nils-herrmann/clean_scirepeval huggingface dataset:
The edge-disjoint paths problem is NP-complete for series-parallel graphs

Many combinatorial problems are NP-complete for general graphs. However, when restricted to series–parallel graphs or partial k-trees, many of these problems can be solved in polynomial time, mostly in linear time. On the other hand, very few problems are known to be NP-complete for series–parallel graphs or partial k-trees. These include the subgraph isomorphism problem and the bandwidth problem. However, these problems are NP-complete even for trees. In this paper, we show that the edge-disjoint paths problem is NP-complete for series–parallel graphs and for partial 2-trees although the problem is trivial for trees and can be solved for outerplanar graphs in polynomial time. ? 2001 Elsevier Science B.V. All rights reserved.
A philosophical and evolutionary approach to cyber-bullying: social networks and the disruption of sub-moralities

Cyber-bullying, and other issues related to violence being committed online in prosocial environments, are beginning to constitute an emergency worldwide. Institutions are particularly sensitive to the problem especially as far as teenagers are concerned inasmuch as, in cases of inter-teen episodes, the deterrent power of ordinary justice (i.e. threaten to sue) is not as effective as it is between adults. In order to develop the most suitable policies, institution should not be satisfied with statistics and sociological perspectives on the phenomenon, but rather seek a deep ethical understanding—also referring to the biological and evolutionary past of human beings. The aim of this paper is to show a way to fill this theoretical gap, offering some answers (and some questions too) that can illuminate future policy-oriented research and reflection. In order to do so, we will start by connecting our argument to evolutionary studies carried out in the past two decades, focusing on gossip as a tool for social assortment, thus endowed with a dual function: protect the group from free riders, intruders and bullies but also bully the deviant members. In the “Mediating gossip through social networks” section, we will see which aspects of gossip, vital for bullying, are co-opted by social network scenarios. A fundamental trait of human social life, that is the subdivision in smaller coalitions, or sub-groups, will be shown as missing in social networks (SN) dynamics—therefore constituting themselves as structurally violent. The “Why and how do social networks empower bullying?” section will deal with techno-ethical and epistemological concerns regarding how gossip, mediated by SN, manages to empower cyber-bullying. The “Self-gossip and self-mobbing in the light of the disruption of sub-moralities” section will characterize cyber-bullying as often sparked by self-gossip (soon degrading into self-mobbing) in a scenario where familiar sub-groups, which also mediate defense and mutual understanding, are disrupted. The “Discussion and conclusion” section will consist of a philosophical summary, divided in two parts: a pars destruens analyzing whether SN, in their actual configurations, are fit for being used by humans-like-us, and a pars construens examining the broad potential consequences of highly enforced regulation aimed at contrasting cyber-bullying.
On the Shear Behavior of Engineered Cementitious Composites

Results of an experimental investigation of structural response of Shear failure is generally brittle in concrete structures. shear beams made of a special class of cementitious composites, Examples of concrete structural failure related to shear referred to as engineered cementitious composites (ECCs), are reloading include bridge deck punching failure [4], corported. ECCs are designed with tailored material structure and have bel failure [5], anchor bolt pull-out [6], and segmental been shoum to exhibit pseudo strain-hardening tensile behavior. The bridge shear key failure [7]. A goal of the work preimproved performance in shear over conventional plain, fibersented here is to modify the brittle failure mode by reinforced, and wire mesh reinforced concrete is demonstrated. It is taking advantage of the unique material behavior of suggested that ECCs can be utilized for structural applications ECCs. There has been a lot of research into the use of where superior ductility and durability performance are desired, fibers as a replacement for shear reinforcement to enADVANCED CEMENT BASED MATERIALS 1993, 1, 142--149 hance shear capacity of concrete beams to ensure a
PeriodShare: A Bloody Design Fiction

In this paper we present PeriodShare, a speculative design proposing a wireless menstrual cup that automatically quantifies and shares menstrual data on social networks. We suggest that PeriodShare is a design fiction that uses both crowd-funding rhetoric and the form of a rather clumsy DIY project to create a particular fictional universe that (1) speculates on a potential near future of quantification of menstruation, and through this (2) encourages to reflection on the dynamics of contemporary technology paradigms like the politics and culture of self-tracking, sharing, and intimate data. As a research through design project and by using these communication threads, PeriodShare thus uses menstruation as a trope to investigate social, cultural and political issues of intimate technologies.
The dorsomedial pathway is not just for reaching: grasping neurons in the medial parieto-occipital cortex of the macaque monkey.

