Miracle-dz/UniFAR-FTUs huggingface dataset:
Disruptive Innovations and Paradigm Shifts in Journalism as a Business: From Advertisers First to Readers First and Traditional Operational Models to the AI Factory

Since the early years of the 21st century, the newspaper industries of Western liberal democracies have been in a perpetual crisis caused by disruptive digital innovators outside the industry. Until recently, the industry and researchers have conveyed a predominantly pessimistic view of the future. This paper argues that after numerous unsuccessful innovations, the industry is now in the middle of a global paradigm shift with a huge impact on journalism as a business and showing a promising path forward. The new “Readers First Paradigm” is replacing “Advertisers First” The former constitutes a new way of doing business, consisting of two main changes: a revised value proposition focusing on reader preferences and subscriptions; a fully digitized operational model built around AI and machine learning. The article shows that until recently, the paradigm shift has been largely overlooked by researchers in the field. Further, a theoretical framework of industrial paradigm shifts is developed to describe and explain the new paradigm’s emergence and growth. Additionally, a generic “ideal-type” systemic description of the paradigm is provided, identifying the individual components and how they work together.
#ShoutYourAbortion on Instagram: Exploring the Visual Representation of Hashtag Movement and the Public’s Responses

The goal of the current study was to explore the visual representation of #ShoutYourAbortion hashtag movement on Instagram. The photos’ content and embedded texts in the photos were examined. And the photos were clustered using k-means clustering algorithm, and the resulting clustered were compared using the same criteria above. Photo features which shows the content- and pixel-level characteristics were extracted and used for comparison between clusters. The photo features were also used to examine their relationships with the public’s responses. It was found that text was the main type of content, and the texts presented in photos were mainly about stories told in first person point of view as a woman. The photos were grouped into two clusters, which differed in terms of content and photo features. And the public’s responses were found to be related to photo features. The results are expected to contribute to the understanding of hashtag movements via photos and making photos in hashtag movements more appealing to the public.
Ethnocentrism: A Short Form Measure (SFGENE-7)

The purpose of this investigation is to develop and validate a short form of the Generalized Ethnocentrism Scale (GENE). The GENE scale measures people’s tendency to consider their culture as the center of the world. A total of four studies were conducted using samples of college students. In the Study 1 has been examined the underlying factor structure of the GENE measure. Based on this study, the Short Form Generalized Ethnocentrism Scale is proposed (SFGENE-7). In the second study, the two-factor structure evidenced in Study 1, was supported by a confirmatory factor analysis (CFA). The psychometric properties of the short form of the GENE scale were also satisfactory. In the third study, the two-factor structure was submitted again to CFA. The investigation of the relationship between ethnocentrism and external variables was also examined. The results supported the convergent, discriminant and incremental validities. Finally, in the fourth study, temporal stability of the short form of the GENE scale has been assessed. The results demonstrated that the SFGENE-7 had adequate test-retest reliability. Overall, results reveal that the SFGENE-7 has adequate levels of reliability, convergent validity, discriminant validity, and incremental validity. We obtained a short, reliable, and valid instrument to assess ethnocentrism.
Deep convolutional neural network for three-dimensional objects classification using off-axis digital Fresnel holography

A Deep Convolutional Neural Network (CNN)-based three-dimensional (3D) objects classification using an augmented holographic phase image dataset is proposed. Off-axis Fresnel digital holography is used for retrieving 3D object information as phase images. The phase image dataset of the five different 3D objects considered in this study is prepared under various recording distances and rotational angles. The CNN based architecture classifies the 3D objects into two sets comprising of TRUE and FALSE classes with higher accuracy and minimal loss on the training set for the phase image dataset. The CNN has achieved higher precision, lower recall, and higher F1-score for the TRUE class on the test set. The experimental results demonstrate that the proposed CNN is capable of classifying 3D objects with an overall accuracy of 86% and AUC of 96% for the phase image dataset. The feasibility and classification performance are deduced from a proof of the concept experiment.
TITLE Understanding the Operational Model of Self-Concept : An Educational Approach

