Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction technique on the effect of human motion on indoor radio wave propagation / Md. Jakirul Islam

Due to the attractive features of millimeter band, its uses are greatly expanding in the indoor wireless communication systems. As the distance between the transmitter and receiver is much shorter in indoor environments than that of the outdoor environment, the radio wave paths of the millimeter band frequencies are highly influenced by the building materials as well as by the human movements. Ray tracing is widely used method to characterize the radio wave propagation for the planning and establishment of the indoor wireless network. Accurate object modeling for the realistic environment and computational time are two classical problems of the ray tracing model. Because, large number of rays that travels in a complex and convoluted indoor environment must be traced. Therefore, an accurate and efficient ray tracing method is proposed here, which is based on the surface separation, object address distribution and surface skipping techniques. The proposed approach considers the effects of human body movement to provide a realistic estimation of the wave propagation. Hence, an approximated human body model is proposed to simulate the activities of humans, whereas three dimensional (3-D) cube or cuboids are used for the remaining objects of the simulation environment. The Red-Black tree data structure provides a faster object retrieval mechanism while object address distribution technique prevents the unnecessary objects to take part in intersection tests, thus accelerates the ray tracing. Conversely, the surface separation is a novel technique that makes the tracing of the ray paths easily and accurately, mostly for the complex 3-D environment. In addition, the calculation of exact ray paths that reach the receiver after multiple reflections, refractions and/or diffractions is also considered in this study. To prove the superiority, complexity analysis and detail comparisons (based on predicted rays, computational time and received power) between the proposed and existing methods are presented in this study. It is observed from the results that the proposed method predicts the same amount of rays and received power as the image and Space Volumetric Partitioning (SVP) methods, which ensures the accuracy of the proposed method. Conversely, the proposed method predicts the higher amount of rays (it is about 10% and 2%) and higher amount of received power (it is about 3.65% and 1.87%) compared to the Ray Launching (RL) and Angular Z-Buffer (AZB) methods, respectively. Moreover, the proposed method is 30.75%, 36.47, 69.30%, and 91.47% faster than the SVP, AZB, Image, and RL methods, respectively, when the simulation was carried out in the simple and complex environment, respectively. Additionally, the proposed method relatively obtaining the constant time for two different environments and the simulation time does not directly follow the number of objects of the indoor environment. The results obtained will be of great interest for the proposed ray tracing method that involves human motion within the simple and complex indoor environments.
Android malware detection through generative adversarial networks

Mobile and cell devices have empowered end users to tweak their cell phones more than ever and introduce applications just as we used to with personal computers. Android likewise portrays an uprise in mobile devices and personal digital assistants. It is an open‐source versatile platform fueling incalculable hardware units, tablets, televisions, auto amusement frameworks, digital boxes, and so forth. In a generally shorter life cycle, Android also has additionally experienced a mammoth development in application malware. In this context, a toweringly large measure of strategies has been proposed in theory for the examination and detection of these harmful applications for the Android platform. These strategies attempt to both statically reverse engineer the application and elicit meaningful information as features manually or dynamically endeavor to quantify the runtime behavior of the application to identify malevolence. The overgrowing nature of Android malware has enormously debilitated the support of protective measures, which leaves the platforms such as Android feeble for novel and mysterious malware. Machine learning is being utilized for malware diagnosis in mobile phones as a common practice and in Android distinctively. It is important to specify here that these systems, however, utilize and adapt the learning‐based techniques, yet the overhead of hand‐created features limits ease of use of such methods in reality by an end user. As a solution to this issue, we mean to make utilization of deep learning–based algorithms as the fundamental arrangement for malware examination on Android. Deep learning turns up as another way of research that has bid the scientific community in the fields of vision, speech, and natural language processing. Of late, models set up on deep convolution networks outmatched techniques utilizing handmade descriptive features at various undertakings. Likewise, our proposed technique to cater malware detection is by design a deep learning model making use of generative adversarial networks, which is responsible to detect the Android malware via famous two‐player game theory for a rock‐paper‐scissor problem. We have used three state‐of‐the‐art datasets and augmented a large‐scale dataset of opcodes extracted from the Android Package Kit bytecode and used in our experiments. Our technique achieves F1 score of 99% with a receiver operating characteristic of 99% on the bytecode dataset. This proves the usefulness of our technique and that it can generally be adopted in real life.
MADPARM: Mobile Agent based Distributed and Parallel Association Rule Mining

