Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental evaluation of segmentation algorithms for corner detection in sonar images

Corners usually appear very distinct from the rest of the scene in a mechanical scanning imaging sonar (MSIS) image, generally characterized by sharp intensities. The detection of corners is particularly useful in human-structured environments such as tanks because the knowledge on their location provides a way to compute the vehicle position. The combination of some basic operations typically used for image segmentation have great potential to detect and localize corners in sonar images automatically. This article proposes and evaluates with experimental data a set of image segmentation algorithms for corner detection in sonar scans. The developed algorithms are evaluated with ground truth, and their performance is analyzed following a few relevant metrics for autonomous navigation.
Motion Sensor-Based Small Cell Sleep Scheduling for 5G Networks

The fifth generation of mobile systems will rely on a dense deployment of small cells in order to meet its ambitious Key Performance Indicator targets. However, this dense deployment will result in a significant increase in the energy consumption of 5G networks, and thus, OPEX costs. Several small cell sleeping algorithms have been proposed in the literature but they require complex algorithms to decide when to switch on/off the small cells. On the basis of a distributed self-organizing approach, in this paper, we propose a low-cost, low-complexity small cell sleep scheduling algorithm to minimize the energy consumption of small cells in 5G and beyond networks. Our control utilizes a motion detection circuit to toggle the small cell between sleep and active modes based on the presence of a user. We evaluate the performance the algorithm on an experimental testbed and the results show that our algorithm achieves up to 20% reduction in energy consumption when compared to ‘always on’ approach.
iMTF-GRN: Integrative Matrix Tri-Factorization for Inference of Gene Regulatory Networks

Gene Regulatory Network (GRN) inference using computational approaches has been a highly pursued problem in bioinformatics. Various approaches have been developed to infer GRNs from gene expression data including statistical, machine learning and information theoretic approaches. However, a large number of regulatory relationships remain unpredicted even in the highly studied model organisms such as Escherichia coli and Saccharomyces cerevisiae. Besides, the regulatory relationships in higher eukaryotes with large genome sizes, such as humans and mice remain mostly unexplored. Majority of the approaches proposed in the literature on GRNs infer molecular interactions from gene expression data alone, despite the fact that gene expression regulation being a product of sequential interactions of multiple biological processes. To capture more regulatory relationships with higher precision, we apply a data fusion and inference model based on Non-negative Matrix Tri-factorization called integrative matrix tri-factorization for GRN inference (iMTF-GRN) that can integrate the diverse type of biological data in a relational learning framework. We, demonstrate that iMTF-GRN model shows improved accuracy in predicting TF-target and miRNA-target gene regulations and performs comparatively better over other state-of-the-art methods.
Skeletonization-Scheme-Based Adaptive Near Field Sampling for Radio Frequency Source Reconstruction

Radio frequency (RF) source reconstruction is useful for RF radiation analysis and interference diagnosis in Internet of Things. Near field (NF) sampling is a critical step of RF source reconstruction. Accurate RF source reconstruction usually requires a large number of NF samples, which results in tremendous effort of NF sampling. This article presents an adaptive NF sampling method based on the skeletonization scheme. First, sources to be reconstructed are related to NF samples through integral equation (IE). Second, the IE is discretized with the method of moments, and thus the interaction matrix between source and field points is found. Third, strong rank-revealing QR factorization is applied to the interaction matrix, which results in a permutation matrix, a row skeleton matrix, and a transformation matrix. Finally, a small number of skeleton sampling points are selected by analyzing the permutation matrix. The fields at skeleton sampling points can be used to calculate the fields at other sampling points through the transformation matrix. Hence, one only needs to perform NF sampling at a small number of skeleton sampling points, which significantly reduces the expenditure of NF sampling. Simulations using synthetic and measurement data are presented to show the effectiveness and advantages of the proposed sampling method.
Using K-Means to Determine Learner Typologies for Project-based Learning: A Case Study of the University of Education, Winneba

Currently, academic instructors in Ghana have some difficulty in grouping students for projects-based courses because of increasing student numbers. One of the recent challenges educational institutions and instructors are facing is the explosive growth of educational data and how to use this data to improve the quality of teaching. K-means clustering is an unsupervised Data Mining technique for grouping large datasets with insightful similarity patterns to expose hidden trends and behavior in each cluster. The purpose of this research is to apply K-means clustering algorithm to analyze students’ clusters for centered project-based learning. This research uses K clusters of 20. The clustering gave a low within cluster Sum of Square Error (SSE) of 3.60889. Clusters 1 and 6 have the highest member set of 32 each whiles clusters 8 and 9 have the lowest member set of 2. The results show that the K-means clustering algorithm is effective in grouping learners based on similar characteristics that indicate their performance. Assessments can also be tailored to suit all categories of learners for efficient results in project-based courses.
Testing Between Different Types of Poisson Mixtures with Applications to Neuroscience

