Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions of women academics regarding work–life balance

Work–life balance in the context of this paper means keeping a balance between home and workplace responsibilities and roles. In more traditional societies, such as in Pakistan, working women often find it difficult to keep a balance between their responsibilities in the workplace and their home responsibilities. This paper is based on findings from a qualitative research study that explored the perceptions of working women regarding their home–work responsibilities and how these impact their lives. Data were collected from 10 female academics working in a university in the north of Pakistan, using semi-structured interviews. Thematic analysis was used to analyse the data. Findings reveal that most women academics find it challenging to keep a balance between their home and workplace responsibilities. Participants faced a range of problems both at home and in the workplace that impacted their life in both places. These problems included lack of support and facilities at home and in the workplace. These problems, however, did not seem to deter them from carrying on with their work–home responsibilities. Some of the strategies the participants adopted to keep this balance included separating and scheduling home–work time, securing support of the family and utilising time in the workplace more productively. The downside of this, however, seemed to be that these women academics found little time for themselves as leisure time or time entirely devoted to their own mental and physical wellbeing. The research has important implications for working women, their employers, families and policy makers in Pakistan and other societies with similar contexts.
A novel motion object detection method based on improved frame difference and improved Gaussian mixture model

The existing motion detection methods include background subtraction and frame difference. But it is prone to exist some holes with frame difference method and it is difficult to build background model using background subtraction method. So the test results did not achieve the ideal state. Aim at these problem, this paper combines frame difference method improved by motion history image with background subtraction method based on improved Gaussian mixture model to detect the motion object. The experimental results show the method has achieved a satisfactory effect.
Optimize My Schedule but Keep It Flexible: Distributed Multi-Criteria Coordination for Personal Assistants

Research projects have begun focusing on deploying personal assistant agents to coordinate users in such diverse environments as offices, distributed manufacturing or design centers, and in support of first responders for emergencies. In such environments, distributed constraint optimization (DCOP) has emerged as a key technology for multiple collaborative assistants to coordinate with each other. Unfortunately, while previous work in DCOP only focuses on coordination in service of optimizing a single global team objective, personal assistants often require satisfying additional individual userspecified criteria. This paper provides a novel DCOP algorithm that enables personal assistants to engage in such multicriteria coordination while maintaining the privacy of their additional criteria. It uses n-ary NOGOODS implemented as private variables to achieve this. In addition, we’ve developed an algorithm that reveals only the individual criteria of a link and can speed up performance for certain problem structures. The key idea in this algorithm is that interleaving the criteria searches — rather than sequentially attempting to satisfy the criteria — improves efficiency by mutually constraining the distributed search for solutions. These ideas are realized in the form of private-g and public-g Multi-criteria ADOPT, built on top of ADOPT, one of the most efficient DCOP algorithms. We present our detailed algorithm, as well as some experimental results in personal assistant domains.
A neuro-fuzzy approach to aerobic fitness classification: a multistructure solution to the context-sensitive feature selection problem

"Divide and conquer" is an old problem-solving principle. In this paper, we propose a neuro-fuzzy approach to the problem of context-sensitive feature selection. The method is based on a fuzzy preclassifier and multistructure feedforward neural networks. The same feature sets are used in the different structures, but the neurocalculation is different in each structure due to different synaptic weights. Finally, the classification output is selected from the multi-network structure using the fuzzy preclassifier's output as a decision criterion. The proposed method is tested in a case of human fitness classification. The research material was clinically obtained and the test subjects were healthy adults. The results are compared to a those obtained with a conventional neural network classifier. The method proposed here using a neuro-fuzzy approach to classification improved the classification accuracy by over 10 percent.
Clearance of yeast eRF-3 prion [PSI+] by amyloid enlargement due to the imbalance between chaperone Ssa1 and cochaperone Sgt2

The cytoplasmic [PSI+] element of budding yeast represents the prion conformation of translation release factor eRF-3 (Sup35). Prions are transmissible agents caused by self-seeded highly ordered aggregates (amyloids). Much interest lies in understanding how prions are developed and transmitted. However, the cellular mechanism involved in the prion clearance is unknown. Recently we have reported that excess misfolded multi-transmembrane protein, Dip5ΔC-v82, eliminates yeast prion [PSI+]. In this study, we showed that the prion loss was caused by enlargement of prion amyloids, unsuitable for transmission, and its efficiency was affected by the cellular balance between the chaperone Hsp70-Ssa1 and Sgt2, a small cochaperone known as a regulator of chaperone targeting to different types of aggregation-prone proteins. The present findings suggest that Sgt2 is titrated by excess Dip5ΔC-v82, and the shortage of Sgt2 led to non-productive binding of Ssa1 on [PSI+] amyloids. Clearance of prion [PSI+] by the imbalance between Ssa1 and Sgt2 might provide a novel array to regulate the release factor function in yeast.
Suppression of β1-Integrin in Gonadotropin-Releasing Hormone Cells Disrupts Migration and Axonal Extension Resulting in Severe Reproductive Alterations

