nils-herrmann/clean_scirepeval huggingface dataset:
Consistency, Regularity, and Frequency Effects in Naming Chinese Characters

Three experiments in naming Chinese characters are presented here to address the relationships between character frequency, consistency, and regularity effects in Chinese character naming. Significant interactions between character consistency and frequency were found across the three experiments, regardless of whether the phonetic radical of the phonogram is a legitimate character in its own right or not. These findings suggest that the phonological information embedded in Chinese characters has an influence upon the naming process of Chinese characters. Furthermore, phonetic radicals exist as computation units mainly because they are structures occurring systematically within Chinese characters, not because they can function as recognized, freestanding characters. On the other hand, the significant interaction between regularity and consistency found in the first experiment suggests that these two factors affect Chinese character naming in different ways. These findings are accounted for within interactive activation frameworks and a connectionist model.
Developmental neurotoxicity of industrial chemicals

Neurodevelopmental disorders such as autism, attention deficit disorder, mental retardation, and cerebral palsy are common, costly, and can cause lifelong disability. Their causes are mostly unknown. A few industrial chemicals (eg, lead, methylmercury, polychlorinated biphenyls [PCBs], arsenic, and toluene) are recognised causes of neurodevelopmental disorders and subclinical brain dysfunction. Exposure to these chemicals during early fetal development can cause brain injury at doses much lower than those affecting adult brain function. Recognition of these risks has led to evidence-based programmes of prevention, such as elimination of lead additives in petrol. Although these prevention campaigns are highly successful, most were initiated only after substantial delays. Another 200 chemicals are known to cause clinical neurotoxic effects in adults. Despite an absence of systematic testing, many additional chemicals have been shown to be neurotoxic in laboratory models. The toxic effects of such chemicals in the developing human brain are not known and they are not regulated to protect children. The two main impediments to prevention of neurodevelopmental deficits of chemical origin are the great gaps in testing chemicals for developmental neurotoxicity and the high level of proof required for regulation. New, precautionary approaches that recognise the unique vulnerability of the developing brain are needed for testing and control of chemicals.
Data Hiding Using Steganography

It is a truth universally acknowledged that "a picture is worth a thousand words". The emerge of digital media has taken this saying to a complete new level. By using steganography, one can hide not only 1000, but thousands of words even in an average sized image. This article presents various types of techniques used by modern digital steganography, as well as the implementation of the least significant bit (LSB) method. The main objective is to develop an application that uses LSB insertion in order to encode data into a cover image. Both a serial and parallel version are proposed and an analysis of the performances is made using images ranging from 1:9 to 131 megapixels.
Fixing a Broken ELBO

Recent work in unsupervised representation learning has focused on learning deep directed latentvariable models. Fitting these models by maximizing the marginal likelihood or evidence is typically intractable, thus a common approximation is to maximize the evidence lower bound (ELBO) instead. However, maximum likelihood training (whether exact or approximate) does not necessarily result in a good latent representation, as we demonstrate both theoretically and empirically. In particular, we derive variational lower and upper bounds on the mutual information between the input and the latent variable, and use these bounds to derive a rate-distortion curve that characterizes the tradeoff between compression and reconstruction accuracy. Using this framework, we demonstrate that there is a family of models with identical ELBO, but different quantitative and qualitative characteristics. Our framework also suggests a simple new method to ensure that latent variable models with powerful stochastic decoders do not ignore their latent code.
EvoRiver: Visual Analysis of Topic Coopetition on Social Media

Cooperation and competition (jointly called “coopetition”) are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects. This model provides a close functional approximation of the coopetition process by depicting how different groups of influential users (i.e., “topic leaders”) affect coopetition. We also design EvoRiver, a time-based visualization, that allows users to explore coopetition-related interactions and to detect dynamically evolving patterns, as well as their major causes. We test our model and demonstrate the usefulness of our system based on two Twitter data sets (social topics data and business topics data).
ADMIT: anomaly-based data mining for intrusions

