Reviewerly/exHarmony huggingface dataset:
Optimization of Drilling Path Using the Bees Algorithm

Optimization is the process of finding the best possible solutions to a problem.It has been widely used in various areas especially in engineering problems.One of the common issues that is faced by some of the manufacturers is finding drilling sequences of multiple holes.By drilling multiple holes with the least total path length, the manufacturer can save a lot of time and it can increase the productivity of the company.Thus, this study focuses on the drilling path of the multiple holes problem which has been solved by other researchers.This study uses the Bees Algorithm to find the best sequence of drilling holes (minimum total path length) and the results found are compared with the result of other algorithms.In addition to results comparison with other algorithms, the results obtained are verified with simulation results using MasterCAM software.The results comparison shows that the Bees Algorithm achieved comparable performance compared to other algorithms.
Collaborative Fault-Tolerant Navigation Method for Unmanned Swarm based on MHSS

In a UAV swarm, the additional measurement information shared among aircrafts can improve adaptability and safety of UAVs. Because the multi-faults of UAV navigation system is difficult to detect in a complex environment, a collaborative fault-tolerant navigation method for aircraft swarm is proposed. First, considering the influence of different measurement characteristics of GNSS pseudorange and aircraft ranging information on detection, a cooperative augmentation positioning model is established. Then, utilizing the cooperative observations, the improved multi-hypothesis solution separation (MHSS) algorithm can be applied in the case of few satellites, and determine the faulty satellites by checking the difference of subset solutions. Simulation results show that the method can effectively guarantee the availability and accuracy of multi-fault detection under the condition of few satellites, and improve the fault-tolerant navigation capability of UAV swarm.
Implementation of Certainty Factor Method to Diagnose Diseases in Pineapple Plants

Indonesia is one of the largest pineapple producing countries in the world which is ranked 9th in the world, with a total annual production of around 1,396,153 tons. In the process of planting pineapples, there are opportunities for pests and diseases that can cause a decrease in the quality of pineapples. It can even cause crop failure which is detrimental to farmers. For this reason, this research will implement the certainty factor method to diagnose diseases in pineapple plants. So that pineapple plant diseases can be identified early and can be handled properly, so that the pineapple fruit produced has good quality. Based on the results of the prediction accuracy test that has been done, an accuracy value of 80% is obtained.
Video super-resolution using a hierarchical recurrent multireceptive-field integration network

Video super-resolution methods always suffer from being computationally expensive and time-consuming because of sequentially performing the temporal frame alignment and spatial feature fusion. To address the issue, a novel video super-resolution method based on a hierarchical recurrent multireceptive-field integration network is proposed. Specifically, a hierarchical recurrent structure, which can fully model the temporal correlation of sequential low-resolution frames, is proposed. Then, a residual multireceptive-field integration module is introduced to obtain the hidden state and rich context features. Finally, an adaptive fusion strategy is employed to further boost video-reconstruction quality using fewer parameters. Moreover, the proposed method is capable of improving the performance and speed of video super-resolution. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves a better balance between the reconstruction performance and speed than existing video super-resolution methods.
Lightweight Cryptographic and Artificial Intelligence Models for Anti-smishing

The utilization of Short Message Service (SMS) in smartphones has expanded quickly during recent years. As text informing is the most prudent choice accessible to arrive at masses, it is broadly utilized for offering buy focal points and as a promoting medium. Roughly billions of instant SMSs are sent each hour worldwide, so the expanding utilization of these messages makes it profoundly suitable for assailants. Over 90% of SMS messages are opened within a few seconds, which shows SMS’s importance, making it the focus of cyber-attacks. Smishing is one of the cybercrime types that combine SMS and phishing in which trespassers send SMS containing suspicious links and content to the victims. Usually, smishing directs users to harmful websites to silently download malware or show interfaces like legitimate sites to attract users to fill in their sensitive information, i.e., passwords. This study proposes two automated models for detecting smishing by employing Lightweight cryptographic and artificial intelligence (AI) techniques. For the first model, and since there is no official validation stage in SMS construction, we developed our first contribution using a lightweight cryptographic SMS model to protect SMS and detect possible phishing or fake content. We select the standard, popular signature schemes, and evaluating the performance according to time and signature size overhead. The results showed the proposed model's effectiveness to guarantee the main security goals, authentication, integrity, and non-repudiation. Our second model deals with legacy SMS, which doesn’t follow the validation stage, so we collected the SMS dataset for an AI model and manually labeled their classes as phishing or legitimate according to their link content. We preprocessed the SMS dataset to extract the link-based distinguishing features, and we applied feature selection techniques to obtain the best performance. We experimented with two artificial intelligence classifiers and evaluated their detection accuracy results using accuracy, precision, recall, and F-score metrics. Random forest classifier yielded the best detection results with an accuracy of 97%.
Focus-tunable experimental optical cryptosystem

