Past Search Research Articles

Indetermsoft-Set-Based D* Extra Lite Framework for Resource Provisioning in Cloud Computing

Cloud computing is an immensely complex, huge-scale, and highly diverse computing platform that allows the deployment of highly resource-constrained scientific and personal applications. Resource provisioning in cloud computing is difficult because of the uncertainty associated with it in terms of dynamic elasticity, rapid performance change, large-scale virtualization, loosely coupled applications, the elastic escalation of user demands, etc. Hence, there is a need to develop an intelligent framework that allows effective resource provisioning under uncertainties. The Indetermsoft set is a promising mathematical model that is an extension of the traditional soft set that is designed to handle uncertain forms of data. The D* extra lite algorithm is a dynamic heuristic algorithm that makes use of the history of knowledge from past search experience to arrive at decisions. In this paper, the D* extra lite algorithm is enabled with the Indetermsoft set to perform proficient resource provisioning under uncertainty. The experimental results show that the performance of the proposed algorithm is found to be promising in performance metrics such as power consumption, resource utilization, total execution time, and learning rate. The expected value analysis also validated the experimental results obtained.

Priming of location (PoL) revisited: Reanalysis of a large-scale database

ABSTRACT Where we search for an object is strongly determined by where we recently found it, even if we don’t expect it to remain at the same position. This priming of location (PoL) phenomenon has gained visibility following Maljkovic and Nakayama’s (Maljkovic, V., & Nakayama, K. (1996). Priming of pop-out: II. The role of position. Perception & Psychophysics, 58(7), 977–991. https://doi.org/10.3758/BF03206826) seminal paper. Here, we revisit these authors’ conclusions on five selected issues: the mechanisms underlying PoL, its spatial profile, the contribution of distractor inhibition, the influence of search context changes, and PoL’s temporal profile. For each issue, we review the relevant follow-up studies and address open questions by taking advantage of an open-source large-scale database of more than 180,000 trials. While we replicate the core findings published on PoL we provide novel insights on each issue. Our findings show that task demands modulate PoL’s spatial characteristics; the inhibitory component was over-estimated in previous studies due to a confound, with PoL mainly indexing facilitation at previous target locations; PoL is sensitive, yet not eliminated, by changes in search context; both passive decay and proactive interference account for PoL’s temporal profile; and the effect of a past search event lasts far longer than previously thought, but its response-based component is much shorter-lived. We discuss limitations and directions for future research.

Search Query Refinement Using Context, Knowledge and Long-term Memorization

In the era of information overload, search tools are crucial, yet users often struggle to articulate their precise dataneeds, resulting in sub-optimal search outcomes. This is often due to users associating products with influencers or celebrities rather than knowing the specific brand or product name. This research aims to enable users to find products based on real-life associ- ations, emphasizing the importance of upgrading search query refinement for accuracy and relevance. A significant challenge faced by existing web tools is refining queries involving unrelated entities. This research addresses this gap by proposing a compre- hensive approach that integrates context, knowledge represen- tation, and long-term memorization. The framework combines contextual information with advanced knowledge representation techniques, enhancing the system’s understanding of user intent and domain-specific concepts. Long-term memorization reduces the time complexity of query refinement by leveraging past search experiences. This study underscores the potential of incorporating context, knowledge representation, and long-term memorization in refining search queries, offering more accurate results. As the digital landscape evolves, our approach has the potential to upgrade the search engine experience, providing personalized and contextually relevant results globally.

“Basically, it’s a History of the Russian State”: Russocentrism, Etatism, and the Ukrainian Question in Stalin’s Editing of the 1937 Short History of the USSR

Abstract Joseph Stalin and the Soviet party leadership launched a major propaganda campaign in 1931 that called for a new approach to Soviet history, not only for scholars and pedagogues but for society as a whole. A veritable “search for a usable past,” this initiative was to bolster the authority and legitimacy of the state and rally the population together in patriotic unity by connecting the prerevolutionary past to the Stalinist present. When this new historical line was finally unveiled in 1937, it challenged earlier Soviet sloganeering on subjects like nationalism, imperialism, and colonialism. This article examines how Stalin attempted to reconcile his new “usable past” with these other ideological priorities, focusing on a case study of the so-called Ukrainian question within the context of the USSR’s broader reevaluation of tsarist-era imperialism and colonial policy.

