nils-herrmann/clean_scirepeval huggingface dataset:
Plant Science View on Biohybrid Development

Biohybrid consists of a living organism or cell and at least one engineered component. Designing robot-plant biohybrids is a great challenge: it requires interdisciplinary reconsideration of capabilities intimate specific to the biology of plants. Envisioned advances should improve agricultural/horticultural/social practice and could open new directions in utilization of plants by humans. Proper biohybrid cooperation depends upon effective communication. During evolution, plants developed many ways to communicate with each other, with animals, and with microorganisms. The most notable examples are: the use of phytohormones, rapid long-distance signaling, gravity, and light perception. These processes can now be intentionally re-shaped to establish plant-robot communication. In this article, we focus on plants physiological and molecular processes that could be used in bio-hybrids. We show phototropism and biomechanics as promising ways of effective communication, resulting in an alteration in plant architecture, and discuss the specifics of plants anatomy, physiology and development with regards to the bio-hybrids. Moreover, we discuss ways how robots could influence plants growth and development and present aims, ideas, and realized projects of plant-robot biohybrids.
Traffic Sign Detection and Recognition Using Feature Based and OCR Method

Traffic sign detection is a technology by which a vehicle is able to recognize the different traffic signs located on the road and used to regulate the traffic. Traffic signs are detected by analyzes color information contained on the images having capability of detection and recognition of traffic signs even with bad visual artifacts those originate from different conditions. The feature based method is intended for traffic sign detection, in this method two sets of features are to be detected in the reference and sensed images, identifying key points in the images and match among those points to find the similarity, the SURF descriptor is used for key points and point matching. After detecting the shape of the traffic sign the optical character recognition (OCR) method is used to recognize the character present in the detected shape. A technique, based on Maximally Stable Extremal Regions (MSER) region and canny edge detector is supervised for character recognition in traffic sign detection.
Automatic Identification of Human Parasite Eggs Based on Multitexton Histogram Retrieving the Relationships between Textons

In order to identify the parasitic diseases, this paper propose the automatic identification of Human Parasite Eggs to eight different species : Ascaris, Uncinarias, Trichuris, Dyphillobothrium-Pacificum, Taenia-Solium, Fasciola Hepetica and Enterobius-Vermicularis from their microscopic images based on Multitexton Histogram - MTH using new structures of textons. This proposed system includes two stages. In first stage, a feature extraction mechanism that is based on MTH descriptor retrieving the relationships between textons. In second stage, an CBIR system has been implemented in orden to detect their correct species of helminths. Finally, simulation results show overall success rates of 94,78% in the detection.
Dual Band-Reject UWB Antenna With Sharp Rejection of Narrow and Closely-Spaced Bands

An ultrawideband (UWB) antenna that rejects extremely sharply the two narrow and closely-spaced U.S. WLAN 802.11a bands is presented. The antenna is designed on a single surface (it is uniplanar) and uses only linear sections for easy scaling and fine-tuning. Distributed-element and lumped-element equivalent circuit models of this dual band-reject UWB antenna are presented and used to support the explanation of the physical principles of operation of the dual band-rejection mechanism thoroughly. The circuits are evaluated by comparing with the response of the presented UWB antenna that has very high selectivity and achieves dual-frequency rejection of the WLAN 5.25 GHz and 5.775 GHz bands, while it receives signal from the intermediate band between 5.35-5.725 GHz. The rejection is achieved using double open-circuited stubs, which is uncommon and are chosen based on their narrowband performance. The antenna was fabricated on a single side of a thin, flexible, LCP substrate. The measured achieved rejection is the best reported for a dual-band reject antenna with so closely-spaced rejected bands. The measured group delay of the antenna validates its suitability for UWB links. Such antennas improve both UWB and WLAN communication links at practically zero cost.
Learning Accurate, Compact, and Interpretable Tree Annotation

We present an automatic approach to tree annotation in which basic nonterminal symbols are alternately split and merged to maximize the likelihood of a training treebank. Starting with a simple Xbar grammar, we learn a new grammar whose nonterminals are subsymbols of the original nonterminals. In contrast with previous work, we are able to split various terminals to different degrees, as appropriate to the actual complexity in the data. Our grammars automatically learn the kinds of linguistic distinctions exhibited in previous work on manual tree annotation. On the other hand, our grammars are much more compact and substantially more accurate than previous work on automatic annotation. Despite its simplicity, our best grammar achieves an F1 of 90.2% on the Penn Treebank, higher than fully lexicalized systems.
Gender differences in negative affect and well-being: the case for emotional intensity.

