Miracle-dz/UniFAR-FTUs huggingface dataset:
Method Use in Behavioural Design: What, How, and Why?

methods one Behavioural design is an important area of research and practice key to addressing behavioural and societal challenges. Behavioural design reflects a synthesis of design and behavioural science, which draws together aspects of abductive, inductive, and deductive reasoning to frame, develop, and deliver behaviour change through purposefully designed interventions. However, this synthesis creates major questions as to how methods are selected, adapted, and used during behavioural design. To take a step toward answering these questions we conducted fifteen interviews with globally recognised experts. Based on these interviews we deliver three main contributions. First, we provide an overview of the methods used in all phases of the behavioural design process. Second, we identify behavioural uncertainty as a key driver of method use in behavioural design. Third, we explain how this creates a tension between design and scientific concerns — related to interactions between abductive, inductive, and deductive reasoning—which must be managed across the behavioural design process. We bring these insights together in a basic conceptual framework explaining how and why methods are used in behavioural design. Together these findings take a step towards closing critical gaps in behavioural design theory and practice. They also highlight several directions for further research on method use and uncertainty as well as behavioural design expertise and professional identity. Relevance to Design Practice – This work describes an overview of methods used across all phases of the behavioural design process, as well as a framework for understanding their selection, adaption, and use. This provides concrete guidance for identifying relevant methods and subsequently applying them in behavioural design.
Title: Enhanced photovoltage for inverted planar heterojunction perovskite solar cells

: The highest power conversion efficiencies (PCEs) reported for perovskite solar cells with inverted planar structures are still inferior to those of PSCs with regular structures, mainly 25 because of lower open-circuit voltages ( V oc ). Herein, we report a strategy to reduce nonradiative recombination
Non-variability of the fine-structure constant over cosmological time scales

A statistical analysis of fine splitting of C IV, N V, O VI, Mg II, Al III and Si IV doublet absorption lines in quasar spectra is carried out in order to estimate a possible time variation of the fine-structure constant α=e 2 /ħc over cosmological time scales t∼10 10 yr. The observational basis of the analysis is a catalogue of 1414 pairs of wavelengths with redshifts z=0.2−3.7, compiled from data published in 1980−1992. Robust statistical estimates like the trimmed mean are used as well as the least squares. No statistically significant time variation of α is found. The estimate α −1 dα/dz=(−0.6±2.8) 10 −4 is obtained
Classification of Alzheimer’s Disease Based on Core-Large Scale Brain Network Using Multilayer Extreme Learning Machine

Various studies suggest that the network deficit in default network mode (DMN) is prevalent in Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Besides DMN, some studies reveal that network alteration occurs in salience network motor networks and large scale network. In this study we performed classification of AD and MCI from healthy control considering the network alterations in large scale network and DMN. Thus, we constructed the brain network from functional magnetic resonance (fMR) images. Pearson’s correlation-based functional connectivity was used to construct the brain network. Graph features of the brain network were converted to feature vectors using Node2vec graph-embedding technique. Two classifiers, single layered extreme learning and multilayered extreme learning machine, were used for the classification together with feature selection approaches. We performed the classification test on the brain network of different sizes including the large scale brain network, the whole brain network and the combined brain network. Experimental results showed that the least absolute shrinkage and selection operator (LASSO) feature selection method generates better classification accuracy on large network size, and that feature selection with adaptive structure learning (FSAL) feature selection technique generates better classification accuracy on small network size.
Automated Threat Detection In X-Ray Imagery For Advanced Security Applications

—In areas of high security, like airports, etc, X-Ray machines are used to scan baggage to look for hazardous objects such as guns, knives, razor blades. But, in a manual scan, it is easy to miss some details. Also manual scan is a tedious and time-consuming process. We investigate and compare several algorithms for detection of harmful/hazardous objects like razor blades/handguns etc in X-Ray images of travellers’ baggage. Deep Convolutional neural networks such as RCNN, Detectron, RetinaNet and Yolo has shown great results in object detection and recognition. We plan to use the object detection techniques and apply them to improvise upon the already existing Automatic baggage screening methods.
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking

We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained entity types and entity descriptions to perform linking. The model performs mention detection, fine-grained entity typing, and entity disambiguation for all mentions within a document in a single forward pass, making it more than 60 times faster than competitive existing approaches. ReFinED also surpasses state-of-the-art performance on standard entity linking datasets by an average of 3.7 F1. The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking. The combination of speed, accuracy and scale makes ReFinED an effective and cost-efficient system for extracting entities from web-scale datasets, for which the model has been successfully deployed.
Assessing Future Changes in the East Asian Summer Monsoon Using CMIP5 Coupled Models

