Successful Search Research Articles

Fast quantum search driven by environmental engineering

Studies have demonstrated that a joined complete graph is a typical mathematical model that can support a fast quantum search. In this paper, we study the implementation of joined complete graphs in atomic systems and realize a quantum search of runtime based on this implementation with a success probability of 50%. Even though the practical systems inevitably interact with the surrounding environment, we reveal that a successful quantum search can be realized through delicately engineering the environment itself. We consider that our study will bring about a feasible way to realize quantum information processing including quantum algorithms in reality.

A New Churn Prediction Model Based on Deep Insight Features Transformation for Convolution Neural Network Architecture and Stacknet

Predicting churn has become a critical issue for service providers around the world. In particular, telecom operators for whom acquiring new customers is four times more costly than retaining existing ones. To keep up with the market, considerable investments are made to develop new anti-churn strategies, including machine learning models that are increasingly used in this field. In the proposed work, we combine three stages. In first stage, by using deepInsight, we transform the attributes of dataset into images in order to take the advantage of the strength of convolution networks in detecting hidden patterns in the dataset. In second stage, we use deep convolutional neural network for features extraction. In the last stage, we built a three-layer Stacknet of eight selected algorithms using a successive split-grid search for classification and churn prediction. The proposed model obtained the best accuracy score of 83,4%, better than the others proposed models in literatures.

Gaming experience predicts UAS operator performance and workload in simulated search and rescue missions

Unmanned aircraft systems (UAS) operator training and selection procedures are still being refined to effectively address challenges related to performance, workload, and stress in UAS operation. Research suggests that experience with commercial videogames may test skills relevant to modern UAS operation. This study investigated the ability of videogame experience to predict operator performance, workload, and stress. Forty-nine participants performed 9 trials of a simulated search and rescue mission. It was expected that participants who more frequently played videogames would report lower levels of distress and workload, higher task engagement, and better overall performance. Results showed that gaming experience was negatively correlated with subjective workload and positively correlated with multiple measures of performance. Furthermore, nearly all observed gender-related differences were not present when gaming experience was controlled for. These results have implications for the role of gaming experience in remotely operated systems operator recruitment, selection, and training. Practitioner summary: This study examined how gaming experience influences UAS operator success in simulated search and rescue missions. Participants reported on their experience playing videogames before completing multiple experimental trials on a desktop computer. Results indicated that experience playing videogames significantly impacted performance, workload, and stress.

Transceiver Decoupling of Multivariate Symmetric Hybrid Precoding Based on 5G

Mobile Internet will promote the continuous change of human interaction, leading to an increase in mobile traffic, so the demand for network bandwidth and data volume is rising rapidly, which is also one of the problems that 5G needs to solve. The mobile communication network of the railway system has the characteristics of high-speed user mobility, large-scale group mobility of users, high certainty of user mobile lines, and high QoS requirements for dispatching information. In order to improve the transmission reliability requirements of the railway system for wireless communication, a quick search method algorithm based on GMCS model to encode the number of each subinterval is proposed. Hybrid precoding is designed according to multivariate symmetry rules. The target beam is designed according to the GMCS model, and the hierarchical training beam is designed to minimize the mean square error between the training beam and the target beam as the objective function. Then, the fast search model based on beam overlap is extended to NLoS to solve the problem of misjudgment caused by multipath. In the simulation experiment, it proves that the search success rate of the research in this paper is 10% higher than that of the traditional algorithm. It improves the search speed and has obvious advantages in complexity. It can provide a dynamic reliable conversion mechanism for the railway communication environment, reduce the transmission power of the base station, and optimize the actual effect of uplink and downlink service requirements.

