nils-herrmann/clean_scirepeval huggingface dataset:
An Ontology-Based Cybersecurity Framework for the Internet of Things

The use of sensors and actuators as a form of controlling cyber-physical systems in resource networks has been integrated and referred to as the Internet of Things (IoT). However, the connectivity of many stand-alone IoT systems through the Internet introduces numerous cybersecurity challenges as sensitive information is prone to be exposed to malicious users. This paper focuses on the improvement of IoT cybersecurity from an ontological analysis, proposing appropriate security services adapted to the threats. The authors propose an ontology-based cybersecurity framework using knowledge reasoning for IoT, composed of two approaches: (1) design time, which provides a dynamic method to build security services through the application of a model-driven methodology considering the existing enterprise processes; and (2) run time, which involves monitoring the IoT environment, classifying threats and vulnerabilities, and actuating in the environment ensuring the correct adaptation of the existing services. Two validation approaches demonstrate the feasibility of our concept. This entails an ontology assessment and a case study with an industrial implementation.
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation

Recently, 3D understanding research sheds light on extracting features from point cloud directly [22, 24], which requires effective shape pattern description of point clouds. Inspired by the outstanding 2D shape descriptor SIFT , we design a module called PointSIFT that encodes information of different orientations and is adaptive to scale of shape. Specifically, an orientation-encoding unit is designed to describe eight crucial orientations, and multi-scale representation is achieved by stacking several orientation-encoding units. PointSIFT module can be integrated into various PointNet-based architecture to improve the representation ability. Extensive experiments show our PointSIFT-based framework outperforms state-ofthe-art method on standard benchmark datasets. The code and trained model will be published accompanied by this paper.
KALMAN FILTERING IN SEMI-ACTIVE SUSPENSION CONTROL

This paper focuses on estimation of the vertical velocity of the vehicle chassis and the relative velocity between chassis and wheel. These velocities are important variables in semi-active suspension control. A model-based estimator is proposed including a Kalman filter and a non-linear model of the damper. Inputs to the estimator are signals from wheel displacement sensors and from accelerometers placed at the chassis. In addition, the control signal is used as input to the estimator. The Kalman filter is analyzed in the frequency domain and compared with a conventional filter solution including derivation of the displacement signal and integration of the acceleration signal.
Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review

With the advances in information technology (IT) criminals are using cyberspace to commit numerous cyber crimes. Cyber infrastructures are highly vulnerable to intrusions and other threats. Physical devices and human intervention are not sufficient for monitoring and protection of these infrastructures; hence, there is a need for more sophisticated cyber defense systems that need to be flexible, adaptable and robust, and able to detect a wide variety of threats and make intelligent real-time decisions. Numerous bio-inspired computing methods of Artificial Intelligence have been increasingly playing an important role in cyber crime detection and prevention. The purpose of this study is to present advances made so far in the field of applying AI techniques for combating cyber crimes, to demonstrate how these techniques can be an effective tool for detection and prevention of cyber attacks, as well as to give the scope for future work.
NEUROPSYCHOLOGICAL DIFFERENTIATION OF PSYCHOPATHIC AND NONPSYCHOPATHIC CRIMINAL OFFENDERS

A battery of neuropsychological measures was administered to 69 right-handed incarcerated male offenders to test the hypothesis that psychopaths would perform more poorly than nonpsychopaths on measures related to left hemisphere (verbal) functioning, frontal lobe (executive) functioning, or both. Psychopathy was assessed with the Psychopathy Checklist and subjects were further subdivided into lowand high-anxious groups. Robust group differences were found on two of six measures (Block Design and Trail Making Test, Part B) predicted to differentiate the groups. Consistent with other studies that subdivided subjects into lowand high-anxious groups, group differences were specific to the low-anxious subjects. The results provided no support for the hypothesis that psychopaths are characterized by verbal or left hemisphere dysfunction. Although the results were not inconsistent with the hypothesis of deficient frontal lobe functioning in psychopaths, the evidence supporting the hypothesis was specific to tasks involving the integration of cognitive-perceptual and motor processes.
Hierarchical Long-term Video Prediction without Supervision

Much of recent research has been devoted to video prediction and generation, yet most of the previous works have demonstrated only limited success in generating videos on short-term horizons. The hierarchical video prediction method by Villegas et al. (2017b) is an example of a state-of-the-art method for long-term video prediction, but their method is limited because it requires ground truth annotation of high-level structures (e.g., human joint landmarks) at training time. Our network encodes the input frame, predicts a high-level encoding into the future, and then a decoder with access to the first frame produces the predicted image from the predicted encoding. The decoder also produces a mask that outlines the predicted foreground object (e.g., person) as a by-product. Unlike Villegas et al. (2017b), we develop a novel training method that jointly trains the encoder, the predictor, and the decoder together without highlevel supervision; we further improve upon this by using an adversarial loss in the feature space to train the predictor. Our method can predict about 20 seconds into the future and provides better results compared to Denton and Fergus (2018) and Finn et al. (2016) on the Human 3.6M dataset.
Investigating preschoolers' categorical thinking about gender through imitation, attention, and the use of self-categories.

