Miracle-dz/UniFAR-FTUs huggingface dataset:
Explicit Not Implicit Preferences Predict Conservation Intentions for Endangered Species and Biomes

Conservation of biodiversity is determined in part by human preferences. Preferences relevant to conservation have been examined largely via explicit measures (e.g., a self-reported degree of liking), with implicit measures (e.g., preconscious, automatic evaluations) receiving relatively less attention. This is the case despite psychological evidence from other contexts that implicit preferences are more informative of behavior. Thus, the type of measure that predicts conservation intentions for biodiversity is unknown. We conducted three studies to examine conservation intentions in light of people’s explicit and implicit preferences toward four endangered species (sea otter, American badger, caribou, yellow-breasted chat) and four biomes (forest, ocean, grassland, tundra). In Study 1 (n = 55), we found that people implicitly preferred caribou most, but explicitly preferred sea otter most, with a significant multiple regression where participants’ explicit preferences dictated their stated intended donations for conservation of each species. In Study 2 (n = 57) we found that people implicitly and explicitly preferred forest and ocean over grassland and tundra. Explicit rather than implicit preferences predicted the intended donation for conservation of the ocean biome. Study 3 involved a broader online sample of participants (n = 463) and also found that explicit preferences dictated the intended donations for conservation of biomes and species. Our findings reveal discrepancies between implicit and explicit preferences toward species, but not toward biomes. Importantly, the results demonstrate that explicit rather than implicit preferences predict conservation intentions for biodiversity. The current findings have several implications for conservation and the communication of biodiversity initiatives.
Optimising perceptuo-motor performance and learning with EEG neurofeedback

The neurobiological functions of an organism serve to assist its adaptation to behaviourally challenging environments, which commonly involves the learning and refinement of perceptuo-motor skills. The intensity and time scale at which this occurs is critical towards survival. Previous work has observed that the neurochemical and neuroelectric (EEG) operation of specific functional systems is upregulated during so-called ‘activated’ states of behaviour. Thus it has recently been shown that artificial (i.e. exogenous) stimulation of such systems via pharmacological or electrical means can successfully modulate as well as enhance learning and associated behavioural performance. We hypothesized that neurofeedback, which is implemented through non-invasive volitional control of electrocortical rhythms (EEG), offers an alternate and natural (i.e. endogenous) way to modulate and thereby stimulate analogous systems. Study 1 shows that neurofeedback is a viable and beneficial method for improving the acquisition and performance of perceptuo-motor skills in trainee microsurgeons, when compared to a wait-list control group. With the aid of transcranial magnetic stimulation (TMS), Study 2 demonstrates for the first time that 30 minutes of a single neurofeedback session directly leads to a robust and correlated change in corticomotor plasticity which is usually associated with learning or observed after exogenous stimulation. Lastly, Study 3 investigates the short-term modulation of one session of‘excitatory’ neurofeedback on the subsequent performance of a serial reaction-time task (SRTT), an experimental paradigm widely used as a model for procedural perceptuo-motor learning. In conclusion, this thesis contributes original evidence of direct as well as long-term functional enhancements following EEG neurofeedback, and supports its use as a safe, non-invasive and natural method for improving human perceptuo-motor performance and learning.
A QoS-Based Trust Approach for Service Selection and Composition via Bayesian Networks

Service oriented computing is being increasingly exploited in the architecture of current web applications. The interactions among the deployed web services are becoming vital to accomplish heterogeneous and compound business goals. Service selection and composition are two common tasks which are highly influenced by the quality of these interactions. Thus, assigning web services QoS-based trust scores that are incrementally updated, provides a means to assist both tasks. Then, services with higher trust scores are more likely to be selected than those with smaller ones. They are, additionally, more prone to be incorporated as part of composite services. In this paper, we propose a probabilistic approach based on Bayesian networks (BN) to learn the composition structure of composite services and compute QoS-based trust scores in an online setting. The learning of the BN structure and parameters is based on modeling the QoS, which is represented by the BN's variables, using a multinomial generalized Dirichlet distribution (MGDD). The effectiveness of our approaches is empirically assessed using real and synthetic data. Our experimental results show that MGDD provides a flexible and accurate representation of the QoS. They also prove the capability of the BN approach to learn the composition structure, and further the responsibility of the constituent services in the quality of the composite service even when their QoS is partially observed.
Morphological characterization of the retinal degeneration in three strains of mice carrying the rd-3 mutation

