Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-consistency, cognitive reactions and autobiographical memory in depression

By adopting Beike & Landoll's (2000) model of Cognitive Reaction to Memory (CRM), four studies were designed to provide a framework for investigating the role of several types of reactions that theoretically would reduce discomfort and resolve inconsistency between a remembered life event and a belief about the lifetime period in which it occurred. Specifically, the experiments investigated providing justifications for the inconsistency, recruiting additional specific events that oppose those recalled and putting the events behind oneself (closure). While Beike and Landoll's (2000) population were university students, the main target of the current study was to examine the usage of these cognitive reactions within three levels of clinical depressed patients (mild, moderate, and severe), and the impact of that on their wellbeing satisfaction. Furthermore, the Autobiographical Memory Interview was administered to examine the predicted over-general style in depression across three life periods (i. e., childhood, early adulthood, recent life). Two methods were employed to generate data: a questionnaire, and interview. Questionnaire and interview data were gathered from 364 Kuwaiti depressed patients and 211-matched control. No support was found for Beike & Landoll's (2000) prediction that cognitive reactions maintain well-being after inconsistent recall only. The findings of this study showed that the three types of cognitive reactions: justification, outweighing, and closure, were found to be ineffective in reducing discomfort and restoring consistency for depressed patients. Their negative mood state appears to affect their usage of cognitive reactions, recall of specific autobiographical memories, and beliefs about the past. Thus, their reactions to memories do not restore consistency and their well-being satisfaction remains correspondingly low. Furthermore, results indicate that the increase recall of overgeneral autobiographical memories is a process that appears to be unique to depression and early adulthood period seemed to be the point in the temporal gradient between the low (reference and mild) and high (severe and moderate) depression group.
I-1/2002

STATSBUDSJETTET 2002 KAP 703: OREMERKET TILSKUDD TIL HELSETJENESTEN TIL INNSATTE I FENGSEL Rundskriv I-1/2002 Til: Kommuner med fengsler Landets fylkeskommuner Dato: 14.01. 2002 Var ref. : HA1 PK/- 1 Innledning.
Identifying the progression of coronary artery disease: prediction of cardiac events

Coronary artery disease (CAD) remains as a principal cause of morbidity and mortality worldwide. Despite the advancements in medical therapy and interventional procedures during the past years, the rate of major adverse cardiac events remains high both in individuals without previous history of heart disease and in patients with known CAD. Improved strategies of risk stratification for identifying subclinical atherosclerosis and asymptomatic individuals at high risk for cardiac events may help in intensifying treatment at targeted population groups, thereby providing enhanced primary prevention. Latest technological progress has made available new methodologies (e.g., fractional flow reserve and virtual surrogates) for assessing coronary anatomy and obstructive lesions precipitating acute cardiac events. Coronary imaging using either invasive (e.g., intravascular ultrasound, optical coherence tomography) or noninvasive (e.g., multislice computed tomography) modalities has enabled the detailed identification of atherosclerotic plaque features and inflammatory processes, thereby providing a “histology‐like” quantification of the disease burden within coronary arteries. Additionally, the local hemodynamic environment, in particular endothelial shear stress, is considered as a primary stimulus for plaque development and progression. In vivo assessment of endothelial shear stress in humans has been made possible using three‐dimensional coronary imaging and computational fluid dynamics techniques, and could potentially provide a more comprehensive evaluation of the risk for disease progression. Combined assessment of cardiovascular burden focusing on the functional significance of obstructive CAD, the anatomic features of plaque vulnerability, and the local hemodynamic milieu could yield an increased predictive capacity for prognostication, and thus enable preemptive strategies that could avert cardiac events.
Detecting and Resolving Flow Entries Collisions in Software Defined Networks

Software-defined network(SDN) provides flexible management by separating control plane and data plane. Multiple function modules distribute flow entries to OpenFlow switches via centering controllers. Unfortunately, making and managing flow entries and policies are often error- prone and complex due to the lack of systematic analysis tools. Since the network updating takes place frequently, the analysis scheme must be efficient enough. In this paper, we propose a Trie based scheme to analysis collision occurring in the data plane. Extensive experiments demonstrate that our method is 3-40× faster than the traditional scheme andcost less memory. Moreover, a policy-oriented strategy was introduced to help resolve the collision, which can be treated as reference advice for administrators. Also, we implement and evaluate our scheme in the simulation environment to verify its practicability.
Evaluation of Pooling Operations and Regularization Parameters in Neural Networks for Drug-drug Interaction Extraction

