Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of sensing systems for improving surgical grasper performance

Minimally invasive techniques play a vital and increasing role in modern surgery. In these procedures, surgical graspers are essential in replacing the surgeon’s fingertips as the main manipulator of delicate soft tissues. Current graspers lack haptic feedback, restricting the surgeon to visual feedback. Studies show that this can frequently lead to morbidity or task errors due to inappropriate application of force. Existing research has sought to address these concerns and improve the safety and performance of grasping through the provision of haptic feedback to the surgeon. However, an effective method of grasping task optimisation has not been found. This thesis explores new sensing approaches intended to reduce errors when manipulating soft tissues, and presents a novel tactile sensor designed for deployment in the grasper jaw. The requirements were first established through discussion with clinical partners and a literature review. This resulted in a conceptual approach to use multi-axis tactile sensing within the grasper jaw as a potential novel solution. As a foundation to the research, a study was conducted using instrumented graspers to investigate the characteristics of grasp force employed by surgeons of varying skill levels. The prevention of tissue slip was identified as a key method in the prevention of grasper misuse, preventing both abrasion through slip and crush damage. To detect this phenomena, a novel method was proposed based on an inductive pressure sensing system. To investigate the efficacy of this technique, experimental and computational modelling investigations were conducted. Computational models were used to better understand the transducer mechanisms, to optimise sensor geometry and to evaluate performance in slip detection. Prototype sensors were then fabricated and experimentally evaluated for their ultimate use in slip detection within a surgical grasper. The work concludes by considering future challenges to clinical translation and additional opportunities for this research in different domains.
A Reference Model for Product Data Profiling in Retail ERP Systems

Due to the high volume of data and the increasing automation in retail, more and more companies are dealing with procedures to improve the quality of product data. A promising approach is the use of machine learning methods that support the user in master data management. The development of such procedures demands error-free training data. This means that product data must be cleaned and labelled which requires extensive data profiling. For typical retail company data bases with usually complex and convoluted structures this exploration step can take a huge and expensive amount of time. In order to speed up this process we present a reference model and best practices for the systematic and efficient profiling and exploration of product data.
Effect Regulated Projection of Robot’s Action Space for Production and Prediction of Manipulation Primitives Through Learning Progress and Predictability-Based Exploration

In this article, we propose an effective action parameter exploration mechanism that enables efficient discovery of robot actions through interacting with objects in a simulated table-top environment. For this, the robot organizes its action parameter space based on the generated effects in the environment and learns forward models for predicting consequences of its actions. Following the intrinsic motivation approach, the robot samples the action parameters from the regions that are expected to yield high learning progress (LP). In addition to the LP-based action sampling, our method uses a novel parameter space organization scheme to form regions that naturally correspond to qualitatively different action classes, which might be also called action primitives. The proposed method enabled the robot to discover a number of lateralized movement primitives and to acquire the capability of predicting the consequences of these primitives. Furthermore, our results suggest the reasons behind the earlier development of grasp compared to push action in infants. Finally, our findings show some parallels with data from infant development where correspondence between action production and prediction is observed.
Towards Profit Optimization During Online Participant Selection in Compressive Mobile Crowdsensing

A mobile crowdsensing (MCS) platform motivates employing participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. In this article, we improve the profit via the data reconstruction method, which brings new challenges, because it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In particular, two Profit-driven Online Participant Selection (POPS) problems under different situations are studied in our work: (1) for S-POPS, the sensing cost of the different parts within the target area is the Same. Two mechanisms are designed to tackle this problem, including the ProSC and ProSC+. An exponential-based quality estimation method and a repetitive cross-validation algorithm are combined in the former mechanism, and the spatial distribution of selected participants are further discussed in the latter mechanism; (2) for V-POPS, the sensing cost of different parts within the target area is Various, which makes it the NP-hard problem. A heuristic mechanism called ProSCx is proposed to solve this problem, where the searching space is narrowed and both the participant quantity and distribution are optimized in each slot. Finally, we conduct comprehensive evaluations based on the real-world datasets. The experimental results demonstrate that our proposed mechanisms are more effective and efficient than baselines, selecting the participants with a larger profit for the platform.
Joint Caching and Routing in Congestible Networks of Arbitrary Topology

