nils-herrmann/clean_scirepeval huggingface dataset:
Leveraging disjoint communities for detecting overlapping community structure

Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overlapping communities from the network. In this paper, we present PVOC (Permanence based Vertex-replication algorithm for Overlapping Community detection), a two-stage framework to detect overlapping community structure. We build on a novel observation that non-overlapping community structure detected by a standard disjoint community detection algorithm from a network has high resemblance with its actual overlapping community structure, except the overlapping part. Based on this observation, we posit that there is perhaps no need of building yet another overlapping community finding algorithm; but one can efficiently manipulate the output of any existing disjoint community finding algorithm to obtain the required overlapping structure. We propose a new post-processing technique that by combining with any existing disjoint community detection algorithm, can suitably process each vertex using a new vertex-based metric, called permanence, and thereby finds out overlapping candidates with their community memberships. Experimental results on both synthetic and large real-world networks show that PVOC significantly outperforms six state-of-the-art overlapping community detection algorithms in terms of high similarity of the output with the ground-truth structure. Thus our framework not only finds meaningful overlapping communities from the network, but also allows us to put an end to the constant effort of building yet another overlapping community detection algorithm.
Automatic Identification of Indicators of Compromise using Neural-Based Sequence Labelling

Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an early stage. Thus, they exert an important role in the field of cybersecurity. However, state-of-the-art IOCs detection systems rely heavily on hand-crafted features with expert knowledge of cybersecurity, and require a large amount of supervised training corpora to train an IOC classifier. In this paper, we propose using a neural-based sequence labelling model to identify IOCs automatically from reports on cybersecurity without expert knowledge of cybersecurity. Our work is the first to apply an end-to-end sequence labelling to the task in IOCs identification. By using an attention mechanism and several token spelling features, we find that the proposed model is capable of identifying the low frequency IOCs from long sentences contained in cybersecurity reports. Experiments show that the proposed model outperforms other sequence labelling models, achieving over 88% average F1-score.
Mind Your Language: Learning Visually Grounded Dialog in a Multi-Agent Setting

The task of visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers. We posit that requiring agents to adhere to rules of human language while also maximizing information exchange is an ill-posed problem, and observe that humans do not stray from a common language because they are social creatures and have to communicate with many people everyday, and it is far easier to stick to a common language even at the cost of some efficiency loss. Using this as inspiration, we propose and evaluate a multi-agent dialog framework where each agent interacts with, and learns from, multiple agents, and show that this results in more relevant and coherent dialog (as judged by human evaluators) without sacrificing task performance (as judged by quantitative metrics).
Combining Syntactic and Sequential Patterns for Unsupervised Semantic Relation Extraction.

This work investigates the impact of syntactic features in a completely unsupervised semantic relation extraction experiment. Automated relation extraction deals with identifying semantic relation instances in a text and classifying them according to the type of relation. This task is essential in information and knowledge extraction and in knowledge base population. Supervised relation extraction systems rely on annotated examples [ , – , ] and extract di erent kinds of features from the training data, and eventually from external knowledge sources. The types of extracted relations are necessarily limited to a pre-defined list. In Open Information Extraction (OpenIE) [ , ] relation types are inferred directly from the data: concept pairs representing the same relation are grouped together and relation labels can be generated from context segments or through labeling by domain experts [ , , ]. A commonly used method [ , ] is to represent entity couples by a pair-pattern matrix, and cluster relation instances according to the similarity of their distribution over patterns. Pattern-based approaches [ , , , , ] typically use lexical context patterns, assuming that the semantic relation between two entities is explicitly mentioned in the text. Patterns can be defined manually [ ], obtained by Latent Relational Analysis [ ], or from a corpus by sequential pattern mining [ , , ]. Previous works, especially in the biomedical domain, have shown that not only lexical patterns, but also syntactic dependency trees can be beneficial in supervised and semi-supervised relation extraction [ , , – ]. Early experiments on combining lexical patterns with di erent types of distributional information in unsupervised relation clustering did not bring significant improvement [ ]. The underlying di culty is that while supervised classifiers can learn to weight attributes from di erent sources, it is not trivial to combine di erent types of features in a single clustering feature space. In our experiments, we propose to combine syntactic features with sequential lexical patterns for unsupervised clustering of semantic relation instances in the context of (NLP-related) scientific texts. We replicate the experiments of [ ] and augment them with dependency-based syntactic features. We adopt a pairpattern matrix for clustering relation instances. The task can be described as follows: if a1, a2, b1, b2 are pre-annotated domain concepts extracted from a corpus, we would like to classify concept pairs a = (a1, a2) and b = (b1, b2) in homogeneous groups according to their semantic relation. We need an e cient
Sentiment and affect analysis of Dark Web forums: Measuring radicalization on the internet