Recently, deep neural networks have been widely used in biomedical relation extraction, especially CNN and LSTM. Relation extraction is a classification task which uses pooling operations in neural networks to reduce dimensions and integration features and it uses regularization to prevent over-fitting. This paper evaluates the performance of CNN and LSTM in Drug-drug interaction extraction. We discuss the models' performance differences using different pooling operations and regularization parameters. Firstly, regularization can prevent over-fitting effectively, but it is important to ensure that the regularization parameters are set within the correct ranges. Secondly, the max pooling is better than other single pooling methods. Max pooling outperforms the others alternatives because is the only one which is invariant to the special pad tokens that are appending to the shorter sentences known as padding.
Real-Time Online Multi-Object Tracking: A Joint Detection and Tracking Framework

In recent years, object detection technology has been continuously developed, and the tracking-by-detection strategy has gradually become the main method of multi-object tracking. Based on detection, the accuracy of the multi-object tracking depends on the detection results to a large extent. However, in many practical applications, especially the case of complex scenes and crowded objects, the detection results are usually inaccurate. In this paper, a joint detection and tracking framework is proposed with a unified confidence scoring function to evaluate tracks confidence and complement low confidence detections with high confidence tracks. In this way, detections and tracks can be combined organically and achieved complementarity. High confidence detection results can prevent long-term tracking drift, while high confidence tracking prediction can deal with false detection and missed detection caused by occlusion during object interaction. Moreover, we trained the ReID appearance feature with higher identification capabilities on the large-scale person re-identification datasets, which has higher identification capability. Extensive experiments are conducted on MOT17 benchmarks to demonstrate the real-time and advanced performance of our tracker.
Effects of Acute Moderate Intensity Exercise on Emotion Based on beta Power in EEG

According to previous studies, high anxiety leads to an increase in beta power, while pleasure and relaxation of emotion reduces beta power. In this experiment, the International Standard Picture Stimulation Library(IAPS) was used to induce positive and negative emotions of subjects, and EEG signals were collected when subjects were looking at pictures. Comparing the beta power of positive and negative EEG induced by before and after exercise, we found that the power of beta stimulated by positive and negative pictures before exercise is generally greater than that after exercise. It indicates that subjects were happier when facing positive stimuli than before exercise, and less negative emotions were produced when facing negative stimuli than before exercise. In short, acute moderate intensity exercise can improve people's negative emotion in the face of negative stimuli. The purpose of this study is to explore the mechanism of the effect of acute moderate intensity exercise on emotion by analyzing beta power in late positive potential (LPP).
Image Aided Point-wise Autonomous Annotation for LiDAR Data

This paper presents an autonomous method to realize the annotation of LiDAR (Light Detection And Ranging) point clouds with the help of images. Different from directly labeling on point clouds, our approach first utilizes mature image segmentation method to derive pixel level labels of images. Point clouds are annotated through the corresponding relationship between 2D and 3D under the guidance of image labels. A point label rectification procedure is proposed using the 3D structure characteristics to reduce the error of annotation. Experimental results demonstrate our approach is able to annotate LiDAR point clouds and rectify the results effectively.
Full-stack SDN: The Next Big Challenge?

This paper challenges the common assumption that SDN networks shall be run only at lowest layers of the stack, i.e., L2 and L3. Using as use case data center networks providing virtualized services, we show how state-of-the-art solutions already employ some application-level processing via a central controller. With this in mind, we question if the lessons learned from a decade of SDN networking can be also extended to the upper layers. We make the case for a full-stack SDN framework that encompasses all protocol layers in the network stack, and call for further research in the area.
Racial mirroring effects on human-agent interaction in psychotherapeutic conversations

Conversational agents are increasingly utilized to deliver mental health interventions. However, these systems are characterized by relatively poor adoption and adherence. Our study explores the "racial mirroring" effects on how people perceive and engage with agents in the context of psychotherapy. We developed a conversational system with racially heterogeneous personas using strong visual cues. We conducted an experiment by randomly assigning participants (N=212) to racial mirroring, non-mirroring and control groups. Our results suggest that racial mirroring did influence human-agent interaction in terms of perceived interpersonal closeness, user satisfaction, disclosure comfort, desire to continue interacting, and projected future relationship with agents. In this paper, we present the conversational system, experimental procedure, and results. We conclude with design recommendations for employing conversational agents in mental health intervention.
Explaining recommendations by means of aspect-based transparent memories

