Reviewerly/exHarmony huggingface dataset:
Improving Precision in IR Considering Dynamic Environments

Much of the research in Information Retrieval (IR) is devoted to studying the improvement of personalized results for specific users in a static environment. Nevertheless, few approaches take advantage of collective past searches in a dynamic context where the number of documents is increased according with the passage of time. In this paper, we present an on-line probabilistic algorithm, which uses the collective past searches in a dynamic context to answer static and dynamic queries. Several experiments were carried out with the aim of evaluating the effectiveness of our algorithm. The algorithm's results were compared with the cosine measure. Following the Cranfield paradigm, simulated datasets were used in the experiments. Final results show that it is possible to improve effectiveness in a dynamic context.
Operational Feasibility Analysis of the Multimodal Controller Working Position “TriControl”

Current Air Traffic Controller working positions (CWPs) are reaching their capacity owing to increasing levels of air traffic. The multimodal CWP prototype TriControl combines automatic speech recognition, multitouch gestures, and eye-tracking, aiming for more natural and improved human interaction with air traffic control systems. However, the prototype has not yet undergone systematic evaluation with respect to feasibility. This paper evaluates the operational feasibility, focusing on the system usability of the approach CWP TriControl and its fulfillment of operational requirements. Fourteen controllers took part in a simulation study to evaluate the TriControl concept. The active approach controllers among the group of participants served as the main core target subgroup. The ratings of all controllers in the TriControl assessment were, on average, generally in slight agreement, with just a few showing statistical significance. However, the active approach controllers performed better and rated the system much more positively. The active approach controllers were strongly positive regarding the system usability and acceptance of this early-stage prototype. Particularly, ease of use, user-friendliness, and learnability were perceived very positively. Overall, they were also satisfied with the command input procedure, and would use it for their daily work. Thus, the participating controllers encourage further enhancements to be made to TriControl.
A Nonlinear Clustering Algorithm via Kernel Function and Locality Structure Learning

Clustering is an important task in machine learning, which is widely used in some applications such as medical image and pattern recognition. However, normal clustering algorithm have the following disadvantages: 1) some proposed clustering algorithms only consider the linear relationship of data. 2)some methods consider the global similarity of features and overlook the local similarity of features. We motivate these and propose nonlinear clustering algorithm via kernel function and locality structure learning. Firstly, our method uses the gaussian kernel function to achieve high dimensional projection so as to make the original data linearly separable.Then our method establishes the similarity matrix of data features in low-dimensional space to conduct local structure learning, which avoids the divergence of sample sets, retains the original nearest-neighbor structure relations, and improves the performance of clustering. Moreover, our method apply the sparse learning to remove the redundant features, which can improve the robustness of the model in the process of learning. Experimental results showed that our proposed method was superior to the normal linear clustering methods.
Technological Support for the needs of personalized training

We present the conceptual design of an e-Learning framework that makes use of an advanced technology in support of the personalized training of young people. This concept builds on the integration of novel functionality that implements an automated objective evaluation of the cognitive effort, the degree of attention and concentration, the emotional condition, and the stress level of a trainee, based on physiological signal processing. Such an awareness helps for the automated adaptation of the training process to the personality traits and to the momentary capacity of each trainee. Such a novel technology allows the development of intelligent training applications that adapt to the momentary capacity of the trainee to comprehend and advance with the current tasks and help towards the optimization of the learning outcome. Such a concept also offers new opportunities for the development of more engaging and more efficient training process and the design of an universally accessible interfaces.
Analysis and Selection of Classifiers for Gesture Characteristics Recognition Based on MGRS Framework

Human gesture is a complex motion that can be encoded with a multitude of information. Gesture recognition is the process of decoding and classifying this information. Currently, there are various computational intelligence techniques that have been developed to recognize gestures. Due to the vast quantity of algorithms available, it is difficult to understand which algorithms perform best for a given gesture recognition problem. Current studies in gesture recognition usually focus on a singular gesture characteristic for recognition leaving a gap in knowledge on the role multiple characteristics play in gesture recognition. In this study, various classification a lgorithms are evaluated by classifying the beat pattern and the articulation of a conducting gesture, two very distinct characteristics of the same gesture. The results of this study show that all of the top performing algorithms for both characteristics arc part of the neural network family, specifically the recurrent neural networks. It was also discovered that some algorithms had significantly different ranges of accuracy depending on the characteristic of the gesture. On the other hand, other algorithms showed less than a 5% difference between the two characteristics. From these results we determine that neural network algorithms should be considered for any gesture recognition problem regardless of its characteristics due to their relatively low variance between characteristics. The existence of potentially significant variance in results between different characteristics for other algorithms reveal that the characteristics of a gesture play an important part in determining the performance of a given algorithm.
Towards a Neural-Symbolic Generative Policy Model