Reproduction in mammals is dependent on the function of hypothalamic neurons whose axons project to the hypothalamic median eminence (ME) where they release gonadotropin-releasing hormone (GnRH) into a specialized capillary network for delivery to the anterior pituitary. These neurons originate prenatally in the nasal placode and migrate into the forebrain along the olfactory-vomeronasal nerves. The complex developmental events leading to the correct establishment of the GnRH system are tightly regulated by the specific spatiotemporal expression patterns of guidance cues and extracellular matrix molecules, the functions of which, in part, are mediated by their binding to β1-subunit-containing integrins. To determine the biological role of these cell-surface proteins in reproduction, Cre/LoxP technology was used to generate GnRH neuron-specific β1-integrin conditional KO (GnRH-Itgb1−/−) mice. Loss of β1-integrin signaling impaired migration of GnRH neurons, their axonal extension to the ME, timing of pubertal onset, and fertility in these mice. These results identify β1-integrin as a gene involved in normal development of the GnRH system and demonstrate a fundamental role for this protein in acquisition of normal reproductive competence in female mice.
A Decision Theoretic Framework for Implicit Relevance Feedback

In the process of developing a search system, there is the di cult question of how to evaluate search results quality. A system can be evaluated on multiple metrics, and we must rely on domain experts to de ne these metrics and their relative importance. This paper presents a decision theoretic approach, which separates the modeling of user behavior from the evaluation (utility) function. The sole task of the model is to predict user behavior, for example predicting explicit relevance labels and implicit `click' behavior. The utility function describes what kind of ranking would be preferred by users. Given a model and the utility function, we can see the ranking problem as an optimal decision problem. We describe this decision theoretic approach in more detail and present a simple example of a decision theoretic model.
MCA: A Developed Associative Memory Using Multi-Connect Architecture

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multiconnect architecture (MCA) associative memory to improve the Hopfield neural network by modifying the net architecture, learning and convergence processes. This modification is to increase the performance of associative memory neural network by avoiding most of the Hopfield neural network limitations. In general, MCA is a single layer neural network uses auto-association tasks and working in two phases, that is learning and convergence phases. MCA was developed based on two principles. First, the smallest net size will be used rather than depending on the pattern size. Second, the learning process will be performed to the limited parts of the pattern only to avoid learning similar parts several times. The experiments performed show promising results when MCA shows high efficiency associative memory by avoiding most of the Hopfield net limitations. The results proved that the MCA net can learn and recognize unlimited patterns in varying size with acceptable percentage noise rate in comparison to the traditional Hopfield neural network.
Detecting of requirements inconsistency: an ontology-based approach

Detecting of requirements inconsistency is key to the development of heterogeneous software system. But at present, although there is a lot of work on the detecting of requirements inconsistency, most of them is limited in detecting of requirements inconsistency at syntax level, we still lack of a promising method for the detection of requirements inconsistency at semantic level. Based on an abstract requirement refinement process model, this paper takes domain ontology as infrastructure for the refinement of software requirements, software requirements can then be seen as interactions between software agents and the ambiences they exist. We assume that the real effect of these interactions is to make the state of ambience changed, so we explicitly represent requirements of software agents as a set of state transition diagrams, each is for one entity in the ambience. And we realized semantic level inconsistency detecting of software requirements based on state transition of ambience.
Platelet-Rich Gel Supernatants Stimulate the Release of Anti-Inflammatory Proteins on Culture Media of Normal Equine Synovial Membrane Explants

The aims were as follows: (1) to evaluate the effects at 48 and 96 h of two concentrations (25 and 50%) of leukocyte and platelet-rich gel (L-PRG) and pure PRG (P-PRG) supernatants on the production/degradation in normal equine synovial membrane explants (SME) of platelet derived growth factor isoform BB, transforming growth factor beta-1, tumor necrosis factor alpha, interleukin (IL-) 4 (IL-4), IL-1 receptor antagonist (IL-1ra), and hyaluronan (HA) synthesis and (2) to correlate these molecules with their respective PRG supernatant treatments. SME from 6 horses were cultured for 96 h with L-PRG and P-PRG supernatants at 25 and 50% concentrations, respectively. SME culture media were changed each 48 h and used for determination by ELISA of the molecules, which were also determined in synovial fluid. 25% L-PRG supernatant produced a sustained release over time of IL-1ra and a gradual release of HA, whereas 50% L-PRG supernatant produced a sustained increase over time of IL-4 and HA. 50% P-PRG supernatant produced an increased and sustained production of IL-1ra and IL-4. The cellular composition and the articular concentration (volume) of a platelet-rich plasma preparation could affect the anti-inflammatory and anabolic joint responses in horses with osteoarthritis.
Seven – eight year oscillatory mode of climate variability 1 and its coherence with geomagnetic activity from 2 stratosphere to troposphere