Affect intensity (AI) may reconcile 2 seemingly paradoxical findings: Women report more negative affect than men but equal happiness as men. AI describes people's varying response intensity to identical emotional stimuli. A college sample of 66 women and 34 men was assessed on both positive and negative affect using 4 measurement methods: self-report, peer report, daily report, and memory performance. A principal-components analysis revealed an affect balance component and an AI component. Multimeasure affect balance and AI scores were created, and t tests were computed that showed women to be as happy as and more intense than men. Gender accounted for less than 1% of the variance in happiness but over 13% in AI. Thus, depression findings of more negative affect in women do not conflict with well-being findings of equal happiness across gender. Generally, women's more intense positive emotions balance their higher negative affect.
Semantic Mapping of Large-Scale Outdoor Scenes for Autonomous Off-Road Driving

Semantic mapping is a very active and growing research area, with important applications in indoor and outdoor robotic applications. However, most of the research on semantic mapping has focused on indoor mapping and there is a need for developing semantic mapping methodologies for large-scale outdoor scenarios. In this work, a novel semantic mapping methodology for large-scale outdoor scenes in autonomous off-road driving applications is proposed. The semantic map representation consists of a large-scale topological map built using semantic image information. Thus, the proposed representation aims to solve the large-scale outdoors semantic mapping problem by using a graph based topological map, where relevant information for autonomous driving is added using semantic information from the image description. As a proof of concept, the proposed methodology is applied to the semantic map building of a real outdoor scenario.
Destructive hostility: the Jeffrey Dahmer case. A psychiatric and forensic study of a serial killer.

We were involved as forensic experts in the case of the serial killer Jeffrey Dahmer. We discuss the scene and victim autopsy findings, with a brief consideration of the basic emotion of hostility. These findings support the thesis that at the basis of this serial killer's behavior were primary unconscious feelings of hate that he had channeled into a sadistic programmed destruction of 17 young men. The interview of the serial killer, the photographic scene documentation, and the autopsy findings stress the ambivalent homosexuality of the killer, his sexual sadism, his obsessive fetishism, and his possible cannibalism and necrophilia.
Comparison between first-order hold with zero-order hold in discretization of input-delay nonlinear systems

In large sampling period nonlinear systems, the Taylor series method was used to improve the performance of the controller. First-order hold (FOH) and zero-order hold (ZOH) are used respectively in the discretization of input time-delay systems. The sampled-data representation of FOH and ZOH are both described. The mathematical structure of new discretization schemes are proposed and characterized as useful methods of establishing concrete connections between numerical and system theoretic properties. The performances of the proposed discretization procedure are compared between FOH with ZOH. Various sampling rates and time-delay values are adopted to inspect the proposed method. The results demonstrate that the proposed discretization scheme can assure the system precision requirements even under the situation of a large sampling period. FOH and ZOH have their own advantages respectively for different input signals.
300-FPS Salient Object Detection via Minimum Directional Contrast

Global contrast considers the color difference between a target region or pixel and the rest of the image. It is frequently used to measure the saliency of the region or pixel. In previous global contrast-based methods, saliency is usually measured by the sum of contrast from the entire image. We find that the spatial distribution of contrast is one important cue of saliency that is neglected by previous works. Foreground pixel usually has high contrast from all directions, since it is surrounded by the background. Background pixel often shows low contrast in at least one direction, as it has to connect to the background. Motivated by this intuition, we first compute directional contrast from different directions for each pixel, and propose minimum directional contrast (MDC) as raw saliency metric. Then an O(1) computation of MDC using integral image is proposed. It takes only 1.5 ms for an input image of the QVGA resolution. In saliency post-processing, we use marker-based watershed algorithm to estimate each pixel as foreground or background, followed by one linear function to highlight or suppress its saliency. Performance evaluation is carried on four public data sets. The proposed method significantly outperforms other global contrast-based methods, and achieves comparable or better performance than the state-of-the-art methods. The proposed method runs at 300 FPS and shows six times improvement in runtime over the state-of-the-art methods.
Emotions, Arousal, and Frontal Alpha Rhythm Asymmetry During Beethoven’s 5th Symphony

