Miracle-dz/UniFAR-FTUs huggingface dataset:
Food Frequency Index as a Measure of Diet Quality in Non-Frail Older Adults

Aims: The objective of the present work was to identify factors associated with diet quality in non-frail older adults measured with the Food Frequency Index (FFI). Methods: In a cross-sectional survey, data from a qualitative 28-item food-frequency questionnaire was used to calculate the FFI in a sample of 444 adults aged 55 years and older living in Vienna, Austria. Results: A higher FFI, indicating a favorable diet quality, was obtained for women as compared to men, younger and better-educated subjects (p for all < 0.01). Dietary supplement users (p = 0.04) and subjects being physically active (p < 0.01) showed significantly higher FFI scores. Eating difficulties (p = 0.04) and gastrointestinal problems (p = 0.03) were found to be risk factors for poor diet quality as measured with the FFI, whereas the number of prescribed daily medications was not associated with diet quality (p for trend = 0.77). Conclusions: Differences in diet quality are evident in subgroups of non-frail older adults living in Vienna as defined by societal factors, such as education, lifestyle-related factors, such as being physically active and health-related factors, such as oral health.
Effect of Biofeedback-assisted Autogenic Training on Headache Activity and Mood States in Korean Female Migraine Patients

Biofeedback with or without combined autogenic training is known to be effective for the treatment of migraine. This study aimed to examine the effect of biofeedback treatment on headache activity, anxiety, and depression in Korean female patients with migraine headache. Patients were randomized into the treatment group (n=17) and monitoring group (n=15). Mood states including anxiety and depression, and psychophysiological variables such as mean skin temperature of the patients were compared with those of the normal controls (n=21). We found greater treatment response rate (defined as ≥50% reduction in headache index) in patients with biofeedback-assisted autogenic training than in monitoring group. The scores on the anxiety and depression scales in the patients receiving biofeedback-assisted autogenic training decreased after the biofeedback treatment. Moreover, the decrease in their anxiety levels was significantly related to the treatment outcome. This result suggests that the biofeedback-assisted autogenic training is effective for the treatment of migraine and its therapeutic effect is closely related to the improvement of the anxiety level.
Toward the Starting Line: A Systems Engineering Approach to Strong AI

Artificial General Intelligence (AGI) or Strong AI aims to create machines with human-like or human-level intelligence, which is still a very ambitious goal when compared to the existing computing and AI systems. After many hype cycles and lessons from AI history, it is clear that a big conceptual leap is needed for crossing the starting line to kick-start mainstream AGI research. This position paper aims to make a small conceptual contribution toward reaching that starting line. After a broad analysis of the AGI problem from different perspectives, a system-theoretic and engineering-based research approach is introduced, which builds upon the existing mainstream AI and systems foundations. Several promising cross-fertilization opportunities between systems disciplines and AI research are identified. Specific potential research directions are discussed.
The tpl promoter of Citrobacter freundii is activated by the TyrR protein

The ability of microorganisms to degrade L-tyrosine to phenol, pyruvate, and ammonia is catalyzed by the inducible enzyme L-tyrosine phenol lyase (EC 4.1.99.2). To investigate possible mechanisms for how the synthesis of this enzyme is regulated, a variety of biochemical and genetic procedures was used to analyze transcription from the tpl promoter of Citrobacter freundii ATCC 29063 (C. braakii). By computer analysis of the region upstream of the tpl structural gene, two segments of DNA bearing strong homology to the known operator targets of the TyrR protein of Escherichia coli were detected. A DNA fragment of 509 bp carrying these operator targets plus the presumptive tpl promoter was synthesized by PCR and used to construct a single-copy tpl-lacZ reporter system. The formation of beta-galactosidase in strains carrying this reporter system, which was measured in E. coli strains of various genotypes, was strongly dependent on the presence of a functional TyrR protein. In strains bearing deletions of the tyrR gene, the formation of beta-galactosidase was reduced by a factor of 10. Several mutationally altered forms of TyrR were deficient in their abilities to activate the tpl promoter. The pattern of loss of activation function was exactly parallel to the effects of the same tyrR mutations on the mtr promoter, which is known to be activated by the TyrR protein. When cells carrying the tpl-lacZ reporter system were grown on glycerol, the levels of beta-galactosidase were 10- to 20-fold higher than those observed in glucose-grown cells. The effect was the same whether or not TyrR-mediated stimulation of the tpl promoter was in effect. By deleting the cya gene, it was shown that the glycerol effect was attributable to stimulation of the tpl promoter by the cyclic AMP (cAMP)-cAMP reporter protein system. A presumptive binding site for this transcription factor was detected just upstream of the -35 recognition hexamer of the tpl promoter. The transcriptional start point of the tpl promoter was determined by chemical procedures. The precise locations of the TyrR binding sites, which were established by DNase I footprinting, agreed with the computer-predicted positions of these regulatory sites. The two TyrR operators, which were centered at coordinates -272.5 and -158.5 with respect to the transcriptional start point, were independently disabled by site-directed mutagenesis. When the upstream operator was altered, activation was completely abolished. When the downstream operator was altered, there was a fourfold reduction in reporter enzyme levels. The tpl system presents a number of intriguing features not previously encountered in TyrR-activated promoters. First among these is the question of how the TyrR protein, bound to widely separated operators, activates the tpl promoter which is also widely separated from the operators.
On the Art of Modeling; Illustrated with the Analysis of the Golf Swing Motion

