Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Feature Selection for Clustering High Dimensional Data Streams

Change in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the stream progresses. This type of change has not received as much attention as concept-level change. Furthermore, a lot of the methods proposed for clustering streams (density-based, graph-based, and grid-based) rely on some form of distance as a similarity metric and this is problematic in high-dimensional data where the curse of dimensionality renders distance measurements and any concept of “density” difficult. To address these two challenges we propose combining them and framing the problem as a feature selection problem, specifically a dynamic feature selection problem. We propose a dynamic feature mask for clustering high dimensional data streams. Redundant features are masked and clustering is performed along unmasked, relevant features. If a feature’s perceived importance changes, the mask is updated accordingly; previously unimportant features are unmasked and features which lose relevance become masked. The proposed method is algorithm-independent and can be used with any of the existing density-based clustering algorithms which typically do not have a mechanism for dealing with feature drift and struggle with high-dimensional data. We evaluate the proposed method on four density-based clustering algorithms across four high-dimensional streams; two text streams and two image streams. In each case, the proposed dynamic feature mask improves clustering performance and reduces the processing time required by the underlying algorithm. Furthermore, change at the feature level can be observed and tracked.
Autonomous Steering of Concentric Tube Robots for Enhanced Force/Velocity Manipulability

Concentric tube robots (CTR) can traverse tightly curved paths and offer dexterity in constrained environments, making them advantageous for minimally invasive surgical scenarios that experience strict anatomical and surgical constraints. Their shape is controlled via rotation and translation of several concentrically arranged super-elastic precurved tubes that form the robot backbone. As the elastic energy accumulated in the backbone due to bending and twist of the tubes increases, robots can exhibit sudden snapping motions, which can damage the surrounding tissues. In this paper, we proposed an approach for closed-loop steering of a redundant CTR that allows for snap-free motion and enhances its force/velocity manipulability, increasing the capacity of the robot to move and/or exercise forces along any direction. First, a controller stabilizes the CTR end-effector on a desired time-variant trajectory. Next, an online optimizer uses the robot’s redundant Degrees of Freedom (DoF) to reshape its manipulability in real-time and steer it away from potentially snapping configurations or increase its capacity in delivering force payloads. Simulations and experiments demonstrate the performance of the proposed control strategy. The controller can steer a generally unstable CTR along trajectories while avoiding instabilities with a mean error of 850$\mu$m, corresponding to 0.6% of arclength, and improves robot ability to exercise forces by 55%.
P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).
Hybrid Machine Learning Techniques Applied in Real Engineering Applications

Smartphone devices, particularly android devices used by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attackers that is a network of interconnected nodes operated by command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks carried Distributed denial of services (DDoS) that causes to steal sensitive data, remote access, spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet using static or dynamic analysis. In this paper, we have proposed a novel hybrid model, which is a combination of static and dynamic method that relies on machine learning to identify android botnet applications having C&C capability. The results evaluated through machine learning classifiers in which Random forest classifier outperform other ML techniques, i.e. Naïve Bayes, Support Vector Machine, and Simple logistics. Our proposed framework can achieve 97.48% accuracy in detecting such harmful applications. Furthermore, we highlight some research directions and possible solutions regarding botnet attacks for the entire community.
First order parallel coupled BPF with wideband rejection based on SRR and CSRR

In this paper a new approach for first order Chebyshev parallel coupled Bandpass filter resonant at 1 GHz is presented to obtain better results (wideband rejection, high selectivity and low bandpass insertion loss) compared to conventional design. The proposed filter (a tri-formation consisting of CSRR, SRR and stubs of stepped impedance are loaded microstrip resonator) can be configured, by laying split ring resonator (SRR) and complementary split ring resonator (CSRR) with 50 Ω microstrip lines, in addition to effect of loading two stubs of stepped impedance around center of midline microstrip with impedance line 55.36 Ω of parallel coupled. The proposed filter produces high selectivity from passband to stopband transition equals to 307.5 dB/GHz and an excellent wide stopband performance extend from 1.22 GHz to 5 GHz (harmonics repression till for 5 ƒ0); all are bellow -20 dB excepting one transmission zero of -19 dB, that can be eliminate the harmonic superior frequencies without using any external Bandstop filter. Also, enhancement low bandpass insertion loss level, where it reaches 0.25 dB at fundamental centered frequency (ƒ0 = 0.96 GHz) with 21% bandwidth. The proposed filter is designed and simulated with computer aided of Ansoft HFSS software package which ordinarily used in microwave application.
Coupled path and motion planning for a rover-manipulator system

