Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Automatically Generated Tweets Using Lexical Analysis and Profile Credibility

Every industry relies heavily on accurate news and information distribution. In the recent decade social media has become one of the main methods of sharing news and other social impacting information online. But there’s an uprising threat to all social media platforms, especially for twitter, known as bots. Not all bots are malicious but these automated accounts are largely responsible for platform manipulation, which is the process of misleading, disrupting the experience of other users by engaging in deceptive, aggressive activities. There are many politically motivated groups in Facebook and Twitter who use various levels of manipulation to influence voters and thereby undermining the democratic process. Platform manipulation is not only carried out by malicious automation, but also with spam and inauthentic accounts (fake accounts). This paper presents novel methodology to detect these bots (automated accounts) using existing research as foundation and builds new research solution to the problem. This methodology can be applied to the news domain to find bots involved in spreading false information. This methodology classifies a given tweet into either fake news or not and use the result as a feature and in addition to that user credibility can also be taken into account.
A 65-nm CMOS Continuous-Time Pipeline ADC Achieving 70-dB SNDR in 100-MHz Bandwidth

We describe the design principles and circuit details of a three-stage continuous-time pipeline (CTP) ADC that achieves 70-dB SNDR in a 100-MHz bandwidth while sampling at 800 MHz. Implemented in 65-nm CMOS, the ADC is easy to drive and incorporates an inherent anti-alias filter that achieves 60-dB rejection in the first Nyquist band. Each pipeline stage is realized using a second-order Rauch-filter-based residue amplifier that incorporates a 9-level resistive DAC and an RC-delay line. A dummy-switching scheme relaxes DAC reference-buffer requirements. The back-end ADC is a $4\times $ time-interleaved 7-bit SAR converter. The Schreier and Walden FoMs of our ADC are 165.4 dB and 56.1 fJ/level, respectively.
Foveal-pit inspired filtering of DVS spike response

In this paper, we present results of processing Dynamic Vision Sensor (DVS) recordings of visual patterns with a retinal model based on foveal-pit inspired Difference of Gaussian (DoG) filters. A DVS sensor was stimulated with varying number of vertical white and black bars of different spatial frequencies moving horizontally at a constant velocity. The output spikes generated by the DVS sensor were applied as input to a set of DoG filters inspired by the receptive field structure of the primate visual pathway. In particular, these filters mimic the receptive fields of the midget and parasol ganglion cells (spiking neurons of the retina) that sub-serve the photo-receptors of the foveal-pit. The features extracted with the foveal-pit model are used for further classification using a spiking convolutional neural network trained with a backpropagation variant adapted for spiking neural networks.
Analyzing Multimodal Sentiment Via Acoustic- and Visual-LSTM With Channel-Aware Temporal Convolution Network

The emotion of human is always expressed in a multimodal perspective. Analyzing multimodal human sentiment remains challenging due to the difficulties of the interpretation in inter-modality dynamics. Mainstream multimodal learning architectures tend to design various fusion strategies to learn inter-modality interactions, which barely consider the fact that the language modality is far more important than the acoustic and visual modalities. In contrast, we learn inter-modality dynamics in a different perspective via acoustic- and visual-LSTMs where language features play dominant role. Specifically, inside each LSTM variant, a well-designed gating mechanism is introduced to enhance the language representation via the corresponding auxiliary modality. Furthermore, in the unimodal representation learning stage, instead of using RNNs, we introduce ‘channel-aware’ temporal convolution network to extract high-level representations for each modality to explore both temporal and channel-wise interdependencies. Extensive experiments demonstrate that our approach achieves very competitive performance compared to the state-of-the-art methods on three widely-used benchmarks for multimodal sentiment analysis and emotion recognition.
Joint Resource Allocation Based on Traffic Flow Virtualization for Edge Computing

Edge computing can provide network services with low latency and real-time processing by operating cloud services on network edges. Edge computing has numerous advantages such as low latency, local characteristics, and network congestion localization, however, associated resource management is becoming a significant challenge because of its features such as hierarchy, decentralization, and heterogeneity. Thus, a joint resource operation and management scheme for edge computing that can greatly reduce the traffic load in the network by considering the generated traffic load and computing resources is proposed in this paper. It is based on the concept of a virtual service flow (VSF) consisting of clients, data sources, a server instance, and external network entities for a specific service. The VSF consists of several drafts according to the expected server location, and each draft estimates a traffic load that can occur according to its characteristics. The location of the edge server of the VSF is determined for each VSF, and it is performed based on the VSF rejection and reconfiguration algorithm using the weighted vector bin packing algorithm that considers the client node coverage of the VSF. The proposed scheme is evaluated based on simulations that consider the actual characteristics of the network services and devices.
Artificial Conversational Agent using Robust Adversarial Reinforcement Learning

