Miracle-dz/UniFAR-FTUs huggingface dataset:
When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images

Automated object detectors on Unmanned Aerial Vehicles (UAVs) are increasingly employed for a wide range of tasks. However, to be accurate in their specific task they need expensive ground truth in the form of bounding boxes or positional information. Weakly-Supervised Object Detection (WSOD) overcomes this hindrance by localizing objects with only image-level labels that are faster and cheaper to obtain, but is not on par with fully-supervised models in terms of performance. In this study we propose to combine both approaches in a model that is principally apt for WSOD, but receives full position ground truth for a small number of images. Experiments show that with just 1% of densely annotated images, but simple image-level counts as remaining ground truth, we effectively match the performance of fully-supervised models on a challenging dataset with scarcely occurring wildlife on UAV images from the African savanna. As a result, with a very limited amount of precise annotations our model can be trained with ground truth that is orders of magnitude cheaper and faster to obtain while still providing the same detection performance.
A Component-based Approach to Hand-based Verification and identification System

Hand-based verification/identification represent a key biometric technology with a wide range of potential applications both in industry and government. Traditionally, hand-based verification and identification systems exploit information from the whole hand for authentication or recognition purposes. To account for hand and finger motion, guidance pegs are used to fix the position and orientation of the hand. In this dissertation, we have investigated a component-based approach to hand-based verification and identification which improves both accuracy and robustness as well as ease of use due to avoiding pegs. Our approach accounts for hand and finger motion by decomposing the hand silhouette in different regions corresponding to the back of the palm and the fingers. To improve accuracy and robustness, verification/recognition is performed by fusing information from different parts of the hand. The proposed approach operates on 2D images acquired by placing the hand on a flat lighting table and does not require using guidance pegs or extracting any landmark points on the hand. To decompose the silhouette of the hand in different regions, we have devised a robust methodology based on an iterative morphological filtering scheme. To capture the geometry of the back of the palm and the fingers, we employ region descriptors based on high-order Zernike moments which are computed using an efficient methodology. The proposed approach has been evaluated both for verification and recognition purposes on a database of 101 subjects with 10 images per subject, illustrating high accuracy and robustness. Comparisons with related ap-
Effect of reject option on classifier performance

Binary classifier systems that provide class membership probabilities as outputs may be augmented by a reject option to refuse classification for cases that either appear to be outliers, or for which the output probability is around 0.5. We investigated the effect of these two reject options (called “distance reject” and “ambiguity reject”, respectively) on the calibration and discriminatory power of logistic regression models. Outliers were found using one-class support vector machines. Discriminatory power was measured by the area under the ROC curve, and calibration by the Hosmer-Lemeshow goodness-offit test. Using an artificial data set and a real-world data set for diagnosing myocardial infarction, we found that ambiguity reject increased discriminatory power, while distance reject decreased it. We did not observe any influence of either reject option on the calibration of the logistic regression models.
Genetic algorithm with fuzzy fitness function for feature selection

Feature selection is an important preprocessing task for any pattern recognition or data mining application. Though lots of well developed statistical and mathematical techniques of feature selection exist they do not match the imprecise and incomplete nature of most of the real world problems. Recently soft computing techniques i.e. neurocomputing, fuzzy logic, genetic algorithm etc. are gaining growing popularity for their remarkable ability of handling real life data like a human being in an environment of uncertainty, imprecision and implicit knowledge. In this work, a genetic algorithm in conjunction with a fuzzy fitness function, a fuzzy measure for evaluation of the quality of a feature has been proposed for feature subset selection. GA based feature selection algorithms are robust but their computation time is high specially when they are used with a classifier for fitness evaluation. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function by separating the two stages feature selection and classification. Simulation over two data sets shows the efficiency of the proposed technique for achieving near optimal solution in practical problems specially when the data set contains a large number of features.
Risk Assessment for Failure Mode and Effects Analysis Using the Bonferroni Mean and TODIM Method

