Miracle-dz/UniFAR-FTUs huggingface dataset:
Multitask Learning for Cross-Domain Image Captioning

Recent artificial intelligence research has witnessed great interest in automatically generating text descriptions of images, which are known as the <italic>image captioning</italic> task. Remarkable success has been achieved on domains where a large number of paired data in multimedia are available. Nevertheless, annotating sufficient data is labor-intensive and time-consuming, establishing significant barriers for adapting the image captioning systems to new domains. In this study, we introduc a novel Multitask Learning Algorithm for cross-Domain Image Captioning (MLADIC). MLADIC is a multitask system that simultaneously optimizes two coupled objectives via a dual learning mechanism: image captioning and text-to-image synthesis, with the hope that by leveraging the correlation of the two dual tasks, we are able to enhance the image captioning performance in the target domain. Concretely, the image captioning task is trained with an encoder–decoder model (i.e., CNN-LSTM) to generate textual descriptions of the input images. The image synthesis task employs the conditional generative adversarial network (C-GAN) to synthesize plausible images based on text descriptions. In C-GAN, a generative model <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula> synthesizes plausible images given text descriptions, and a discriminative model <inline-formula><tex-math notation="LaTeX">$D$</tex-math></inline-formula> tries to distinguish the images in training data from the generated images by <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula>. The adversarial process can eventually guide <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula> to generate plausible and high-quality images. To bridge the gap between different domains, a two-step strategy is adopted in order to transfer knowledge from the source domains to the target domains. First, we pre-train the model to learn the alignment between the neural representations of images and that of text data with the sufficient labeled source domain data. Second, we fine-tune the learned model by leveraging the limited image–text pairs and unpaired data in the target domain. We conduct extensive experiments to evaluate the performance of MLADIC by using the MSCOCO as the source domain data, and using Flickr30k and Oxford-102 as the target domain data. The results demonstrate that MLADIC achieves substantially better performance than the strong competitors for the cross-domain image captioning task.
Using adaptive ruled layers for Rapid Prototyping: principles and first results

Current 2.5D layered rapid prototyping has as disadvantage the staircase effect, requiring thin layers to be used to achieve a reasonable accuracy. Slices with inclined outer surfaces can be constructed using linear interpolation between adjacent contours, resulting in ruled slices. A methodology to approximate a given model geometry within a specified accuracy using ruled slices and an adaptive layer thickness is described. This involves matching successive contours and analysing the geometry for curvature and inclination to calculate allowed layerthicknesses. First results show a significant reduction in the number of layers when compared to adaptive slicing using 2.5D layers. A proof-of-concept software, the Delft University of Technology Improved Slicer (DUTIS) has been developed to perform the adaptive slicing using either 2.5D or ruled layers allowing a comparison between the two alternative methods.
Clairvoyant Restarts in Branch-and-Bound Search Using Online Tree-Size Estimation

We propose a simple and general online method to measure the search progress within the Branch-and-Bound algorithm, from which we estimate the size of the remaining search tree. We then show how this information can help solvers algorithmically at runtime by designing a restart strategy for MixedInteger Programming (MIP) solvers that decides whether to restart the search based on the current estimate of the number of remaining nodes in the tree. We refer to this type of algorithm as clairvoyant. Our clairvoyant restart strategy outperforms a state-of-the-art solver on a large set of publicly available MIP benchmark instances. It is implemented in the MIP solver SCIP and will be available in future releases.
EFFICIENT DATA MINING TECHNIQUES FOR BIG DATA ANALYSIS: A SURVEY

Technology revolution has been facilitating millions of people by generating tremendous data, resulting in big data. It has been a confirmed phenomenon that enormous amount of data have been generated continuously at unprecedented and ever increasing scales. Even though, big data bears greater value, it brings tremendous challenges to extract hidden knowledge and more valuable insights from big data. The valuable information in big data can be obtained by applying data mining techniques in big data. The goal of big data mining techniques go beyond fetching the requested information or even uncovering some hidden relationships and patterns between data. Big data mining techniques involves various process like feature selection, clustering and classification. In this article, a detailed comparative survey on different processes of big data mining techniques such as dimensionality reduction, clustering and classification for big data analysis is presented. At first, different dimensionality reduction, clustering and classification methods proposed for big data analysis in previous researches are studied in detail. After that, a comparative and state-of-the-art analysis is carried out to identify the limitations in those methods.
Multi-Projector Content Preservation with Linear Filters