Educational factors such as self-concept and academic achievement have consistently been of great interest to parents, teachers, and administrators. Divergent views of the selfconcept of students have led to the proliferation of constructs, instruments, and interpretations. The perceptual notion that "self concept is an undifferentiated and interrelated perceptions of the self" puts teachers in precarious positions when designing Individualized Education Programs for their students. The operational definition that "self-concept is an individual's repertoire of self-descriptive behaviors" present proper understanding of school-related and non-school-related tasks which confront students. In addition, such an understanding makes it easier to design Individualized Education Programs, and reduce labels/categorizations which are prevalent in education today. The authors have discussed both perceptual and operational models of self-concept as thej relate to teaching, and provided empirical findings to support the operational
The Relationship between Self-reported Traffic Crashes and Driver Behavior in the Road Transportation of Goods and Freight in Bali

Road safety stakeholders in Bali have highlighted the need to investigate and better understand road user behavior as a means of reducing the high number of crashes in the goods and freight distribution sector. This study aims to analyze the relationship between driver behavior and traffic crashes reported by drivers transporting goods and freight in Denpasar, Bali. A driving safety survey was distributed to 350 goods and freight transport drivers to obtain a range of demographic information, self-reported crash and offence data, and psychosocial data associated with driving safety. Analysis identified four areas of concern with distinctions between different driving practices within the sample, consisting of aggressive driving behavior, driving errors, traffic code violations and normlessness. The results of this study demonstrate that normlessness consisting of behaviors such as taking a slight risk when overtaking and ignoring speed limits were the most common forms of unsafe behavior reported by the goods vehicle transport drivers. The study found that speeding influenced more self-reported driving errors, aggressive behaviors, traffic rule violations and crashes. In addition, drink driving was found to be positively related to traffic crashes. The implications of these results are discussed.
Leadership and Marketing Capabilities in Small Businesses of Subsistence Marketplaces

Small businesses are vital in developing countries. Usually, the marketplaces in the most impoverished areas are subsistence marketplaces, and small businesses are frequently created as the only form of subsistence for the entrepreneur and their family. These businesses working in subsistence marketplaces have distinct marketing needs. These businesses may require leadership to use their marketing capabilities to be competitive. This paper analyzes the mediation of marketing capabilities and leadership as sources of competitive advantage in subsistence small businesses using a partial least square technique and timber industry data from a region in Argentina. This paper confirms that transformational leadership and marketing capabilities are a source of competitive advantage in subsistence small businesses. In particular, leaders use marketing capabilities to generate a competitive advantage.
Learning to Live With Ambiguity: Rethinking Ambiguous Loss for Mothers of Children With Disabilities

The purpose of this study was to explore the mothering experiences of children with disabilities. The theory of ambiguous loss was applied to interpret the meanings that mothers give to the process of caregiving and the ways they stay proactive in the midst of challenges. Eleven mothers of children ages 6 to 35 with various disabilities participated in in-depth interviews to talk about their child’s educational history, their family’s routine, and the rewards and challenges of raising a child with disabilities. Findings illustrate that the mothers faced different experiences of ambiguity depending on the visible or invisible nature of their child’s disabilities. However, regardless of the nature of the disabilities, the mothers had learned to live with ambiguity in the face of new and continuous challenges. They did this by redefining their lives through maintaining valued identities as mothers and redefining the meaning of family. Findings also imply that the scope of ambiguous loss needs to be expanded.
Neural Mechanisms Underlying Expectation-Guided Decision-Making

Many decisions are guided by expectations about their outcomes. For instance, we may decide to visit a restaurant because we anticipate the food to be outstanding. How these expectations are represented in the brain, and how they allow us to make adaptive choices are important questions for understanding the neural basis of behavior. Work across species has revealed brain areas that signal expected rewards (Haber and Knutson, 2010; Kahnt, 2018). This work typically focuses on neural correlates of the value of choice options (Padoa-Schioppa, 2011), that is, how desirable an option is. Activity in many brain areas, including the striatum, ventromedial prefrontal cortex and orbitofrontal cortex (OFC), is correlated with expected value. However, expected outcomes are more than their value—they have a specific identity. Even though we may equally desire pizza funghi and spaghetti arrabiata, they are not the same, and representing expectations about the identity of outcomes is important for adaptive decision-making. In this opinion, I will summarize recent work from my lab that has shown how the lateral OFC represents expectations about specific outcomes, how these expectations are learned, and how they can be used for adaptive decision-making. Finally, I will summarize evidence that disrupting activity in OFC networks that represent specific outcome expectations impairs adaptive behavior. Together, these findings support the view that the OFC contributes to expectation-guided decision-making by enabling us to simulate the consequences of our choices.
Multiplexed Visualization Method to Explore Complete Targeting Regulatory Relationships Among Circadian Genes for Insomnia Treatment

