Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled-line sensor setup with liquids and solids permittivity sensing capability developed with the use of 3D printing technology

A novel coupled-line sensor setup is introduced allowing for permittivity change monitoring and characterization of both liquid and solid dielectric materials. The four-port coupled-line sensor is proposed together with the 3D printed assembly. A via-shorted edge-coupled two-sided coupled line section is realized to ensure identical sensitivity on the material-under-test placed on both sides of the sensor. The advantage of such a construction is that solid calibration samples with well-defined properties can be used to calibrate the setup when either solid or, what is especially useful, liquid materials are to be measured. An exemplary setup was fabricated using laminate and FDM (Fused Deposition Modeling) type 3D printing technologies and utilized for measurement of various dielectric materials including liquids (butan-2-ol, ethanol, propan-1-ol and propan-2-ol) and solid samples (Arlon 25N and Arlon AD600 laminates). The obtained results prove the usefulness of the proposed setup for characterization of materials featuring frequency dependent complex permittivity.
Combining machine translation and text similarity metrics to identify paraphrases in Turkish

Paraphrase identification (PI) is to recognize whether given two sentences are restatements of each other or not. In our study we propose an approach that exploits machine translation and text similarity metrics as features for PI. Machine learning algorithms like Support Vector Machine (SVM) with three different kernels, C4.5 Decision tree and Multinomial Naïve Bayes (NB) are trained with these features. We evaluated our system on Parder, Turkish paraphrase corpus. The experimental results show that the proposed approach offers state-of-the-art results.
Echo state network-based visibility graph method for nonlinear time series prediction

For an echo state network (ESN), the reservoir topology plays a very important role in network performance. However, the computing capability of classical random structure is limited, and it can not meet the prediction accuracy requirement of some highly complicated prediction tasks. Therefore, different algorithms to construct new reservoir topology based on complex network theory have been proposed, amongst of which, the visibility graph algorithm shows to be more effective and simple. In this paper, a new ESN based on the visibility graph (VGESN) is proposed to perform the prediction of Mackey-Glass chaotic system (MGS) and nonlinear autoregressive moving average (NARMA) time series. Furthermore, the network features of associated graphs are analysed, including the small-world feature and scale- free property. The simulation results indicate that the proposed VGESN has better prediction performance compared with traditional ESN. The analytical results of network characteristics also reveal that the MGS associated graph has small-world feature, while the NARMA associated graph has both small-world and scale-free property.
Iterative roll angle estimation from dense disparity map

The v-disparity map is predominantly used to estimate the parameters of the vertical profile of the road surface. Once the road surface is modelled, an object that lies away from it can be detected and determined as either an obstacle or a pothole. The accuracy of this estimation is largely affected by the clarity of the v-disparity map which can be vastly improved by eliminating the effect of a non-zero roll angle. With a rotation around the roll angle for the disparity map, a better v-disparity histogram can be provided. This paper presents a method for accurate roll angle estimation through analysis of the disparity and v-disparity maps. Since the quality of the v-disparity map is improved by rotating the disparity map by the estimated roll angle, this leads to improved road modelling. The more accurate the roll angle estimation, the larger this improvement is.
Pseudo-color processing of forward looking sonar image: An adaptive hot metal coding algorithm

According to the features of forward looking sonar image, an adaptive hot metal coding algorithm with maximum entropy threshold (AH-M) is proposed to complete pseudo-color processing in forward looking sonar image. Specifically, the parabolic eight-point interpolation is used to interpolate the beams and the sampling points. Meanwhile, the backward mapping coordinate transformation is adopted to generate forward looking sonar image. On this basis, a new hot metal coding algorithm is proposed to complete pseudo-color processing. In order to perform pseudo-color processing according to the features of forward looking sonar image, the node values of the hot metal coding are selected adaptively by the maximum entropy threshold method. The experimental results demonstrate that the proposed AH-M can eliminate mosaic phenomenon and get detail-prominent sonar images. And it can get rich and distinct colors in generated sonar images with relatively fast speed.
Frequency Stabilization in a Sub-Terahertz Gyrotron With Delayed Reflections of Output Radiation

We have studied, both theoretically and experimentally, the frequency stabilization process in a 320-GHz double-beam gyrotron with reflections of output radiation from a nonresonant remote load. In the experiments with reflections from a Teflon disk located at a distance of 3.5 m from the gyrotron output, a twofold increase in frequency stability compared with the case of free-running (reflectionless) generation was obtained. The results of the experiments are in good agreement with the theoretical analysis which was carried out within the framework of a self-consistent time-domain model with modified boundary conditions, as well as based on the direct particle-in-cell simulations.
Network log analysis based on the topic word mover's distance