Distributed and Parallel data mining requires flexible and extensible framework to mining the useful knowledge from distributed database sites. Frequent Item-set mining is the primary step in association rule mining. Plenty of research work had been done in the distributed data mining especially association rule mining. Recently researcher deployed mobile agents for distributed association rule mining (IDMA, AEDM, AMAARM, EMADS, MADM , AeMSAR and MAD-ARM). Most of the approaches focused on reducing the communication cost by deploying multiple mobile agents and establish protocol for communication between them. This paper introduces the new novel framework which improves MADARM with parallel access of data from distributed site’s database using mobile agents (MADPARM). To improve the performance, we used compact bit table approach for mining frequent item-set (FI) from each site. Finally we present the result which shows the proposed framework gives better result than the MADARM in distributed environment.
Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.
Development of a Compact Wall-Climbing Robot Capable of Transitioning among Floor, Vertical Wall and Ceiling

橋梁やトンネルなどの社会インフラは,人々の暮らしを支え るうえで必要不可欠なものであるが,時間の経過とともにその 機能が低下していく. なかでも,日本の社会インフラは高度経 済成長期以降に集中的に整備されたものが多く,老朽化が進ん でいる. 橋梁に着目すると,建設後 50 年以上経過したものの 割合が 2017年時点で 23%になっている [1]. そして,この割合 は 2027年には 48%にまで増加すると予想されている. そのた め,橋梁の老朽化対策は喫緊の課題となっている. 現在,老朽化した橋梁の損傷箇所を早期発見し補修するために 定期的に点検が行われている. この点検は,足場や特殊クレー ン車を用いて点検員が近接目視によって行っている. しかし, この点検方法では莫大な費用と時間を要する. また,点検員が 高所から落下するなどの危険を伴っている. そのため,橋梁の 定期点検の大幅な費用削減と安全性の向上を図ることのできる ロボットによる検査に注目が高まっている [2]. 近年,橋梁検査用に様々なロボットが開発されている. 鋼橋
From Validation of Medical Devices towards Validation of Adaptive Cyber-Physical Systems

Conventionally, a technical system is defined in the design phase and considers all important requirements and aspects. The expected operations and the circumstances of operations are to be known in advance. If these are known, then the designed system can even be validated before it is produced or launched on the market. Validation is typically based on predictive analyses or simulations. However, these do not apply completely in the case of smart systems such as smart cyber-physical systems (S-CPSs) which self-manage their operation, or at least a part of it. Being able to adapt during run-time and evolve over time, S-CPSs cannot be validated using conventional deterministic approaches. Typical examples of these self-managing systems are S-CPSs already used as instrumentation in the medical field. The above circumscribed situation stimulated our background research, the results of which are concisely summarized and critically concluded in this paper. The literature has been found fairly narrow in terms of novel validation approaches for self-managing systems. The literature proposes to share the tasks of operational and behavioral validation among the system designers and the technical systems themselves. While designers need prognostic approaches to validate system operation, systems need to construct validation plans and execute them at run-time. This requires additional, validation-specific functionalities and context-dependent mechanisms such as run-time validation frameworks or meta-models, objective-sensitive self-monitoring mechanisms, self-constraining and self-supporting mechanisms, and other enablers. Extensive foundational research and system prototype testing are deemed to be indispensable. To make the first small step in this direction, this paper proposes a concept for the validation of smart medical CPSs. This relies on the following hypothesis: If a system has the freedom for self-adaptation, then it should also be equipped with a self-control mechanism, meta-knowledge, and a supervisory controller. These additional resources enable purpose- and context-dependent semantic reasoning about the operational objectives and behavioral states. This paper suggests a number of topics for future research towards a run-time validation engine.
Camera Pose Estimation with Semantic 3D Model

In computer vision, estimating camera pose from correspondences between 3D geometric entities and their projections into the image is a widely investigated problem. Although most state-of-the-art methods exploit simple primitives such as points or lines, and thus require dense scene models, the emergence of very effective CNN-based object detectors in the recent years have paved the way to the use of much lighter 3D models composed solely of a few semantically relevant features. In that context, we propose a novel model-based camera pose estimation method in which the scene is modeled by a set of virtual ellipsoids. We show that 6-DoF camera pose can be determined by optimizing only the three orientation parameters, and that at least two correspondences between 3D ellipsoids and their 2D projections are necessary in practice. We validate the approach on both simulated and real environments.
Sensitive Information Topics-Based Sentiment Analysis Method for Big Data