Brain control of prehension is thought to rely on two specific brain circuits: a dorsomedial one (involving the areas of the superior parietal lobule and the dorsal premotor cortex) involved in the transport of the hand toward the object and a dorsolateral one (involving the inferior parietal lobule and the ventral premotor cortex) dealing with the preshaping of the hand according to the features of the object. The present study aimed at testing whether a pivotal component of the dorsomedial pathway (area V6A) is involved also in hand preshaping and grip formation to grasp objects of different shapes. Two macaque monkeys were trained to reach and grasp different objects. For each object, animals used a different grip: whole-hand prehension, finger prehension, hook grip, primitive precision grip, and advanced precision grip. Almost half of 235 neurons recorded from V6A displayed selectivity for a grip or a group of grips. Several experimental controls were used to ensure that neural modulation was attributable to grip only. These findings, in concert with previous studies demonstrating that V6A neurons are modulated by reach direction and wrist orientation, that lesion of V6A evokes reaching and grasping deficits, and that dorsal premotor cortex contains both reaching and grasping neurons, indicate that the dorsomedial parieto-frontal circuit may play a central role in all phases of reach-to-grasp action. Our data suggest new directions for the modeling of prehension movements and testable predictions for new brain imaging and neuropsychological experiments.
Electrically Programmable Fuse (eFUSE): From Memory Redundancy to Autonomic Chips

Electrical fuse (eFUSE) has become a popular choice to enable memory redundancy, chip identification and authentication, analog device trimming, and other applications. We will review the evolution and applications of electrical fuse solutions for 180 nm to 45 nm technologies at IBM, and provide some insight into future uses in 32 nm technology and beyond with the eFUSE as a building block for the autonomic chip of the future.
Observed responses of captive stoats ( Mustela erminea ) to nest boxes and metal collars used to protect kaka ( Nestor meridionalis ) nest cavities

Artificial barriers, such as nest boxes and metal collars, are sometimes used, with variable success, to exclude predators and/or competitors from tree nests of vulnerable bird species. This paper describes the observed response of captive stoats (Mustela erminea) to a nest box design and an aluminium sheet collar used to protect kaka (Nestor meridionalis) nest cavities. The nest box, a prototype for kaka, was manufactured from PVC pipe. Initial trials failed to exclude stoats until an overhanging roof was added. All subsequent trials successfully prevented access by stoats. Trials with a 590 mm wide aluminium collar were less successful, but this was mainly due to restrictions enforced by enclosure design: Stoats gained access above the collar via the enclosure walls and ceiling. In only one of twelve trials was a stoat able to climb past the collar itself. The conservation implications of these trials and directions for future research are discussed. __________________________________________________________________________________________________________________________________
Verifiable Dynamic Symmetric Searchable Encryption: Optimality and Forward Security

Symmetric Searchable Encryption (SSE) is a very efficient and practical way for data owners to outsource storage of a database to a server while providing privacy guarantees. Such SSE schemes enable clients to encrypt their database while still performing queries for retrieving documents matching some keyword. This functionality is interesting to secure cloud storage, and efficient schemes have been designed in the past. However, security against malicious servers has been overlooked in most previous constructions and these only addressed security against honest-but-curious servers. In this paper, we study and design the first efficient SSE schemes provably secure against malicious servers. First, we give lower bounds on the complexity of such verifiable SSE schemes. Then, we construct generic solutions matching these bounds using efficient verifiable data structures. Finally, we modify an existing SSE scheme that also provides forward secrecy of search queries, and make it provably secure against active adversaries, without increasing the computational complexity of the original scheme.
Executable Object Modeling with Statecharts

This paper reports on an effort to develop an integrated set of diagrammatic languages for modeling object-oriented systems, and to construct a supporting tool. The goal is for models to be intuitive and well-structured, yet fully executable and analyzable, enabling automatic synthesis of usable and efficient code in object-oriented languages such as C++. At the heart of the modeling method is the language of statecharts for specifying object behavior, and a hierarchical OMT-like language for describing the structure of classes and their inter-relationships, that we call O-charts. Objects can interact by event generation, or by direct invocation of operations. In the interest of keeping the exposition manageable, we leave out some technically involved topics, such as multiple-thread concurrency and active objects.
Fasting intact proinsulin is a highly specific predictor of insulin resistance in type 2 diabetes.