We present a first implementation of an optical encryption setup in the fractional Fourier domain using an electrically focus-tunable lens. Fractional Fourier transform cryptosystems allow the use of the fractional order of the transform as an encryption parameter. In our proposed scheme, the tunable lens enables fast control of this parameter and replaces the use of mechanical alternatives, increasing the stability and reducing the alignment requirements compared to previous fractional cryptosystems. The active electrically focus-tunable lens was accurately calibrated to ensure proper implementation and control of the tunable optical cryptosystem. We tested the encryption-decryption range of the electro-optical lens by encrypting objects using different focal lengths. Then we analyzed the tolerance to decryption with keys registered with different focal lengths. In this way, we found an operation range that enables the implementation of an effective multi-user encryption protocol. All results presented in this work are supported by numerical simulations and experimental results, demonstrating the viability and versatility of our proposal.
A Multi-Stage Advanced Deep Learning Graphics Pipeline

In this paper we propose the Advanced Deep Learning Graphics Pipeline (ADLGP). ADLGP is a novel approach that uses existing deep learning architectures to convert scene data into rendered images. Our goal of generating frames from semantic data has produced successful renderings with similar structures and composition as target frames. We demonstrate the success of ADLGP with side-by-side comparisons of frames generated through standard rendering procedures. We assert that a fully implemented ADLGP framework would reduce the time spent in visualizing 3D environments, and help selectively offload the requirements of the current graphics rendering pipeline.
Evolving Complementary Sparsity Patterns for Hardware-Friendly Inference of Sparse DNNs

Sparse deep learning models are known to be more accurate than their dense counterparts for equal parameter and computational budgets. Unstructured model pruning can deliver dramatic compression rates, yet the consequent irregular sparsity patterns lead to severe computational challenges for modern computational hardware. Our work introduces a set of complementary sparsity patterns to construct both highly expressive and inherently regular sparse neural network layers. We propose a novel training approach to evolve inherently regular sparsity configurations and transform the expressive power of the proposed layers into a competitive classification accuracy even under extreme sparsity constraints. The structure of the introduced sparsity patterns engenders optimal compression of the layer parameters into a dense representation. Moreover, the constructed layers can be processed in the compressed format with full-hardware utilization in minimally modified non-sparse computational hardware. The experimental results demonstrate superior compression rates and remarkable performance improvements in sparse neural network inference in systolic arrays.
Investigating the Effect of Feature Distribution Shift on the Performance of Sleep Stage Classification with Consumer Sleep Trackers

The performance of consumer wearable sleep trackers could vary significantly from user to user. This paper presents a pilot study on the effect of the feature distribution shift between training and test datasets on the performance of sleep classification models. We used Anderson-Darling (AD) statistic to quantify the shift in feature distribution, which is then correlated to the several model performance metrics. Our results show that the distribution shift of some features were negatively correlated to the model accuracy for REM and NREM sleep classification. Future studies on consumer sleep tracking technologies may focus on addressing the dataset shift problem in the model training process.
AHDet: A dynamic coarse-to-fine gaze strategy for active object detection

In the work setting of deep learning, most of the neural networks employed for visual object detection in recent years are based on bounding box regression. The performance of active detectors through multi-step decision-making is limited by the rough model design. However, from the perspective of cognitive science, the recognition in the human visual system is a decision process from coarse to fine. Based on the theory of “see the forest first, then the trees”, this paper proposes a dynamic coarse-to-fine gaze strategy for active object detection, named AHDet, which takes the key points as the realization carrier of the coarse-to-fine concept. The detection process is divided into two steps, AIM and HIT. In the step of AIM, the positioning and prior bounding boxes for objects are given by detecting the center points, referring to the first glance. In the step of HIT, bounding boxes are dynamically adjusted to obtain compact bounding boxes with the help of the corner points, referring to the careful observation. With the design of the two-step coarse-to-fine gaze process, AHDet outperforms traditional approaches. A series of experiments performed on MS-COCO and PASCAL VOC dataset demonstrate the advantages of AHDet.
Knowledge structure driven prototype learning and verification for fact checking

