Reviewerly/exHarmony huggingface dataset:
An Infusion Containers Detection Method Based on YOLOv4 with Enhanced Image Feature Fusion

The detection of infusion containers is highly conducive to reducing the workload of medical staff. However, when applied in complex environments, the current detection solutions cannot satisfy the high demands for clinical requirements. In this paper, we address this problem by proposing a novel method for the detection of infusion containers that is based on the conventional method, You Only Look Once version 4 (YOLOv4). First, the coordinate attention module is added after the backbone to improve the perception of direction and location information by the network. Then, we build the cross stage partial-spatial pyramid pooling (CSP-SPP) module to replace the spatial pyramid pooling (SPP) module, which allows the input information features to be reused. In addition, the adaptively spatial feature fusion (ASFF) module is added after the original feature fusion module, path aggregation network (PANet), to facilitate the fusion of feature maps at different scales for more complete feature information. Finally, EIoU is used as a loss function to solve the anchor frame aspect ratio problem, and this improvement allows for more stable and accurate information of the anchor aspect when calculating losses. The experimental results demonstrate the advantages of our method in terms of recall, timeliness, and mean average precision (mAP).
The complexity landscape of claim-augmented argumentation frameworks

Claim-augmented argumentation frameworks (CAFs) provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective; they extend Dung AFs by associating a claim to each argument representing its conclusion. This additional layer offers various possibilities to generalize abstract argumentation semantics, i.e. the re-interpretation of arguments in terms of their claims can be performed at different stages in the evaluation of the framework: One approach is to perform the evaluation entirely at argument-level before interpreting arguments by their claims (inherited semantics); alternatively, one can perform certain steps in the process (e.g., maximization) already in terms of the arguments' claims (claim-level semantics). The inherent difference of these approaches not only potentially results in different outcomes but, as we will show in this paper, is also mirrored in terms of computational complexity. To this end, we provide a comprehensive complexity analysis of the four main reasoning problems with respect to claim-level variants of preferred, naive, stable, semi-stable and stage semantics and complete the complexity results of inherited semantics by providing corresponding results for semi-stable and stage semantics. Furthermore, we provide complexity results for these types of frameworks when restricted to specific graph classes and when parameterized by the number of claims within the framework. Moreover, we show that deciding, whether for a given framework the two approaches of a semantics coincide (concurrence) can be surprisingly hard, ranging up to the third level of the polynomial hierarchy.
Revision, defeasible conditionals and non-monotonic inference for abstract dialectical frameworks

For propositional beliefs, there are well-established connections between belief revision, defeasible conditionals, and nonmonotonic inference. In argumentative contexts, such connections have not yet been investigated. On the one hand, the exact relationship between formal argumentation and nonmonotonic inference relations is a research topic that keeps on eluding researchers despite recently intensified efforts, whereas argumentative revision has been studied in numerous works during recent years. In this paper, we show that relationships between belief revision, defeasible conditionals, and nonmonotonic inference similar to those in propositional logic hold in argumentative contexts as well. We first define revision operators for abstract dialectical frameworks, and use such revision operators to define dynamic conditionals by means of the Ramsey test. We show that such conditionals can be equivalently defined using a total preorder over three-valued interpretations, and study the inferential behaviour of the resulting conditional inference relations.
A Graph-Attention-Based Method for Single-Resident Daily Activity Recognition in Smart Homes

In ambient-assisted living facilitated by smart home systems, the recognition of daily human activities is of great importance. It aims to infer the household's daily activities from the triggered sensor observation sequences with varying time intervals among successive readouts. This paper introduces a novel deep learning framework based on embedding technology and graph attention networks, namely the time-oriented and location-oriented graph attention (TLGAT) networks. The embedding technology converts sensor observations into corresponding feature vectors. Afterward, TLGAT provides a sensor observation sequence as a fully connected graph to the model's temporal correlation as well as the sensor's location correlation among sensor observations and facilitates the feature representation of each sensor observation through receiving other sensor observations and weighting operations. The experiments were conducted on two public datasets, based on the diverse setups of sensor event sequence length. The experimental results revealed that the proposed method achieved favorable performance under diverse setups.
Vehicle Detection on Occupancy Grid Maps: Comparison of Five Detectors Regarding Real-Time Performance