The role of a model is to provide adequate knowledge to handle a particular problem. The work of modeling starts on the basis of the feel and knowledge of the object and proceeds by developing guesses about the structure of the object. In this paper characteristics of this process are demonstrated with the example of the analysis of the golf swing motion.
Fast and Exact Top-k Search for Random Walk with Restart

Graphs are fundamental data structures and have been employed for centuries to model real-world systems and phenomena. Random walk with restart (RWR) provides a good proximity score between two nodes in a graph, and it has been successfully used in many applications such as automatic image captioning, recommender systems, and link prediction. The goal of this work is to find nodes that have top-k highest proximities for a given node. Previous approaches to this problem find nodes efficiently at the expense of exactness. The main motivation of this paper is to answer, in the affirmative, the question, 'Is it possible to improve the search time without sacrificing the exactness? '. Our solution, K-dash, is based on two ideas: (1) It computes the proximity of a selected node efficiently by sparse matrices, and (2) It skips unnecessary proximity computations when searching for the top-k nodes. Theoretical analyses show that K-dash guarantees result exactness. We perform comprehensive experiments to verify the efficiency of K-dash. The results show that K-dash can find top-k nodes significantly faster than the previous approaches while it guarantees exactness.
Do Injected Faults Cause Real Failures? A Case Study of Linux

Software fault injection (SFI) has been used to intentionally cause "failures" in software components and assess their impacts on the entire software system. A key property that SFI should satisfy is the representative ness of injected failures, the failures caused by SFI should be as close as possible to failures in the wild. If injected failures do not represent realistic failures, the measured resilience or tolerance against failures of the investigated system is not trustworthy. To the best of the authors' knowledge, the representative ness of "faults" has been investigated. However, it is an open problem whether the failures caused by injected faults represent realistic failures. In this paper, we report the preliminary results of the investigation on the representative ness of injected failures. To compare injected failures with real failures, we have collected 43,742 real crash logs of Linux from the Red Hat repository, and conducted a fault injection campaign on Linux, using SAFE, a state-of-theatre injector of software faults. In the fault injection campaign, 50,000 faults are injected to the Linux file system and 71,470 runs of a workload are executed. The crash logs generated by SFI are compared with the real Red Hat logs with respect to crash causes, crashed system calls, and crashed modules. Our preliminary results suggest that failures caused by injected faults do not represent real failures, probably because injected faults are not representative enough or because the selected workload is not realistic.
Resolving Partnership Ambivalence: A Randomized Controlled Trial of Very Brief Cognitive and Experiential Interventions with Follow-Up

Both the experiential two-chair approach (TCA) and the cognitive decision-cube technique (DCT) have been used for the treatment of ambivalence in counselling. The aims of this study were (a) to show that partnership ambivalence is reduced after a brief stand-alone intervention using either TCA or DCT, and (b) to test the hypothesized mechanisms of change processual activation and clarification. Fifty ambivalent participants were randomly assigned to either TCA or DCT interventions of two sessions each. Results indicated that partnership ambivalence was reduced after both interventions. The TCA was associated with a significantly higher processual activation than was the DCT.
Journal of Theoretical and Applied Information Technology Rule Based Expert System for Selecting Software Development Methodology

Software development methodology is a formalized approach that is used to plan and manage the process of developing a software system. Since there are many software development methodologies, one of the challenges faced by software developers is to decide which methodology to apply in a software project. This paper presents the modeling and development of a prototype expert system that helps software project managers and software engineers in selecting the appropriate software development methodology. The developed system is successfully designed as rule based expert system supported with object oriented modeling. The user interaction with the system is based on a user-friendly graphical interface.
Connecting Conscious and Unconscious Processing

