Reviewerly/exHarmony huggingface dataset:
Lightweight Outsourced Privacy-Preserving Heart Failure Prediction Based on GRU

The medical service provider establishes a heart failure prediction model with deep learning technology to provide remote users with real-time and accurate heart failure prediction services. Remote users provide their health data to the health care provider for heart failure prediction through the network, thereby effectively avoiding the damage or death of vital organs of the patient due to the onset of acute heart failure. Obviously, sharing personal health data in the exposed data sharing environment would lead to serious privacy leakage. Therefore, in this paper, we propose a privacy-preserving heart failure prediction (PHFP) system based on Secure Multiparty Computation (SMC) and Gated Recurrent Unit (GRU). To meet the real-time requirements of the PHFP system, we designed a series of data interaction protocols based on additional secret sharing to achieve lightweight outsourcing computing. Through these protocols, we can protect the user’s health data privacy while ensuring the efficiency of the heart failure prediction model. At the same time, to provide high-quality heart failure prediction services, we also use the new mathematical fitting method to directly construct the safety activation function, which reduces the number of calls to the security protocol and optimizes the accuracy and efficiency of the system. Besides, we built a security model and analyzed the security of the system. The experimental results show that PHFP takes into account the safety, accuracy, and efficiency in the application of heart failure prediction.
In-Air Exchange of Small Payloads Between Multirotor Aerial Systems

Exchanging payloads between multiple vehicles is a challenging problem that finds applications in sustained monitoring and long-range payload transportation. In this paper, we take the problem one step further and explore the design and implementation of a system of aerial vehicles that can exchange a small cargo/payload while air-borne. The proposed approach exploits the hover-capabilities of multirotor vehicles, and works within the constraints and the challenges imposed by such a task (such as proximity and contact forces). It is also portable to a wide range of vehicles, and does not require custom flight maneuvers in order to be viable. A simulation framework is utilized to highlight the key challenges addressed by the system. We present detailed results from experiments conducted outdoors that demonstrate two vehicles exchanging a small 60 g payload unit between them.
Automatic Generation and Assessment of Student Assignments for Parallel Programming Learning

The course of parallel programming is becoming more and more important for the education of students majoring in computer science. However, it is not easy to learn parallel programming well due to its high theory and practice requirements. In this paper, we design and implement an automatic assignment generation and assessment system to help students learn parallel programming. The assignments can be generated according to user behaviors and thus able to guide students to learn parallel programming step by step. Besides, it can automatically generate an overall assessment of student assignments by using fuzzy string matching, which provides an approximate reference score of objective questions. Subjective questions can be assessed directly by comparing the answer to the reference answer. This system also provides a friendly user interface for students to complete online assignments and let teachers manage their question database. In our teaching practice, students can learn parallel programming more effectively with the help of such an assignment generation and assessment system.
Kernel compositional embedding and its application in linguistic structured data classification

In many applications such as natural language processing, speech recognition, and computer vision, there are inputs with hierarchical compositional structure and long relation among their subcomponents. Introducing this information in the definition of a model can improve its performance in dealing with this type of data. On the other side, the high generalization power of kernel methods is proven in traditional machine learning problems. If we can employ some idea of these methods on handling structured objects, we can benefit from improving the performance and the generalization capability of compositional models. Accordingly, a new approach is introduced in this paper to realize the idea of simultaneously leveraging advantages of both kernel methods and compositional embedding to provide powerful representations and classifiers for structured data. Based on this approach, which is named Kernel Compositional Embedding (KCE), we propose two methods: Direct KCE (DKCE), and Indirect KCE (IKCE). In the DKCE method, we directly deal with a potentially infinite dimensional embedding space, i.e., the embeddings are used implicitly in the classification task. In IKCE method, instead of implicitly performing all operations inside embedding space, only some elements of one or more reproducing kernel Hilbert spaces are used to embed structured objects into low-dimensional Euclidean spaces. To evaluate the performance of the proposed methods, we apply them on two common computational linguistic tasks, i.e, sentiment analysis and natural language inference. The experimental results illustrate that the classification performance of the proposed methods is higher than or competitive to some well-known competing methods.
A Prototype of Effective Online Teaching Tools with Adaptive Navigation Support