Security of computer systems is essential to their acceptance and utility. Computer security analysts use intrusion detection systems to assist them in maintaining computer system security. This paper deals with the problem of differentiating between masqueraders and the true user of a computer terminal. Prior efficient solutions are less suited to real time application, often requiring all training data to be labeled, and do not inherently provide an intuitive idea of what the data model means. Our system, called ADMIT, relaxes these constraints, by creating user profiles using semi-incremental techniques. It is a real-time intrusion detection system with host-based data collection and processing. Our method also suggests ideas for dealing with concept drift and affords a detection rate as high as 80.3% and a false positive rate as low as 15.3%.
Signal processing of sensor node data for vehicle detection

We describe an algorithm and experimental work for vehicle detection using sensor node data. Both acoustic and magnetic signals are processed for vehicle detection. We propose a real-time vehicle detection algorithm called the adaptive threshold algorithm (ATA). The algorithm first computes the time-domain energy distribution curve and then slices the energy curve using a threshold updated adaptively by some decision states. Finally, the hard decision results from threshold slicing are passed to a finite-state machine, which makes the final vehicle detection decision. Real-time tests and offline simulations both demonstrate that the proposed algorithm is effective.
Action, outcome, and value: a dual-system framework for morality.

Dual-system approaches to psychology explain the fundamental properties of human judgment, decision making, and behavior across diverse domains. Yet, the appropriate characterization of each system is a source of debate. For instance, a large body of research on moral psychology makes use of the contrast between "emotional" and "rational/cognitive" processes, yet even the chief proponents of this division recognize its shortcomings. Largely independently, research in the computational neurosciences has identified a broad division between two algorithms for learning and choice derived from formal models of reinforcement learning. One assigns value to actions intrinsically based on past experience, while another derives representations of value from an internally represented causal model of the world. This division between action- and outcome-based value representation provides an ideal framework for a dual-system theory in the moral domain.
The causes and consequences of genetic heterogeneity in cancer evolution

Recent studies have revealed extensive genetic diversity both between and within tumours. This heterogeneity affects key cancer pathways, driving phenotypic variation, and poses a significant challenge to personalized cancer medicine. A major cause of genetic heterogeneity in cancer is genomic instability. This instability leads to an increased mutation rate and can shape the evolution of the cancer genome through a plethora of mechanisms. By understanding these mechanisms we can gain insight into the common pathways of tumour evolution that could support the development of future therapeutic strategies.
LSSL-SSD: Social Spammer Detection with Laplacian Score and Semi-supervised Learning

The rapid development of social networks makes it easy for people to communicate online. However, social networks always suffer from social spammers due to their openness. Spammers deliver information for economic purposes, and they pose threats to the security of social networks. To maintain the long-term running of online social networks, many detection methods are proposed. But current methods normally use high dimension features with supervised learning algorithms to find spammers, resulting in low detection performance. To solve this problem, in this paper, we first apply the Laplacian score method, which is an unsupervised feature selection method, to obtain useful features. Based on the selected features, the semi-supervised ensemble learning is then used to train the detection model. Experimental results on the Twitter dataset show the efficiency of our approach after feature selection. Moreover, the proposed method remains high detection performance in the face of limited labeled data.
A Review of Feature Reduction Techniques in Neuroimaging

Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.
Design of low-cost 4W & 6W MMIC high power amplifiers for Ka-band modules

Two Ka-band power amplifier MMICs, 4W and 6W, with high power density and gain are presented. Each amplifier was designed using a 5-stage topology to demonstrate over 30dB of gain. The 4W design exhibited a peak saturated output power of 37.2dBm and a chip output power density of 532 mW/mm/sup 2/. This is the highest recorded power density for a Ka-band power amplifier design to date. The high gain and power density make them ideal for low-cost Ka-band transmit systems.
k-DPPs: Fixed-Size Determinantal Point Processes

Determinantal point processes (DPPs) have recently been proposed as models for set selection problems where diversity is preferred. For example, they can be used to select diverse sets of sentences to form document summaries, or to find multiple nonoverlapping human poses in an image. However, DPPs conflate the modeling of two distinct characteristics: the size of the set, and its content. For many realistic tasks, the size of the desired set is known up front; e.g., in search we may want to show the user exactly ten results. In these situations the effort spent by DPPs modeling set size is not only wasteful, but actually introduces unwanted bias into the modeling of content. Instead, we propose the k-DPP, a conditional DPP that models only sets of cardinality k. In exchange for this restriction, k-DPPs offer greater expressiveness and control over content, and simplified integration into applications like search. We derive algorithms for efficiently normalizing, sampling, and marginalizing kDPPs, and propose an experts-style algorithm for learning combinations of k-DPPs. We demonstrate the usefulness of the model on an image search task, where k-DPPs significantly outperform MMR as judged by human annotators.
Discovering statistically significant biclusters in gene expression data