As a safety and reliability analysis technique, failure mode and effects analysis (FMEA) has been used extensively in several industries for the identification and elimination of known and potential failures. However, some shortcomings associated with the FMEA method have limited its applicability. This study aims at presenting a comprehensive FMEA model that could efficiently handle the preference interdependence and psychological behavior of experts in the process of failure modes ranking. In this model, a linguistic variable expressed by the interval-valued Pythagorean fuzzy number (IVPFN) is utilized by experts to provide preference information with regard to failure modes’ evaluation and risk factors’ weight. Then, to depict the interdependent relationships between experts’ preferences, the Bonferroni mean operator is extended to IVPFN to aggregate the experts’ preference. Subsequently, an extended TODIM approach in which the dominance degree of failure modes is calculated by grey relational analysis is utilized to determine the risk priority of failure modes. Finally, a practical example concerning the risk assessment of a nuclear reheat valve system is provided to demonstrate the effectiveness and feasibility of the presented method. In addition, a sensitivity analysis and comparison analysis are conducted, and the results show that the preference interdependence and psychological behavior of experts have an important effect on the risk priority of failure modes.
Improving Abstractive Document Summarization with Salient Information Modeling

Comprehensive document encoding and salient information selection are two major difficulties for generating summaries with adequate salient information. To tackle the above difficulties, we propose a Transformer-based encoder-decoder framework with two novel extensions for abstractive document summarization. Specifically, (1) to encode the documents comprehensively, we design a focus-attention mechanism and incorporate it into the encoder. This mechanism models a Gaussian focal bias on attention scores to enhance the perception of local context, which contributes to producing salient and informative summaries. (2) To distinguish salient information precisely, we design an independent saliency-selection network which manages the information flow from encoder to decoder. This network effectively reduces the influences of secondary information on the generated summaries. Experimental results on the popular CNN/Daily Mail benchmark demonstrate that our model outperforms other state-of-the-art baselines on the ROUGE metrics.
Full Virtual ADAS Testing. Application to the Typical Emergency Braking EuroNCAP Scenario

The requirements in terms of sensing accuracy and the evolution of road safety legislation directly impact the development of perception sensors. In this paper, we present how a combination of 3D electromagnetic physics simulation, real-time scenario, sensor processing simulation and dynamic connection to ADAS software can be implemented to fulfil advanced sensor simulation. The challenges to lift for testing the development of new optical and electromagnetic sensors and processing software to support early detection of hazards and collision avoidance is addressed. The study illustrated in the following can be applied to any type of ADAS function and autonomous vehicles development. The Car-To-Car-Rear-Braking (CCRB) Euro-NCAP testing scenario is shown and the simulation results are discussed.
Unleashing the Power of Learning: An Enhanced Learning-Based Approach for Dynamic Binary Translation

Dynamic binary translation (DBT) is a key system technology that enables many important system applications such as system virtualization and emulation. To achieve good performance, it is important for a DBT system to be equipped with high-quality translation rules. However, most translation rules in existing DBT systems are created manually with high engineering efforts and poor quality. To solve this problem, a learning-based approach was recently proposed to automatically learn semantically-equivalent translation rules, and symbolic verification is used to prove the semantic equivalence of such rules. But, they still suffer from some shortcomings. In this paper, we first give an in-depth analysis on the constraints of prior learning-based methods and observe that the equivalence requirements are often unduly restrictive. It excludes many potentially high-quality rule candidates from being included and applied. Based on this observation, we propose an enhanced learning-based approach that relaxes such equivalence requirements but supplements them with constraining conditions to make them semantically equivalent when such rules are applied. Experimental results on SPEC CINT2006 show that the proposed approach can improve the dynamic coverage of the translation from 55.7% to 69.1% and the static coverage from 52.2% to 61.8%, compared to the original approach. Moreover, up to 1.65X performance speedup with an average of 1.19X are observed.
Efficient Processing of Range Queries in Main Memory