Testing Between Different Types of Poisson Mixtures with Applications to Neuroscience by Yunran Chen Department of Statistical Science Duke University Date: Approved: Surya Tokdar, Supervisor Alexander Volfovsky Mike West An abstract of a thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the Department of Statistical Science in the Graduate School of Duke University 2019 Copyright c © 2019 by Yunran Chen All rights reserved
Mobility Analysis of Mobile VLC with Optical Zoom Antenna

This paper specifically analyse the mobility performance in optical zoom receiving mobile VLC. Optical zoom antenna adjust the optical gain by changing the FOV and then effect the signal to noise ratio (SNR) at the receiver. Firstly, with same transmit power, we analysis the communicable area between fixed focus VLC systems at different FOV. Besides, we contrast the mobility between fixed-focus and optical zoom VLC. Comparing with fixed focus VLC, the mobility can be increased by 90% with optical zoom receiving at 800Mbps. As data rate increasing, fixed focus system is completely disrupted, while the optical zoom antenna still ensure well mobility. According to these analysis, optical zoom antenna can guarantee a stable and high speed communication over a wide range in mobile VLC.
METRIC$^{+}$+: A Metamorphic Relation Identification Technique Based on Input Plus Output Domains

Metamorphic testing is well known for its ability to alleviate the oracle problem in software testing. The main idea of metamorphic testing is to test a software system by checking whether each identified metamorphic relation (MR) holds among several executions. In this regard, identifying MRs is an essential task in metamorphic testing. In view of the importance of this identification task, METRIC (METamorphic Relation Identification based on Category-choice framework) was developed to help software testers identify MRs from a given set of complete test frames. However, during MR identification, METRIC primarily focuses on the input domain without sufficient attention given to the output domain, thereby hindering the effectiveness of METRIC. Inspired by this problem, we have extended METRIC into METRIC+ by incorporating the information derived from the output domain for MR identification. A tool implementing METRIC+ has also been developed. Two rounds of experiments, involving four real-life specifications, have been conducted to evaluate the effectiveness and efficiency of METRIC+. The results have confirmed that METRIC+ is highly effective and efficient in MR identification. Additional experiments have been performed to compare the fault detection capability of the MRs generated by METRIC+ and those by μMT (another MR identification technique). The comparison results have confirmed that the MRs generated by METRIC+ are highly effective in fault detection.
LambdaLoss: Metric-Driven Loss for Learning-to Rank

How to directly optimize ranking metrics such as Normalized Dis-counted Cumulative Gain (NDCG) is an interesting but challenging problem, because ranking metrics are either flat or discontinuous everywhere. Among existing approaches, LambdaRank is a novel algo-rithm that incorporates metrics into its learning procedure. Though empirically effective, it still lacks theoretical justification. For example, what is the underlying loss that LambdaRank optimizes for? Due to this, it is unclear whether LambdaRank will always converge. In this paper, we present a well-defined loss for Lamb-daRank in a probabilistic framework and show that LambdaRank is a special configuration in our framework. This framework, which we call LambdaLoss, provides theoretical justification for Lamb-daRank. Furthermore, we propose a few more metric-driven loss functions in our LambdaLoss framework. Our loss functions have clear connection to ranking metrics and can be optimized in our framework efficiently. Experiments on three publicly available data sets show that our methods significantly outperform the state-of-the-art learning-to-rank algorithms. This confirms both the theoretical soundness and the practical effectiveness of the LambdaLoss framework.
Aspect-based sentiment analysis based on dynamic attention GRU

Aspect-level sentiment analysis is a fine-grained task that aims to identify the sentiment polarity (i.e., negative, neutral, or positive) of a specific target opinion in its context. Since the sentiment polarity of a target depends on the target itself and the semantics of the context, the target and the sentence should be treated equally and modeled interactively. For aspect-level sentiment analysis, we propose (1) a method to encode the aspect and sentence simultaneously, and (2) a neural network based on a dynamic attention gated recurrent unit. The simultaneous encoding manner can generate the target representation, which contains more contextual clues. The dynamic attention mechanism can achieve the attention values of contextual words and further generate the target representation dynamically. Experimental results achieved on a SemEval 2014 dataset (Laptop and Restaurant) show that our approach achieves a significant improvement in the accuracy rates over the standard attention-based models.
Joint feature fusion and optimization via deep discriminative model for mobile palmprint verification

