Miracle-dz/UniFAR-FTUs huggingface dataset:
Regression based robotic grasp detection using Deep learning and Autoencoders

Solving Intelligent object grasping problem in an unstructured environment by a robot manipulator is a challenging task. To grasp an object, the robot should know the position of the object in an environment, decide how and where the hand gripper should be moved and then finally determine how the object is to be held. We have proposed a Hybrid architecture for detecting optimal robotic grasp. The algorithms till now have successfully been applied to RGB images for training and for testing as well. A new hybrid architecture presented in this paper is as follows First, a convolutional neural network (ResNet-50) pre-trained by transfer learning performs regression to grasping rectangles, which will generate multiple rectangles on a single image. Second, an Auto-Encoder would predict quality score for all rectangles regressed by convolutional neural network and choose an optimal rectangle among them. Cornell Grasping Dataset has been used for training and testing purposes but since the dataset is very small, to generalize the model, augmentation has been performed on the images to generate more images. The hybrid architecture gives accuracy of 75.34% on object-wise split and 75.81% on image-wise split.
Robust Anomaly Detection via Sequential Ensemble Learning

Abstract —In this paper, we propose a robust anomaly detection method to detect anomalies in the observed data via a sequential ensemble based approach in an unsupervised framework. The proposed model, ﬁrst Isolation Forest algorithm distills anomalies from normal instances. Later, the k-Nearest Neighbor (KNN) algorithm estimates the density of the data and scores the anomaliness of the future samples. Although KNN is not robust to the data containing anomalies, we show that the KNN algorithm achieves robustness via the proposed unsupervised distilling method via the IForest algorithm. Our method is quickly applicable to the real world anomaly detection problems since it does not require supervised distilling or data labeling. Through comprehensive set of experiments, we show that the proposed model signiﬁcantly outperforms the commonly used anomaly detection algorithms in the literature.
Advances in hydrogen sulphide utilisation: phase transfer catalysed selective reduction of nitronaphthalene

The current study demonstrated the selective reduction of 1-nitronaphthalene (1-NN) by hydrogen sulphide (H2S) absorbed in aqueous N-methyldiethanolamine (MDEA), which is a commonly encountered process in amine treating unit (ATU) of petroleum refinery. The modified Zinin reduction has been carried out using tetra-n-butylphosphonium bromide (TBPB) as phase transfer catalyst under Liquid–Liquid (L–L) biphasic mode. The selectivity of product 1-aminonapthalene was found to be 100% and the reaction was kinetically controlled with the activation energy of 20.77 kJ mol−1. The influence of the process parameters like stirring speed, concentration of 1-NN, concentration of aqueous sulphide, concentration of catalyst, MDEA concentration, elemental sulphur loading at different reaction time on the reactant conversion and the reaction rate of 1-NN were studied for establishment of the reaction mechanism. The kinetic model and mechanism of complex L–L phase transfer catalytic process have been developed and then the model has been validated against the experimental data. This approach of reducing 1-NN by H2S-laden MDEA can substitute the energy-expensive Claus process, which gives no other product than sulphur.
IntellCache: An Intelligent Web Caching Scheme for Multimedia Contents

The traditional reactive web caching system is getting less popular day by day due to its inefficiency in handling the overwhelming requests for multimedia content. An intelligent web caching system intends to take optimal cache decisions by predicting future popular contents (FPC) proactively. In recent years, a few approaches have proposed some intelligent caching system where they were concerned about proactive caching. Those works intensified the importance of FPC prediction using the prediction models. However, only FPC prediction may not help to get the optimal solution in every scenario. In this paper, a technique named IntellCache has been proposed that increases the caching efficiency by taking a cache decision i.e. content storing decision before storing the predicted FPC. Different deep learning models such as- multilayer perceptron (MLP), Long short-term memory (LSTM) of Recurrent Neural Network (RNN) and ConvLSTM a combination of LSTM and Convolutional Neural Network (CNN) are compared to identify the most efficient model for FPC. The information on the contents of 18 years from the MovieLens data repository has been mined to evaluate the proposed approach. Results show that this proposed scheme outperforms previous solutions by achieving a higher cache hit ratio and lower average delay and thus, ensures users' satisfaction.
Designing Recommendations and Road Map of Governance for Quality Management System of Online SKCK Based on Information Security Using ISO 9001: 2015 and ISO 27001: 2013 (Case Study: Ditintelkam Polda ABC)

