Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighting-based semantic similarity measure based on topological parameters in semantic taxonomy

Abstract Semantic measures are used in handling different issues in several research areas, such as artificial intelligence, natural language processing, knowledge engineering, bioinformatics, and information retrieval. Hierarchical feature-based semantic measures have been proposed to estimate the semantic similarity between two concepts/words depending on the features extracted from a semantic taxonomy (hierarchy) of a given lexical source. The central issue in these measures is the constant weighting assumption that all elements in the semantic representation of the concept possess the same relevance. In this paper, a new weighting-based semantic similarity measure is proposed to address the issues in hierarchical feature-based measures. Four mechanisms are introduced to weigh the degree of relevance of features in the semantic representation of a concept by using topological parameters (edge, depth, descendants, and density) in a semantic taxonomy. With the semantic taxonomy of WordNet, the proposed semantic measure is evaluated for word semantic similarity in four gold-standard datasets. Experimental results show that the proposed measure outperforms hierarchical feature-based semantic measures in all the datasets. Comparison results also imply that the proposed measure is more effective than information-content measures in measuring semantic similarity.
SUV/Patlak-4D whole-body PET/CT dynamic and parametric imaging: clinical demonstration and validation of SUV synthesis from dynamic passes

Whole-body (WB) PET/CT imaging currently involves single-frame, i.e., static, PET acquisitions over a set of axial bed positions to form standardized uptake value (SUV) images. However, SUV is considered semi-quantitative due to its dependence on post-injection scan time and subject physiology. Recently, we introduced a class of novel WB dynamic PET/CT scan protocols capable of acquiring PET data over multiple time frames at each bed to enable quantitative WB parametric PET imaging. In this study, we are validating a special dynamic WB 18F-FDG PET/CT framework supporting synthesis of staticequivalent SUV as well as dynamic and parametric Patlak WB images from a single set of WB passes acquired within the standard-of-care post-injection time window of 18F-FDG PET/CT exams. The proposed protocol, streamlined for wide clinical adoption, consists of four unidirectional WB passes of constant bed frames (30 sec) or equivalent bed speeds, beginning 1h after injection. The acquired data at each bed may later be used by a direct 4D Patlak reconstruction algorithm to obtain parametric WB Ki images after utilizing a populationbased model to infer the missing first 1h of the input function. Moreover, the same data can be added at each bed to synthesize static PET frames and equivalent SUV WB PET images. For the clinical evaluation of the proposed method, we consented patients scheduled for their regular WB 18F-FDG PET/CT exam to undergo afterwards a second PET-only scan, for the same total scan time, using our protocol. SUV mean comparisons at focal uptake ROIs between the step-and-shoot (S&S) static SUV and the respective synthetic SUV images yielded <14% and <20%. differences, primarily attributed to post-smoothing, when synthesis was conducted on S&S and continuous bed motion scan modes respectively. In addition, the direct 4D Patlak Ki WB images exhibited similar noise levels, while offering similar or better lesion contrast and enhanced quantification relative to SUV.
Gait Analysis based Speed Control of Walking Assistive Robot

The lack of caregivers in an aged society with low birth rate has become a serious social problem. The independent living ability of elderly people are expected, and walking ability is necessary to live an independent life. We are developing a walking assistive robot that can be adapted to the user's walking condition. This study uses proximity sensors, which were attached to the front of the walking support robot, to measure the walking of a user gait analysis and control of the robot according to the gait. Gait analysis was performed based on the relative distance of the user's leg to the inner front of the robot. Various experiments were conducted to study the difference between normal walking and waling with pseudo-disability, and to compare different control strategies based on gait analysis. The results showed that the measurement method is feasible to measure walking while using a walking assistive robot, and that speed control considering the characteristics of walking is a more suitable control method than linear speed change. These results will be useful for the development of automatic rehabilitation robots according to an individual's walking condition.
A data privacy model based on internet of things and cyber-physical systems reference architectures

Data privacy concerns in the Internet of Things (IoT) and cyber - physical systems (CPS) are real, valid and accentuated. In this paper it is argued that data privacy compliance in IoT and CPS should be addressed at both technical and non-technical levels. Methods to ensure data privacy protection based on both system and organisational reference architectures are therefore required. Based on an analysis of existing reference architectures for IoT and CPS, this paper proposes a consolidated architecture relevant for ensuring data privacy for both IoT and CPS. The proposed architecture is then combined with an enterprise architecture reference framework to propose a data privacy model for IoT and CPS with a focus on both organisational and technological features and positioned to guide compliance with the South African Protection of Personal Information Act 4 of 2013 (POPI Act). 1
Improving Pseudo-Relevance Feedback With Neural Network-Based Word Representations