Understanding the complete map of melatonin synthesis, the information transfer network among circadian genes in pineal gland, promises to resolve outstanding issues in endocrine systems and improve the clinical diagnosis and treatment level of insomnia, immune disease and hysterical depression. Currently, some landmark studies have revealed some genes that regulate circadian rhythm associated with melatonin synthesis. However, these studies don't give a complete map of melatonin synthesis, as transfer information among circadian genes in pineal gland is lost. New biotechnology, integrates dynamic sequential omics and multiplexed imaging method, has been used to visualize the complete process of melatonin synthesis. It is found that there are two extremely significant information transfer processes involved in melatonin synthesis. In the first stage, as the light intensity decreased, melatonin synthesis mechanism has started, which is embodied in circadian genes, Rel, Polr2A, Mafk, and Srbf1 become active. In the second stage, circadian genes Hif1a, Bach1, Clock, E2f6, and Per2 are regulated simultaneously by four genes, Rel, Polr2A, Mafk, and Srbf1 and contribute genetic information to Aanat. The expeditious growth in this technique offer reference for an overall understanding of gene-to-gene regulatory relationship among circadian genes in pineal gland. In the study, dynamic sequential omics and the analysis process well provide the current state and future perspectives to better diagnose and cure diseases associated with melatonin synthesis disorder.
A Characterization of Optimal Designs for Observational Studies

SUMMARY An empirical investigation of the effects of a treatment is an observational study if it involves the comparison of treated and control groups that were not formed by randomization. In such studies, treated and control groups may differ systematically with respect to pretreatment measures or covariates, and addressing these pre- treatment differences is a central concern. Matching and subclassification are two standard methods of adjusting for observed pretreatment differences; they may be used alone (e.g. Cochran (1968)) or in conjunction with analytical or model-based adjustments (e.g. Rubin (1973, 1979), Holford (1978), Rosenbaum and Rubin (1984), section 3.3, and Rosenbaum (1987, 1988a)). In particular, Rubin's simulation studies suggest that model-based adjustments applied to matched or subclassified samples are more robust than model-based adjustments applied to unmatched samples. The purpose of this paper is to identify the structure of an optimal subclassification, i.e. one in which the treated and control subjects in the same subclass are, on average, as similar as possible with respect to observed covariates. It turns out that this structure is simple and intuitive. While adjustments may control imbalances in observed covariates, they cannot
A Review on the Role of Affective Stimuli in Event-Related Frontal Alpha Asymmetry

Frontal alpha asymmetry refers to the difference between the right and left alpha activity over the frontal brain region. Increased activity in the left hemisphere has been linked to approach motivation and increased activity in the right hemisphere has been linked to avoidance or withdrawal. However, research on alpha asymmetry is diverse and has shown mixed results, which may partly be explained by the potency of the used stimuli to emotionally and motivationally engage participants. This review gives an overview of the types of affective stimuli utilized with the aim to identify which stimuli elicit a strong approach-avoidance effect in an affective context. We hope this contributes to better understanding of what is reflected by alpha asymmetry, and in what circumstances it may be an informative marker of emotional state. We systematically searched the literature for studies exploring event-related frontal alpha asymmetry in affective contexts. The search resulted in 61 papers, which were categorized in five stimulus categories that were expected to differ in their potency to engage participants: images & sounds, videos, real cues, games and other tasks. Studies were viewed with respect to the potency of the stimuli to evoke significant approach-avoidance effects on their own and in interaction with participant characteristics or condition. As expected, passively perceived stimuli that are multimodal or realistic, seem more potent to elicit alpha asymmetry than unimodal stimuli. Games, and other stimuli with a strong task-based component were expected to be relatively engaging but approach-avoidance effects did not seem to be much clearer than the studies using perception of videos and real cues. While multiple factors besides stimulus characteristics determine alpha asymmetry, and we did not identify a type of affective stimulus that induces alpha asymmetry highly consistently, our results indicate that strongly engaging, salient and/or personally relevant stimuli are important to induce an approach-avoidance effect.
Embodied Interaffectivity in the Emergence and Maintenance of Group Cohesion

