Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF MICROWAVE OVEN EXPOSED DIET ON SPERMATOGENESIS IN TESTICULAR TISSUE OF MICE AND COMPARATIVE EFFECTS OF MENTHA PIPERITA AND MELATONIN

Objective: To observe the effects of microwave oven exposed diet on spermatogenesis in the testis of mice and comparative effects of Mentha piperita and melatonin.Study Design: Laboratory based randomized controlled trial.Place and Duration of Study: Anatomy Department, Army Medical College Rawalpindi, in collaboration with National Institute of Health (NIH), Islamabad, from Apr 2015 to May 2015.Material and Method: Study comprised of 32 adult male mice (BALBc strain) weighing 25-30 gms. Selection criteria based on non-probability (purposive) simple random sampling. Mice were divided into four equal groups of 8 mice each. Group 1, taken as control, was given standard diet 5-10gm/animal/day daily for four weeks. Group 2 was given 5-10 gm/animal/day of microwave oven exposed mice pellets for four weeks. Group 3 received Mentha piperita leaf extract (1g/kg b.wt./day) along with microwave oven exposed mice pellets (5-10gm/animal/day) for 4 weeks and group 4 received oral dosage of melatonin 12mg/kg/day along with microwave oven exposed mice pellets (5-10gm/animal/day) for 4 weeks. After four weeks animals were dissected. The shape, color and any abnormal finding of the testis were observed. Testis were processed, embedded and stained for histological study. Spermatogenesis was assessed by the Johnsons scoring. SPSS 21 was used for statistical analysis. Chi square test was applied for intergroup comparison.Results: Spermatogenesis was suppressed and Johnsons score was decreased from normal spermatogenesis (10) to (6-8) in the experimental group 2 and was more improved in the Mentha piperita treated group as compare to the melatonin.Conclusion: Microwave oven exposed mice pellets suppressed spermatogenesis and Mentha piperita had better ameliorative effects than melatonin on the testis of mice. , ,
Throughput Maximization using Spatial Reusability in Multi-Hop Wireless Networks

: The main issue of routing in multi-hop wireless networks is to get high end-to-end throughput. So it is hard to find the optimal path from the source node to the destination node. There are number of routing protocols have been implemented to find the path with least transmission time for sending a single packet. Such transmission time reduces protocols cannot be ensured to get high end-to-end throughput. Spatial reusability aware single-path routes and any path routing protocols consider the both condition to achieve high end to end throughput and to find the path with least transmission time. And also compare them with existing single-path routing and any path routing protocols respectively. The evaluation results show that both protocols significantly increase the end-to-end throughput compared with existing protocols.
Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors

Word embedding models typically learn two types of vectors: target word vectors and context word vectors. These vectors are normally learned such that they are predictive of some word co-occurrence statistic, but they are otherwise unconstrained. However, the words from a given language can be organized in various natural groupings, such as syntactic word classes (e.g. nouns, adjectives, verbs) and semantic themes (e.g. sports, politics, sentiment). Our hypothesis in this paper is that embedding models can be improved by explicitly imposing a cluster structure on the set of context word vectors. To this end, our model relies on the assumption that context word vectors are drawn from a mixture of von Mises-Fisher (vMF) distributions, where the parameters of this mixture distribution are jointly optimized with the word vectors. We show that this results in word vectors which are qualitatively different from those obtained with existing word embedding models. We furthermore show that our embedding model can also be used to learn high-quality document representations.
MOBILE VISUAL ACUITY ASSESSMENT APPLICATION: AcuMob

This paper presents a mobile healthcare (mHealth) system for estimation of visual impairment that provides easiness by specifying the degree of an eye as orthoscopes. Our proposed system called AcuMob which is an Android based mobile application aimed to be used by patients who have myopia. In the crowd society, our proposed app will be implemented faster than the traditional ophthalmologic examination treatments as an alternative. Because AcuMob can be used in everywhere in any time slot, it is offered in the area where the ophthalmologist is not available. The system is developed with using Xamarin framework and voice commands are used to interact with mobile app. Some preferable letters that are suggested by the ophthalmologists were used in the system. The letter categories are specified according to letters’ sizes. In the start-up screen, the biggest letter is demonstrated and if the user responds correct answer, the letter’s size is being smaller. However, if the user says wrong answer three times consecutively, eyesight ratio is produced by the system to the user referencing to Snellen Chart’s information. This article has aimed at making a prediction about the visual impairment’s degree. Thanks to AcuMob, people can get idea about their visual acuity without consulting to an eye medical doctor (MD). For the evaluation of systems’ reliability, field tests were performed at Bayrampasa Goz Vakfi Hospital in Istanbul with two ophthalmologist specialists. At the end of trials, the actual diagnosed degrees and the equivalent degree of eyesight ratios according to Snellen Chart’s information is compared and the success rates are shown. The system achieved at the 65% of average success rate, which can give users an idea about current condition of their visions.
On the parameter space of generative lexicalized statistical parsing models

