Miracle-dz/UniFAR-FTUs huggingface dataset:
Family planning in context: Sensitivity of fertility desires and intentions to ecological cues

Although fertility decision-making has been the source of considerable theoretical and empirical investigation, the effect of several contextual variables on individuals’ fertility decision-making processes are not yet understood. For example, are individuals more strongly influenced by social forces that are informational or normative? Also, do individuals change their fertility intentions based on their current and developmental economic conditions? Further, how ‘shared’ are reproductive decisions within a couple, are males or females more likely to get what they want? This 3-study program of research used both experimental and exploratory qualitative methods to elucidate the nature of these unresolved issues within the domain of fertility decisionmaking. Study 1 (N = 344, Mage = 23, SDage =6.41, 59.3% female) found that highly motivated individuals’ fertility intentions were more susceptible to informational, compared to normative messages (the opposite was true for unmotivated participants). Study 2 (N = 249, Mage = 24, SDage =6.10, 61.4% female) found that exposure to mortality primes up-regulated fertility intentions for individuals with “fast” life history strategies, but facilitated the down-regulation of fertility intentions for individuals with “slow” life history strategies. Interestingly, resource scarcity primes were associated with the postponement of fertility plans in individuals’ with “fast” life history strategies. Study 3 (N = 120, Mage = 21, SDage =4.96, 50% female) found that, contrary to predictions, the similarity of couples’ gender role attitudes, career-orientations, and education levels did not significantly predict the frequency of their use of statements coded as compromise and agreement or persuasion and disagreement in their discussions regarding their future reproductive plans. Findings do suggest that individuals with higher levels of education were more likely to use persuasion and disagreement statements in their child timing and number discussions with their romantic partner, indicative of greater decision-making power in that particular social exchange. Further, men and women in study 3 were equally likely to use statements coded as compromise and agreement, persuasion and disagreement, and concession when discussing both their future fertility plans as well as their future financial plans. FAMILY PLANNING IN CONTEXT: SENSITIVITY OF FERTILITY DESIRES AND INTENTIONS TO ECOLOGICAL CUES
Design and Code Inspections to Reduce Errors in Program Development

Substantial net improvements in programming quality and productivity have been obtained through the use of formal inspections of design and of code. Improvements are made possible by a systematic and efficient design and code verification process, with well-defined roles for inspection participants. The manner in which inspection data is categorized and made suitable for process analysis is an important factor in attaining the improvements. It is shown that by using inspection results, a mechanism for initial error reduction followed by ever-improving error rates can be achieved.
The Hybrid Approach to Part-of-Speech Disambiguation

In this paper a hybrid approach to the part-of-speech disambiguation for Russian is presented. It combines the rule-based and the arti cial neural networks oriented approaches. The methods are applied independently, then the results are compared, and the decision about the right part of speech for the word-form is made. The main advantage of the presented algorithm is the ability to suggest several parts of speech when it is di cult to select the right one with a fair degree of con dence. It reduces the probability of error during the syntactic and semantic levels analysis. Java implementation of the proposed algorithm is published under EPL.
Automated Classification of MR Brain Images based on Transfer Learning Approach

Accurate and precise classification of MR images plays a vital role in the clinical evaluation of patients with brain tumors. However, a fundamental challenge arises from the disparity between the intricate details perceptible to human assessors and the information captured by MRI machines. Traditional methods for categorization often emphasize significant features and incorporate manually crafted attributes. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have shown success in image classification. Despite its reliance on substantial training data, deep learning effectively captures features and seamlessly integrates feature extraction and categorization through self-learning. Yet, applying CNN training directly to small medical imaging datasets poses challenges due to their limited size. To overcome this issue, we propose a solution involving transfer learning, utilizing a pre-trained deep-supervised CNN model specialized in discriminating between tumor and non-tumor classes. We validate our approach using a benchmark dataset. Notably, our method eschews handcrafted features, requires minimal preprocessing, and achieves an impressive accuracy of 80.67%, underscoring its broader applicability. We compare our results with alternative deep learning techniques employing CNNs.
Auditory evoked potential P300 in adults: reference values

ABSTRACT Objective To establish reference intervals for cognitive potential P300 latency using tone burst stimuli. Methods This study involved 28 participants aged between 18 and 59 years. P300 recordings were performed using a two-channel device (Masbe, Contronic). Electrode placement was as follows: Fpz (ground electrode), Cz (active electrode), M1 and M2 (reference electrodes). Intensity corresponded to 80 dB HL and frequent and rare stimulus frequencies to 1,000Hz and 2,000Hz, respectively. Stimuli were delivered binaurally. Results Mean age of participants was 35 years. Average P300 latency was 305ms. Conclusion Maximum acceptable P300 latency values of 362.5ms (305 + 2SD 28.75) were determined for adults aged 18 to 59 years using the protocol described.
Información económica de Agencias Internacionales de Noticias: Impacto en la imagen de Venezuela