In-network caching constitutes a promising approach to reduce traffic loads and alleviate congestion in both wired and wireless networks. In this article, we study the joint caching and routing problem in congestible networks of arbitrary topology (JoCRAT) as a generalization of previous efforts in this particular field. We show that JoCRAT extends many previous problems in the caching literature that are intractable even with specific topologies and/or assumed unlimited bandwidth of communications. To handle this significant but challenging problem, we develop a novel approximation algorithm with guaranteed performance bound based on a randomized rounding technique. Evaluation results demonstrate that our proposed algorithm achieves near-optimal performance over a broad array of synthetic and real networks, while significantly outperforming the state-of-the-art methods.
Short Term Load Forecasting Using Recurrent and Feedforward Neural Networks

Accurate load forecasting greatly influences energy production planning. If the demand forecast is inaccurate this could lead to blackouts or waste of precious energy. This paper compares innovative neural networks on the basis of accuracy. The first is a feedforward neural network (FFNN). Next we look at different models of Recurrent Neural Networks (RNN) specifically long short term Memory (LSTM). Finally we explore combining the two approaches into a hybrid network. We predict load with an hourly granularity also known as short term load forecasting (STLF). We apply these approaches to real world data sets from the Energy Information Administration (EIA) over a period of about 4 years. Our approach focuses on the integration of historical time features from the last hour, day, month, etc. with the inclusion of RNN methods. We show that the included time features reduce the overall error and increase generalizability. We combine this with features such as weather, cyclical time features, cloud cover, and the day of the year to further reduce the error. We then compare the approaches to reveal that the correct handling of time features significantly improves the model by learning hidden
An Insight in to Privacy Preserving Data Mining Methods

— Recent advances in information, communications, data mining, and security technologies have gave rise to a new era of research, known as Privacy Preserving Data Mining (PPDM). Several data mining algorithms, incorporating privacy preserving mechanisms, have been developed that allow one to extract relevant knowledge from large amount of data, while hide sensitive data or information from disclosure or inference. PPDM is a new attempt; thus, several research questions have often being asked. For instance: (1) How to measure the performance of these algorithms? (2) How effective of these algorithms in terms of privacy preserving? (3) Will they impact the accuracy of data mining results? and (4) Which one can better protect sensitive information? To help answer these questions, we conduct an extensive review on literature. We present a classification scheme, adopted from early studies, to guide the review process. Finally, we share directions for future research.
Comparison study on secrecy probability of AF-NOMA and AF-OMA networks

The strictly positive secrecy capacity (SPSC) performance is examined in a new design of non-orthogonal multiple access (NOMA) considered in this study. This system model employs Amplify-and-Forward (AF) relaying scheme to serve far users. In this scheme, a transmitter sends confidential signal to far users. It can be raised falling performance in the presence of an external eavesdropper in such NOMA system. With regard to orthogonal multiple access (OMA), performance of NOMA system model is compared. In particular, tradeoff the SPSC performance and transmit SNR is examined. In this study, the SPSC is evaluated as the secrecy metric to limit impacts of the practical passive eavesdropper in real scenario. It is confirmed that the secrecy performance of NOMA is significant lower than OMA due to related parameters characterization in NOMA, and it should be controlled by varying related coefficients. As main results, both of NOMA and OMA against to impact of eavesdropper is studied in terms of analytically result and numerically result.
Evaluation of Cyber Defense Exercises Using Visual Analytics Process

This Innovative Practice Full Paper addresses modern cyber ranges which represent unified platforms that offer efficient organization of complex hands-on exercises where participants can train their cybersecurity skills. However, the functionality targets mostly learners who are the primary users. Support of organizers performing analytic and evaluation tasks is weak and ad-hoc. It makes harder to improve the quality of an exercise, particularly its impact on learners. In this paper, we present an application of a well-structured visual analytics process to the organization of cyber exercises. We illustrate that the classification derived from the adoption of the visual analytics process helps to clarify and formalize analytical tasks of educators and enables their systematic support in cyber ranges. We demonstrate an application of our approach on a particular series of eight exercises we have organized in last three years. We believe the presented approach is beneficial for anyone involved in preparation and execution of any complex exercise.
Task Scheduling in Graphic Processing Units Heterogeneous With Density of The Graph

