Miracle-dz/UniFAR-FTUs huggingface dataset:
The Double Sphere Camera Model

Vision-based motion estimation and 3D reconstruction, which have numerous applications (e.g., autonomous driving, navigation systems for airborne devices and augmented reality) are receiving significant research attention. To increase the accuracy and robustness, several researchers have recently demonstrated the benefit of using large field-of-view cameras for such applications. In this paper, we provide an extensive review of existing models for large field-of-view cameras. For each model we provide projection and unprojection functions and the subspace of points that result in valid projection. Then, we propose the Double Sphere camera model that well fits with large field-of-view lenses, is computationally inexpensive and has a closed-form inverse. We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i.e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians. We also provide qualitative results and discuss the performance of all models.
Effective Reformulation of Query for Code Search Using Crowdsourced Knowledge and Extra-Large Data Analytics

Software developers frequently issue generic natural language queries for code search while using code search engines (e.g., GitHub native search, Krugle). Such queries often do not lead to any relevant results due to vocabulary mismatch problems. In this paper, we propose a novel technique that automatically identifies relevant and specific API classes from Stack Overflow Q & A site for a programming task written as a natural language query, and then reformulates the query for improved code search. We first collect candidate API classes from Stack Overflow using pseudo-relevance feedback and two term weighting algorithms, and then rank the candidates using Borda count and semantic proximity between query keywords and the API classes. The semantic proximity has been determined by an analysis of 1.3 million questions and answers of Stack Overflow. Experiments using 310 code search queries report that our technique suggests relevant API classes with 48% precision and 58% recall which are 32% and 48% higher respectively than those of the state-of-the-art. Comparisons with two state-of-the-art studies and three popular search engines (e.g., Google, Stack Overflow, and GitHub native search) report that our reformulated queries (1) outperform the queries of the state-of-the-art, and (2) significantly improve the code search results provided by these contemporary search engines.
Network Connectivity Optimization: Fundamental Limits and Effective Algorithms

Network connectivity optimization, which aims to manipulate network connectivity by changing its underlying topology, is a fundamental task behind a wealth of high-impact data mining applications, ranging from immunization, critical infrastructure construction, social collaboration mining, bioinformatics analysis, to intelligent transportation system design. To tackle its exponential computation complexity, greedy algorithms have been extensively used for network connectivity optimization by exploiting its diminishing returns property. Despite the empirical success, two key challenges largely remain open. First, on the theoretic side, the hardness, as well as the approximability of the general network connectivity optimization problem are still nascent except for a few special instances. Second, on the algorithmic side, current algorithms are often hard to balance between the optimization quality and the computational efficiency. In this paper, we systematically address these two challenges for the network connectivity optimization problem. First, we reveal some fundamental limits by proving that, for a wide range of network connectivity optimization problems, (1) they are NP-hard and (2) (1-1/e) is the optimal approximation ratio for any polynomial algorithms. Second, we propose an effective, scalable and general algorithm (CONTAIN) to carefully balance the optimization quality and the computational efficiency.
Computerised Interpretation Systems for Cardiotocography for Both Home and Hospital Uses

Improving the accuracy and consistency of interpretation results for foetal monitoring has been an active research direction in both obstetrics and gynaecology. In this paper, we have developed computer-aided analysis systems for use both in hospitals and at home that incorporate automatic scoring functions to evaluate the foetal conditions in the cavity of the uterus. These systems can analyse any segment of data in a foetal monitoring record. Our novel systems can accurately identify the CTG patterns, such as FHR baseline, foetal movements, uterine contractions, accelerations and type of decelerations, thus making the interpretation results more accurate. There are two modes of scoring: automatic and manual, and the system consists of a number of popular scoring methods, including the Kreb's, Fischer, and improved Fischer scoring methods and the ACOG three-tier classification methods. According to clinical tests in hospitals, the systems have comparable accuracy to obstetricians' interpretations. Computerised interpretation thus provides a supplement to traditional analysis that could help obstetricians function more effectively.
Assessing the Readability of Policy Documents: The Case of Terms of Use of Online Services