Music is capable of inducing emotional arousal. While previous studies used brief musical excerpts to induce one specific emotion, the current study aimed to identify the physiological correlates of continuous changes in subjective emotional states while listening to a complete music piece. A total of 19 participants listened to the first movement of Ludwig van Beethoven’s 5th symphony (duration: ~7.4 min), during which a continuous 76-channel EEG was recorded. In a second session, the subjects evaluated their emotional arousal during the listening. A fast fourier transform was performed and covariance maps of spectral power were computed in association with the subjective arousal ratings. Subjective arousal ratings had good inter-individual correlations. Covariance maps showed a right-frontal suppression of lower alpha-band activity during high arousal. The results indicate that music is a powerful arousal-modulating stimulus. The temporal dynamics of the piece are well suited for sequential analysis, and could be necessary in helping unfold the full emotional power of music.
A decision support system for quayside operations in a container terminal

a r t i c l e i n f o Keywords: Quayside operations Berth allocation Crane scheduling Container terminal operations This paper proposes a decision support system for optimizing operations on the quayside of a container terminal. Due to the existence of multiple parties involved in the decision making processes within port operations, it is essential to pay attention to each parties' concerns and demands which by nature are frequently conflicting with each other. This calls for a DSS that offers the flexibility of adjusting the balance within conflicting objectives, guiding the decision maker towards the final decision. Consequently, this study provides a DSS that determines the berthing and crane allocations simultaneously. To show the practical application of the DSS presented, a real life case study at a container terminal has been conducted. Implementation of the model shows that improvements ranging from 10% to 25% on service time and costs can be attained. The prolonged economic recession together with weak economic growth is leading container terminals to follow a cautious approach when serving their customers. Before the economic crisis, where more optimistic figures were prospected, concentrating on satisfying these customers to the highest level has been the more focused approach to gain share in the market. Nowadays, the necessity for considering cost issues while providing quality service to the customers has increased. The decisions for operations within the terminal depend on the balance of influence between terminal operators and shipping companies. From the terminal operator's perspective operating for high productivity and container throughput at low costs is a critical element to stay competitive. However, from the shipping companies' perspective low turnaround time and reliability regarding adherence to promised handling times are more critical elements. Hence, there are different views existing between the parties involved at the container terminals [24,29]. With shipping companies on one side and terminal operators on the other, each having its own concerns and demands and which by nature are frequently conflicting with each other, the decision makers call for supporting instruments that help them to attain a balance among those differing intentions. Yet, recent literature on quayside operations within a container terminal does not provide adequate support to resolve the issue via practical considerations. Subsequently, this study attempts to provide a decision support tool that determines the berthing and crane allocations simultaneously under multiple objectives. At a container terminal, vessels are docked on a berth …
Personalized Travel Package Recommendation

As the worlds of commerce, entertainment, travel, and Internet technology become more inextricably linked, new types of business data become available for creative use and formal analysis. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To this end, we first analyze the characteristics of the travel packages and develop a Tourist-Area-Season Topic (TAST) model, which can extract the topics conditioned on both the tourists and the intrinsic features (i.e. locations, travel seasons) of the landscapes. Based on this TAST model, we propose a cocktail approach on personalized travel package recommendation. Finally, we evaluate the TAST model and the cocktail approach on real-world travel package data. The experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is thus much more effective than traditional recommendation methods for travel package recommendation.
The fundamental social motives that characterize dark personality traits

A useful way of understanding personality traits is to examine the motivational nature of a trait because motives drive behaviors and influence attitudes. In two cross-sectional, self-report studies (N=942), we examined the relationships between fundamental social motives and dark personality traits (i.e., narcissism, psychopathy, sadism, spitefulness, and Machiavellianism) and examined the role of childhood socio-ecological conditions (Study 2 only). For example, we found that Machiavellianism and psychopathy were negatively associated with motivations that involved developing and maintaining good relationships with others. Sex differences in the darker aspects of personality were a function of, at least in part, fundamental social motives such as the desire for status. Fundamental social motives mediated the associations that childhood socio-ecological conditions had with the darker aspects of personality. Our results showed how motivational tendencies in men and women may provide insights into alternative life history strategies reflected in dark personality traits.
Empathy-Related Responses to Depicted People in Art Works

