Reviewerly/exHarmony huggingface dataset:
Risk-aware Trajectory Planning in Urban Environments with Safe Emergency Landing Guarantee

In-flight aircraft failures are never avoidable entirely, inducing a significant risk to people and properties on the ground in an urban environment. Existing risk-aware trajectory planning approaches minimize the risk by determining trajectories that might result in less damage in the case of failure. However, the risk of the loss of thrust can be eliminated by executing a safe emergency landing if a landing site is reachable. Therefore, we propose a novel risk-aware trajectory planning that minimizes the risk to people on the ground while an option of a safe emergency landing in the case of loss of thrust is guaranteed. The proposed method has been empirically evaluated on a realistic urban scenario. Based on the reported results, an improvement in the risk reduction is achieved compared to the shortest and risk-aware only trajectory. The proposed risk-aware planning with safe emergency landing seems to be suitable trajectory planning for urban air mobility.
STD PONG 2.0: Field Evaluation of a Mobile Persuasive game for Discouraging Risky Sexual Behaviours among Africans Youths

Playing mobile games has become a very popular activity in our society today. Aside from being a very common leisure-time activity, researchers have begun to design games to solve real-life problems in many domains, including in the domain of health and wellness. A major challenge in the domain of health and wellness is the spread of Sexually Transmitted Diseases (STDs). This paper presents the design and field evaluation of a mobile persuasive game intervention aimed at promoting change in risky sexual behaviour among African youths titled STD PONG 2. 0. The results of the field evaluation on 41 African youths showed that STD PONG 2.0 was effective at motivating change in risky sexual behaviours by promoting a positive attitude, intention, and self-efficacy against risky sexual behaviours. The game also led to a significant increase in the knowledge about STDs and its preventive measures. Finally, we propose some design suggestions for developing persuasive games targeted at Africans generally and African rural communities based on our findings.
Motion Planning for Kinematically Redundant Mobile Manipulators with Genetic Algorithm, Pose Interpolation, and Inverse Kinematics

Motion planning for kinematic redundancy is an area of great importance for maximizing the mobility of robotic systems. However, generating optimized motions for this type of system is a challenging task given the large search space of possible configurations. Previously proposed methods do not address path following tasks with constrained end-effector position and orientation for a mobile manipulator system with more than 6 degrees of freedom (DoF). This paper presents a novel computational method for simultaneous optimization of base and manipulator robotic system with 8 DoF for welding tasks, constraining both end-effector position and orientation. The mobile manipulator consists of a 2 DoF non-holonomic base and a 6 DoF manipulator. The proposed method applies a Genetic Algorithm (GA) to solve for optimized configurations for the base and manipulator for strategically sampled end-effector waypoints. The base configurations and end-effector orientations are interpolated between the GA solutions and used as inputs for an inverse kinematics solver to find the optimal manipulator pose. The experiment results show that the proposed methods create optimized smooth and continuous motions for both the base and manipulator while constraining the end-effector position and orientation. The proposed method is a novel application of GA optimization, with improved results for path following motion planning by including sampling, interpolation, and inverse kinematics steps within the methodology.
An improved firefly algorithm with dynamic self-adaptive adjustment

The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.
Virtual onboarding: lessons from the pandemic

This article explores lessons learned during the COVID-19 pandemic on how to best onboard new library employees in a remote working environment.
Artificial Intelligence Potential Trends in Military

Artificial intelligence (AI) is trending in the military and safety-critical application sectors. Currently, the private sector is helping the government sector to implement new advanced techniques to bring a revolution for different government and public sector management. It also helps to provide sustainable accountability in the accounting field; at present, AI is bringing a revolution in concept building. It is bringing potential revolutions by using novel approaches in such directions. This paper is a novel approach in the same direction; our research aim of this paper is to emphasize the AI in the militaries, what are the latest trend and usages recently worldwide used for AI applications in militaries. In this paper, we not only discuss the usage of AI applications in the military but also in the civil defense and health industry. We review and discuss that AI has potential benefits in military applications, HRMS, decision making, disaster prevention and response, GIS, service personalization, interoperability, extensive data analysis, anomaly and pattern recognition, intrusion detection, and new solution discovery using the highly configurable system and real-time simulation.
Localizing category-related information in speech with multi-scale analyses