Occupancy grid maps are widely used as an environment model that allows the fusion of different range sensor technologies in real-time for robotics applications. In an autonomous vehicle setting, occupancy grid maps are especially useful for their ability to accurately represent the position of surrounding obstacles while being robust to discrepancies between the fused sensors through the use of occupancy probabilities representing uncertainty. In this article, we propose to evaluate the applicability of real-time vehicle detection on occupancy grid maps. State of the art detectors in sensor-specific domains such as YOLOv2/YOLOv3 for images or PIXOR for LiDAR point clouds are modified to use occupancy grid maps as input and produce oriented bounding boxes enclosing vehicles as output. The five proposed detectors are trained on the Waymo Open automotive dataset and compared regarding the quality of their detections measured in terms of Average Precision (AP) and their real-time capabilities measured in Frames Per Second (FPS). Of the five detectors presented, one inspired from the PIXOR backbone reaches the highest AP0.7 of 0.82 and runs at 20 FPS. Comparatively, two other proposed detectors inspired from YOLOv2 achieve an almost as good, with a AP0.7 of 0.79 while running at 91 FPS. These results validate the feasibility of real-time vehicle detection on occupancy grids.
Democracy Without Development: An Assessment of the Moderating Factors in Nigeria

This paper assesses the performance of democratic leadership in the Nigerian state in sub-Saharan Africa to determine if democracy dividends accrue to the citizens. The study suggests that in the fight against corruption, the government must guarantee the independence of the anticorruption institutions and fix the socioeconomic factors plaguing the nation. Evidence from the descriptive statistical outputs of the moderator factors such as corruption, inflation, infrastructure and insecurity suggests a deleterious moderation in the relationship between ‘democratic leadership performance’ and ‘democracy dividends’. Thus, the diminutive influence of the former on the latter potentially leaves the citizens vulnerable.
Distributed Data Integrity Verification Scheme in Multi-Cloud Environment

Most existing data integrity auditing protocols in cloud storage rely on proof of probabilistic data possession. Consequently, the sampling rate of data integrity verification is low to prevent expensive costs to the auditor. However, in the case of a multi-cloud environment, the amount of stored data will be huge. As a result, a higher sampling rate is needed. It will also have an increased cost for the auditor as a consequence. Therefore, this paper proposes a blockchain-based distributed data integrity verification protocol in multi-cloud environments that enables data verification using multi-verifiers. The proposed scheme aims to increase the sampling rate of data verification without increasing the costs significantly. The performance analysis shows that this protocol achieved a lower time consumption required for verification tasks using multi-verifiers than a single verifier. Furthermore, utilizing multi-verifiers also decreases each verifier's computation and communication costs.
Recognize News Transition from Collective Behavior for News Recommendation

In the news recommendation, users are overwhelmed by thousands of news daily, which makes the users’ behavior data have high sparsity. Therefore, only considering a single user’s personalized preferences cannot support the news recommendation. How to improve the relatedness of news and users and reduce data sparsity has become a hot issue. Recent studies have attempted to use graph models to enrich the relationship between users and news, but they are still limited to modeling the historical behaviors of a single user. To fill the gap, we integrate user-news relationships and the overall user historical clicked news sequences to construct a global heterogeneous transition graph. And a refinement approach is proposed to recognize the news transition patterns in the graph. Based on the global heterogeneous transition graph, we propose a heterogeneous transition graph attention network to capture the common behavior patterns of most users to enhance the representation of user interest. Fusing the users’ personalized and common interest, we propose the GAINRec model to recommend news effectively. Extensive experiments are conducted on two public news recommendation datasets, and the results show the superiority of the proposed GAINRec model compared with the state-of-the-art news recommendation models. The implementation of our model is available at https://github.com/newsrec/GAINRec .
A Perspective on Lifelong Open-Ended Learning Autonomy for Robotics through Cognitive Architectures

This paper addresses the problem of achieving lifelong open-ended learning autonomy in robotics, and how different cognitive architectures provide functionalities that support it. To this end, we analyze a set of well-known cognitive architectures in the literature considering the different components they address and how they implement them. Among the main functionalities that are taken as relevant for lifelong open-ended learning autonomy are the fact that architectures must contemplate learning, and the availability of contextual memory systems, motivations or attention. Additionally, we try to establish which of them were actually applied to real robot scenarios. It transpires that in their current form, none of them are completely ready to address this challenge, but some of them do provide some indications on the paths to follow in some of the aspects they contemplate. It can be gleaned that for lifelong open-ended learning autonomy, motivational systems that allow finding domain-dependent goals from general internal drives, contextual long-term memory systems that all allow for associative learning and retrieval of knowledge, and robust learning systems would be the main components required. Nevertheless, other components, such as attention mechanisms or representation management systems, would greatly facilitate operation in complex domains.
Phase Deflectometry for Defect Detection of High Reflection Objects