Abstract This paper proposes a prototype of educational system based on adaptive navigation support to help teacher manage their courses online. In a university context, a teacher can have many courses in one semester. The courses consists of several topics with several material and assignments on it. In a such circumstances, a teacher must prepare the topic’s material before they can go to the class. This situation can cause the teacher to overwhelmingly getting busy to identify which courses they want to focus on and which one they do not. Therefore, they need a tools as an aid to choose which courses they should focus on. In this study, a prototype has been built based on adaptive navigation approach to help teacher overcome the situation. Link hiding techniques were used as a mechanism to help user focus on one task at a time.
Improving the Convergence Speed of Deep Neural Networks with Biased Sampling

Deep Neural Networks (DNNs) are usually trained using a stochastic gradient descent algorithm or its variants. This involves the gradient being computed on small batches of samples. The decision of which samples will be selected is an important step in the training process, as it can greatly affect the DNN's performance. This paper proposes a novel method for batch selection, that involves replacing easy training examples with difficult ones every epoch, which results in an acceleration of the training process of deep networks. The resulting algorithm was tested on four datasets, representing both regression (Boston Housing) and image classification (MNIST, CIFAR10, CIFAR100) problems. The proposed approach, called Batch Selection with Biased Sampling (BSBS), yields faster convergence rates. Experiments indicate that the model can be trained in up to 50% less epochs.
An Incremental Technique for Mining Coverage Patterns in Large Databases

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. Typically, pattern mining is carried out from huge databases, which tend to get updated several times. Consequently, as a given database is updated, some of the patterns discovered may become invalid, while some new patterns may emerge. This has motivated significant research efforts in the area of Incremental Mining. The goal of incremental mining is to efficiently and incrementally mine patterns when a database is updated as opposed to mining all of the patterns from scratch from the complete database. Incidentally, research efforts are being made to develop incremental pattern mining algorithms for extracting different kinds of patterns such as frequent patterns, sequential patterns and utility patterns. However, none of the existing works addresses incremental mining in the context of coverage patterns, which has important applications in areas such as banner advertising, search engine advertising and graph mining. In this regard, the main contributions of this work are three-fold. First, we introduce the problem of incremental mining in the context of coverage patterns. Second, we propose the IncCMine algorithm for efficiently extracting the knowledge of coverage patterns when incremental database is added to the existing database. Third, we performed extensive experiments using two real-world click stream datasets and one synthetic dataset. The results of our performance evaluation demonstrate that our proposed IncCMine algorithm indeed improves the performance significantly w.r.t. the existing CMine algorithm.
Beyond Sessions: Exploiting Hybrid Contextual Information for Web Search

It is essential to fully understand user intents for the optimization of downstream tasks such as document ranking and query suggestion in web search. As users tend to submit ambiguous queries, numer- ous studies utilize contextual information such as query sequence and user clicks for the auxiliary of user intent modeling. Most of these work adopted Recurrent Neural Network (RNN) based frame- works to encode sequential information within a session, which is hard to realize parallel computation. To this end, we plan to adopt attention-based units to generate context-aware representations for elements in sessions. As intra-session contexts are deficient for handling the data sparsity and cold-start problems in session search, we would also attempt to integrate cross-session dependen- cies by constructing session graphs on the whole corpus to enrich the representation of queries and documents.
A Method for Collision-free Guidance of Mobile Robots in Unknown Dynamic Environments with Moving Obstacles

In this paper, a collision-free navigation algorithm for mobile robots is proposed. The algorithm is applied to dynamic environments. Shapes of moving obstacles may be non-convex, speed and angular velocity are variable. The algorithm is a sliding mode control law, which switches between two modes, namely, target approaching mode and obstacle avoidance mode. Moreover, a rigorous analysis of the proposed algorithm is provided and the performance is demonstrated with simulation results. It is a really practical algorithm as there is not too many assumptions for the obstacles compared with others.
Identifying, Classifying and Searching Graphic Symbols in the NOTAE System

The use of graphic symbols in documentary records from the 5th to the 9th century has so far received scant attention. What we mean by graphic symbols are graphic signs (including alphabetical ones) drawn as a visual unit in a written text and representing something other or something more than a word of that text. The Project NOTAE represents the first attempt to investigate these graphic entities as a historical phenomenon from Late Antiquity to early medieval Europe in any written sources containing texts generated for pragmatic purposes (contracts, petitions, official and private letters, lists etc.). Identifying and classifying graphic symbols on such documents is a task that requires experience and knowledge of the field, but software applications may come in help by learning to recognize symbols from previously annotated documents and suggesting experts potential symbols and likely classification in newly acquired documents to be validated, thus easing the task. This contribution introduces the NOTAE system that, in addition to the aforementioned task, provides non expert users with tools to explore the documents annotated by experts.
Dilated Temporal Convolutional Neural Network Architecture with Independent Component Layer for Human Activity Recognition