Dark Web forums are heavily used by extremist and terrorist groups for communication, recruiting, ideology sharing, and radicalization. These forums often have relevance to the Iraqi insurgency or Al-Qaeda and are of interest to security and intelligence organizations. This paper presents an automated approach to sentiment and affect analysis of selected radical international Ahadist Dark Web forums. The approach incorporates a rich textual feature representation and machine learning techniques to identify and measure the sentiment polarities and affect intensities expressed in forum communications. The results of sentiment and affect analysis performed on two large-scale Dark Web forums are presented, offering insight into the communities and participants.
Distributed Inter-BS Cooperation Aided Energy Efficient Load Balancing for Cellular Networks

We propose a distributed cooperative framework among base stations (BS) with load balancing (dubbed as inter-BS for simplicity) for improving energy efficiency of OFDMA-based cellular access networks. Proposed inter-BS cooperation is formulated following the principle of ecological self-organization. Based on the network traffic, BSs mutually cooperate for distributing traffic among themselves and thus, the number of active BSs is dynamically adjusted for energy savings. For reducing the number of inter-BS communications, a three-step measure is taken by using estimated load factor (LF), initializing the algorithm with only the active BSs and differentiating neighboring BSs according to their operating modes for distributing traffic. An exponentially weighted moving average (EWMA)-based technique is proposed for estimating the LF in advance based on the historical data. Various selection schemes for finding the best BSs to distribute traffic are also explored. Furthermore, we present an analytical formulation for modeling the dynamic switching of BSs. A thorough investigation under a wide range of network settings is carried out in the context of an LTE system. Results demonstrate a significant enhancement in network energy efficiency yielding a much higher savings than the compared schemes. Moreover, frequency of inter-BS correspondences can be reduced by over 80%.
Ranking experts using author-document-topic graphs

Expert search or recommendation involves the retrieval of people (experts) in response to a query and on occasion, a given set of constraints. In this paper, we address expert recommendation in academic domains that are different from web and intranet environments studied in TREC. We propose and study graph-based models for expertise retrieval with the objective of enabling search using either a topic (e.g. "Information Extraction") or a name (e.g. "Bruce Croft"). We show that graph-based ranking schemes despite being "generic" perform on par with expert ranking models specific to topic-based and name-based querying.
Humorist Bot: Bringing Computational Humour in a Chat-Bot System

A conversational agent, capable to have a ldquosense of humourrdquo is presented. The agent can both generate humorous sentences and recognize humoristic expressions introduced by the user during the dialogue. Humorist Bot makes use of well founded techniques of computational humor and it has been implemented using the ALICE framework embedded into an Yahoo! Messenger client. It includes also an avatar that changes the face expression according to humoristic content of the dialogue.
Deep Learning for Visual Question Answering

This project deals with the problem of Visual Question Answering (VQA). We develop neural network-based models to answer open-ended questions that are grounded in images. We used the newly released VQA dataset (with about 750K questions) to carry out our experiments. Our model makes use of two popular neural network architecture: Convolutional Neural Nets (CNN) and Long Short Term Memory Networks (LSTM). We use state-of-the-art CNN features for encoding images, and word embeddings to encode the words. Our Bag-of-word + CNN model obtained an accuracy of 44.47%, while our CNN+LSTM model obtained an accuracy of 47.80% on the validation set of the VQA dataset. The code has been open sourced under the MIT License, and is the first open-source project to work with the VQA dataset.
Analysis and Design of LLC Resonant Converters With Capacitor–Diode Clamp Current Limiting

This paper presents a design methodology for LLC resonant converters with capacitor-diode clamp for current limiting in overload conditions. A new fundamental harmonic approximation-based equivalent circuit model is obtained through the application of describing function techniques, by examining the fundamental behavior of the capacitor-diode clamp. An iterative procedure to determine the conduction point of the diode clamp is also given. The behavior of this type of converter is analyzed and guidelines for designing the current limiting characteristics are discussed. The characterization of a 90 W converter design using the proposed methodology is presented. The converter voltage gain and the voltage-current characteristics under different overload conditions and operating frequencies are predicted using the proposed model, which accuracies are validated against the prototype with good correlation.
Prevalence of picky eating behaviour in Chinese school-age children and associations with anthropometric parameters and intelligence quotient. A cross-sectional study