An efficient scheduling algorithm is significant to achieve high performance in heterogeneous consisted by the central processing units (CPU) and the graphic processing unit (GPU). However, most of the scheduling algorithms are not suitable to meet the present demands of the heterogeneous. According to the characteristics of the direction acyclic graph, the direction acyclic graph nodes density list scheduling algorithm (DAG-NDLS) was proposed. The algorithm had two important phase, the first phase was task prioritizing, selected the ready task with highest priority, defined by the summation of the node density and the transmission between the node and child nodes, and the second phase was processor selection, selected the processors for the tasks with the earliest finish time minimum. In the end, compared the performances of the proposed algorithm with the heterogeneous earlier finish time algorithm and the high performance task scheduling algorithm, the results of the proposed algorithm were the best in case of the efficiency and utilization.
Massive MIMO Detection Techniques: A Survey

Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.
Pattern Attributes Extraction of Flight Data Based on Rough Set

This thesis proposes a method of extracting pattern attributes of flight data based on rough set theory, which is aimed to solve thedifficulty in constructing knowledge base using the expert system. The method uses self-organizing map to get the discrete attributes first, thensimplifies the information through genetic algorithm, and finally uses prior knowledge of domain experts to process the simplified information.Results of experiment prove the feasibility of this method.
A Comparative Study of Requirements Engineering Process Model

In current digital era, software evolution is continuously put into practice by practitioners in order to meet up the changing stakeholder’s requirements. It has been investigated that for success of any software system, requirements engineering is very critical phase. This brings an evolution in the scope of the requirements engineering process, along with novel challenges of managing the requirements. Requirements Engineering is considered as a collection of processes that functions on various levels, which incorporate at organizational, product and project level. This paper presents a comparative study of different requirements engineering process models. The objective of the study is to find the vital aspects that contribute to RE process model selection from the viewpoint of practitioners in the business. Several possible future directions for RE process in software engineering are presented, including application specific elicitation techniques, requirements pre-processing, requirements prioritization etc. Keywords: Requirements Elicitation, Requirements Engineering, Requirement Engineering Process, Requirement Engineering Practice, Research Framework, etc.
Cloud-Fog based framework for drought prediction and forecasting using artificial neural network and genetic algorithm

ABSTRACT Drought is one of the most recurrent natural disasters with cataclysmic effects on water budget, crop production, economic progression and public health. These consequences are magnified by the climate change leading to more intense drought conditions. A number of drought indices have been presented to calibrate the drought severity with its own strengths and limitations. Many of them are region-specific and unable to exhibit the alterations in significant drought inducing elements. Internet of Things (IoT) is well-suited for continuous monitoring, collection and analysis of different environmental phenomena. The dimensionality of the data collected about drought inducing attributes temperature, humidity, precipitation, evapotranspiration, groundwater, soil moisture at different depths, streamflow and season is reduced using PCA (Principal Component Analysis) at fog layer. Cloud layer estimates the drought severity level using Artificial Neural Network (ANN) whose parameters are optimised with Genetic Algorithm (GA) to get more accurate system and ARIMA method is used to forecast the drought for different time frames. Experimentation done on data collected from government websites shows that proposed system performs well in terms of accuracy, sensitivity, specificity, precision and F-measure with values 95.03%, 90.6%, 96.73%, 91.42% and 91.01%.
Recurrent and Non-recurrent Congestion Based Gridlock Detection on Chula-SSS Urban Road Network

Ei Ei Mon1, Hideya Ochiai 2, Chaiyachet Saivichit 1, Chaodit Aswakul1 1 Wireless Network and Future Internet Research Unit, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Thailand 6071456921@student.chula.ac.th, Chaiyachet.S@chula.ac.th, chaodit.a@chula.ac.th 2 Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Japan jo2lxq@hongo.wide. ad.jp
DEVELOPMENT OF ENCRYPTION AND DECRYPTION TECHNIQUE TO SECURE THE CONFIDENTIAL DATA