Phase coherence between geomagnetic activity and temperature vari7 ability from stratosphere to troposphere is quantified and statistically tested in 8 the oscillatory mode with the period 7–8 years using the ERA-40/ERA-Interim 9 and NCEP/NCAR reanalysis monthly air temperature data. Conditional phase 10 coherence is used to identify the North Atlantic Oscillation (NAO) as a mediator 11 transferring the geomagnetic influence from the stratosphere to the troposphere 12 and near-surface air temperature. Cross-frequency phase–amplitude causal cou13 pling is evaluated in daily surface air temperature using the conditional mutual 14 information in order to understand the role of the 7–8 year cycle in the climate 15 variability. Statistically significant causal influence of the phase of the 7–8 year 16 cycle on temperature variability with temporal scales shorter than 12 months is 17 observed. Within a large area of Europe, yearly conditional means of surface air 18 temperature change within the 7–8 year cycle in the range 1.2–1.6 K. 19
The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks

A general method for building and training multilayer perceptrons composed of linear threshold units is proposed. A simple recursive rule is used to build the structure of the network by adding units as they are needed, while a modified perceptron algorithm is used to learn the connection strengths. Convergence to zero errors is guaranteed for any boolean classification on patterns of binary variables. Simulations suggest that this method is efficient in terms of the numbers of units constructed, and the networks it builds can generalize over patterns not in the training set.
Goal Setting for Cognitive Rehabilitation in Mild to Moderate Parkinson's Disease Dementia and Dementia with Lewy Bodies

Alongside the physical symptoms associated with Parkinson's disease dementia and dementia with Lewy bodies, health services must also address the cognitive impairments that accompany these conditions. There is growing interest in the use of nonpharmacological approaches to managing the consequences of cognitive disorder. Cognitive rehabilitation is a goal-orientated behavioural intervention which aims to enhance functional independence through the use of strategies specific to the individual's needs and abilities. Fundamental to this therapy is a person's capacity to set goals for rehabilitation. To date, no studies have assessed goal setting in early-stage Parkinson's disease dementia or dementia with Lewy bodies. Semistructured interviews were carried out with 29 participants from an ongoing trial of cognitive rehabilitation for people with these conditions. Here, we examined the goal statements provided by these participants using qualitative content analysis, exploring the types and nature of the goals set. Participants' goals reflected their motivations to learn new skills or improve performance in areas such as technology-use, self-management and orientation, medication management, and social and leisure activities. These results suggest that goal setting is achievable for these participants, provide insight into the everyday cognitive difficulties that they experience, and highlight possible domains as targets for intervention. The trial is registered with ISRCTN16584442 (DOI 10.1186/ISRCTN16584442 13/04/2015).
Schooling and Labor Market Impact of the 1968 Nine-Year Education Program in Taiwan

The extension of basic schooling from six to nine years in 1968 was the largest expansion of education in Taiwan's modern history. More than 140 new junior high schools were opened in 1968 under this program, increasing the number of junior high schools by 70 percent from 1967 to 1968. We evaluate the effect of this program on education and wages by analyzing cohort differences in educational attainment induced by the timing of the program and by combining these cohort differences with differences across counties in the number of schools built. These estimates suggest that children who were between the ages of 6 and 11 in 1968 received 0.6 additional years of education for every school constructed per 1000 children between the ages of 12 to 14. We use the exogenous variation in schooling due to this program to construct instrumental variable (IV) estimates of the returns to education. We find that IV estimates based on cohort differences in education are lower than the corresponding OLS estimates, but IV estimates based on regional differences in inter-cohort patterns are not significantly different from the OLS estimates.
Research methods in computing: what are they, and how should we teach them?