A method for detecting the surface defects of high reflection objects using phase deflection is proposed. The abrupt change in the surface gradient at the defect leads to the change in the fringe phase. Therefore, Gray code combined with a four-step phase-shift method was employed to obtain the surface gradients to characterize the defects. Then, through the double surface illumination model, the relationship between illumination intensity and phase was established. The causes of periodic error interference were analyzed, and the method of adjusting the fringe width to eliminate it was proposed. Finally, experimental results showed the effectiveness of the proposed method.
Analysis of Transient Characteristics of Induced Polarization in Innovative Mineral Exploration Solutions (from Case Studies of Gold Deposits)

Abstract —The paper discusses current state of the spectral approach to analysis of induced polarization (IP), its possibilities and limitations, along with directions for the method development. Reviewed are the main methods and approaches for estimation of spectral IP parameters and their specific features. Our practical experiments have shown the advantages of analysis of transient IP characteristics over standard approaches to the IP data analysis and processing. Results of the standard and spectral approaches application to analysis of IP data when solving mineral exploration problems at the Maletoyvayam gold deposit (Kamchatka Peninsula) and the Yasnoe gold occurrence (Taimyr Peninsula) are compared. Here, we demonstrate that low efficiency of works is erroneously regarded as one of the main factors hindering a broad adoption of the spectral IP method in mineral exploration activities. Rather, it is a lack of automated techniques for analysis of transient IP characteristics. We propose a new approach to spectral analysis of IP data, based on the algorithms for image recognition of a reference object, and describe pioneering efforts of its practical application.
A study on surprisal and semantic relatedness for eye-tracking data prediction

Previous research in computational linguistics dedicated a lot of effort to using language modeling and/or distributional semantic models to predict metrics extracted from eye-tracking data. However, it is not clear whether the two components have a distinct contribution, with recent studies claiming that surprisal scores estimated with large-scale, deep learning-based language models subsume the semantic relatedness component. In our study, we propose a regression experiment for estimating different eye-tracking metrics on two English corpora, contrasting the quality of the predictions with and without the surprisal and the relatedness components. Different types of relatedness scores derived from both static and contextual models have also been tested. Our results suggest that both components play a role in the prediction, with semantic relatedness surprisingly contributing also to the prediction of function words. Moreover, they show that when the metric is computed with the contextual embeddings of the BERT model, it is able to explain a higher amount of variance.
A novel observation points‐based positive‐unlabeled learning algorithm

Abstract In this study, an observation points‐based positive‐unlabeled learning algorithm (hence called OP‐PUL) is proposed to deal with positive‐unlabeled learning (PUL) tasks by judiciously assigning highly credible labels to unlabeled samples. The proposed OP‐PUL algorithm has three components. First, an observation point classifier ensemble (OPCE) algorithm is constructed to divide unlabeled samples into two categories, which are temporary positive and permanent negative samples. Second, a temporary OPC (TOPC) is trained based on the combination of original positive samples and permanent negative samples and then the permanent positive samples that are correctly classified with TOPC are retained from the temporary positive samples. Third, a permanent OPC (POPC) is finally trained based on the combination of original positive samples, permanent positive samples and permanent negative samples. An exhaustive experimental evaluation is conducted to validate the feasibility, rationality and effectiveness of the OP‐PUL algorithm, using 30 benchmark PU data sets. Results show that (1) the OP‐PUL algorithm is stable and robust as unlabeled samples and positive samples are increased in unlabeled data sets and (2) the permanent positive samples have a consistent probability distribution with the original positive samples. Moreover, a statistical analysis reveals that POPC in the OP‐PUL algorithm can yield better PUL performances on the 30 data sets in comparison with four well‐known PUL algorithms. This demonstrates that OP‐PUL is a viable algorithm to deal with PUL tasks.
Breast Cancer Detection Using Machine Learning

One of the leading causes of mortality in women is breast cancer. For women, carcinoma is regarded as one of the most serious problem. As per the International Agency for Research on Cancer (IARC) figures issued in December 2020, carcinoma has now surpassed as the most frequently diagnosed cancer in women globally. Early diagnosis of these conditions aids in the prevention of cancer. If cancer is identified early on, the chance of survival is extremely good. Particularly in the medical industry, where such procedures are commonly used in diagnostics for higher processes. This study discusses data visualization and productivity comparisons across several machine learning approaches. The major purpose is to evaluate each algorithm's correctness in term of accuracy, sensitivity, and uniqueness in terms of data categorizing performance and quality. In the experiments, our experimental findings had the best accuracy (92.10%).
Study of Exploration and Exploitation Mechanisms in Nature Inspired Metaheuristics for Global Optimization