Future changes in the East Asian summer monsoon (EASM) are estimated from historical and Repre- sentative Concentration Pathway 6.0 (RCP6) experiments of the ﬁfth phase of the Coupled Model Intercomparison Project (CMIP5). The historical runs show that, like the CMIP3 models, the CMIP5 models produce slightly smaller precipitation. A moisture budget analysis illustrates that this precipitation deﬁcit is due to an underestimation in evaporation and ensuing moisture ﬂux convergence. Of the two components of the moisture ﬂux convergence (i.e., moisture convergence and horizontal moist advection), moisture convergence associated with mass convergence is underestimated to a greater degree. Precipitation is anticipated to increase by 10%–15% toward the end of the twenty-ﬁrst century over the major monsoonal front region. A statistically signiﬁcant increase is predicted to occur mostly over the Baiu region and to the north and northeast of the Korean Peninsula. This increase is attributed to an increase in evaporation and moist ﬂux convergence (with enhanced moisture convergence contributing the most) in- duced by the northwestward strengthening of the North Paciﬁc subtropical high (NPSH), a characteristic feature of the future EASM that occurred in CMIP5 simulations. Along the northern and northwestern ﬂank of the strengthened NPSH, intensiﬁed southerly or southwesterly winds lead to the increase in moist convergence, enhancing precipitation over these areas. However, future precipitation over the East China Sea is projected to decrease. In the EASM domain, a local mechanism prevails, with increased moisture and moisture convergence leading to a greater increase in moist static energy in the lower troposphere than in the upper troposphere, reducing tropospheric stability.
Rationalizing Dark Patterns: Examining the Process of Designing Privacy UX Through Speculative Enactments

locality, anonymity and pseudonymity, security and access, and recourse. These are primary principles in the GDPR as well. Moreover, the legal requirements for these principles have been operationalized in the form of GDPR consent notices , in which users are presented with explicit choices regarding their privacy when accessing a website (Degeling et al., 2019). Research has found that these screens paradoxically limit user choice by virtue
Mapping the Long-Term Evolution of the Post-Event Deformation of the Guang’an Village Landslide, Chongqing, China Using Multibaseline InSAR Techniques

On 21 October 2017, days of heavy rainfall triggered a landslide in Guang’an Village, Wuxi County, Chongqing, China. According to the field investigation after the incident, there is still a massive accumulation body, which could possibly reactivate the landslide. In this study, to explore the long-term evolution of the deformation after the initial Guang’an Village Landslide, a time-series InSAR technique (TS-InSAR) was applied to the 128 ascending Sentinel-1A datasets spanning from October 2017 to March 2022. A new approach is proposed to enhance the conventional TS-InSAR method by integrating LiDAR data into the TS-InSAR process chain. The spatial–temporal evolution of post-event deformation over the Guang’an Village Landslide is analyzed based on the time-series results. It is found that the post-event deformation can be divided into three main stages: the post-failure stage, the post-failure and reactivation stage, and the reactivation stage. It is also suggested that, although the study area is currently under the reactivation stage, there are two active deformation zones that may become the origin of a secondary landslide triggered by heavy rainfall in the future. Moreover, the nearby Yaodunzi landslide might also play an important role in the generation and reactivation of a secondary Guang’an Village Landslide. Therefore, continuous monitoring for post-event deformation of the Guang’an Village Landslide is important for early warning of a secondary landslide in the near future.
An Energy-Efficient Partial Data Offloading-Based Priority Rate Controller Technique in Edge-Based IoT Network to Improve QoS

In last few years, the Internet of Things (IoT) platform, which comprises a significant number of devices equipped with sensors and utilized for monitoring and actuation operations, has seen significant growth. Massive volumes of data are collected by IoT devices, which are subsequently sent to the cloud for analysis and forecasting. IoT data that is directly sent to the cloud can congest IT infrastructure that is used for other purposes. As a result, data offloading has become a popular issue in academic and industrial sectors, specifically for traffic-intensive applications that can benefit from offloading to local edge and cloud infrastructure. A lot of in-depth studies have been undertaken in areas where sensor device mobility is high in order to ensure efficient data offloading. Nevertheless, very few studies have taken into account the issue of data offloading with sensor devices that have very limited mobility, like industrial IoT sensors, which really needs more attention as it is one of the most data-trafficking areas. To counter this problem, a data offloading-based heuristic technique using edge computing is proposed in IIoT-based applications. This will reduce data transferring quantity, resulting in lower data migration capacity, bandwidth usage, and load, lowering operational costs. It is being put through extensive testing to see how it compares to existing algorithms. The experimental results show that the proposed algorithm is superior in terms of energy consumption, dependability, and resilience under various situations.
Ofﬂine Policy Evaluation with New Arms