Measurements of the physical outputs of speech—vocal tract geometry and acoustic energy—are high-dimensional, but linguistic theories posit a low-dimensional set of categories such as phonemes and phrase types. How can it be determined when and where in high-dimensional articulatory and acoustic signals there is information related to theoretical categories? For a variety of reasons, it is problematic to directly quantify mutual information between hypothesized categories and signals. To address this issue, a multi-scale analysis method is proposed for localizing category-related information in an ensemble of speech signals using machine learning algorithms. By analyzing how classification accuracy on unseen data varies as the temporal extent of training input is systematically restricted, inferences can be drawn regarding the temporal distribution of category-related information. The method can also be used to investigate redundancy between subsets of signal dimensions. Two types of theoretical categories are examined in this paper: phonemic/gestural categories and syntactic relative clause categories. Moreover, two different machine learning algorithms were examined: linear discriminant analysis and neural networks with long short-term memory units. Both algorithms detected category-related information earlier and later in signals than would be expected given standard theoretical assumptions about when linguistic categories should influence speech. The neural network algorithm was able to identify category-related information to a greater extent than the discriminant analyses.
Are We Modeling the Evidence or Our Own Biases? A Comparison of Conceptual Models Created from Reports

Conceptual modeling requires expertise in the application area and in modeling. Many research groups fulfill this requirement by analyzing qualitative data produced by subject matter experts and constructing their own representations of this evidence base as conceptual models. The final models are often portrayed as objective and directly based on the evidence, suggesting that modelers are merely vessels through which qualitative data becomes structured as a model. In this paper, we measure for the first time the extent to which a final model is shaped by the modeler's own interpretation. To analyze differences among modelers, we (i) compare the conceptual models produced individually in terms of structure and semantics and (ii) track knowledge provenance by automatically comparing which parts of the evidence base were utilized. Results demonstrate that modelers may interpret the evidence base differently, which stresses the need to disclose how modelers translate evidence before engaging in knowledge aggregation.
Anomaly detection in the time-series data of industrial plants using neural network architectures

Industrial process control systems collect a vast amount of production data, especially time-series data from various sensors in a plant. This data provides a plethora of potential use cases for AI and machine learning that can help plant operators better understand and predict complex plant situations and thereby run their plants more effectively and safely. A relevant use case is anomaly detection, due to the time and cost-intensive process of detecting and rectifying issues, e.g., plant equipment failures. In this paper, the anomaly detection problem for multivariate industrial time-series data is addressed with the help of neural network architectures. In particular, three architectures are evaluated and compared: Dense Autoencoders, LSTM Autoencoders, and LSTMs. The evaluation is performed to detect 20 simulated plant equipment failures for a real-world industrial process found in oil production fields, called the three-phase separator process. The evaluation is done by 1) measuring to what extent the anomaly detection models succeed in detecting the anomalies, and 2) how well the models are able to explain the anomaly root cause. The main contribution of this paper is to find an anomaly detection model that is best suited for the detection and explanation of various failure cases in such an industrial process setting. The evaluation results show the quantitative comparison of the three models metrics and their performances. Here, it is observed that the Dense Autoencoders performed best for the given 20 failure cases.
BECA: A Blockchain-Based Edge Computing Architecture for Internet of Things Systems

The scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage.
Blockchain and Recordkeeping: Editorial

Distributed ledger technologies (DLT), including blockchains, combine the use of cryptography and distributed networks to achieve a novel form of records creation and keeping designed for tamper-resistance and immutability. Over the past several years, these capabilities have made DLTs, including blockchains, increasingly popular as a general-purpose technology used for recordkeeping in a variety of sectors and industry domains, yet many open challenges and issues, both theoretical and applied, remain. This editorial introduces the Special Issue of Computers focusing on exploring the frontiers of blockchain/distributed ledger technology and recordkeeping.
Explaining Bad Forecasts in Global Time Series Models