Abstract. With recent advances in pattern recognition and computer vision, mobile palmprint authentication has become an emerging field to provide better facilities and ubiquitous computing for scientific and commercial communities. To effectively streamline this issue, researchers focus on improving authentication performance by designing deep convolutional neural networks. Despite the high potential of the state-of-the-art methods, the challenges of preprocessing computation cost, lack of training samples for big data application, and discriminative feature optimization remain to be carefully addressed. A deep mobile palmprint verification framework focusing on discriminative feature representation is proposed. To this end, an automatic feature mapping is learned from two well-known deep architectures via an effective weighted loss function. Thereafter, a convolution-based feature fusion block is followed by a surrogate model in the feature-matching phase for palmprint verification. From a practical point of view, our framework is cost-effective and can represent discriminative features with high performance. We demonstrate the effectiveness of our framework and mobile database for palmprint verification task beating the state-of-the-art on standard benchmarks. Moreover, experimental results show that our model outperforms previous ones, especially for the few-shot learning application, achieving equal error rates of 0.0281% and 0.0197% for IIT Delhi Touchless Palmprint Database and Hong Kong PolyU Palmprint databases, respectively. It is notable that all codes are open-source and may be accessed online.
Development of a practical system for computerized evaluation of descriptive answers of middle school level students

ABSTRACT Assessment plays an important role in education. Recently proposed machine learning-based systems for answer grading demand a large training data which is not available in many application areas. Creation of sufficient training data is costly and time-consuming. As a result, automatic long answer grading is still a challenge. In this paper, we propose a practical system for long or descriptive answer grading that can assess in a small class scenario. The system uses an expert-written reference answer and computes the similarity of a student answer with it. For the similarity computation, it uses several word level and sentence level similarity measures including TFIDF, Latent Semantic Indexing, Latent Dirichlet Analysis, TextRank summarizer, and neural sentence embedding-based InferSent. The student answer might contain certain facts that do not occur in the model answer. The system identifies such sentences, examine their relevance and correctness, and assigns extra marks accordingly. In the final phase, the system uses a clustering-based confidence analysis. The system is tested on an assessment of school-level social science answer books. The experimental results demonstrate that the system evaluates the answer books with high accuracy, the best root mean square error value is 0.59 on a 0–5 scoring scale.
Provisioning of Edge Computing Resources for Heterogeneous IoT Workload

With the evolution of cellular networks, the number of smart connected devices have witnessed a tremendous increase to reach billions by 2020 as forecasted by Cisco, constituting what is known today as the Internet of Things (IoT). With such explosion of smart devices, novel services have evolved and invaded almost every aspect of our lives; from e-health to smart homes and smart factories, etc. Such services come with stringent QoS requirements. While the current network infrastructure (4G) is providing an acceptable QoE to the end users, it will be rendered obsolete when considering the critical QoS requirements of such new services. Hence, to deliver the seamless experience these services provide, MEC has emerged as a promising technology to offer the cloud capabilities at the edge of the network, and hence, meeting the low latency requirements of such services. Moreover, another QoS parameter that needs to be addressed is the ultra high reliability demanded by the IoT services. Therefore,5G has evolved as a promising technology supporting ultra Reliable Low Latency Communication (uRLLC) and other service categories. While integrating uRLLC with MEC would help in realizing such services, it would however raise some challenges for the network operator. Thus, in this thesis, we address some of these challenges. Specifically, in the second chapter, we address the problem of MEC Resource Provisioning and Workload Assignment (RPWA) in an IoT environment, with heterogeneous workloads demanding services with stringent latency requirements. We formulate the problem as an MIP with the objective to minimize the re-sources deployment cost. Due to the complexity of the problem, we will develop a decomposition approach (RPWA-D) to solve the problem and study through different simulations, the performance of our approach. In chapter 3, we consider both ultra high reliability and low latency requirements of different IoT services, and solve the Workload Assignment problem (WA) in an IoT environment. We formulate the problem as an MIP with the objective of maximizing the admitted workload to the network. After showing the complexity of the problem and the non scalability of the WA-MIP, we propose two different approaches; WA-D and WA-Tabu. The results show that WA-Tabu was the most efficient and scalable.
A modeling and computational study of the frustration index in signed networks