Selective Query Processing: A Risk-Sensitive Selection of Search Configurations

In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches, and these optimized parameters are then used as the search configuration for all subsequent queries. A better approach, however, would be to adapt the parameters to fit the query at hand. Selective query expansion is one such an approach, in which the system decides automatically whether or not to expand the query, resulting in two possible search configurations. This approach was extended recently to include many other parameters, leading to many possible search configurations where the system automatically selects the best configuration on a per-query basis. One problem with this approach is the system training, which requires evaluation of each training query with every possible configuration. In real-world systems, so many parameters and possible values must be evaluated that this approach is impractical, especially when the system must be updated frequently, as is the case for commercial search engines. In general, the more configurations, the greater the effectiveness when configuration selection is appropriate but also the greater the risk of decreasing effectiveness in the case of an inappropriate configuration selection. To determine the ideal configurations to be used for each query in real-world systems, we have developed a method in which a limited number of possible configurations are pre-selected, then used in a meta-search engine that decides the best search configuration for each query. We define a risk-sensitive approach for configuration pre-selection that considers the risk-reward tradeoff between the number of configurations kept and system effectiveness. We define two alternative risk functions to apply to different goals. For final configuration selection, the decision is based on query feature similarities. We compare two alternative risk functions on two query types (ad hoc and diversity) and compare these to more sophisticated machine learning based methods. We find that a relatively small number of configurations (20) selected by our risk-sensitive model is sufficient to obtain results close to the best achievable results for each query. Effectiveness is increased by about 15% according to the P@10 and nDCG@10 evaluation metrics when compared to traditional grid search using a single configuration and by about 20% when compared to learning to rank documents. Our risk-sensitive approach works for both diversity- and ad hoc oriented searches. Moreover, the similarity-based selection method outperforms the more sophisticated approaches. Thus, we demonstrate the feasibility of developing per-query information retrieval systems, which will guide future research in this direction.

Density-clustering of continuous gravitational wave candidates from large surveys

Searches for continuous gravitational waves target nearly monochromatic gravitational wave emission from, e.g., nonaxisymmetric fast-spinning neutron stars. Broad surveys often require us to explicitly search for a very large number of different waveforms, easily exceeding $\ensuremath{\sim}{10}^{17}$ templates. In such cases, for practical reasons, only the top, say $\ensuremath{\sim}{10}^{10}$, results are saved and followed up through a hierarchy of stages. Most of these candidates are not completely independent of neighboring ones, but arise due to some common cause: a fluctuation, a signal, or a disturbance. By judiciously clustering together candidates stemming from the same root cause, the subsequent follow-ups become more effective. A number of clustering algorithms have been employed in past searches based on iteratively finding symmetric and compact overdensities around candidates with high detection statistic values. The new clustering method presented in this paper is a significant improvement over previous methods: it is agnostic about the shape of the overdensities, is very efficient and it is effective: at a very high detection efficiency, it has a noise rejection of $99.99%$, is capable of clustering two orders of magnitude more candidates than attainable before and, at fixed sensitivity it enables more than a factor of 30 faster follow-ups. We also demonstrate how to optimally choose the clustering parameters.

35307 Skin tone bias of Google image results for common dermatologic diseases

Google Images is readily accessible and commonly used by patients. We sought to understand whether Google Image results are representative of how skin conditions appear in varying skin tones. Because the Google algorithm is personalized based on past searches of each user, to assess baseline results a browser without any prior search history was used. Searches for acne, eczema, psoriasis, shingles, and hives were performed and the first 50 images of patients that resulted were graded as either lighter skin or darker skin.

Dynamic openness for network-enabled product and process innovation: a panel-data analysis

PurposePrior research on open innovation has not investigated changes in knowledge acquisition strategies of firms over time overlooking how learning from past knowledge acquisition can change subsequent search strategies. Also, prior research has focused principally on product innovation overlooking process innovation. The purpose of the paper is to introduce the concept of dynamic openness, which is defined as temporal changes in external knowledge search strategy. We explore four dynamic openness strategies – closing down, opening up, persistent open and persistent closed – and examine the impact of these strategies on both product and process innovation.Design/methodology/approachThe authors used a panel dataset of 16,021 firms based on five waves (2009–2017) of the UK Community Innovation Survey (UKIS). All models are estimated using firm and year fixed effects (FE) method to control for endogeneity that arises from unobserved heterogeneity. Endogeneity and robustness tests were carried out to ensure the validity of results.FindingsThe results show that firms do use dynamic openness strategies over time leveraging learning from past searches. Specifically, the study indicates that closing down is not an effective strategy for either type of innovation. For process innovation, firms should pursue opening up strategy rather than persistent open strategy, whereas for product innovation firms could pursue either strategy, highlighting important contextual differences.Originality/valueThe paper contributes to the literature on knowledge acquisition in open innovation: (1) by theorizing the underlying reasons – learning from past collaborations, absorptive capacity and external knowledge heterogeneity – why firms pursue one dynamic openness strategy over another and (2) by extending literature by delineating the dynamic openness strategies that firms should pursue in process innovation vs product innovation.