Consciousness remains a mystery-"a phenomenon that people do not know how to think about-yet" (Dennett, 1991, p. 21). Here, I consider how the connectionist perspective on information processing may help us progress toward the goal of understanding the computational principles through which conscious and unconscious processing differ. I begin by delineating the conceptual challenges associated with classical approaches to cognition insofar as understanding unconscious information processing is concerned, and to highlight several contrasting computational principles that are constitutive of the connectionist approach. This leads me to suggest that conscious and unconscious processing are fundamentally connected, that is, rooted in the very same computational principles. I further develop a perspective according to which the brain continuously and unconsciously learns to redescribe its own activity itself based on constant interaction with itself, with the world, and with other minds. The outcome of such interactions is the emergence of internal models that are metacognitive in nature and that function so as to make it possible for an agent to develop a (limited, implicit, practical) understanding of itself. In this light, plasticity and learning are constitutive of what makes us conscious, for it is in virtue of our own experiences with ourselves and with other people that our mental life acquires its subjective character. The connectionist framework continues to be uniquely positioned in the Cognitive Sciences to address the challenge of identifying what one could call the "computational correlates of consciousness" (Mathis & Mozer, 1996) because it makes it possible to focus on the mechanisms through which information processing takes place.
Distributed asynchronous winner-take-all structures and conflict detection

Although WinnerTake-All (WTA) networks have found wide applications in neural networks, they face several dMicrdties such as committing premature decisions, and scafing poorly with input size. In particular, they do not work prop erly in an asynchronous environment. We propose an alternative to the cl~ic WTA structures: the reh.zed alternate update W2’A (RA-WTA) networks and a generaf mechanism to implement the RA-WTA network in random-update asynchronous environments. The RA-WTA networks use a twostage updati function that makes the network easy to scale, more adaptive to varying input size and number of competing agents. Moreover, such an update function enables the network to work properly with a relaxed alternate-update. These form a base to detect conflicts in inputs. We will describe a general form of RA-WTA networks that use a distributed representation. We then present a distributed hand-shaking update method that effectively transforms a totally random-update WTA network to an RA-WTA network without loss of its functionality, and that works with WTA networks using a distributed representation. Finally, the experimental results are presented,
Distributed control of a fleet of batteries

Battery storage is increasingly important for grid-level services such as frequency regulation, load following, and peak-shaving. The management of a large number of batteries presents a control challenge: How can we solve the apparently combinatorial problem of coordinating a large number of batteries with discrete, and possibly slow rates of charge/discharge? The control solution must respect battery constraints, and ensure that the aggregate power output tracks the desired grid-level signal. A distributed stochastic control architecture is introduced as a potential solution. Extending prior research on distributed control of flexible loads, a randomized decision rule is defined for each battery of the same type. The power mode at each time-slot is a randomized function of the grid-signal and its internal state. The randomized decision rule is designed to maximize idle time of each battery, and keep the state-of-charge near its optimal level, while ensuring that the aggregate power output can be continuously controlled by a grid operator or aggregator. Numerical results show excellent tracking, and low stress to individual batteries.
Discovering human interactions in videos with limited data labeling

We present a novel approach for discovering human interactions in videos. Activity understanding techniques usually require a large number of labeled examples, which are not available in many practical cases. Here, we focus on recovering semantically meaningful clusters of human-human and human-object interaction in an unsupervised fashion. A new iterative solution is introduced based on Maximum Margin Clustering (MMC), which also accepts user feedback to refine clusters. This is achieved by formulating the whole process as a unified constrained latent max-margin clustering problem. Extensive experiments have been carried out over three challenging datasets, Collective Activity, VIRAT, and UT-interaction. Empirical results demonstrate that the proposed algorithm can efficiently discover perfect semantic clusters of human interactions with only a small amount of labeling effort.
Human upper body posture recognition and upper limbs motion parameters estimation

We propose a real-time human motion capturing system to estimate the upper body motion parameters consisting of the positions of upper limb joints based on the depth images captured by using Kinect. The system consists of the action type classifier and the body part classifiers. For each action type, we have a body part classifier which segment the depth map into 16 different body parts of which the centroids can be linked to represent the human body skeleton. Finally, we exploit the temporal relationship between of each body part to correct the occlusion problem and determine the occluded depth information of the occluded body parts. In the experiments, we show that by using Kinect our system can estimate upper limb motion parameters of a human object in real-time effectively.
Helping Children with Aggression and Conduct Problems: Best Practices for Intervention

