Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable size sampling to support high uniformity confidence in sensor data streams

In order to rapidly process large amounts of sensor stream data, it is effective to extract and use samples that reflect the characteristics and patterns of the data stream well. In this article, we focus on improving the uniformity confidence of KSample, which has the characteristics of random sampling in the stream environment. For this, we first analyze the uniformity confidence of KSample and then derive two uniformity confidence degradation problems: (1) initial degradation, which rapidly decreases the uniformity confidence in the initial stage, and (2) continuous degradation, which gradually decreases the uniformity confidence in the later stages. We note that the initial degradation is caused by the sample range limitation and the past sample invariance, and the continuous degradation by the sampling range increase. For each problem, we present a corresponding solution, that is, we provide the sample range extension for sample range limitation, the past sample change for past sample invariance, and the use of UC-window for sampling range increase. By reflecting these solutions, we then propose a novel sampling method, named UC-KSample, which largely improves the uniformity confidence. Experimental results show that UC-KSample improves the uniformity confidence over KSample by 2.2 times on average, and it always keeps the uniformity confidence higher than the user-specified threshold. We also note that the sampling accuracy of UC-KSample is higher than that of KSample in both numeric sensor data and text data. The uniformity confidence is an important sampling metric in sensor data streams, and this is the first attempt to apply uniformity confidence to KSample. We believe that the proposed UC-KSample is an excellent approach that adopts an advantage of KSample, dynamic sampling over a fixed sampling ratio, while improving the uniformity confidence.
Tools for discovery, refinement and generalization of functional properties by enumerative testing

This thesis presents techniques for discovery, refinement and generalization of properties about functional programs. These techniques work by reasoning from test results: their results are surprisingly accurate in practice, despite an inherent uncertainty in principle. These techniques are validated by corresponding implementations in Haskell and for Haskell programs: Speculate, FitSpec and Extrapolate. Speculate discovers properties given a collection of black-box function signatures. Properties discovered by Speculate include inequalities and conditional equations. These properties can contribute to program understanding, documentation and regression testing. FitSpec guides refinements of properties based on results of black-box mutation testing. These refinements include completion and minimization of property sets. Extrapolate generalizes counterexamples of test properties. Generalized counterexamples include repeated variables and side-conditions and can inform the programmer what characterizes failures. Several example applications demonstrate the effectiveness of Speculate, FitSpec and Extrapolate.
The C11 and C++11 concurrency model

Relaxed-memory concurrency is now mainstream in both hardware and programming languages, but there is little support for the programmer of such systems. In this highly non-deterministic setting, ingrained assumptions like causality or the global view of memory do not hold. It is dangerous to use intuition, specifications are universally unreliable, and testing outcomes are dependent on hardware that is getting more permissive of odd behaviour with each generation. Relaxed-memory concurrency introduces complications that pervade the whole system, from processors, to compilers, programming languages and software. There has been an effort to tame some of the mystery of relaxed-memory systems by applying a range of techniques, from exhaustive testing to mechanised formal specification. These techniques have established mathematical models of hardware architectures like x86, Power and ARM, and programming languages like Java. Formal models of these systems are superior to prose specifications: they are unambiguous, one can prove properties about them, and they can be executed, permitting one to test the model directly. The clarity of these formal models enables precise critical discussion, and has led to the discovery of bugs in processors and, in the case of Java, x86 and Power, in the specifications themselves. In 2011, the C and C++ languages introduced relaxed-memory concurrency to the language specification. This was the culmination of a six-year process on which I had a significant impact. This thesis details my work in mathematically formalising, refining and validating the 2011 C and C++ concurrency design. It provides a mechanised formal model of C and C++ concurrency, refinements to the design that removed major errors from the specification before ratification, a proof in HOL4 (for a restricted set of programs) that the model supports a simpler interface for regular programmers, and, in collaboration with others, an online tool for testing intuitions about the model, proofs that the language is efficiently implementable above the relaxed x86 and Power architectures, a concurrent reasoning principle for proving specifications of libraries correct, and an in-depth analysis of problems that remain in the design.
A 4.8-μVrms-Noise CMOS-Microelectrode Array With Density-Scalable Active Readout Pixels via Disaggregated Differential Amplifier Implementation

We demonstrate a 4.8-μVrms noise microelectrode array (MEA) based on the complementary-metal-oxide-semiconductor active-pixel-sensors readout technique with disaggregated differential amplifier implementation. The circuit elements of the differential amplifier are divided into a readout pixel, a reference pixel, and a column circuit. This disaggregation contributes to the small area of the readout pixel, which is less than 81 μm2. We observed neuron signals around 100 μV with 432 electrodes in a fabricated prototype chip. The implementation has technological feasibility of up to 12-μm-pitch electrode density and 6,912 readout channels for high-spatial resolution mapping of neuron network activity.
A review on 3D terrain visualization of GIS data: techniques and software