Axion emission from nuclear magnetic dipole transitions

Nuclear transitions are one possible source of axions but past searches were restricted to specifc transitions. In this manuscript, we propose to extend the search for axions and axion-like particles to more a complex environment that would result in a number of correlated observables. By including creation mechanisms that have their origin in the Carbon-Nitrogen-Oxygen (CNO) cycle, we show that the search for solar axions should not only be restricted to the keV-mass region. We discuss limitations, such as the lifetime and the mass, that create a challenge for an Earth-bound experiments. We show that it is possible to use the same creation mechanisms as used in solar axions to search with a comparable rate at nuclear power reactors.

Out of Thin Air? Astrobiology and Atmospheric Chemotrophy.

The emerging understanding of microbial trace gas chemotrophy as a metabolic strategy to support energy and carbon acquisition for microbial survival and growth has significant implications in the search for past, and even extant, life beyond Earth. The use of trace gases, including hydrogen and carbon monoxide as substrates for microbial oxidation, potentially offers a viable strategy with which to support life on planetary bodies that possess a suitable atmospheric composition, such as Mars and Titan. Here, we discuss the current state of knowledge of this process and explore its potential in the field of astrobiological exploration.

Search for sub-eV axion-like resonance states via stimulated quasi-parallel laser collisions with the parameterization including fully asymmetric collisional geometry

We have searched for axion-like resonance states by colliding optical photons in a focused laser field (creation beam) by adding another laser field (inducing beam) for stimulation of the resonance decays, where frequency-converted signal photons can be created as a result of stimulated photon-photon scattering via exchanges of axion-like resonances. A quasi-parallel collision system (QPS) in such a focused field allows access to the sub-eV mass range of resonance particles. In past searches in QPS, for simplicity, we interpreted the scattering rate based on an analytically calculable symmetric collision geometry in both incident angles and incident energies by partially implementing the asymmetric nature to meet the actual experimental conditions. In this paper, we present new search results based on a complete parameterization including fully asymmetric collisional geometries. In particular, we combined a linearly polarized creation laser and a circularly polarized inducing laser to match the new parameterization. A 0.10 mJ/31 fs Ti:sapphire laser pulse and a 0.20 mJ/9 ns Nd:YAG laser pulse were spatiotemporally synchronized by sharing a common optical axis and focused into the vacuum system. Under a condition in which atomic background processes were completely negligible, no significant scattering signal was observed at the vacuum pressure of 2.6 × 10−5 Pa, thereby providing upper bounds on the coupling-mass relation by assuming exchanges of scalar and pseudoscalar fields at a 95% confidence level in the sub-eV mass range.

Survey on Text Transformation using Bi-LSTM in Natural Language Processing with Text Data

Communication using text can be understood by human beings as language plays a vital role in their life. Computers need to understand such human readable languages in order to explore documents in a wider manner. For such cases, machine learning algorithms as well as natural language processing algorithms can be used. Computers understand the human readable language and convert them as machine readable form. Replication of data stands as a most challenging problem in document exploration as the document may contain many repetitive words. The system makes use of classifier named LSTM to remove the words appearing frequently in the entire document. Firstly the text is transformed using NLP techniques followed by feature extraction and finally Bi-LSTM is used to classify the text data. LSTM mainly focus on reduction of repetitive words from the entire document and also maintains the integrity of the document. Bi-Lstm is an effective method to connect two different independent RNN together that helps to follow the sequence of statement front and back path every now and then.Multi directional ways of running the code will help in future prediction by saving the sentence for future purpose also.When the independent hidden states are joined it will be preserved for future and also past search. Although the word count is reduced, content of the word document is maintained in the LSTM method which stands as a major advantage. The experimental results showcase the efficacy of the system.

Deep exploration for continuous gravitational waves at 171–172 Hz in LIGO second observing run data

We pursue a novel strategy towards a first detection of continuous gravitational waves from rapidly-rotating deformed neutron stars. Computational power is focused on a narrow region of signal parameter space selected by a strategically-chosen benchmark. We search data from the 2nd observing run of the LIGO Observatory with an optimised analysis run on graphics processing units. While no continuous waves are detected, the search achieves a sensitivity to gravitational wave strain of $h_0 = 1.01{\times}10^{-25}$ at 90% confidence, 24% to 69% better than past searches of the same parameter space. Constraints on neutron star deformity are within theoretical maxima, thus a detection by this search was not inconceivable.