Reinforcement learning (R.L.) is an effective and practical means for resolving problems where the broker possesses no information or knowledge about the environment. The agent acquires knowledge that is conditioned on two components: trial-and-error and rewards. An R.L. agent determines an effective approach by interacting directly with the setting and acquiring information regarding the circumstances. However, many modern R.L. -based strategies neglect to theorise considering there is an enormous rift within the simulation and the physical world due to which policy-learning tactics displease that stretches from simulation to physical world Even if design learning is achieved in the physical world, the knowledge inadequacy leads to failed generalization policies from suiting to test circumstances. The intention of robust adversarial reinforcement learning(RARL) is where an agent is instructed to perform in the presence of a destabilizing opponent(adversary agent) that connects impedance to the system. The combined trained adversary is reinforced so that the actual agent i.e. the protagonist is equipped rigorously.
Effects of Acid and Na2SiO3 Modification on the Dissolution Behavior of 2CaO·SiO2-3CaO·P2O5 Solid Solution in Aqueous Solutions

Steelmaking slag with high P 2 O 5 content is considered as an alternative resource of P. In such slag, P is mainly concentrated in 2CaO·SiO 2 -3CaO·P 2 O 5 (C 2 S–C 3 P) solid solution, which has higher water solubility than other mineralogical phases, which indicates the possibility of selective leaching of P. In order to recover P from steelmaking slag, its dissolution ratio from C 2 S–C 3 P into aqueous solutions must be increased. In this study, the effects of acid as leaching agent, high temperature modification of C 2 S–C 3 P by Na 2 SiO 3 , and pH on the dissolution behavior of C 2 S–C 3 P in aqueous solutions have been investigated. The obtained results show that when oxalic acid (H 2 C 2 O 4 ) or citric acid (H 3 C 6 H 5 O 7 ) was used to leach P from C 2 S–C 3 P, dissolved Ca 2 + ions were removed by CaC 2 O 4 precipitation or by the formation of CaC 6 H 5 O 7 − complex, respectively. As a result, the precipitation of phosphates was suppressed, and P dissolution increased dramatically. On the other hand, the P-rich phase changed from the original C 2 S–C 3 P into 2CaO·SiO 2 -2CaO·Na 2 O·P 2 O 5 with higher water solubility after Na 2 SiO 3 addition, thus improving P dissolution in the H 2 C 2 O 4 and H 3 C 6 H 5 O 7 solution. At higher pH (pH ≥ 7), H 2 C 2 O 4 was beneficial for P dissolution from the modified C 2 S–C 3 P. However, at lower pH (5 ≤ pH < 7), H 3 C 6 H 5 O 7 was an optimal leaching agent for extracting soluble P. At pH 5, the dissolution ratio of P from the modified C 2 S–C 3 P in the H 3 C 6 H 5 O 7 solution reached 85.7%.
Robust Three-Dimensional Shape Sensing for Flexible Endoscopic Surgery Using Multi-Core FBG Sensors

In this letter, we propose a novel 3D shape sensing algorithm for flexible endoscopic surgery using multi-core fiber Bragg grating (FBG) sensors. Considering the signal noises and environmental perturbations, the direct use of FBG measurements for shape sensing and position estimation is regarded as far from accurate and stable, especially when utilized in the sensing of long and flexible surgical instruments. To solve this problem, a novel and generic model-based filtering technique for the iterative curvature/twist estimation by taking advantage of the configurations of the multi-core FBGs in the optical fiber is introduced to remedy the sensory noises. Besides, we introduce an enhanced moving average approach to smooth the estimated curvatures and twists spatially on the fiber. We extensively validate our algorithm by conducting shape sensing tasks in simulations under varying conditions, and in experiments using a robotic-assisted colonoscope system integrated with a multi-core FBG fiber. The results prove our method substantially outperforms the conventional approach in the aspect of estimation accuracy and robustness, showing the superiority and application feasibility.
Ultrasonic study of molecular interactions in binary liquid mixtures: acrolien & cinnamaldehyde in methanol, cyclohexane & p-dioxane

The velocity of ultrasonic waves (10 MHz) for different compositions of mixtures of methanol, cyclohexane and p-dioxane with acrolein and cinnamaldehyde has been measured in the temperature range 10-40 °C. The excess compressibility β a E and excess molar volume V E parameters have been discussed in the light of inter-molecular AB interactions and resulting disorder in these mixtures. It is observed that excess parameters β a E and V E can be used for classification of all types of molecular interactions in binary liquid mixtures.
Genetic Algorithm For Solving University Course Timetabling Problem Using Dynamic Chromosomes

