Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Causes of Staff Turnover at Takoradi Polytechnic : The Brown Typology

It is vital that organisations retain their valuable staff in order to achieve their organisational goals. The study explored factors that influence staff turnover at the Takoradi Polytechnic with the main lens being Brown’s (2007) typology. The quantitative research method was employed for this study. The data used for the study were obtained from 222 employees using the stratified random sampling procedure. A questionnaire was used to obtain the data. Simple frequencies, Student t-test and ANOVA were used to analyse the data. The study revealed that staff were concerned with inadequate resources and support to perform their duties effectively and poor monetary rewards. These findings suggest that many staff can easily exit the polytechnic. It was recommended that management looks into the nature of monetary rewards at the polytechnic for possible improvements in order to retain staff. Additionally, more resources such as teaching and learning materials should be provided to raise staff commitment levels.
Thermal Management with In-Situ Process-Temperature Sensor for TSV 3 D-ICs

In TSV (through-silicon-via) 3D-ICs, stacking multiple dies faces a severe challenge of the thermal stress and Vt scatter. In this paper, a thermal management with an insitu process-temperature (PT) sensor for TSV 3D-ICs is proposed using thermal guard rings and thermal TSVs. Additionally, the in-situ PT sensor is proposed to monitor the transistor variations (Vtn, Vtp) and temperature of the intra-die for 3D-ICs. Based on TSMC 65nm CMOS process, this PT sensor achieves 367.5 pJ per conversion, and the sensitivities of Vtn, Vtp and the inaccuracy of temperature are merely ±1.6mV, ±0.8mV, and ±1.5oC, respectively. Moreover, the simulation results also show the thermal management with an insitu PT sensor can reduce temperature and detect heat dissipation of TSV 3D-ICs significantly.
Relative Per formance Evaluation: A Review of M anagerial Accounting Research

Relativepe1formance evaluation (RPE) involves using information about the perfl ormanceof agroup of peers when evaluating the performance of spec面c individuals, teams, or orga iizationa1 units. RPE within and across organizations has drawn much attention fl'om both a的demies and practitioners. Despite its theoretical appeal, empirical research on evidence for RPE usage has reported mixed results. This paper describes a sample of managerial accounting research that theoretically and empirically addresses RPE within and across firms, and suggests further manageriaL1 accounting research onRPE.
Discovering aroma patterns in food products using Latent Dirichlet Allocation and Jensen Shannon divergence

Aroma Extract Dilution Analysis (AEDA) evaluates volatile compounds most likely contributing to the overall aroma of a food sample by means of flavour dilution (FD) factors. In the food industry, this can be useful to compare aroma-active profiles of raw materials or finished products and to select those that are statistically similar. When multiple samples are analysed, the high number of variables makes it difficult to take conclusions. Principal Component Analysis (PCA) should not be applied to FD values as they are discrete numbers. To our knowledge, there are no appropriate methods available to interpret AEDA results from multiple samples. In this study, a new rapid methodology to interpret AEDA results was developed. Latent Dirichlet Allocation (LDA) was developed in the context of text analysis as a mean of dimensionality reduction and has been successfully applied for the analysis of AEDA outcomes. Furthermore, Jensen Shannon divergence measure was a useful tool to compare the distribution of volatile compounds with similar descriptions ("berries", "cheese" or "fruits") among different samples.
A Comparative Mention-Pair Models for Coreference Resolution in DARI Language for Information Extraction

Coreference resolution plays an important role in Information Extraction.This paper covers the investigation of two strategies based on a mention-pair resolver using Decision Tree classifier on structured and unstructured dataset, targeting coreference resolution in Dari language. Strategies are (1) training separate models which is specialized in particular categories (e.g., lexical, syntactic and semantic) and types of mentions (e.g. pronouns, proper nouns) and (2) using a structured dataset on a machine learning library that is designed to classify numerical values. Moreover, these modifications and comparative models describe a contribution of comprehensive factors involved in the resolution of texts. Specifically, we developed the first Dari corpus (’DariCoref’) based on OntoNotes and WikiCoref scheme. Both strategies are produced f-score of state-of-the-art.
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The skull of the enigmatic turtle Compsemys victa Leidy, 1856 is described. A number of unique characteristics are apparent, including the extremely thick nature of all cranial bones, the presence of rod-like epipterygoids, placement of the foramen posterius canalis carotici interni halfway along the contact between the pterygoid and basisphenoid, lack of cheek emarginations, and the reduction of the size of the cavum tympani relative to the orbit. Two differing global turtle analyses and one paracryptodiran analysis were performed to determine the phylogenetic placement of C. victa. Both global analyses converged by placing C. victa within Paracryptodira, herein defined as the most inclusive clade that includes Pleurosternon bullockii and Baena arenosa, but no species of living turtle, whereas the paracryptodiran analysis places C. victa outside of Baenoidea, herein defined as the least inclusive clade that contains P. bullockii and B. arenosa. Although a number of similarities are apparent between C. victa and the uncommon, extant testudinoid Platysternon megacephalum, the available data indicate that these similarities are convergent, likely due to their carnivorous diet. Taphonomic evidence reveals that basal paracryptodires, including C. victa, preferred slow moving or ponded water environments. The riverine habitat preference of baenodds must therefore be derived.
MODE AND RATE OF POSTNATAL GROWTH OF MACACA MULATTA - BASIC ADAPTIVE TRENDS AND SEXUAL DMORPHISM