El presente articulo tiene como objetivo analizar la influencia de las informaciones economicas generadas por las agencias internacionales de noticias sobre la imagen de Venezuela. Para lograr este proposito, se planteo un cuerpo teorico basado en diversos autores y se desarrollo una investigacion descriptiva, cuya poblacion estuvo integrada por periodicos de America Latina inscritos en la Sociedad Interamericana de Prensa (SIP), en especial los que mantienen suscripcion con las agencias internacionales de noticias; siendo las unidades de informacion los editores y directores de la fuente de economia de dichos diarios. Los resultados arrojaron que las informaciones que difunden las agencias internacionales de noticias en Latinoamerica influyen en la formacion de la imagen de Venezuela, partiendo de que el 70% de la muestra nunca ha visitado el pais y que su opinion esta basada en las informaciones economicas y politicas que recibe a diario de estas empresas noticiosas.Apartir de estas resenas se generan opiniones sobre Venezuela con un matiz politico, economico y social negativo, contrario a la percepcion hacia sus habitantes basada en calidez y hospitalidad y en paisajes hermosos.
The detection of somatic mutations of thyrotropin receptor gene in fine needle biopsy samples from thyroid nodules.

OBJECTIVE To evaluate the detection possibility of TSH receptor gene mutation within the third cytoplasmic loop and the sixth transmembrane domain in the cytological material obtained by means of fine needle biopsy of autonomous and non-autonomous nodules. METHODS The study has been carried out in 16 women with goitre showing no clinical signs of hyperthyroidism. According to the thyroid scintigraphy and serum level of thyrotropin (TSH) the patients were divided into two groups: 1. 6 patients with autonomous nodules; 2. 10 patients with non-autonomous nodules. Genomic DNA has been isolated from the cytological material and the peripheral blood nuclear cells in order to confirm possible somatic character of TSH receptor gene mutations. DNA has been amplified in polymerase chain reaction (PCR) with the use of a specific pair of primers. Purified PCR products have been subjected to further automatic sequencing. RESULTS Among 6 autonomous nodules tested one heterozygotic somatic mutation of adenine for cytosine at 1804 nucleotide of TSH receptor gene was detected. This mutation resulted in the change of threonine (codon ACC) at 632 position of TSH receptor protein for proline (codon CCC). Among the non-autonomous nodules one heterozygotic somatic mutation of adenine for cytosine at 1870 nucleotide of receptor TSH gene has been detected. From this mutation followed the change of lysine (codon AAG) at 624 position of the polypeptide chain for glutamine (codon CAG) followed as a consequence. CONCLUSIONS We emphasize the validity of fine needle biopsy in the detection of somatic mutations in the TSH receptor gene. For the first time the somatic mutation in the TSH receptor gene in a non-autonomous nodule has been reported.
Grouping Based Wrong Way Vehicle Detection

The Vehicle location is utilized to recognize the vehicles in any video or picture document. The course of recognition of vehicles incorporates the item location by thinking about the vehicles as the essential article. By taking the pictures structure elevated or even view and from street or stopping utilizing observation cameras, the location interaction can be started. Vehicle identification and order assumes critical part in rush hour gridlock checking and the executives. The use of vehicle recognition and order is exceptionally immense. Vehicle identification is utilized on streets, roadways, leaving or some other spot to distinguish or follow the quantity of vehicles present on the spot. This will assist the reconnaissance with passing judgment on the traffic vehicles, normal speed and classification of vehicle. There are number of item recognition methods are accessible to distinguish and arrange them.
Ubiquitous graphics: combining hand-held and wall-size displays to interact with large images

Ubiquitous Graphics addresses the problem of interacting with very large computer graphics images, for instance an online map or a large digitized painting. It uses a combination of mobile and stationary displays to show both overview and detail. The main image is displayed using a projector or other large traditional display. To access details, the user holds a mobile device in front of the stationary display. Using ultrasonic tracking the smaller display is aligned with the overview, giving access to a corresponding portion of the image in higher resolution. Alternatively the system provides "magic lens" functionality that can show additional information. Users may add free-form annotations and pre-defined graphical objects by interacting directly with the mobile device. In a user study, subjects drew better descriptive maps using the system than an ordinary map application. The system is robust and was demonstrated to several thousand people in a week-long public exhibit.
Social Touch Technology: A Survey of Haptic Technology for Social Touch

