Data Imputation through the Identification of Local Anomalies

We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e.g., an occluder in the case of a visual recording. Within this framework, we propose i) a novel algorithm to efficiently separate, i.e., detect and localize, possible corruptions from a given suspicious data instance and ii) a Maximum A Posteriori (MAP) estimator to impute the corrupted data. As a generalization to Euclidean distance, we also propose a novel distance measure, which is based on the ranked deviations among the data attributes and empirically shown to be superior in separating the corruptions. Our algorithm first splits the suspicious instance into parts through a binary partitioning tree in the space of data attributes and iteratively tests those parts to detect local anomalies using the nominal statistics extracted from an uncorrupted (clean) reference data set. Once each part is labeled as anomalous vs normal, the corresponding binary patterns over this tree that characterize corruptions are identified and the affected attributes are imputed. Under a certain conditional independency structure assumed for the binary patterns, we analytically show that the false alarm rate of the introduced algorithm in detecting the corruptions is independent of the data and can be directly set without any parameter tuning. The proposed framework is tested over several well-known machine learning data sets with synthetically generated corruptions; and experimentally shown to produce remarkable improvements in terms of classification purposes with strong corruption separation capabilities. Our experiments also indicate that the proposed algorithms outperform the typical approaches and are robust to varying training phase conditions

ADRMX: Additive Disentanglement of Domain Features with Remix Loss

The common assumption that train and test sets follow similar distributions is often violated in deployment settings. Given multiple source domains, domain generalization aims to create robust models capable of generalizing to new unseen domains. To this end, most of existing studies focus on extracting domain invariant features across the available source domains in order to mitigate the effects of inter-domain distributional changes. However, this approach may limit the model's generalization capacity by relying solely on finding common features among the source domains. It overlooks the potential presence of domain-specific characteristics that could be prevalent in a subset of domains, potentially containing valuable information. In this work, a novel architecture named Additive Disentanglement of Domain Features with Remix Loss (ADRMX) is presented, which addresses this limitation by incorporating domain variant features together with the domain invariant ones using an original additive disentanglement strategy. Moreover, a new data augmentation technique is introduced to further support the generalization capacity of ADRMX, where samples from different domains are mixed within the latent space. Through extensive experiments conducted on DomainBed under fair conditions, ADRMX is shown to achieve state-of-the-art performance. Code will be made available at GitHub after the revision process

Genetic deterioration and repair in pea (Pisum sativum L.) seeds during storage

SIGLEAvailable from British Library Document Supply Centre- DSC:DX171706 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Source Free Domain Adaptation of a DNN for SSVEP-based Brain-Computer Interfaces

This paper presents a source free domain adaptation method for steady-state visually evoked potential (SSVEP) based brain-computer interface (BCI) spellers. SSVEP-based BCI spellers help individuals experiencing speech difficulties, enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in the current methods requires an extensive calibration period before using the system, leading to discomfort for new users. We address this issue by proposing a method that adapts the deep neural network (DNN) pre-trained on data from source domains (participants of previous experiments conducted for labeled data collection), using only the unlabeled data of the new user (target domain). This adaptation is achieved by minimizing our proposed custom loss function composed of self-adaptation and local-regularity loss terms. The self-adaptation term uses the pseudo-label strategy, while the novel local-regularity term exploits the data structure and forces the DNN to assign the same labels to adjacent instances. Our method achieves striking 201.15 bits/min and 145.02 bits/min ITRs on the benchmark and BETA datasets, respectively, and outperforms the state-of-the-art alternative techniques. Our approach alleviates user discomfort and shows excellent identification performance, so it would potentially contribute to the broader application of SSVEP-based BCI systems in everyday life.Comment: 11 pages (including one page appendix), 5 figure

The Impact of Sustainable Supply Chain Management and Supply Chain Collaboration on Turkish Firms Performance: Moderator Effect of Uncertainty

Supply Chain-Related Sustainability Cases offer organizations the challenge of enriching environmental, social, and economic performance within supply networks. Firms are increasingly implementing environmental and social dimensions of sustainability. During the implementation, they check the collaboration efforts for getting information outside of the organizations to develop and improve both firm and supply chain performance. Due to drastic changes in the business environment, firms face uncertainty. This study aims to analyze the impact of sustainable supply chain management and collaboration under the supply chain uncertainty on firms' performance. Based on the literature review the conceptual framework was developed. To test the research hypotheses, multi-item scales and survey questionnaires were adopted from prior research. The research is based on a quantitative approach using a questionnaire survey. We obtained 240 usable questionnaires from 112 companies. The Partial Least Square method was used to test the proposed conceptual model. The results show that sustainable supply chain management is positively associated with supply chain performance and supply chain collaboration. Also, we found that supply chain collaboration has a positive effect on supply chain performance. Supply chain performance is positively associated with firm performance. Furthermore, supply chain uncertainty moderates the relationship between collaboration, sustainable supply chain management, and supply chain performance

Evaluation of effective demographic variables in competition performances of Turkish wrestling referees

Management of wrestling competitions of equal competitors is often assigned to successful referees. For this reason, it is important to determine the demographic variables that influence the referees’ level of success. In this context, the aim of the study was to evaluate the effective demographic variables in competition performances of Turkish wrestling referees using the logistic regression analysis. The purpose of this research was explained to the referees and voluntary participation was provided. The research data consist of demographic variables and the referee evaluation reports of the year 2016 calculated by the Central Referee Committee of the Turkish Wrestling Federation. The referees were classified as successful (between 7-8.5 points) and unsuccessful (between ≥8.5-10 points) according to their performance scores. Accordingly, the international referees were 49 times more successful than the national referees in a competition. The referees who wrestled at the international level were 6 times more successful than the referees wrestling at the national level and who did not wrestle. Referees whose ages 31-40 and ≥41 were 7.9 and 24.9 times more successful than ≤30 age respectively. Moreover, it was determined that those who were refereeing for social identity, a hobby, and other reasons in a competition were, respectively, 6, 39 and 22 times more possible to be successful than those who were refereeing for social status. Consequently, if a successful referee performance was desired, the ones should be selected as who were over 41 years of age and wrestled at the international level, and who were international level referees that were refereeing as a hobby or other. Thus, a more successful referee performance can be achieved in competitions

Designing and analyzing park sensor system for efficient and sustainable car park area management

Many problems have been seen in cities because of increasing vehicle density. One of these problems is vehicle density in parking lots. People look for empty parking areas and they spend too much time. While people look for empty parking areas, CO2 (carbon dioxide) emission and energy consumption increase due to density in parking lots. We worked to solve these problems by doing Magnetic Car Park Sensor. Magnetic Car Park Sensor is the system which detects cars in car parks. After cars detected with the system, the system sends information to center server and we can see information data in the system interface. The system helps people to find empty parking lots. As people find empty car park areas fastly, energy consumption and CO2 emission are decreased significantly