ПРОБЛЕМА РОЗВИТКУ ОБ’ЄКТНИХ ВІДНОСИН В ІДЕЯХ ПСИХОАНАЛІТИКІВ «НЕЗАЛЕЖНОЇ» ГРУПИ

The article reveals the key ideas of psychoanalysts of the «independent» group of the British Psychoanalytic Association. To date, the framework of the modern anthropological system consists of psychoanalytic theories of personality development. In addition to the orthodox (classical) psychoanalysis of S. Freud, individual psychology of A. Adler and analytical psychology of Jung, an important place in the system of psychoanalytic directions is occupied by the theory of object relations - psychoanalytic current, which is based on the idea that as much the satisfaction of internal urges, as Freud argued, as the successful search and establishment of relationships with others. However, in addition to the work of M. Klein and the followers of her scientific and practical school, the creative work of other representatives of British psychoanalysis remains little studied for Ukrainian science. On the basis of the theoretical analysis of literary sources the main principles of psychoanalytic pedagogy within the theory of object relations are revealed. The main features of the theory of object relations, around which the representatives of the «independent» group gathered, have been identified. Emphasis is placed on the common and different views of British psychoanalysts on the formation of the child's personality, as well as criticism of the ideas of classical psychoanalysis. In particular, it is proved that the representatives of the «independent» group departed from the classical Freudian idea of the dominant influence of the Oedipus complex and the rigid «Super Ego» on the formation of personality. It is proved that the representatives of the «independent» group adhered to three common ideas: 1) each person from birth seeks to object relations, rather than to meet needs; 2) the interaction of the newborn with the outside world, especially in the dyad with the mother, has a decisive influence on the evolution of his inner world, which is considered through the prism of introjected inner objects; 3) a person's attitude to external objects is determined by imaginary internal object relations, which were formed in the child's psyche at an early age. It was concluded that the activities of analysts of the «independent» group focused on the problems of interpersonal communication, the child's interaction with parents at an early age, as well as the problems of identification and self-perception. Prospects for further research are planned in revealing the peculiarities of the work of Balint’s groups and the organization of scientific and practical measures for the prevention of pedagogical burnout.

A Broyden-based algorithm for multi-objective local-search optimization

In multi-objective optimization, the direction vectors in the objective space that improve the current non-dominated set are named improvement directions. The Improvement Direction Mapping (IDM) methods apply a spatial transformation to map improvement directions in the objective space to search for directions in the variable space. Jacobian based transformations can perform the mapping, however, they require the analytic expressions of the objectives and their derivatives. Hence, they serve as a reference in academic problems but are impractical in real-world applications. Furthermore, they are commonly ill-conditioned and under-determined; consequently, they deviate the search as the current non-dominated set approaches to the Pareto set. This work proposes a solution via an iterative updating based on the Broyden method. The proposal promotes parallelism between the displacement of the non-dominated solutions in the objective space and improvement directions delivered by Chebyshev scalarizing functions. Compared to other transformations reported in the specialized literature, the proposed method demonstrates a better conditioning that provokes to err at a lesser extent in providing successful search directions. This impacts the algorithm performance, mainly in locations close to the Pareto set. These advantages are demonstrated using benchmark functions and metrics.

Barriers to the effective exploitation of migrants' social and cultural capital in hospitality and tourism: A dual labour market perspective

This article examines migrants' labour market entry experiences and positions in hospitality and tourism from a dual labour market theory standpoint. We conducted thirty interviews with migrants from Latin America, the Middle East, and Sub-Saharan Africa. The transcripts were first analysed through an open coding framework. That was followed by emotion and sentiment analyses for the interviewees' transcripts using Natural Language Processing approach (NLP) with a programming code written in Python. This article found that migrants' high representation in tourism and hospitality is linked to the immense cultural capital they harbour. However, migrants are often at the sector's lower end and fail to build on their social and cultural capital. The research contributes to the understanding that migrants' positions within tourism and hospitality are misaligned with migrants' credentials. Ethnicity impacts job search success and attainment of higher ranks significantly. The underemployment of migrants in hospitality and tourism leads to human capital loss for both migrants and host economies.

Trends in Technological Innovation in Education During a Time of Pandemic Disruption: Examining The Perspective of Teaching Digital Pedagogy

Since the world has been disturbed by the increasing impact of Covid-19, various countries have applied distance learning, responding to the pandemic policy on an international scale. The author believes that the learning of technological innovation for Indonesia is different from that of developed countries, so this study tries to understand the digital pedagogical perspective of teaching. A series of successful electronic literature searches were carried out on several scientific journal publications, books, and other website literature to get the intended understanding. The data that we have obtained are then analyzed in-depth, involving critical coding and high evaluation to obtain understanding and conclusions that can answer the core questions of this study. We used all data from secondary data, namely scientific evidence published between 2010 and 2021. Then the design of this study was in a descriptive qualitative system under a phenomenological approach, an effort to explore data as widely as possible. After discussing the results, we included that pedagogical technology, which is a method that discusses how teachers handle their duties assisted by technological innovation. However, the pedagogy also helps teachers become student learning facilitators by using existing technological facilities to improve learning outcomes. While at the same time developing students' potential even though they are being afflicted with learning disorders due to pandemics. We hope this result can be a contributing input for further studies.