The Decree of the Chief of the Indonesian National Police No. Pol. SKEP / 360 / VI / 2005 regulates The implementation of the online SKCK (Surat Keterangan Catatan Kepolisian, an Indonesian police certificate of good conduct) is a concrete manifestation of the National Police Grand Strategy 2005-2025 Strive For Excellent regarding the strategic plan of the National Police. The decree explained that each area of the Police’s duties must implement the use of technology and efforts to achieve superior public services (Strive for Excellence). Ditintelkam Polda ABC is responsible for and supervises every online SKCK implementation process guided by the Chief of Police Regulation Number 18 of 2014 and Standard Operating Procedure (SOP) regarding online SKCK payments which only regulates the procedures for issuing and paying online SKCK. In that regard, it is necessary to design recommendations and road map for online SKCK service quality governance at the ABC Police Directorate General of Information Security using ISO 9001: 2015 (QMS) and ISO 27001: 2013 (SMKI). This research used the six stages in the Design Science Research Methodology (DSRM) and PDCA (Plan, Do, Check Act) concept for the design process and formulation of governance recommendation. The research produced a five-year road map of governance for information security-based online SKCK services at Ditintelkam Polda ABC.
Scavenging of Priority Organic Pollutants from Aqueous Waste using Granular Activated Carbon

Compliances with stringent effluent discharge limits imposed by environmental protection agencies (EPA) and the most economic way of achieving it without loss of production has led to continued refinement, recognition and development of promising wastewater treatment technologies. Many organic compounds present in industrial and domestic wastewaters are carcinogenic in nature. Removal of these organic compounds from wastewater has become a great challenge to wastewater treatment technologies, as many of them are non-biodegradable in nature. Adsorption on granular activated carbon (GAC) has emerged an efficient and economically viable technology for removal of final traces of a broad spectrum of toxic organic compounds from domestic and industrial wastewater. In the present investigation adsorption of some priority organic pollutants, namely phenol, o-cresol, p-nitrophenol, m-methoxyphenol, benzoic acid and salicylic acid on granular activated carbon, was studied in a batch system at laboratory scale. Experiments were carried out to determine adsorption isotherms and kinetics for adsorbate when present in aqueous solutions as single, bi- and tri-solute systems. The commercially available bituminous coal based granular activated carbon Filtrasorb 300 (F-300) was used as adsorbent. The results indicate that p-nitrophenol is most strongly adsorbed as compared to other phenols studied. Aqueous phase solubility of the adsorbate plays a deciding role in multi-component systems as more hydrophobic p-nitrophenol adsorbs to a greater extent than less hydrophobic phenol, o-cresol and m-methoxyphenol. The preferential adsorption of strongly adsorbable solute over a weakly adsorbable one has been observed, as the solutes are competing for the available surface area of the adsorbent for adsorption.
Weak Ground Truth Determination of Continuous Human-Rated Data

The article presents a novel weak ground truth (WGT) determination procedure on continuous human-rated data. The notion of WGT is essential in cases where there is no direct empirical evidence for an observed construct and human annotations provide the most reliable means for determining the ground truth. The core idea behind the proposed procedure is to transform the ratings to reduce rater bias, maximize inter-rater agreement, and improve WGT. The procedure was evaluated on two behavioral datasets containing continuous annotations of several expressive dimensions. The results show that the procedure improves the size of WGT data by removing the disagreement originating from rater-specific distortions, such as rater mean and scaling bias. The entropy of residuals decreases after WGT optimization, meaning that more relevant information is retained. The average improvement of WGT data size is between 10.1 and 20.9 percentage points, depending on the respective dimension. However, in cases where the rater bias is small, the procedure does not substantially modify WGT. This indicates that the proposed optimization only removes rater biases derived from rater-specific distortions, while retaining and improving valid WGT. The proposed procedure is generalizable on any type and size of continuous or discrete numerical data where multiple raters are involved.
Highlighting of Lecture Video Closed Captions

The main purpose of this study is to automatically highlight important regions of lecture video subtitles. Even though watching videos is an effective way of learning, the main disadvantage of video-based education is limited interaction between the learner and the video. With the developed system, important regions that are automatically determined in lecture subtitles will be highlighted with the aim of increasing the learner’s attention to these regions. In this paper ﬁrst the lecture videos are converted into text by using an automatic speech recognition system. Then continuous space representations for sentences or word sequences in the transcriptions are generated using Bidirectional Encoder Representations from Transformers (BERT). Important regions of the subtitles are selected using a clustering method based on the similarity of these representations. The developed system is applied to the lecture videos and it is found that using word sequence representations in determining the important regions of subtitles gives higher performance than using sentence representations. This result is encouraging in terms of automatic highlighting of speech recognition outputs where sentence boundaries are not deﬁned explicitly.
Investigation of Effects of PA Non-linearities on the Fully Digital Massive MIMO Systems

