Confirmation Bias in Simulated Child Sexual Abuse (CSA) Interviews: Abuse Assumption Influences Interviewing and Decision-making Processes

PurposeResearch has shown that confirmation bias plays a role in legal and forensic decision-making processes. However, to our knowledge, no study has examined how it manifests itself when interviewing an allegedly abused child. MethodIn the present study, we used data from a series of experiments in which participants interviewed child avatars to examine how an assumption of abuse based on preliminary information influenced decision-making and interviewing style. Interview training data from eight studies with students, psychologists and police officers were included in the analyses.ResultsWe found that interviewers’ preliminary assumption of sexual abuse having taken place predicted 1) a conclusion of abuse by the interviewers after the interview; 2) higher confidence in their judgment; 3) more frequent use of not recommended question types and 4) a decreased likelihood of reaching a correct conclusion given the same number of available relevant details. ConclusionThe importance of considering how preliminary assumptions of abuse affect interview behaviour and outcomes and the implications for the training of investigative interviewers were discussed.

Rapid detection of social interactions is the result of domain general attentional processes

Using visual search displays of interacting and non-interacting pairs, it has been demonstrated that detection of social interactions is facilitated. For example, two people facing each other are found faster than two people with their backs turned: an effect that may reflect social binding. However, recent work has shown the same effects with non-social arrow stimuli, where towards facing arrows are detected faster than away facing arrows. This latter work suggests a primary mechanism is an attention orienting process driven by basic low-level direction cues. However, evidence for lower level attentional processes does not preclude a potential additional role of higher-level social processes. Therefore, in this series of experiments we test this idea further by directly comparing basic visual features that orient attention with representations of socially interacting individuals. Results confirm the potency of orienting of attention via low-level visual features in the detection of interacting objects. In contrast, there is little evidence for the representation of social interactions influencing initial search performance.

Impact of Environmental Factors on Seed Germination and Seedling Emergence of White Clover (Trifolium repens L.)

White clover (Trifolium repens L.) is cultivated as a forage crop and planted in various landscapes for soil conservation. There are numerous reports of failed white clover stands each year. A good understanding of the seed germination biology of white clover in relation to environmental factors is essential to achieve successful stand establishment. A series of experiments were conducted to investigate the impacts of light, temperature, planting depth, drought, and salt stress on seed germination and the emergence of white clover. White clover is negatively photoblastic, and seed germination averaged 63 and 66% under light and complete dark conditions 4 weeks after planting (WAP), respectively. Temperature affected the seed germination speed and rate. At 1 WAP, seeds incubated at 15 to 25 °C demonstrated a significantly higher germination rate than the low temperatures at 5 and 10 °C; however, the germination rate did not differ among the temperature treatments at 4 WAP. The results suggest that white clover germination decreases with increasing sowing depths, and the seeds should be sown on the soil surface or shallowly buried at a depth ≤1 cm to achieve an optimal emergence. White clover seeds exhibited high sensitivity to drought and salinity stress. The osmotic potential and NaCl concentration required to inhibit 50% seed germination were −0.19 MPa and 62.4 mM, respectively. Overall, these findings provide quantifiable explanations for inconsistent establishment observed in field conditions. The results obtained in this research can be used to develop effective planting strategies and support the successful establishment of white clover stands.

Combining vLAPS and Nudging Data Assimilation

The combination of techniques that incorporate observational data may improve numerical weather prediction forecasts; thus, in this study, the methodology and potential value of one such combination were investigated. A series of experiments on a single case day was used to explore a 3DVAR-based technique (the variational version of the Local Analysis and Prediction System; vLAPS) in combination with Newtonian relaxation (observation and analysis nudging) for simulating moist convection in the Advanced Research version of the Weather Research and Forecasting model. Experiments were carried out with various combinations of vLAPS and nudging for a series of forecast start times. A limited subjective analysis of reflectivity suggested all experiments generally performed similarly in reproducing the overall convective structures. Objective verification indicated that applying vLAPS analyses without nudging performs best during the 0–2 h forecast in terms of placement of moist convection but worst in the 3–5 h forecast and quickly develops the most substantial overforecast bias. The analyses used for analysis nudging were at much finer temporal and spatial scales than usually used in pre-forecast analysis nudging, and the results suggest that further research is needed on how to best apply analysis nudging of analyses at these scales.

Construction and Analysis of Integral User-Oriented Trustworthiness Metrics

Trustworthiness metrics help users to understand information system’s or a device’s security, safety, privacy, resilience, and reliability level. These metrics have different types and natures. The challenge consists of the integration of these metrics into one clear, scalable, sensitive, and reasonable metric representing overall trustworthiness level, useful for understanding if the users can trust the system or for the comparison of the devices and information systems. In this research, the authors propose a novel algorithm for calculation of an integral trustworthiness risk score that is scalable to any number of metrics, considers their criticality, and does not perform averaging in a case when all metrics are of equal importance. The obtained trustworthiness risk score could be further transformed to trustworthiness level. The authors analyze the resulting integral metric sensitivity and demonstrate its advantages on the series of experiments.

Knowledge graph embedding for data mining vs. knowledge graph embedding for link prediction – two sides of the same coin?

Knowledge Graph Embeddings, i.e., projections of entities and relations to lower dimensional spaces, have been proposed for two purposes: (1) providing an encoding for data mining tasks, and (2) predicting links in a knowledge graph. Both lines of research have been pursued rather in isolation from each other so far, each with their own benchmarks and evaluation methodologies. In this paper, we argue that both tasks are actually related, and we show that the first family of approaches can also be used for the second task and vice versa. In two series of experiments, we provide a comparison of both families of approaches on both tasks, which, to the best of our knowledge, has not been done so far. Furthermore, we discuss the differences in the similarity functions evoked by the different embedding approaches.

