Miracle-dz/UniFAR-FTUs huggingface dataset:
Preserved recognition and selection of facial expression of happiness in mild Alzheimer’s Disease

A decline in the ability to process facial expression of emotions has been reported in individuals with Alzheimer’s disease (AD). However, the low number of participants and the lack of diversity in the tasks being used in previous studies leaves a gap in our knowledge about this issue. We recruited 169 participants including healthy older adults (HOA), participants with mild cognitive impairment (MCI) and AD patients at different stages of the disease (mild to moderate). Four tasks including recognition, selection, matching and declarative knowledge about facial expression of emotions were used. Face identification was used as a control task. Our results revealed that compared with HOA, MCI participants did not show any significant deficits in none of the tasks. AD patients did not show any impairment in the control task. However, they were impaired in the processing of facial expression of negative emotions across all four affective tasks. Interestingly, recognition and selection of happiness were intact in AD patients at the mild stage of the disease. Our findings suggest that despite the pathology affecting distributed areas in the brain, the less challenging aspects (recognition and selection) of the ability to process the facial expression of happiness were preserved at the early stage of the disease. By recruiting a large number of participants using several different tasks our study provides a comprehensive picture of the disorders of facial emotion processing in Alzheimer’s Disease. Our findings have significant implications for improving the AD patients’ quality of life and the quality of their social interaction with others. Future studies might start to investigate the processing of emotions in AD patients in other modalities rather than visual. in AD and MCI participants on the control task of face identity discrimination. Our study is valuable for it sheds new light on different aspects of facial expression processing in AD patients by using a variety of tasks in a large sample size of participants. Our study could potentially help to replicate the findings of the previous studies as well as adding to the current knowledge about the different aspects of facial expression processing in AD patients.
A Credibility-Based Congestion Control Method

A novel congestion control method works at the network layer, which is credibility-based and under supervision is proposed. The method supposes that network elements are all well-reputed, which means that most of them will control their transmission rate consciously and the total demanding bandwidth will not exceed the capacity of the network at high probability. The scheme is also capable to deal with situations where a few elements violate the rules and thus lead congestion. The whole network is divided into several domains independently in the scheme, each domain includes a congestion control server (CCS) and several control modules (CM). The CCS serves to collect credit information and to make punishment decisions based on the credit information, while the CM generates signaling packets to calculate the responsibility mark. Violators will get punished according to the responsibility mark. The method is distributed and can adapt to the dynamic state of network, and is quite simple to implement. It relies on sources self-restriction to avoid congestion and the expensive traffic shaping needs only to be done at sources in this way.
Web Information Extraction Using Web-specific Features

Several problems exist with traditional HMM based approaches for Web information extraction (IE) due to the lack of consideration on Web-specific features. To address this issue we present a Generalized Hidden Markov Model (GHMM) that extends HMMs by making use of Web-specific information for Web IE. In GHMM based approach, Web content blocks instead of terms are used as basic extraction unit. Besides, instead of using the traditional sequential state transition order, GHMM decides the state transition order based on layout structure of the corresponding web page. Furthermore, GHMM uses multiple emission features derived from Web information instead of single emission feature. Experimental study shows that GHMM based approach can effectively improve Web IE comparing to traditional HMM based approaches.
Cloud file system

Cloud computing became mainstream in the field of information technology. Cloud computing represents a distributed computing paradigm that focuses on providing a wide range of users with distributed access to scalable, virtualized hardware and/or software infrastructure over the Internet. The presented paper proposes a new file system as SaaS service of a cloud. The paper presents architecture and implementation details and comparison with the present data store methods.
Multi-kernel support vector machine and Levenberg-Marquardt classification approach for neonatal brain MR images

This paper focuses on the development of an accurate neonatal brain MRI segmentation algorithm and its clinical application to characterize normal brain growth and explore the neuro-anatomical correlates of cognitive impairments. The segmentation of MR images of the neonatal brain is a fundamental step in the study and assessment of infant brain development. The highest level of development techniques for adult brain MRI segmentation are not suitable for neonatal brain, because of substantial contrasts in structure and tissue properties between newborn and fully developed brains. Current newborn brain MRI segmentation techniques either depend on manual interaction or require the utilization of atlases or templates, which unavoidably presents a bias of the results towards the population that was utilized to derive the atlases. In this paper, we proposed an atlas-free approach for the segmentation of neonatal brain MRI, based on the neural network approach. The segmentation is primary stage to obtain quantitative analysis of regional brain tissues. These measurements allow characterization of the regional brain growth and inspect the correlations with clinical factors.
Axonal and glial microstructural information obtained with diffusion-weighted magnetic resonance spectroscopy at 7T