Despite a lack of consensus on the nature of Computing Research Methods (CRM), a growing number of programs are exploring models and content for CRM courses. This report is one step in a participatory design process to develop a general framework for thinking about and teaching CRM.We introduce a novel sense-making structure for teaching CRM. That structure consists of a road map to the CRM literature, a framework grounded in questions rather than answers, and two CRM skill sets: core skills and specific skills. We integrate our structure with a model for the process a learner goes through on the way to becoming an expert computing researcher and offer example learning activities that represent a growing repository of course materials meant to aid those wishing to teach research skills to computing students.Our model is designed to ground discussion of teaching CRM and to serve as a roadmap for institutions, faculty, students and research communities addressing the transition from student to fully enfranchised member of a computing research community of practice. To that end, we offer several possible scenarios for using our model.In computing, research methods have traditionally been passed from advisor to student via apprenticeship. Establishing a richer pedagogy for training researchers in computing will benefit all (see Figure 1).
Towards Benchmarked Sleep Detection with Inertial Wrist-worn Sensing Units

The monitoring of sleep by quantifying sleeping time and quality is pivotal in many preventive health care scenarios. A substantial amount of wearable sensing products have been introduced to the market for just this reason, detecting whether the user is either sleeping or awake. Assessing these devices for their accuracy in estimating sleep is a daunting task, as their hardware design tends to be different and many are closed-source systems that have not been clinically tested. In this paper, we present a challenging benchmark dataset from an open source wrist-worn data logger that contains relatively high-frequent (100Hz) 3D inertial data from 42 sleep lab patients, along with their data from clinical polysomnography. We analyse this dataset with two traditional approaches for detecting sleep and wake states and propose a new algorithm specifically for 3D acceleration data, which operates on a principle of Estimation of Stationary Sleep-segments (ESS). Results show that all three methods generally over-estimate for sleep, with our method performing slightly better (almost 79% overall median accuracy) than the traditional activity count-based methods.
Optimizing the memory bandwidth with loop fusion

The memory bandwidth largely determines the performance and energy cost of embedded systems. At the compiler level, several techniques improve the memory bandwidth at the scope of a basic block, but often fail to exploit all. We propose a technique to optimize the memory bandwidth across the boundaries of a basic block. Our technique incrementally fuses loops to better use the available bandwidth. The resulting performance depends on how the data is assigned to the memories of the memory layer. At the same time, the assignment also strongly influences the energy cost. Therefore, we combine in our approach the fusion and assignment decisions. Designers can use our output to trade-off the energy cost with the system's performance.
Analysis and optimization of routing trust values in a Kademlia-based Distributed Hash Table in a malicious environment

Trust management in peer-to-peer (P2P) networks is a current topic. Most of the current work focuses on a “clean routing table”, aiming to keep it free from malicious nodes. We follow a different approach which can be characterized as “clean routing process”: All kinds of nodes may be contained in a node's routing table. During the routing process, only trustworthy nodes shall be used for determining the next hop and finally for storing or retrieving content items. The trust values are stored in a distributed way and the ratings must be given and stored verifiably. In this paper, we present the results of implementing our trust concept into a Kademliabased Distributed Hash Table (DHT). We analyze the distribution of the routing trust values and introduce malicious nodes into the network. The results show that our concept is able to identify them precisely.
A diffuse bubble-like radio-halo source MRC 0116+111: imprint of AGN feedback in a low-mass cluster of galaxies

We present detailed observations of MRC 0116+111, revealing a luminous, miniradio halo of ∼240-kpc diameter located at the centre of a cluster of galaxies at redshift z = 0.131. Our optical and multiwavelength Giant Metrewave Radio Telescope and Very Large Array radio observations reveal a highly unusual radio source: showing a pair of giant (∼100-kpc diameter) bubble-like diffuse structures, that are about three times larger than the analogous extended radio emission observed in M87 – the dominant central radio galaxy in the Virgo cluster. However, in MRC 0116+111 we do not detect any ongoing active galactic nucleus (AGN) activity, such as a compact core or active radio jets feeding the plasma bubbles. The radio emitting relativistic particles and magnetic fields were probably seeded in the past by a pair of radio jets originating in the AGN of the central cD galaxy. The extremely steep high-frequency radio spectrum of the north-western bubble, located ∼100 kpc from cluster centre, indicates radiation losses, possibly because having detached, it is rising buoyantly and moving away into the putative hot intracluster medium. The other bubble, closer to the cluster centre, shows signs of ongoing particle re-acceleration. We estimate that the radio jets which inflated these two bubbles might have also fed enough energy into the intracluster medium to create an enormous system of cavities and shock fronts, and to drive a massive outflow from the AGN, which could counter-balance and even quench a cooling flow. Therefore, this source presents an excellent opportunity to understand the energetics and the dynamical evolution of radio jet inflated plasma bubbles in the hot cluster atmosphere.