Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy saving in smart homes based on consumer behavior: A case study

This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that mines consumer behavior data only and applies machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system looks for frequent and periodic patterns in the event data provided by the digitalSTROM home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects opportunities to save energy without decreasing the comfort level, it sends a recommendation to the inhabitants. The system was implemented and deployed to a set of test homes. The test participants were able to rate the impact of the recommendations on their comfort. This feedback was used to adjust the system parameters and make it more accurate during a second test phase. The historical data set provided by digitalSTROM contained 33 homes with 3521 devices and over 4 million events. The system produced 160 recommendations on the first phase and 120 on the second phase. The ratio of useful recommendations was close to 10%. We found out that a recommender system that uses an algorithm that mines patterns based on their confidence, independent of their frequency and periodicity, might achieve better results and a higher acceptance by users.
Impact on Health-Related Quality of Life and Perceived Burden of Informal Caregivers of Individuals with Alzheimer’s Disease

This study assessed the impact on health-related quality of life (HRQL) and the perceived burden of informal caregivers of individuals with Alzheimer’s disease (AD) on the Canary Islands (Spain). We utilized a multicenter, cross-sectional design, based on questionnaire responses of 237 informal caregivers of AD patients. Patients were classified according to the degree of severity utilizing the Clinical Dementia Rating Scale. Sociodemographic, HRQL (EQ-5D) and functional dependency data were gathered together with the degree of caregiver burden. Caregivers had a higher frequency of problems than did the general population for every EQ-5D dimension. Caregivers’ HRQL was inversely associated with the subject dependency level and caregiver age. HRQL was higher for more educated caregivers. Variables with a negative and/or significant effect on caregivers’ HRQL were high feelings of burden, more committed time to care, and older age. The caregiver burden quantified by the Zarit scale showed 83.3% of caregivers with a high level of burden.
Foreign Direct Investment and Indian Economic Growth

The present study investigates the impact of the foreign direct investment (FDI) on Indian economic development during the period 1981 to 2016. The annual data of Index of Industrial Production (IIP) have been considered as a proxy of Indian economic development. The estimated results of the Johansen's cointegration test and vector error correction model (VECM) suggest that there exists a long-run positive cointegrating relationship between FDI and IIP. The result of the VECM indicates that any change in the value of FDI causes to change the value of IIP in the long run. But in the long run, change in IIP does not have any causal effect on FDI. The results of short-run causality test among the variables based on Granger causality test shows a bidirectional short-run causal relationship between FDI and IIP, that is in short-run the value of FDI significantly affect the movement of IIP and vice versa. So, with the help of the estimated results, the study concludes that FDI plays a crucial role in enhancing the economic growth and development of the country, as the flow of FDI leads to improve the economic development indicator used in this study.
Solution of Partial Differential Equations Combination of Variables

We encounter partial differential equations routinely in transport phenomena. Some examples are unsteady flow in a channel, steady heat transfer to a fluid flowing through a pipe, and mass transport to a falling liquid film. Here, we shall learn a method for solving partial differential equations that complements the technique of separation of variables. We shall also learn when the method can be used. We consider the same model problem, namely the motion induced in fluid contained between two long and wide parallel plates placed with a distance b between them as shown in the sketch below.
Biological Aspects of Shortfin Scad (Decapterus macrosoma) in Makassar Waters, South Sulawesi, Indonesia

The shortfin scad is a pelagic fisheries resource that plays an important role in the economy of the South Sulawesi marine fisheries sector. Besides its use for consumption by local communities, shortfin scad are also used as bait in the tuna fishery and exported frozen. Over the past five years there has been a decline in catches caused by uncontrolled exploitation. To date, there is still no control over the exploitation of shortfin scad, and it is feared the sustainability of shortfin scad as a fisheries resource is threatened. This research aimed to analyse specific aspects of shortfin scad biology for this population, including sex ratio, size at first maturity, and growth pattern. The study was conducted in the waters off Makassar, in South Sulawesi, Indonesia, using direct observation with a survey method. Fish samples were obtained from catches landed by fishermen at the Paotere fishing harbour in Makassar City. Sampling was conducted from early April to early August 2017. The shortfin scad sample obtained comprised 201 male fishes and 169 female fishes. The results showed that the sex ratio of the shortfin scad (male:female) was 1.1:1.0. The shortfin scad reached first maturity at a fork length of 18.24 − 19.54 cm for males and 16.45 − 17.24 cm for females. The Gonad Maturity Index (GMI) of male fish was dominated by immature gonad classes (GMI I and GMI II), while for females GMI III and GMI IV dominated the sample, indicating that females mature earlier than males. Male and female growth patterns were allometric positive with b>3, indicating that the gain in weight was faster than the increase in length.
Thermal Analysis of Facial Muscles Contractions