Using aligned overlapping image projectors provides several ad-vantages when compared to a single projector: increased bright-ness, additional redundancy, and increased pixel density withina region of the screen. Aligning content between projectors isachieved by applying space transformation operations to the de-sired output. The transformation operations often degrade the qual-ity of the original image due to sampling and quantization. Thetransformation applied for a given projector is typically done in iso-lation of all other content-projector transformations. However, it ispossible to warp the images with prior knowledge of each othersuch that they utilize the increase in effective pixel density. Thisallows for an increase in the perceptual quality of the resultingstacked content. This paper presents a novel method of increas-ing the perceptual quality within multi-projector configurations. Amachine learning approach is used to train a linear filtering basedmodel that conditions the individual projected images on each other
Applying Visualization and Collective Intelligence for Rapid Group Decision Making

There is a need to rapidly collect subjective data from a diverse group of contributors to facilitate argumentation and group decision making that reflects the breadth and depth of the knowledge of the collective group. Methods currently exist, although they are subject to biases and lack traceability. A method and tool have been developed to apply collective intelligence and visualization concepts to enable such rapid decision making while overcoming the identified weaknesses. A pilot study to select the best technology to solve a Naval mission requirement was performed to assess the ability of the tool to: Enable informed decision making, assess differences in contributor mental models, and identify where there is or is not consensus. The method/tool was useful to these ends, warranting further research. Lessons learned were gathered and the validity of the approach was notionally examined, driving the identification of future work to be performed.
Modelling and Control of Quadrotor Control System using MATLAB/Simulink

: Generally, Quadrotor type Unmanned Aerial Vehicles are unstable in nature, so to stabilize it, the controller is used. This paper observes the PD controller that make use of UAV to control the adjust of quadrotor UAV even as in the air. The gain parameters of the PD controller, the proportional gain K p and the derivative gain K d are apply to be stable and good performance. Unmanned aerial vehicles (UAV) are becoming increasingly common and span a huge range of size and shape. After integrating PD controllers into the systems, quadcopter settling time of roll, pitch and yaw system. Simulations result and comparison of X, Yand Yaw control techniques are presented at the end of this paper. This controller monitors the controlled process variable, and compares it with the reference or set point. The difference between actual and desired value of the process variable, called the error signal. Error is applied as feedback to generate a control action to bring the controlled process variable to the same value as the set point. Control action in which the output is proportional to a linear combination of the input and the time rate of change of input.
Medical Images Segmentation Operations

Extracting various valuable medical information from head MRI and CT series is one of the most important and challenging tasks in the area of medical image analysis. Due to the lack of automation for many of these tasks, they require meticulous preprocessing from the medical experts. Nevertheless, some of these problems may have semi-automatic solutions, but they are still dependent on the person's competence. The main goal of our research project is to create an instrument that maximizes series processing automation degree. Our project consists of two parts: a set of algorithms for medical image processing and tools for its results interpretation. In this paper we present an overview of the best existing approaches in this field, as well the description of our own algorithms developed for similar tissue segmentation problems such as eye bony orbit and brain tumor segmentation based on convolutional neural networks. The investigation of performance of different neural network models for both tasks as well as neural ensembles applied to brain tumor segmentation is presented. We also introduce our software named "MISO Tool" which is created specifically for this type of problems. It allows tissues segmentation using pre-trained neural networks, DICOM pixel data manipulation and 3D reconstruction of segmented areas.
Recent Advances in Particle Swarm Optimization for Large Scale Problems

Accompanied by the advent of current big data ages, the scales of real world optimization problems with many decisive design variables are becoming much larger. Up to date, how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of bio-inspired computation. So addressing these complex large scale problems to produce truly useful results is one of the presently hottest topics. As a branch of the swarm intelligence based algorithms, particle swarm optimization (PSO) for coping with large scale problems and its expansively diverse applications have been in rapid development over the last decade years. This review paper mainly presents its recent achievements and trends, and also highlights the existing unsolved challenging problems and key issues with a huge impact in order to encourage further more research in both large scale PSO theories and their applications in the forthcoming years.
A real-time infrared image enhancement algorithm based on improved CLAHE

Due to that the infrared thermal imaging system has the characteristics of low contrast and small dynamic range, this paper proposed an real-time infrared image enhancement algorithm based on Limit Contrast Adaptive Histogram Equalization (CLAHE) and also provided the algorithm implementation. The algorithm firstly divides the pretreated image data into several sub-regions of size, and then the histogram of the sub-region is calculated respectively, the clipping threshold of histogram is determined according to the image gradient information, the captured pixels are evenly distributed to each gray level. Finally, bi-linear interpolation is used to remove the unbalance effect of block edge transition. Experimental results show that compared with traditional algorithms, this algorithm is capable of suppressing the noise and highlight the edges and details of the image, as well as meeting the real-time requirement.
Simulation model of selective assembly of two parts with sorting by the estimated values