In this thesis, we apply as well as develop techniques and methodologies for the examination of the complex systems that are lexicalized statistical parsing models. The primary idea is that of treating the “model as data”, which is not a particular method, but a paradigm and a research methodology. Our argument is that lexicalized statistical parsing models have become increasingly complex, and therefore require thorough scrutiny, both to achieve the scientific aim of understanding what has been built thus far, and to achieve both the scientific and engineering goal of using that understanding for progress. In this thesis, we take a particular, dominant type of parsing model and perform a macro analysis, to reveal its core (and design a software engine that modularizes the periphery), and we also crucially perform a detailed analysis, which provides for the first time a window onto the efficacy of specific parameters. These analyses have not only yielded insight into the core model, but they have also enabled the identification of “inefficiencies” in our baseline model, such that those inefficiencies can be reduced to form a more compact model, or exploited for finding a better-estimated model with higher accuracy, or both.
The Discrete-Continuous Logic and its possible quantum realizations

We propose a new version of generalized probabilistic propositional logic, namely, discrete-continuous logic (DCL) in which every generalized proposition (GP) is represented as 2x2 nondiagonal positive matrix with unit trace. We demonstrate that on the set of propositions of this kind one can define both the discrete logical operations (connectives) such as negation and strong logical disjunction and in addition one parameter group of continuous operations (logical rotations). We prove that an arbitrary classical proposition (which in this logic is represented by the purely diagonal matrix) can be considered as the result of strong disjunction of two identical GP. This fact gives one a good reason to presume the DCL as a prime logical substructure underlying to ordinary propositional logic, which is recorded by our consciousness. We believe that proposed version of DCL will find many applications both in physics (quantum logic) and also in cognitive sciences (mental imagery) for better understanding of the pecular nature of mental brain operations.
Towards a Usable Anomaly Diagnosis System among Internet Firewalls’ Rules

While configuring firewalls, firewall rule editing, ordering, and distribution must be done with extreme caution on each of cooperative firewalls. However, network operators are prone to incorrectly configuring firewalls because commonly there are hundreds of thousands of filtering rules (i.e., rules in the Access Control List file; or ACL for short) which could be set up in a firewall, not mention these rules among firewalls can affect mutually. To complete the crucial but laboring inspection of rule configuration on firewalls effectively and efficiently, this paper describes two of our developed diagnosis mechanisms which can speedily discover rule anomalies within/among firewalls with two innovative data structures – Rule Anomaly Relationship tree (RAR tree) and Adaptive RAR tree (ARAR tree). With the assistance of these data structures and associated algorithms, two of our developed mechanisms show significant improvements on system performance and scalability in rule anomaly diagnosis for Internet firewalls.
Factors that affect asker's pay intention in trilateral payment‐based social Q&A platforms: From a benefit and cost perspective

More and more social Q&A platforms are launching a new business model to monetize online knowledge. This monetizing process introduces a more complicated cost and benefit tradeoff to users, especially for askers' concerns. Much of the previous research was conducted in the context of free‐based Q&A platform, which hardly explains the triggers that motivate askers' pay intention. Based on the theories of social exchange and social capital, this study aims to identify and examine the antecedents of askers' pay intention from the perspective of benefit and cost. We empirically test our predictions based on survey data collected from 322 actual askers in a well‐known trilateral payment‐based social Q&A platform in China. The results by partial least squares (PLS) analysis indicate that besides noneconomic benefits including self‐enhancement, social support, and entertainment, financial factors such as cost and benefit have significant influences on the perceived value of using trilateral payment‐based Q&A platforms. More important, we further identify that the effect of financial benefit is moderated by perceived reciprocity belief, and the effect of perceived value is moderated by perceived trust in answerers. Our findings contribute to the previous literature by proposing a theoretical model that explains askers' behavioral intention, and the practical implications for payment‐based Q&A service providers and participants.
A GLRT-Based Mechanism for Detecting Relay Misbehavior in Clustered IoT Networks