In this study, effects of power amplifier (PA) nonlinearities on the performance of fully digital massive MIMO systems are studied. Fully digital systems have been studied extensively in the literature. However, research on the effects of PA non-linearities is limited to assessment of out-of-band (OOB) radiation pattern of the received signal. In this paper, we introduce another metric to evaluate the performance of the digital MIMO systems, which are subject to PA non-linearities, in terms of achievable information rate (AIR). In addition, we compare different techniques which can be used to compensate the non-linear effects either in the transmitter or the receiver side by considering both OOB radiation and AIR. Keywords—Non-linear power amplifiers, Massive MIMO.
Intelligent Trajectory Design for Secure Full- Duplex MIMO-UAV Relaying Against Active Eavesdroppers: A Model-Free Reinforcement Learning Approach

Unmanned aerial vehicle (UAV) assisted wireless communication has recently been recognized as an inevitably promising component of future wireless networks. Particularly, UAVs can be utilized as relays to establish or improve network connectivity thanks to their flexible mobility and likely line-of-sight channel conditions. However, this gives rise to more harmful security issues due to potential adversaries, particularly active eavesdroppers. To combat active eavesdroppers, we propose an artificial-noise beamforming based secure transmission scheme for a full-duplex UAV relaying scenario. In the considered scheme, we investigate a UAV-relay equipped with multiple antennas to securely serve multiple ground users in the presence of randomly located active eavesdroppers. We formulate a novel average system secrecy rate (ASSR) maximization problem under some quality of service (QoS) and mission time constraints. Since the ASSR optimization problem is too hard to solve by conventional optimization methods due to the unavailability of the environment’s dynamics and complex model, we develop some model-free reinforcement learning-based algorithms, i.e., Q-learning, SARSA, Expected SARSA, Double Q-learning, and SARSA(<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>), to efficiently solve the problem without substantial UAV-network data exchange. Using the proposed algorithms, we can maximize ASSR via finding an optimal UAV trajectory and proper resource allocation. Simulation results demonstrate that all the proposed learning-based algorithms can train the UAV-relay to learn the environment by iterative interactions, thus finding an optimal trajectory, intelligently. Particularly, we find that SARSA(<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>) based proposed algorithm with <inline-formula> <tex-math notation="LaTeX">$\lambda =0.1$ </tex-math></inline-formula> outperforms the others in terms of the ASSR.
Safety Score: A Quantitative Approach to Guiding Safety-Aware Autonomous Vehicle Computing System Design

High automated vehicles rely on the computing system in the car to understand the environment and make driving decisions. Therefore, computing system design is essential for ensuring the driving safety. However, to our knowledge, no clear guideline exists so far regarding how to guide the safety-aware autonomous vehicle (AV) computing system design. To understand the safety requirement of AV computing system, we performed a field study by operating industrial Level-4 AV fleets in multiple locations for three months. The field study indicates that traditional computing system performance metrics, such as tail latency, average latency, maximum latency, and timeout, cannot fully satisfy the safety requirement for AV computing system design. To address this issue, we propose the “safety score” as a primary metric for measuring the level of safety in AV computing system design.
Continual Learning with Sparse Progressive Neural Networks

Human brain effectively integrates prior knowledge to new skills by transferring experience across tasks without suffering from catastrophic forgetting. In this study, to continuously learn a visual classiﬁcation task sequence (PermutedMNIST), we employed a neural network model with lateral connections, sparse group Least Absolute Shrinkage And Selection Operator (LASSO) regularization and projection regularization to decrease feature redundancy. We show that encouraging feature novelty on progressive neural networks (PNN) prevents major performance decrease on sparsiﬁcation, sparsiﬁcation of a progressive neural network produces fair results and decreases the number of learned task-speciﬁc parameters on novel tasks.
Siamese Networks for Online Map Validation in Autonomous Driving

Many state-of-the-art autonomous driving systems require prior knowledge in the form of high definition map data, which provide information about the road and its environment. These data can be erroneous or outdated due to the frequency of updates and because of short- or long-term changes in the road. Any autonomous system that relies on such potentially invalid information needs to detect deviations from the map while driving, by the use of its own, or external sensors, a problem often called online map validation. This paper proposes a novel approach to online map validation, where map data are compared against the readings of onboard sensor measurements by a deep learning classifier. This classifier is based on the Siamese Network architecture, an architecture known from similarity learning. The classifier is trained on data from real world test drives and evaluated on both correct maps and incorrect maps as found in construction sites. Results show that the classifier reaches an F1 score of 89.1 %, whereby misclassified scenes mostly stem from the limited variability in the training data and the lacking evidence of construction sites in the input data.
Connectivity Preservation and Collision Avoidance of Multi-Unmanned Surface Vehicles Via Adaptive Sliding Control