Building of university timetable is one of the problems that are difficult to be solved because of the large number of lectures and conflicts between them. This makes it difficult to introduce schedule-building restrictions that consume time and effort for table production. Many methods have been suggested that the computer is used to solve this problem, including a Genetic Algorithm (GA) where the main purpose of the algorithm is to reduce the number of conflicts in the timesheet and to reduce the search space encoding. This paper proposes an automated system to build a faculty timetable using a genetic algorithm. A genetic algorithm had been used to schedule the timetable of the faculty of engineering and information technology with a dynamic chromosome size that is flexible with the course numbers of each department. This algorithm can be applied in different institutions (i.e. faculties, or institutes) According to their limitations. The proposed system achieved great results during the evaluation phase of around 93% compared to manual scheduling or the systems available.
A Low-Power Tunable Frequency Selective Surface for Multiplexed Remote Sensing

In this interdisciplinary work, a low-power varactor-tunable frequency selective surface is presented for use with a multiplexed remote sensor system. Combined with a low power sensor and control system, voltage biasing of the proposed frequency selective surface panel can be used to modulate the center frequency of a reflection peak, which in turn may be detected by remote interrogation through a radar system. The control board and frequency selective surface were found to draw only $200~\mu \text{A}$ of current during typical operation from two 3V CR2032 button batteries, enabling continuous operation for weeks at a time (even longer if operated in wake-up mode) when used in conjunction with a suitably low power sensor. Centered at 3 GHz, the backscatter peak can be modulated across a range of 200 MHz, and the combined system was shown to successfully differentiate between various concentrations of ammonia under test conditions.
SAFE LINEAR BANDITS

Bandit algorithms have various applications in safety-critical systems, where it is important to respect the system's underlying constraints. The challenge is that such constraints are often unknown as they depend on the bandit's unknown parameters. In this talk, we formulate a linear stochastic multi-armed bandit problem with safety constraints that depend linearly on an unknown parameter vector. As such, the learner is unable to identify all safe actions and must act conservatively in ensuring that their actions satisfy the safety constraint at all rounds (at least with high probability). For these bandits, we propose new upper-confidence bound (UCB) and Thompson-sampling algorithms, which include necessary modifications to respect the safety constraints. For two settings -with and without bandit feedback information on the constraint- we prove regret bounds and discuss their optimality in relation to corresponding bounds in the absence of safety restrictions. For example, for a setting with bandit-feedback information on the constraint, we present a frequentist regret of order $\mathcal{O}\left(d^{3/2}log^{1/2}d\sqrt{T}log^{2/3}T\right)$, which remarkably matches the results provided by [1] for the standard linear Thompson-sampling algorithm. We highlight how the inherently randomized nature of Thompson-sampling helps expand the set of safe actions the algorithm has access to at each round. Finally, we discuss related problem variations with stage-wise baseline constraints, in which the learner must choose actions that not only maximize cumulative reward across the entire time horizon, but they further satisfy a linear baseline constraint taking the form of a lower bound on the instantaneous reward. The content of this talk is based on [2]–[4].
Methods for Classification of Items for Inventory Management

Association rules are used for finding associations among multi-items by mining sale transaction data. Inventory for getting better profits and to minimize inventory cost replenishment policy can be created using association rule. However finding purchase dependency is an extremely difficult task. Moreover among thousands of items available in an organization, only few catch management's close attention. It is generally observed that all items do not have same degree of control. Different methods of classification are used to classify different items. ABC classification is the most widely used technique for purpose of selective inventory control. In this paper we summarize the research done under purchase dependency and ABC classification for better inventory management and also propose a method to calculate optimal ordering policy. At the end of the paper various ways for further future research are identified.
Flexible and Stretchable Dry Active Electrodes With PDMS and Silver Flakes for Bio-Potentials Sensing Systems