Cryptography is the science of converting one form of data into another form, to provide security. Converting plain text to cipher text is encryption and converting cipher text to plain text is decryption. In the today’s leading world huge amount of data are stored and transferred every second. But the issue is to provide the security to that data. For this Encryption and Decryption is considered as one of the better techniques. Two types of approach are used for encryption and decryption that is Symmetric key cryptography and Asymmetric key cryptography. RSA is one of the algorithms used in Asymmetric key cryptography. RSA algorithm is better in terms of security, but this algorithm reduces the speed of mutual authentication. In this paper, we have developed a modified RSA approach where the speed of Mutual authentication is enhanced with the help of modified RSA algorithm and a strong key is used to provide better security.
Predicting Future Instance Segmentation with Contextual Pyramid ConvLSTMs

Despite the remarkable progress in instance segmentation, the problem of predicting future instance segmentation remains challenging due to the unobservability of future data. Existing methods mainly address this challenge by forecasting pyramid features to represent unobserved future frames. However, they mainly predict features for each pyramid level independently, and ignore the underlying structural relationship between features of different levels. In this paper, we propose a novel framework called Contextual Pyramid ConvLSTMs, which contains a set of ConvLSTMs to exploit intra-level spatio-temporal contexts for predicting features of each individual level. Moreover, we also add pathway connections among the ConvLSTMs to transmit information across different ConvLSTMs, which allows our system to capture more inter-level spatio-temporal contextual information. We experimentally show that the proposed method can achieve state-of-the-art performance on two video instance segmentation benchmarks for future instance segmentation prediction.
Stability analysis of a consensus algorithm involving transmission delay

This paper provides a theoretical analysis to solve delay-induced consensus of a multi-agent system. Due to the individual differences of agents, the uncertain congestion of communication channels, and the physical characteristics of medium, time delay is ubiquitous at the moment of information transmission. We first present a generalisation of consensus algorithms, dealing with multiple agents and transmission delay and then obtain a sufficient condition for the system to experience consensus by constructing a Lyapunov function. It is proved that the system can reach consensus when the network of dynamic agents is strongly connected and detail-balanced. Numerical simulations show that increasing time delay may slow down the convergence rate of the system.
Scene Recognition via Semi-Supervised Multi-Feature Regression

With the development of visual sensor equipment (e.g., personal smart phones, vehicle cameras, surveillance videos and camcorders), scene recognition technology has attracted much attention due to its latent applications in visual surveillance, intelligent traffic and aerial remote sensing. Although some progress has been made in the field of scene recognition in recent years, the complexity of scene images and the inadequate numbers of labeled data pose challenges in this area. Hence, to effectively fuse the multiple features of each image and employ the information of both labeled and unlabeled images for scene recognition, we proposed a semi-supervised multi-feature regression (SSMFR) model in this paper. The SSMFR model possesses three advantages. First, the model propagates the labels of labeled data to unlabeled data by utilizing graph-based semi-supervised learning techniques so that both the information regarding unlabeled data and labeled data can be exploited to gain better performance. Second, SSMFR employs multiple graphs to characterize the structures of multiple feature spaces and adaptively assigns the weight to different graphs. Therefore, SSMFR can efficiently preserve the manifold structure of samples in each feature space and adequately exploit the complementary information of multiple features. Moreover, SSMFR adopts a $l_{2,1}$ -norm constraint to learn a sparse and robust classifier for scene recognition. To solve the SSMFR model, we proposed a simple and efficient iterative update optimization scheme. Finally, we also proved the convergence of SSMFR by theoretical analysis and experiments. Experiments were conducted on several benchmark scene datasets, and the experimental results demonstrated that the proposed SSMFR model can obtain better performance for scene recognition than some other state-of-the-art algorithms.
Unite projection and template match to segment license plate characters

A method to segment the cracked,adhesive,dirtied and partial covered characters of license plate is presented.License plate image that is rectified is projected.The cracked and adhesive characters are processed.Characters' segmentation is firstly completed.Then the processed characters' image is matched according to the template designed.The characters are segmented once more by the little error's template.Tests show that the method has a wide adaptability and robustness.It could get the optimization matching location to segment the characters.
Support Vector Machine Framework for Multi-View Metric Learning

In this article we tackle the supervised multi-view learning problem with kernel methods and metric learning. In this context we consider a recently developed multi-view metric learning (MVML) framework, and propose a SVM-based algorithm that jointly learns the classifier and metrics between views. These metrics permit taking into account the multi-view characteristics of the learning problem. Experiments on real data were performed to evaluate the performance of the proposed algorithm.