Whereas traditional theories of gender development have focused on individualistic paths, recent analyses have argued for a more social categorical approach to children's understanding of gender. Using a modeling paradigm based on K. Bussey and A. Bandura (1984), 3 experiments (N = 62, N = 32, and N = 64) examined preschoolers' (M age = 52.9 months) imitation of, and memory for, behaviors of same-sex and opposite-sex children and adults. In all experiments, children's imitation of models varied according to the emphasis given to the particular category of models, despite equal attention being paid to both categories. It is suggested that the categorical nature of gender, or age, informs children's choice of imitative behaviors.
Who invests in renewable electricity production ? Empirical evidence and suggestions for further research

Tranforming energy systems to fulfill the needs of a low-carbon economy requires large investments in renewable electricity production (RES-E). Recent literature underlines the need to take a closer look at the composition of the RES-E investor group in order to understand the motives and investment processes of different types of investors. However, existing energy policies generally consider RES-E investments made on a regional or national level, and target investors who evaluate their RES-E investments according to least-cost high-profit criteria. We present empirical evidence to show that RES-E investments are made by a heterogeneous group of investors, that a variety of investors exist and that their formation varies among the different types of renewable sources. This has direct implications for our understanding of the investment process in RES-E and for the study of motives and driving forces of RES-E investors. We introduce a multi-dimensional framework for analyzing differences between categories of investors, which not only considers to the standard economic dimension which is predominant in the contemporary energy literature, but also considers the entrepreneurship, innovation-adoption and institutional dimensions. The framework emphasizes the influence of four main investor-related factors on the investment process which should be studied in future research: motives, background, resources and personal characteristics.
Along-Wind Load Effects on Tall Buildings : Comparative Study of Major International Codes and Standards

Most international codes and standards provide guidelines and procedures for assessing the along-wind effec structures. Despite their common use of the ‘‘gust loading factor’’ ~GLF! approach, sizeable scatter exists among the wind eff predicted by the various codes and standards under similar flow conditions. This paper presents a comprehensive assessment o of this scatter through a comparison of the along-wind loads and their effects on tall buildings recommended by major internation and standards. ASCE 7-98 ~United States !, AS1170.2-89~Australia!, NBC-1995~Canada!, RLB-AIJ-1993 ~Japan!, and Eurocode-1993 ~Europe! are examined in this study. The comparisons consider the definition of wind characteristics, mean wind loads, GLF, eq static wind loads, and attendant wind load effects. It is noted that the scatter in the predicted wind loads and their effects arises from the variations in the definition of wind field characteristics in the respective codes and standards. A detailed example is pre illustrate the overall comparison and to highlight the main findings of this paper.  Buildings, highrise; Building codes; Wind loads; Dynamics; Wind velocity.
Deep Spatial Pyramid Match Kernel for Scene Classification

Several works have shown that Convolutional Neural Networks (CNNs) can be easily adapted to different datasets and tasks. However, for extracting the deep features from these pre-trained deep CNNs a fixedsize (e.g., 227×227) input image is mandatory. Now the state-of-the-art datasets like MIT-67 and SUN-397 come with images of different sizes. Usage of CNNs for these datasets enforces the user to bring different sized images to a fixed size either by reducing or enlarging the images. The curiosity is obvious that “Isn’t the conversion to fixed size image is lossy ?”. In this work, we provide a mechanism to keep these lossy fixed size images aloof and process the images in its original form to get set of varying size deep feature maps, hence being lossless. We also propose deep spatial pyramid match kernel (DSPMK) which amalgamates set of varying size deep feature maps and computes a matching score between the samples. Proposed DSPMK act as a dynamic kernel in the classification framework of scene dataset using support vector machine. We demonstrated the effectiveness of combining the power of varying size CNN-based set of deep feature maps with dynamic kernel by achieving state-of-the-art results for high-level visual recognition tasks such as scene classification on standard datasets like MIT67 and SUN397.
A fast and robust automatic object detection algorithm to detect small objects in infrared images