In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering algorithm of Cheng and Church (2000).
Recent Advances in Wearable Sensors for Health Monitoring

Wearable sensor technology continues to advance and provide significant opportunities for improving personalized healthcare. In recent years, advances in flexible electronics, smart materials, and low-power computing and networking have reduced barriers to technology accessibility, integration, and cost, unleashing the potential for ubiquitous monitoring. This paper discusses recent advances in wearable sensors and systems that monitor movement, physiology, and environment, with a focus on applications for Parkinson's disease, stroke, and head and neck injuries.
DeepEye: Visualizing Your Data by Keyword Search

Do you dream to create good visualizations for your dataset simply like a Google search? If yes, our visionary systemDeepEye is committed to fulfill this task. Given a dataset and a keyword query, DeepEye understands the query intent, generates and ranks good visualizations. The user can pick the one he likes and do a further faceted search to easily navigate the visualizations. We detail the architecture of DeepEye, key components, as well as research challenges and opportunities.
Map merging of Multi-Robot SLAM using Reinforcement Learning

Using `Simultaneous Localization and Mapping' (SLAM), mobile robots can become truly autonomous in the exploration of their environment. However, once these environments becomes too large, Multi-Robot SLAM becomes a requirement. This paper will outline how a mobile robot should decide when best to merge its maps with another robot's upon rendezvous, as opposed to doing so immediately. This decision will be based on the current status of the mapping particle filters and the current status of the environment. Using Reinforcement Learning, a model can be established and then trained upon to determine a policy capable of deciding when best to merge. This will allow the robot to incur less error during a merge compared to simply merging immediately. This policy is trained and validated using simulated mobile robot datasets.
Longitudinal Risk Factors for Cyberbullying in Adolescence

Cyberbullying has emerged as a new form of antisocial behaviour in the context of online communication over the last decade. The present study investigates potential longitudinal risk factors for cyberbullying. A total of 835 Swiss seventh graders participated in a short-term longitudinal study (two assessments 6 months apart). Students reported on the frequency of cyberbullying, traditional bullying, rule-breaking behaviour, cybervictirnisation, traditional victirnisation, and frequency of online communication (interpersonal characteristics). In addition, we assessed moral disengagement, empathic concern, and global self-esteem (intrapersonal characteristics). Results showed that traditional bullying, rule-breaking behaviour, and frequency of online communication are longitudinal risk factors for involvement in cyberbullying as a bully. Thus, cyberbullying is strongly linked to real-world antisocial behaviours. Frequent online communication may be seen as an exposure factor that increases the likelihood of engaging in cyberbullying. In contrast, experiences of victimisation and intrapersonal characteristics were not found to increase the longitudinal risk for cyberbullying over and above antisocial behaviour and frequency of online communication. Implications of the findings for the prevention of cyberbullying are discussed.
Contributions of an avian basal ganglia–forebrain circuit to real-time modulation of song

Cortical–basal ganglia circuits have a critical role in motor control and motor learning. In songbirds, the anterior forebrain pathway (AFP) is a basal ganglia–forebrain circuit required for song learning and adult vocal plasticity but not for production of learned song. Here, we investigate functional contributions of this circuit to the control of song, a complex, learned motor skill. We test the hypothesis that neural activity in the AFP of adult birds can direct moment-by-moment changes in the primary motor areas responsible for generating song. We show that song-triggered microstimulation in the output nucleus of the AFP induces acute and specific changes in learned parameters of song. Moreover, under both natural and experimental conditions, variability in the pattern of AFP activity is associated with variability in song structure. Finally, lesions of the output nucleus of the AFP prevent naturally occurring modulation of song variability. These findings demonstrate a previously unappreciated capacity of the AFP to direct real-time changes in song. More generally, they suggest that frontal cortical and basal ganglia areas may contribute to motor learning by biasing motor output towards desired targets or by introducing stochastic variability required for reinforcement learning.
Smart Grid DNP3 Vulnerability Analysis and Experimentation