We study ofﬂine policy evaluation in a setting where the target policy can take actions that were not available when the data was logged. We analyze the bias of two popular regression-based estimators in this setting, and upper-bound their biases by a quantity we refer to as the reward regression risk . We show that the estimators can be asymptotically unbiased and uniformly convergent if the reward regression risk asymptotically goes to zero. We then upper-bound the reward regression risk using tools from domain adaptation. This analysis motivates using domain adaptation algorithms to train reward predictors for ofﬂine policy evaluation. It also suggests future directions for developing improved ofﬂine policy optimization algorithms
PerKGQA: Question Answering over Personalized Knowledge Graphs

Previous studies on question answering over knowledge graphs have typically operated over a single knowledge graph (KG). This KG is assumed to be known a priori and is lever-aged similarly for all users’ queries during inference. However, such an assumption is not applicable to real-world settings, such as healthcare, where one needs to handle queries of new users over unseen KGs during inference. Furthermore, privacy concerns and high computational costs render it infeasible to query the single KG that has information about all users while answering a specific user’s query. The above concerns motivate our question answering setting over personalized knowledge graphs (P ER KGQA) where each user has restricted access to their KG. We observe that current state-of-the-art KGQA methods that require learning prior node representations fare poorly. We propose two complementary approaches, P ATH CBR and P ATH RGCN for P ER KGQA. The former is a simple non-parametric technique that employs case-based reasoning, while the latter is a parametric approach using graph neural networks. Our proposed methods circumvent learning prior representations, can generalize to unseen KGs, and outperform strong baselines on an academic and an internal dataset by 6.5% and 10.5%.
Tonsillar and Lung-Cultured Mast Cells Gene Expression Profiles between Human and OX40 on Human T Cells: Comparison of between OX40 Ligand on Human Mast Cells T Cell Proliferation by Direct Cross-Talk

Mast cells (MCs) are the primary effector cells in allergic reactions and have also been found to activate T cells and to reside in close physical proximity to T cells. However, the molecular mechanisms involved in the MC-T cell interaction remain unclear. We hypothesized that human tonsillar MCs, which locate in the interfollicular areas, might interact with T cells. Thus, we ﬁrst established a culture system of human tonsillar MCs and then compared gene expression proﬁles of tonsillar MCs with that of lung MCs before and after aggregation of Fc (cid:1) RI by using high-density oligonucleotide probe arrays. Here we show that resting tonsillar MCs, when compared with lung MCs, revealed signiﬁcantly higher expression levels for CC chemokines (CCL3 and 4), which recruit T cells, and for TNFR superfamilies (OX40 ligand and 4-1BB ligand), which induce proliferation of T cells. After aggregation of Fc (cid:1) RI, not only tonsillar MCs but also lung MCs up-regulated the expression of these molecules. We conﬁrmed that T cell proliferation is induced in direct cross-talk by the MC surface molecule OX40 ligand. These results suggest that human MCs may play important roles in adaptive immunity through the T cell responses. The Journal of Immunology, 2004, 173: 5247–5257.
The Vector representation of the Standard Quantum Process Tomography

The characterization of the evolution of a quantum system is one of the main tasks to accomplish to achieve quantum information processing. The standard quantum process tomography (SQPT) has the unique property that it can be applied without introducing any additional quantum resources. In present work, we shall focus on the following two topics about the SQPT. At first, in the SQPT protocol for a $d$-dimensional system, one should encounter a problem in solving of a set of $d^4$ linear equations in order to get the matrix containing the complete information about the unknown quantum channel. Until now, the general form of the solution is unknown. And a long existed conviction is that the solutions are not unique. Here, we shall develop a self-consistent scheme, in which bounded linear operators are presented by vectors, to construct the set of linear equations. With the famous Cramer's rule for the set of linear equations, we are able to give the general form of the solution and prove that it is unique. In second, the central idea of the SQPT is to prepare a set of linearly independent inputs and measuring the outputs via the quantum state tomography (QST). Letting the inputs and the measurements be prepared by two sets of the rank-one positive-operator-valued measures [POVMs], where each POVM is supposed to be linearly independent and informationally complete (IC), we observe that SQPT now is equivalent to deciding a unknown state with a set of product IC-POVM in the $d^2$-dimensional Hilbert space. Following the general linear state tomography theory, we show that the product symmetric IC-POVM should minimize the mean-square Hilbert-Schmidt distance between the estimator and the true states. So, an optimal SQPT can be realized by preparing both the inputs and the measurements as the symmetric IC-POVM.
mitochon-Occurrence of a Natural Intergeneric Hybrid between Rhodeus pseudosericeus and Acheilognathus signifer (Pisces: Cyprinidae) from the Namhangang (river), Korea