The process of one-parameter selective assembly of two parts is considered. Methods for reducing the probability of sorting errors are given. It is suggested to perform the sorting process according to the values of the parameter estimates. For this estimate, a recursion algorithm (the Kalman filter) is used in each cycle. In the environment of GPSS World, a simulation model of selective acquisition and assembly of two parts for determining the parameters of the assembly process is constructed. Comparative modeling results are presented that prove the effectiveness of the algorithm for estimating parameters for selective assembly. The variants of using the algorithm and the prospects for further research are suggested.
German‑Arab encounters: A cross‑cultural Training approach

Abstract The paper addresses intercultural differences which should contribute to raising awareness of sociolinguists in particular with respect to the role religion plays in shaping talk in Arabic and to serious confusion and misunderstandings that may occur when communicators from different cultural backgrounds meet. Successful intercultural interactions are at the heart of what intercultural communication is all about. So what does it mean that Arabs interact successfully with Germans (Westerners) and vice versa? This is the focus of this paper which led to the development of an intercultural communicative competence framework (Byram 1997) based on critical incidents (Schumann 2012a/b). In utilizing such a framework, our efforts toward developing intercultural competence in our students can be included in a more comprehensive, integrated approach instead of having too much trust in the mass-market course book not necessarily always tailored to the teaching context.
Saliency detection with FCNN based on low-level feature optimization

In order to improve the accuracy of saliency target recognition in digital images, this paper proposes a saliency detection algorithm based on low-level feature optimization for full convolution neural networks. Firstly, a fully convolutional neural network is constructed and trained on the basis of the VGG-16 network, and the initial saliency map is obtained through the output of the full convolutional neural network. Then, the input image is super-pixel divided, and the super pixel is regarded as a vertex of a graph to compose. On the basis of the initial saliency map, the superpixel saliency division is performed. The selected initial seed points are selected based on the central prior, and the low-level eigenvalues such as the superpixel RGB eigenvalues are calculated, and the saliency region merging is performed to obtain the saliency optimization map based on the low-level feature optimization. Finally, the initial saliency map and the saliency optimization map are combined to obtain the final saliency map. The comparison experiments show that the proposed algorithm achieves the excellent precision compared with other algorithms, and illustrates the effectiveness of the algorithm.
Embedding constructive alignment of reading lists in course design

Reading list practices are long-standing but cause confusion and misunderstanding between module leaders and students. Constructive alignment (Biggs and Tang, 2011), although widely applied in course design across the UK Higher Education sector, has not previously been applied to the practice of reading lists but offers a practical and pedagogically sound method for reinventing reading list practice and bridging the gap of understanding between the intentions of module leaders and the interpretation of students. The module leaders of seven modules were offered the support of a project led by Oxford Brookes Library to redesign their modules so that the reading lists were constructively aligned with the learning outcomes of the modules. After an initial run of the redesigned modules the module leaders were asked whether they would embed the practice of constructively aligned reading lists in their modules. Five of the modules were redesigned and continued with the redesign past the initial instance, one of the modules exited the project before it was redesigned, and one of the modules returned to the pre-project module design and reading list practice. The project was successful in embedding constructively aligned reading list practice in Oxford Brookes University modules past the first run of the module, but several barriers to effective learning and teaching were identified with the most significant being a lack of student engagement with the redesigned reading lists. The implication for practice is that constructively aligned reading lists should include an element of summative assessment to increase the chances of student engagement and the successful embedding of constructively aligned reading lists in the design of modules.
Modeling Non-Uniform Memory Access on Large Compute Nodes with the Cache-Aware Roofline Model

NUMA platforms, emerging memory architectures with on-package high bandwidth memories bring new opportunities and challenges to bridge the gap between computing power and memory performance. Heterogeneous memory machines feature several performance trade-offs, depending on the kind of memory used, when writing or reading it. Finding memory performance upper-bounds subject to such trade-offs aligns with the numerous interests of measuring computing system performance. In particular, representing applications performance with respect to the platform performance bounds has been addressed in the state-of-the-art Cache-Aware Roofline Model (CARM) to troubleshoot performance issues. In this paper, we present a Locality-Aware extension (LARM) of the CARM to model NUMA platforms bottlenecks, such as contention and remote access. On top of this, the new contribution of this paper is the design and validation of a novel hybrid memory bandwidth model. This new hybrid model quantifies the achievable bandwidth upper-bound under above-described trade-offs with less than 3 percent error. Hence, when comparing applications performance with the maximum attainable performance, software designers can now rely on more accurate information.
The Wisdom of the Silent Crowd: Predicting the Match Results of World Cup 2018 through Twitter