In information retrieval, query expansion methods, such as pseudo-relevance feedback, are designed to enrich users’ queries with relevant terms for comprehensively interpreting the desired information. One of the key issues for query expansion is how to obtain high-quality expansion terms to capture the information needs. Recent advances in neural network language models have demonstrated that these models can learn powerful distributed word representations, which have been successfully applied to solve various natural language processing tasks. In this paper, we propose a novel query expansion framework based on neural network-based word representations. Our framework first selects abundant candidate expansion terms using a modified term-dependency method and then generates term features for candidate terms based on word representations to encode relationships between given queries and corresponding candidate terms. Furthermore, we adopt learning-to-rank methods to train term-ranking models with the generated features for term refinement. We conduct extensive experiments to examine the performance of the learned term-ranking models and compare the effectiveness of the representation-based and context-based features for selecting relevant expansion terms. Experimental results using four TREC collections show that neural network-based word representations are effective in query expansion and can significantly improve retrieval performance.
A Parametric Level set Method for Imaging Multiphase Conductivity Using Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an imaging modality that provides cross-sectional images of objects that carry contrasts in electrical conductivity. EIT is suitable for example for monitoring industrial processes involving multiple phases with different conductivities. This paper presents a parametric level set (PLS) based reconstruction scheme for an EIT-imaging of conductivity distributions within multiphase systems. The proposed scheme involves applying a multiphase level set model to solve the inverse problem of finding interfaces between the regions having different conductivity values and estimating the conductivities of phases. The unknown conductivity to be reconstructed is assumed to be piecewise constant while the interface between the regions are represented by the two PLS functions employing Gaussian radial basis functions (GRBF). The level set-based scheme handles topological merging and breaking naturally during the evolution process. It also offers several advantages compared to the traditional pixel-based approaches. For example, the representation of the PLS function by using GRBF provides flexibility in describing a large class of shapes with fewer unknowns. Numerical simulations and phantom experiments are performed to validate the proposed method.
Design of LPG Injection Mechanism with PID Control for 1 kVA Generator Set

Injection of LPG fuel in generator set engine using carburetor technology have not been controlled yet so that LPG fuel consumption become inefficient at dynamic condition such as variation of fuel quality and different load on the generator. The inefficient fuel combustion will produce the bad emissions of combustion waste. Thus, it needs to be controlled in order to reduce the emission on fuel control mechanisms. This research proposes a microcontroller based fuel control system using openloop and closed-loop control in the 1 kVA generator set. The open-loop control test is performed to obtain the characteristic of throttle angle and injection duration. After that, the result of open-loop control test is used as inputs for closed-loop control that use PI and PID control at different load scenarios. The system is tested in two scenarios, scenario 1 is the generator driving 335 Watt load and scenario 2 the generator is driving 555 Watt load. On the PI Control test with Kp at 4.14 and Ki at 1.67, the generator time response is 3.1 seconds at scenario 1 and 3.6 seconds at scenario 2. Then on the PID control test with Kp at 5.52, Ki at 1 and Kd at 0.25, time response at the generator is 2.6 seconds at scenario 1 and 2.7 seconds at scenario 2. The fuel pressure can be reduced to 2.45 psi at loading scenario 1 and reduced to 2.82 psi at loading scenario 2 compared when using a carburetor. That closed-loop control is applied to increase the efficiency of fuel consumption that is directly influenced the reducing of emission from combustion.
Work environment and the performance of forest rangers in South West Mau Forest, Kenya

Etemesi NI, Sirmah PK, Chepkwony J. 2018. Work environment and the performance of forest rangers in South West Mau Forest, Kenya. Asian J For 2: 46-53. The objective of this research was to evaluate and understand how the working environment of Kenya Forest Service (KFS) forest rangers affects their performance. The Rangers, under the Enforcement and Compliance Division (ENCOM) of KFS are mandated to implement the enforcement of laws and policies pertaining to forests and its allied resources as prescribed in the Kenya Forest Act of 2005. Qualitative research approach was employed in the data collection by the use of structured questionnaires in four forest stations. From a population of 46 rangers, 32 rangers, 8 serving in each of the three forest stations {Londiani, Masaita, and Sorget} of Kericho Zone, Mau complex and the Kericho Ecosystem Conservator's Office were sampled randomly. A pre-tested questionnaire on demographic trends, duration of service, work environment variables constituting of remuneration, living conditions, motivation, appraisals, rewards, empowerment, communication, work tools, mobility, uniforms, challenges, and personal life were administered in January 2016. The performance indicators gave dissatisfaction rates of 59% and 63% in most of the parameters tested. Comparison of the finding of this study with the findings of the surveys of 2010 and 2013 in different conservancies in Kenya gave an index of 51.4% and 56.74% satisfaction respectively. The results, therefore, denote a progressive correlation between the working conditions drivers and the performance of forest rangers. Kenya Forest Service under ENCOM Division has a responsibility and large task to improve the working conditions and environment of the rangers. No matter how efficient conservation and regeneration programs may be undertaken while enforcement lags behind the cumulative performance of the entire process shall ever record dismal performance. More radical measures must be undertaken to enhance the performance and productivity of rangers through motivation, improving both their intrinsic and extrinsic working environment. KFS must as well acknowledge that low employee satisfaction rates shall ever incapacitate the forest rangers performance.
Locating and Controlling Unsound Transitions in Workflow Systems Based on Workflow Net With Data Constraints