Part I: Nature of the Problem. Characteristics and Developmental Course. Risk and Protective Factors Targeted in Intervention. Part II: Assessment and Identification. Assessment Procedures. Part III: Child and Family Interventions. Social Competence Training. Parent and Family Skills Training. Mental Health Treatments. Part IV: Contextually Based Interventions. School-based Interventions for Academic Engagement and Skill Building. Schoolwide Interventions to Promote Positive Behavior. Classroom and Individual Interventions to Promote Positive Behavior. Community Interventions for Child and Family Support. Part V: Integration and Challenges. Comprehensive Multicomponent and Coordinated Services Programs. Challenges in Implementing Effective Interventions: Engaging Families and Coordinating Service Systems. Epilogue: Future Directions.
CASP: Concurrent Autonomous Chip Self-Test Using Stored Test Patterns

CASP, concurrent autonomous chip self-test using stored test patterns, is a special kind of self-test where a system tests itself concurrently during normal operation without any downtime visible to the end-user. CASP consists of two ideas: 1. Storage of very thorough test patterns in non-volatile memory; and, 2. Architectural and system-level support for autonomous testing of one or more cores in a multi-core system using stored patterns, concurrently with normal system operation, without bringing down the entire system. CASP enables design of robust systems with built-in features for circuit failure prediction, error detection, self-diagnosis and self-repair. Such systems are necessary to overcome major reliability challenges in scaled-CMOS technologies. Implementation of CASP in the OpenSPARC Tl multi-core processor demonstrates its effectiveness and practicality.
Platform-based design for videotext regions filter system

This paper performs the analysis of structural videotext regions and proposes a videotext filter system on DSP platform. Although more videotext exists in the video content, usually some videotext is the advertisements and it is not needed for some people. Thus, a video completion filter is requested to remove the videotexts for various purposes. However, few existing methods can handle the completion of the original region occupied by motion videotext well due to the complex background of motion videotext. In addition, the completed region should be generated as soon as possible based on the fast processing consideration. Thus, we design a fast and regular videotext region filter system to recover the original region occupied by the scrolling videotexts. The characteristic of the canny edge map of frames is utilized to form structural edge in the occupied region. The spatial texture region completion is carried out by our proposed video gradient interpolation approach. We have completed the whole system on TI C64xx DSP platform with some optimized techniques. The experimental results show that this videotext filter is accomplished as a stand-alone system for fast processing application.
Semantics-aware trace analysis

As computer systems continue to become more powerful and complex, so do programs. High-level abstractions introduced to deal with complexity in large programs, while simplifying human reasoning, can often obfuscate salient program properties gleaned from automated source-level analysis through subtle (often non-local) interactions. Consequently, understanding the effects of program changes and whether these changes violate intended protocols become difficult to infer. Refactorings, and feature additions, modifications, or removals can introduce hard-to-catch bugs that often go undetected until many revisions later. To address these issues, this paper presents a novel dynamic program analysis that builds a semantic view of program executions. These views reflect program abstractions and aspects; however, views are not simply projections of execution traces, but are linked to each other to capture semantic interactions among abstractions at different levels of granularity in a scalable manner. We describe our approach in the context of Java and demonstrate its utility to improve regression analysis. We first formalize a subset of Java and a grammar for traces generated at program execution. We then introduce several types of views used to analyze regression bugs along with a novel, scalable technique for semantic differencing of traces from different versions of the same program. Benchmark results on large open-source Java programs demonstrate that semantic-aware trace differencing can identify precise and useful details about the underlying cause for a regression, even in programs that use reflection, multithreading, or dynamic code generation, features that typically confound other analysis techniques.
Lightweight Detection of Denial-of-Service Attacks in Practical Wireless Sensor Networks : A Systematic Approach

Wireless sensor networks (WSNs) are especially susceptible to denial-of-service attacks due to the resourceconstrained nature of motes. We follow a systematic approach to analyze the impacts of these attacks on the network behavior; therefore, we first identify a large number of metrics easily obtained and calculated without incurring too much overhead. Next, we statistically test these metrics to assess whether they exhibit significant different values under attack when compared to those of the baseline operation. The metrics look into different aspects of the motes and the network, for example, MCU and radio activities, network traffic statistics, and routing related information. Then, to show the applicability of the metrics to different WSNs, we vary several parameters, such as traffic intensity and transmission power. We consider the most common topologies in wireless sensor networks such as central data collection and meshed multi-hop networks by using the collection tree and the mesh protocol. Finally, the metrics are grouped according to their capability of distinction into different classes. In this work, we focus on jamming and blackhole attacks. Our experiments reveal that certain metrics are able to detect a jamming attack on all motes in the testbed, irrespective of the parameter combination, and at the highest significance value. To illustrate these facts, we use a standard testbed consisting of the widely-employed TelosB motes.