Adapting systematic scoping study methods to identify cancer-specific physical activity opportunities in Ontario, Canada

BackgroundIdentifying cancer-specific physical activity programs and post-secondary courses targeting students in academic settings (i.e., “real world” opportunities) may promote physical activity behaviors among cancer survivors. Using knowledge synthesis methods such as systematic scoping study methods may facilitate knowledge tool development and guide evidence-based practice to improve knowledge transfer. However, identifying these opportunities poses a challenge as systematic scoping study methods have yet to be applied and adapted to this context. Thus, to extend systematic scoping study methods, the purpose of the current investigation is to describe the adaptation of systematic scoping study methods in the context of cancer-specific “real world” opportunities in Ontario, Canada.MethodsSystematic scoping study methods were adapted to develop a knowledge tool, which was a credible resource website for researchers, clinicians, and survivors. Three search strategies including Advanced Google Search, targeted website search, and consultations with experts were used to identify eligible (e.g., appropriate for cancer survivors, offered in the community) cancer-specific physical activity programs. Only the targeted website search was used to search post-secondary institutions because they are centralized onto one government website.ResultsFifty-eight programs and 10 post-secondary courses met the eligibility criteria. Relevant data from these opportunities were extracted, charted, synthesized, and uploaded onto the resource website. The most successful search strategy for cancer-specific physical activity programs was the targeted website search followed by Google Advanced Search and consultations with content experts.ConclusionsChallenges were experienced due to lack of standard reporting among opportunities, bias of potentially relevant records, and changing nature of resulting records. The current investigation demonstrated that systematic scoping study methods can be applied to cancer-specific physical activity programs and post-secondary courses in the context of cancer survivorship in Ontario yielding robust results. The method can be further adapted and updated in future knowledge syntheses in health-related contexts.Systematic review registrationThe systematic scoping review method protocol has not been registered.

Unpacking High-Impact Practices in the Arts: Predictors of College, Career, and Community Engagement Outcomes

ABSTRACT Research on high-impact practices (HIPs) demonstrates positive links to student learning and development, but generally does not focus on discipline-specific activities, such as working with an artist in the community and portfolio completion. This study seeks to identify beneficial HIPs for arts training through an analysis of 23,916 arts alumni from 77 postsecondary institutions. A series of regression models suggest that HIP participation was associated with gains in academic abilities and career skills, higher levels of college satisfaction, more successful job searches, greater likelihood of employment in the arts and avocational arts practice, and more frequent arts community involvement.

Natural product anticipation through synthesis.

Natural product synthesis remains one of the most vibrant and intellectually rewarding areas of chemistry, although the justifications for pursuing it have evolved over time. In the early years, the emphasis lay on structure elucidation and confirmation through synthesis, as exemplified by celebrated studies on cocaine, morphine, strychnine and chlorophyll. This was followed by a phase where the sheer demonstration that highly complex molecules could be recreated in the laboratory in a rational manner was enough to justify the economic expense and intellectual agonies of a synthesis. Since then, syntheses of natural products have served as platforms for the demonstration of elegant strategies, for inventing new methodology 'on the fly' or to demonstrate the usefulness and scope of methods established with simpler molecules. We now add another aspect that we find fascinating, viz. 'natural product anticipation'. In this Review, we survey cases where the synthesis of a compound in the laboratory has preceded its isolation from nature. The focus of our Review lies on examples where this anticipation of a natural product has triggered a successful search or where synthesis and isolation have occurred independently. Finally, we highlight cases where a potential natural product structure has been suggested as a result of synthetic endeavours but not yet confirmed by isolation, inviting further collaborations between synthetic and natural product chemists.

