, focusing in particular on effective resource allocation and appropriate punishment schemes. We present an audit game model that is a natural generalization of a standard security game model for resource allocation with an additional punishment parameter. Computing the Stackelberg equilibrium for this game is challenging because it involves solving an optimization problem with non-convex quadratic constraints. We present an additive FPTAS that efficiently computes the solution.
Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling

Stochastic Gradient Descent (SGD) is a popular optimization method which has been applied to many important machine learning tasks such as Support Vector Machines and Deep Neural Networks. In order to parallelize SGD, minibatch training is often employed. The standard approach is to uniformly sample a minibatch at each step, which often leads to high variance. In this paper we propose a stratified sampling strategy, which divides the whole dataset into clusters with low within-cluster variance; we then take examples from these clusters using a stratified sampling technique. It is shown that the convergence rate can be significantly improved by the algorithm. Encouraging experimental results confirm the effectiveness of the proposed method.
Non-Negative Matrix Factorization Clustering on Multiple Manifolds

Nonnegative Matrix Factorization (NMF) is a widely used technique in many applications such as clustering. It approximates the nonnegative data in an original high dimensional space with a linear representation in a low dimensional space by using the product of two nonnegative matrices. In many applications with data such as human faces or digits, data often reside on multiple manifolds, which may overlap or intersect. But the traditional NMF method and other existing variants of NMF do not consider this. This paper proposes a novel clustering algorithm that explicitly models the intrinsic geometrical structure of the data on multiple manifolds with NMF. The idea of the proposed algorithm is that a data point generated by several neighboring points on a specific manifold in the original space should be constructed in a similar way in the low dimensional subspace. A set of experimental results on two real world datasets demonstrate the advantage of the proposed algorithm.
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset

The recent work of Clark et al. (2018) introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set. That paper includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them; however, it does not include clear definitions of these types, nor does it offer information about the quality of the labels. We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset. Using ten annotators and a sophisticated annotation interface, we analyze the distribution of labels across the Challenge Set and statistics related to them. Additionally, we demonstrate that although naive information retrieval methods return sentences that are irrelevant to answering the query, sufficient supporting text is often present in the (ARC) corpus. Evaluating with human-selected relevant sentences improves the performance of a neural machine comprehension model by 42 points.
SCENE: a scalable two-stage personalized news recommendation system

Recommending news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Traditional news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. However, the latent relationships among different news items, and the special properties of new articles, such as short shelf lives and value of immediacy, render the previous approaches inefficient.
 In this paper, we propose a scalable two-stage personalized news recommendation approach with a two-level representation, which considers the exclusive characteristics (e.g., news content, access patterns, named entities, popularity and recency) of news items when performing recommendation. Also, a principled framework for news selection based on the intrinsic property of user interest is presented, with a good balance between the novelty and diversity of the recommended result. Extensive empirical experiments on a collection of news articles obtained from various news websites demonstrate the efficacy and efficiency of our approach.
Attending to Characters in Neural Sequence Labeling Models

•Sequence of tokens mapped to word embeddings. •Bidirectional LSTM builds context-dependent representations for each word. •A small feedforward layer encourages generalisation. •Conditional Random Field (CRF) at the top outputs the most optimal label sequence for the sentence. •Unable to model unseen words, learns poor representations for infrequent words, and unable to capture character-level patterns.
SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity

Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and function and as a component of protein 3D structure prediction pipelines. Most predictors use a combination of machine learning and profiles, and thus must be retrained and assessed periodically as the number of available protein sequences and structures continues to grow.



We present newly trained modular versions of the SSpro and ACCpro predictors of secondary structure and relative solvent accessibility together with their multi-class variants SSpro8 and ACCpro20. We introduce a sharp distinction between the use of sequence similarity alone, typically in the form of sequence profiles at the input level, and the additional use of sequence-based structural similarity, which uses similarity to sequences in the Protein Data Bank to infer annotations at the output level, and study their relative contributions to modern predictors. Using sequence similarity alone, SSpro's accuracy is between 79 and 80% (79% for ACCpro) and no other predictor seems to exceed 82%. However, when sequence-based structural similarity is added, the accuracy of SSpro rises to 92.9% (90% for ACCpro). Thus, by combining both approaches, these problems appear now to be essentially solved, as an accuracy of 100% cannot be expected for several well-known reasons. These results point also to several open technical challenges, including (i) achieving on the order of ≥ 80% accuracy, without using any similarity with known proteins and (ii) achieving on the order of ≥ 85% accuracy, using sequence similarity alone.



SSpro, SSpro8, ACCpro and ACCpro20 programs, data and web servers are available through the SCRATCH suite of protein structure predictors at
Machine Learning Techniques and Chi-Square Feature Selection for Cancer Classification Using SAGE Gene Expression Profiles

Recently developed Serial Analysis of Gene Expression (SAGE) technology enables us to simultaneously quantify the expression levels of tens of thousands of genes in a population of cells. SAGE is better than Microarray in that SAGE can monitor both known and unknown genes while Microarray can only measure known genes. SAGE gene expression profiling based cancer classification is a better choice since cancers may be due to some unknown genes. Whereas a wide range of methods has been applied to traditional Microarray based cancer classification, relatively few studies have been done on SAGE based cancer classification. In our study we evaluate popular machine learning methods (SVM, Naive Bayes, Nearest Neighbor, C4.5 and RIPPER) for classifying cancers based on SAGE data. In order to deal with the high dimensional problem, we propose to use Chi-square for tag/gene selection. Both binary classification and multicategory classification are investigated. The experiments are based on two human SAGE datasets: brain and breast. The results show that SVM and Naive Bayes are the top-performing SAGE classifiers and that Chi-square based gene selection can improve the performance of all the five classifiers investigated.
Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates

Recent years have seen a surge of interest in stance classification in online debates. Oftentimes, however, it is important to determine not only the stance expressed by an author in her debate posts, but also the reasons behind her supporting or opposing the issue under debate. We therefore examine the new task of reason classification in this paper. Given the close interplay between stance classification and reason classification, we design computational models for examining how automatically computed stance information can be profitably exploited for reason classification. Experiments on our reason-annotated corpus of ideological debate posts from four domains demonstrate that sophisticated models of stances and reasons can indeed yield more accurate reason and stance classification results than their simpler counterparts.
SkinMarks: Enabling Interactions on Body Landmarks Using Conformal Skin Electronics

The body provides many recognizable landmarks due to the underlying skeletal structure and variations in skin texture, elasticity, and color. The visual and spatial cues of such body landmarks can help in localizing on-body interfaces, guide input on the body, and allow for easy recall of mappings. Our main contribution are SkinMarks, novel skin-worn I/O devices for precisely localized input and output on fine body landmarks. SkinMarks comprise skin electronics on temporary rub-on tattoos. They conform to fine wrinkles and are compatible with strongly curved and elastic body locations. We identify five types of body landmarks and demonstrate novel interaction techniques that leverage SkinMarks' unique touch, squeeze and bend sensing with integrated visual output. Finally, we detail on the conformality and evaluate sub-millimeter electrodes for touch sensing. Taken together, SkinMarks expands the on-body interaction space to more detailed, highly curved and challenging areas on the body.