Global optimization aims to identify the most interesting solution in the overall space of search, which is known as global optimum. But in reality, it is a misnomer because it is only the best of all discovered solutions in the explored search space, which depends, in turn, on initial positions of searching points over possibilities space. Practically, it is impossible to cover the entire search space of an NP hard problem, in an acceptable time, whatever the method used, since the size of this space is generally excessive. That is what justify, currently, the increased use of metaheuristics based mainly on two mechanisms: exploitation and exploration. Exploration guarantees that the used algorithm will reach the widest possible extent of the undiscovered areas in the search space, whereas exploitation guarantees that such algorithm will search for the best solutions inside the most promising areas i.e. around the best local and global optimums. Authors study, here, different implementations of exploration and exploitation mechanisms used in 12 well-known nature inspired metaheuristics. This study compares the aforementioned metaheuristics using 4 uni-model and 9 multi-model benchmarks. Uni-model benchmarks are used to test exploitation while multi-model benchmarks are used to test exploration. Obtained results show the superiority of TSO metaheuristic to find the adequate balance between exploration and exploitation leading it to discover, always, global optimum or one of the best near global optimums.
Real-Time Automated Face Recognition System for Online Exam Examinee Verification

Amidst Covid-19, identity fraud in exams is becoming more and more advanced. Therefore, identifying these fake candidates accurately has become a difficult problem in exam administration. The novelty of this work is the introduction of optimized Inception ResNetV2 for an ideal online testing system where the system can detect the faces of test-takers and generate a report of the automated monitoring of the candidate. The point of the work is to design a biometric module that is to be implemented as the candidate authentication and identity verification proctoring method for the exams. For research, the suggested method concentrates on a single image. The system integrates various technologies, such as facial imaging, human-computer interaction, data transmission, and communication. Using video proctoring, the system can continuously monitor the examinee’s behaviour by live streaming. This is done by using, MediaPipe Face Mesh, MediaPipe Face Detection, Pretrained Inception-ResNet-v2 Convolutional Neural Network, Django Framework, and OpenCV. The proposed work involves two modules: Online Examination Management and Face Recognition Proctor. This research attempts to build automated face recognition and identity features in order to detect fraud.
Named Entity Recognition for Algerian Arabic Dialect in Social Media

Named entities recognition (NER) has become, over time, a potentially helpful pre-processing for several NLP tasks. It mainly identifies and classifies entities in the text into predefined categories, such as a person, location, and organization. Most of the work done in Arabic named entity recognition (ANER) has focused on Modern Standard Arabic (MSA). However, most scripts on the internet, especially in social media (which are currently a source for corpus development), are in a dialectal form that does not follow standard writing rules. This paper investigates the possibility of deep learning based on named entity recognition in Algerian dialect script through a comparative study between 5 models: AraBERT, DziriBERT, MARBERT, ARBERT, and mBERT. We chose these five models for two significant reasons; the first one, that these models are already pre-trained on the MSA and Arabic dialect text, the second reason, that they have proved their efficiency for other tasks such as Part of Speech Tagging, sentiment analysis, and ANER.
An Image Encryption-Based Method for Handwritten Digit Recognition

Recognition of handwritten digits is a dominant research problem in the field of image analysis and pattern recognition. In particular, feature extraction-based appearance approaches solve many research problems. In this work, we introduce a new method using encryption techniques such as the feature extraction stage. Furthermore, this method is very skilled for handwritten digit recognition as it is less affected by variations in shape and slant. The proposed method is carried in two steps: first, we concatenate the entire image with its encrypted image. Second, the image pixels are collected into a vector. The performance of the proposed approach is evaluated for recognizing the isolated handwritten digits from the CVL dataset. The experimental results showed recognition with high accuracy.
Predictive Model for Solar Insolation Using the Deep Learning Technique

In this study, prediction performances of a regression model and deep learning-based predictive models were comparatively analyzed for the prediction of hourly insolation in regions located at the temperate climate and microthermal climate with high precipitation. Unlike linear regression models, artificial neural networks (ANN) and long short-term memory- (LSTM-) based models achieved reliable predictive performances with CV(RMSE) of 14.0% and 15.8%, respectively. This study proposed the direction of future research by improving the performance of predicting insolation at 1 hour after the current time-step, which has time-dependent characteristics, by utilizing insolation at 24 hours before the current time-step and insolation at the current time-step in addition to the forecasted weather data. In the proposed models, a large error occurred at sunrise and sunset times, suggesting the possibility of improving predictive performance by utilizing variables related to sunrise and sunset in the future. Along with Cheongju, the proposed model could properly predict the hourly insolation in other regions around the world. The results of predicting other regions derived slightly higher prediction errors than Cheongju. However, it is expected that it will be possible to predict the hourly insolation in other regions with better prediction performance if variables related to geographical location are additionally considered in the future.