In later stages of type 2 diabetes, proinsulin and proinsulin-like molecules are secreted in increasing amounts with insulin. A recently introduced chemiluminescence assay is able to detect the uncleaved "intact" proinsulin and differentiate it from proinsulin-like molecules. This investigation explored the predictive value of intact proinsulin as an insulin resistance marker.



In total, 48 patients with type 2 diabetes (20 women and 28 men, aged 60 +/- 9 years [means +/- SD], diabetes duration 5.1 +/- 3.8 years, BMI 31.2 +/- 4.8 kg/m2, and HbA1c 6.9 +/- 1.2%) were studied by means of an intravenous glucose tolerance test and determination of fasting values of intact proinsulin, insulin, resistin, adiponectin, and glucose. Insulin resistance was determined by means of minimal model analysis (MMA) (as the gold standard) and homeostatis model assessment (HOMA).



There was a significant correlation between intact proinsulin values and insulin resistance (MMA P<0.05 and HOMA P<0.01). Elevation of intact proinsulin values above the reference range (>10 pmol/l) showed a very high specificity (MMA 100% and HOMA 92.9%) and a moderate sensitivity (MMA 48.6% and HOMA 47.1%) as marker for insulin resistance. Adiponectin values were slightly lower in the insulin resistant group, but no correlation to insulin resistance could be detected for resistin in the cross-sectional design.



Elevated intact proinsulin seems to indicate an advanced stage of beta-cell exhaustion and is a highly specific marker for insulin resistance. It might be used as arbitrary marker for the therapeutic decision between secretagogue, sensitizer, or insulin therapy in type 2 diabetes.
Recovery and mental health: a review of the British literature.

Recovery is not a new concept within mental health, although in recent times, it has come to the forefront of the policy agenda. However, there is no universal definition of recovery, and it is a contested concept. The aim of this study was to examine the British literature relating to recovery in mental health. Three contributing groups are identified: service users, health care providers and policy makers. A review of the literature was conducted by accessing all relevant published texts. A search was conducted using these terms: 'recovery', 'schizophrenia', 'psychosis', 'mental illness' and 'mental health'. Over 170 papers were reviewed. A thematic analysis was conducted. Six main themes emerged, which were examined from the perspective of the stakeholder groups. The dominant themes were identity, the service provision agenda, the social domain, power and control, hope and optimism, risk and responsibility. Consensus was found around the belief that good quality care should be made available to service users to promote recovery both as inpatient or in the community. However, the manner in which recovery was defined and delivered differed between the groups.
Autoencoder-based feature learning for cyber security applications

This paper presents a novel feature learning model for cyber security tasks. We propose to use Auto-encoders (AEs), as a generative model, to learn latent representation of different feature sets. We show how well the AE is capable of automatically learning a reasonable notion of semantic similarity among input features. Specifically, the AE accepts a feature vector, obtained from cyber security phenomena, and extracts a code vector that captures the semantic similarity between the feature vectors. This similarity is embedded in an abstract latent representation. Because the AE is trained in an unsupervised fashion, the main part of this success comes from appropriate original feature set that is used in this paper. It can also provide more discriminative features in contrast to other feature engineering approaches. Furthermore, the scheme can reduce the dimensionality of the features thereby signicantly minimising the memory requirements. We selected two different cyber security tasks: networkbased anomaly intrusion detection and Malware classication. We have analysed the proposed scheme with various classifiers using publicly available datasets for network anomaly intrusion detection and malware classifications. Several appropriate evaluation metrics show improvement compared to prior results.
Cloud Guided Stream Classification Using Class-Based Ensemble