Computing the frustration index of a signed graph is a key step toward solving problems in many fields including social networks, political science, physics, chemistry, and biology. The frustration index determines the distance of a network from a state of total structural balance. Although the definition of the frustration index goes back to the 1950s, its exact algorithmic computation, which is closely related to classic NP‐hard graph problems, has only become a focus in recent years. We develop three new binary linear programming models to compute the frustration index exactly and efficiently as the solution to a global optimization problem. Solving the models with prioritized branching and valid inequalities in Gurobi, we can compute the frustration index of real signed networks with over 15 000 edges in less than a minute on inexpensive hardware. We provide extensive performance analysis for both random and real signed networks and show that our models outperform all existing approaches by large factors. Based on resolution time, algorithm output, and effective branching factor we highlight the superiority of our models to both exact and heuristic methods in the literature.
Clustering Users by Their Mobility Behavioral Patterns

The immense stream of data from mobile devices during recent years enables one to learn more about human behavior and provide mobile phone users with personalized services. In this work, we identify clusters of users who share similar mobility behavioral patterns. We analyze trajectories of semantic locations to find users who have similar mobility “lifestyle,” even when they live in different areas. For this task, we propose a new grouping scheme that is called Lifestyle-Based Clustering (LBC). We represent the mobility movement of each user by a Markov model and calculate the Jensen–Shannon distances among pairs of users. The pairwise distances are represented by a similarity matrix, which is used for the clustering. To validate the unsupervised clustering task, we develop an entropy-based clustering measure, namely, an index that measures the homogeneity of mobility patterns within clusters of users. The analysis is validated on a real-world dataset that contains location-movements of 50,000 cellular phone users that were analyzed over a two-month period.
Video Summarization And Video Highlight Selection Tools To Facilitate Fire Incident Management

This paper reports on the added value of combining different types of sensor data and geographic information for fire incident management. A survey was launched within the Belgian fire community to explore the need of added value and the use of new types of sensor data during a fire incident. This evaluation revealed that people are visually-oriented and that video footages and images are of great value to gain insights in a particular problem. However, due to the limited available time (i.e., fast decisions need to be taken) and the large amount of cameras it is not feasible to analyze all video footages sequentially. To solve this problem we propose a video summarization mechanism and a video highlight selection tool based on the automatic generated image and video tags.
Immersive Analytics: Theory and Research Agenda

Advances in a variety of computing fields, including “big data,” machine learning, visualization, and augmented/mixed/virtual reality, have combined to give rise to the emerging field of immersive analytics, which investigates how these new technologies support analysis and decision making. Thus far, we feel that immersive analytics research has been somewhat ad hoc, possibly owing to the fact that there is not yet an organizing framework for immersive analytics research. In this paper, we address this lack by proposing a definition for immersive analytics and identifying some general research areas and specific research questions that will be important for the development of this field. We also present three case studies that, while all being examples of what we would consider immersive analytics, present different challenges, and opportunities. These serve to demonstrate the breadth of immersive analytics and illustrate how the framework proposed in this paper applies to practical research.
Safety benefit evaluation of intelligent driving systems based on multisource data mining

Different manufacturers achieve intelligent driving system function diversely, which imposes higher impartiality requirements for the evaluation method of the third party. To this end, this article presents a safety benefit evaluation method of intelligent driving systems based on multi-source data mining. On the basis of the discussion over the nature of general system identification, this approach uses neural network to learn the behavior of the evaluated object using the running vehicle data collected and provided by the manufacturer. Combined with the trained network controller, the test scenario model and the car following model extracted from the field operational tests data, and with the occupant injury model obtained from the accident data, Monte Carlo random simulation is used to calculate the injury risk with or without the evaluated system, then the safety benefit by comparison is estimated. In this article, the adaptive cruise system and the automatic emergency braking system are evaluated. The results show that the neural network can accurately imitate the behavior of the object to be evaluated. There is only 0.01 error between the evaluation results using this network and the real object.
Multiple Hypothesis Tracking for Detecting and Tracking Humans

— This paper presents a whole perception system of moving purpose detection associate degreed target trailing for robustly following target humans in an unknown indoor dynamic surroundings. To find moving points beneath grid-based formulation, a changed inverse observation model is planned to beat many often happened detection limitations. Next, connected human-extraction techniques area unit planned to filter less attainable clusters for sleuthing potential human target from these moving points. Finally, the multiple hypothesis trailing algorithmic program is enforced to take care of the information association downside for enhancing the reliableness and lustiness of the human trailing once measurements area unit howling. totally different levels of experiments are performed to guage the effectiveness of the planned algorithmic program framework.