To facilitate information sharing between systems and devices in a distributed environment, unstructured data from various sensors must be analysed accordingly. Recent work has developed the notion of a context-dependant generative policy framework capable of learning generative policy models from strings and text-based data in a tabular format. However, it is vital that unstructured contextual information can be analysed alongside tabular data, potentially at the edge of the network to enable generative policy models to be applied to more complex tasks. This paper performs a deep-dive into the field of neuralsymbolic machine learning with a view towards enabling future neural-symbolic generative policy models that are capable of analysing both structured and unstructured data, whilst providing full transparency and enabling edge of network reasoning capability. Firstly, an existing technique called DeepProbLog is investigated and applied to a policy inferencing task based on unstructured data and secondly, a recent inductive logic programming technique currently used for learning generative policy models is evaluated with respect to a policy learning task also based on unstructured data. Finally, the results of both tasks are discussed to provide a platform for future research into enabling neural-symbolic generative policy models.
Multi-Label Feature Selection Using Particle Swarm Optimization: Novel Local Search Mechanisms

Multi-label feature selection has become an indispensable preprocessing step of a multi-label classification problem which can reduce the number of features while maintaining or even improving the classification performance. Particle swarm optimization (PSO) has been widely applied to feature selection, but mainly for single-label classification. In comparison with single-label feature selection, multi-label feature selection is a more challenging task due to the interaction between the class labels. On such a large and complex search space, PSO usually loses its population diversity and converges to a local optimal quickly. PSO usually maintains the best position discovered during its evolutionary process, called gbest which plays an essential role in the search of PSO. We propose a novel local search strategy which improves the gbest with an expectation of preventing the premature convergence problem. The proposed local search employs a flipping o perator to search f or better feature subsets surrounding the current gbest. Experimental results on eight real-world datasets show that the proposed local search can assist PSO to evolve better feature subsets.
Sampled-data Synchronization of Recurrent Neural Networks with Multi-GPUs

This paper investigates the multi-rate sampled-data synchronization problem for recurrent reural networks with the multi-GPUs which have each different variable sampling rate. To handle the multi-GPU system with multi-sampling rate, the sampled-data sychronization error system is expressed as a summation of feedback subsystems with multi-sampling intervals. For the sampled-data controller design, Lyapunov functions with looped functions are constructed to use the information of the multi-rate sampling, and the modified free-matrix inequality is exploited to estimate the tighter upper bound intergral term. Finally, the simulation results show the effectiveness of the proposed method.
Recommender Systems - The Foundation of an Intelligent Financial Platform: Prospects of Development

Modern recommender systems in the financial sector are considered smart and experienced "financial managers" who are knowledgeable and know the financial situation of each client, regardless of where he is located. At present, a certain limit has been reached for the development of recommender systems in the financial sector. Without determining clear economic feasibility, financial intermediaries may suspend further research in this area. The costs of further promotion and development of recommender systems can only be justified by the economic effect that they can give - additional contacts with customers and the provision of related services, expanding the customer base, developing financial services, and increasing revenues.
Efficient Implementation of Fault-Tolerant 4:1 Quantum Multiplexer (QMUX) Using Clifford+T-Group

Since last couple of years quantum computing has made tremendous advancement towards developing the next generation computing paradigm and much to this cause several investigations already have started to make efficient implementation for quantum logic circuit. By considering several design constraints especially from noisy sources, new design models(like Nearest Neighbor design, Fault-tolerant architecture, single T-depth design) are evolving on daily basis. Focusing on this need, here in this work we show an efficient implementation of Quantum Multiplexer circuit towards realizing the quantum ALU. Two different design models are shown here, where in the first model, we have proposed the ancilla free garbage based implementation of MUX circuit and in the second model, we improve the previous design to make it garbage free. In our design phase, initially we have derived smaller modules and then they are integrated to make the generalized representation. For, ensuring fault-tolerant property in our made designs, we have used the functional power of Clifford +T-group. All the made circuits are tested over input vectors and the logical correctness of the designs have been verified individually.
ShadowFPE: New Encrypted Web Application Solution Based on Shadow DOM