To inhibit the spread of rumorous information, fact checking aims at retrieving relevant evidence to verify the veracity of a given claim. Previous work on fact checking typically uses knowledge graphs (KGs) as external repositories and develop reasoning methods to retrieve evidence from KGs. Domain knowledge structure, including category hierarchy and attribute relationships, can be utilized as discriminative information to facilitate KG based learning and verification. However, in previous fact checking research, category hierarchy and attribute information was often scattered in a KG and treated as the ordinary triple facts in the learning process like other types of information, or was utilized in a limited way without the consideration of category hierarchy or the combination of category hierarchy with the learning process. Thus to better utilize category hierarchy and attribute relationships, in this paper, we propose an end-to-end knowledge structure driven prototype learning and verification method for fact checking. For improving intra-category compactness and inter-category separation, we develop a hierarchical prototype learning technique that jointly learns a prototype for each sub-category to enhance entity embeddings and optimize embedding representations using high-level category. For further enhancing embedding learning, we propose a graph attention network to aggregate information from neighboring attribute nodes. We construct a real-world dataset on food domain, and experimental results on the benchmark datasets and our domain dataset show the effectiveness of our method compared to both previous fact checking methods and representative KG reasoning methods.
Transparent Aspect-Level Sentiment Analysis Based on Dependency Syntax Analysis and Its Application on COVID-19

Aspect-level sentiment analysis identifies fine-grained emotion for target words. There are three major issues in current models of aspect-level sentiment analysis. First, few models consider the natural language semantic characteristics of the texts. Second, many models consider the location characteristics of the target words, but ignore the relationships among the target words and among the overall sentences. Third, many models lack transparency in data collection, data processing, and results generating in sentiment analysis. In order to resolve these issues, we propose an aspect-level sentiment analysis model that combines a bidirectional Long Short-Term Memory (LSTM) network and a Graph Convolutional Network (GCN) based on Dependency syntax analysis (Bi-LSTM-DGCN). Our model integrates the dependency syntax analysis of the texts, and explicitly considers the natural language semantic characteristics of the texts. It further fuses the target words and overall sentences. Extensive experiments are conducted on four benchmark datasets, i.e., Restaurant14, Laptop, Restaurant16, and Twitter. The experimental results demonstrate that our model outperforms other models like Target-Dependent LSTM (TD-LSTM), Attention-based LSTM with Aspect Embedding (ATAE-LSTM), LSTM+SynATT+TarRep and Convolution over a Dependency Tree (CDT). Our model is further applied to aspect-level sentiment analysis on “government” and “lockdown” of 1,658,250 tweets about “#COVID-19” that we collected from March 1, 2020 to July 1, 2020. The experimental results show that Twitter users’ positive and negative sentiments fluctuated over time. Through the transparency analysis in data collection, data processing, and results generating, we discuss the reasons for the evolution of users’ emotions over time based on the tweets and on our models.
Designing Personalized OS Update Message based on Security Behavior Stage Model

As one of the scales which assess the end-user’s security behavior, the security behavior stage model (SeBeST) [1] is a practical approach to characterize similar groups of users (precontemplation, contemplation, preparation, action and maintenance stages) and provide customized remedies to improve their security behavior. For example, in OS update message customization, a group that does not update OS continuously may require a message indicating the ease of OS update; on the other hand, updating users need a message indicating the importance of OS update. In this paper, we propose a personalized OS update message interface based on SeBeST. We conduct two online surveys to evaluate effective appearance and message as the personalized user interface (UI). First, we assess the interface’s appearance individually for the three behavior stages (preparation, action, and maintenance) and then combine the customized messages and the selected impressions for these stages. We confirmed that appropriate appearances are different for each stage. For example, a highlighted red button is efficient for users in the preparation stage. On the other hand, the red background is suitable for users of the action and maintenance stages. We discovered that the combination of the message indicating the disadvantage of the OS update and the UI which is the highlighted red button is suitable for the preparation and action stages. In addition, we confirmed the best combination for users of the maintenance stage is a message indicating the ease of OS update and the UI which is mouse over pop-up representation. Therefore, it is necessary for each user to show the appropriate message and UI.
Can a camera tell the weather?