Retinal development in 3 strains of rd-3/rd-3 mutant mice, previously shown to have different rates of degeneration, was studied using light, electron, and immunofluorescence microscopy. The time course and phenotype of the degeneration as well as details on the mechanism of massive photoreceptor cell loss are compared with other known retinal degenerations in mice. Up until postnatal day (P) 10, the retinas of all three strains (RBF, 4Bnr, In-30) develop similarly to those of pigmented and nonpigmented controls. TUNEL-positive cells appear in the outer nuclear layer (ONL) by P14, and reach a maximum in all three mutant strains around P21. Scattered rods and cones form a loose, monolayered ONL by 8 weeks in the albino RBF strain, by 10 weeks in the albino 4Bnr strain, and by 16 weeks in the pigmented In-30 strain. Though the initial degeneration begins in the central retina, there is no preferred gradient of cell death between central and peripheral photoreceptors. Rods and cones are present at all ages examined. During development, stacks of outer segments (OS) form in all three strains though they never achieve full adult lengths, and often have disorganized, atypical OS. Rod opsin is expressed in the developing OS but is redistributed into plasma membrane as OS degeneration proceeds. Retinal pigment epithelial (RPE) cells of all mutant strains contain packets of phagocytosed OS, and their apical processes associate with the distal ends of the OS. At their synaptic sites, photoreceptor terminals contain ribbons apposed to apparently normal postsynaptic triads. As photoreceptors are lost, Müller cells fill in space in the ONL but they do not appear to undergo significant hypertrophy or migration, though during the degeneration, glial fibrillary acidic protein (GFAP) expression is gradually upregulated. Macrophage-like cells are found frequently in the subretinal space after the onset of photoreceptor apoptosis. As OS disappear, the RPE apical processes revert to simple microvilli. Late in the degeneration, some RPE cells die and neighboring cells appear to flatten as if to maintain confluence. In regions of RPE cell loss that happen to lie above retina where the ONL is gone, cells of the inner nuclear layer (INL), wrapped by Müller cell processes, may front directly on Bruch's membrane.
Kinematics, hydrodynamics and force production of pleopods suggest jet-assisted walking in the American lobster (Homarus americanus)

SUMMARY The American lobster (Homarus americanus) displays a diverse set of locomotory behaviours that includes tail flips, walking and paddling. Paddling is carried out by the four pairs of paddle-shaped pleopods on the ventral abdomen. Although it is recognized that pleopod-generated fluid flows have some locomotory role in adults, reports on their relative importance in locomotion are inconsistent. This paper integrates experimental kinematics and hydrodynamics of lobster pleopod beating to determine the mechanism and magnitude of pleopod force production. A kinematic analysis of pleopod beating in live lobsters showed that the pleopods execute an adlocomotory metachronal beating pattern. We modelled in vivo pleopod kinematics with a set of simple trigonometric functions, and used these functions to program a mechanical lobster model consisting of motor-driven pleopods on a lobster abdomen exoskeleton. Based on flow visualizations obtained from applying particle image velocimetry to the lobster model, we propose that the unsteady metachronal kinematics of the pleopods can maximize thrust by exploiting forces arising from individual pleopod activity and interactions among adjacent pairs. The pleopods continuously entrain fluid surrounding the lobster and create a caudally directed fluid jet oriented parallel to the substratum. Inputting wake morphology and velocity data into a simplified model for steady jet thrust showed that the pleopods of the lobster model produced 27–54 mN of thrust, which is comparable to the propulsive forces generated by other proficient swimmers. These results suggest that lobster pleopods are capable of producing forces of a magnitude that could assist the walking legs in forward propulsion.
Extracting Multi-item Sequential Patterns by Wap-tree Based Approach

Sequential pattern mining constitutes a basis for solution of problems in web mining, especially in web usage mining. Research on sequence mining continues seeking faster algorithms. WAP-Tree based algorithms that emerged from the web usage mining literature have shown a remarkable performance on single-item sequence databases. In this study, we investigate the application of WAP-Tree based mining to multi-item sequential pattern mining and we present MULTI-WAP-Tree, which extends WAP-Tree for multi-item sequence databases. In addition, we propose a new algorithm MULTI-FOF-SP (MULTI-FOF-Sibling Principle) that extracts patterns on MULTI-WAP-Tree. MULTI-FOF-SP is based on the previous WAP-Tree based algorithm FOF (First Occurrence Forest) and an early pruning strategy called ”Sibling Principle” from the literature. Experimental results reveal that MULTI-FOF-SP finds patterns faster than PrefixSpan on dense multi-item sequence databases with small alphabets.
Survival of K+ permeability and gating currents in squid axons perfused with K+-free media