Data has a great influence on the analysis of the correctness of workflow systems. How to ensure that a system runs without data errors is very important. Workflow nets with data constraints (WFDC-nets) are a kind formal model that is good at specifying data oriented functional requirements in workflow systems. A sound WFDC-net can guarantee that the system is not only functionally correct but also meets all data requirements. Methods and tools for the soundness verification of WFDC-nets have been designed. However, how to locate unsound transitions in a WFDC-net and then effectively control them is still an open topic. This paper attempts to solve the issue and gives methods to optimize unsound workflow systems in design stage. In our solution, we first construct a minimal complete configuration tree based on the configuration graph with data constraints (C-graph) of the WFDC-net, and then we define four kinds of paths and algorithms to characterize unsound transitions. Furthermore, a pseudo intermediate set is constructed according to different paths and unsound transitions. Based on it, a 4-step controlling strategy is taken to optimize the system and finally makes the controlled system satisfy the data requirements. An e-commerce system is used to illustrate the feasibility and effectiveness of the proposed methods.
Towards software architecture runtime models for continuous adaptive monitoring

A software architecture runtime model provides an abstraction that allows to reason about a running system. For example, a self-adaptive system can employ the model to detect phenomena which make an adaptation beneficial. Over time other phenomena can become interesting and thus make the monitoring of different sys-tem properties necessary. Typically properties are declared in a meta-model as part of application specific model element classifiers. In this case adding new properties requires the creation of a new runtime model instance based on the updated meta-model version. In contrast, a more flexible approach allows altering the set of properties in the runtime model without creating a new model instance and thus without interrupting the phenomena detection process. In this paper we elaborate requirements for a runtime model modeling language which shall enable continuous adaptive monitoring.
Metabolic regulation of T cell development by Sin1–mTORC2 is mediated by pyruvate kinase M2

Abstract Glucose metabolism plays a key role in thymocyte development. The mammalian target of rapamycin complex 2 (mTORC2) is a critical regulator of cell growth and metabolism, but its role in early thymocyte development and metabolism has not been fully studied. We show here that genetic ablation of Sin1, an essential component of mTORC2, in T lineage cells results in severely impaired thymocyte development at the CD4−CD8− double negative (DN) stages but not at the CD4+CD8+ double positive (DP) or later stages. Notably, Sin1-deficient DN thymocytes show markedly reduced proliferation and glycolysis. Importantly, we discover that the M2 isoform of pyruvate kinase (PKM2) is a novel and crucial Sin1 effector in promoting DN thymocyte development and metabolism. At the molecular level, we show that Sin1–mTORC2 controls PKM2 expression through an AKT-dependent PPAR-γ nuclear translocation. Together, our study unravels a novel mTORC2−PPAR-γ−PKM2 pathway in immune-metabolic regulation of early thymocyte development.
LWA Rate Adaption by Enhanced Event-Triggered Reporting

Unlicensed spectrum has been utilized to open up additional capacity to support long-term evolution (LTE) service. LTE and wireless local area network (WLAN) Aggregation (LWA) is a perfect solution that satisfies this need. In 2017, Taiwan has deployed the first commercial LWA service. The deployed LWA network reuses and is nicely merged with the existing individual LTE and WLAN networks. To provide appropriate WLAN transmission bandwidth, the user equipment periodically measures the WLAN quality and reports the results to the eNB (the LTE base station). The eNB then decides if the transmission rate should be adapted. We investigate the performance of periodical reporting and then propose the enhanced event-triggered reporting to improve the performance of WLAN transmission rate adaption. We suggest how to select appropriate frequency to minimize the network traffic of periodical reporting. We also show that enhanced event-triggered reporting outperforms periodical reporting. Our solution utilizes the standard 3GPP protocols and does not modify the mobile phone's software.
What if the World Were Different? Gradient-Based Exploration for New Optimal Policies