Previous studies have demonstrated the importance of eating behaviour regarding dietary variety and nutrient intake of children. However, the association between picky eating and growth of children is still a topic of debate. This study sought to estimate the prevalence of picky eating and to identify possible associations with the growth of school-age children in China. In this survey, 793 healthy children aged 7-12 years were recruited from nine cities and rural areas in China using a multi-stage cluster sampling method. Data collected included socio-demographic information and parents' perceptions of picky eating using a structured questionnaire, nutrient intake using 24-hour dietary recall, weight and height using body measurements, and intelligence using the Wechsler Intelligence Scale for Children. Blood samples were collected and analysed for minerals. The prevalence of picky eating reported by parents was 59.3% in children. Compared with non-picky eaters, picky eaters had a lower dietary intake of energy, protein, carbohydrates, most vitamins and minerals, and lower levels of magnesium, iron, and copper in the blood (p < 0.05), and also had a 0.184 z-score lower in height for age (95% CI: -0.332, 0.036; p = 0.015), a 0.385 z-score lower in weight for age (95% CI: -0.533, -0.237; p < 0.001), a 0.383 z-score lower in BMI for age (95% CI: -0.563, -0.203; p < 0.001), and scored 2.726 points higher on the intelligence test (95% CI: 0.809, 4.643; p = 0.006) when adjusted for children's birth weight and food allergy, mothers' education, and family income. Picky eating behaviour towards meat, eggs and vegetables showed negative associations with growth. Picky eating behaviour is prevalent in school-age children in China and may have a negative effect on growth.
Cross‐talk between circadian clocks, sleep‐wake cycles, and metabolic networks: Dispelling the darkness

Integration of knowledge concerning circadian rhythms, metabolic networks, and sleep-wake cycles is imperative for unraveling the mysteries of biological cycles and their underlying mechanisms. During the last decade, enormous progress in circadian biology research has provided a plethora of new insights into the molecular architecture of circadian clocks. However, the recent identification of autonomous redox oscillations in cells has expanded our view of the clockwork beyond conventional transcription/translation feedback loop models, which have been dominant since the first circadian period mutants were identified in fruit fly. Consequently, non-transcriptional timekeeping mechanisms have been proposed, and the antioxidant peroxiredoxin proteins have been identified as conserved markers for 24-hour rhythms. Here, we review recent advances in our understanding of interdependencies amongst circadian rhythms, sleep homeostasis, redox cycles, and other cellular metabolic networks. We speculate that systems-level investigations implementing integrated multi-omics approaches could provide novel mechanistic insights into the connectivity between daily cycles and metabolic systems.
Seeing fearful body language rapidly freezes the observer's motor cortex

Fearful body language is a salient signal alerting the observer to the presence of a potential threat in the surrounding environment. Although detecting potential threats may trigger an immediate reduction of motor output in animals (i.e., freezing behavior), it is unclear at what point in time similar reductions occur in the human motor cortex and whether they originate from excitatory or inhibitory processes. Using single-pulse and paired-pulse transcranial magnetic stimulation (TMS), here we tested the hypothesis that the observer's motor cortex implements extremely fast suppression of motor readiness when seeing emotional bodies - and fearful body expressions in particular. Participants observed pictures of body postures and categorized them as happy, fearful or neutral while receiving TMS over the right or left motor cortex at 100-125 msec after picture onset. In three different sessions, we assessed corticospinal excitability, short intracortical inhibition (SICI) and intracortical facilitation (ICF). Independently of the stimulated hemisphere and the time of the stimulation, watching fearful bodies suppressed ICF relative to happy and neutral body expressions. Moreover, happy expressions reduced ICF relative to neutral actions. No changes in corticospinal excitability or SICI were found during the task. These findings show extremely rapid bilateral modulation of the motor cortices when seeing emotional bodies, with stronger suppression of motor readiness when seeing fearful bodies. Our results provide neurophysiological support for the evolutionary notions that emotion perception is inherently linked to action systems and that fear-related cues induce an urgent mobilization of motor reactions.
From Shortest-Path to All-Path: The Routing Continuum Theory and Its Applications