Whether for using online services or dealing with legal issues, citizens are often requested to sign/accept policy documents that are intended to commit them to specific rights and duties. Usually such documents are difficult to read due to their nature, the length of sentences, complex terms used, etc. Since understanding is a prerequisite to making an informed decision, it is important to assess whether citizens, on average, would have the ability to comprehend those texts. We consider here that the authors of those documents should keep in mind their target audience and tailor their texts to their profiles. A good indicator that can be used in this context is the average education level of the citizens of a country. On the other hand, readability measures/scores of texts have been used to assess if educational material is suitable for the intended students' level of education attainment. In this paper, we use this information to assess the readability of terms of use of online services and correlate this information with the education attainment of the countries of their target audience. Our analysis shows that more efforts need to be put into making such policy documents understandable by a broader audience, uncovering a need for standards and tools in this area.
Floating Drain-Based Measurement of ON-State Voltage of an OTFT for Sensing Applications

The threshold voltage of an organic thin-film transistor (OTFT) is sensitive to several variables, which makes it attractive for sensing applications. However, its measurement is not straightforward, especially, when mobility is gate voltage-dependent, and source and drain resistances are significant. The on-state voltage of a TFT does not suffer from these drawbacks, and thus, offers an alternative variable for use as a sensing variable. However, conventional measurement of on-state voltage marking the onset of conduction in the device requires measurement of very low current values, and extrapolation as a result of which it is not commonly employed. This paper demonstrates a simpler alternative method for measurement of on-state voltage that requires measurement of floating drain voltage as source voltage is linearly ramped while maintaining gate voltage constant. The drain voltage tracks the source voltage until the channel is completely removed. 2-D numerical simulation results are presented to validate that the proposed technique for the extraction of on-state voltage yields reliable estimates under different device conditions. Experimental results obtained with pentacene/poly (4-vinyl phenol)-based top contact bottom-gate OTFTs are also presented to illustrate the proposed concept.
A Distributed Systems Perspective on Industrial IoT

Industrial Internet of Things (IoT) is frequently mentioned as one of the emerging areas in computing that may have a high potential real-world impact in the coming decade. In this paper, we analyze the challenges posed and opportunities offered by industrial IoT solutions from the distributed systems perspective. We focus on the sensing and actuation layer, which results from the the tight coupling of such solutions with the physical objects they monitor and control. We analyze this layer with respect to interoperability, scalability, and dependability, which are key features of many distributed systems.
Automatic Attendance Capturing Using Histogram of Oriented Gradients on Facial Images

Humans mostly use faces to identify/recognise individuals and the recent improvement in the capability of computing now allow recognition and detection automatically. However, there still exist quite a number of problems in the automatic recognition of facial images. Histogram of Oriented Gradients (HOG) has been recently adopted and seen as a standard for efficient face recognition and object detection generally. In this paper, we investigate and discuss a simple but effective approach to capturing student’s attendance register in a lecture hall by making use of HOG features for detecting and recognising students face at different moods, orientations, and illuminations. Our experiment detection and recognition output show a good performance on our facial image database obtained from the University of Johannesburg, this performance is due to HOG descriptors attributes which are robust to changes in rotation and illuminations. Our system will help to save instructional staff/lecturer time by eliminating manual calling of students name and also help monitor students.
Parallel Error Detection Using Heterogeneous Cores

Microprocessor error detection is increasingly important, as the number of transistors in modern systems heightens their vulnerability. In addition, many modern workloads in domains such as the automotive and health industries are increasingly error intolerant, due to strict safety standards. However, current detection techniques require duplication of all hardware structures, causing a considerable increase in power consumption and chip area. Solutions in the literature involve running the code multiple times on the same hardware, which reduces performance significantly and cannot capture all errors. We have designed a novel hardware-only solution for error detection, that exploits parallelism in checking code which may not exist in the original execution. We pair a high-performance out-of-order core with a set of small low-power cores, each of which checks a portion of the out-of-order core's execution. Our system enables the detection of both hard and soft errors, with low area, power and performance overheads.
GraphU: A Unified Vertex-Centric Parallel Graph Processing Platform

Many synchronous and asynchronous distributed platforms based on the Bulk Synchronous Parallel (BSP) model have been built for large-scale vertex -centric graph processing. Unfortunately, a program designed for a synchronous platform may not work properly on an asynchronous one. As a result, given the same problem, end users may be required to design different parallel algorithms for different platforms. Recently, we have proposed a unified programming model, DFA-G (Deterministic Finite Automaton for Graph processing), which expresses the computation at a vertex as a series of message-driven state transitions. It has the attractive property that any program modeled after it can run properly across synchronous and asynchronous platforms. In this demo, we first propose a framework of complexity analysis for DFA-G automaton and show that it can significantly facilitate complexity analysis on asynchronous programs. Due to the existing BSP platforms' deficiency in supporting efficient DFA-G execution, we then develop a new prototype platform, GraphU. GraphU was built on the popular open-source Giraph project. But it entirely removes synchronization barriers and decouples remote communication from vertex computation. Finally, we empirically evaluate the performance of various DFA-G programs on GraphU by a comparative study. Our experiments validate the efficacy of the proposed complexity analysis approach and the efficiency of GraphU.
Parking Spot Estimation and Mapping Method for Mobile Robots