Over the last two decades, the hardware landscape has fundamentally changed, shaping the architecture of modern database systems. First, main memory becomes a popular choice for primary data storage, since current server machines can feature up to several terabytes of main memory. Second, modern CPUs get tremendously parallel providing both dataand task-level parallelism. They support SIMD instructions, which can simultaneously process multiple data elements. Moreover, stand-alone CPUs host an ever increasing number of cores, which can be considered as independent processing units. Recent many-core CPUs install up to 72 cores on one chip. Database systems employ index structures as means to accelerate search queries. Over the last years, the research community has proposed many different in-memory approaches that optimize cache misses instead of disk I/O, as opposed to disk-based systems, and make use of the grown parallel capabilities of modern CPUs. However, these techniques mainly focus on single-key lookups, but neglect equally important range queries. Range queries are an ubiquitous operator in data management commonly used in numerous domains, such as genomic analysis, sensor networks, or online analytical processing. The main goal of this dissertation is thus to improve the capabilities of main-memory database systems with regard to executing range queries. To this end, we first propose a cache-optimized, updateable main-memory index structure, the cache-sensitive skip list, which targets the execution of range queries on single database columns. Second, we study the performance of multidimensional range queries on modern hardware, where data are stored in main memory and processors support SIMD instructions and multi-threading. We re-evaluate a previous rule of thumb suggesting that, on disk-based systems, scans outperform index structures for selectivities of approximately 15-20% or more. To increase the practical relevance of our analysis, we also contribute a novel benchmark consisting of several realistic multidimensional range queries applied to real-world genomic data. Third, based on the outcomes of our experimental analysis, we devise a novel, fast and space-efficient, main-memory based index structure, the BB-Tree, which supports multidimensional range and point queries and provides a parallel search operator that leverages the multi-threading capabilities of modern CPUs.
ArbEngVec : Arabic-English Cross-Lingual Word Embedding Model

Word Embeddings (WE) are getting increasingly popular and widely applied in many Natural Language Processing (NLP) applications due to their effectiveness in capturing semantic properties of words; Machine Translation (MT), Information Retrieval (IR) and Information Extraction (IE) are among such areas. In this paper, we propose an open source ArbEngVec which provides several Arabic-English cross-lingual word embedding models. To train our bilingual models, we use a large dataset with more than 93 million pairs of Arabic-English parallel sentences. In addition, we perform both extrinsic and intrinsic evaluations for the different word embedding model variants. The extrinsic evaluation assesses the performance of models on the cross-language Semantic Textual Similarity (STS), while the intrinsic evaluation is based on the Word Translation (WT) task.
Enhancing the Usability of Brain-Computer Interface Systems

Brain-computer interfaces (BCIs) aim to enable people to interact with the external world through an alternative, nonmuscular communication channel that uses brain signal responses to complete specific cognitive tasks. BCIs have been growing rapidly during the past few years, with most of the BCI research focusing on system performance, such as improving accuracy or information transfer rate. Despite these advances, BCI research and development is still in its infancy and requires further consideration to significantly affect human experience in most real-world environments. This paper reviews the most recent studies and findings about ergonomic issues in BCIs. We review dry electrodes that can be used to detect brain signals with high enough quality to apply in BCIs and discuss their advantages, disadvantages, and performance. Also, an overview is provided of the wide range of recent efforts to create new interface designs that do not induce fatigue or discomfort during everyday, long-term use. The basic principles of each technique are described, along with examples of current applications in BCI research. Finally, we demonstrate a user-friendly interface paradigm that uses dry capacitive electrodes that do not require any preparation procedure for EEG signal acquisition. We explore the capacitively measured steady-state visual evoked potential (SSVEP) response to an amplitude-modulated visual stimulus and the auditory steady-state response (ASSR) to an auditory stimulus modulated by familiar natural sounds to verify their availability for BCI. We report the first results of an online demonstration that adopted this ergonomic approach to evaluating BCI applications. We expect BCI to become a routine clinical, assistive, and commercial tool through advanced EEG monitoring techniques and innovative interface designs.
Learning to Relate from Captions and Bounding Boxes

In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object bounding box annotations as the sole source of supervision. Our proposed approach uses a top-down attention mechanism to align entities in captions to objects in the image, and then leverage the syntactic structure of the captions to align the relations. We use these alignments to train a relation classification network, thereby obtaining both grounded captions and dense relationships. We demonstrate the effectiveness of our model on the Visual Genome dataset by achieving a recall@50 of 15% and recall@100 of 25% on the relationships present in the image. We also show that the model successfully predicts relations that are not present in the corresponding captions.
Reinforcement learning algorithm for 5G indoor device‐to‐device communications

