Negative Labels Research Articles

MENCEGAH DAN MENANGANI STIGMA SOSIAL TERKAIT COVID 19 (SUATU PENDEKATAN STUDI LITERATURE REVIEW)

<p class="16aJudulAbstrak">The COVID-19 virus was first discovered in late December 2019 in Wuhan, China, the number of people infected with the coronavirus is increasing rapidly all over the world, and public and anxiety levels are increasing in many areas. During the COVID-19 outbreak, a wave of fear and phenomena in society emerged. So that stigma arises which is a negative label attached to someone given by society. Efforts to change community stigma are by designing effective anti-stigma programs to break down misperceptions about COVID-19, increase public knowledge about COVID-19, and spread positive messages and support. The research method is a literature review study and includes qualitative data sources from a study. The search strategy for relevant Indonesian and English articles, Literature searches are carried out through online searches on Google Scholar and also the SINTA website (Science and Technology Index). There are 15 articles that are grouped into several stigma topics related to COVID-19. 4 articles discuss the incidence of stigma, 7 articles discuss the psychological impact of stigma related to COVID-19, and 4 articles discuss social stigma related to COVID-19. Conclusion To fight stigmatization related to COVID-19 is to Spread the facts, Engage social influencers, and Amplify the voices of people who are recovering from COVID-19.</p>

Consumer Reactions to Positive and Negative Front-of-Package Food Labels

The National Academy of Medicine recommends that the U.S. adopt an interpretative front-of-package food labeling system, but uncertainty remains about how this system should be designed. This study examined reactions to front-of-package food labeling systems that use positive labels to identify healthier foods, negative labels to identify unhealthier foods, or both. In August 2021, U.S. adults (N=3,051) completed an online randomized experiment. Participants were randomly assigned to 1 of 4 labeling conditions: control (calorie), positive, negative, or both positive and negative labels. Labels were adapted from designs for a 'healthy' label drafted by the Food and Drug Administration and displayed on the front of product packaging. Participants selected products to purchase, identified healthier products, and reported reactions to the labels. Analyses, conducted in 2022, examined the healthfulness of participants' selections using the Ofcom Nutrient Profiling Model score (0-100, higher scores indicate being healthier). Participants exposed to only positive labels, only negative labels, or both positive and negative labels had healthier selections than participants in the control arm (differences vs control=1.13 [2%], 2.34 [4%] vs 3.19 [5%], respectively; all p<0.01). The both-positive-and-negative-labels arm outperformed the only-negative-labels (p=0.03) and only-positive-labels (p<0.001) arms. The only-negative-labels arm outperformed the only-positive-labels arm (p=0.005). All the 3 interpretative labeling systems also led to improvements in the identification of healthier products and beneficial psychological reactions (e.g., attention, thinking about health effects; all p<0.05). Front-of-package food labeling systems that use both positive and negative labels could encourage healthier purchases and improve understanding more than systems using only positive or only negative labels.

An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features

The identification of cucumber fruit is an essential procedure in automated harvesting in greenhouses. In order to enhance the identification ability of object detection models for cucumber fruit harvesting, an extended RGB image dataset (n = 801) with 3943 positive and negative labels was constructed. Firstly, twelve channels in four color spaces (RGB, YCbCr, HIS, La*b*) were compared through the ReliefF method to choose the channel with the highest weight. Secondly, the RGB image dataset was converted to the pseudo-color dataset of the chosen channel (Cr channel) to pre-train the YOLOv5s model before formal training using the RGB image dataset. Based on this method, the YOLOv5s model was enhanced by the Cr channel. The experimental results show that the cucumber fruit recognition precision of the enhanced YOLOv5s model was increased from 83.7% to 85.19%. Compared with the original YOLOv5s model, the average values of AP, F1, recall rate, and mAP were increased by 8.03%, 7%, 8.7%, and 8%, respectively. In order to verify the applicability of the pre-training method, ablation experiments were conducted on SSD, Faster R-CNN, and four YOLOv5 versions (s, l, m, x), resulting in the accuracy increasing by 1.51%, 3.09%, 1.49%, 0.63%, 3.15%, and 2.43%, respectively. The results of this study indicate that the Cr channel pre-training method is promising in enhancing cucumber fruit detection in a near-color background.