While increasing empirical evidence suggests that global time series forecasting models can achieve better forecasting performance than local ones, there is a research void regarding when and why the global models fail to provide a good forecast. This paper uses anomaly detection algorithms and explainable artificial intelligence (XAI) to answer when and why a forecast should not be trusted. To address this issue, a dashboard was built to inform the user regarding (i) the relevance of the features for that particular forecast, (ii) which training samples most likely influenced the forecast outcome, (iii) why the forecast is considered an outlier, and (iv) provide a range of counterfactual examples to understand how value changes in the feature vector can lead to a different outcome. Moreover, a modular architecture and a methodology were developed to iteratively remove noisy data instances from the train set, to enhance the overall global time series forecasting model performance. Finally, to test the effectiveness of the proposed approach, it was validated on two publicly available real-world datasets.
Parallel Privacy-Preserving Shortest Path Algorithms

In this paper, we propose and present secure multiparty computation (SMC) protocols for single-source shortest distance (SSSD) and all-pairs shortest distance (APSD) in sparse and dense graphs. Our protocols follow the structure of classical algorithms—Bellman–Ford and Dijkstra for SSSD; Johnson, Floyd–Warshall, and transitive closure for APSD. As the computational platforms offered by SMC protocol sets have performance profiles that differ from typical processors, we had to perform extensive changes to the structure (including their control flow and memory accesses) and the details of these algorithms in order to obtain good performance. We implemented our protocols on top of the secret sharing based protocol set offered by the Sharemind SMC platform, using single-instruction-multiple-data (SIMD) operations as much as possible to reduce the round complexity. We benchmarked our protocols under several different parameters for network performance and compared our performance figures against each other and with ones reported previously.
Ad Text Classification with Bidirectional Encoder Representations

In this study, a natural language processing-based (NLP-based) method is proposed for the sector-wise automatic classification of ad texts created on online advertising platforms. Our data set consists of approximately 21,000 labeled advertising texts from 12 different sectors. In the study, the Bidirectional Encoder Representations from Transformers (BERT) model, which is a transformer-based language model that is recently used in fields such as text classification in the natural language processing literature, was used. The classification efficiencies obtained using a pre-trained BERT model for the Turkish language are shown in detail.
Garbage Detection Algorithm Based on Deep Learning

Rapid and accurate detection of garbage has become a hot issue in the field of computer vision. In this paper, we propose an intelligent garbage detection algorithm to improve the accuracy and speed of garbage detection. For the self-made garbage dataset, to reduce the annotation workload and the problem of missing annotation and wrong annotation, YOLOv4 combined with self-learning strategy is used to realize the training of garbage detection model and improve the accuracy of detection tasks. At the same time, the fast NMS algorithm is introduced to accelerate the prediction process and improve the model prediction speed. The experimental results show that the detection effect of the algorithm in this paper is significant, and it can effectively identify six different kind of garbage, with mean Average Precision (mAP) of 95.36% and a detection time of 25ms for a single image, which is expected to be applied to the recognition and detection of waste targets in industrial conveying scenarios.
Modeling and Verifying the CKB Blockchain Consensus Protocol

The Nervos CKB (Common Knowledge Base) is a public permissionless blockchain designed for the Nervos ecosystem. The CKB consensus protocol is the key protocol of the Nervos CKB, which improves the limit of the consensus’s performance for Bitcoin. In this paper, we developed the formal model of the CKB consensus protocol using timed automata. Based on the model, we formally verified various important properties of the Nervos CKB to provide a sufficient trustworthiness assurance. Especially, the security of the Nervos CKB against the selfish mining attacks to the protocol was investigated.
Hate Speech Dataset from Turkish Tweets

