Miracle-dz/UniFAR-FTUs huggingface dataset:
Subjectivity Analysis of Movie Reviews using Machine Learning to Perform Sentence Level Classification

This research conducts a short review, comparative study and performs sentence level subjectivity analysis using three different machine learning algorithms for movie reviews. Support Vector Machine (SVM) and Naïve Bayes Model have been used for subjective/objective classification using the same data-set (rotten tomato movie reviews) to analyze the result as well as to find the best outcome. Multilayer perceptron (MLP) has been implemented later to the feasibility of it’s in differentiating subjective and objective notions. In sentence level classification, using stop words produce a better result than not using stops words. A comparative analysis has also been included to support this claim.
A Deep Learning Approach for Load Demand Forecasting of Power Systems

Power load forecasting has been an important problem in which machine learning and neural networks have been largely studied, providing effective solutions. Nevertheless, there are still discrepancies among generated load forecasts and actual demands in real life environments, where big amounts of data are continuously created. In this paper we propose an approach for evaluating existing short term load forecasting and predicting such discrepancies. Our approach is based on generating and using deep convolutional - recurrent neural networks that are able to process the data, either as time series, or as 2-D information. An experimental study is provided that illustrates the ability of this approach to improve the accuracy of forecasting in a real life scenario.
A Tensorflow Based Neural Network to Predict Drone Strikes in Pakistan

Drones are special pilotless planes to carry missiles and attack a target intelligently. Pakistan has been a target of drone attacks since 2004 as part of the US war-on-terrorism; since then many attacks have been made although Pakistan openly condemns them. In the new political setup of Pakistan and changing relationship with US, solutions are needed to counter these strikes. In this context, we propose a neural network based framework called DAF (Drone Analytics Framework) to predict the future occurrences of drones. We validate DAF with the original dataset of drone attacks in Pakistan and results show that DAF has a potential to minimize the adverse effects of these unwanted strikes by providing a prediction framework which can assist in making a counter strategy.
Exploring Lexical Differences Between Indonesian and Malay

Contemporary Indonesian and Malay share the same origin and therefore show remarkable similarities. However, as society and culture have great impacts on language, differences between these two languages could not be neglected. This paper studies the lexical differences of these two languages from three angles: words appearing only in one language. , interlingual homographs and words frequently used in one language but not in the other. , by analyzing large-scale Indonesian and Malay news texts. Using the techniques in natural language processing and statistics. , we effectively extracts the wanted words and reveal that obvious differences exist between the vocabulary of these two languages. Our work somehow fill the gap in the contrastive study of Indonesian and Malay. , as well as facilitate research on the influences of society and culture on language.
New Cyber Threat Discovery from Darknet Marketplaces

Recently, cyber threats are growing in frequency and severity. A majority of them leave their digital traces in the darknet long time before they are disclosed and cause damage publicly. In this paper, we introduce a lightweight framework for discovering new cyber threats in darknet marketplaces by filtering out newly discovered terms related to cybersecurity, aiming at warning to related authorities and leaving enough time for making patchworks or other countermeasures, to prevent possible cyber attacks or at least reduce losses. Our framework leverages the items and the text in their titles and descriptions in the darknet marketplaces, and employ machine learning and data mining techniques. Currently, our framework generates 143 warnings, of which 35 are considered as new threats, including new hacking tools (e.g. raior) and variants of existing threats (e.g. GozNym 2.0 and AlienSpy RAT 5.0).
A Statistical Evaluation of Vector-space Models for Text Categorisation

In our paper, we statistically evaluate categorisation performance of a distributed embedding technique called word2vec, and popular sparse representations, on the labelled 20-newsgroups dataset and unlabelled United States political news dataset. We deploy extensive parametric variations of vector-space models for both supervised and unsupervised topic-categorisation, relatively gauge them, and report the best results. We introduce a methodology to deploy distributed embeddings for unsupervised learning using Principal Component Analysis, which performs exceedingly well on both datasets, both by topic coherence scores, and visual interpretation of token content of topic mixtures learnt. Our motivation is primarily driven by proving that dense word embeddings can perform as good as, if not better than, traditional frequency-based vector space models. In addition, this paper demonstrates that distributed embeddings based Support Vector Machines performs best for supervised publisher categorisation on the political news dataset, whereas Term-Frequency document Frequency based Support Vector Machines outperforms supervised topic categorisation in the 20-newsgroups dataset.
On the Performance Significance of Boundary Strategies for Firefly Algorithm