Extraction of Frozen Berry Raw Materials with a Vibration Tray Device

Introduction. Products containing natural extracts are in great demand. However, poor production technologies make them too expensive to satisfy consumer demand. As a result, a variety of intensification methods have been developed to increase the economic efficiency of extraction, e.g. low-frequency mechanical vibrations. However, frozen raw materials have to be processed at low temperatures, which makes the method less efficient. The research objective was to intensify the extraction process from frozen berries in a vibration tray device by increasing the temperature of the system of interacting phases. Study objects and methods. The research involved frozen cranberries and blueberries. They grow everywhere in Western Siberia and are rich vitamins and minerals. The berries were subjected to slow freezing at –18°C, which destroyed the cell structure and increased the processing efficiency. The study was carried out in a lab device with a vibrating tray. All parameters were measured by standard methods. Results and discussion. The extraction device was equipped with a jacket into which a coolant was fed, i.e. water with a temperature of 55°C. A preliminary series of experiments revealed two negative aspects associated with the supply of coolant into the jacket. First, the surface layer started to thaw, which reduced the efficiency of grinding. Second, the processing time increased. A new method was developed to solve these problems: the coolant was supplied at the end of the grinding. The time of the coolant supply depended on the type of raw materials. The processes that occurred within the device depended on two factors: the frequency of vibrations of the tray and the diameter of the holes in the tray. These factors could be adjusted to intensify the process, but they increased the power costs and energy consumption. A series of experiments determined the optimal values of these parameters. A mathematical analysis revealed regression equations, i.e. how the destruction time and power costs affected the main parameters. The established optimal process parameters made it possible to determine the minimal time of the destruction process: for cranberries – 2.5 min, for blueberries – 1.5 min. The minimal power consumption was 17.8 watts for cranberries and 11.7 watts for blueberries. Conclusion. The research increased the economic efficiency of the technological process of natural extraction, which can reduce the cost of the finished product and increase its availability. The values of the process parameters can be used to design new similar devices and serve as practical recommendations for berry extraction in vibration tray devices.

The world’s first glyphosate-resistant case of Avena fatua L. and Avena sterilis ssp. ludoviciana (Durieu) Gillet & Magne and alternative herbicide options for their control

Avena fatua and A. ludoviciana (commonly known as wild oats) are the most problematic winter grass species in fallows and winter crops in the northeast region of Australia. A series of experiments were conducted to evaluate the performance of glyphosate and alternative post-emergence herbicides on A. fatua and A. ludoviciana. This study reports the world’s first glyphosate-resistant (GR) biotypes of A. fatua and A. ludoviciana. The glyphosate dose required to kill 50% of the plants (LD50) and to reduce 50% of the biomass (GR50) for the GR biotype of A. fatua was 556 g a.e./ha and 351 g a.e./ha, respectively. These values for A. ludoviciana were 848 g a.e./ha and 289 g a.e./ha. Regardless of the growth stage (3–4 or 6–7 leaf stages), clethodim (120 g a.i./ha), haloxyfop (78 g a.i./ha), pinoxaden (20 g a.i./ha), and propaquizafop (30 g a.i./ha) were the best alternative herbicide options for the control of A. fatua and A. ludoviciana. The efficacy of butroxydim (45 g a.i./ha), clodinafop (120 g a.i./ha), imazamox + imazapyr (36 g a.i./ha), and paraquat (600 g a.i./ha) reduced at the advanced growth stage. Glufosinate (750 g a.i./ha), flamprop (225 g a.i./ha), and pyroxsulam + halauxifen (20 g a.i./ha) did not provide effective control of Avena species. This study identified alternative herbicide options to manage GR biotypes of A. fatua and A. ludoviciana.

The impact of invisible-spreaders on COVID-19 transmission and work resumption

The global impact of coronavirus disease 2019 (COVID-19) is unprecedented, and many control and prevention measures have been implemented to test for and trace COVID-19. However, invisible-spreaders, who are associated with nucleic acid detection and asymptomatic infections, have received insufficient attention in the current COVID-19 control efforts. In this paper, we analyze the time series infection data for Italy, Germany, Brazil, India and Sweden since the first wave outbreak to address the following issues through a series of experiments. We conclude that: 1) As of June 1, 2020, the proportion of invisible-spreaders is close to 0.4% in Sweden, 0.8% in early Italy and Germany, and 0.4% in the middle and late stages. However, in Brazil and India, the proportion still shows a gradual upward trend; 2) During the spread of this pandemic, even a slight increase in the proportion of invisible-spreaders could have large implications for the health of the community; and 3) On resuming work, the pandemic intervention measures will be relaxed, and invisible-spreaders will cause a new round of outbreaks.

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

Advancement in smart sensing and computing technologies has provided a dynamic opportunity to develop intelligent systems for human activity monitoring and thus assisted living. Consequently, many researchers have put their efforts into implementing sensor-based activity recognition systems. However, recognizing people’s natural behavior and physical activities with diverse contexts is still a challenging problem because human physical activities are often distracted by changes in their surroundings/environments. Therefore, in addition to physical activity recognition, it is also vital to model and infer the user’s context information to realize human-environment interactions in a better way. Therefore, this research paper proposes a new idea for activity recognition in-the-wild, which entails modeling and identifying detailed human contexts (such as human activities, behavioral environments, and phone states) using portable accelerometer sensors. The proposed scheme offers a detailed/fine-grained representation of natural human activities with contexts, which is crucial for modeling human-environment interactions in context-aware applications/systems effectively. The proposed idea is validated using a series of experiments, and it achieved an average balanced accuracy of 89.43%, which proves its effectiveness.