Reviewerly/exHarmony huggingface dataset:
Compressive HDR Light Field Imaging Using a Single Multi-ISO Sensor

In this paper, we propose a new design for single sensor compressive HDR light field cameras, combining multi-ISO photography with coded mask acquisition, placed in a compressive sensing framework. The proposed camera model is based on a main lens, a multi-ISO sensor and a coded mask located in the optical path between the main lens and the sensor that projects the coded spatio-angular information of the light field onto the 2D sensor. The model encompasses different acquisition scenarios with different ISO patterns and gains. Moreover, we assume that the sensor has a built-in color filter array (CFA), making our design more suitable for consumer-level cameras. We propose a reconstruction algorithm to jointly perform color demosaicing, light field angular information recovery, HDR reconstruction, and denoising from the multi-ISO measurements formed on the sensor. This is achieved by enabling the sparse representation of HDR light fields using an overcomplete HDR dictionary. We also provide two HDR light field data sets: one synthetic data set created using the Blender rendering software with two baselines, and a real light field data set created from the fusion of multi-exposure low dynamic range (LDR) images captured using a Lytro Illum light field camera. Experimental results show that, with a sampling rate as low as 2.67%, using two shots, our proposed method yields a higher light field reconstruction quality compared to the fusion of multiple LDR light fields captured with different exposures, and with the fusion of multiple LDR light fields captured with different ISO settings.
A Novel Learning and Response Generating Agent-based Model for Symbolic - Numeric Knowledge Modeling and Combination

Many modern applications require both modeling and generative capabilities, so that they can produce novel outcomes that address requirements beyond the solutions used in model training. Current AI approaches arguably emphasize modeling but pay much less attention to generative capabilities. This paper presents a new learning and response generating (LRG) agent-based model, in which interacting agents continuously learn symbolic - numeric knowledge and create new outcomes (responses) using a set of five ways to combine concepts. Each way has both fast, reactive and a slow, planned versions. Experiments present the characteristics of an agent's modeling and generating capabilities.
Validation of Helioclim-3 irradiance with ground observations in Senegal using four typical climatic zones

This work evaluates the quality of the irradiance data produced by Heliosat-2 algorithm with the Meteosat images (data accessible from the Helioclim-3-HC3 database) with the ground observations in four typical climatic zones of Senegal. For each station, the global horizontal irradiance (GHI) is given from the 1st of January to the 31th of December 2005 with a time step of hour (8760 cases). The results show that for all the four stations representative of the different climates of Senegal, there is a good agreement between the global irradiation measured at the ground and those from satellite. The powerful quality control method applied on one year of hourly data (8760 h) resulted in a lot of data being lost. But the goal is to see this good match between the two data sets. After application of quality control 38.24% of cases remain for Dakar, 29.20% for St Louis, 29.73% for Tambacounda and 27.02% for Ziguinchor. The root mean square error varies from site to another with low values ranging from 10.46% to 12.78%. The mean bias is between −0.967% and 0.396%. This study gives us an assurance for the use of the HelicoClim-3 database over a long period for mapping purposes and contributes to overcome the barrier of development of solar projects in Senegal in reducing the gap due to lack of ground data..
Better Prevent than React: Deep Stratified Learning to Predict Hate Intensity of Twitter Reply Chains

Given a tweet, predicting the discussions that unfold around it is convoluted, to say the least. Most if not all of the discernibly benign tweets which seem innocuous may very well attract inflammatory posts (hate speech) from people who find them non-congenial. Therefore, building upon the aforementioned task and predicting if a tweet will incite hate speech is of critical importance. To stifle the dissemination of online hate speech is the need of the hour. Thus, there have been a handful of models for the detection of hate speech. Classical models work retrospectively by leveraging a reactive strategy – detection after the postage of hate speech, i.e., a backward trace after detection. Therefore, a benign post that may act as a surrogate to invoke toxicity in the near future, may not be flagged by the existing hate speech detection models. In this paper, we address this problem through a proactive strategy initiated to avert hate crime. We propose DRAGNET, a deep stratified learning framework which predicts the intensity of hatred that a root tweet can fetch through its subsequent replies. We extend the collection of social media discourse from our earlier work [1], comprising the entire reply chains up to $\sim$5k root tweets catalogued into four controversial topics Similar to [1], we notice a handful of cases where despite the root tweets being non-hateful, the succeeding replies inject an enormous amount of toxicity into the discussions. DRAGNET turns out to be highly effective, significantly outperforming six state-of-the-art baselines. It beats the best baseline with an increase of 9.4% in the Pearson correlation coefficient and a decrease of 19% in Root Mean Square Error. Further, DRAGNET’S deployment in Logically’s advanced AI platform designed to monitor real-world problematic and hateful narratives has improved the aggregated insights extracted for understanding their spread, influence and thereby offering actionable intelligence to counter them
A Word-Granular Adversarial Attacks Framework for Causal Event Extraction