In this paper, a comparison of a few selected boundary strategies are presented for popular optimization Firefly Algorithm (FA). The problem of boundary constrained optimization was already extensively studied for well-known heuristic optimization Particle Swarm Optimization (PSO). This suggesting importance for similar research for other swarm-based algorithms, like FA. Also, the simple measurement on the tendency of FA particle to violate defined boundaries is presented as well. The recent CEC 17 benchmark suite is used for the performance comparison of the methods and the results are compared and tested for statistical significance.
Adaptive Critic Design for Extreme Learning Machines applied to noisy and drifting industrial processes

Natural and manmade continuous-time dynamic systems are susceptible to adverse digressions, i.e. periods of rapid deterioration of performance. Conceptually, digressions of a given type are engendered when specific parameters of the system get into definitive combinatorial relationships. Machine Learning (ML) techniques can in principle identify in real time the acquisition (formation) of such relationships and release warnings that consequently command actions that can return the system to normality. Here a manufacturing system, namely the continuous casting process of steel making, is the object of study and both Artificial Neural Networks (ANNs) and Extreme Learning Machines (ELMs) have been shown to perform the above function. A specific characteristic of industrial systems isprocess drift; in such processes the drift is induced into the inter-parameter data relationship learnt by the ML mechanism and the latter has to adapt to the evolving relationship else lose out on accuracy. Adaptive-Critic techniques can function as enablers for the ML mechanism to adapt to this drift. In this work two such Adaptive-Critic techniques are developed, the first for ANNs and then for ELMs, which are demonstrated to work successfully for the industrial process of interest. Importantly, this is the first development in public domain of an Adaptive-Critic technique using ELMs for adaptation to industrial drift. The techniques are generic and amenable for drifting processes in any industrial environment.
Generalized Multitasking for Evolutionary Optimization of Expensive Problems

Conventional evolutionary algorithms (EAs) are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multitasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial EA (MFEA), leading to a faster convergence of the expensive problem. However, existing MFEAs do not work well in solving multitasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing MFEA is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized MFEA (G-MFEA), empirical studies have been conducted on eight multitasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed G-MFEA works more efficiently for multitasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization.
A Hybrid PLL Using Low-Power GRO-TDC for Reduced In-Band Phase Noise

In this brief, we propose a hybrid phase locked loop (PLL) which employs a coarse resolution gated ring oscillator time-to-digital converter in the digital integral (I) path and a switched RC circuit in the analog proportional (P) path, which provide lower in-band noise than a bang-bang phase detector-based hybrid PLL (BB-HPLL). We also present noise analysis of the proposed PLL which shows that in-band noise can be further reduced by increasing the integral path gain, which is contrary to conventional design of BB-HPLLs where I path gain is minimized. A prototype chip fabricated in the 65-nm CMOS achieves 13-dB improvement of in-band phase noise compared to a conventional hybrid PLL and 2.08 psrms jitter at 4.8 GHz, while consuming 2.22 mW from a 1.0-V supply.
The Stator Slot-Type Optimization of Electrical Excitation Flux-Switching Motor and Its Maximum Torque/Copper Loss Control

A novel single-slot type electrical excitation flux-switching motor (EFSM) was proposed to overcome the disadvantages of small magnetic load and complex assembly process caused by the difference between the excitation and armature slots. The double-winding is used to design the excitation winding and the armature winding coil to be laid down as a distributed winding with a pitch of 2, which simplifies the manufacturing process. The working principle of the single-slot type EFSM was analyzed by a finite element method. Furthermore, the maximum torque/copper (MTPC) loss of the single-slot type EFSM was proposed based on $i_{d}\,= \,0$ vector control strategy. At last, a single-slot type EFSM prototype was developed. The working principle and MTPC control strategy of the single-slot type EFSM were verified well by experiments.
Enhancing the Electrical Conductivity of Graphene Oxide Reduced by L-Ascorbic Acid via Microwave-Assisted Method