Growth studies in primates usually concentrate on the investigation of body mass and, in some cases, several other parameters such as sitting height. There are only several really complex long-term research projects on ontogeny. Grant project (206/93/1029) of the Grant Agency of the Czech Republic — Complex study of postnatal ontogeny of higher primates — ranks among such projects. Besides somatic growth, many other aspects of primate ontogeny are being studied in this pilotproject, like the ontogeny of behaviour and the development of social structure, reproduction and sex maturation, physiology, biochemistry and ecological and health aspects of growth. The main task of this study was preparation and verification ofmethodsfor the complex longitudinal study of ontogeny; however, the pilot study yielded a number of quite original data. The longitudinal study currently includes 73 regularly-examined individuals subdivided into three age groups: A) a group of 22 individuals born in 1991 and 1992 (15females and 7 males); B) a group of 24 individuals (10 males, 14 females) bom in 1993; C) a group of 27 individuals born in 1994 (15 males and 12 females). A full set of 48 measurements is taken for each individual. This study deals only with the analysis of growth trends and growth velocity ofoverall body size and individual body segments. For these purposes, the polynomial fits for the most important measurements and indices are analyzed. We have made a detailed analysis of changes of general body size parameters (body mass and body height) for various age intervals. The analysis of development of individual body segments is also presented. According to our results, the head segment develops relatively very independently, with steady and marked differences between the sexes. The trunk and limb parameters have developed in a similar way, and their polynomial fits significantly differfrom those of head. The head segment, trunk segment, lower limb segment and upper limb segment have specific modes and rates of growth. The most important result is the indication ofgrowth acceleration infemales in the pre-puberty and early puberty period. This phenomenon resembles the prepubertal spurts in girls. It is dificult to give a precise interpretation of this growth pattern because there are in fact no systematic studies of the maturation process in higher primates. Compared to the other rhesus monkey groups examined, the group from Konårovice is the most gracile, with a medium sexual dimorphism. The population studied by van Wagenen and Cathpole is more heavy, with lower sexual dimorphism. Females from the Gavan and Huthchinson sample are comparable in body mass, but the males are significantly more heayy; sexual dimorphism is very marked. One important fact results from the comparison Of ontogenetic development of recent man and rhesus macaques up to the first stage of sexual maturation. While there are marked differences in body mass growth curves, the body height changes are relatively very similar.
Self-Supervised Segmentation of Dynamic Objects

We address the problem of automatically segmenting dynamic objects in an urban environment from a moving camera without manual labelling, in an online, selfsupervised learning manner. We use input images obtained from a single uncalibrated camera placed on top of a moving vehicle, extracting and matching pairs of sparse features that represent the optical flow information between frames. This optical flow information is initially divided into two classes, static or dynamic, where the static class represents features that comply to the constraints provided by the camera motion and the dynamic class represents the ones that do not. This initial classification is used to incrementally train a Gaussian Process (GP) classifier to segment dynamic objects in new images. The hyperparameters of the GP covariance function are optimized online during navigation, and the available selfsupervised dataset is updated as new relevant data is added and redundant data is removed, resulting in a near-constant computing time even after long periods of navigation. The output is a vector containing the probability that each pixel in the image belongs to either the static or dynamic class (ranging from 0 to 1), along with the corresponding uncertainty estimate of the classification. Experiments conducted in an urban environment, with cars and pedestrians as dynamic objects and no prior knowledge or additional sensors, show promising results even when the vehicle is moving at considerable speeds (up to 50 km/h). This scenario produces a large quantity of featureless regions and false matches that is very challenging for conventional approaches. Results obtained using a portable camera device also testify to our algorithm’s ability to generalize over different environments and configurations without any fine-tuning of parameters.
Difficult training improves team performance: an empirical case study of US college basketball