Flexible and stretchable dry active electrodes for multi bio-potentials sensing based on Ag flakes / polydimethylsiloxane (PDMS) electrically conductive composite (ECC) are developed and characterized. The proposed dry active electrode consists of soft substrate, electrode, and simple circuits with an amplifier and a capacitor. The soft substrate is made of silicone with a molding process, while the electrode is fabricated by bar coating the ECC on the patterned substrate. Furthermore, circuits interconnect, soldering of chip and other components, and connection with flexible PCB (FPC) are all implemented with ECC directly, which simplifies the fabrication process. A portable bio-potential sensing system is also designed and implemented to work with the proposed electrodes. Various experiments were carried out to verify the proposed electrodes as well as the whole sensing system. The connectivity and proper functionality of the electrodes and cables remain stable during the stretching test. The system and the sensors yield good signal quality for multiple bio-potentials. Compared with conventional Ag/AgCl wet passive electrodes in electrocardiogram (ECG) sensing, the proposed dry active electrodes showed comparable noise floor and less sensitivity to power line interferences and motion artifacts. In electroencephalogram (EEG) sensing, the observed alpha rhythm was 12.66 dB higher than the baseline in the eye-close state. The proposed sensors exhibited potential applications in wearable systems in the electromyogram (EMG) grip force measurement and classification test. Overall, the proposed sensors could provide better comforts during long-time wearing according to its better matching modulus with skin and can meet the requirements of high quality multi bio-potential sensing.
FDS Measurement-Based Moisture Estimation Model for Transformer Oil-Paper Insulation Including the Aging Effect

Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer. The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation. However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored. Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm. In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture. Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm. The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions. In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures.
Rejuvenation Addresses Partial Discharge in Power Cables

Rejuvenation of power cables addresses water trees, voids, or electrical trees. Partial discharge (PD) in joints or terminations are addressed by the sustained pressure rejuvenation process, which replaces all terminations and joints. In this study, partial discharge inception voltage (PDIV) is found to increase by 27% for each 0.1%w in crease in fluid concentration in field-aged rejuvenated cable samples. The mechanism of rejuvenation is discussed in which PD mechanism is prevented by plasticizing the dielectric, filling the voids, smoothing electrical stress, and sequestering free electrons in field-aged cables.
Novel switched-capacitor-based multilevel inverter topology for renewable energy

A capacitor-based multi-level inverter is presented in this paper. The proposed inverter creates 27 levels at output voltage. Capacitor charge balance is done naturally without the help of external circuits, which is one of the advantages of the proposed inverter. The proposed inverter also has an expanded topology that increases the number of levels and the voltage gain. Compared to other similar topologies, the proposed inverter has more levels, which shows the advantage of the proposed topology. Finally, the performance of the proposed topology is proved by the results obtained from the simulation using PSCAD/EMTDC software.
A Study on Applying Interleaved Switching Pattern on a Double-Input/Single-Output Zeta Converter

This paper presents behavior of a double-input/single-output (DISO) zeta converter by applying interleaved switching pattern in continuous conduction mode (CCM). This converter is implemented by combining two H-bridge cells and a conventional zeta converter, based on DISO buck-boost topologies. The converter can be utilized in either step-down or step-up modes. By adopting the interleaved switching pattern, a delay time is generated between gate pulses of H-bridge cells transistors. As a result, the converter output voltage can be varied by changing the delay time while the pulse duty cycles are kept constant. Moreover, the output voltage ripple, which is depended on the duty cycles is also kept constant. According to the chosen duty cycles and delay time, the converter can operate either in step-up or step-down modes. The output voltage ripple value of the proposed converter is less than that of both buck-boost converters, and the output voltage polarity is positive as opposed to DISO buck-boost converter. As the input filter inductor is placed in a parallel branch, adding several numbers of H-bridge cells in series as the input stage of zeta converter is very easy and does not need any changes in the formation of the converter components, which is a great advantage of utilizing zeta converter in comparison to cuk and SEPIC. Steady state analyses and calculations are done, and the proposed converter is simulated in both step-up and step-down modes.
Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings

Great concern regarding energy efficiency has led the research community to develop approaches which enhance the energy awareness by means of insightful representations. An example of intuitive energy representation is the parts-based representation provided by Non-Intrusive Load Monitoring (NILM) techniques which decompose non-measured individual loads from a single total measurement of the installation, resulting in more detailed information about how the energy is spent along the electrical system. Although there are previous works that have achieved important results on NILM, the majority of the NILM systems were only validated in residential buildings, leaving a niche for the study of energy disaggregation in non-residential buildings, which present a specific behavior. In this article, we suggest a novel fully-convolutional denoising auto-encoder architecture (FCN-dAE) as a convenient NILM system for large non-residential buildings, and it is compared, in terms of particular aspects of large buildings, to previous denoising auto-encoder approaches (dAE) using real electrical consumption from a hospital facility. Furthermore, by means of three use cases, we show that our approach provides extra helpful funcionalities for energy management tasks in large buildings, such as meter replacement, gap filling or novelty detection.