Group cohesion is an affect-laden construct, with a large body of research indicating its importance for success of teams. Surprisingly, it has received scant attention in collaborative learning contexts, and little is known about its development as dynamically emergent in the spontaneous, interdependent actions of actors during groupwork. This paper details an illustrative case analysis which took an embodied perspective to explore the role of interaffectivity in the emergence and maintenance of cohesion in one small group of university students who reported a highly positive and productive experience of collaborative science activities over a semester. The case analysis made visible group cohesion as unfolding and enactive in the myriad ephemeral and seemingly inconsequential microlevel behaviors that evolved into macro-temporal patterns of positive embodied interaffectivity, magnifying their visibility and collective impact. A fine-grained embodiment lens unveiled how participants cocreated collaborative affordances in actions that involved corporeal orientation as well as use of space, task, and other material artifacts. Task-related humor within routine task interaction offered the potential for establishing group cohesion in early group life, but also posed a potential threat to task-focused cohesiveness, requiring careful modulation at critical task points. Attentiveness not only to the task but importantly, to one another as interpersonal attentiveness, appeared to be a key factor in developing and maintaining group cohesion, also demonstrating collaborative learning as a process of orienting to and understanding tasks through one another. An embodiment lens highlighted mutual attentiveness in the ongoing orienter-orientee microprocesses that facilitated group orientation early in group life, and in reorienting to positive embodied interaffectivity when the group reconvened for their joint science activities in subsequent weeks.
Electronic Toys Decrease the Quantity and Lexical Diversity of Spoken Language Produced by Children With Autism Spectrum Disorder and Age-Matched Children With Typical Development

Many young children with autism spectrum disorder (ASD) have language delays. Play-based interactions present a rich, naturalistic context for supporting language and communication development, but electronic toys may compromise the quality of play interactions. This study examined how electronic toys impact the quantity and lexical diversity of spoken language produced by children with ASD and age-matched children with typical development (TD), compared to traditional toys without electronic features. Twenty-eight parent-child dyads (14 per group) played with both electronic and traditional toy sets in a counter-balanced order. We transcribed child speech during both play sessions and derived the number of utterances and number of different word (NDW) roots per minute that children produced. Children with ASD and children with TD talked significantly less and produced significantly fewer unique words during electronic toy play than traditional toy play. In this way, children appear to take a “backseat” to electronic toys, decreasing their communicative contributions to play-based social interactions with their parents. These findings highlight the importance of understanding how toy type can affect parent-child play interactions and the subsequent learning opportunities that may be created. Play-based interventions for children with ASD may be most effective when they incorporate traditional toys, rather than electronic toys.
Design and Optimization of Hardware Accelerators for Deep Learning

Deep Neural Networks (DNNs) are the state-of-art solution in a growing number of tasks including computer vision, speech recognition, and genomics. However, DNNs are computationally expensive as they are carefully trained to extract and abstract features from raw data using multiple layers of neurons with millions of parameters. In this dissertation, we primarily focus on inference, e.g., using a DNN to classify an input image. This is an operation that will be repeatedly performed on billions of devices in the datacenter, in self-driving cars, in drones, etc. We observe that DNNs spend a vast majority of their runtime to runtime performing matrix-by-vector multiplications (MVM). MVMs have two major bottlenecks: fetching the matrix and performing sum-of-product operations. To address these bottlenecks, we use in-situ computing, where the matrix is stored in programmable resistor arrays, called crossbars, and sum-of-product operations are performed using analog computing. In this dissertation, we propose two hardware units, ISAAC and Newton.In ISAAC, we show that in-situ computing designs can outperform DNN digital accelerators, if they leverage pipelining, smart encodings, and can distribute a computation in time and space, within crossbars, and across crossbars. In the ISAAC design, roughly half the chip area/power can be attributed to the analog-to-digital conversion (ADC), i.e., it remains the key design challenge in mixed-signal accelerators for deep networks. In spite of the ADC bottleneck, ISAAC is able to out-perform the computational efficiency of the state-of-the-art design (DaDianNao) by 8x. In Newton, we take advantage of a number of techniques to address ADC inefficiency. These techniques exploit matrix transformations, heterogeneity, and smart mapping of computation to the analog substrate. We show that Newton can increase the efficiency of in-situ computing by an additional 2x. Finally, we show that in-situ computing unfortunately cannot be easily adapted to handle training of deep networks, i.e., it is only suitable for inference of already-trained networks. By improving the efficiency of DNN inference with ISAAC and Newton, we move closer to low-cost deep learning that in turn will have societal impact through self-driving cars, assistive systems for the disabled, and precision medicine.