As a crucial operation, routing plays an important role in various communication networks. In the context of data and sensor networks, routing strategies such as shortest-path, multi-path and potential-based (“all-path”) routing have been developed. Existing results in the literature show that the shortest path and all-path routing can be obtained from L1 and L2 flow optimization, respectively. Based on this connection between routing and flow optimization in a network, in this paper we develop a unifying theoretical framework by considering flow optimization with mixed (weighted) L1/L2-norms. We obtain a surprising result: as we vary the trade-off parameter θ, the routing graphs induced by the optimal flow solutions span from shortest-path to multi-path to all-path routing-this entire sequence of routing graphs is referred to as the routing continuum. We also develop an efficient iterative algorithm for computing the entire routing continuum. Several generalizations are also considered, with applications to traffic engineering, wireless sensor networks, and network robustness analysis.
Enhancing gold recovery from electronic waste via lixiviant metabolic engineering in Chromobacterium violaceum

Conventional leaching (extraction) methods for gold recovery from electronic waste involve the use of strong acids and pose considerable threat to the environment. The alternative use of bioleaching microbes for gold recovery is non-pollutive and relies on the secretion of a lixiviant or (bio)chemical such as cyanide for extraction of gold from electronic waste. However, widespread industrial use of bioleaching microbes has been constrained by the limited cyanogenic capabilities of lixiviant-producing microorganisms such as Chromobacterium violaceum. Here we show the construction of a metabolically-engineered strain of Chromobacterium violaceum that produces more (70%) cyanide lixiviant and recovers more than twice as much gold from electronic waste compared to wild-type bacteria. Comparative proteome analyses suggested the possibility of further enhancement in cyanogenesis through subsequent metabolic engineering. Our results demonstrated the utility of lixiviant metabolic engineering in the construction of enhanced bioleaching microbes for the bioleaching of precious metals from electronic waste.
Dynamic IoT Device Clustering and Energy Management With Hybrid NOMA Systems

Fog computing, as a promising technique, is with huge advantages in dealing with large amounts of data and information with low latency and high security. We introduce a promising multiple access technique entitled nonorthogonal multiple access to provide communication service between the fog layer and the Internet of Things (IoT) device layer in fog computing, and propose a dynamic cooperative framework containing two stages. At the first stage, dynamic IoT device clustering is solved to reduce the system complexity and the delay for the IoT devices with better channel conditions. At the second stage, power allocation based energy management is solved using Nash bargaining solution in each cluster to ensure fairness among IoT devices. Simulation results reveal that our proposed scheme can simultaneously achieve higher spectrum efficiency and ensure fairness among IoT devices compared to other schemes.
Diversity in landraces and cultivars of bottle gourd (Lagenaria siceraria; Cucurbitaceae) as assessed by random amplified polymorphic DNA

Bottle gourd (Lagenaria siceraria) is an African crop with an ancient pantropicaldistribution. Morphological analyses and archaeological evidencesuggest the oceanic dispersals of wild bottle gourd fruits fromAfrica to Asia and the Americas by 10000–15000 B.P., followedby independent domestications on all three continents. AlthoughAfrican and New World landraces (ssp. siceraria) are morphologically distinct from Asian landraces(ssp. asiatica), siceraria-typemorphological characters in New Guinea germplasm has raised questionsconcerning the origins of Asian landraces, including whether or notthere was early germplasm exchange between Asia and the New World.Another area of confusion in bottle gourd systematics and evolutionconcerns the origins and circumscriptions of commercial cultivars,many of which have been subject to undocumented hybridization andselection practices over the past 100–200 years. To clarify theevolutionary histories of bottle gourd landraces and cultivars, 64random amplified polymorphic DNA (RAPD) markersrepresenting 30 primers were examined in 31 landraces and 43 cultivaraccessions of L. siceraria,as well as in a wild relative, L.sphaerica. Principal component analyses of thecorrelation matrices of the band presence/absence data revealedthe following: 1) southern African germplasm represents adivergent lineage from which several cultivars have been selected;2) New World germplasm is distinct and primarily of Africanorigin(s), but possesses Asian affinities as well; 3)landraces from New Guinea are not related to New World germplasm aspreviously hypothesized; 4) commercial cultivars are diverse intheir geographic origins and genetic compositions; and 5) theintegrity and circumscriptions of some cultivars have been confoundedby production practices allowing gene flow among cultivars.
Transient thermal analysis using both lumped-circuit approach and finite element method of a permanent magnet traction motor

This work presents the transient thermal analysis of a permanent magnet (PM) synchronous traction motor. The motor has magnets inset into the surface of the rotor to give a maximum field-weakening range of between 2 and 2.5. Both analytically based lumped circuit and numerical finite element methods have been used to simulate the motor. A comparison of the two methods is made showing the advantages and disadvantages of each. Simulation results are compared with practical measurements.