Facial expressions can be systematically coded using the Facial Action Coding System (FACS) that describes the specific action unit (AU) or combination of AUs elicited during different kinds of expressions. This study investigated the thermal patterns concomitant to specific action units performance. As thermal imaging can track dynamic patterns in facial temperature at any distance (>; 0.4 m), with high temporal (<; 20 m) and thermal (<; 20 mK@300 K) resolutions, this noninvasive technique was tested as a method to assess fluctuations of facial heat patterns induced by facial muscles contractions. Four FACS-trained coders produced nine different AUs or combination of AUs at various speeds and intensities. Using a spatial pattern approach based on PCA decomposition of the thermal signal, we showed that thermal fluctuations are specific to the activated AUs and are sensitive to the kinetics and intensities of AU production. These results open new avenues for studying patterns of facial muscle activity related to emotion or other cognitively induced activities, in a noninvasive manner, avoiding potential lighting issues.
Sparse Discriminant Preserving Projections for Face Recognition

Previous works have demonstrated that dimensionality reduction algorithms that combine sparse subspace learning (SSL) and discriminant information of sample data can improve the classification performance for some pattern recognition problems. However, most of these approaches introduce within-class sparse reconstruction matrix and within-class discriminative structure simultaneously, which may affect each other. To address this problem, in this paper, we propose a new dimensionality reduction algorithm called sparse discriminant preserving projections (SDPP). Different from the existing methods, SDPP uses between-class scatter as global discriminant information and integrates it with sparse reconstructive structure of samples in each class to establish the objective function. Since SDPP only takes into account the within-class sparsity reconstructive relationship and the between-class scatter information, it not only well preserves the sparse reconstruction relationship, but also improves classification performance. Experiments on face image databases demonstrate the superiority of the proposed algorithm.
Mean-shift based object detection and clustering from high resolution remote sensing imagery

Object detection from remote sensing images has inherent difficulties due to cluttered backgrounds and noisy regions from the urban area in high resolution images. Detection of objects with regular geometry, such as circles from an image uses strict feature based detection. Using region based segmentation techniques such as K-Means has the inherent disadvantage of knowing the number of classes apriori. Contour based techniques such as Active contour models, sometimes used in remote sensing also has the problem of knowing the approximate location of the region and also the noise will hinder its performance. A template based approach is not scale and rotation invariant with different resolutions and using multiple templates is not a feasible solution. This paper proposes a methodology for object detection based on mean shift segmentation and non-parametric clustering. Mean shift is a non-parametric segmentation technique, which in its inherent nature is able to segment regions according to the desirable properties like spatial and spectral radiance of the object. A prior knowledge about the shape of the object is used to extract the desire object. A hierarchical clustering method is adopted to cluster the objects having similar shape and spatial features. The proposed methodology is applied on high resolution EO images to extract circular objects. The methodology found to be better and robust even in the cluttered and noisy background. The results are also evaluated using different evaluation measures.
ABNORMAL SUBENDOCARDIAL PERFUSION IN CARDIAC SYNDROME X DETECTED BY CARDIOVASCULAR MAGNETIC RESONANCE IMAGING

A BSTRACT Background In cardiac syndrome X (a syndrome characterized by typical angina, abnormal exercisetest results, and normal coronary arteries), conventional investigations have not found that chest pain is due to myocardial ischemia. Magnetic resonance techniques have higher resolution and therefore may be more sensitive. Methods We performed myocardial-perfusion cardiovascular magnetic resonance imaging in 20 patients with syndrome X and 10 matched controls, both at rest and during an infusion of adenosine. Quantitative perfusion analysis was performed by using the normalized upslope of myocardial signal enhancement to derive the myocardial perfusion index and the myocardial-perfusion reserve index (defined as the ratio of the myocardial perfusion index during stress to the index at rest). Results In the controls, the myocardial perfusion index increased in both myocardial layers with adenosine (in the subendocardium, from a mean [±SD] of 0.12±0.03 to 0.16±0.03 [P=0.02]; in the subepicardium, from 0.11±0.02 to 0.17±0.05 [P=0.002]); in patients with syndrome X, the myocardial perfusion index did not change significantly in the subendocardium (0.13±0.02 vs. 0.14±0.03, P=0.11; P=0.09 as compared with controls) but increased in the subepicardium (from 0.11±0.02 to 0.20±0.04, P<0.001; P=0.11 for the comparison with controls). Adenosine provoked chest pain in 95 percent of patients with syndrome X and 40 percent of controls (P<0.001). Conclusions In patients with syndrome X, cardiovascular magnetic resonance imaging demonstrates subendocardial hypoperfusion during the intravenous administration of adenosine, which is associated with intense chest pain. These data support the notion that the chest pain may have an ischemic cause. (N Engl J Med 2002;346:1948-53.)
Age‐related Changes in the NMDA Receptor/Nitric Oxide/cGMP Pathway in the Hippocampus and Cerebellum of Freely Moving Rats Subjected to Transcerebral Microdialysis