K+ currents were recorded in squid axons internally perfused with impermeant electrolyte. Total absence of permeant ions inside and out leads to an irreversible loss of potassium conductance with a time constant of approximately 11 min at 8 degrees C. Potassium channels can be protected against this effect by external K+, Cs+, NH4+, and Rb+ at concentrations of 100-440 mM. These experiments suggest that a K+ channel is normally occupied by one or more small cations, and becomes nonfunctional when these cations are removed. A large charge movement said to be related to K+ channel gating in frog skeletal muscle is absent in squid giant axons. However, deliberate destruction of K+ conductance by removal of permeant cations is accompanied by measurable loss in asymmetric charge movement. This missing charge component is large enough to contain a contribution from K+ gating charge movements of more than five elementary charges per channel.
Power and Weight Distribution Design Criteria for Cooperative Diversity Channels

The paper considers the single relay orthogonal cooperative diversity decode and forward scenario as a block fading channel model. A closed form expression for the pairwise error probability is derived which leads to design criteria for full diversity and coding gain. Furthermore, the power distribution between the source and relay units is introduced as a design parameter. Fixed power distribution allocation has the potential of improving performance with no increase in the system complexity. Several techniques for improving the coding gain are given based on the power distribution during the cooperation phase of the transmission. As an example, the (1,7/5) convolutional code was considered. Simulation results for this example show that a 1 dB performance gain can be achieved by modifying the power distribution of the source and relay. For the code considered, the equal power allocation method most commonly used gave the worst performance. The best performance was achieved when the source was silent during the cooperation phase. This observation simplifies protocol design.
Research on Cock States Detection of Cock Oven Inspection Robot Based on YOLO-v3

The state switching of the coke oven gas pipeline exchange cock plays a vital role in the safe production of coking. This paper designs and manufactures a rail-type mobile robot applied to coke oven basement inspection tasks to troubleshoot coke oven heat exchange equipment. Three detection methods are proposed for the opening and closing state of the exchange cock. Each detection method is analyzed according to the actual production process and requirements. The cock images are collected by the robot and made into a dataset. YOLO-v3 is used to train models of three detection methods on the same experimental platform and compare them. Finally, the optimal detection model is selected and deployed on the inspection robot. The detection accuracy rate reaches 99.1%, which effectively reduces the accident rate and maintains the stability of the coking system.
CSI: Charged System Influence Model for Human Behavior Prediction

Social influence has been widely studied in areas of viral marketing, information diffusion and health care. Currently, most influence models only deal with a single influence without the interference of other influences. Also, the influence spreading in previous models must be triggered by individuals who have been activated by the influence. In this paper, we argue that it is the attraction from a specific influence makes an individual choose to spread it among multiple influences. Inspired by charged system theory in physics, a new influence model is proposed, considering individual features and social structure features. It also gives a natural description about how individuals make decisions among multiple influences. Then a novel algorithm based on this model is provided to predict human behavior. Extensive experiments on three real-world datasets demonstrate that our model and algorithm statistically outperform the state-of-the-art methods in terms of prediction accuracy.
Are there distinctive clusters of higher and lower status universities in the UK?

In 1992 the binary divide between universities and polytechnics was dismantled to create a nominally unitary system of higher education for the UK. Just a year later, the first UK university league table was published, and the year after that saw the formation of the Russell Group of self-proclaimed ‘leading’ universities. This paper asks whether there are distinctive clusters of higher and lower status universities in the UK, and, in particular, whether the Russell Group institutions can be said to constitute a distinctive elite tier. Cluster analysis of publicly available data on the research activity, teaching quality, economic resources, academic selectivity, and socioeconomic student mix of UK universities demonstrates that the former binary divide persists with Old (pre-1992) universities characterised by higher levels of research activity, greater wealth, more academically successful and socioeconomically advantaged student intakes, but similar levels of teaching quality, compared to New (post-1992) institutions. Among the Old universities, Oxford and Cambridge emerge as an elite tier, whereas the remaining 22 Russell Group universities appear to be undifferentiated from the majority of other Old universities. A division among the New universities is also evident, with around a quarter of New universities forming a distinctive lower tier.
Memory-efficient pattern matching architectures using perfect hashing on graphic processing units