Recommender Systems have seen substantial progress in terms of algorithmic sophistication recently. Yet, the systems mostly act as black boxes and are limited in their capacity to explain why an item is recommended. In many cases recommendations methods are employed in scenarios where users not only rate items, but also convey their opinion on various relevant aspects, for instance by the means of textual reviews. Such user-generated content can serve as a useful source for deriving explanatory information to increase system intelligibility and, thereby, the user's understanding. We propose a recommendation and explanation method that exploits the comprehensiveness of textual data to make the underlying criteria and mechanisms that lead to a recommendation more transparent. Concretely, the method incorporates neural memories that store aspect-related opinions extracted from raw review data. We apply attention mechanisms to transparently write and read information from memory slots. Besides customary offline experiments, we conducted an extensive user study. The results indicate that our approach achieves a higher overall quality of explanations compared to a state-of-the-art baseline. Utilizing Structural Equation Modeling, we additionally reveal three linked key factors that constitute explanation quality: Content adequacy, presentation adequacy, and linguistic adequacy.
E-DAM: Encoder-Decoder with Attention Mechanism for City-scale Taxi Trajectory Prediction

Taxi trajectory prediction plays an important role in perceiving urban traffic conditions and analyzing taxi passengers' travel behaviors. In this paper, an urban taxi trajectory prediction model is proposed based on attention mechanism and Long-short Term Memory (LSTM). In this model, road networks is partitioned into grids, and the road segment with the grid which the road segment lies in is represented with embedding vector. Encoder-decoder framework is adopted, and both the encoder and decoder are implemented with LSTM, in which encoder is used to turn the taxi trajectory into coding vector and decoder is employed to transform the coding vector back into taxi trajectory. Furthermore, in order to improve the performance of the trajectory prediction model, attention mechanism is introduced to put the attention on the combination among road segments during prediction. In the experiment, the proposed model was fully verified using Xi'an taxi GPS data. The result shows that the model can effectively predict the city-scale taxi trajectory, and its prediction performance is better than that of the traditional time series prediction models and the existing deep network models.
Radical Repair of Inguinal Hernia in Same Day Surgery

Due to advances in minimally-invasive surgical treatment, diseases that have conventionally required hospitalization, can now be treated in same day surgery. Among such treatments, same day inguinal hernioplasty is now the worldwide gold standard. Due to the National Health Insurance (NHI) point system, however, such day surgery is not yet standard in Japan. In our clinic, day surgery for inguinal hernia has been performed for 873 patients since 2014, with favorable outcomes. It is extremely important to perform under balanced anesthesia, and to occlude the hernia orifice with a minimum delamination procedure due to the surgical techniques necessary for safe day surgery. Day surgery is less-invasive and low in cost with many benefits for busy modern people who have a difficulty in taking off work. We should further improve surgical techniques to continuously perform such a minimally invasive the day surgery in the future.
TAPrec: supporting the composition of trigger-action rules through dynamic recommendations

Nowadays, users can personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules. As the number of supported technologies grows, however, so does the design space, i.e., the combinations between different triggers and actions: without proper support, users often experience difficulties in discovering rules and their related functionality. In this paper, we introduce TAPrec, an End-User Development platform that supports the composition of trigger-action rules with dynamic recommendations. By exploiting a hybrid and semantic recommendation algorithm, TAPrec suggests, at composition time, either a) new rules to be used or b) actions for auto-completing a rule. Recommendations, in particular, are computed to follow the user's high-level intention, i.e., by focusing on the rules' final purpose rather than on low-level details like manufacturers and brands. We compared TAPrec with a widely used trigger-action programming platform in a study on 14 end users. Results show evidence that TAPrec is appreciated and can effectively simplify the personalization of connected entities: recommendations promoted creativity by helping users personalize new functionality that are not easily noticeable in existing platforms.
TurboEPC: Leveraging Dataplane Programmability to Accelerate the Mobile Packet Core

Recent architectures of the mobile packet core advocate the separation of the control and dataplane components, with all signaling messages being processed by the control plane entities. This paper presents the design, implementation, and evaluation of TurboEPC, a redesign of the mobile packet core that revisits the division of work between the control and data planes. In TurboEPC, the control plane offloads a small amount of user state to programmable dataplane switches, using which the switches can correctly process a subset of signaling messages within the dataplane itself. The messages that are offloaded to the dataplane in TurboEPC constitute a significant fraction of the total signaling traffic in the packet core, and handling these messages on dataplane switches closer to the end-user improves both control plane processing throughput and latency. We implemented the TurboEPC design using P4-based software and hardware switches. The TurboEPC hardware prototype shows throughput and latency improvements by up to 102x and 98% respectively when the switch hardware stores the state of 65K concurrent users, and 22× and 97% respectively when the switch CPU is busy forwarding dataplane traffic at linerate, over the traditional EPC.
Navigation-by-preference: a new conversational recommender with preference-based feedback