Abstract One major challenge facing policy-makers is to design education and workplace training programs that are appropriately challenging. We review previous research that suggests that difficult training is better than easy training. However, surveys we conducted of students and of expert sport coaches showed that many prescribed easy rather than difficult training for those they coached. We analyzed the performance of National Collegiate Athletic Association (NCAA) basketball teams in postseason tournaments to see whether the existing research, largely on individuals in short-term situations, would generalize to teams in the long run. Indeed, playing difficult nonconference (training) games modestly improved performance for NCAA teams in the postseason. Difficult training particularly benefitted teams that lost many nonconference games, and the effect of difficulty was positive within the range of difficulty NCAA teams actually encounter, making it clear that difficult training is superior. We suggest that our results can be generalized beyond sports, although with careful consideration of differences between NCAA basketball teams and other teams that may limit generalizability. These results suggest that policy-makers might consider amplifying the difficulty of team training exercises under certain conditions.
Developing Chinese Complimenting in a Study Abroad Program

This article reports on an empirical study investigating what and how two college-level American learners of Mandarin Chinese developed their own understanding of the Chinese complimenting speech act while participating in a 7-week intensive language study abroad program. A case study approach was adopted with a focus on participants’ self-reflection data supplemented with the researcher’s observation data to unpack each participant’s pragmatic developmental process throughout the program. Guided by sociocultural theory, the study uncovered the dynamic, complex and highly individualized developmental process each participant experienced. The findings revealed that Chinese people’s special treatment of the two American students provided more constraints than opportunities for them to acquire Chinese complimenting. However, the learners’ own motivation and approaches to learning also significantly shaped their learning process and outcomes. Pedagogical suggestions for Chinese pragmatic development in study abroad contexts are provided.
Workplace Bullying and Psychological Distress in Public Institutions in Ghana

Sex differences and psychological distress associated with workplace bullying were investigated in a total of 1,273 employees in three public institutions in Ghana. The effect of level of occupation (junior vs. senior) was also explored. Victimisation from bullying was measured with an abbreviated version of the Work Harassment Scale (WHS-7), and mental health associations with workplace bullying were assessed with an indicator of psychological distress (General Health Questionnaire, GHQ-12). 19.1% of the respondents had been bullied “often” or “very often”. There were no sex differences in frequency of victimisation from bullying. Occupational status was significantly associated with bullying: junior staff members reported higher levels of victimisation from bullying and higher levels of psychological distress than senior staff members. Workplace bullying appears to b common in public institutions in Ghana, and has significant negative outcomes for individuals, especially junior staff members. The findings have implications for policy-makers, employers, and employees.
Novel terminal arrival airspace robustness metrics via topological density clustering

We leverage topological density clustering to analyze radar trajectory data, resulting in novel airspace robustness metrics with respect to convective weather in the terminal arrival airspace. Our method extracts trajectory clusters that occur during varying weather conditions and derives metrics based off of the quantity and quality of these clusters. We study these clusters for Chicago O’Hare International Airport (ORD), and obtain insights into the spatially-varying robustness of ORD arrival resources in convective weather conditions. We found that the southern arrival airspace resources at ORD fared worse in convective weather, indicated by a higher prevalence of and population within anomalous clusters. Our airspace robustness metrics via trajectory density clustering allow for cross-airport comparisons of resiliency, enhancing the ability of airport operators to learn and adapt traffic management strategies utilized at peer airports with similar airfield and airspace attributes.
An Instruction Level GPU Power Modeling Method