VICTIM BLAMING: LABELING FOR WOMEN VICTIMS OF SEXUAL VIOLENCE IN HUMAN RIGHTS PERSPECTIVE

The problem of blaming victims is related to the strong traditions and culture of the community that still perpetuate gender stereotypes. This study aims to examine the implications of patriarchal culture on the position of women as subordinates, the concept of blaming the victim against the women victims of sexual violence and human rights studies on victim blaming and labeling for women victims of sexual violence. The culture of gender injustice in the society makes women often get negative labels, in which the women who look beautiful and dress up using rather tight and sexy clothes are always considered as inviting the opposite sex to commit sexual harassment. This is then always used and associated with labeling of victims of sexual harrasment in which the society tends to blame victims of sexual harrasment or victim blaming. This type of research is a normative juridical research that is prescriptively providing solutions to the legal protection for victims related to victim blaming and labeling women as victims of sexual violence in the view of human rights that difinitely violates the constitution and law.

MRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels.

Passive detection of risk factors (that may influence unhealthy or adverse behaviors) via wearable and mobile sensors has created new opportunities to improve the effectiveness of behavioral interventions. A key goal is to find opportune moments for intervention by passively detecting rising risk of an imminent adverse behavior. But, it has been difficult due to substantial noise in the data collected by sensors in the natural environment and a lack of reliable label assignment of low- and high-risk states to the continuous stream of sensor data. In this paper, we propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of an adverse behavior. Next, to circumvent the lack of any confirmed negative labels (i.e., time periods with no high-risk moment), and only a few positive labels (i.e., detected adverse behavior), we propose a new loss function. We use 1,012 days of sensor and self-report data collected from 92 participants in a smoking cessation field study to train deep learning models to produce a continuous risk estimate for the likelihood of an impending smoking lapse. The risk dynamics produced by the model show that risk peaks an average of 44 minutes before a lapse. Simulations on field study data show that using our model can create intervention opportunities for 85% of lapses with 5.5 interventions per day.

Evidence-Based Taxonomy: Labels as Illocutionary Acts

Concepts in science have an important role: They delimit and specify objects, activities, processes, and abstract entities. When terms are diffuse, mean different things to different persons, and lead more to qualifications than demarcation, they cease to be concepts and may become labels, which are informal alternative designations. There are many labels in science and they have become abundant in taxonomy: α-taxonomy, integrative-taxonomy, iterative-taxonomy, etc., are only a few examples. α-taxonomy is a negative label that obtained popularity at the same time the term α-diversity became popular in ecology. The label α-taxonomy conveys a negative meaning to taxonomy because the nature of its evidence—originally morphological—is seen by many as “merely descriptive” and, thereby, supposedly inferior to other forms of evidence. This has contributed to substantial and unwarranted damage to the status of this science and to the careers of taxonomists. The recent history of methodologies for species delimitation shows that what some have considered of low value (morphology), compared to “new” data (molecular), is in many cases the critical factor to delimit species. We propose to eschew these kinds of labels and simply refer to taxonomy to avoid stigmatizing of any kind of practicing taxonomist, whether focused on morphology, cytology, molecular biology, or other fields of biology. Taxonomy implies the use of the current best evidence, theories, and methods to demarcate species and their relationships.