Today, while the content produced by users on online platforms increases rapidly due to the spread of the internet, hate speech expressions on these platforms also increase similarly. Social media platforms with millions ofusers are especially among the areas where hate speech expressions are shared frequently. Popular social media companies form their own policies within the scope of combating hate speech. However, the size of the data on the internet makes it almost impossible to do this manually. Consequently, especially in recent years, many studies have been conducted on the automatic detection of hate speech. While most of the studies in the literature are on English, there are published studies on hate speech detection in many languages such as German, French, Arabic, Indonesian, Portuguese. One of the main reasons for fewer studies in languages other than English is the smaller number and size of publicly shared hate speech datasets in those languages. There is a similar situation for Turkish. Therefore, within the scope of the study, a hate speech dataset comprising 10,224 Turkish tweets was generated and shared publicly. Tweets were labeled as hate, offensive, and none, and tweets tagged as hate were assigned subclass labels such as ethnic, religious, sexist, and political, which express the type of hate. In the first step of the labeling process, two annotators labeled all tweets separately. In the comparison made after this process, it was seen that the agreement rate in the given labels was 92.5%. Afterwards, the two annotators discussed the tweets they gave different labels by exchanging ideas and increased the agreement rate to 98.4%. For the remaining tweets, the opinion of the third evaluator was sought. After the labeling process, it was seen that the rate of hate speech in the data set was 22.8%. This publicly available data set, which is a first for Turkish in terms of its scope and size, is expected to be an important resource for automatic hate speech detection studies in Turkish.
Quantification of Kaolinite and Halloysite Using Machine Learning from FTIR, XRF, and Brightness Data

Quantification of halloysite and kaolinite in clay deposits from X-ray diffraction (XRD) commonly requires extensive sample preparation to differentiate the two phyllosilicates. When assessing hundreds of samples for mineral resource estimations, XRD analyses may become unfeasible due to time and expense. Fourier transform infrared (FTIR) analysis is a fast and cost-effective method to discriminate between kaolinite and halloysite; however, few efforts have been made to use this technique for quantified analysis of these minerals. In this study, we trained machine- and deep-learning models on XRD data to predict the abundance of kaolinite and halloysite from FTIR, chemical composition, and brightness data. The case study is from the Cloud Nine kaolinite–halloysite deposit, Noombenberry Project, Western Australia. The residual clay deposit is hosted in the saprolitic and transition zone of the weathering profile above the basement granite on the southwestern portion of the Archean Yilgarn Craton. Compared with XRD quantification, the predicted models have an R2 of 0.97 for kaolinite and 0.96 for halloysite, demonstrating an excellent fit. Based on these results, we demonstrate that our methodology provides a cost-effective alternative to XRD to quantify kaolinite and halloysite abundances.
Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots

Collaborative robots are currently deployed in professional environments, in collaboration with professional human operators, helping to strike the right balance between mechanization and manual intervention in manufacturing processes required by Industry 4.0. In this paper, the contribution of gesture recognition and pose estimation to the smooth introduction of cobots into an industrial assembly line is described, with a view to performing actions in parallel with the human operators and enabling interaction between them. The proposed active vision system uses two RGB-D cameras that record different points of view of gestures and poses of the operator, to build an external perception layer for the robot that facilitates spatiotemporal adaptation, in accordance with the human's behavior. The use-case of this work is concerned with LCD TV assembly of an appliance manufacturer, comprising of two parts. The first part of the above-mentioned operation is assigned to a robot, strengthening the assembly line. The second part is assigned to a human operator. Gesture recognition, pose estimation, physical interaction, and sonic notification, create a multimodal human-robot interaction system. Five experiments are performed, to test if gesture recognition and pose estimation can reduce the cycle time and range of motion of the operator, respectively. Physical interaction is achieved using the force sensor of the cobot. Pose estimation through a skeleton-tracking algorithm provides the cobot with human pose information and makes it spatially adjustable. Sonic notification is added for the case of unexpected incidents. A real-time gesture recognition module is implemented through a Deep Learning architecture consisting of Convolutional layers, trained in an egocentric view and reducing the cycle time of the routine by almost 20%. This constitutes an added value in this work, as it affords the potential of recognizing gestures independently of the anthropometric characteristics and the background. Common metrics derived from the literature are used for the evaluation of the proposed system. The percentage of spatial adaptation of the cobot is proposed as a new KPI for a collaborative system and the opinion of the human operator is measured through a questionnaire that concerns the various affective states of the operator during the collaboration.