A system using Convolutional Neural Network was developed for Human Activity Recognition (HAR) application. The proposed method uses the algorithm to detect the daily activities of people with cognitive disabilities. As an initial application, daily hygienic activities were monitored to assist people with cognitive disabilities who often need prompts to start and finish activities. The proposed system discussed in the paper uses Dilated Temporal Convolutional Neural Network Architecture with Independent Component Layers (DT-CNN-IC) to detect daily hygienic activities. The proposed system can be trained to obtain high average classification accuracy of 92.66 percent from only using a single channel accelerometer information. This method can reduce the necessary computational resources to train the proposed neural network allowing the algorithm to be implemented on low-cost platforms.
Online Embedding and Clustering of Data Streams

Number of connected devices is steadily increasing and these devices continuously generate data streams. These data streams are often high dimensional and contain concept drift. Real-time processing of data streams is arousing interest despite many challenges. Clustering is a method that does not need labeled instances (it is unsupervised) and it can be applied with less prior information about the data. These properties make clustering one of the most suitable methods for real-time data stream processing. Moreover, data embedding is a process that may simplify clustering and makes visualization of high dimensional data possible. There exist several data stream clustering algorithms in the literature, however no data stream embedding method exists. UMAP is a data embedding algorithm that is suitable to be applied on data streams, but it cannot adopt concept drift. In this study, we have developed a new method to apply UMAP on data streams, adopt concept drift and cluster embedded data instances using any distance based clustering algorithms.
Cross-Media Image-Text Retrieval Combined with Global Similarity and Local Similarity

In this paper, we study the problem of image-text matching in order to make the image and text have better semantic matching. In the previous work, people just simply used the pre-training network to extract image and text features and project directly into a common subspace, or change various loss functions on this basis, or use the attention mechanism to directly match the image region proposals and the text phrases. This is not a good match for the semantics of the image and the text. In this study, we propose a method of cross-media retrieval based on global representation and local representation. We constructed a cross-media two-level network to explore better semantic matching between images and text, which contains subnets that handle both global and local features. Specifically, we not only use the self-attention network to obtain a macro representation of the global image but also use the local fine-grained patch with the attention mechanism. Then, we use a two-level alignment framework to promote each other to learn different representations of cross-media retrieval. The innovation of this study lies in the use of more comprehensive features of image and text to design the two kinds of similarity and add them up in some way. Experimental results show that this method is effective in image-text retrieval. Experimental results on the Flickr30K and MS-COCO datasets show that this model has a better recall rate than many of the current advanced cross-media retrieval models.
Improved Path Planning for Mobile Robot Based on Firefly Algorithm

Aiming at the disadvantage that (Firefly Algorithm) FA is easy to fall into local optimum, (Modified Firefly Algorithms) MFA is proposed. By controlling the step factor α, the algorithm improves the adaptive control of α, and the convergence speed of the algorithm by controlling the parameter β <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> . The simulation results show that MFA has better accuracy for other algorithms. The robot path optimization using MFA is shorter than that using FA, which proves the effectiveness of MFA.
Optimizing Deep Learning Based Semantic Video Segmentation on Embedded GPUs

Decision making in many industries today is being improved drastically thanks to artificial intelligence and deep learning. New algorithms address challenges such as genome mapping, medical diagnostics, self-driving cars, autonomous robots and more. Deep learning in embedded systems requires high optimization due to the high computational demand, given that power, heat dissipation, size and price constraints are numerous. In this paper we analyze several acceleration methods which include utilization of GPUs for most complex variants of deep learning, such as semantic video segmentation operating in real time. Specifically, we propose mapping of acceleration routines commonly present within deep learning SDKs to different network layers in semantic segmentation. Finally, we evaluate one implementation utilizing the enumerated techniques for semantic segmentation of front camera in autonomous driving front view.
Scalability and Parallelization of Sequential Processing: Big Data Demands and Information Algebras