“Why would they call me an orphan when I have parents”: Care leavers’ experiences of labelling and stereotyping whilst living in residential care facilities in Zimbabwe

Care leaving research has focused on the experiences of young people when they make the transition from residential care facilities (RCCFs) back into mainstream society. Past research has found evidence of social exclusion and documents challenges that care leavers face when re-entering the society when they age-out of care. Some challenges include a lack of preparation for independent living, including limited financial, housing and employment support. There exists a gap in care leaving literature about the extent to which the labelling and stereotyping of care leavers during their time in RCCFs affects their transitions into adulthood. This paper presents an analysis of interviews conducted with care leavers from six childcare facilities in Zimbabwe (n = 30). The paper argues that the labelling and stereotyping of care leavers has a negative effect on how they are accepted and reintegrated into the society. The thematic analysis of the data provides clear evidence of some of the negative labels such as being called an“orphan” in the case of some non-orphans in RCCFs, and the negative stereotypes which stuck with the care leavers years into their adulthood. These labels and stereotypes also affected care leavers’ own perceptions of themselves as well as their interpersonal relationships with others. The paper identifies some implications for social work practice and makes recommendations towards the promotion of a more positive social image of care leavers.

Coping with Stigmatizing Labels: Self-labeling vs. Label-rejection

Negative group labels (i.e., slurs) have historically been used as a tool to reinforce stigma and help enforce the lower status of stigmatized groups. One strategy that has emerged to cope with negative group labels (e.g., chink, slut) and their associated stigma is self-labeling, a stigmatized group member’s intentional use of a negative group label to refer to themselves (i.e., “I am X”). While previous research has focused on the benefits of self-labeling, or increasing group power and reducing label negativity, we reveal the social costs for individuals who use the strategy, such as reduced likeability and promotability, by introducing a related label-coping strategy, that of label-rejection (i.e., “I am not X”) and comparing its effects to those of the self-labeling strategy. In four studies, participants were presented with scenarios in which stigmatized group members used either self-labeling or label-rejection. We found that while self-labeling is more effective than label-rejection in decreasing majority group members’ perceptions of the negativity of the label, the self-labeler was seen as less likeable and less promotable than the label-rejecter, an effect that generalizes across women and racial minorities and across different scenarios (Study 1-3). We also found that this social cost was mediated through threatened affect, which led to greater oversensitivity attributions to self-labelers (Study 4). Lastly, we found that the social cost associated with self-labeling was diminished when the strategy was used in a reactive vs. in a spontaneous context (Study 5). These results suggest that stigmatized groups’ use of self-labeling to attenuate stigma is potentially accompanied by negative social consequences for individuals, even as it contributes to reducing the power of the label itself.

Cross-modal distillation with audio–text fusion for fine-grained emotion classification using BERT and Wav2vec 2.0

Fine-grained emotion classification for mood- and emotion-related physical-characteristics detection and its application to computer technology using biometric sensors has been extensively researched in the field of affective computing. Although text modality has achieved a considerably high performance from the perspective of sentiment analysis, which simply classifies a positive or negative label, fine-grained emotion classification requires additional information besides text. An audio feature can be adopted as the additional information as it is closely associated with text, and the characteristics of the changes in sound pulses can be employed in fine-grained emotion classification. However, the multimodal datasets related to fine-grained emotion are limited, and the scalability and efficiency are insufficient for multimodal training to be applied extensively via the self-supervised learning (Self-SL) approach, which can adequately represent modality. To address these limitations, we propose cross-modal distillation (CMD), which induces the feature spaces of student models with a few parameters while receiving those of the teacher models that can adequately express each modality based on Self-SL. The proposed CMD performs the mapping of a feature space between teacher-student models based on contrastive learning, while two attention mechanisms—cross-attention between audio and text features and self-attention for features in modality—are performed during knowledge distillation. Wav2vec 2.0 and BERT, which are already adequately trained for audio and text via Self-SL, were adopted as teacher models; audio–text transformer models were used as student models. Accordingly, the CMD-based representation learning applies a lightweight model for IEMOCAP, MELD, and CMU–MOSEI datasets with the task of multi-class emotion classification, while exhibiting better fine-grained emotion classification performance than benchmark models with a considerably low uncertainty for prediction.