Memory architectures have been widely adopted in network intrusion detection system for inspecting malicious packets due to their flexibility and scalability. Memory architectures match input streams against thousands of attack patterns by traversing the corresponding state transition table stored in commodity memories. With the increasing number of attack patterns, reducing memory requirement has become critical for memory architectures. In this paper, we propose a novel memory architecture using perfect hashing to condense state transition tables without hash collisions. The proposed memory architecture achieves up to 99.5% improvement in memory reduction compared to the traditional two-dimensional memory architecture. We have implemented our memory architectures on graphic processing units and tested using attack patterns from Snort V2.8 and input packets form DEFCON. The experimental results show that the proposed memory architectures outperform state-of-the-art memory architectures both on performance and memory efficiency.
An Evaluation Research of Topic Extraction Results Based on External Metadata

With the increasing scale of literature publication and the rapid growth in the number of publications, topic extraction methods have also made great progress, yet the extent to which the topic extraction results reflect the true subject structure of the scientific literature remains an open question. In the development of topic extraction, the validity of topic extraction results has been an issue of concern to scholars. This paper contributes to validating the topic extraction method by using an external validation method to evaluate the topic extraction results. Considering that each piece of literature may involve more than one research topic, this thesis completes topic extraction using four overlapping community detection algorithms, COPRA, DEMON, SLPA, and GCE, based on direct citation network construction. The topic extraction results were then investigated for validation with the help of external metadata as the validation dataset.
UDLex: Towards Cross-language Subcategorization Lexicons

This paper introduces UDLex , a computational framework for the automatic extraction of argument structures for several languages. By exploiting the versatility of the Universal Dependency annotation scheme, our system acquires subcat-egorization frames directly from a dependency parsed corpus, regardless of the input language. It thus uses a universal set of language-independent rules to detect verb dependencies in a sentence. In this paper we describe how the system has been developed by adapting the LexIt (Lenci et al., 2012) framework, originally designed to describe argument structures of Italian predicates. Practical issues that arose when building argument structure representations for typologically different languages will also be discussed.
Usability testing of notification interfaces: are we focused on the best metrics?

Notification interfaces that continuously present peripheral information have received increasing interest within the HCI community, especially those supporting awareness of others' activities. While recent empirical studies have focused on information design aspects of peripheral displays, there have been few reported studies that comparatively evaluate actual systems. To this end, this article describes our efforts in comparing three interfaces that inform a remote user about activities within a given setting. Our data allow conclusions about comparative interface usability and preference, and provide an indication about metrics that are valuable to focus on in evaluations for these types of interfaces. In particular, we find that quantitative, performance related metrics, such as the correctness of notification interpretation and interruption to a primary task, are much less conclusive for fully implemented peripheral interfaces than qualitative judgments based on the usage experience.
Predicting the Impact of Socio-Demographic Risk Factors on COVID-19 Based on Hybrid ANN-CNN Model

Global health has been greatly influenced by the COVID-19 pandemic, especially in low- and middle-income nations like Nigeria. Despite the catastrophic effects of the pandemic, little is known about how sociodemographic risk factors that affects the number of COVID-19 infections and deaths in Nigeria. Using Spearman heat map correlation analysis, this study examined these parameters and developed a hybrid ANN-CNN model to forecast the influence of sociodemographic characteristics against COVID-19 confirmed cases and mortality cases in Nigeria. The Nigerian COVID-19 confirmed and death cases data from May 1, 2020, to April 30, 2021, as well as sociodemographic risk factor statistics, were the datasets used in this study. The experiment was completed by training and testing the models, and based on MAEs and RMSEs models performance evaluation metrics, the developed Hybrid ANN-CNN model outperformed the other five state-of-the-art machine learning models involving Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Least Absolute Shrinkage and Selection Operator (LASSO). With mean absolute errors of (0.0157) and (0.0181) for confirmed and death cases, respectively, the developed Hybrid ANN-CNN model outperformed the others. Similarly, with RMSEs of (0.0842) and (0.0813) for confirmed and death cases, respectively, the developed Hybrid ANN-CNN model fared better than other models. The developed Hybrid ANN-CNN model can be helpful in tracking and containing pandemic outbreaks, both in the present and the future.