Planning under uncertainty assumes a model of the world that speciﬁes the probabilistic eﬀects of the actions of an agent in terms of changes of the state. Given such model, planning proceeds to determine a policy that deﬁnes for each state the choice of action that the agent should follow in order to maximize a reward function. In this work, we realize that the world can be changed in more ways than those possible by the execution of the agent’s repertoire of actions. These additional conﬁgurations of the world may allow new policies that let the agent accumulate even more reward than that possible by following the optimal policy of the original world. We introduce and formalize the problem of planning while considering these additional possible worlds. We then present an approach that models feasible changes to the world as modiﬁcations to the probability transition function, and show that the problem of computing the conﬁguration of the world that allows the most rewarding optimal policy can be formulated as a constrained optimization problem. Finally, we contribute a gradient-based algorithm for solving this optimization problem. Experimental evaluation shows the eﬀectiveness of our approach in multiple problems of practical interest.
SLL Reduction of Circular Array by Weighting Using Gamma Function

Restriction of communication area is preferable in view point of security. Two kinds of beams are used in a communication area restriction system which has been proposed by the authors; one is for transmitting and the other is for jamming [1]. In the conventional method, a binomial coefficient is used; however, sidelobe level is raised in case of virtual linear array. This paper proposes a new weighting method using gamma function for virtual linear array to reduce the sidelobe level. The result of analysis shows that the undesired radiation is reduced by the proposed method.
Evaluation of Sialyllactose Supplementation of a Prebiotic-Containing Formula on Growth, Intestinal Development, and Bacterial Colonization in the Neonatal Piglet

Abstract Background Sialyllactose (SL) is a highly abundant oligosaccharide in human milk that has been shown to influence intestinal maturation and cognitive development and exert bifidogenic effects on the gut microbiota. The SL content of infant formula is significantly less than that of human milk, therefore there is interest in determining the effect of supplementing SL to infant formula at the levels in human milk on neonatal outcomes. Objective The aim of this study was to investigate the effect of varying doses of dietary SL compared with a milk replacer formula on weight gain, gastrointestinal development, and microbiota composition in piglets. Methods Thirty-eight intact male piglets were randomly assigned to 1 of 4 experimental diets from 2 to 32–33 d of age. Diets were formulated to contain SL at 0 mg/L (CON), 130 mg/L (LOW), 380 mg/L (MOD), or 760 mg/L (HIGH). At 32–33 d of age, blood was collected for serum chemistry and blood cellular analyses, and coagulation time. Immediately after humane killing, the small intestine was excised and intestinal segments fixed for quantification of mucin-producing goblet cells and morphologic analysis. In addition, mucosal disaccharide activity was assessed. Colonic luminal contents and feces were collected for measurement of pH, dry matter, volatile fatty acids, and the microbiota. Results SL at ≤760 mg/L supported normal growth, intestinal development, and enzyme activity as well as serum chemistries and hematology (P > 0.05). In addition, SL supplementation did not affect overall microbiota structure and diversity in ascending colon contents and feces, but had minor effects on the relative abundances of specific microbes. Conclusions The findings in this study demonstrate that SL addition to a prebiotic-containing formula was well-tolerated by neonatal piglets, supported normal growth, and did not result in any adverse effects on serum chemistries or intestinal development.
Improving the Performance of User-level Runtime Systems for Concurrent Applications

Concurrency is an essential part of many modern large-scale software systems. Applications must handle millions of simultaneous requests from millions of connected devices. Handling such a large number of concurrent requests requires runtime systems that efficiently manage concurrency and communication among tasks in an application across multiple cores. Existing low-level programming techniques provide scalable solutions with low overhead, but require non-linear control flow. Alternative approaches to concurrent programming, such as Erlang and Go, support linear control flow by mapping multiple user-level execution entities across multiple kernel threads (M:N threading). However, these systems provide comprehensive execution environments that make it difficult to assess the performance impact of user-level runtimes in isolation. This thesis presents a nimble M:N user-level threading runtime that closes this conceptual gap and provides a software infrastructure to precisely study the performance impact of user-level threading. Multiple design alternatives are presented and evaluated for scheduling, I/O multiplexing, and synchronization components of the runtime. The performance of the runtime is evaluated in comparison to event-driven software, systemlevel threading, and other user-level threading runtimes. An experimental evaluation is conducted using benchmark programs, as well as the popular Memcached application. The user-level runtime supports high levels of concurrency without sacrificing application performance. In addition, the user-level scheduling problem is studied in the context of an existing actor runtime that maps multiple actors to multiple kernel-level threads. In particular, two locality-aware work-stealing schedulers are proposed and evaluated. It is shown that locality-aware scheduling can significantly improve the performance of a class of applications with a high level of concurrency. In general, the performance and resource utilization of large-scale concurrent applications depends on the level of concurrency that can be expressed by the programming model. This fundamental effect is studied by refining and customizing existing concurrency models.