Clustering Internet of Things (IoT) networks, to alleviate the network scalability problem, provides an opportunity for an adversary to compromise a set of nodes by simply compromising the relay they are associated with. In such scenarios, an adversary who has compromised the relay can affect the network’s performance by deliberately dropping the packets transmitted by the IoT devices and/or by corrupting the packets to be forwarded by the relay. In this way, the adversary can successfully mimic a bad radio channel between the IoT devices and the relay, thereby requiring the IoT devices to retransmit more frequently. Such a strategy increases the processing load on the IoT devices and will drain their batteries at a faster rate. To detect such an attack, we present hybrid intrusion detection systems that rely on the monitoring of uplink and downlink packets transmitted between IoT devices and the relay. Specifically, we compare the observed packet drop probabilities against their long-term expected values. The detection rules proposed originate from the generalized likelihood ratio test, where the adversary parameters are estimated using maximum likelihood estimation. A semi-analytical approach to obtain the expressions for the false alarm probability is presented in order to determine the decision thresholds. Results presented show the effectiveness of the proposed detection systems, demonstrate the impact of the choice of adversary parameters on them, and validate the expressions obtained for the false alarm probability.
Discovering Transit-Oriented Development Regions of Megacities Using Heterogeneous Urban Data

Public transport is of great significance in megacities. Transit-oriented development (TOD) has become a reliable solution to urban sustainable development, which can reshape the urban form and improve its quality. This paper focuses on leveraging heterogeneous mega urban data to answer three critical questions in TOD: what region looks like under TOD concept, which regions have the potential to be TOD regions, and how to construct these TOD regions. For region partition, we propose a connected component-based clustering algorithm, which merges the large amount of public transport stops into representative cluster ones as region centers, and then apply the Voronoi algorithm to locate the region boundaries according to the cluster centers. For TOD region identification, we present a link importance-based random walk method that considers the importance of various transits and further identifies the most valuable regions to be TOD. For discovering functions of TOD regions, we introduce a multifactor-based function characterization approach that combines both the static linguistic factor and human mobility factor together and then derives the actual function distributions. The experiments, which are conducted on three real data sets, show the superiority of the proposed methods to solve the problems of region partition, TOD region identification, and function characterization for the megacities. In the meantime, the results provide support for the government to formulate public policy to construct a TOD city.
From Network Traffic Measurements to QoE for Internet Video

Video streaming is a dominant contributor to the global Internet traffic. Consequently, monitoring video streaming Quality of Experience (QoE) is of paramount importance to network providers. Monitoring QoE of video is a challenge as most of the video traffic of today is encrypted. In this paper, we consider this challenge and present an approach based on controlled experimentation and machine learning to estimate QoE from encrypted video traces using network level measurements only. We consider a case of YouTube and play out a wide range of videos under realistic network conditions to build ML models (classification and regression) that predict the subjective MOS (Mean Opinion Score) based on the ITU P.1203 model along with the QoE metrics of startup delay, quality (spatial resolution) of playout and quality variations, and this is using only the underlying network Quality of Service (QoS) features. We comprehensively evaluate our approach with different sets of input network features and output QoE metrics. Overall, our classification models predict the QoE metrics and the ITU MOS with an accuracy of 63-90% while the regression models show low error; the ITU MOS (1–5) and the startup delay (in seconds) are predicted with a root mean square error of 0.33 and 2.66 respectively.
Active Surveillance and Prostate Cancer Screening

textabstractThe aim of this thesis is to contribute to the evaluation of active surveillance as an option for selected men with adenocarcinoma of the prostate. Active surveillance (AS) entails an active observation policy for men with presumably curable prostate cancer (PCa) at diagnosis. The concept of AS is to avoid unnecessary treatment, with its inherent side effects and costs, while preserving the window of cure with active surveillance, in these males with PCa. Though it sounds and even feels contradictory to delay treatment in a PCa patient, the rationale of AS can be explained by several convincing arguments.
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation

Traditional model training for sentence generation employs cross-entropy loss as the loss function. While cross-entropy loss has convenient properties for supervised learning, it is unable to evaluate sentences as a whole, and lacks flexibility. We present the approach of training the generation model using the estimated semantic similarity between the output and reference sentences to alleviate the problems faced by the training with cross-entropy loss. We use the BERT-based scorer fine-tuned to the Semantic Textual Similarity (STS) task for semantic similarity estimation, and train the model with the estimated scores through reinforcement learning (RL). Our experiments show that reinforcement learning with semantic similarity reward improves the BLEU scores from the baseline LSTM NMT model.
Generalized Wireless-Powered Communications: When to Activate Wireless Power Transfer?