Telecommunication networks continuously generate various system logs which include plentiful information of system status. So these logs can be used to detect whether a network is under a fault scenario or not. In this paper, we propose an improved word mover's distance (WMD) called Topic Word Mover's Distance (T-WMD) to measure the distance between two log samples and then classify different fault logs to determine the fault root cause. Compared with original WMD, T-WMD takes topic information into consideration and provides more latent semantic information of log corpus. Experiments of k-nearest neighbor (k-nn) fault logs classification show that our T-WMD metric outperforms the original WMD.
An extended multi-population genetic algorithm for resource allocation in service hosting platforms

Nowadays, there is an increasingly rise in the number of needs for processing and the need for larger and more powerful computing systems has been felt and the data centers are rapidly moving towards the required technologies. Since the expansion of the dedicated hosting platforms is not justifiable for economic reasons, there is a strong incentive to use shared resources among services which is called “Shared hosting platforms”. One of the main challenges in the hosting platforms is the resource allocation. The resources should be allocated according to the users' requests with the least possible time and expenses in order to be beneficial for providers. In this paper, we define the resource allocation problem for the shared hosting platforms as an optimization problem and solve it. We have used a special multi-population genetic algorithm to solve the resource allocation problem in shared hosting platforms namely self-adaptive multi-population genetic algorithm (SAMPGA). Extensive simulation experiments were conducted for a comparative performance evaluation with respect to standard genetic algorithm (SGA) using two performance measures. The results demonstrated that the SAMPGA has a much better performance than the SGA.
Execution-Guided Neural Program Decoding

We present a neural semantic parser that translates natural language questions into executable SQL queries with two key ideas. First, we develop an encoder-decoder model, where the decoder uses a simple type system of SQL to constraint the output prediction, and propose a value-based loss when copying from input tokens. Second, we explore using the execution semantics of SQL to repair decoded programs that result in runtime error or return empty result. We propose two model-agnostics repair approaches, an ensemble model and a local program repair, and demonstrate their effectiveness over the original model. We evaluate our model on the WikiSQL dataset and show that our model achieves close to state-of-the-art results with lesser model complexity.
Acidosis and Phosphate Directly Reduce Myosin’s Force-Generating Capacity Through Distinct Molecular Mechanisms

Elevated levels of the metabolic by-products, including acidosis (i.e., high [H+]) and phosphate (Pi) are putative agents of muscle fatigue; however, the mechanism through which they affect myosin’s function remain unclear. To elucidate these mechanisms, we directly examined the effect of acidosis (pH 6.5 vs. 7.4), alone and in combination with elevated levels of Pi on the force-generating capacity of a mini-ensemble of myosin using a laser trap assay. Acidosis decreased myosin’s average force-generating capacity by 20% (p < 0.05). The reduction was due to both a decrease in the force generated during each actomyosin interaction, as well as an increase in the number of binding events generating negative forces. Adding Pi to the acidic condition resulted in a quantitatively similar decrease in force but was solely due to an elimination of all high force-generating events (>2 pN), resulting from an acceleration of the myosin’s rate of detachment from actin. Acidosis and Pi also had distinct effects on myosin’s steady state ATPase rate with acidosis slowing it by ∼90% (p > 0.05), while the addition of Pi under acidic conditions caused a significant recovery in the ATPase rate. These data suggest that these two fatigue agents have distinct effects on myosin’s cross-bridge cycle that may underlie the synergistic effect that they have muscle force. Thus these data provide novel molecular insight into the mechanisms underlying the depressive effects of Pi and H+ on muscle contraction during fatigue.
Intelligent surveillance system for abandoned luggage

In this paper, an automatic method based on computer vision for the detection of abandoned luggage/bag in public spaces is proposed. First, with the background subtraction and foreground modeling technique, objects that were not present in the previous frames and have appeared later on as a static object are detected and then whether they are luggage or not are determined using the Faster Region Proposal Convolutional Neural Network (Faster R-CNN) technique. At the same time, the person closest to the luggage is identified and defined as the owner. After that, the event analysis phase is being started. If the identified person is not in the luggage area within a certain period of time (e.g. 30 seconds), the luggage is defined as “abandoned” with a bounding box. The assessments show that the proposed approach is effective and rapid.
Addressing the Barriers to Interlingual Rule-based Machine Translation with a Concept Specification and Abstraction Semantic Representation