We present Navigation-by-Preference, n-by-p, a new conversational recommender that uses what the literature calls preference-based feedback. Given a seed item, the recommender helps the user navigate through item space to find an item that aligns with her long-term preferences (revealed by her user profile) but also satisfies her ephemeral, short-term preferences (revealed by the feedback she gives during the dialog). Different from previous work on preference-based feedback, n-by-p does not assume structured item descriptions (such as sets of attribute-value pairs) but works instead in the case of unstructured item descriptions (such as sets of keywords or tags), thus extending preference-based feedback to new domains where structured item descriptions are not available. Different too is that it can be configured to ignore long-term preferences or to take them into account, to work only on positive feedback or to also use negative feedback, and to take previous rounds of feedback into account or to use just the most recent feedback. We use an offline experiment with simulated users to compare 60 configurations of n-by-p. We find that a configuration that includes long-term preferences, that uses both positive and negative feedback, and that uses previous rounds of feedback is the one with highest hit-rate. It also obtains the best survey responses and lowest measures of effort in a trial with real users that we conducted with a web-based system. Notable too is that the user trial has a novel protocol for experimenting with short-term preferences.
Human-in-the-loop AI in government: a case study

In this paper, we present a novel application where Human-Computer Interaction (HCI) meets Artificial Intelligence (AI) and discuss obstacles that need to be resolved on the long journey from research to production. Unlike academia and industries that have been at the forefront of automation for decades, government is a new player in the field, though an important one. We build systems that are used on a large scale, we collect data to inform policymakers. Using the example of the Household Budget Survey, we demonstrate how government agencies can apply Human-in-the-Loop AI to automate the production of official statistics. The aim is time and resource saving on repetitive, labour-intensive tasks which machines are good at, allowing humans to focus on value added tasks requiring flexibility and intelligence. One major challenge is the human factor. How will the users, who are accustomed to manual tasks, react to the complexity of AI? How should we design the interface to give them a good user experience? How do we measure success? Indeed, one key step towards production is to secure funding, which requires presenting potential success in a way that the stakeholder can understand. Stressing the importance of formulating problems from a practical business viewpoint, we hope to bridge the communication gap and help the research community reach out to more potential users and help solve more novel real-world problems.
With a little help from my peers: depicting social norms in a recommender interface to promote energy conservation

How can recommender interfaces help users to adopt new behaviors? In the behavioral change literature, nudges and norms are studied to understand how to convince people to take action (e.g. towel re-use is boosted when stating that `75% of hotel guests' do so), but what is advised is typically not personalized. Most recommender systems know what to recommend in a personalized way, but not much research has considered how to present such advice to help users to change their current habits. We examine the value of presenting normative messages (e.g. `75% of users do X') based on actual user data in a personalized energy recommender interface called `Saving Aid'. In a study among 207 smart thermostat owners, we compared three different normative explanations (`Global', `Similar', and `Experienced' norm rates) to a non-social baseline (`kWh savings'). Although none of the norms increased the total number of chosen measures directly, we show evidence that the effect of norms seems to be mediated by the perceived feasibility of the measures. Also, how norms were presented (i.e. specific source, adoption rate) affected which measures were chosen within our Saving Aid interface.
Preparedness for health-related SDGs among healthcare workers in a rural district of Maharashtra with reference to achievements of MDGs 4, 5 and 6

Background: Healthcare workers at field level constitute a major pillar in the large public health infrastructure of India. At this juncture, it becomes necessary to understand their role in achieving MDGs, issues, and challenges on the field and how 'prepared' they are to embark upon the new responsibilities in the coming 15 years to achieve the SDGs. This will form a springboard for the next generation of healthcare providers to successfully achieve the SDGs. Materials and Methods: This qualitative research study was conducted in the rural part of Thane district from September 2016- March 2017. Four Focus Group Discussions (FGDs) were done to assess the role, activities, reasons for successes and shortcomings of MDG indicators for healthcare providers and thereby assess preparedness for achieving health-related SDGs at the grass-root level. Results: Major challenges faced in the field were cultural barriers, poverty, illiteracy, fear, disregard for the health workers. There were challenges in human resource management such as workload, unpaid work, dissatisfaction, grievance redressal, leaves, etc., Suggested technical and health-centric interventions were skill development, supportive supervision, incentives and better implementation of new policies. Training in soft skills is needed. Conclusions: The health workers seem to be unaware of the term MDGs/SDGs but, showed a deep sense of commitment towards improving the health of people and meeting their work targets despite the challenges faced in the field. Their justified concerns need to be addressed to have better retention and improved performance.