Diffusion-weighted magnetic resonance spectroscopy (DWS) offers unique access to compartment-specific microstructural information on tissue, and potentially sensitive detection of compartment-specific changes in disease. The specificity of DWS is, however, offset by its relative low sensitivity, intrinsic to all MRS-based methods, and further exacerbated by the signal loss due to the diffusion weighting and long echo times. In this work we first provide an experimental example for the type of compartment-specific information that can be obtained with DWS from a small volume of interest (VOI) in brain white matter. We then propose and discuss a strategy for the analysis of DWS data, which includes the use of models of diffusion in compartments with simple geometries. We conclude with a broader discussion of the potential role of DWS in the characterization of tissue microstructure and the complementarity of DWS with less-specific but more sensitive microstructural tools such as diffusion tensor imaging.
Self-regulation of circumscribed brain activity modulates spatially selective and frequency specific connectivity of distributed resting state networks

The mechanisms of learning involved in brain self-regulation have still to be unveiled to exploit the full potential of this methodology for therapeutic interventions. This skill of volitionally changing brain activity presumably resembles motor skill learning which in turn is accompanied by plastic changes modulating resting state networks. Along these lines, we hypothesized that brain regulation and neurofeedback would similarly modify intrinsic networks at rest while presenting a distinct spatio-temporal pattern. High-resolution electroencephalography preceded and followed a single neurofeedback training intervention of modulating circumscribed sensorimotor low β-activity by kinesthetic motor imagery in eleven healthy participants. The participants were kept in the deliberative phase of skill acquisition with high demands for learning self-regulation through stepwise increases of task difficulty. By applying the corrected imaginary part of the coherency function, we observed increased functional connectivity of both the primary motor and the primary somatosensory cortex with their respective contralateral homologous cortices in the low β-frequency band which was self-regulated during feedback. At the same time, the primary motor cortex—but none of the surrounding cortical areas—showed connectivity to contralateral supplementary motor and dorsal premotor areas in the high β-band. Simultaneously, the neurofeedback target displayed a specific increase of functional connectivity with an ipsilateral fronto-parietal network in the α-band while presenting a de-coupling with contralateral primary and secondary sensorimotor areas in the very same frequency band. Brain self-regulation modifies resting state connections spatially selective to the neurofeedback target of the dominant hemisphere. These are anatomically distinct with regard to the cortico-cortical connectivity pattern and are functionally specific with regard to the time domain of coherent activity consistent with a Hebbian-like sharpening concept.
Load balancing with Mobile Base Stations in Tactical Information Communication Networks

A Tactical Information Communication Network (TICN) enables communication among military entities during warfare. In a TICN, base stations (BSs) are required to move along with mobile devices in order to provide the communication wherever they are. This type of BS is referred to as Mobile Base Station (MBS). The backhaual of an MBS is composed of a wireless communication link so that the performance of a TICN can be limited by the unstable backhaul link capacity. In this paper, we propose load balancing algorithms considering the capacity of backhaul links in a TICN. Association control based load balancing algorithms and transmit power control based load balancing algorithms are proposed and analyzed. The simulation results demonstrate that the proposed algorithms improve the system performance of a TICN by reducing the outage rate significantly.
Social networks identification and analysis using call detail records

The telecommunication industry has grown tremendously within the past few years. The number of subscribers is increasing day by day so it is becoming more and more difficult to monitor the activity of users in the mobile network. All the subscribers are not playing important role in the network and mobile companies might need to identify the valued customers for offering new services or promotions of existing services. Also the data generated by the telecom industry can be useful for the Law enforcing agencies to find out the network of some criminal by analyzing its call detail records. This paper discusses the usefulness of the analysis of call detail records for discovering and analyzing social networks and prominence of an actor in the network.
Respiratory Syncytial Virus Disease in Preterm Infants in the US Born at 32–35 Weeks Gestation Not Receiving Immunoprophylaxis