As a data augmentation method, masking word is commonly used in many natural language processing tasks. However, most mask methods are based on rules and are not related to downstream tasks. In this paper, we propose a novel masking word generator, named Actor-Critic Mask Model (ACMM), which can adaptively adjust the mask strategy according to the performance of downstream tasks. In order to demonstrate the effectiveness of the method, we conducted experiments on two causal event extraction datasets. Experiment results show that, compared with various rule-based masking methods, the masked sentences generated by our proposed method can significantly enhance the generalization of the model and improve the model performance.
Detection of Auxiliary Equipment in Engine Room Based on Improved SSD

Abstract To improve the intelligent operation and maintenance of ships, the research on the perception of engine room auxiliary equipment (A/E) will realize AUTO-0 and replace the engineers. Due to compact layout of the cabin and the different shapes of equipment, the Single Shot MultiBox Detector (SSD) that uses the traditional convolution method for feature extraction cannot adapt to the complex environment. Therefore, an improved A/E detection algorithm based on SSD is proposed. Firstly, we collected the images of actual ships, annotated them manually, and used the K-Means clustering algorithm to analyze them. Secondly, we replaced the classification loss of SSD with the focal loss, balancing the positive and negative samples in the dataset. Finally, we added repulsion loss for the overlapping targets to improve the detection effect of the model against dense occlusion &amp; overlap. The experiment is based on the dataset containing 18185 training object boxes, and 2044 test object boxes, the mean average precision (mAP) in this paper reaches 78.95%, which is 5.63% higher than the original SSD, and the detection boxes are more suitable.
Oblique decision tree induction by cross-entropy optimization based on the von Mises–Fisher distribution

Abstract Oblique decision trees recursively divide the feature space by using splits based on linear combinations of attributes. Compared to their univariate counterparts, which only use a single attribute per split, they are often smaller and more accurate. A common approach to learn decision trees is by iteratively introducing splits on a training set in a top–down manner, yet determining a single optimal oblique split is in general computationally intractable. Therefore, one has to rely on heuristics to find near-optimal splits. In this paper, we adapt the cross-entropy optimization method to tackle this problem. The approach is motivated geometrically by the observation that equivalent oblique splits can be interpreted as connected regions on a unit hypersphere which are defined by the samples in the training data. In each iteration, the algorithm samples multiple candidate solutions from this hypersphere using the von Mises–Fisher distribution which is parameterized by a mean direction and a concentration parameter. These parameters are then updated based on the best performing samples such that when the algorithm terminates a high probability mass is assigned to a region of near-optimal solutions. Our experimental results show that the proposed method is well-suited for the induction of compact and accurate oblique decision trees in a small amount of time.
Correlated High Utility Itemset Mining Based on Item Decomposition

Correlated High utility itemset mining discovers itemsets whose correlation and utility are above the minimum thresholds. However, performance limitations relevant to memory and time result in scalability issues when exploring the search space in a large dataset. Additionally, the original database must be scanned multiple times. This paper addresses the combinatorial explosion in the search space by clustering the items and applying the mining process to each cluster separately or simultaneously. The proposed clustering-based technique discovers potential correlated items by studying the similarity between items' behaviors, which are made up of a set of utility-oriented sequential transactions carried out in a specific order. The items are first clustered using the hierarchical clustering technique, where correlated items are grouped. Then, the correlated and profitable patterns are derived by applying a pattern mining algorithm to each cluster. Experimental results show that the item-clustering technique has more efficiency and effectiveness than the transaction-clustering technique. Moreover, the proposed item-clustering algorithm outperforms the state-of-the-art CoHUIM algorithms in terms of memory and time, and the ratio of the accuracy reaches up to 99% for all cases.
Joint Client Selection and Task Assignment for Multi-Task Federated Learning in MEC Networks