Automatic object detection in infrared images is a vital task for many military defense systems. The high detection rate and low false detection rate of this phase directly affect the performance of the following algorithms in the system as well as the general performance of the system. In this work, a fast and robust algorithm is proposed for detection of small and high intensity objects in infrared scenes. Top-hat transformation and mean filter was used to increase the visibility of the objects, and a two-layer thresholding algorithm was introduced to calculate the object sizes more accurately. Finally, small objects extracted by using post processing methods.
Controlling Information Aggregation for Complex Question Answering

Complex question answering, the task of answering complex natural language questions that rely on inference, requires the aggregation of information from multiple sources. Automatic aggregation often fails because it combines semantically unrelated facts leading to bad inferences. This paper proposes methods to address this inference drift problem. In particular, the paper develops unsupervised and supervised mechanisms to control random walks on Open Information Extraction (OIE) knowledge graphs. Empirical evaluation on an elementary science exam benchmark shows that the proposed methods enables effective aggregation even over larger graphs and demonstrates the complementary value of information aggregation for answering complex questions.
A new mobile-based multi-factor authentication scheme using pre-shared number, GPS location and time stamp

Lots of Web accounts are being hacked everyday even though the public Web servers like Gmail, Yahoo and Hotmail deploy the best-known security mechanisms. However, attacks usually happen due to personal faults. It is too difficult to hack the Gmail or Yahoo servers, but usually attackers try to gain access to user's system itself to get some information about his password. In this paper a new mechanism of securing Web accounts is proposed to overcome these personal faults. It enhances security by generating a secret hash code based on a pre-shared number and user's current GPS location.
Interventions targeting social isolation in older people: a systematic review

Targeting social isolation in older people is a growing public health concern. The proportion of older people in society has increased in recent decades, and it is estimated that approximately 25% of the population will be aged 60 or above within the next 20 to 40 years. Social isolation is prevalent amongst older people and evidence indicates the detrimental effect that it can have on health and wellbeing. The aim of this review was to assess the effectiveness of interventions designed to alleviate social isolation and loneliness in older people.



Relevant electronic databases (MEDLINE, EMBASE, ASSIA, IBSS, PsycINFO, PubMed, DARE, Social Care Online, the Cochrane Library and CINAHL) were systematically searched using an extensive search strategy, for randomised controlled trials and quasi-experimental studies published in English before May 2009. Additional articles were identified through citation tracking. Studies were included if they related to older people, if the intervention aimed to alleviate social isolation and loneliness, if intervention participants were compared against inactive controls and, if treatment effects were reported. Two independent reviewers extracted data using a standardised form. Narrative synthesis and vote-counting methods were used to summarise and interpret study data.



Thirty two studies were included in the review. There was evidence of substantial heterogeneity in the interventions delivered and the overall quality of included studies indicated a medium to high risk of bias. Across the three domains of social, mental and physical health, 79% of group-based interventions and 55% of one-to-one interventions reported at least one improved participant outcome. Over 80% of participatory interventions produced beneficial effects across the same domains, compared with 44% of those categorised as non-participatory. Of interventions categorised as having a theoretical basis, 87% reported beneficial effects across the three domains compared with 59% of interventions with no evident theoretical foundation. Regarding intervention type, 86% of those providing activities and 80% of those providing support resulted in improved participant outcomes, compared with 60% of home visiting and 25% of internet training interventions. Fifty eight percent of interventions that explicitly targeted socially isolated or lonely older people reported positive outcomes, compared with 80% of studies with no explicit targeting.



More, well-conducted studies of the effectiveness of social interventions for alleviating social isolation are needed to improve the evidence base. However, it appeared that common characteristics of effective interventions were those developed within the context of a theoretical basis, and those offering social activity and/or support within a group format. Interventions in which older people are active participants also appeared more likely to be effective. Future interventions incorporating all of these characteristics may therefore be more successful in targeting social isolation in older people.
Counterfeit Currency Detection Techniques

The currency which is imitated with illegal sanction of state and government is counterfeit currency. Every country incorporates a number of security features for its currency security. Currency counterfeiting is always been a challenging term for financial system of any country. The problem of counterfeiting majorly affects the economical as well as financial growth of a country. In view of the problem various studies about counterfeit detection has been conducted using various techniques and variety of tools. This paper focuses on the researches and studies that have been conducted by various researchers. The paper highlighted the methodologies used and the particular characteristics features considered for counterfeit money detection.
Trilobite body patterning and the evolution of arthropod tagmosis.