Most of users hesitate to use third-party web applications because of security and privacy concerns. An ideal solution would be to allow apps to work with encrypted data, so that users might be more willing to provide just the encrypted version of their sensitive data. ShadowCrypt, proposed in CCS 2014, is the first and so far only solution that can achieve this by leveraging the encapsulation provided by Shadow DOM V0, without the need for the users to trust neither server nor client codes of web applications. Unfortunately, researchers have shown that ShadowCrypt is vulnerable to several attacks. Note that ShadowCrypt is no longer compliant to the updated W3C standard since 2015. Furthermore, some attacks on ShadowCrypt have been proposed. Hence, currently there is no effective and secure solution to guarantee the privacy of users. In this paper, we present ShadowFPE, a novel format-preserving encryption that makes use of a robust property in Shadow DOM to obtain a feasible solution. Compared with ShadowCrypt, ShadowFPE does not destroy the data format and makes the data usable in most of cloud web applications. We confirmed the effectiveness and security of ShadowFPE through case studies on web applications. Our results show that ShadowFPE is practical since it has low computational overhead and requires minimal modification in existing applications.
Swheel: Low-Cost Smart Wheelchair with Wireless Control

The aim of this project is to make a low cost, multi-modal control and an easy to operate a smart wheelchair. To meet the user-specific needs we have implemented a prototype for a smart wheelchair having two major features: (1) Android based application which includes touch-enabled buttons, gesture recognition, voice recognition, and (2) Remote OS installation mechanism. The proposed system is capable of obstacle detection with object detection using Ultrasonic Sensor with real-time object distance, and slope detection using three-axis accelerometer. The accuracy of our system is 91.58% for slope detection as calculated by the 3-axis accelerometer on a variety of slopes and 85.85% for obstacle detection for a variety of obstacles as detected by the ultrasonic sensor. Also, it can send an SOS message which is a notable feature of this project. All these features are controlled wirelessly using Bluetooth on an Android-based application.
An application of Virtual Reality technology in Education

The use of digital technologies influences practically almost all aspects of our daily life. Computer technologies have seen dramatic improvements, and laid the foundation for worldwide virtual reality (VR) development. The main goal of virtual reality is simulation of realistic human world. One of the key features of virtual reality is real-time interactivity, which means that the computer or the system has to be able to detect a user's input and modify the virtual world instantaneously. There are lots of educational difficulties related to understanding of complex objects like chemical compounds or parts of human body. A way to overcome them is the use of VR as appropriate technique which can improve the learning process to became more efficient. The aim of the paper is to present an approach for constructing molecules with different structural complexity. In addition, the development of VR models for human anatomy parts is also considered.
Performance Enhancement of GPU Parallel Computing Using Memory Allocation Optimization

The Fourier transform converts a signal from its original domain to a representation in the frequency domain. Applications of the Fourier Transform are far-reaching, spanning fields such as intelligent information processing, machine vision, physics, mathematics, medical science, and telecommunications; hence, its applications have become an indispensable part in our daily life. Therefore, it is essential to construct efficient and high-reliability schemes to guarantee smooth performance of the systems using Fourier Transforms. This study compares performances of Fast Fourier Transforms on a host CPU, GPU parallel computing, and GPU parallel computing with memory allocation optimization. From the experimental results, GPU parallel computing is proven to be effective in enhancing computation speed of the FFT; the speedup ratio of GPU parallel computing over the CPU can reach 48 when operating on 32678 8-byte complex input data. In addition, by optimizing GPU memory allocation, the computation speed of the FFT can be further enhanced; the speedup ratio of GPU parallel computing with memory allocation optimization over the CPU can reach 114.7 when operating on 32678 8-byte complex input data.
Secure Semantic Interoperability for IoT Applications with Linked Data