This paper introduces a motion planning strategy aimed at the coordination of a rover and manipulator. The main purpose is to fetch samples of scientific interest that could be placed on difficult locations, requiring to maximize the workspace of the combined system. In order to validate this strategy, a simulation environment has been built, based on the VORTEX Studio platform. A virtual model of the ExoTer rover prototype, owned by the European Space Agency, has been used together with the same robot control software. Finally, we show in this paper the benefits of validating the proposed strategy on simulation, prior to its future use on the real experimental rover.
Influence of neutral word removal on sentiment analysis

Nowadays, lots of information is shared on the Internet, including opinions. In order to deal with these opinions, computers perform sentiment analysis, which provides insight into the sentiment of a piece of text. Currently, not a lot of research is done for Dutch sentiment analysis. Preliminary experiments found that a larger lexicon enhances sentiment analysis, but this increase was too small compared to previous literature. A possible cause for this is the large amount of neutral words the larger lexicon contained after extension. This paper will investigate the inﬂuence of neutral word removal on the performance of sentiment analysis. Two experiments were conducted, one on an unbalanced dataset and one on a balanced dataset. Neutral words were gradually removed from the extended lexicon and the performance was measured. The two experiments both show that neutral word removal enhances sentiment analysis, but the diﬀerences are small. Furthermore, the experiment on the balanced dataset shows that a larger lexicon does not enhance sentiment analysis. Due to small enhancements and the opposite results compared to literature, no conclusion is drawn.
Comparison of Subjective QoE Models for Auto-Stereoscopic 3DTV

Measuring Quality of Experience (QoE) of stereoscopic 3D video is a hot research topic. Subjective models are considered as the most reliable and facilitate development of objective models. However, to collect user opinion scores takes long time. Therefore, new subjective assessment models should be proposed providing not only time-efficiency but also good accuracy and reliability. In this study, two novel subjective QoE models are proposed as alternative to the conventional Double Stimulus Continuous Quality Scale method. Also, a fair comparison method is proposed to evaluate performances of the three subjective methods using the same stimuli prepared with the most recent multi-view video codec on an auto-stereoscopic 3DTV. Correlations are calculated using two objective QoE measures using depth maps and structural similarities. The results demonstrate that the performances of the proposed models are comparable to each other and both models are superior to the conventional method.
Stuck Like Glue: The Formation Of Product Attachments Among Salespeople

Determining why salespeople put forth more effort than others is of particular interest to sales researchers. Identifying factors that influence the intensity of effort is an important area of research. This research proposes that salesperson product attachment is a key factor in determining effort levels. Expectancy theory has been frequently used to support the use of quotas in that salespeople engage in behaviors that will be perceived by them as most likely to yield valued rewards. Thus, according to this theory, salespeople will place the most effort on products that are expected to yield the best results in regards to the quota system. However, this research uses attachment theory and the theory of human needs to demonstrate that salespeople may act in less rational ways, by placing more effort on products to which they feel strongly attached even if these specific products are less likely to yield the best results financially. Through qualitative interviews, antecedents of attachment are identified. These antecedents were empirically tested in Study 1, using a Qualtrics panel of salespeople. Using structural equation modeling, identification and ownership were shown to directly and positively impact attachment. Study 2 consisted of paired results from salespeople and managers at a large food brokerage company. In this study, the antecedents of identification and ownership were again found to significantly lead to attachment, thus demonstrating the robustness of the results. In addition, it was determined that attachment directly and positively impacts effort level and
Combining the Attention Network and Semantic Representation for Chinese Verb Metaphor Identification