Fifth generation (5G), the next generation telecommunications will be striking the markets in near future. Device‐to‐device (D2D) communication would be a part of 5G to serve communication needs for billions of connected devices to support high data rate ultrareliable low latency communications. Indoor 5G will be relying on distributed small cell solutions and D2D along with machine‐to‐machine connections. Machine learning is one of the most promising tools for providing the best set of solutions to learn the influential scenarios and certain parameters of the communication networks. This research proposes reinforcement‐learning‐based latency controlled D2D connectivity (RL‐LCDC) algorithm and its Q‐learning approach in an indoor D2D communication network for strong 5G connectivity with minimum latency. The proposed approach, RL‐LCDC efficiently discovers the neighbors, decides the D2D link, and adaptively controls the communication range for maximum network connectivity. The results show that RL‐LCDC optimizes the connectivity with lower end‐to‐end delay and better energy efficiency with efficient convergence time when compared with other conventional schemes.
An Experience Report on Implementing a Custom Agile Methodology on a C++/Python Project

The usage of a relational database seemed the perfect solution to this: it could allow the execution of several production tasks at the same time, having the certainty that the data was kept consistent thanks to the isolation and synchronisation given by the transactional model. Implementing transactions in the C programs in order to keep the data in text files, would have been a very difficult task, and an obvious waste of time.
Processing English Compounds: Investigating Semantic Transparency

Semantic transparency is widely believed to affect the processing of compound words. It has been described as the degree to which the meaning of the constituent is retained in the meaning of the whole compound, but also as the degree to which the meaning of the compound is predictable from the meaning of the constituents. Furthermore, semantic transparency has been operationalized in various ways (e.g., Libben 2010; Libben et al. 2003; Sandra 1990). We describe a study in which transparency was measured based on: 1) linguistic criteria used by informed judges, 2) participant ratings of a) how predictable a compound’s meaning was from its parts, and b) the extent that each constituent retains its meaning in the compound, 3) Latent Semantic Analysis (LSA; Landauer & Dumais 1997) scores for the compound and each constituent. We used these measures to test the claim that meaning retention ratings reflect the semantic similarity between a compound’s meaning and the constituent meaning, whereas the predictability ratings indicate the degree of semantic compositionality of the compound’s concept (see Marelli & Luzzatti 2012). We did not find support for these specific conceptualizations of semantic transparency. We then investigated the relationships among these different measures of semantic transparency to determine whether they reflect the same underlying construct, and in particular, the extent to which the LSA scores and participant ratings can predict the classification by informed judges using linguistic criteria. Finally, we used the various measures to predict typing times (a measure of processing) of compound words. The results from these various analyses indicate that the various methods of measuring semantic transparency do not reflect the same underlying aspects of semantic transparency.
Quality Assessment of Peer-Produced Content in Knowledge Repositories using Development and Coordination Activities

Abstract We develop a method to assess the quality of peer-produced content in knowledge repositories using their development and coordination histories. We also develop a process to identify relevant features for quality assessment models and algorithms for processing datasets in large-scale knowledge repositories. Models using these features, on English language Wikipedia articles, outperform existing methods for quality assessment. We achieve an overall accuracy of 81 percent which is a 7 percent improvement over existing models. In addition, our features improve the precision and recall of each class up to 9 percent and 17 percent respectively. Finally, our models are robust to ten-fold cross validation and techniques used for classification. Overall, our research provides a comprehensive design science framework for both identifying and efficiently extracting features related to development and coordination activities and assessing quality using these features. We also provide details of potential implementation of a quality assessment system for knowledge repositories.
EROFS: A Compression-friendly Readonly File System for Resource-scarce Devices

Smartphones usually have limited storage and runtime memory. Compressed read-only file systems can dramatically decrease the storage used by read-only system resources. However, existing compressed read-only file systems use fixedsized input compression, which causes significant I/O amplification and unnecessary computation. They also consume excessive runtime memory during decompression and deteriorate the performance when the runtime memory is scarce. In this paper, we describe EROFS, a new compression-friendly read-only file system that leverages fixed-sized output compression and memory-efficient decompression to achieve high performance with little extra memory overhead. We also report our experience of deploying EROFS on tens of millions of smartphones. Evaluation results show that EROFS outperforms existing compressed read-only file systems with various micro-benchmarks and reduces the boot time of realworld applications by up to 22.9% while nearly halving the storage usage.