Preservation permitting patterns of developmental evolution can be reconstructed within long extinct clades, and the rich fossil record of trilobite ontogeny and phylogeny provides an unparalleled opportunity for doing so. Furthermore, knowledge of Hox gene expression patterns among living arthropods permit inferences about possible Hox gene deployment in trilobites. The trilobite anteroposterior body plan is consistent with recent suggestions that basal euarthropods had a relatively low degree of tagmosis among cephalic limbs, possibly related to overlapping expression domains of cephalic Hox genes. Trilobite trunk segments appeared sequentially at a subterminal generative zone, and were exchanged between regions of fused and freely articulating segments during growth. Homonomous trunk segment shape and gradual size transition were apparently phylogenetically basal conditions and suggest a single trunk tagma. Several derived clades independently evolved functionally distinct tagmata within the trunk, apparently exchanging flexible segment numbers for greater regionally autonomy. The trilobite trunk chronicles how different aspects of arthropod segmentation coevolved as the degree of tagmosis increased.
Stress and Coping Theories

This article first presents two theories representing distinct approaches to the field of stress research: Selye's theory of `systemic stress' based in physiology and psychobiology, and the `psychological stress' model developed by Lazarus. In the second part, the concept of coping is described. Coping theories may be classified according to two independent parameters: traitoriented versus state-oriented, and microanalytic versus macroanalytic approaches. The multitude of theoretical conceptions is based on the macroanalytic, trait-oriented approach. Examples of this approach that are presented in this article are `repression–sensitization,' `monitoringblunting,' and the `model of coping modes.' The article closes with a brief outline of future perspectives in stress and coping research.
A 2.4 GHz-868 MHz dual-band wake-up radio for wireless sensor network and IoT

Wake-up radio is an emerging technology with the ambitious goal of reducing the communication power consumption in smart sensor networks and Internet of Things. This reduction in power consumption will enable a new generation of applications which could achieve a longer lifetime than is achievable today. Wake up radios are required to work with a low power budget and should exhibit low latency coupled with high sensitivity and addressing capabilities. Typically they are combined with existing radio transceiver and power management techniques to reduce the overall communication power while maintaining the same communication performance. This paper presents a dual band (2.4GHz and 868MHz) wake up radio with the above mentioned characteristics. The dual band solution is exploited to increase the flexibility of the wake up radio, allowing interoperability with the two most common frequencies used in Wireless Sensors Networks and Internet of Things. Simulation results present a system able to exploit the two bands with sensitivity as low as -53dBm at 868MHz and -45dBm at 2450MHz. Experimental results on power consumption demonstrate the low power consumption of the proposed solution with only 1.276μW of power consumption in listening mode. The addressing is performed by an ultra low power on board PIC microcontroller with 40nW of power consumption when the wake up radio is in listening mode and only 70 μW when the data are received and parsed.
The Effect of a Combined Visual Efficiency and Perceptual-Motor Training Programme on the Handwriting Performance of Children with Handwriting Difficulties : A Pilot Study

The purpose of this study was to investigate the effect of a combined visual efficiency and perceptual-motor training programme on the handwriting performance of Chinese children aged 6 to 9 years with handwriting difficulties (HWD).  Twenty-six children with HWD were assigned randomly and equally into two groups. The training programme was provided over eight consecutive weeks with one session per week. The perceptual-motor group received training only on perceptual-motor functions, including visual spatial relationship, visual sequential memory, visual constancy, visual closure, graphomotor control and grip control. The combined training group received additional training components on visual efficiency, including accommodation, ocular motility, and binocular fusion. Visual efficiency, visual perceptual skills, and Chinese handwriting performance were assessed before and after the training programme.  The results showed statistically significant improvement in handwriting speed after the training in both groups. However, the combined training gave no additional benefit on improving handwriting speed (ANCOVA: F=0.43, p=0.52). In terms of visual efficiency, participants in the combined training group showed greater improvement in amplitude of accommodation measured with right eye (F=4.34, p<0.05), left eye (F=5.77, p<0.05) and both eyes (F=11.08, p<0.01).  Although the additional visual efficiency training did not provide further improvement in the handwriting speed of children with HWD, children showed improvement in their accommodation amplitude. As accommodative function is important for providing sustainable and clear near vision in the process of reading and word recognition for writing, the effect of the combined training on handwriting performance should be further investigated.