Multimodal Contrastive Supervised Learning to Classify Clinical Significance MRI Regions on Prostate Cancer.

Clinically significant regions (CSR), captured over multi-parametric MRI (mp-MRI) images, have emerged as a potential screening test for early prostate cancer detection and characterization. These sequences are able to quantify morphology, micro-circulation, and cellular density patterns that might be related to cancer disease. Nonetheless, this evaluation is mainly carried out by expert radiologists, introducing inter-reader variability in the diagnosis. Therefore, different deep learning models were proposed to support the diagnosis, but a proper representation of prostate lesions remains limited due to the non-alignment among sequences and the dependency of considerable amounts of labeled data for learning. The main limitation of such representation lies in the cross-entropy minimization that only exploits inter-class variation, being insufficient data augmentation and transfer learning strategies. This work introduces a Supervised Contrastive Learning (SCL) strategy that fully exploits the inter and intra-class variability of prostate lesions to robustly represent MRI regions. This strategy extracts lesion sample tuples, with positive and negative labels, regarding a query lesion. Such tuples are involved into an easy-positive, and semi-hard negative mining to project samples that better update the deep representation. The proposed learning strategy achieved an average ROC-AVC of 0.82, to characterize prostate cancer in MRI, using only the 60% of the available annotated data. Clinical relevance - A robust learning scheme that properly finds representations in limited data scenarios to classify clinically significant MRI regions on prostate cancer.

Development of the Nurse Occupational Stigma Scale.

IntroductionOccupational stigma toward nurses exerts a negative impact on the physical and mental health and work performance of nurses. However, hitherto, there has been no quantitative instrument designed to assess occupational stigma toward nurses.ObjectiveThe present study aimed to develop the Nurse Occupational Stigma Scale (NOSS) and test its reliability and validity in the Chinese context.MethodsThe items of the scale were formed through the open-ended interview and literature review. A questionnaire survey was administered among 765 patients using NOSS and the Caring Behaviors Inventory (CBI-24).ResultsIn the initial questionnaire, a total of 21 items were developed, and 5 items were dropped for cross-loadings. The formal scale consists of 16 items divided into three dimensions of negative label, nurse-patient relationship, and devaluation and discrimination. The results of confirmatory factor analysis (CFA) indicated that the three-factor model fitted well (χ2/df=2.635, RMSEA=0.064, RFI=0.926, CFI=0.962, NFI=0.941, IFI=0.953). The total scores of the NOSS and the scores of all dimensions were significantly negatively correlated with the scores of the CBI-24. The internal consistency coefficients of the scale and all dimensions were between 0.827–0.920, and the split-half reliability coefficients were between 0.826–0.942. The NOSS had the measurement invariance across gender.ConclusionWith its good reliability and validity, the NOSS can be an appropriate instrument for researchers to conduct studies about nurse occupational stigma.

Componential Analysis of English Verbs.