Social networks have attracted much attention thanks to their availability, being the source of information in almost everything related to life, and giving the chance for users to discover the things and people related to them. Global and highly popular events such as the FIFA World Cup are always welcomed by social networking sites. Furthermore, these social networking sites customize themselves and provide some specific content for this global events. From this point of view, we have collected 38,371,358 tweets during the FIFA World Cup 2018 which were posted by 7,876,519 unique Twitter users. The aim of this study is proposing a prediction system that evaluates the teams who qualified for the FIFA World Cup 2018 through their squad, and their performances in the early stages of the competition in order to predict the match results of the further stages. For this reason, a number of different types of machine learning algorithms are utilized after building a machine learning model which is based on novel features. According to the experimental results, the best accuracy of the proposed system for the match result prediction is calculated as high as 87.5% which is quite better than the related work. The experimental results demonstrate the effectiveness of social network based features.
Trajectory Tracking Control System Design For Autonomous Two-Wheeled Robot

A trajectory tracking control system design of an autonomous two-wheeled robot (TWR) is presented. The control system objective is to steer the TWR move on a desired trajectory in planar space. The TWR has two kinds of movement: moving forward/backward and turning to the right/left, where the movements are represented by a non-linear kinematics equation. Simplifying the trajectory tracking control system design, the non-linear kinematics equation is approximated by a linear kinematics equation. Linear quadratics regulator (LQR) method is applied to design the trajectory tracking control system. The designed control system is evaluated through computer simulation. Simulation results show that the designed control system is able to make the TWR track a desired trajectory that located 1.4 meter away from the TWR initial position within 3 seconds.
Improvement Performance of Clustered Shortest Geopath Routing Protocol Using Data Aggregation in Wireless Sensor Network Environments

The wireless sensor network is one of the wireless networks which consists of many sensor nodes placed in a particular region for a certain specific functionality. Sensor nodes are generally small and have limited memory, processing resources, energy and lifetime. Limitations of sensor nodes on the energy side can affect network performance such as network lifetime and delay on the event detection. There are many methods that have been done to increase network lifetime, one of which is Clustered Shortest Geopath Routing protocol (CSGP). CSGP is a method to increase network lifetime, this method the development of the shortest geopath routing protocol (SGP) and Low Energy Adaptive Clustering Hierarchy (LEACH). the cluster formation is based on the geographic information of the network while the cluster head selection is based on the position of the node closest to the clusters center point and the data sensing obtained by sensor node will be sent to the cluster head which will forward them to the sink using SGP. However, this method has the limitation on the cluster head selection which is not optimal and the density of network transmission on the SGP line so that the energy consumption of nodes in the path is greater than other sensor nodes. This research proposes a schema selection and maintenance of the cluster head to determine the adaptive path, then aggregation data to reduce the number of network transmission. The data aggregation is the process of collecting and combining useful information in a particular region. Implementation of the stages will be performed in the simulation environment by utilizing SIDNet SWANS simulator, a simulator that used for simulating the wireless sensor network.
Per-Flow Throughput Fairness in Ring Aggregation Network with Multiple Edge Routers

Ring aggregation networks are often employed by network carriers because of their efficiency and high fault tolerance. A fairness scheme is required in ring aggregation to achieve per-flow throughput fairness and bufferbloat avoidance, because frames are forwarded along multiple ring nodes. N Rate N + 1 Color Marking (NRN + 1CM) was proposed to achieve fairness in ring aggregation networks consisting of Layer-2 Switches (SWs). With NRN + 1CM, frames are selectively discarded based on color and the frame-dropping threshold. To avoid the accumulation of a queuing delay, frames are discarded at upstream nodes in advance through the notification process for the frame-dropping threshold. However, in the previous works, NRN + 1CM was assumed to be employed in a logical daisy chain topology linked to one Edge Router (ER). The currently available threshold notification process of NRN + 1CM cannot be employed for ring networks with multiple ERs. Therefore, this paper proposes a method for applying NRN + 1CM to a ring aggregation network with multiple ERs. With the proposed algorithm, an SW dynamically selects the dropping threshold to send in order to avoid excess frame discarding. The performance of the proposed scheme was confirmed through computer simulations.