Accurate determination of weather conditions from a single camera captured image is invaluable not only for meteorological applications but also for automated image processing tasks. The few approaches, existing currently, suffer from one or both of two major drawbacks. They either require complex networks for application of sophisticated machine learning techniques which consume both computational resources and time or else, fail to achieve the desired level of performance. In this paper, we propose a novel weather classification technique using sky based features from camera-captured images combined with very basic machine learning algorithms.The contribution of this article is two-fold. A highly accurate sky detection algorithm is used to compute the proposed sky relevant features. These features are then harnessed to predict the weather condition using simple and fast techniques such as a multi-label Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN). Experimental results using publicly available standard weather databases establish the superiority in performance of the proposed algorithm in weather classification.
Quantum Resistance Deep Learning based Drone Surveillance System

Some countries have designed anti-drone systems i.e., detecting, jamming, and camera units. It is a multidis-ciplinary experienced system particularly designed to protect regions and people from cyber-terrorist and oppose unauthorized drones. Security and surveillance are two of the leading areas in the growing drone sector. Moreover, machine learning or deep learning could help in object detection because of its high accuracy and acceptable delay performance. Hence, this paper proposed a modified streaming protocol for drone surveillance with post-quantum cryptography that ensures the drone's data confidentiality. This paper also provided a deep learning receiver to perform object detection by using YOLOv2-Tiny, YOLOv3-Tiny, and YOLOv4-Tiny respectively. The 72 experiment results showed that all configurations on the 30-FPS input produced big overhead and huge delay. This leaves the option to set the FPS input to be lower than 30, yet the FPS benchmark result showed that even with the highest FPS configuration, the results were capped at a maximum of 14-FPS. Nevertheless, the results of the proposed methods confirmed the feasibility of using the developed surveillance drone on low-energy architecture.
On the Sound of Successful Meetings: How Speech Prosody Predicts Meeting Performance

This paper investigates the degree to which meeting success can be predicted through holistic, acoustic-prosodic measurements. The analyzed meetings are taken from the Parking Lot Corpus in which 70 groups of three to six students discuss the traffic situation at their university and come up with parking and transportation recommendations. The number, feasibility, and quality of these recommendations as well as the mean effectiveness and satisfaction ratings across group members provide the basis for correlations with three sets 15 acoustic-prosodic features that cover pitch, duration/timing, intensity, and the absolute frequencies of local events such as silent pauses. Results show that meeting success is, in fact, considerably correlated with the overall “sound” of the individual meetings, with pitch features being the most diverse and powerful predictors. In addition, we found that the “sound” of subjectively effective meetings differs from the “sound” of objectively productive meetings, i.e. meetings that generate a high output of feasible and/or high-quality recommendations. The prosodic feature patterns suggest that effective meetings are short and matter-of-fact, whereas the productive meetings are longer and have a lively speech melody that makes these meetings stimulating. We discuss the implications of our findings for future research and technological innovation.
An effective modified possibilistic Fuzzy C-Means clustering algorithm for noisy data problems

Clustering is a machine learning method that consists in grouping data points by similarity. Different Fuzzy C-Means clustering algorithms have been proposed, for example FCM, PCM, PFCM, most of those algorithms encounter several problems like choice of the adequate distance, efficiency against noise and outliers. In this study, we propose a new robust algorithm named Modified Possibilistic Fuzzy C-Means algorithm (MPFCM) based on possibilistic approach, which improves PFCM algorithm and overcomes those shortcomings. MPFCM is an extension of PFCM algorithm. Furthermore, the MPFCM allows to use more sophisticated norms for different complex problems. In another hand, MPFCM detects cluster centers more accurately in noisy data environment and with nonlinearly separable input data space. Experiments and results showed the effectiveness of our proposed algorithm MPFCM.
State Estimation with Linear Equality Constraints Based on Trajectory Function of Time and Karush-Kuhn-Tucker Conditions

In this paper, we address the constrained target tracking problem based on the approach of the trajectory function of time, in which the target state satisfies equality constraints. What is different from the previous work is that we handle equality constraints by means of utilizing Karush-Kuhn-Tucker conditions within the trajectory curve fitting framework. A simple numerical example has been used for demonstrating the advantage of our proposed approach in comparison with the exiting Kalman filter based on the projection approach and the pseudo-observation approach.
MDEForgeWL: Towards cloud-based discovery and composition of model management services

Model management services play an essential role while developing complex systems by means of model-driven engineering (MDE) practices. They carry out several model management operations (MMOs), including model transformation, validation, comparison, and merging, which are exposed as remotely consumable services. However, the adoption of MMOs on cloud-based model repositories has raised issues related to their discovery and orchestration. Notably, it is an arduous and error-prone task to carry out the composition and execution of complex workflows involving different modeling artefacts consumed by various model management services.This paper presents MDEForgeWL, a complete infrastructure to support the execution of MMO workflows that are remotely available as dedicated services. MDEForgeWL consists of i) a DSL and supporting engine for defining and executing user-defined workflows of model management services, and ii) a cluster infrastructure to register new services and make them available for defining workflows. A prototypical implementation of MDEForgeWL is presented by applying it to an illustrative example.