Computational lexical resources such as WordNet, PropBank, VerbNet, and FrameNet are in regular use in various NLP applications, assisting in the never-ending quest for richer, more precise semantic representations. Coherent class-based organization of lexical units in VerbNet and FrameNet can improve the efficiency of processing by clustering similar items together and sharing descriptions. However, class members are sometimes quite different, and the clustering in both can gloss over useful fine-grained semantic distinctions. FrameNet officially eschews syntactic considerations and focuses primarily on semantic coherence, associating nouns, verbs and adjectives with the same semantic frame, while VerbNet considers both syntactic and semantic factors in defining a class of verbs, relying heavily on meaning-preserving diathesis alternations. Many VerbNet classes significantly overlap in membership with similar FrameNet Frames, e.g., VerbNet Cooking-45.3 and FrameNet Apply_heat, but some VerbNet classes are so heterogeneous as to be difficult to characterize semantically, e.g., Other_cos-45.4. We discuss a recent addition to the VerbNet class semantics, verb-specific semantic features, that provides significant enrichment to the information associated with verbs in each VerbNet class. They also implicitly group together verbs sharing semantic features within a class, forming more semantically coherent subclasses. These efforts began with introspection and dictionary lookup, and progressed to automatic techniques, such as using NLTK sentiment analysis on verb members of VerbNet classes with an Experiencer argument role, to assign positive, negative or neutral labels to them. More recently we found the Brandeis Semantic Ontology (BSO) to be an invaluable source of rich semantic information and were able to use a VerbNet-BSO mapping to find fine-grained distinctions in the semantic features of verb members of 25 VerbNet classes. This not only confirmed the assignments previously made to classes such as Admire-31.2, but also gave a more fine-grained semantic decomposition for the members. Also, for the Judgment-31.1 class, the new method revealed new, more fine-grained existing semantic features for the verbs. Overall, the BSO mapping produced promising results, and as a manually curated resource, we have confidence the results are reliable and need little (if any) further hand-correction. We discuss our various techniques, illustrating the results with specific classes.

Youth as Educators: Cultivating Youth Digital Contribution to Community Education

Frequently, youth is stigmatized as trouble makers and immature groups. Thus, they still need to be educated, nurtured and supervised, especially in this millennial era. In spite of negative label attributed to youth, youth is potential community educators, especially during this digital world. Their natural interest to social media and technology can be investment to engage them in community education through digital works. This study aims to explore educational aspects that several young students emphasize and educate to their online peers and digital community through their digital works creation which they upload at YouTube. The study adopts qualitative methods and employs thematic analysis to examine 50 uploaded videos. The study reveals that digital works can be media for youth to educate character, non-violence and environmental-health education to their peers and other communities. This also encourages youth to learn to be active and responsible citizens, undertake direct experiential learning process, enhance their social media responsibility, provide role models for others, promote community well being and alleviate negative youth stigma.

Passing the Baton: Black Women Track Stars and American Identity

Passing the Baton frames the intersectionality of femininity and athleticism through an examination of the challenges experienced by Black women who compete in track and field. Sport historians will appreciate Cat Ariail's use of a historical timeline of Black women's accomplishments in track and field to express the golden age of athletic competition. Another bonus to this book is the notes section at the end of the book. The notes could be used as a guide toward continuing research on Black women in track and field, as the author thoughtfully provided thorough details on each section of the book.Insightfully, the introduction grabs the attention of the reader through teasing topics like boundaries of belonging, Black women's athleticism, and the athletic agency of Black American track women. Then the book transitions to telling the story of Alice Coachman, who specialized in the high jump and was the first African American woman to win an Olympic medal. The chapter uncovers how Coachman's presence at the Olympic games contradicted the masculine identity of the American elite athlete. To verify this notion, the author creatively uses legendary baseball player Jackie Robinson to convey the destabilization of normative Americanness during the postwar period in sports. However, the portrayal of Jackie Robinson also discloses the challenges for Black women negotiating their athletic identity while seeking to achieve American acceptance.This is further explored through an introduction to the women's track and field club the Tuskegee Tigerettes. Known for their poise, femininity, and athleticism, the Tigerettes began competing in sports in the early twentieth century. The Tigerettes challenged negative labels affixed to female athletes participating in speed and strength sports by exemplifying normative American, southern values, which they coined the Tuskegee Model.Chapter 2 explores Black women tracksters as political actors. The author shows how Olympians competing on a global stage earned the honor and power of being representatives of their countries. Black women became the face of American culture and athleticism, making them a global symbol of America.What makes this book a priceless contribution to the field of sport history, however, is Ariail's argument that the athletic victories of Black women in track and field surpassed the sports stage and directly impacted political relationships with the United States and forged America's image. The author critically examines this point in chapter 3, where she highlights the story of Aertwentha Mae Faggs. Faggs presented Tennessee State University's famed Tigerbelles to the sports world. The Tigerbelles became internationally known for their superior athleticism in women's sports, and in 1952 Faggs won an Olympic gold medal, making her one of the first Tigerbelles to earn one. Like the Tigerettes, the athletic success and representation of the Tigerbelles provided the United States an opportunity to cast a picture of gender and racial equality on a global scale.Chapter 4 focuses on the notion of heteronormativity and its influence on Black women becoming symbols of American democracy. As such, the author states, “Heteronormativity and predominant blackness of American women's track and field meant that the squad assembled for the inaugural US-Soviet Union dual aligned with the American social order” (120). Chapter 5 then unpacks the story of Wilma Rudolph, who first appears in the introduction of the book. Through these two sections, the author creatively untangles Rudolph's story by examining the influences of the Cold War and the civil rights movement on the identity of elite Black women athletes.A highlight of chapter 5 is when Ariail discusses how Rudolph's image complicated the intersection of race, gender, and sexuality. The author contends that Rudolph's beauty was aligned with America's obsession with light skin, straight hair, and femininity, which propelled her celebrity status and morphed American traditions into accepting Black women as fierce athletic competitors.The book ends by detailing how the enduring efforts of Black women competing in speed and strength sports opens doors for white female athletes. For example, through her image, Wilma Rudolph made elite athleticism obtainable for white female athletes who were shunned for participating in strength sports, which attacked the dominant white gender ideology.In conclusion, I highly recommend this book as it intermingles foreign politics, American values, and challenges experienced by Black women in track and field seeking to reach the epitome of athleticism.