With the rapid development of the Internet, more and more users expressed their views on the Internet. Therefore, the big data of texts are generated on the Internet. In the era of big data, mining the sentiment tendencies contained in massive texts on the Internet through natural language processing technology has become an important way of public opinion supervision. In this paper, the sensitive information topics-based sentiment analysis method for big data is proposed. This method integrates topic semantic information into text representation through a neural network model. The attention mechanism is introduced into the neural network, and context-aware vector is introduced to calculate the weight of each word. In addition, in order to make the model more adaptable, the method of sentiment dictionary tagging is used to obtain the training data. The experimental results show that the proposed model can effectively improve the accuracy of sentiment analysis results.
Clustering Prominent Named Entities in Topic-Specific Text Corpora

Named Entity Recognition (NER) refers to the computational task of identifying real-world entities in text documents. A research challenge is to use computational techniques to identify and utilize these entities to improve several NLP applications. In this paper, a method that clusters prominent names of people and organizations based on their semantic similarity in a text corpus is proposed. The method relies on common named entity recognition techniques and word embeddings models. Semantic similarity scores generated using word embeddings models for named entities are used to cluster similar entities of the people and organizations types. A human judge evaluated ten variations of the method after it was run on a corpus that consists of 4,821 articles on a specific topic. The performance of the method was measured using three quantitative measures. The results of these three metrics demonstrate that the method is effective in clustering semantically similar named entities.
Title Confounding , homogeneity and collapsibility for causal effectsin epidemiologic studies

Detection of confounding and confounders is important for observational studies, and especially so for epidemiologic studies. Miettinen and Cook (1981) derived two criteria for detecting confounders. Using a model, Wickramaratne and Holford (1987) proved that the two criteria are necessary but not sufficient conditions for confounders. We take uniform nonconfounding to mean there is no confounding at a coarse-subpopulation-level obtained by pooling any number of subpopulations. We discuss the necessity and sufficiency of the two criteria for uniform nonconfounding. The concepts of homogeneity and collapsibility for causal effects are also defined, and the relation among confounding, homogeneity and collapsibility is discussed. We show that the common causal effect over all fine subpopulations is just the causal effect of the whole population.
A SECURE AND ROBUST CONGESTION CONTROL MODEL WITH FRAGMENTATION FOR CLUSTERED DISTRIBUTED DATABASE SYSTEM

The purpose of this paper is to present a secure and robust way to adopt Distributed Database System (DDBS). Distributed database is becoming very popular now a day. Today’s business environment has an increasingly need for distributed database and client/server applications. We know that the aim of current technologies is to fulfil the desire for consistent, scalable, reliable and accessible information i.e. steadily growing information. In Distributed Database System, the data fragmentation and distributed database site’s clustering problems are NP-Hard in nature and difficult to solve. One of the points considered that may cause loss of data means, while upgrading from Relational Database System, data distribution over different data-sites uses fragmentation. The database queries to access the applications on the data-sites and should be performed effectively. Therefore, the fragments that accessed by queries are needed to be allocated to the distributed database sites. Thus, fragmentation should be as secure as data be lossless during query execution. And, the second one may cause expensiveness of technology changes. Thus, we present a method for grouping the sites (clustering) of Distributed Database Systems in order to achieve not only secure fragmentation but also robust and cost effective congestion control.
Trademark Image Retrieval Based on Faster R-CNN

Automatic retrieval of digital trademark images is significant for improving the efficiency of trademark examination and management. In this paper, we proposed a method based on deep learning for trademark retrieval. Faster R-CNN was first applied to trademark retrieval. The global feature descriptor of the image is extracted by Faster R-CNN and the local feature of the image is extracted through the object proposal regions by the Region Proposal Networks (RPN). Retrieval strategies consist of initial ranking and spatial reranking. The experimental results show that our proposed method achieves remarkable performance in trademark image retrieval.
Orchestrating Big Data Analysis Workflows in the Cloud

Interest in processing big data has increased rapidly to gain insights that can transform businesses, government policies, and research outcomes. This has led to advancement in communication, programming, and processing technologies, including cloud computing services and technologies such as Hadoop, Spark, and Storm. This trend also affects the needs of analytical applications, which are no longer monolithic but composed of several individual analytical steps running in the form of a workflow. These big data workflows are vastly different in nature from traditional workflows. Researchers are currently facing the challenge of how to orchestrate and manage the execution of such workflows. In this article, we discuss in detail orchestration requirements of these workflows as well as the challenges in achieving these requirements. We also survey current trends and research that supports orchestration of big data workflows and identify open research challenges to guide future developments in this area.
Machine Learning Techniques for Annotations of Large Financial Text Datasets