Interlingual machine translation offers the prospect of better preservation of meaning and requires fewer language/translation models than statistical machine translation approaches. However, it lacks popularity primarily because of the extensive training and labour required to define the language rules. In the present work, we introduce a semantic representation designed to address this. The novel representation treats all bits of meaning as individual concepts that refine or further specify one another to build a network that relates entities in space and time. Its "specifying" nature replaces the need for almost all case roles found in typical thematic-role-based representations. Also, the representation can encapsulate propositions and treat them as new concepts, meaning that concepts can be defined in terms of other concepts. This allows the approach to abstract away some technical linguistic terms and ontological details. The proposed natural language generation, parsing, and translation strategies using this representation are also amenable to probabilistic modeling and thus to learning directly from example data.
Trinity: A Distributed Publish/Subscribe Broker with Blockchain-based Immutability

Internet-of-Things (IoT) and Supply Chain monitoring applications rely on messaging protocols for exchanging data. Contemporary IoT deployments widely use the publish-subscribe messaging model because of its resource-efficiency. However, the systems with publish-subscribe messaging model employ a centralized architecture, wherein the data from all the devices in the application network flows via a central broker to the subscribers. Such a centralized architecture make publish-subscribe messaging model susceptible to a central point of failure. Besides, it provides an opportunity for the organization that owns the broker to tamper with the data. In this work, we contribute Trinity, a novel distributed publish-subscribe broker with blockchain-based immutability. Trinity distributes the data published to one of the brokers in the network to all the brokers in the network. The distributed data is stored in an immutable ledger through the use of the blockchain technology. Furthermore, Trinity executes smart contracts to validate the data before saving the data on the blockchain. Through the use of a blockchain network, Trinity can guarantee persistence, ordering, and immutability across trust boundaries. Our evaluation results show that Trinity consumes minimal resources, and the use of smart contracts enable the stakeholders to automate the data management processes. To the best of our knowledge, Trinity is the first framework that combines the components of the blockchain technology with the publish-subscribe messaging model.
Monitoring the transaction selection policy of Bitcoin mining pools

Mining pools are collection of workers that work together as a group in order to collaborate in the proof of work and reduce the variance of their rewards when mining. In order to achieve this, Mining pools distribute amongst the workers the task of finding a block so that each worker works on a different subset of the candidate solutions. In most mining pools the selection of transactions to be part of the next block is performed by the pool manager and thus becomes more centralized. A mining Pool is expected to give priority to the most lucrative transactions in order to increase the block reward however changes to the transaction policy done without notification of workers would be difficult to detect. In this paper we treat the transaction selection policy performed by miners as a classification problem; for each block we create a dataset, separate them by mining pool and apply feature selection techniques to extract a vector of importance for each feature. We then track variations in feature importance as new blocks arrive and show using a generated scenario how a change in policy by a mining pool could be detected.
LEAD: An Adaptive 3D-NoC Routing Algorithm with Queuing-Theory Based Analytical Verification

2D-NoCs have been the mainstream approach used to interconnect multi-core systems. 3D-NoCs have emerged to compensate for deficiencies of 2D-NoCs such as long latency and power overhead. A low-latency routing algorithm for 3D-NoC is designed to accommodate high-speed communication between cores. Both simulation and analytical models are applied to estimate the communication latency of NoCs. Generally, simulations are time-consuming and slow down the design process. Analytical models provide, within a fraction of the time, nearly accurate results which can be used by simulation to fine-tune the design. In this paper, a high performance and adaptive routing algorithm has been proposed for partially connected 3D-NoCs. Latency of the routing algorithm under different traffic patterns, different number of elevators and different elevator assignment mechanisms are reported. An analytical model, tailored to the adaptivity of the algorithm and under low traffic scenarios, has been developed and the results have been verified by simulation. According to the results, simulation and analytical results are consistent within a 10 percent margin.
Social media comparison and analysis: The best data source for research?