Background: The Respiratory Syncytial Virus (RSV) Respiratory Events Among Preterm Infants Outcomes and Risk Tracking (REPORT) study evaluated RSV disease burden in US preterm infants 32–35 weeks gestational age (wGA) not receiving RSV prophylaxis. Methods: Preterm infants <6 months of age as of November 1st were followed prospectively at 188 clinics from September to May 2009–2010 or 2010–2011. Nasal and pharyngeal swabs were collected for medically attended acute respiratory illnesses (MAARI) and tested for RSV by qRT-polymerase chain reaction. Risk factors were assessed using multivariate Cox proportional hazard model adjusted for seasonality. Results: Of 1642 evaluable infants, 287 experienced RSV MAARI. Rates of RSV-related MAARI, outpatient lower respiratory tract illness, emergency department visits and hospitalization (RSVH) during November to March were 25.4, 13.7, 5.9 and 4.9 per 100 infant-seasons, respectively. Preschool-aged, nonmultiple-birth siblings and daycare attendance were consistently associated with increased risk of RSV. RSVH rates were highest in infants 32–34 and 35 wGA who were <6 months of age during November to March with daycare attendance or nonmultiple-birth, preschool-aged siblings (8.9 and 9.3 per 100 infant-seasons, respectively, versus 3.5 for all other infants, P<0.001). Chronologic age <3 months was associated with a higher RSVH rate for infants 35 wGA but not for infants 32–34 wGA. Conclusions: In US preterm infants who were 32–35 wGA, <6 months on November 1st and not receiving RSV prophylaxis, the burden of RSV MAARI was 25 per 100 infant-seasons. The highest RSVH rates occurred among those with daycare attendance or nonmultiple-birth, preschool-aged siblings while they were <6 months of age during the RSV season.
Influence Blocking Maximization in Social Network Using Centrality Measures

Online social networks play an important role as a suitable platform for information diffusion. While positive news diffusion on social network has a great impact in people’s life, the negative news can also spread as fast as positive ones. To make the social network a reliable place, it is necessary to block inappropriate, unwanted and contamination diffusion. In this paper, we study the notion of competing negative and positive campaigns in a social network and address the influence blocking maximization (IBM) problem to minimize the bad effect of misinformation. IBM problem can be summarized as identifying a subset of nodes to adopt the positive influence under Multi-campaign Independent Cascade Model (MCICM) as diffusion model to minimize the number of nodes that adopt the negative influence at the end of both propagation processes. We proposed Centrality_IBM algorithm based on centrality measures for finding an appropriate candidate subset of nodes for spreading positive diffusion in order to minimizing the IBM problem. Then, we experimentally compare the performance of the proposed algorithm using some centrality measures to choose the appropriate subset of positive influential nodes. The experiments on different real datasets reveal that the closeness centrality measure outperforms the alternative centrality measures in most of the cases.
Understanding Medication Nonadherence from Social Media: A Sentiment-Enriched Deep Learning Approach

Medication nonadherence (MNA) causes severe health ramifications and costs the U.S. healthcare systems $290 billion annually. Understanding patients’ MNA reasons is an urgent goal for researchers, practitioners, and the pharmaceutical industry to mitigate those health and economic consequences. Past years have witnessed soaring patient engagement in social media, making it a cost-efficient and rich information source that can complement prior survey studies and deepen the understanding of MNA. Yet, such a dataset is untapped in existing MNA studies due to technical challenges such as negative decision-making in long texts, varied patient vocabulary, and sparse relevant information. In this work, we develop Sentiment-Enriched DEep Learning (SEDEL) to address these challenges and extract MNA reasons. We evaluate SEDEL on 53,180 reviews of about 180 drugs and achieve a precision of 89.25%, a recall of 88.48%, and an F1 score of 88.86%. SEDEL significantly outperforms the state-of-the-art baseline models. Nine categories of MNA reasons are identified and verified by domain experts. This study contributes to IS research in two aspects. First, we devise a novel deep-learning-based approach for reason mining. Second, our results provide direct implications for the health industry and practitioners to design interventions.
Enabling efficient access control with dynamic policy updating for big data in the cloud

Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud, because the cloud has capabilities of storing big data and processing high volume of user access requests. Attribute-Based Encryption (ABE) is a promising technique to ensure the end-to-end security of big data in the cloud. However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes. A trivial implementation is to let data owners retrieve the data and re-encrypt it under the new access policy, and then send it back to the cloud. This method incurs a high communication overhead and heavy computation burden on data owners. In this paper, we propose a novel scheme that enabling efficient access control with dynamic policy updating for big data in the cloud. We focus on developing an outsourced policy updating method for ABE systems. Our method can avoid the transmission of encrypted data and minimize the computation work of data owners, by making use of the previously encrypted data with old access policies. Moreover, we also design policy updating algorithms for different types of access policies. The analysis show that our scheme is correct, complete, secure and efficient.
Socially-aware market mechanism for participatory sensing

By using personal mobile devices, participatory sensing provides an alternative to deploying dedicated mobile nodes to perform data acquisition tasks in areas where human carriers are already present. However, this application comes with some unique challenges. In this paper, we study the challenges resulting from social concerns of participants in a participatory sensing application and propose a socially aware auction mechanism to address them. Through simulations, we compare the proposed mechanism to several existing mechanisms. The results demonstrate that, with the right configuration, our proposed mechanism can decrease the cost for the data sinks and decrease the privacy loss and battery usage of participants while preserving their participation.
Estimation of Fish Biomass Using Environmental DNA