Metaphor is the central issue of language and thinking. Metaphor identification plays a significant preliminary role in the field of machine translation, reading comprehension, and automatic summarization, making it a focus of natural language processing. Recently, research into Chinese verb metaphor identification has become a widespread concern. The main problem is that the usage of semantic resources is relatively simple, and there is a lack of deep semantic support. Therefore, this paper proposes a word representation method suitable for metaphor classification tasks, which combines the traditional word vector with structural information from the Synonym Thesaurus, so that the word vector can contain the abstraction degree of the word in the metaphor. On this basis, we propose a verb metaphor attention network based on subject–predicate and verb–object relationships, which gives full consideration to the global syntactic information when we perform LSTM encoding and calculate the weight of each word. At the same time, it can facilitate the understanding of literalness and non-literalness. The experimental results show that the identification effect improves on the existing results, indicating that word representation combining semantic resources and attention network can improve the verb metaphor identification performance.
Transduction matrix to enable sensor-less application of DC motor

Sensorless technology is one of the popular topic in motor industry. The aim is to reduce number of sensor and sensor size in the system. One of the method to achieve this sensor-less application of a motor is thru transduction matrix method. Transduction matrix is a 2x2 matrix that show the relationship between electrical input and mechanical output of the motor. By obtaining the transduction matrix of a chosen motor, the mechanical output of the motor can be calculated from its electrical input. This paper explains the theory behind and the method to obtain the transduction matrix of a DC motor. The DC motor is chosen because it is the common motor in industry.
Developing a novel based productivity model by investigating potential bounds of production plant: A case study.

Productivity level is based on reliability impression which is the rudimentary aspect of automatic assembly line for continuous production. Productivity forecasting is a professional tool helping to enhance production system and attain the client petition by using precise model. Due to mechanisms complexity of assembly lines, analysis of failure factors contributes a significant role for investigating potential bounds that require analytial approach for comparison the current and proposed model of productivity effects. The issues related to production losses need additional space for improvement of productivity model which may not present a close comparison between the current and proposed model of productivity. Through this paper the development of a novel based productivity model is attempted to extant comparisons for availability of assembly line workspaces of an automobile tire manufacturing plant in Pakistan. For investigating the potential bounds of productivity losses, DMAIC and PACE techniques are used. It is verified that novel productivity model yielded better results of 3.358 % errors showing its accuracy as compared to real productivity level at different workspaces. Different rates of production losses followed by the workspaces blockage timing issues are effectively discussed with engineering maintainable explanation for productivity enhancement within the tire curing assembly line.
QUALITY METRIC DESIGN USING INTERNAL CONTROL METHOD TO CONTROL THE QUALITY OF FIBER OPTIC INSTALLATION PROJECT

To provide internet network, one of public Telecommunication provider in Indonesia conduct several project related to fiber optic cable installation such as the STO Modernization Feeder project. In every project implementation, the company is concerned with quality in order to remain competitive with competitors. Deliverable which meets the project quality will produce the product in accordance with the requirements. STO Garut Feeder Modernization Project was experiencing delays which is caused by the non-conformity project deliverable to project specification during monitoring & controlling phase. At project planning, the quality metric was not designed, which can be used as a guideline for vendor in carrying out the project task and to assist the process of quality control. Quality metric design is carried out using internal control methods that can identify possible errors in each project activity and critical success criteria that define a successful activity that will be performed. Result shows that only 67.1% statement (critical success criteria) form critical activity is implemented. This result can answer why the project delay because the activity on critical path is mostly re-worked due to non-conformity issues
Joint Beam and Resource Allocation in 5G mmWave Small Cell Systems