In the past decade, social media has become an important part of our everyday life. The employment of different social media changes the way we communicate, collaborate, gather information and consequently perceive the world around us. Thus, researchers from different fields exploit the social media to provide deeper insight into human behaviour. Each social media possesses its own privacy politics and access to publicly available data. In this paper, we present a generic framework along with the tools to analyse different social media. The analysis shows basic usage statistics, reach and engagement differences, language, sentiment and gender identification of each social network data. We compare data from Twitter, Facebook, Tumblr, Google+ and YouTube. The results reveal specifics of each social media, which to some extent also depend on the data available and the selected seed keywords. We uncover that popularity of selected topics in social media is proportional to the number of hits on Google, celebrities and politicians are the most talked topics and that behaviour of users across social media is different. For example, Twitter users prefer to post more, while Facebook and Youtube users prefer to comment. The majority of all social media posts are in English, larger number of them are negative and often written by male users. The results of the proposed framework should serve as a tool to identify the appropriate source of data for the representative analysis of social media.
Primal-dual optimization of the fractional-order variational optical flow model for image registration

Image registration is one of the fundamental and essential tasks for image fusion and image analysis. However, now there are still some problem exiting in non-rigid image registration. For example, in large displacement condition, the image registration accuracy is not satisfied. To deal with it, we present a novel fractional-order variational method to estimate optical flow, using the primal-dual method to construct convex function for global optimization, and applying pyramid iteration to calculate large displacement. Numerical experiments show that the proposed approach can not only obtains more accuracy than classical optical flow method but also works well in large displacement condition. It also demonstrates that fractional-order method as generalization of integer-order method has substantial improvements in image registration accuracy.
Energy Optimal Task Scheduling with Normally-Off Local Memory and Sleep-Aware Shared Memory with Access Conflict

The rapid development of the Real-Time and Embedded System (RTES) has increased the requirement on the processing capabilities of sensors, mobiles and smart devices, etc. Meanwhile, energy efficiency techniques are in desperate need as most devices in RTES are battery powered. Following the above trends, this work explores the memory system energy efficiency for a general multi-core architecture. This architecture integrates a local memory in each processing core, with a large off-chip memory shared among multiple cores. Decisions need to be made on whether tasks will be executed with the shared memory or the local memory to minimize the total energy consumption within real-time constraints. This paper proposes optimal schemes as well as a polynomial-time approximation algorithm with constant ratio. The problem complexity analysis for different task and system models is also presented. Experimental results show that the proposed approximation scheme performs close to the optimal solution in average.
The bacterial community structure and functional profile in the heavy metal contaminated paddy soils, surrounding a nonferrous smelter in South Korea

Abstract The pollution of agricultural soils by the heavy metals affects the productivity of the land and has an impact on the quality of the surrounding ecosystems. This study investigated the bacterial community structure in the heavy metal contaminated sites along a smelter and a distantly located paddy field to elucidate the factors that are related to the alterations of the bacterial communities under the conditions of heavy metal pollution. Among the study sites, the bacterial communities in the soil did not show any significant differences in their richness and diversity. The soil bacterial communities at the three study sites were distinct from one another at each site, possessing a distinct set of bacterial phylotypes. Among the study sites, significant changes were observed in the abundances of the bacterial phyla and genera. The variations in the bacterial community structure were mostly related to the general soil properties at the phylum level, while at the finer taxonomic levels, the concentrations of arsenic (As) and lead (Pb) were the significant factors, affecting the community structure. The relative abundances of the genera Desulfatibacillum and Desulfovirga were negatively correlated to the concentrations of As, Pb, and cadmium (Cd) in the soil, while the genus Bacillus was positively correlated to the concentrations of As and Cd. According to the results of the prediction of bacterial community functions, the soil bacterial communities of the heavy metal polluted sites were characterized by the more abundant enzymes involved in DNA replication and repair, translation, transcription, and the nucleotide metabolism pathways, while the amino acid and lipid metabolism, as well as the biodegradation potential of xenobiotics, were reduced. Our results showed that the adaptation of the bacterial communities to the heavy metal contamination was predominantly attributed to the replacement process, while the changes in community richness were linked to the variations in the soil pH values.
Active fault tolerant control design for UAV using nonsingular fast terminal sliding mode approach

In this paper, an active fault tolerant controller is proposed for the unmanned aerial vehicle (UAV) attitude control system using nonsingular fast terminal sliding mode control technique. Firstly, the nonlinear attitude dynamics model of UAV with actuator faults and external disturbance is described. Next, a neural network-based fault estimation observer is designed to obtain the estimated value of unknown actuator faults and external disturbance, and the stability of the designed observer is also proved. Then, an active fault tolerant controller is designed based on the fault estimation observer and nonsingular fast terminal sliding mode control technique. The Lyapunov stability analysis shows that the proposed fault tolerant controller can guarantee the finite time asymptotic stability for nonlinear closed-loop attitude control systems of UAV with actuator faults and external disturbances. Finally, numerical simulation results are presented to illustrate the benefit of the proposed scheme.