3D terrain visualization of geographic information systems (GIS) data has become an important issue in recent years. This is due to the emergence of new geo-browsers such as Google Earth, widely popular among users. The availability of 3D representation tools has increased the demand for 3D terrain visualization. The aim of this paper is to review the literature related to the 3D terrain visualization of GIS data from the first map produced until the online mapping era. The reviews are divided into four different sections: manual visualization of 3D terrain, automated visualization of 3D terrain, online visualization of 3D terrain, and software for visualizing 3D terrain. Then, the paper compares between the different types of systems developed by various authors based on the capabilities and the limitations of the system. Some of the techniques have their own strengths and limitations which solve the problem in 3D terrain visualization. However, the research on improving 3D terrain visualization is still ongoing. This is due to the popularity of online environments and mobile devices that render 3D terrain. This review paper will help interested users understand the current state of 3D terrain visualization of GIS data in a better way.
Personalized service recommendations for travel using trajectory pattern discovery

Service recommendations help travelers locate en route traffic information service of interest in a timely manner. However, recommendations based on simple traffic information, such as the number of requests for the location of a facility, fail to consider an individual’s preferences. Most existing work on improving service recommendations has continued to utilize the same ratings and rankings of services without consideration of diverse users’ demands. The challenge remains to push forward the modeling of spatiotemporal trajectories to improve service recommendations. In this research, we proposed a new method to address the above challenge. We developed a personalized service-trajectory correlation that could recommend the most appropriate services to users. In addition, we proposed the use of “congeniality” probability to measure the service demand similarity of two travelers based on their service-visiting behaviors and preferences. We employed a clustering-based scheme, taking into account the spatiotemporal dimensions to refine the trajectories at each spot where travelers stayed at a certain point in time. Experiments were conducted employing a real global positioning system–based dataset. The test results demonstrated that our proposed approach could reduce the deviation of the trajectory measurement to 10% and enhance the success rates of the service recommendations to 60%.
Attention Deficits in a Comorbidity-Free Sample of Euthymic Pediatric Bipolar Disorder

Attention deficits are considered one of the potential endophenotypic markers of Bipolar Disorder (BD). Pediatric bipolar disorder (PBD) likely has stronger genetic underpinnings than adult onset BD; therefore, demonstrating attention deficits in PBD can be both strategic and convincing in attesting their status as one of the potential endophenotypic markers of BD. However, unlike adult literature, uncertainty exists regarding the magnitude of attention deficits in PBD. In this regard, one key unresolved question is the potential impact of attention deficit hyperactivity disorder (ADHD). The main goal of the study was to examine attention deficits in a comorbidity-free sample of euthymic PBD patients. Thirty (21 boys, 9 girls) remitted PBD patients without co-morbidity and thirty age (<17 years), sex, handedness, and Full-Scale IQ matched control subjects were compared on performance on attention tasks. Working memory (WM), which might potentially confound with the attention task performances, was also examined. Compared to controls, PBD patients performed poorly on various tests of attention, but not on any WM tasks. Further, it was found that observed attention deficits were independent of residual mood symptoms, medication effect or illness characteristics. Such attention deficits in this comorbidity-free PBD sample further endorses its status as an endophenotypic marker of bipolar disorders and establishes continuity with deficits found in adult bipolar patients.
Context-driven monitoring and control of buildings ventilation systems using big data and Internet of Things–based technologies

Ventilation systems are deployed in buildings to maintain good indoor air quality, especially in specific periods, or in the absence of buildings’ windows. These systems perform automatically this task by regulating the injected air according to the actual indoor CO2 concentration. Several control approaches have been implemented and deployed in real-setting scenarios, but most of them are either time-triggered or based on fixed threshold values. In this paper, we introduce a platform that integrates recent advanced Internet of Things and big-data technologies for context-driven monitoring and control of ventilation systems. The aim is to gather, process and extract contextual data, mainly indoor/outdoor CO2 concentration, to be used for maintaining a suitable ventilation rate that balances between energy consumption and occupants’ well-being. A prototype was developed and deployed for conducting experiments of different ventilation control approaches. We have developed two control approaches, ON/OFF and proportional–integral–derivative control, and compared them with the proposed state-feedback control approach. Experiments have been conducted in our Energy-Efficient Building Laboratory to evaluate these approaches in terms of the indoor CO2 concentration, the ventilation rates, and the power consumption. The experimental results show that the state-feedback control outperforms proportional–integral–derivative and ON/OFF control approaches in terms of energy efficiency and comfort.
Spontaneous Activation of Event Details in Episodic Future Simulation