Associations between Categorization Rules and Categorical Visual Search

One of the tasks of modern categorization theories is the search for cognitive functions associated with categorical learning. Foreign research has demonstrated an association between visual search and continuum category learning (categorical representation): success of the visual search based on single-feature categorical rules does not change with increasing number of distractors, but collapses when the search is supported by information integration categorical rules. The purpose of the current study was to identify links between learning discrete categories and the success of visual search in order to test the previously obtained effect on another type of categories — verbal rules (explicit type) and prototypes (implicit type). It was assumed that, since the representation based on prototypes would have a lower level of awareness, its support for the visual search based (success) would be lower than while forming verbal rules. Participants (N = 121) completed a task where they learnt a new artificial category belonging to one of two types of rules and immediately after that performed a categorical visual search task where they were asked to search for a target relevant to the category they learnt. We found that after learning the verbal rule as well as forming prototypes, visual search success did not collapse with increasing number of distractors. We also found that the higher the success rate in learning a new category, the more effective the visual search was, regardless of the type of rule. Thus, we have shown for the first time that visual search can be supported by different types of categories, both explicit and implicit. We explain the present results and their difference from the results of the previous study by the fact that categories based on discrete features (as opposed to continuums) allows to create more robust representations that are easier to use in non-categorical tasks such as visual search.

Searching a High Performance Feature Extractor for Text Recognition Network.

Feature extractor plays a critical role in text recognition (TR), but customizing its architecture is relatively less explored due to expensive manual tweaking. In this article, inspired by the success of neural architecture search (NAS), we propose to search for suitable feature extractors. We design a domain-specific search space by exploring principles for having good feature extractors. The space includes a 3D-structured space for the spatial model and a transformed-based space for the sequential model. As the space is huge and complexly structured, no existing NAS algorithms can be applied. We propose a two-stage algorithm to effectively search in the space. In the first stage, we cut the space into several blocks and progressively train each block with the help of an auxiliary head. We introduce the latency constrain into the second stage and search sub-network from the trained supernet via natural gradient descent. In experiments, a series of ablation studies are performed to better understand the designed space, search algorithm, and searched architectures. We also compare the proposed method with various state-of-the-art ones on both hand-written and scene TR tasks. Extensive results show that our approach can achieve better recognition performance with less latency. Code is avaliable at https://github.com/AutoML-Research/TREFE.

A Fast and Scalable Algorithm for Prior Art Search

Companies and research institutes are acquiring monopolized technologies through patents to survive in a rapidly changing market. The applicant is guaranteed by law the exclusive right to the technology for a certain period. A prior art search is an essential task for this purpose. Therefore, the need for a successful prior art search is increasing. Previous studies have disadvantages because relevant fields are limited, requiring an in-depth understanding of specific technologies. This paper proposes a method to overcome these limitations using patent text and citation information. The proposed method searches for prior art using a citation network based on the shortest path derived from a breadth-first search. We examined based on experiments whether the proposed method could be applied to the industry. Based on the results, the proposed algorithm can efficiently search for prior art. Furthermore, it was possible to construct a counterstrategy based on the characteristics of the applicant. The code will be made available at github.com/lee-ju/k_step_PIC.

Bluetooth Low Energy for Close Detection in Search and Rescue Missions With Robotic Platforms: An Experimental Evaluation

In the field of rescue robotics, data collection about the environment and efficient communications are fundamental for the success of search and rescue missions. Digitalization provides new ways of detecting and localizing potential victims via the wireless devices carried by the users. Nowadays, the number of personal Bluetooth low energy wearables in use (smartbands, smartwatches, earbuds...) increases constantly, being a yet-to-be-exploited personal radio frequency beacon in the case of an emergency, where the user may not be localized and unconscious. In this paper, the results of experimental tests of a Bluetooth low energy based detection system ported by terrestrial and aerial robots are provided, in order to test the feasibility of such system for the localization of the victims in unknown complex disaster areas. The results show that the tested devices can be reliably detected up to 15 meters away when using transmission power values typical of a smartphone, while being able to detect even lightly burdened devices. These results support the idea of developing an algorithm for the delimitation of areas of interest for the search and rescue groups, influencing the routes followed by the robot with the objective of exploring the detected devices area in the search of victims.

Learning Temporal-Correlated and Channel- Decorrelated Siamese Networks for Visual Tracking

Recently, Siamese network based trackers have attracted growing popularity in visual tracking, which tackle the tracking by template matching between the initial template and successive search regions. The initial template patch is generally encoded into a convolutional feature for matching. However, the limited representational capability of the template feature limits the tracking accuracy. Besides, this fixed representation also fails to adapt to the target appearance changes. To alleviate these issues, we improve the Siamese trackers by introducing temporal correlation and channel decorrelation mechanisms. On the one hand, we consider the channel-wise correlations between the initial and historical template features to adaptively aggregate informative channel-wise representations for template update. On the other hand, we propose a decorrelation regularization to weaken the channel-wise correlations of individual template features. By end-to-end training, we learn a more complete and adaptive template for accurate object tracking. We demonstrate the generality of our approach by applying it to two prevalent Siamese trackers, i.e., SiamFC and SiamRPN. Extensive experiments on seven benchmark datasets verify the effectiveness of our method.

