Objective: We examined the effects of aid reliability and disclosure on human trust in reliance a combat identification (CID) aid. tested whether acts as mediating factor between belief CID Background: Individual systems have been developed to reduce friendly fire incidents. However, these cannot positively identify target that does not working transponder. Therefore, when feedback is “unknown”, could be hostile, neutral, or friendly. Soldiers difficulty relying this type imperfect...

Deep learning has achieved great success in the past few years. However, performance of deep is likely to impede face non-IID situations. Domain generalization (DG) enables a model generalize an unseen test distribution, i.e., learn domain-invariant representations. In this paper, we argue that features should be originating from both internal and mutual sides. Internal invariance means can learned with single domain capture intrinsic semantics data, property within domain, which agnostic...

In survival analysis, the primary goal is to monitor several entities and model occurrence of a particular event interest. such applications, it quite often case that interest may not always be observed during study period this gives rise problem censoring which cannot easily handled in standard regression approaches. addition, obtaining sufficient labeled training instances for learning robust prediction very time consuming process can extremely difficult practice. paper, we propose...

Large language models (LLMs) are capable of generating coherent summaries from very long contexts given a user query. Extracting and properly citing evidence spans could help improve the transparency reliability these summaries. At same time, LLMs suffer positional biases in terms which information they understand attend to, affect citation. Whereas previous work has focused on citation with predefined levels granularity (e.g. sentence, paragraph, document, etc.), we propose task...

Delirium is an acute neurocognitive disorder that affects up to half of older hospitalized medical patients and can lead dementia, longer hospital stays, increased health costs, death. Although delirium be prevented treated, it difficult identify predict.

This paper focuses on researching the scientific problem of deep extraction and inference favorable geological geochemical information about mineralization at depth, based which a mineral resources prediction model is established machine learning approaches are used to carry out quantitative prediction. The main contents include: (i) discussing method 3D anomaly under multi-fractal content-volume (C-V) models, extracting 12 element anomalies constructing data volume for laying foundation...

Data exfiltration is a serious threat to organizations. Such exfiltrations cause breach events that can lead millions of dollars loss. Perimeter defense not enough by itself since successful exploits from insiders also be very damaging. Internal network user activities need monitored detect malicious actions. Automatic machine learning methods applied for anomaly detection, but they create lot false alarms. Domain experts identify users, are unable process large volumes data. Interactive...

Incomplete data clustering is often encountered in practice. Here the treatment of missing attribute value and optimization procedure are important factors impacting performance. In this study, a becomes an information granule represented as certain interval. To avoid intervals determined by different cluster information, we propose congeneric nearest-neighbor rule-based architecture preclassification result, which can improve effectiveness estimation Furthermore, global fuzzy approach using...

Research on email anomaly detection has typically relied specially prepared datasets that may not adequately reflect the type of data occurs in industry settings. In our research, at a major financial services company, privacy concerns prevented inspection bodies emails and attachment details (although subject headings filenames were available). This made labeling possible anomalies resulting redacted more difficult. Another source difficulty is high volume combined with scarcity resources...

Obesity is one of the leading preventable causes death in United States (U.S.). Risk factor analysis a pro­cess to identify and understand risk factors contributing particular disease, an imperative component de­velopment efficient effective prevention intervention efforts. Most existing methods usually aim build one-size-fits-all model at population-level. However, this type does not take into consideration heterogeneity population. To overcome limitation, we formulate subpopulation...

Peer-review plays a critical role in the scientific writing and publication ecosystem. To assess efficiency efficacy of reviewing process, one essential element is to understand evaluate reviews themselves. In this work, we study content structure peer under argument mining framework, through automatically detecting (1) argumentative propositions put forward by reviewers, (2) their types (e.g., evaluating work or making suggestions for improvement). We first collect 14.2K from major machine...

Obesity has become a significant threat to health. Identifying and understanding the underlying obesity risk factors (ORFs) are crucial for optimizing prevention, intervention treatment obesity. Most existing methodological approaches factor analysis employed within single task learning (STL) framework learn ranked list of ORFs whole population. However, is multi-faced health outcome. Some highly specific certain subpopulation others universal entire Multi-task (MTL) offers solution connect...

We study the problem of zero-shot classification in which we don't have labeled data target domain. Existing approaches learn a model from source domain and apply it without adaptation to domain, is prone shift problem. To solve problem, propose novel Learning Discriminative Instance Attribute(LDIA) method. Specifically, projection matrix for both jointly also use prototype attribute space regularise learned matrix. Therefore, information can be effectively transferred The experimental...

K-means is a well-known prototype based clustering algorithm for its simplicity and efficiency. However, most k-means methods assume different classes are represented by one prototype, which makes limit of algorithms. Recently, multi-prototype have been raised to tackle this problem, composed two stages: split stage merge stage. For algorithms, proper number plays vital role in the performance it generally given users trial error way. In paper, new incremental designed determine appropriate...

In human-robot interaction areas, the robot is often expected to recognize identity of speaker in some specific scenarios. It a kind biometric modality, and general using statistical model classical powerful method dealing with identification problem. this paper, we apply Gaussian mixture (GMM) on speech feature distribution modeling build system under MATLAB platform. Experiments are conducted practical database also further give insights into extraction, different length input utterances...

To propose the new prediction method of Kernel Fuzzy C-Means (KFCM) for business failure. (FCM) algorithm fails to deal with non-spherical clusters and incomplete data, while kernel can map low-dimensional data into highdimensional feature space which is easier be separated. Therefore, integrated FCM solve problems FCM. fully reflect performance different functions, KFCM respectively adopts three functions include Gaussian, Polynomial, Sigmoid kernel. The paper employs financial from Chinese...

Despite many data clustering methods are available, most of them uncover compactness or connectivity as the intrinsic structure unlabeled data. Very few approaches explicitly consider cluster size distribution, especially over‐dispersed (high variance), which may represent yet another important aspect structural information In this paper, we propose a novel joint mixture model framework to estimate distribution together with (density). Our is sufficiently flexible and general capture wide...

Space-borne gravitational wave detectors like TianQin might encounter data gaps due to factors micro-meteoroid collisions or hardware failures. Such glitches will cause discontinuity in the and have been observed LISA Pathfinder. The existence of such presents challenges analysis for TianQin, especially massive black hole binary mergers, since its signal-to-noise ratio (SNR) accumulates a non-linear way, gap near merger could lead significant loss SNR. It introduce bias estimate noise...