Environmental DNA (eDNA) from aquatic vertebrates has recently been used to estimate the presence of a species. We hypothesized that fish release DNA into the water at a rate commensurate with their biomass. Thus, the concentration of eDNA of a target species may be used to estimate the species biomass. We developed an eDNA method to estimate the biomass of common carp (Cyprinus carpio L.) using laboratory and field experiments. In the aquarium, the concentration of eDNA changed initially, but reached an equilibrium after 6 days. Temperature had no effect on eDNA concentrations in aquaria. The concentration of eDNA was positively correlated with carp biomass in both aquaria and experimental ponds. We used this method to estimate the biomass and distribution of carp in a natural freshwater lagoon. We demonstrated that the distribution of carp eDNA concentration was explained by water temperature. Our results suggest that biomass data estimated from eDNA concentration reflects the potential distribution of common carp in the natural environment. Measuring eDNA concentration offers a non-invasive, simple, and rapid method for estimating biomass. This method could inform management plans for the conservation of ecosystems.
Strategic Recruitment Across Borders: An Investigation of Multinational Enterprises

As a result of globalization, large-scale modern-day businesses extend across borders as they engage in multinational enterprises. Such enterprises must conduct operations in disparate, culturally diverse contexts, which present challenges for implementing human resource management activities, such as whether to standardize or localize activities across borders. The current study focuses on recruitment activities, as they represent firms’ initial efforts to attract highly qualified talent. However, the extant recruitment literature has primarily been conducted in a single context or in Westernized societies; thus, it is unclear how organizations recruit across borders. Drawing on signaling theory, we explore how Fortune 1000 firms use recruiting signals in their domestic and international operations. In general, we find that firms standardize the recruiting signals across their domestic and international operations. Yet, the amount that each signal is emphasized differs in domestic and international operations and is contingent upon language. Furthermore, cultural distance between the home and host country largely does not explain the standardization of the recruiting signals. We summarize the findings and provide direction intended to guide future research.
NBTI Recovery in SRAM Arrays through Enhanced Recovery Boosting

Negative bias temperature instability (NBTI) is an important lifetime reliability problem in microprocessors. SRAM-based structures within the processor are especially susceptible to NBTI since one of the pMOS devices in the memory cell always has an input of “0”. Previously proposed recovery techniques for SRAM cells aim to balance the degradation of the two pMOS de-vices by attempting to keep their inputs at a logic “0” exactly 50% of the time. However, one of the devices is always in the negative bias condition at any given time. In this paper, we propose a technique called Recovery Boosting that allows both pMOS devices in the memory cell to be put into the recovery mode by slightly modifying to the design of conventional SRAM cells. We evaluate the circuit-level design of a physical register file and an issue queue that use such cells through SPICE-level simulations. We then conduct an architecture-level evaluation of the performance and reliability of using area-neutral designs of these two structures. We show that Recovery Boosting provides significant improvement in the static noise margins of the register file and issue queue while having very little impact on power consumption and performance.
LGB Q&A: An Investigation of the Influence of Sexual Orientation on Professional Practice Among LGBQ-Affiliated Forensic Mental Health Professionals

Recent decades have seen an increase in research examining the experiences of Lesbian, Gay, Bisexual, Queer, and other-identifying (LGBQ) individuals in the workplace, including surveys and empirical studies exploring how an individual’s sexual orientation influences their work, instances of workplace discrimination due to sexual orientation, and the impact of antidiscrimination legislation in changing workplace culture. However, research has yet to examine the experience of LGBQ-identifying forensic mental health professionals. A survey of 37 forensic psychologists and psychiatrists who self-identified with the LGBQ community found half of all participants viewed their sexual orientation as impacting their professional identity in some way. Qualitative analyses suggest this may include an interest in research about or a desire to work with LGBQ-identifying individuals. Although one third of the participants in this study reported they did not believe there was LGBQ related prejudice or discrimination in the field of forensic mental health, 81% of participants reported experiencing some form of prejudice/discrimination. Further, over half of all participants reported changing their professional practice, physical appearance, and/or behaviors to influence others’ perceptions of their sexual orientation. In total, these data suggest LGBQ-identifying forensic mental health professionals may simultaneously feel as though the field is particularly accepting of nonheterosexuality while also identifying ways in which their sexual orientation impacts their workplace behaviors and environment.