The N‐methyl‐d‐aspartate (NMDA) receptor/nitric oxide synthase/guanylate cyclase pathway was studied during aging by monitoring extracellular cGMP in the rat hippocampus and cerebellum during in vivo microdialysis. In the hippocampus the basal cGMP efflux decreased by 50% from 3 to 12 months of age, whereas it remained constant with age in the cerebellum. Locally perfused NMDA (1 mM) evoked remarkable cGMP responses in 3‐month‐old rats; in the hippocampus the cGMP production was already dramatically reduced at 12 months, whereas in the cerebellum a similar impairment occurred much later (24 months). The nitric oxide donor S‐nitroso‐N‐penicillamine (1 mM) elicited cGMP responses which slightly decreased from 3 to 12–24 months in the hippocampus, while no significant decrement with age could be seen in the cerebellum. Local perfusion of the phosphodiesterase inhibitor 3‐isobutyl‐1 methylxanthine (IBMX, 1 mM) produced large increases in hippocampal cGMP levels. The response decreased at 12 and 24 months, apparently in parallel with the fall in the basal level of cGMP. No significant differences across ages were observed following IBMX infusion in the cerebellum. The decreases in basal outflow and in the NMDA‐evoked cGMP response seen in the aged hippocampus were not compensated for by supplying l‐arginine. Infusion of d‐serine (1 mM) enhanced (150–200%) extracellular cGMP in the cerebellum with no age‐related differences. The activity in vitro, of hippocampal nitric oxide synthase at 24 months was 33% lower than at 3 months, whereas the cerebellar enzyme did not show any age‐related decay. Aging seems therefore to affect differentially the NMDA receptor/nitric oxide synthase/cGMP pathway in the rat hippocampus versus the cerebellum. In the hippocampus the early fall in the NMDA‐evoked cGMP response seems to originate from deficits in NMDA receptor function and nitric oxide synthase and guanylate cyclase activities; in the cerebellum, the decreased response to NMDA in the old animals seems essentially to be due to impairment of NMDA receptor function.
Pelvic inflammatory disease

Pelvic inflammatory disease (PID) is a clinical syndrome comprising a spectrum of infectious and inflammatory diseases of the upper female genital tract. The diagnosis of pelvic inflammatory disease (PID) can include any combination of endometritis, salpingitis, tubo-ovarian abscess, or pelvic peritonitis. [1] Each of these disease processes is characterized by ascending spread of organisms from the vagina or cervix to the structures of the upper female genital tract. Although PID is most notable for the associated risk of severe, long-term sequelae, the infections may be asymptomatic ("silent") or overt with mild to severe symptoms. The clinical syndrome of acute (and subacute) PID—usually defined as symptoms for fewer than 30 days—can be due to a variety of pathogens, often including, but not limited to, Neisseria gonorrhoeae and Chlamydia trachomatis. [2] In contrast, chronic pelvic inflammatory disease (symptoms for greater than 30 days) is a separate disorder usually related to infection by Mycobacterium tuberculosis or Actinomyces species (Table 1). [2] This module will focus on acute and subacute PID.
A hybrid neural network with fuzzy rules for categorical and numeric input

This paper is concerned with the architecture and training of a hybrid neural network that may be used to represent a function that has both numeric and categorical independent variables. The numerical variables are separated from the categorical variables. It is assumed that the function to be represented really consists of several functions whose independent variables are the numerical variables. The hybrid network consists of two networks, the FFNN and the CIN (categorical input network). The FFNN accepts the numerical component as input and the CIN accepts the categorical input. Each produces a vector. These vectors are subsequently combined through the dot product to produce the final output of the combined network. The FFNN actually consists of several FFNN's with an MLP for each function to be represented. In general the output vector of the CIN will be 1-of-n and in that case the categorical component of the independent variables in effect is used to select the output of one of the MLP's and thus selects one of the functions represented. The approach suggested is shown to be quite effective.
Bayesian network learning by compiling to weighted MAX-SAT

The problem of learning discrete Bayesian networks from data is encoded as a weighted MAX-SAT problem and the MaxWalkSat local search algorithm is used to address it. For each dataset, the per-variable summands of the (BDeu) marginal likelihood for different choices of parents ('family scores') are computed prior to applying MaxWalkSat. Each permissible choice of parents for each variable is encoded as a distinct propositional atom and the associated family score encoded as a 'soft' weighted single-literal clause. Two approaches to enforcing acyclicity are considered: either by encoding the ancestor relation or by attaching a total order to each graph and encoding that. The latter approach gives better results. Learning experiments have been conducted on 21 synthetic datasets sampled from 7 BNs. The largest dataset has 10,000 datapoints and 60 variables producing (for the 'ancestor' encoding) a weighted CNF input file with 19,932 atoms and 269,367 clauses. For most datasets, MaxWalkSat quickly finds BNs with higher BDeu score than the 'true' BN. The effect of adding prior information is assessed. It is further shown that Bayesian model averaging can be effected by collecting BNs generated during the search.
Complexity classes in communication complexity theory