Episodic future simulation is supported by both the retrieval and recombination of episodic details. It remains unclear, however, how individuals retrieve episodic details from memory to construct possible future scenarios; for this people must use details related to the planned future events appropriately. A potentially relevant cognitive process is the spontaneous activation of intention observed in prospective memory (i.e., the intention superiority effect). Previous studies on prospective memory have shown that the approximation of retrieval opportunities for future intentions activate related information, suggesting that the intention superiority effect is context-sensitive. We hypothesized that the same cognitive process underlies future simulation—that is, details related to future events should spontaneously become activated at the appropriate moment of future simulation to make that simulation plausible. In Experiment 1, participants took part in future experiments and formed intentions to perform particular actions for the next experiments. Subsequently, they imagined events that could occur up until they arrived at the experimental room on the day of the next experiment. During this exercise, they did not imagine engaging in the required experimental task. We measured the conceptual activation of intention-related information via a recognition task using intended action words as targets. The results showed the intention superiority effect—concepts related to participants’ future intentions became active when envisioning future events approaching the next experiment. In Experiments 2 and 3, we ensured that the intention superiority effect in future simulation was context-sensitive by adding a control condition that required participants to imagine events other than the approaching future experiments. These results indicated that concepts related to the intended actions were spontaneously activated when imagined future events became both temporally and spatially close to the future simulation. Our finding suggests that spontaneous activation of details approaching the context of a future simulation helps in constructing plausible future scenarios.
Secure distributed framework based on seamless key session for smart home environment

Smart home environment comprises high-capacity and low-capacity sensors. These sensor nodes interact with daily activities of users and they communicate with each other to perform particular tasks. A set of instructions in insecure network during communication can result in a deadlock due to attack of adversary. Such deadlocks can be a severe risk for the security of user’s activities in smart home environment. Recent critical security attacks in smart home environment exposed that how communication channels turned low-capacity sensors into harmful attack for illegal activities which later lead to damage security and privacy. Therefore, a suitable framework is needed to overcome security pitfalls and to protect user’s activities by establishing secure key session between sensor nodes. This study has proposed new secure distributed framework which is based on secure cryptic setup and efficient key-session authentication process to strengthen resource-limited devices. Secure distributed framework shares attributes of seamless key session among low-capacity nodes after sensing daily activities of user based on activity model to establish cryptic link by keeping identity of devices faceless. Distributed activity key-session model of secure distributed framework stores the user’s activities via sensor nodes and these nodes forward the data to nearby gateway in smart home environment. This scheme regularly updates token authentication process and enables stringency of nodes against severe attacks during communication. Secure distributed framework originates from distributed trajectories’ connection among sensors in network by avoiding deadlock and suspicious activities. Moreover, secure distributed framework works more efficiently on resources-restraint nodes as compared to previous schemes from literature while security is significantly enhanced. This article has focused on new encrypted key-session attributes of encrypted daily living activities to fortify low computational devices and enables wireless nodes for secure trajectory association. Preliminary simulation is performed in well-popular security tool automated validation of Internet security protocol applications to check the feasibility of proposed secure distributed framework and the required results are achieved.
Graph Theoretical Characteristics of EEG-Based Functional Brain Networks in Patients With Epilepsy: The Effect of Reference Choice and Volume Conduction

It is well-established that both volume conduction and the choice of recording reference (montage) affect the correlation measures obtained from scalp EEG, both in the time and frequency domains. As a result, a number of correlation measures have been proposed aiming to reduce these effects. In our previous work, we have showed that scalp-EEG based functional brain networks in patients with epilepsy exhibit clear periodic patterns at different time scales and that these patterns are strongly correlated to seizure onset, particularly at shorter time scales (around 3 and 5 h), which has important clinical implications. In the present work, we use the same long-duration clinical scalp EEG data (multiple days) to investigate the extent to which the aforementioned results are affected by the choice of reference choice and correlation measure, by considering several widely used montages as well as correlation metrics that are differentially sensitive to the effects of volume conduction. Specifically, we compare two standard and commonly used linear correlation measures, cross-correlation in the time domain, and coherence in the frequency domain, with measures that account for zero-lag correlations: corrected cross-correlation, imaginary coherence, phase lag index, and weighted phase lag index. We show that the graphs constructed with corrected cross-correlation and WPLI are more stable across different choices of reference. Also, we demonstrate that all the examined correlation measures revealed similar periodic patterns in the obtained graph measures when the bipolar and common reference (Cz) montage were used. This includes circadian-related periodicities (e.g., a clear increase in connectivity during sleep periods as compared to awake periods), as well as periodicities at shorter time scales (around 3 and 5 h). On the other hand, these results were affected to a large degree when the average reference montage was used in combination with standard cross-correlation, coherence, imaginary coherence, and PLI, which is likely due to the low number of electrodes and inadequate electrode coverage of the scalp. Finally, we demonstrate that the correlation between seizure onset and the brain network periodicities is preserved when corrected cross-correlation and WPLI were used for all the examined montages. This suggests that, even in the standard clinical setting of EEG recording in epilepsy where only a limited number of scalp EEG measurements are available, graph-theoretic quantification of periodic patterns using appropriate montage, and correlation measures corrected for volume conduction provides useful insights into seizure onset.
Improved continuous enhancement routing solution for energy-aware data aggregation in wireless sensor networks