In the present work, L-Ascorbic Acid (LAA) has been employed as a green reducing agent for graphene oxide (GO) by simple and cost-effective microwave-assisted process. Complete GO reduction was achieved within 20 minutes using LAA and different radiation time on the low mode level of microwave radiation. The resulting sample was characterized by the four-point probe, X-ray diffraction (XRD), Fourier Transform Infrared (FTIR), Electrochemical Impedance Spectroscopy (EIS), and Scanning electron microscopy (SEM) characterizations. The SEM images of graphene oxide reduced (rGO) by LAA provides clear evidence for the formation of the sheets of graphene (rGO). The electrical conductivity of rGO was affected by time microwave radiation and the annealing process. The highest electrical conductivity of 3281 S/m was obtained at 20 minutes in low mode of microwave radiation. This conductivity still increased after annealing at a temperature of 300°C. By varying the heating duration, the optimum electrical conductivity was achieved up to 5910 S/m. Resulting in charge transfer resistance (Rct) value of 10.52 ohm from the sample rGO with low mode 20 minutes. The result presented here may pave the way for promising application in high energy storage system.
Performance Analysis of Uplink Massive MIMO in Inverse-Gamma Shadowing Channels

The promise of future 5G networks is to provide very high capacity and ultra-reliable communication. Performance analysis of wireless systems is a key towards insightful design of current and future wireless systems. In this paper, we investigate the uplink performance of a single cell multi-user massive multiple-input multiple-output (MIMO) system over inverse-Gamma shadowing channels. Assuming perfect knowledge of the channel state information (CSI), we derive closed-form expressions for outage probability, average achievable rate and bit error rate (BER). Additionally, we obtain an approximate expression of the signal-to-interference-plus-noise ratio with imperfect CSI, and we derive the corresponding outage probability expression. The obtained expressions are assessed through numerous simulations and numerical examples that confirm the accuracy of our analysis. Furthermore, the results give a clear view on the impact of various parameter settings (including fading severity, transmit power, and the number of base station antennas) on the system’s performance.
Poster: Mix-Zones Everywhere: A Dynamic Cooperative Location Privacy Protection Scheme

Inter-vehicle communications disclose rich information about vehicle whereabouts. Pseudonymous authentication secures communication while enhancing user privacy. To enhance location privacy, cryptographic mix-zones are proposed where vehicles can covertly update their credentials. But, the resilience of such schemes against linking attacks highly depends on the geometry of the mix-zones, mobility patterns, vehicle density, and arrival rates. In this poster, we propose “mix-zones everywhere”, a cooperative location privacy protection scheme to mitigate linking attacks during pseudonym transition. Time-aligned pseudonyms are issued for all vehicles to facilitate synchronous pseudonym updates. Our scheme thwarts Sybil-based misbehavior, strongly maintains user privacy in the presence of honest-but-curious system entities, and is resilient against misbehaving insiders.
Semantic Video Recommendation System Based on Video Viewers Impression from Emotion Detection

Huge video data that available at many video hosting providers create a new problem. One of them is a problem in video discovery system. One of many solutions that solve the problem is video search function. But, video search function cannot provide best user experience despite using a good keyword. This problem encourages many video hosting service providers to get deeper insight of the video data that they have and used them to give the best experience to the user. A video service provider such as Youtube will perform video analysis based on the internal video contents such as colours, textures, shapes, music signal, video title provided by users and other features that exist in video. The result of this analysis was used to understand its video content and also user preference on a video. At the last, an insight about user personalities can be known and video provider can personalize video for each user to give them best user experience. With technological developments in Machine Learning and Computer Vision technology, video analysis can be done based on other things beyond the video. It was the audience's impression of a video. An analysis of audience impressions in real-time was expected to get insights of a video content by a real feeling of the viewers. By those insights, a new video recommendation system can be built to deliver a new experience for the user in video discovery system. This system is intended to be able to deliver variative videos which match their personal emotion at the time. Based on this research results, 65% users love more than half in video list that provided by impression-based recommender system.
Swarm Clustering Algorithm: Let the Particles Fly for a while

Swarm Intelligence (SI) and Evolutionary Algorithms (EAs) have been widely used for cluster analysis of spatial data. However, in most existing SI, particles are encoded to represent the centers of clusters. In this paper, inspired by Particle Swarm optimization (PSO), a novel Swarm Clustering Algorithm (SCA) is proposed, which has the potential ability to deal with the data of the arbitrary number, shape and size of clusters. In SCA, a particle is a point in the dataset under cluster analysis. Thus, the number of particles in the swarm is equal to the size of the dataset. All particles interact dynamically with similar particles, and fly to the denser areas to form clusters. The experimental results show that our algorithm is effective.
The Effect of Sensor Position Uncertainties in Passive Target Tracking

We investigate the effect of sensor position uncertainties in passive target tracking in this paper, using the noisy time difference of arrival measurements. The sensor position uncertainty is considered to be a random noise in the TDOA measurement model. Because the TDOA measurement function is nonlinear to the unknown true sensor positions, we develop an augmented cubature Kalman filter method to deal with it. In comparison with the classical standard cubature Kalman filtering method, the proposed method suffers from higher computational complexity, however, its tracking performance is considerably improved. The performance of the proposed approach is verified through numerical Monte Carlo simulations.
Indonesian's Dangdut Music Classification Based on Audio Features