We propose a novel class-based micro-classifier ensemble classification technique (MCE) for classifying data streams. Traditional ensemble-based data stream classification techniques build a classification model from each data chunk and keep an ensemble of such models. Due to the fixed length of the ensemble, when a new model is trained, one existing model is discarded. This creates several problems. First, if a class disappears from the stream and reappears after a long time, it would be misclassified if a majority of the classifiers in the ensemble does not contain any model of that class. Second, discarding a model means discarding the corresponding data chunk completely. However, knowledge obtained from some classes might be still useful and if they are discarded, the overall error rate would increase. To address these problems, we propose an ensemble model where each class information is stored separately. From each data chunk, we train a model for each class of data. We call each such model a micro-classifier. This approach is more robust than existing chunk-based ensembles in handling dynamic changes in the data stream. To the best of our knowledge, this is the first attempt to classify data streams using the class-based ensembles approach. When the number of classes grow in the stream, class-based ensembles may degrade in performance (speed). Hence, we sketch a cloud-based solution of our class-based ensembles to handle a large number of classes effectively. We compare our technique with several state-of-the-art data stream classification techniques on both synthetic and benchmark data streams, and obtain much higher accuracy.
Forecasting Popularity of Videos Using Social Media

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards.
Fast Distributed Deep Learning via Worker-adaptive Batch Sizing

In heterogeneous or shared clusters, distributed learning processes are slowed down by straggling workers. In this work, we propose LB-BSP, a new synchronization scheme that eliminates stragglers by adapting each worker's training load (batch size) to its processing capability. For training in shared production clusters, a prerequisite for deciding the workers' batch sizes is to know their processing speeds before each iteration starts. To this end, we adopt NARX, an extended recurrent neural network that accounts for both the historical speeds and the driving factors such as CPU and memory in prediction.
Chronic boron exposure and human semen parameters.

Boron found as borates in soil, food, and water has important industrial and medical applications. A panel reviewing NTP reproductive toxicants identified boric acid as high priority for occupational studies to determine safe versus adverse reproductive effects. To address this, we collected boron exposure/dose measures in workplace inhalable dust, dietary food/fluids, blood, semen, and urine from boron workers and two comparison worker groups (n=192) over three months and determined correlations between boron and semen parameters (total sperm count, sperm concentration, motility, morphology, DNA breakage, apoptosis and aneuploidy). Blood boron averaged 499.2 ppb for boron workers, 96.1 and 47.9 ppb for workers from high and low environmental boron areas (p<0.0001). Boron concentrated in seminal fluid. No significant correlations were found between blood or urine boron and adverse semen parameters. Exposures did not reach those causing adverse effects published in animal toxicology work but exceeded those previously published for boron occupational groups.
An efficient approach for device identification and traffic classification in IoT ecosystems

Internet of Things arises as a computational paradigm that promotes the interconnection of objects to the Internet and enables interaction, operational efficiency, and communication. With the increasing inclusion in the network of intelligent objects that have characteristics such as diversity, heterogeneity, mobility and low computational power, it is fundamental to develop mechanisms that allow management and control. In addition, it is important to identify whether the assets are working properly or have anomalies. Traffic classification techniques are important to aid in network analysis and to handle many other key aspects such as security, management, access control, provisioning, and resource allocation. In order to promote the identification of network devices, especially IoT, this article presents a technique that uses Random Forest, a supervised automatic learning algorithm, together with the inspection of the contents of the packages for this purpose. Also, we use the same algorithm to perform the classification of network traffic. In the end, the identification of the devices showed an accuracy of approximately 99%.
Word Mover's Embedding: From Word2Vec to Document Embedding

While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings. Recent work has demonstrated that a distance measure between documents called Word Mover’s Distance (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy. However, WMD is expensive to compute, and it is hard to extend its use beyond a KNN classifier. In this paper, we propose the Word Mover’s Embedding (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings. In our experiments on 9 benchmark text classification datasets and 22 textual similarity tasks, the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.
HHMM-based Chinese Lexical Analyzer ICTCLAS

This document presents the results from Inst. of Computing Tech., CAS in the ACLSIGHAN-sponsored First International Chinese Word Segmentation Bakeoff. The authors introduce the unified HHMM-based frame of our Chinese lexical analyzer ICTCLAS and explain the operation of the six tracks. Then provide the evaluation results and give more analysis. Evaluation on ICTCLAS shows that its performance is competitive. Compared with other system, ICTCLAS has ranked top both in CTB and PK closed track. In PK open track, it ranks second position. ICTCLAS BIG5 version was transformed from GB version only in two days; however, it achieved well in two BIG5 closed tracks. Through the first bakeoff, we could learn more about the development in Chinese word segmentation and become more confident on our HHMM-based approach. At the same time, we really find our problems during the evaluation. The bakeoff is interesting and helpful.