Resource allocation in mmWave small cell is a key issue of the fifth generation (5G) systems. To solve this problem, we first present a hybrid beamforming structure for base station of small cell, which consists of orthogonal frequency division multiplexing (OFDM), identity matrix and switched-beam. Next, we formulate the joint allocation of the beam and transmitted power on the sub-carriers into a mixed integer non-linear programming problem (MINLP). Due to the non-convexity of this problem, we decompose it into two subproblems which are the beam selection and the power allocation. For the beam selection, we propose an algorithm based on cooperative games. For the power allocation, we present an optimal and sub-optimal solution based on Lagrange duality and non-cooperative games respectively. Simulation results show that the beam allocation algorithm proposed in this paper can effectively improve the sum rate of the system, and the optimal power allocation solution is far superior to the sub-optimal one in sum rate.
Cellular and Network Mechanisms for Temporal Signal Propagation in a Cortical Network Model

The mechanisms underlying an effective propagation of high intensity information over a background of irregular firing and response latency in cognitive processes remain unclear. Here we propose a SSCCPI circuit to address this issue. We hypothesize that when a high-intensity thalamic input triggers synchronous spike events (SSEs), dense spikes are scattered to many receiving neurons within a cortical column in layer IV, many sparse spike trains are propagated in parallel along minicolumns at a substantially high speed and finally integrated into an output spike train toward or in layer Va. We derive the sufficient conditions for an effective (fast, reliable, and precise) SSCCPI circuit: (i) SSEs are asynchronous (near synchronous); (ii) cortical columns prevent both repeatedly triggering SSEs and incorrectly synaptic connections between adjacent columns; and (iii) the propagator in interneurons is temporally complete fidelity and reliable. We encode the membrane potential responses to stimuli using the non-linear autoregressive integrated process derived by applying Newton's second law to stochastic resilience systems. We introduce a multithreshold decoder to correct encoding errors. Evidence supporting an effective SSCCPI circuit includes that for the condition, (i) time delay enhances SSEs, suggesting that response latency induces SSEs in high-intensity stimuli; irregular firing causes asynchronous SSEs; asynchronous SSEs relate to healthy neurons; and rigorous SSEs relate to brain disorders. For the condition (ii) neurons within a given minicolumn are stereotypically interconnected in the vertical dimension, which prevents repeated triggering SSEs and ensures signal parallel propagation; columnar segregation avoids incorrect synaptic connections between adjacent columns; and signal propagation across layers overwhelmingly prefers columnar direction. For the condition (iii), accumulating experimental evidence supports temporal transfer precision with millisecond fidelity and reliability in interneurons; homeostasis supports a stable fixed-point encoder by regulating changes to synaptic size, synaptic strength, and ion channel function in the membrane; together all-or-none modulation, active backpropagation, additive effects of graded potentials, and response variability functionally support the multithreshold decoder; our simulations demonstrate that the encoder-decoder is temporally complete fidelity and reliable in special intervals contained within the stable fixed-point range. Hence, the SSCCPI circuit provides a possible mechanism of effective signal propagation in cortical networks.
Physical mechanism study of N-well doping effects on the single-event transient characteristic of PMOS

N-well doping concentration plays an important role in singleevent transient (SET) characteristic of transistors. While adjusting the Nwell doping parameter within a proper range, it has little effect on the basic electrical performance of P-type MOSFET. The physical mechanism of well doping effects on the SET of PMOS is observed. The collapse and recovery of N-well potential, which are the critical factors that influences the SET pulse width, are analyzed by TCAD simulations. The result shows that well doping concentration could affect not only the rate of well potential recover to its equilibrium value, but the potential gradient induced by localized collapse of N-well potential between ion-strike path and source. Energy band analysis is also carried out under the triple well structure, and results show that an increase in well doping concentration will elevate the barrier of reverse-biased P-N junction and therefore cause the accumulation of holes in well, elevating the well potential, and consequently, inhibiting the parasitic bipolar amplification effect.
An Introductory Review of Methods for the Articulation of Strategy in Design

This paper presents an introductory review of methods to articulate strategy in design. Articulation is here seen as a process in which strategy is more or less strictly and explicitly identified and described, e.g. in relation to key ideas, directions to follow, goals and expected results. The paper surveys existing literature in design research and identifies two broad categories of methods for the articulation of strategy: (1) structured frameworks, and (2) vocabularies and principles.