a survey of ichthyofauna at the Jucheongang (stream) of the Namhangang (river) in Korea, we collected a natural hybrid between R. pseudosericeus and A. signifer in their sympatric area. So the aim of the present study is to report the morphological characters and analysis of mitochon-Occurrence ABSTRACT A natural intergeneric hybrid between Rhodeus pseudosericeus and Acheilognathus signifer was collected in their sympatric area, the Jucheongang (stream), the Namhangang (river), Korea. Morphological characters as well as mitochondrial cytochrome b ( mt - cyb ) gene were investigated to confirm the natural hybrid origin. In the comparison of morphological characters, the natural hybrid appeared to have intermediate characters between two parental species in several key characters such as the band of dorsal fins, the color pattern of body, and the body spot and stripe. Also, the collected specimen had an incomplete lateral line with a large number of pored scales. In analysis of mt - cyb gene, it revealed that the maternal species of the natural hybrid appeared to be R. pseudosericeus due to their 100% sequence identity. Thus, such results gave convincing evidence for the occurrence of a natural intergeneric hybrid between Rhodeus pseudosericeus and Acheilognathus signifer .
Hydrological Extremes and their Association with ENSO Phases in Ethiopia

Ethiopia is a rain fed agriculture country, which is subjected to high climate variability in space and time, leading to hydrological extremes causing loss of life and property more frequently. Droughts are more common and sometime floods are experienced in various parts of the country. Being a tropical country, the inter-annual climate variability in Ethiopia is dominated by ENSO (ElNino and Southern Oscillation). In this study, an attempt has been made to determine the occurrence of droughts and floods on monthly basis, by calculating the monthly SPI (Standardized Precipitation Index) using the available rainfall data during (1975-2005) at selected 26 stations that spread across the country. Based on the monthly SPI values computed, the droughts and floods of different intensities; extreme, severe and dry have been determined for all stations. The frequencies of the droughts and floods on monthly scale during the two rainy seasons, Belg (Feb-May) and Kiremt (Jun-Sep) seasons have been determined. For instance, during Belg season, there were 11 extreme (SPI < -2.0) droughts at Nazreth, 10 severe (SPI between -1.99 and -1.50) droughts at Diredawa and 14 moderate (SPI between -1.49 and -1.00) droughts at Kulumsa. Total number of droughts of all intensities over the study period is also highest (22) at Kulumsa and lowest (8) at Mekelle. Moreover, at Kulumsa both the numbers of droughts (22) and floods (22) during Belg are more, which shows that the rainfall variability is the highest at this station. The association of the hydrological extremes during the two rainy seasons, belg and kiremt with the ENSO phases has also been examined, which forms a basis for the prediction of the occurrence of droughts (dry conditions) and floods (wet conditions) at individual stations using ENSO phases. Therefore, this study aims at determining the frequencies of droughts and floods at various rainfall stations in Ethiopia using the Standardized Precipitation Index (SPI) and examining their association with SOI phases within the historical record of rainfall data.
A MACHINE LEARNING ALGORITHM WITH SELF-UPDATE PARAMETER CALIBRATION TO IMPROVE INTRUSION DETECTION OF DDOS IN COMMUNICATION NETWORKS

Currently DDoS attack has become one of the most common network attacks worldwide. This is largely due to the fact that we live in the age of the Internet of Things, with the rapid development of computer and communication technology evolving into big, complex and distributed systems that are exposed to several kinds of attacks in addition to new threats. In order to detect intruders in an efficient and timely manner, a real time detection mechanism, proficient in dealing with a variety of forms of attacks is highly important. However, due to the uniformity and evolution of DDoS attack modes and the variable size of attack traffic, there has not yet been a detection method with satisfactory detection accuracy at present and considerable effort made by both the scientific research and industry for several years to mitigate DDoS detection potential DDoS target indicate that DDoS attacks have not been fully addressed. This study therefore aimed at developing a machine learning a Machine learning algorithm with self-update parameter calibration to improve intrusion detection of DDoS in communication networks, in two steps: Feature extraction and model detection that is, we extract DDoS attack traffic characteristics with large proportion and compare the data packages according to the protocol in the Feature extraction stage whereas in the model detection stage, the features that were extracted are used as the input features in machine learning after which the Random Forest algorithm used to train the developed detection model. Finally, the model was validated by three metrics (accuracy, false negative rate and false positive rate). The results show that the DDoS attack detection method based on machine learning proposed in this study has a good detection rate and accuracy compared to the current popular DDoS attack detection methods. The developed model achieved accuracy of 96% over a real-time dataset.