Interoperability stands for the capacity of a system to interact with the units of another entity. Although it is quite easy to accomplish this within the products of the same brand, it is not facile to provide compatibility for the whole spectrum of the Internet-of-Things (IoT) and the Linked Data (LD) world. Currently, the different applications and devices operate in their own cloud/platform, without supporting sufficient interaction with different vendor-products. As it concerns the meaning of data, which is the main focus of this paper, semantics can settle commonly agreed information models and ontologies for the used terms. However, as there are several ontologies for describing each distinct 'Thing', we need Semantic Mediators (SMs) in order to perform common data mapping across the various utilized formats (i.e. XML or JSON) and ontology alignment (e.g. resolve conflicts). Our goal is to enable end-to-end vertical compatibility and horizontal cooperation at all levels (field/network/backend). Moreover, the implication of security must be taken into consideration as the unsafe adoption of semantic technologies exposes the linking data and the user's privacy, issues that are neglected by the majority of the semantic-web studies. A motivating example of smart sensing is described along with a preliminary implementation on real heterogeneous devices. Two different IoT platforms are integrating in the case study, detailing the main SM features. The proposed setting is secure, scalable, and the overall overhead is sufficient for runtime operation, while providing significant advances over state-of-the-art solutions.
New Approach to Improve the Classification Process of Multi-Class Objects

In recent years, several descriptors have been proposed in many image classification applications. Accelerated-KAZE (A-KAZE) is considered one of the descriptors that has shown high performance for feature extraction. A-KAZE uses a binary descriptor called modified-local difference binary, which is very efficient and invariant to changes in rotation and scale. This representation does not allow spatial information to be considered between objects in the image, which makes it possible to reduce the performances of the classification of the images. This article broaches a new approach to improve the performance of the A-KAZE descriptor for image classification. The authors first establish the connection between the A-KAZE descriptor and the bag of feature model. Then the Spatial Pyramid Matching (SPM) is adopted by exploiting the A-KAZE descriptor to reinforce its robustness by introducing spatial information. The results of the experiments on several datasets show that the A-KAZE descriptor with SPM gives very satisfactory results compared with other existing methods in the state of the art.
Overview of Cloud Computing Resource Allocation and Management Technology

In the process of power system reform, the construction of power market and trading platform is undoubtedly the most challenging, and also the key link to the success of power reform. In order to better design and structure the cloud platform for power trading, this paper summarizes the resource management related to cloud platform. Resource management of cloud platforms is very important for building efficient cloud computing architecture. In this paper, we investigate various cloud computing resource allocation and management technologies, which includes workload prediction, resource scheduling, resource mapping etc. And analyzed advantages and disadvantages of each method. We hope that through our research and analysis, we can lay a foundation for the future development of a new cloud platform resource management model, and hope that on this basis, we can put forward the key resource architecture of cloud platform for power trading.
Non-intrusive behavior awareness for residents of a smart house

Behavior and activities awareness of people within a smart house is studied using pressure sensors on the floor. In turn, Machine Learning algorithms are used to predict unusual behavior of the house residents. The research focuses in people with supported needs trying to live a normal life in their own houses, allowing them more independence. The system relies on using pressure sensors on the floor to estimate the position of a person in some regions of interest, by measuring their presence on the sensors. System is designed as an Internet of Things (IOT) platform. An array of sensors on the floor are connected through routing devices to an Internet gateway that publishes the data to a Cloud System and into a Database. Machine learning (ML) is used to predict the activities of a person (standing, sitting, lying down), and to determine whether their behavior is common or not. IOT enables technologies that allow having remote monitoring of activities and behavior of residents. Unlike other researches done using pressure sensors that concentrates on predicting people's activities on small area of the room, we are utilizing the sensors to predict people's activities and behavior on all areas of the room.
Using Timed Sequential Patterns in the Transportation Industry

Data mining techniques can be used in several significant types of research in various domains to understand the flows of data, discover hidden knowledge, and improve quality of life. Sequential pattern mining, which is one data mining method, has emerged in the transportation domain to recognize the dynamic behavior of vehicles, trains, people...etc. The idea of sequential pattern mining is to find the frequent subsequences in a database of sequences. Although multiple sequential pattern mining techniques can mine sequential patterns, timestamps are only used to order sequences and time between sequence events is ignored. This information is important in real applications, such as traffic recommendation system and transportation safety. Though knowing that measurement Y occurs after measurement X is valuable, it is more valuable to know the estimated time before the appearance of measurement Y, perhaps, for example, to schedule maintenance at the right time in order to prevent railway damages. In this paper, we propose an algorithm called Minits (MINIng Timed Sequential patterns) to find the frequent sequential patterns and include the transition time between events in these patterns. However, approaches that depend on serial architecture are not effective anymore due to the massive data that are frequently generated from different sources. Therefore, we exploit parallelism using multicore CPUs to improve the performance of Minits to handle big data. Extensive experiments on real and synthetic datasets are reported and show the significance and advantages of this approach. Also, the execution time using a multicore outperform the single core when it deals with big data.