We take a complexity theoretic view of A. C. Yao's theory of communication complexity. A rich structure of natural complexity classes is introduced. Besides providing a more structured approach to the complexity of a variety of concrete problems of interest to VLSI, the main objective is to exploit the analogy between Turing machine (TM) and communication complexity (CC) classes. The latter provide a more amicable environment for the study of questions analogous to the most notorious problems in TM complexity. Implicitly, CC classes corresponding to P, NP, coNP, BPP and PP have previously been considered. Surprisingly, pcc = Npcc ∩ coNPcc is known [AUY]. We develop the definitions of PSPACEcc and of the polynomial time hierarchy in CC. Notions of reducibility are introduced and a natural complete member in each class is found. BPPcc ⊆ Σ2cc ∩ Π2cc [Si2] remains valid. We solve the question that BPPcc ? NPcc by proving an Ω(√n) lower bound for the bounded-error complexity of the coNPcc- complete problem "disjointness". Similar lower bounds follow for essentially any nontrivial monotone graph property. Another consequence is that the deterministically exponentially hard "equality" relation is not NPcc-hard with respect to oracle-protocol reductions. We prove that the distributional complexity of the disjointness problem is O(√n log n) under any product measure on {0, 1}n × {0, 1}n. This points to the difficulty of improving the Ω(√n) lower bound for the B2PP complexity of "disjointness". The variety of counting and probabilistic classes appears to be greater than in the Turing machine versions. Many of the simplest graph problems (undirected reachability, planarity, bipartiteness, 2-CNF-satisfiability) turn out to be PSPACEcc-hard. The main open problem remains the separation of the hierarchy, more specifically, the conjecture that Σ2cc ≠ Π2cc. Another major problem is to show that PSPACEcc and the probabilistic class UPPcc are not comparable.
Electric vehicle charging scheduling under local renewable energy and stochastic grid power price

In the paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a renewable energy aided charging station with multiple charge points. The uncertainty of the EV arrival, the intermittence of the renewable energy, and the variation of the grid power price are taken into account. In each period, the station determines the number of charging EVs during this period. Meanwhile, it also chooses the amount of renewables used for charging (the rest amount of energy will be purchased from the grid). The goal is to minimize the mean waiting time of EVs under the long-term constraint on the cost. We formulate a stochastic optimization problem, in which the charging EV number sequence and the allocated renewable energy sequence compose the two-dimensional optimization variable vector sequence, to investigate this scheduling problem. We derive the formal solution of the problem. Specifically, we prove that the optimal variable vector can be successively obtained: the optimal number of charging EVs is the solution of a reduced stochastic optimization problem and the greedy renewable energy allocation is optimal for given number of charging EVs. Finally, based on theoretical analysis, we propose two strategies for the problem and we investigate the proposed strategies numerically.
Detecting I/O and Internal Feedback Bridging Faults

The testing of bridging faults (short circuits) has become increasingly important with the increasing density in VLSI (very large scale integration) chips. Yet very little work has been done in this area. In this correspondence, based on a two-state sequential machine model, we present the conditions for a circuit with feedback bridgings to oscillate and to exhibit stable sequential behavior. It is shown that only two test patterns are sufficient to detect feedback bridging faults between input and output lines of a general combinational network. We derive a simple equation to generate test patterns for detecting feedback bridging faults among internal lines of a general combinational network.
Dynamic test generation for large binary programs

Abstract : This thesis develops new methods for addressing the problem of software security bugs, shows that these methods scale to large commodity software, and lays the foundation for a service that makes automatic, effective security testing available at a modest cost per bug found. We make the following contributions * We introduce a new search algorithm for systematic test generation that is optimized for large applications with large input files and exhibiting long execution traces where the search is bound to be incomplete (Chapter 2); * We introduce optimizations for checking multiple properties of a program simultaneously using dynamic test generation, and we formalize the notion of active property checking (Chapter 3) * We describe the implementation of tools that implement dynamic test generation of large binary programs for Windows and for Linux: SAGE and SmartFuzz. We explain the engineering choices behind their symbolic execution algorithm and the key optimization techniques enabling both tools to scale to program traces with hundreds of millions of instructions (Chapters 2 and 4) * We develop methods for coordinating large scale experiments with fuzz testing techniques including methods to address the defect triage problem (Chapter 4).