In this paper, we investigate the multi-task federated learning in mobile edge computing (MEC) networks where a central server assigns different federated learning tasks to different MEC servers and select feasible clients to participate in the federated learning training process. The problem is formulated as a joint client selection and task assignment problem to maximize the total utility of all tasks, subject to the trained model quality and total training latency. Since the above-mentioned problem is NP-Hard, it poses challenges to obtain the optimal solution within polynomial time, the problem is transformed into a many-to-one-to-one 3D matching problem. To further reduce the computation while ensuring the matching stability, we first adopt the spectral clustering algorithm to cluster the clients into multiple client clusters. Then we reformulate the problem as a 3-Partite weighted hypergraph total weight maximization problem. Finally, we propose a greedy and local search (GLS) based algorithm to resolve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm as compared with baseline schemes.
Building Smart Contracts for COVID19 Pandemic Over the Blockchain Emerging Technologies

This research aims to improve and integrate hospital's healthcare applications with Blockchain and smart contracts technologies to provide huge and secure storage that is immutable. This application will be able to record the patients' medical history like appointments, medical tests, etc.; As a matter of fact, these resources should be recorded to be securely retrieved, modified, and stored by an authorized party only. The utilization of these critical resources will increase the validity for participants with a high level of liability, where building a scheduling appointment system using the blockchain-based on a smart contract will enhance patients' privacy and provides a safer method to keep data away from altering through an unofficial use. COVID-19 Coronavirus is a global disaster that requires a reliable and stable network-based application with a giant and secure platform to hold a huge number of people and settings. The simulated outcomes of the developed system were significant and extremely noteworthy according to immutability and correctness.
Intermediate results processing for aggregated SPARQL queries

Aggregated search approach in the web of data is to look for results of a single query by aggregating pieces of data from distributed data sources and integrating them, if possible, into an entire entity. However, it may be possible that some parts of the query return null results which affects answers processing. In this work, we propose a star-group patterns-based solution to prepare a SPARQL query to be executed over distributed data sources without having prior knowledge of contributing data sources. The first objective of this work is identifying the complementarity between query parts after decomposing it, and the second one is rewriting the user’s query considering only parts with not null results based on star-group patterns in order to return semantically significant answers. The evaluation of our proposed solution shows that this query rewriting method allows to return as much as possible the sought information.
A Proposed Approach to Secure Automated Teller Machine-Based Financial Transactions

This paper presents a proposed anti-spoofing third-factor authentication approach for Automated Teller Machines (ATMs). It is called Keypad Typing Rhythm Identifier (KTRID). The objective of this novel user-transparent unavoidable authentication approach is to secure ATM-based financial transactions from spoofing attacks via identity theft. The main motivation to conduct this research is that in 2020 ATMs were the top compromised asset that was successfully attacked. ATM keypad typing rhythm refers to a user’s unique keys hits practice that is difficult to mimic by spoofers. KTRID complements the authentication procedure currently used in ATMs in order to hinder spoofing attacks. It is based on exploiting users’ unique typing rhythm behavior on the ATMs keypads. It boosts identity affirmation by exploiting the timing variances of keys hits to distinguish a legitimate bank customer from a spoofer. KTRID detects anomalies in the legitimate keypad typing rhythm outlier status of a smart card user. Such that, in case a smart card is stolen or lost, and the accompanying Personal Identification Number (PIN) is exposed or guessed, still the spoofer will not be able to carry out a successful ATM-based financial transaction. Through KTRID, the impersonator will be impinged by the unique typing rhythm behavior of the legitimate bank customer on the ATM keypad. The security evaluation demonstrated that through detecting outliers in a keypad typing rhythm, KTRID effectively prevented spoofing attacks. KTRID is a vital authentication approach, essentially for bank customers who cannot keep control of their smart cards and/or accompanying 4-digit PINs. To the best of the author’s knowledge, this paper presents the first proposed approach to employ the typing rhythm behavioral-based biometrics for the purpose of securing ATM-based financial transactions.
RGB-D Face Recognition using Reconstruction based Shared Representation

Low cost time-of-flight based depth sensors such as Kinect have opened new avenues for their usage in video surveillance scenarios. RGB-D images obtained from such sensors have shown their utility in improved face recognition capabilities. Generally, existing RGB-D face recognition methods fuse the depth information with RGB information which results in enhanced recognition performance. However, in the real world surveillance scenarios, cameras are placed at a distance too large for low cost depth sensors to capture good quality depth information. Such poor quality depth information may not contribute significantly to face recognition. In this paper, we present a novel representation learning algorithm by learning shared representation of RGB and depth information using a reconstruction based deep neural network. The proposed network, once trained in the offline mode, can generate a shared representation of RGB and depth data using only the RGB image. This feature rich representation is then utilized for face identification. This allows the framework to be used in scenarios where low quality or no depth image is captured. Experiments on multiple real world databases show the effectiveness of the proposed approach.
Purging Compliance from Database Backups by Encryption