Self-driving vehicles rely on detailed semantic maps of the environment for operating. In this letter, we propose a method to autonomously generate such a semantic map enriched with knowledge of parking spot locations. Our method detects and uses parked vehicles in the surroundings to estimate parking lot topology and infer vacant parking spots via a graph-based approach. We show that our method works for parking lot structures in different environments, such as structured parking lots, unstructured/unmarked parking lots, and typical suburban environments. Using the proposed graph-based approach to infer the parking lot structure, we can extend the estimated parking spots by $57\%$, averaged over six different areas with ten trials each. We also show that the accuracy of our algorithm increases when combining multiple trials over multiple days. With ten trials combined, we managed to estimate the whole parking lot structure and detected all parking spots in four out of the six evaluated areas.
A State-of-the-Art Review of Machine Learning Techniques for Fraud Detection Research

The area of fraud detection has been traditionally correlated with data mining and text mining. Even before the "big data" phenomena started in 2008, text mining and data mining were used as instruments of fraud detection. However, the limited technological capabilities of the pre-big data technologies made it very difficult for researchers to run fraud detection algorithms on large amounts of data. This paper reviews the existing research done in fraud detection across different areas with the aim of investigating the machine learning techniques used and find out their strengths and weaknesses. It used the systematic quantitative literature review methodology to review the research studies in the field of fraud detection research within the last decade using machine learning techniques. Various combinations of keywords were used to identify the pertinent articles and were retrieved from ACM Digital Library, IEEE Xplorer Digital Library, Science Direct, Springer Link, etc. This search used a sample of 80 relevant articles (peer-reviewed journals articles and conference papers). The most used machine learning techniques were identified, and their strengths and weaknesses. Finally, the conclusion, limitations and future work have been shown.
Group Exploration of Dynamic Tori

Mobile agents (agents) are activities which can move autonomously in a networked system and execute actions at visited nodes. One of the most fundamental problems of agents is exploration, which requires that each node should be visited by at least one agent. For a long time, researchers focus on exploration of static networks. However, exploration of dynamic networks comes to be studied recently. In this paper, we consider exploration of a dynamic torus under some constraints on the dynamics (or topology changes). An n × m torus (3 ≤ n ≤ m) is considered as a collection of n row rings and m column rings. The constraint on the dynamics is that each ring should be 1-interval connected, which allows at most one link to be missing at any time in each ring. On this n × m dynamic torus, we propose exploration algorithms with and without the link presence detection. With the link presence detection, an agent can detect which incident links are missing (if exist) before determining its next move. On the other hand, without the link presence detection, an agent has to determine its next move without knowing which incident links are missing, which makes the agent stay on the same node when the link necessary to the move is missing. We prove for exploration of the n × m dynamic torus that, without the link presence detection, n+1 agents are necessary and sufficient, and, with the link presence detection, ⌈n/2⌉ + 1 agents are necessary and sufficient. Moreover, for both cases, we propose asymptotically optimal algorithms with respect to both the numbers of agents and rounds when n = m.
The Dark Side of DNN Pruning

DNN pruning has been recently proposed as an effective technique to improve the energy-efficiency of DNN-based solutions. It is claimed that by removing unimportant or redundant connections, the pruned DNN delivers higher performance and energy-efficiency with negligible impact on accuracy. However, DNN pruning has an important side effect: it may reduce the confidence of DNN predictions. We show that, although top-1 accuracy may be maintained with DNN pruning, the likelihood of the class in the top-1 is significantly reduced when using the pruned models. For applications such as Automatic Speech Recognition (ASR), where the DNN scores are consumed by a successive stage, the workload of this stage can be dramatically increased due to the loss of confidence in the DNN. An ASR system consists of a DNN for computing acoustic scores, followed by a Viterbi beam search to find the most likely sequence of words. We show that, when pruning the DNN model used for acoustic scoring, the Word Error Rate (WER) is maintained but the execution time of the ASR system is increased by 33%. Although pruning improves the efficiency of the DNN, it results in a huge increase of activity in the Viterbi search since the output scores of the pruned model are less reliable. Based on this observation, we propose a novel hardware-based ASR system that effectively integrates a DNN accelerator for pruned models with a Viterbi accelerator. In order to avoid the aforementioned increase in Viterbi search workload, our system loosely selects the N-best hypotheses at every time step, exploring only the N most likely paths. To avoid an expensive sort of the hypotheses based on their likelihoods, our accelerator employs a set-associative hash table to keep track of the best paths mapped to each set. In practice, this solution approaches the selection of N-best, but it requires much simpler hardware. Our approach manages to efficiently combine both DNN pruning and Viterbi search, and achieves 9x energy savings and 4.2x speedup with respect to the state-of-the-art ASR solutions.
UniLoc: A Unified Mobile Localization Framework Exploiting Scheme Diversity