Time and position aware resource search algorithm for the mobile peer-to-peer network using ant colony optimisation

The progressive growth of wireless mechanisms and the wide popularity of intelligent devices are attracting particular attention to peer-to-peer mobile ad hoc networks (P2P MANET). Increasing the efficiency to search the resources became an important research topic in the P2P MANETs. Most existing research gives more emphasis on position-based clustering techniques and does not give preference to time. Due to not giving any preference to time, the existing approaches are not so appropriate to improve the efficiency to search the resources in P2P MANETs. This paper suggests a navel resource search approach that first uses position-aware peer clustering strategy. Then, using an ant colony optimisation method with pheromones, it chooses the preeminent neighbour's peer that is of time-aware neighbouring peer's resource preferences and the time-aware neighbouring peer's availability. Based on the results of the experiment, this approach outweighs other approaches in terms of search delay, overhead traffic, and search success rate.

Feeling Positive, Negative, or Both? Examining the Self-Regulatory Benefits of Emotional Ambivalence

According to self-regulation theories, affect plays a crucial role in driving goal-directed behaviors throughout employees’ work lives. Yet past work presents inconsistent results regarding the effects of positive and negative affect with theory heavily relying on understanding the separate, unique effects of each affective experience. In the current research, we integrate tenets of emotional ambivalence with self-regulation theories to examine how the conjoint experience of positive and negative affect yields benefits for behavioral regulation. We test these ideas within a self-regulatory context that has frequently studied the benefits of affect and has implications for all employees at one point in their careers: the job search. Adopting a person-centered (i.e., profile-based) perspective across two within-person investigations, we explore how emotional ambivalence relates to job search success (i.e., interview invitations, job offers) via job search self-regulatory processes (i.e., metacognitive strategies, effort). Results illustrate that the subsequent week (i.e., at time t + 1; Study 1) and month (Study 2) after job seekers experience emotional ambivalence (i.e., positive and negative affect experienced jointly at similar levels at time t), they receive more job offers via increased job search effort and interview invitations. Theoretical and practical implications for studying emotional ambivalence in organizational scholarship are discussed. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1553 .

A Social Interpolation Model of Group Problem-Solving.

How do people use information from others to solve complex problems? Prior work has addressed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (SIM), an agent-based model to explore the cognitive mechanisms that can underlie exploratory behavior in groups. Through results from simulation experiments, we conclude that "exploration" may not be a single cognitive property, but rather the emergent result of three distinct behavioral and cognitive mechanisms, namely, (a) breadth of generalization, (b) quality of prior expectation, and (c) relative valuation of self-obtained information. We formalize these mechanisms in the SIM, and explore their effects on group dynamics and success at solving different kinds of problems. Our main finding is that broad generalization and high quality of prior expectation facilitate successful search in environments where exploration is important, and hinder successful search in environments where exploitation alone issufficient.

Multisensory-motor integration in olfactory navigation of silkmoth, Bombyx mori, using virtual reality system.

Most animals survive and thrive due to navigational behavior to reach their destinations. In order to navigate, it is important for animals to integrate information obtained from multisensory inputs and use that information to modulate their behavior. In this study, by using a virtual reality (VR) system for an insect, we investigated how the adult silkmoth integrates visual and wind direction information during female search behavior (olfactory behavior). According to the behavioral experiments using a VR system, the silkmoth had the highest navigational success rate when odor, vision, and wind information were correctly provided. However, the success rate of the search was reduced if the wind direction information provided was different from the direction actually detected. This indicates that it is important to acquire not only odor information but also wind direction information correctly. When the wind is received from the same direction as the odor, the silkmoth takes positive behavior; if the odor is detected but the wind direction is not in the same direction as the odor, the silkmoth behaves more carefully. This corresponds to a modulation of behavior according to the degree of complexity (turbulence) of the environment. We mathematically modeled the modulation of behavior using multisensory information and evaluated it using simulations. The mathematical model not only succeeded in reproducing the actual silkmoth search behavior but also improved the search success relative to the conventional odor-source search algorithm.