Leveraging Neuroscience to Fight Stigma Around Mental Health.

Labels serve as identifiers and convenient descriptors of inanimate and animate objects. In humans, given labels can easily become part of an individual’s self-perceived identity. Negative labels ascribed to a person can result in internalized stigma, a state that will shape the subject’s biography. This can ultimately impact the person’s mental and physical health since perceived and/or anticipated stigma discourages the use of social and health services. Per definition, stigma involves labeling of persons with physical, mental, or social characteristics that do not match the observer’s arbitrarily conditioned and calibrated sense of norms (public stigma); such labeling may eventually become embedded in rules, regulations, and laws (structural stigma). Internalized stigma projects onto a person’s emotions and actions. Public (enacted) stigma results from stereotyping (collectively agreed-upon notions about a group of persons that are used to categorize these people) and devaluation, which subsequently leads to social distancing, discrimination, and blatant abuse of human rights. Much of what we know about stigma results from research in the psychosocial sciences and, more recently, from social neuroscience. The stigma around mental health has generated much attention in the field of psychiatry where, to date, most research has focussed on epidemiology and anti-stigma interventions. This essay intends to stimulate thought, debate, and research within the behavioral neuroscience community and, therefore, to inform evidence-based design and implementation of neuroscience-based approaches by other professionals working towards the elimination of the stigma attached to mental illness. The article starts by considering the concept of stigma and the psychological processes that give rise to the phenomenon; it also considers how projected and perceived stigma are multiplied. Finally, after a brief review of the few existing neuroscientific explorations of stigma, gaps in our knowledge of the neurobiological basis of stigma are identified and discussed.

Effects of Labelling Process, Stigma And Stereotype Toward Malaysian Women with HIV Positive