The uniqueness of modern dangdut music today is in the beat and melody of music that is relatively faster like techno dance music, and the progressive melody arrangement from synthesizer keyboard. In this paper, we will extract audio features from music, and then used that features for classification based on machine learning methods. Support Vector Machine (SVM) was used in this study. In this paper's experiment we use 32 audio music files, consisting of 16 for dangdut music and 16 for techno dance music. The results of this testing get varying accuracy levels, between 80% to 90% for each music audio file that has been successfully classified.
A Frequency-Limited Adaptive Controller for Underwater Vehicle-Manipulator Systems Under Large Wave Disturbances

Standard adaptive control approaches may not be able to sufficiently stabilize underwater vehicle-manipulator systems (UVMSs) when wave disturbances are large, leading to high-frequency oscillations of large amplitude in its dynamic model parameters. Such parameters bring about undesired oscillations in the vehicle body control and state. This paper extends a frequency-limited adaptive control approach to the vehicle body. An auxiliary model is obtained from the approximated model through a low-pass filter and is used to reduce the problematic oscillations. The resultant stable vehicle body is a necessary premise for successful end-effector tracking. In addition, this paper proposes a sufficient condition of the control gains for guaranteed asymptotical stability of the controlled robotic system. Numerical simulations have demonstrated the effectiveness of the presented approach, compared to the standard adaptive control.
A CPG-Inspired Assist-As-Needed Controller for an Upper-Limb Rehabilitation Robot

Robot assisted rehabilitation training is promising to be an effective therapy for post stroke patients, while it's still a challenge for robot control to achieve the objective of “Assist As Needed” (AAN) This paper introduces a new method to implement AAN training for repetitive upper limb rehabilitation training in three steps: (1) the subject's motion frequency and phase are estimated adaptively using the artificial central pattern generator (CPG) algorithm, (2) the robot reference trajectory is generated based on the minimum-jerk principle and the patient's instantaneous motion phase, and (3) the robot provides assistance according to errors between the reference and actual trajectories based on impedance control method. This method takes advantage of the repetitive feature of rehabilitation training, and generates the training trajectory on-line by considering both the subject's active motion intention and human normal motion requirements. Besides, the algorithm does not need a complex human-robot interaction model and is easy to implement on multi-DOF rehabilitation robots.
SCALP: Self-configurable 3-D Cellular Adaptive Platform

Parallel computation has appeared as the most promising technique to circumvent the limitations imposed by power consumption in order to continue increasing computation power, making thus manycore architectures a promising computer organization approach. Interconnecting and coordinating such high amount of computation nodes in an efficient manner is a hot research topic, several approaches to Network-on-chip architectures propose solutions for this. This paper presents a 3D multi-FPGA hardware platform permitting to prototype 3D NoC architectures with dynamic topologies. More precisely, we intend to use it to prototype self-adaptive and self-organizing hardware architectures in which the computation performed by a node and the interconnections between these nodes can be dynamically modified, being these modifications triggered by the platform itself. This paper presents the overall hardware organization and gives some hints about how to use it.
Human Pose Estimation in Real Traffic Scenes

This work evaluates human pose estimation from a moving vehicle for use in road traffic applications such as automated or autonomous driving. The lack of annotated human pose datasets is a challenge for research on autonomous driving with sufficient safety for pedestrians and cyclists. Hence, a dedicated dataset was created, which was recorded in real traffic and contains both pedestrians and cyclists. The dataset represents diverse conditions in road traffic and different appearances of people and allows for a realistic evaluation of the performance of human pose estimation. In order to obtain ground truth, the data were labeled manually and $3D$ poses were measured by means of an intersection equipped with a wide angle stereo camera system. One recent method for $2D$ pose estimation and another approach for $3D$ pose estimation from literature are investigated. As shown by this research, $2D$ poses estimated in traffic scenes achieve a similar accuracy as state-of-the-art results obtained on other datasets. Moreover, $3D$ pose estimation based on single images outperforms a naive distance measurement of individual joints in disparity maps obtained by a stereo camera and the reached accuracy suggests that the use of $3D$ pose estimation for intention detection and trajectory forecast is feasible. Overall, a dependency of the results on the distance of the respective person and less reliable results for cyclists compared to pedestrians were observed.