Wireless-powered communication network (WPCN) is a key technology to power energy limited massive devices, such as on-board wireless sensors in autonomous vehicles, for Internet-of-Things applications. Conventional WPCNs rely only on dedicated downlink wireless power transfer (WPT), which is practically inefficient due to the significant energy loss in wireless signal propagation. Meanwhile, ambient energy harvesting is highly appealing as devices can scavenge energy from various existing energy sources (e.g., solar energy and cellular signals). Unfortunately, the randomness of the availability of these energy sources cannot guarantee stable communication services. Motivated by the above statements, we consider a generalized WPCN where the devices can not only harvest energy from a dedicated multiple-antenna power station (PS), but can also exploit stored energy stemming from ambient energy harvesting. Since the dedicated WPT consumes system resources, if the stored energy is sufficient, WPT may not be needed to maximize the weighted sum rate. To analytically characterize this phenomenon, we derive the condition for WPT activation and reveal how it is affected by the different system parameters. Subsequently, we further derive the optimal resource allocation policy for the cases that WPT is activated and deactivated, respectively. In particular, it is found that when WPT is activated, the optimal energy beamforming at the PS does not depend on the devices’ stored energy, which is shown to lead to a new unfairness issue. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed optimal resource allocation.
Advanced Management Training for Sustainable Development of the Mining Region

The article presents the experience of advanced training and retraining for mining enterprises in the region. Special emphasis is placed on the training of specialists required for the management of innovative production, solving the problem of digital transformation of the economy. The analysis revealed the need to change approaches to learning and transition to new trends aimed at the formation of a new level of specialist with cross-functional knowledge on a wide range of issues and expertise in one of the subject areas. The achievement of such results in training is based on the introduction into the educational process of Advanced Learning Technologies components, involving the merger of “learning” and “e-learning” environments, the creation of an intellectual learning environment; taking into account the individual needs and abilities of trainees; offering individual learning paths. The presented approaches are implemented within the framework of the Presidential program of management training for the national economy, which presents technologies and special training modules on the use of digital technologies and platform solutions in the economy and management.
Frequent Pattern Mining with Serialization and De-Serialization

Competent frequent pattern mining techniques be critical for finding relationship rules. Here, examination of subject of finding association rules for objects into an enormous DB of customer purchase entries is discussed. Ruling huge patterns following DB entry set had guided lots of techniques. Like Apriori, DHP, ECLAT, FP Growth etc. At this point, we projected new technique known as Frequent Pattern Mining with Serialization and De-Serialization (FPMSD), that's proficient for finding frequent patterns. FPMSD utilizes DLI(down-level-index) i.e. patterns which co-found with delegate item can be recognized rapidly and straightly using effortless and quickest method. This would happen to advantageous compare to other frequent pattern mining techniques. Additionally serialization will save produced frequent patterns into a file and de- serialization will pull through saved patterns from file. This Serialization and De-serialization Technique(SDT) takes lesser time for patterns entry set gathering than getting this from scratch. Keywords - Association rule, Frequent pattern mining, DLI, SDT
Sizing and dynamic modelling and simulation of a standalone PV based DC microgrid with battery storage system for a remote community in Nigeria

In this paper, a solar PV powered DC microgrid is proposed and designed for Umuokpo Amumara in Nigeria with 800 households and a number of community installations which include churches, schools, shops, and a water pumping system. The appropriate sizes of system components are determined to meet the all-time load demand. A Techno-economic feasibility study was carried out in Homer Pro to determine the energy needs of the community and as well t he system size and configuration that best suits the community. The energy requirement of the community was obtained to be 3.16MWh/day. The battery storage system was also sized in this work and a battery system capacity of 21,944Ah was able to meet the community energy requirement for up to a day without renewable energy supply. The dynamic model of proposed the microgrid was simulated in MATLAB/SIMULINK to observe the system’s dynamic response in view of the power quality, load impact, and battery storage charging. The results obtained from the simulation depicted a stand-alone DC microgrid that is capable of meeting the daily electrical energy requirements of the system with good voltage stability. The PV system used in the system could function at maximum power conditions even with variation in the weather conditions. This was achieved by employing the Incremental Conductance MPPT system.