This paper investigates the connectivity preservation and collision avoidance problems of a multi-unmanned surface vehicle (USV) system. In order to achieve these two goals more effectively, an improved artificial potential function (APF) is designed. For the sake of solving the issue of nonlinear disturbance, a fuzzy sliding mode control method combined with fuzzy radial basis function neural network (Fuzzy-RBFNN) is introduced. Then by Lyapunov method, it can be proved that the system which employs the control scheme proposed in this paper is stable. In addition, the theoretical deduction proves that the USV can track a given ideal signal, and the connectivity preservation and collision avoidance of the multi-USV system can be achieved during formation. Finally, a multi-USV system model including four USVs is established, and the control strategy designed in this paper is adopted to conduct simulation experiments with MATLAB. The results testify that this control project is valid.
Proceedings of the International Workshops ABC-Salt (II) and HiTAC 2011

S OF ORAL CONTRIBUTIONS 2 International workshop on Actinide Brine Chemistry in a Salt-Based Repository 7 – 8 Nov 2011 List of contributions WIPP Update and Proposed Salt-Chemistry Studies A. van Luik 21 WIPP Two-Year Investigation Focus and Underground Tests R. Patterson and D.T. Reed 23 Preliminary Safety Analysis Gorleben G. Bracke 25 THEREDA – Thermodynamic Reference Database H. C. Moog 27 Sorel cement as geotechnical barrier in salt formations D. Freyer 29 Thermodynamics of long-term metastable magnesium (chloro) hydroxo carbonates at 25°C C. Bube, M. Altmaier, V. Metz, V. Neck†, D. Schild, B. Kienzler 31 Gas Generation due to Anoxic Corrosion of Steel and Lead in Na-Cl ± Mg Dominated Brines G. T. Roselle 33 Thermodynamic Properties of the Na-Ra-Cl-SO4-H2O SystemEstimating Pitzer Parameters for RaCl2 Y. O. Rosenberg, V. Metz and J. Ganor 35 An Overview of Research on Microbial Interactions in the Waste Isolation Pilot Plant J. Swanson, D. Ams, K. Simmons, H. Khaing, D.T. Reed 37 Microorganisms in potential nuclear waste disposal host rocks A. Geissler, H. Moll, V. Bachvarova, L. Lutke, S. Selenska-Pobell, G. Bernhard 39 Can we use ultramicroelectrodes to perform electrochemistry in brines ? M. Perdicakis, A. Chebil, M. Etienne 41 Redox chemistry of Np(V/VI) under hyperalkaline conditions: aqueous speciation, solubility and chemical analogies with Pu X. Gaona, D. Fellhauer, K. Dardenne, J. Rothe, M. Altmaier 43 Actinide Speciation and Oxidation-state Distribution in the WIPP D.T. Reed, M. Borkowski, M. Richmann, J.-F. Lucchini, J. Swanson and D. Ams 45 Trivalent cations retention upon brucite precipitation N. Finck, K. Dardenne 47 Solubility and speciation of Np in dilute to concentrated CaCl2 solutions under different redox conditions D. Fellhauer, M. Altmaier, V. Neck, J. Runke, J. Lutzenkirchen, X. Gaona, Th. Fanghanel 49
Classification of Radar Platform Motion Types

The detection of an emitting radar and accurate tracking of the associated platform direction are the most important goals of Electronic warfare (EW) systems. EW systems use Kalman filter in order to compansate the measurement errors that occur during instantaneous direction measurements of detected radars. The compatability of the Kalman filter prediction model and parameters with the platform motion type improves the quality of the values shown on the screen. In this paper, it is aimed to determine the platform types associated with the radars using radar parameters that are measured by EW systems. First, an efficient pre-processing method including quantization for interval values and grouping of class values is applied to improve classification performance. Then, Multi-task Learning (MTL) neural network and Support Vector Machine (SVM) techniques are applied to the problem. It is observed that SVM technique outperforms MTL technique. It is evaluated that the result of the classification process can be utilized for selecting the type and the parameters of the filter used for tracking.
Triaxial accelerometer calibration using an extended two-step methodology

Accelerometers are versatile devices employed in a variety of areas. However, depending on the application, requirements for accuracy and reliability vary substantially. For instance, stand-alone inertial navigation requires high quality sensors, while integrated navigation can be implemented using inferior - but less costly - accelerometers, especially MEMSs (Microeletromechanical Systems). Nevertheless, such sensors can often have the performance improved by a procedure known as calibration, which estimates and compensate for their systematic errors. As main contribution of this study, we adapt and implement a calibration methodology originally designed for magnetometers in a consumer-grade, MEMS, triaxial accelerometer. The technique, called extended two-step, is based on a pseudo least squares estimation, and is suitable for in-field implementation. Since it is both simple and efficient, it is worth using the methodology for accelerometers as well. This is only possible because, instead of using the Earth's local magnetic field vector, local gravity is employed as reference. In this version, the sensor must be rotated to multiple orientations and kept static, while data is acquired. Biases, scale factors and the nonorthogonalities between the sensors' axes are estimated. Calibration with simulated and real data is conducted in order to validate the adaptation.