Existing theories of empathic response to visual art works postulate the primacy of automatic embodied reaction to images based on mirror neuron mechanisms. Arguing for a more inclusive concept of empathy-related response and integrating four distinct bodies of literature, we discuss contextual, and personal factors which modulate empathic response to depicted people. We then present an integrative model of empathy-related responses to depicted people in art works. The model assumes that a response to empathy-eliciting figural artworks engages the dynamic interaction of two mutually interlinked sets of processes: socio-affective/cognitive processing, related to the person perception, and esthetic processing, primarily concerned with esthetic appreciation and judgment and attention to non-social aspects of the image. The model predicts that the specific pattern of interaction between empathy-related and esthetic processing is co-determined by several sets of factors: (i) the viewer's individual characteristics, (ii) the context variables (which include various modes of priming by narratives and other images), (iii) multidimensional features of the image, and (iv) aspects of a viewer's response. Finally we propose that the model is implemented by the interaction of functionally connected brain networks involved in socio-cognitive and esthetic processing.
On the Depth of Deep Neural Networks: A Theoretical View

People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound. In margin bound, expected error is upper bounded by empirical margin error plus Rademacher Average (RA) based capacity term. First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth. This indicates negative impact of depth on test performance. Second, we show that deeper networks tend to have larger representation power (measured by Betti numbers based complexity) than shallower networks in multi-class setting, and thus can lead to smaller empirical margin error. This implies positive impact of depth. The combination of these two results shows that for DNN with restricted number of hidden units, increasing depth is not always good since there is a tradeoff between positive and negative impacts. These results inspire us to seek alternative ways to achieve positive impact of depth, e.g., imposing margin-based penalty terms to cross entropy loss so as to reduce empirical margin error without increasing depth. Our experiments show that in this way, we achieve significantly better test performance.
The Experience of Parkinson's Disease: A Systematic Review and Meta-Ethnography

There has been little attempt to summarise and synthesise qualitative studies concerning the experience and perception of living with Parkinson's disease. Bringing this information together would provide a background to understand the importance of an individual's social identity on their well-being and hope. Three primary aims were identified (a) understanding the importance of social identity and meaningful activities on individuals' well-being, (b) identifying factors and strategies that influence well-being and hope, and (c) establishing a model that relates to an individual's hope and well-being. Three stages were undertaken including a traditional electronic search, a critical appraisal of articles, and a synthesis of studies. Qualitative articles were included that considered the experience of living with Parkinson's disease. Thirty seven articles were located and included in the review. Five themes were identified and the themes were used to inform development of a new model of hope enablement. The current review furthered understanding of how physical symptoms and the experience of Parkinson's disease affect the individual's well-being and hope. Social identity was established as a key factor that influenced an individual's well-being. Being able to maintain, retain, or develop social identities was essential for the well-being and hope of individuals with Parkinson's disease. Understanding the factors which prevent or can facilitate this is essential.
Comparative Study of AODV , DSDV and DSR Routing Protocols in MANET Using Network Simulator-2

Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes that dynamically form a network temporarily without any support of central management. Moreover, Every node in MANET moves arbitrarily making the multi-hop network topology to change randomly at uncertain times. There are several familiar routing protocols like AODV,DSR,DSDV etc... which have been proposed for providing communication among all the nodes in the wireless network. This paper presents a performance comparison and study of reactive and proactive protocols AODV,DSR and DSDV based on metrics such as throughput, control overhead ,packet delivery ratio and average end-toend delay by using the NS-2 simulator.
The Effect of Multiple Grammatical Errors on Processing Non-Native Writing

In this work, we estimate the deterioration of NLP processing given an estimate of the amount and nature of grammatical errors in a text. From a corpus of essays written by English-language learners, we extract ungrammatical sentences, controlling the number and types of errors in each sentence. We focus on six categories of errors that are commonly made by English-language learners, and consider sentences containing one or more of these errors. To evaluate the effect of grammatical errors, we measure the deterioration of ungrammatical dependency parses using the labeled F-score, an adaptation of the labeled attachment score. We find notable differences between the influence of individual error types on the dependency parse, as well as interactions between multiple errors.
Real-time prediction of meme burst

Predicting meme burst is of great relevance to develop security-related detecting and early warning capabilities. In this paper, we propose a feature-based method for real-time meme burst predictions, namely “Semantic, Network, and Time” (SNAT). By considering the potential characteristics of bursty memes, such as the semantics and spatio-temporal characteristics during their propagation, SNAT is capable of capturing meme burst at the very beginning and in real time. Experimental results prove the effectiveness of SNAT in terms of both fixed-time and real-time meme burst prediction tasks.