Wireless sensor networks consist of hundreds or thousands of nodes with limited energy resources, and thus, efficient use of energy is necessary for these networks. Given that transmissions are the most energy-demanding operation, routing algorithms should consider efficient use of transmissions in their designs in order to extend the network lifetime. To tackle these challenges, a centralized algorithm is proposed, called improved continuous enhancement routing (ICER), for computing routing trees of refined quality, based on data aggregation while being aware of the battery energy state. Comparisons between ICER and other known solutions in the literature are performed. Our experiments show that ICER is able to ensure, on average, the survival of 99.6% and the connectivity of 99.3% of the network nodes compared to 90.2% and 72.4% in relation to the best-compared algorithm. The obtained results show that ICER significantly extends the network lifetime while maintaining the quality of the routing tree.
Mechanical Work and Physiological Responses to Simulated Flat Water Slalom Kayaking

The purpose of this study was to assess the physical work demand in relation to metrics of force and subsequent physiological response to a simulated flatwater slalom competition. Eight New Zealand team members completed a standard incremental step-test to ascertain power:oxygen consumption relationship. This was followed by a simulated race run where breath-by-breath analysis along with force and power data logged at 50 Hz to determine stroke length, impulse, peak force, time to peak force, and rate of peak force per stroke. Physiological response to negotiating a flatwater slalom course was greater than straight-line paddling (36.89 ± 2.01 vs. 32.17 ± 1.97 ml⋅kg-1⋅min-1, p = 0.0065) at the same power output. Mean power output for the duration of the simulated race (91.63 ± 7.19 s) was 203.8 ± 45.0 W, incurring an oxygen deficit of 1.386 ± 0.541 L⋅min-1 translating to an overall anaerobic contribution of 32 ± 18% and aerobic contribution of 68 ± 18%. Moderate to strong relationships between time duration and stroke peak force (R2 = 0.354, R2 = 0.485) and rate of peak force development (R2 = 0.345, R2 = 0.426) but not for stroke length (R2 = 0.022, R2 = 0.012), impulse (R2 = 0.088, R2 = 0.097) or time to peak force (R2 = 0.001, R2 = 0.0001) for left and right strokes, respectively. The number of propulsive (<0.6 s) strokes outweighed turning/driving (>0.6 s) strokes with a ratio of 94:6%. Longer stroke duration was significantly correlated to greater impulse (R2 = 0.507, p < 0.0001) and time to peak force (R2 = 0.851, p < 0.0001), but a lower rate of force development (R2 = 0.107, p < 0.0001). The results show that a flatwater slalom under simulated race conditions entails initial supra-maximal (anaerobic) work rate with a subsequent transition to one associated with maximal aerobic capacity. Inability to sustain work done and the subsequent decline in peak force and force profile per stroke requires further research regarding strategies to enhance performance.
Alpha-Synuclein Continues to Enhance SNARE-Dependent Vesicle Docking at Exorbitant Concentrations

Recently, Parkinson’s disease-associated α-synuclein (αS) has emerged as an important regulator for SNARE-dependent vesicle fusion. However, it is controversial if excessive accumulation of αS, even in the absence of aggregation, impairs neurotransmission. Here we use a single vesicle fusion assay with ms time resolution capable of dissecting the impact of αS on each step of membrane fusion. Unlike the previous results from various in vitro, cellular, and in vivo studies, we find that non-aggregated αS promotes vesicle merger even at exorbitant concentrations. The enhancement has been seen as much as 13 fold. Delving into the kinetics of the intermediate states for vesicle fusion reveals that αS stimulates vesicle docking without altering the dynamics of bilayer merger (lipid mixing). However, minute amounts of soluble aggregated species abolish SNARE-dependent bilayer merger completely. Thus, the results show that excessive accumulation of non-aggregated αS may not be toxic for neurotransmitter release.