The purpose of this paper is to evaluate the labelling process including stigma and stereotype that Human Immunodeficiency Virus (HIV) women patients must go through. The stigma stems from a lack of knowledge and understanding of HIV itself. It is even worse when HIV positive people are also associated with Acquired Immune Deficiency Syndrome (AIDS) even though not all HIV positive people have AIDS. Data were collected from 20 women with HIV positive in Besut, Terengganu and Pasir Puteh, Bachok and Kota Bharu, Kelantan. Purposive sampling with some help from key informant were used to get the total of 20 respondents. Result of the study indicated that those women experience severe stigma, stereotyping and negative labels even though they were not involved in any immoral activities but instead they are a victim, in which the infection came from their own husband. Some people who are not emotionally strong enough to face the negative perceptions and judgement made by the society cause them to live in fear, anxiety, isolation, depression, and some choose to quit their job and lives, avoid getting proper treatment, meeting, and interacting with others. It is not easy to ensure that society do not label or stigmatize HIV positive patients. But it is the duty of all parties to provide knowledge of Positive HIV and it is a disease that is not easily transmitted to the public. So, there is no reason for the public to fear and punish the patients.

The intelligent decision-making of copper flotation backbone process based on CK-XGBoost

This study creatively puts forward XGBoosts with correlation-based and knowledge-based function (CK-XGBoost) to design flotation backbone process according to the natural properties of a copper mine. The decision-making of flotation backbone process of copper mine is a multi label problem with high feature dimension and small instance set. The proposed CK-XGBoost selects label-specific features and trains a binary XGBoost for each label, and the information of other labels is used to assist the classifier modeling through correlation-based function and knowledge-based function. The correlation-based function utilizes the Pearson correlation coefficient between labels in training set, so that the predicted values of two strong positive correlation labels are similar and the predicted values of two strong negative correlation labels are contrary. The knowledge-based function utilizes domain knowledge, which is independent of the distribution of training set. The experimental results demonstrate the significantly superiority of the proposed CK-XGBoost in the decision-making of copper flotation backbone process.

Embryo Grading With Unreliable Labels Due to Chromosome Abnormalities by Regularized PU Learning With Ranking.

We propose a method for human embryo grading with its images. This grading has been achieved by positive-negative classification (i.e., live birth or non-live birth). However, negative (non-live birth) labels collected in clinical practice are unreliable because the visual features of negative images are equal to those of positive (live birth) images if these non-live birth embryos have chromosome abnormalities. For alleviating an adverse effect of these unreliable labels, our method employs Positive-Unlabeled (PU) learning so that live birth and non-live birth are labeled as positive and unlabeled, respectively, where unlabeled samples contain both positive and negative samples. In our method, this PU learning on a deep CNN is improved by a learning-to-rank scheme. While the original learning-to-rank scheme is designed for positive-negative learning, it is extended to PU learning. Furthermore, overfitting in this PU learning is alleviated by regularization with mutual information. Experimental results with 643 time-lapse image sequences demonstrate the effectiveness of our framework in terms of the recognition accuracy and the interpretability. In quantitative comparison, the full version of our proposed method outperforms positive-negative classification in recall and F-measure by a wide margin (0.22 vs. 0.69 in recall and 0.27 vs. 0.42 in F-measure). In qualitative evaluation, visual attentions estimated by our method are interpretable in comparison with morphological assessments in clinical practice.

Karakteristik dan Model Bimbingan Pendidikan Islam ABK Tuna Laras

Humans are created with various potentials that exist within themselves. Humans have a unique diversity and all humans are perfect in their own way. This does not mean that someone who has a handicap or disorder is an imperfect human being. In everyday life, people with special needs have the same rights as other humans. Characteristics and types of special needs are different, one of which is social and emotional behavior disorders. By reviewing more deeply the characteristics of children with special needs (ABK) with mental retardation, so that they are able to give more attention to them. Based on the literature, it can be interpreted that not all naughty children are evil. Before giving them a negative label, they should be able to identify what makes their behavior different from the others. The provision of services and guidance on a regular basis both in the school, home, and community environment will further direct the mentally retarded crew to be better. One of the guidance models in this mentally retarded ABK is to provide Islamic education and inclusive education so that they can return to real human nature.

Semi-supervised nonnegative matrix factorization with positive and negative label propagations

Semi-supervised nonnegative matrix factorization with positive and negative label propagations