Data compliance laws establish rules intended to protect privacy. These define both retention durations (how long data must be kept) and purging deadlines (when the data must be destroyed in storage). To comply with the laws and to minimize liability, companies must destroy data that must be purged or is no longer needed. However, database backups generally cannot be edited to purge ``expired'' data and erasing the entire backup is impractical. To maintain compliance, data curators need a mechanism to support targeted destruction of data in backups. In this paper, we present a cryptographic erasure framework that can purge data from across database backups. We demonstrate how different purge policies can be defined through views and enforced without violating database constraints.
[Geo-accumulation Index Method to Optimize the Evaluation Method of Polymetallic Environment Quality: Taking Developed Agricultural Areas as an Example].

An accurate and reasonable soil pollution assessment method is the premise of regional soil pollution assessment. Triangular fuzzy numbers were introduced into the geo-accumulation index method, combined with α-cut technology and the Latin hypercube sampling (LHS) stochastic simulation method, to evaluate the accumulation of heavy metals in five different soil types (moisture soil, aeolian sandy soil, cinnamon soil, loessal soil, and alluvial soil), with large differences in heavy metal (Cd, Hg, As, Pb, and Cr) content in the study area. This method can avoid the inaccuracy of the evaluation results caused by the traditional geological accumulation index method in the selection of background values and the great difference between local and whole heavy metal contents in the study area and can comprehensively and truly represent the regional soil heavy metal pollution status, thus providing a theoretical basis for scientific decision-making. The results showed that the improvement in the method will not affect the evaluation results when the heavy metal content difference is small, that is, when the evaluation results of the geological accumulation index at all points are within a certain interval level or when the evaluation results of the geological accumulation index at all points are less than 0. The traditional and geological cumulative index method based on the evaluation with triangular fuzzy numbers provides results for a certain level, which is determined by the heavy metal content in the study area average; conversely, differences in the heavy metal content of soil sampling points throughout the study area can result in a decrease or increase in the recorded heavy metal contents in larger areas, making the evaluation result inaccurate. Combined with the LHS sampling method, the possibility of changing the evaluation results into various pollution levels can greatly improve the limitations of traditional evaluation methods and make the evaluation results more reasonable and accurate. Combined with the geographic information system (GIS) method, the regional heavy metal pollution accumulation concentration can also be visualized.
Hand-Eye Calibration Technology of Intelligent Picking Robot Using Multiple Photoelectric Sensor Information Fusion

Multiple photoelectric sensors are designed for information fusion, in order to restrict the movement of robot end-effecter and camera, and then construct the hand-eye equation to obtain the corresponding hand-eye pose. First, the mathematical model of hand-eye calibration (HEC) is studied; moreover, the control system based on multiple photoelectric sensors with NImyRIO-1900 microprocessor as the core is designed. Its upper layer includes infrared sensor, acoustic photoelectric sensor, and gas photoelectric sensor, which can collect the position characteristics of the robot; finally, an automatic HEC system (HECS) is designed based on the control. The system combines the control system composed of multiple photoelectric sensors and the HEC method. It can collect data in real time, expand the camera field of vision, and improve the efficiency of HEC. When the calibration result reaches the predetermined accuracy, the HECS is terminated. In the experiment, the control system based on multiple photoelectric sensors helps the automatic calibration system to complete the picking task within 15 minutes. The success rate of pose planning of the end-effector of the system is 85%, the calibration attitude error is only 0.0586, and the position error is only 12.6685 mm, which verifies that the calibration system based on multiple photoelectric sensors is effective.
Analyzing the Linear Keystream Biases in AEGIS

AEGIS is one of the authenticated encryption designs selected for the final portfolio of the CAESAR competition. It combines the AES round function and simple Boolean operations to update its large state and extract a keystream to achieve an excellent software performance. In 2014, Minaud discovered slight biases in the keystream based on linear characteristics. For family member AEGIS-256, these could be exploited to undermine the confidentiality faster than generic attacks, but this still requires very large amounts of data. For final portfolio member AEGIS-128, these attacks are currently less efficient than generic attacks. We propose improved keystream approximations for the AEGIS family, but also prove upper bounds below 2−128 for the squared correlation contribution of any single suitable linear characteristic.