This survey provides an overview of work on haptic technology for social touch. Social touch has been studied extensively in psychology and neuroscience. With the development of new technologies, it is now possible to engage in social touch at a distance or engage in social touch with artificial social agents. Social touch research has inspired research into technology mediated social touch, and this line of research has found effects similar to actual social touch. The importance of haptic stimulus qualities, multimodal cues, and contextual factors in technology mediated social touch is discussed. This survey is concluded by reflecting on the current state of research into social touch technology, and providing suggestions for future research and applications.
Solving POMDPs by Searching the Space of Finite Policies

Solving partially observable Markov decision processes (POMDPS) is highly intractable in general, at least in part because the optimal policy may be infinitely large. In this paper, we explore the problem of finding the optimal policy from a restricted set of policies, represented as finite state automata of a given size. This problem is also intractable, but we show that the complexity can be greatly reduced when the POMDP andlor policy are further constrained. We demonstrate good empirical results with a branch-and-bound method for finding globally optimal deterministic policies, and a gradient-ascent method for finding locally optimal stochastic policies.
Building sentiment Lexicons applying graph theory on information from three Norwegian thesauruses

Sentiment lexicons are the most used tool to automatically predict sentiment in text. To the best of our knowledge, there exist no openly available sentiment lexicons for the Norwegian language. Thus in this paper we applied two different strategies to automatically generate sentiment lexicons for the Norwegian language. The first strategy used machine translation to translate an English sentiment lexicon to Norwegian and the other strategy used information from three different thesauruses to build several sentiment lexicons. The lexicons based on thesauruses were built using the Label propagation algorithm from graph theory. The lexicons were evaluated by classifying product and movie reviews. The results show satisfying classification performances. Different sentiment lexicons perform well on product and on movie reviews. Overall the lexicon based on machine translation performed the best, showing that linguistic resources in English can be translated to Norwegian without losing significant value.
Scalable Overlapping Community Detection

Recent advancements in machine learning algorithms have transformed the data analytics domain and provided innovative solutions to inherently difficult problems. However, training models at scale over large data sets remains a daunting challenge. One such problem is the detection of overlapping communities within graphs. For example, a social network can be modeled as a graph where the vertices and edges represent individuals and their relationships. As opposed to the problem of graph partitioning or clustering, an individual can be part of multiple communities which significantly increases the problem complexity. In this paper, we present and evaluate an efficient parallel and distributed implementation of a Stochastic Gradient Markov Chain Monte Carlo algorithm that solves the overlapping community detection problem. We show that the algorithm can scale and process graphs consisting of billions of edges and tens of millions of vertices on a compute cluster of 65 nodes. To the best of our knowledge, this is the first time that the problem of deducing overlapping communities has been learned for problems of such a large scale.
An automatic translation of tags for multimedia contents using folksonomy networks

This paper proposes a novel method to translate tags attached to multimedia contents for cross-language retrieval. The main issue in this problem is the sense disambiguation of tags given with few textual contexts. In order to solve this problem, the proposed method represents both tags and its translation candidates as networks of co-occurring tags since a network allows richer expression of contexts than other expressions such as co-occurrence vectors. The method translates a tag by selecting the optimal one from possible candidates based on a network similarity even when neither the textual contexts nor sophisticated language resources are available. The experiments on the MIR Flickr-2008 test set show that the proposed method achieves 90.44% accuracy in translating tags from English into German, which is significantly higher than the baseline methods of a frequency based translation and a co-occurrence-based translation.
Swarm intelligence: A reliable solution for extracting endmembers from hyperspectral imagery

Swarm intelligence has played a more and more important role in solving combinatorial optimization or continuous optimization problems in extracting endmembers from hyperspectral imagery. This paper summarizes the progress of pure pixel-based algorithms using swarm intelligence, and introduces a new minimum volume-based algorithm adopting the artificial bee colony (ABC) model. The experiments with synthetic and real hyperspectral data prove that these swarm intelligence-based algorithms can get comparable or better results than some other state-of-the-art methods. It can be concluded that swarm intelligence is a reliable solution for extracting endmembers from hyperspectral imagery.
Stepped Supply Voltage Switching for energy constrained systems