Chemical Guidance in the Search for Past and Extant Life on Mars

NASA should design missions to Mars for the purpose of generating Aha! discoveries to jolt scientists contemplating the molecular origins of life. These missions should be designed with an understanding of the privileged chemistry that likely created RNA prebiotically on Earth, and universal chemical principles that constrain the structure of Darwinian molecules generally.

Forward Privacy Preservation in IoT-Enabled Healthcare Systems

In recent years, Internet of Things (IoT)-enabled health monitoring wearable devices have become a trend in healthcare systems, regularly collecting vital sign data from patients and uploading them to the cloud. Through on-demand search queries, data are shared with third-party healthcare service providers to monitor patients' health status and provide timely diagnoses. To ensure privacy and security, patient health data should be encrypted before being uploaded to the cloud. The cloud can give search encryption services. However, current searchable encryption (SE) technologies still have problems with forward privacy security and verifiability. This article proposes an IoT-cloud-enabled healthcare data system incorporating a SE method with forward privacy and verifiability. By designing a trapdoor permutation function, we render the resulting output indistinguishable from meaningless random data to the adversary. Thus, the adversary cannot judge the relationship between a newly inserted record and a past search token, and therefore, the system realizes forward privacy or forward secrecy. We propose a multikeyword search verification mechanism based on a pseudo-random function. Our approach solves verifying the correctness of search results in the top-k search scenario with partial search results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>A</b></i> formal security analysis proves that our scheme achieves forward privacy preservation, which can help guarantee healthcare data privacy. Additionally, a performance evaluation shows that our method is efficient and effective, providing an information security system to preserve patient privacy in IoT-enabled healthcare systems.

Learnable Evolutionary Search Across Heterogeneous Problems via Kernelized Autoencoding

The design of the evolutionary algorithm with learning capability from past search experiences has attracted growing research interests in recent years. It has been demonstrated that the knowledge embedded in the past search experience can greatly speed up the evolutionary process if properly harnessed. Autoencoding evolutionary search (AEES) is a recently proposed search paradigm, which employs a single-layer denoising autoencoder to build the mapping between two problems by configuring the solutions of each problem as the input and output for the autoencoder, respectively. The learned mapping makes it possible to perform knowledge transfer across heterogeneous problem domains with diverse properties. It has shown a promising performance of learning and transferring the knowledge from past search experiences to facilitate the evolutionary search on a variety of optimization problems. However, despite the success enjoyed by AEES, the linear autoencoding model cannot capture the nonlinear relationship between the solution sets used in the mapping construction. Taking this cue, in this article, we devise a kernelized autoencoder to construct the mapping in a reproducing kernel Hilbert space (RKHS), where the nonlinearity among problem solutions can be captured easily. Importantly, the proposed kernelized autoencoding method also holds a closed-form solution which will not bring much computational burden in the evolutionary search. Furthermore, a kernelized autoencoding evolutionary-search (KAES) paradigm is proposed that adaptively selects the linear and kernelized autoencoding along the search process in pursuit of effective knowledge transfer across problem domains. To validate the efficacy of the proposed KAES, comprehensive empirical studies on both benchmark multiobjective optimization problems as well as real-world vehicle crashworthiness design problem are presented.