Today, a number of automated methods exist to augment predictive models with annotations when working on huge collections of unstructured texts. One such method involves the use of machine learning techniques. This work seeks to investigate the use of those techniques for annotations on UIMA a framework used for the analysis of unstructured data. By using the design science research methodology, an annotator pipeline is created as the main artifact. It takes news and blog articles as input and extracts entities, which are then annotated with a sentiment. Concurrently a demonstration is taking place, using a set of 12,480 gold annotated documents on German car manufacturers as training data. By harnessing a multitude of validations methods and machine learning algorithms, all results are thoroughly tested and evaluated. As a result, this work provides a blueprint for research into the use of UIMA and machine learning techniques for domain constrained datasets.
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling

Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis. The core of the library is an optimization module providing model computational classes, solvers and proximal operators for regularization. tick relies on a C++ implementation and state-of-the-art optimization algorithms to provide very fast computations in a single node multi-core setting. Source code and documentation can be downloaded from this https URL
Encryption Cipher Text Retrieval Scheme Based on Fully Homomorphic Encryption Enterprise Cloud Storage

With the development of computer networks, cloud storage has become the mainstream way for people to store information. However, some essential information is disclosed during the storage process and the security of information has become the most concerned issue at present. In order to use the powerful computing of cloud storage to realize resource sharing between relevant departments without revealing information of other unrelated departments, this paper proposes an encryption cipher text retrieval scheme based on fully homomorphic encryption enterprise cloud storage (ECRS), and designs an Enterprise-side security model. The model uses full homomorphic encryption, decryption and spatial vector cipher text retrieval to achieve the security of information and resource sharing among enterprise departments. ECRS determines the employee who can encrypt or decrypt the files, and who has access to the files by modifying the number of attributes intersected between attribute sets. The solution can provide the security for cloud storage, resources for different departments, and improve the accuracy for cipher text retrieval.
From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Internet Interdisciplinary Institute (IN3) Doctor of Network and Information Technologies From Metaheuristics to Learnheuristics: Applications to Logistics, Finance, and Computing by Laura Calvet Liñán A large number of decision-making processes in strategic sectors such as transport, production and finance involve NP-hard problems. Trends such as globalization make systems larger and more complex. Frequently, these problems are characterized by high levels of uncertainty and dynamism. Metaheuristics have become predominant methods for solving challenging optimization problems in reasonable computing times. However, they frequently assume that the inputs, the objective functions, and the set of optimization constraints are deterministic and known in advance. These constitute strong assumptions that lead to work on oversimplified versions of real-world problems. As a consequence, the solutions obtained may have a poor performance when implemented. Simheuristics integrate simulation into metaheuristics to solve stochastic optimization problems in a natural way. Similarly, learnheuristics combine statistical learning and metaheuristics to tackle optimization problems in dynamic enviroments, where inputs may depend on the structure of the solution. From a methodological perspective, the main contributions of this thesis are the design of learnheuristics and a classification of works hybridizing statistical / machine learning and metaheuristics. It discusses the potential of learnheuristics in a number of fields and studies two specific cases. The first is a routing problem in which the depots are heterogeneous, in terms of their commercial offer, and customers show different willingness to consume depending on how well the assigned depot fits their preferences. Thus, different customer-depot assignment maps lead to different customer-expenditure levels. Regression models are employed to capture the relationship between each customer’s willingness to spend as a function of several variables, including the assigned depot as well as other customer’s features (age, gender, etc.). The second case describes a vehicle routing problem where each customer’s demand depends on the order in which the customers are visited. Moreover, several applications are presented in transport, production, finance, and computing. In the first field, the smart design of routes, including capacitated depots and vehicles, are addressed analyzing stochastic demands, and sustainability indicators. Moreover, the waste collection problem and a routing problem with a heterogeneous fleet, asymetric costs and site-dependency are studied. In the production arena, the optimization of jobs’ sequences under stochasticity, considering multiple production lines and a common deadline, is discussed. Strategies to invest on risky assets are proposed and assessed. Finally, the parameter fine-tuning of metaheuristics and the effect of the number of agents and the computing time on metaheuristics’ performance are investigated.