Current localization schemes on mobile devices are experiencing great diversity that is mainly shown in two aspects: the large number of available localization schemes and their diverse performance. This paper presents UniLoc, a unified framework that gains improved performance from multiple localization schemes by exploiting their diversity. UniLoc predicts the localization error of each scheme online based on an error model and real-time context. It further combines the results of all available schemes based on the error prediction results and an ensemble learning algorithm. The combined result is more accurate than any individual schemes. With the flexible design of error modeling and ensemble learning, UniLoc can easily integrate a new localization scheme. The energy consumption of UniLoc is low, since its computation, including both error prediction and ensemble learning, only involves simple linear calculation. Our experience with extensive experiments tells that such easy aggregation incurs little overhead in integrating and training a localization scheme, but gains substantially from the scheme diversity. UniLoc outperforms individual localization schemes by 1.6X in a variety of environments, including >89% new places where we did not train the error models.
Technical Debt-Related Information Asymmetry between Finance and IT

This position paper proposes a new stream of research targeted at technical debt as a source of information asymmetry between finance and IT professionals involved in information technology investment decisions. Finance teams interact with technology teams in several ways, predominantly when business cases require review and during the annual budgeting process. During these discrete interactions, finance teams are required to digest large amounts of technical strategy and architectural information chockfull of technical terminology and diagrams. Typically, the estimates for effort are soft and risk is difficult to measure. It is within this context that finance approves budgets and projects that inevitably result in the accumulation of technical debt. This paper discusses some of the dynamics at work between finance and IT teams within large complex organizations when they meet to make technology investment decisions. In addition, future research is proposed aimed at reducing information asymmetry, thereby leading to improved IT investment decisions and better management of technical debt.
Online Parameter Selection for Web-based Ranking Problems

Web-based ranking problems involve ordering different kinds of items in a list or grid to be displayed in mediums like a website or a mobile app. In most cases, there are multiple objectives or metrics like clicks, viral actions, job applications, advertising revenue and others that we want to balance. Constructing a serving algorithm that achieves the desired tradeoff among multiple objectives is challenging, especially for more than two objectives. In addition, it is often not possible to estimate such a serving scheme using offline data alone for non-stationary systems with frequent online interventions. We consider a large-scale online application where metrics for multiple objectives are continuously available and can be controlled in a desired fashion by changing certain control parameters in the ranking model. We assume that the desired balance of metrics is known from business considerations. Our approach models the balance criteria as a composite utility function via a Gaussian process over the space of control parameters. We show that obtaining a solution can be equated to finding the maximum of the Gaussian process, practically obtainable via Bayesian optimization. However, implementing such a scheme for large-scale applications is challenging. We provide a novel framework to do so and illustrate its efficacy in the context of LinkedIn Feed. In particular, we show the effectiveness of our method by using both offline simulations as well as promising online A/B testing results. At the time of writing this paper, the method described was fully deployed on the LinkedIn Feed.
Transform Blockchain into Distributed Parallel Computing Architecture for Precision Medicine

This paper provides a vision and proposes mechanisms to transform the blockchain duplicated computing into distributed parallel computing architecture by transforming smart contract which features data driven from the ground up to support moving computing to native data strategy. This new distributed parallel computing architecture can be employed to build a large size of data set from various distributed hosted medical data sets which might consist of personal electronic medical record (EMR) and various medical data. This large medical data set will enable researchers to jump start the deep learning research for medical domain. Distributed data management, distributed data sharing, and distributed learning are the core mechanisms in the new architecture. The required new researches and developments to employ Google federated learning and transfer learning algorithms in this new architecture are discussed. The approach and mechanism enabled by the new architecture is illustrated to build a real world evidence of clinical trial toward personal and precision medicine. Research issues and technical challenges are provided.