This paper highlights different security threats and vulnerabilities that is being challenged in smart-grid utilizing Distributed Network Protocol (DNP3) as a real time communication protocol. Experimentally, we will demonstrate two scenarios of attacks, unsolicited message attack and data set injection. The experiments were run on a computer virtual environment and then simulated in DETER testbed platform. The use of intrusion detection system will be necessary to identify attackers targeting different part of the smart grid infrastructure. Therefore, mitigation techniques will be used to ensure a healthy check of the network and we will propose the use of host-based intrusion detection agent at each Intelligent Electronic Device (IED) for the purpose of detecting the intrusion and mitigating it. Performing attacks, attack detection, prevention and counter measures will be our primary goal to achieve in this research paper.
Indexing Evolving Events from Tweet Streams

Tweet streams provide a variety of real-life and real-time information on social events that dynamically change over time. Although social event detection has been actively studied, how to efficiently monitor evolving events from continuous tweet streams remains open and challenging. One common approach for event detection from text streams is to use single-pass incremental clustering. However, this approach does not track the evolution of events, nor does it address the issue of efficient monitoring in the presence of a large number of events. In this paper, we capture the dynamics of events using four event operations (create, absorb, split, and merge), which can be effectively used to monitor evolving events. Moreover, we propose a novel event indexing structure, called Multi-layer Inverted List (MIL), to manage dynamic event databases for the acceleration of large-scale event search and update. We thoroughly study the problem of nearest neighbour search using MIL based on upper bound pruning, along with incremental index maintenance. Extensive experiments have been conducted on a large-scale real-life tweet dataset. The results demonstrate the promising performance of our event indexing and monitoring methods on both efficiency and effectiveness.
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem

Collaborative filtering is one of the most successful and widely used methods of automated product recommendation in online stores. The most critical component of the method is the mechanism of finding similarities among users using product ratings data so that products can be recommended based on the similarities. The calculation of similarities has relied on traditional distance and vector similarity measures such as Pearson’s correlation and cosine which, however, have been seldom questioned in terms of their effectiveness in the recommendation problem domain. This paper presents a new heuristic similarity measure that focuses on improving recommendation performance under cold-start conditions where only a small number of ratings are available for similarity calculation for each user. Experiments using three different datasets show the superiority of the measure in new user cold-start conditions.
RLC Matched GaN HEMT Power Amplifier with 2 GHz Bandwidth

We have demonstrated a RLC matched GaN HEMT power amplifier with 12 dB gain, 0.05-2.0 GHz bandwidth, 8 W CW output power and 36.7-65.4% drain efficiency over the band. The amplifier is packaged in a ceramic S08 package and contains a GaN on SiC device operating at 28 V drain voltage, alongside GaAs integrated passive matching circuitry. A second circuit designed for 48 V operation and 15 W CW power over the same band, obtains over 20 W under pulsed condition with 10% duty cycle and 100 mus pulse width. CW measurements are pending after assembly in an alternate high power package. These amplifiers are suitable for use in wideband digital cellular infrastructure, handheld radios, and jamming applications.
Schemas for Narrative Generation Mined from Existing Descriptions of Plot

Computational generation of literary artifacts very often resorts to template-like schemas that can be instantiated into complex structures. With this view in mind, the present paper reviews a number of existing attempts to provide an elementary set of patterns for basic plots. An attempt is made to formulate these descriptions of possible plots in terms of character functions, an abstraction of plot-bearing elements of a story originally formulated by Vladimir Propp. These character functions act as the building blocks of the Propper system, an existing framework for computational story generation. The paper explores the set of extensions required to the original set of character functions to allow for a basic representation of the analysed schemata, and a solution for automatic generation of stories based on this formulation of the narrative schemas. This solution uncovers important insights on the relative expressive power of the representation of narrative in terms of character functions, and their impact on the generative potential of the framework is discussed.