Nowadays, the prominent computing horse power of graphics processing units (GPUs) grants them dramatically higher performance than their CPU counterpart. As a result, GPUs are pervasively utilized to accelerate a wide variety of scientific applications. This trend has been promoted with the recent advancements in programming paradigms such as CUDA, which greatly simplify the way to exploit the tremendous computation capacity of GPUs for general purpose applications. Meanwhile, due to significant power consumption of GPUs, it is important to take the factor of energy efficiency into consideration when designing GPU applications. However, comprehensive understanding of how programming affects the power consumption of GPU applications is missing and how to optimize GPU applications to achieve higher energy efficiency still remains a challenge. Although it is indispensable to acquire the ability to measure GPU power consumption accurately before further investigating the energy issues, existing approaches for GPU power measurement are quite deficient. Since the current GPUs are not equipped with internal power sensors, the GPU power is either observed from a separate power meter or deduced from the probed voltage and current on the GPUs. Such approaches require additional hardware devices and are not practical solutions. Whereas, software approaches to estimate the power consumption are still in the early stages, which could not provide accurate power prediction for GPU applications due to its coarse granularity. Moreover, the existing approaches primarily rely on performance profiling and cannot provide any insight on energy facet from programming perspective. CUDA, proposed by NVIDIA, is a widely accepted programming paradigm for general purpose computing in both academy and industry. Thus it would be effective to understand the power consumption of GPUs in terms of CUDA program. Especially, how CUDA program impacts the GPU power consumption from the way it is designed and implemented. However, in order
20 0 v 1 2 1 O ct 2 00 3 Stochastic inflation on the brane

Chaotic inflation on the brane is considered in the context of stochastic inflation. It is found that there is a regime in which eternal inflation on the brane takes place. The corresponding probability distributions are found in certain cases. The stationary probability distribution over a comoving volume and the creation probability of a de Sitter braneworld yield the same exponential behaviour. Finally, nonperturbative effects are briefly discussed.
Do We Need Binary Features for 3 D Reconstruction ?

Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors. They have been shown with promising results on some real time applications, e.g., SLAM, where the matching operations are relative few. However, in computer vision, there are many applications such as 3D reconstruction requiring lots of matching operations between local features. Therefore, a natural question is that is the binary feature still a promising solution to this kind of applications? To get the answer, this paper conducts a comparative study of binary features and their matching methods on the context of 3D reconstruction in a recently proposed large scale mutliview stereo dataset. Our evaluations reveal that not all binary features are capable of this task. Most of them are inferior to the classical SIFT based method in terms of reconstruction accuracy and completeness with a not significant better computational performance.
Certified Control for Self-Driving Cars

Certified control is a new architectural pattern for achieving high assurance of safety in autonomous cars. As with a traditional safety controller or interlock, a separate component oversees safety and intervenes to prevent safety violations. This component (along with sensors and actuators) comprises a trusted base that can ensure safety even if the main controller fails. But in certified control, the interlock does not use the sensors directly to determine when to intervene. Instead, the main controller is given the responsibility of presenting the interlock with a certificate that provides evidence that the proposed next action is safe. The interlock checks this certificate, and intervenes only if the check fails. Because generating such a certificate is usually much harder than checking one, the interlock can be smaller and simpler than the main controller, and thus assuring its correctness is more feasible.
Lazer : A Memory-Efficient Framework for Large-Scale Genome Assembly

Genome sequencing technology has witnessed tremendous progress both in terms of throughput as well as the cost per base pair. However, when it comes to sequence assembly, there still exists a dilemma in the state-of-the-art technology. On one hand, we have a number of distributed assemblers that can utilize several nodes but require massive amounts of memory. On the other hand, there are a few assemblers that can assemble mammalian genomes on a single node but cannot scale up. In this paper, we present a distributed assembler that is both scalable and memory efficient. Using partitioned De Bruijn graphs we enhance memory-to-disk swapping and reduce network communication in the cluster. Experimental results show that our framework can assemble a human genome dataset (452GB) in 14.5 hours using two nodes and 23 minutes using 128 nodes.
Efficient Inference at the Edge with Fault Resilient Voltage Scaling of Embedded Memories

Design of edge devices are driven by the need for the lowest possible cost and energy consumption, which are both strongly affected by on-chip memories. One way to reduce energy consumption is by reducing the supply voltage. However, this causes memory cell hard fault rates to rise exponentially, thus degrading yield at low voltage and increasing cost. Though most of the deep learning Neural Network applications are known to be resilient to faults, beyond a certain voltage, the inference accuracy degrades significantly. We propose DL-FaultLink, a methodology for software managed embedded memories where software construction tools, such as compilers and linkers, are used to efficiently map the critical code/layers onto the nonfaulty segments of the memory and the non-critical fault tolerant sections in the faulty memory segments so that the voltage can be scaled down without degrading the accuracy. Our evaluation on 5 real micro-controller class chip shows that up-to 100mV (average 70mV) reduction in voltage can be achieved without any loss in accuracy for a fully connected network and 50mV reduction for a convolutional neural network, both tested using MNIST dataset. DL-FaultLink can also be considered as an efficient fault tolerance technique as it tolerates 25x (upto 362x) increase in byte error rate with minimal impact on inference accuracy.