The energy consumed in switching the voltage on the power rail (VDD switching energy) is a significant overhead in systems using Dynamic Voltage Scaling (DVS) and/or power gating. In this work we propose and demonstrate the use of Stepped Supply Voltage Switching (SVS) for reducing VDD switching energy. We show the analysis, benefits, and overheads of using SVS for DSP algorithms implemented with voltage scalable adders and multipliers in simulation and silicon. SVS helps achieve 45% and 60% net savings in VDD switching energy while switching from 0.3V to 1.2V using one and two intermediate steps respectively. Power supply noise, another concern in systems using power gating or DVS, is also analyzed. SVS helps reduce noise by over 40%.
Performance evaluation of M2M protocols over cellular networks in a lab environment

According to the vision of the Internet of Things the seamless and flexible networking of everyday objects will become an important field of application for Internet-based communication. The simple integration of these devices into a communication system often requires wireless technologies, especially when there is no wired infrastructure available. Cellular networks of the third and fourth generation are promising enablers for embedding a variety of different devices into the Internet of Things. However, cellular networks use a completely different approach for data transmission and media access than wired networks like Ethernet. Therefore, it is necessary to investigate the transmission behavior of common protocols for machine-to-machine (M2M) communication with respect to the peculiarities of cellular networks. In this paper, three M2M protocols - CoAP, MQTT and OPC UA - are compared to each other with regard to their transport mechanisms to evaluate the transmission times and analyzing potentials for optimization. For the evaluation a laboratory test environment with cellular network emulators for EDGE, UMTS and LTE is used to analyze the protocols without interference of delays caused by the Internet or by other users allocating resources of the cellular network.
Self-Calibrating Head-Mounted Eye Trackers Using Egocentric Visual Saliency

Head-mounted eye tracking has significant potential for gaze-based applications such as life logging, mental health monitoring, or the quantified self. A neglected challenge for the long-term recordings required by these applications is that drift in the initial person-specific eye tracker calibration, for example caused by physical activity, can severely impact gaze estimation accuracy and thus system performance and user experience. We first analyse calibration drift on a new dataset of natural gaze data recorded using synchronised video-based and Electrooculography-based eye trackers of 20 users performing everyday activities in a mobile setting. Based on this analysis we present a method to automatically self-calibrate head-mounted eye trackers based on a computational model of bottom-up visual saliency. Through evaluations on the dataset we show that our method 1) is effective in reducing calibration drift in calibrated eye trackers and 2) given sufficient data, can achieve gaze estimation accuracy competitive with that of a calibrated eye tracker, without any manual calibration.
Heart Rate Variability During a Working Memory Task: Does Touching a Dog or Person Affect the Response?

ABSTRACT The presence of a dog has been associated with reduced responses to stressors in several, but not all, previous studies. The presence of a dog has also been related to improved performance on some cognitive tasks. The current study was designed to evaluate the effect of touching a dog on stress responses to a working memory task (WMT). Heart rate variability (HRV) responses to a WMT while touching a dog were compared with responses while touching a stuffed dog and a person. HRV was recorded while 53 university students aged 18 to 41 years sat on the floor next to a randomly assigned co-participant (real dog, stuffed dog, or human) and listened to a reading before and after completing a WMT. This procedure was repeated with the two other co-participants, in a randomized block order. All participants wore an HRV monitor and placed their non-dominant hand on the co-participant throughout the appropriate phase. The WMT involved pointing to increasingly complicated sequences of geometric shapes until the participant failed three times at one level. Linear mixed models analysis for nested data revealed that the memory task was stressful, with parasympathetic nervous system arousal significantly lower during the memory task than the preor post-memory listening tasks (rMSSD: ps < 0.001, = 0.023; high frequency: ps < 0.001, = 0.001). Neither of the measures differed significantly among the three conditions. There also were no significant interactions, indicating that the stress response to the memory task did not differ when the participants were touching the three different co-participants. Touching a dog did not reduce arousal in response to a stressful memory task. This research suggests that a dog may not be effective at reducing stress responses to this type of stressor. Another possibility is that the presence of a dog is more effective than touching a dog at reducing stress responses. Further investigation will be required to evaluate these possibilities.
A general methodology for designing globally convergent optimization neural networks

In this paper, we present a general methodology for designing optimization neural networks. We prove that the neural networks constructed by using the proposed method are guaranteed to be globally convergent to solutions of problems with bounded or unbounded solution sets, in contrast with the gradient methods whose convergence is not guaranteed. We show that the proposed method contains both the gradient methods and nongradient methods employed in existing optimization neural networks as special cases. Based on the theoretical results of the proposed method, we study the convergence and stability of general gradient models in case of unisolated solutions. Using the proposed method, we derive some new neural network models for a very large class of optimization problems, in which the equilibrium points correspond to exact solutions and there is no variable parameter. Finally, some numerical examples show the effectiveness of the method.