Procedures of sequential updating of information are important for “big data streams” processing because they avoid accumulating and storing large data sets. As a model of information accumulation, we study the Bayesian updating procedure for linear experiments. Analysis and gradual transformation of the original processing scheme in order to increase its efficiency lead to certain mathematical structures - information spaces. We show that processing can be simplified by introducing a special intermediate form of information representation. Thanks to the rich algebraic properties of the corresponding information space, it allows unifying and increasing the efficiency of the information updating. It also leads to various parallelization options for inherently sequential Bayesian procedure, which are suited for distributed data processing platforms, such as MapReduce. Besides, we will see how certain formalization of the concept of information and its algebraic properties can arise simply from adopting data processing to big data demands. Approaches and concepts developed in the paper allow to increase efficiency and uniformity of data processing and present a systematic approach to transforming sequential processing into parallel.
Research on Autonomous Driving based on a Highly Flexible Prototype Vehicle

Based on a flexible prototype vehicle and object recognition algorithms from robotic science, the present research aims at new approaches in the development of autonomous driving vehicles. An advanced prototype vehicle, driven by four independent hub motors that provide maneuverability and driving function far beyond automotive standards, serves as the basis for investigations. Enhanced know-how from robotic disciplines comprises sensor technology, navigation algorithms and the autonomous vehicle control system. With the integrated research vehicle, several autonomous driving testing scenarios are carried out to enable comprehensive evaluation and potential assessment of different technologies and development approaches in view of future R&D activities. The study shows, that: A)4WID-4WIS (four wheel independent driving and four wheel independent steering) electric vehicle is much more flexible and efficient to achieve superior performance in aspect of dynamics and drive ability than traditional vehicles. B)Object recognition and path planning algorithms in the robotics science can be implemented in the development of autonomous driving techniques and have achieved convincing results. C)This research ensures a decoupling of the basic vehicle control from the overall robotic path planning and following system. A commonly defined interface enables the adaption of the overall control system to various vehicle types.
A Graph-based Image Clustering Method Using Mutual Information Maximization

In this paper, we present a novel image clustering method that converts a typical image clustering problem into a graph node classification task, given only unlabelled data samples. Graph convolutional network is utilized to perform graph representation encoding. Unlike traditional clustering approaches relying on hand-crafted criteria, our method learns to cluster graph-structured data by maximizing mutual information between the global-graph representation and local-graph representations. The learnt graph embeddings can preserve global information in all locations of local-graphs such as pseudo labels for image clustering tasks. To test the generalization and robustness of the proposed method, we conduct experiments on two different benchmarks, such as fashion classification and finegrained object classification. The preliminary experiment results show that the proposed method outperforms all the baselines.
A novel clear-sky index for color sky imagery used in short-term irradiance forecasting

A method for mapping color sky images of convective fair-weather cumuli to a scalar irradiance metric is presented. While basic interpretations of irradiance from color sky imagery are sufficient for many irradiance forecasting applications, sky images containing convective fair-weather cumuli can often contain gray-colored regions. Gray regions of a cloud commonly relate cloud thickness, indicating a region that has a high potential to attenuate surface irradiance from the sun. However, existing irradiance metrics are not found to translate such gray regions in accordance with their importance to a clear-sky index for short-term forecasting. This method exploits the special structure of sky images in three-dimensional red-green-blue (RGB) Cartesian space. By applying principal component analysis and basic clustering operations, a planar discriminant for cloudy-to-clear-sky classification is found. Once the {cloudy, clear-sky} identity of all image pixels is determined, the cloudy and clear-sky pixel distributions are separately remapped in RGB space so that gray pixels score a higher optical depth value than white pixels, and clear-sky pixels are mapped to low optical depth values. The resulting clear-sky index is simply the grayscale interpretation of the remapped image. The method is described and demonstrated with two actual sky images, and errors for sequences of several hundred images are tabulated and discussed. Remapped images are shown to represent gray cloud regions with a relatively high cloud optical depth in comparison with white cloud regions. The proposed metric is shown, by comparison with experimentally measured irradiance time series, to produce a more accurate clear-sky index in comparison with other methods.
Human AI Symbiosis: The Role of Artificial Intelligence in Stratifying High-Risk Outpatient Senior Citizen Fall Events in a Non-connected Environments

Senior Citizen Falls are debilitating and harmful events. Not only does it negatively affect morality, psychology, self-esteem but also tends to be very repetitive and life costly. To prevent future falls, the outpatient senior citizen needs to be equipped with real-time monitoring sensors such as, a wrist band or a sensor necklace. Nonetheless, In the world where real-time sensor monitoring systems are not available due to connectivity limitations and economic affordability, the onus of senior citizen fall predicting, and preventing, needs to be on cognitive systems that are democratized in nature and yield learning from population health analysis. In this paper, we apply population collaborative filtering techniques and artificial intelligent models to cohort high risk senior citizen clusters and alert healthcare professionals and primary care family members.