The Palimpsest of the Said and the Unsaid: Peter Carey’s Search for the Past

The article explores strategies of elliptical writing in The Chemistry of Tears (2012) by Peter Carey, a novel concerned with the presentation of the processes of concealing and revealing the troublesome past and its connections with the present. It argues that, systematically oscillating between the secret and the revealed, the novel tries to negotiate its way through the palimpsest of the past to re-construct the versions so far unrepresented. The narrative introduces and then slowly fills in narrative gaps, omissions and allusions, becoming a dramatisation of the elliptical strategies of prose. The first level of the operation of elliptical strategies concerns personal stories of characters; the next is represented by larger history and its ramifications for the present; while the last includes historiography and literary tradition. In the centre of the numerous layers of the text and its parallel plots stands the central image of the automaton connected with the process of revelation and restoration: it encapsulates the novel’s elliptical strategies by filling in various textual gaps and yet simultaneously hinting at the impossibility of any such operation. Artykuł bada eliptyczne strategie pisania zastosowane w Chemii łez (2012) Petera Carey’a – powieści, która koncentruje się na problemach ukrywania i ujawniania problematycznej przeszłości i jej związków ze współczesnością. Dowodzi on, iż oscylując między tym, co ukryte i co ujawnione, powieść próbuje przedrzeć się poprzez palimpsest przeszłości i zrekonstruować jej dotychczas niereprezentowane wersje. Powieść wprowadza, a następnie wypełnia szereg luk, niedopowiedzeń i aluzji, fabularyzując tym samym eliptyczne strategie, którymi się zajmuje. Ich pierwszy poziom to dzieje bohaterów; drugi – to szerzej rozumiana przeszłość i jej konsekwencje dla współczesności; ostatni to problematyka historiografii i gry z literacką tradycją. W centrum tych licznych warstw i równoległych wątków stoi centralna figura automatu, który niczym zwornik wiąże tematy ujawniania i przywracania, zbiera w sobie eliptyczne strategie powieści i poprzez wypełnienie narracyjnych luk, jednocześnie wskazuje na niemożliwość takich operacji.

A Queering-to-Come

This introduction outlines the idea of the queer customary and how various African articulations of it engage, contest, and nuance central concerns of queer theory produced in the global North, particularly around ideas of normativity—hetero and homo. It speculates on the customary’s reworking of temporality and what that reworking does to historical time and the problems and possibilities in reading the colonial archive in the search for a useable past for both lived African sexual and gendered experience and the academic study of it. The customary is seen as an iterative containment of ancestral time, a powerful form of self-fashioning in the present, and as an invitation to futurity. Brief framings of how the various essays in the special issue elaborate what we are calling the queer customary follow.

Past, Present and Future of the Soviet University: Jubilee Histories of Higher Education Institutions in the 1930s – 1980s

Introduction. Jubilee histories of the Soviet and Russian universities (higher education institutions) draw scholars’ attention as tools for the construction of traditions and search for a usable past. Historians scrutinize primary the past of universities as it is depicted in historical texts, but as a rule, these texts also treat the present and future of educational institutions. Exploring narratives that put all three times together, the article examines jubilee texts in their integrity. Methods and sources. The study is based on both published and unpublished narrative sources on histories of universities that usually are somehow connected with jubilees. Special attention is paid to discursive features of the texts under study. The author employs the method of discourse-analysis, as well as the method of contextualization of the examined written sources within political and social developments of the Soviet era. Analysis. The 1930s saw a gradual development of the jubilee history genre. While a lot of norms and regulations had not been implemented yet, there were distinctly visible anachronisms and factual errors. The exact place of the revolution and other landmark events had not been determined yet. The new genre gained momentum during and after the thaw period. The history of the universities of that time showed greater attention to historical detail and accuracy. At the same time, a certain model of linear history became well established; past, present, and future were clearly ordered and delineated. Results. Typical jubilee texts of the 1930s – 1980s had quite a stable structure, albeit they could vary in content. They were efficient and flexible enough for ongoing ideological campaigns. More importantly, these qualities widened the opportunities for the articulation of various visions of the university that didn’t necessarily fully chime with the official ideology although they didn’t explicitly confront it either.

Atmospheric Neutrino and Proton Decay Results in Super-Kamiokande

Super-Kamiokande (SK) is a 50 kiloton water Cherenkov detector aiming for the detection of several physics such as solar, atmospheric, astrophysical neutrinos, proton decay, WIMP dark matter, etc. It has been running over 20 years since 1996, and achieved several remarkable outcomes in the field of the particle and astrophysics, one of which is the discovery of the neutrino oscillation, bringing the Nobel Prize in physics 2015. SK still accumulates a large number of neutrino events, and simultaneously the physic target and its sensitivity are extended along with the improvement of the analysis method, such as event reconstruction and background rejection. One of the strong motivations for the atmospheric neutrino oscillation measurement is to measure the mass ordering (hierarchy) between ½2 and ½3. The atmospheric neutrino is sensitive to the mass hierarchy with help of the matter effect which is given when passing through the Earth. We have performed a detailed analysis to discriminate small signature of the mass hierarchy due to the matter effect. Proton decay is a direct signature anticipated by the grand unified theory (GUT) which is the physics beyond the standard model. Though the major decay modes and GUT models are excluded by the past searches, the efforts to search a glimpse of the proton decay signal are being continued with better event reconstruction and analysis method. In this paper the recent results of the atmospheric neutrino measurement and proton decay search using the most updated dataset taken until 2017 spring are described.