Traceability to ensure food safety and consumer protection as typified by case studies of three meat processing plants

Ensuring food safety is a legal obligation of the manufacturer or of the entity that places the product on sale. Traceability is one of the tools that are used to ensure food safety. It allows the withdrawal of a dangerous or non-compliant product from the market and determines the source of a threat. The aim of the study was to compare the functioning and effectiveness of traceability systems in selected approved meat industry plants. The system functioning in a large meat processing plant, in which the circulation of documents was implemented in a computer system, was compared with two smaller ones, in which paper documentation was carried out, but supported by a computer system. In these plants, the traceability system was based on internal procedures. Properly developed traceability procedures and simulations support and enable response in a crisis. Computer systems streamline and facilitate the traceability process. However, the comparative analysis showed that the use of paper records allowed for efficient identification of the source of the threat. The possibility of performing product traceability was confirmed in these plants. Internal markings and codes and documentation flow, staff training, and awareness proved helpful

SSO Based Fingerprint Authentication of Cloud Services for Organizations

Access to a pool of programmable resources, such as storage space, applications, services, and on-demand networks, is made possible by cloud computing technology. Involving the cloud with the organization reduces its efforts to meet the needs of its customers. The Single Sign-On (SSO) method, which enables users to access various application services using a single user credential, is one of the key benefits of cloud computing. There are numerous problems and difficulties with cloud computing that need to be highlighted. However, protecting user agent privacy against security assaults is far more challenging. To combat security and privacy assaults, this study suggests SSO-based biometric authentication architecture for cloud computing services. Since end devices are computationally inefficient for processing user information during authentication, biometric authentication is effective for resources controlled by end devices at the time of accessing cloud services. As a result, the proposed design minimizes security attacks in cloud computing. An innovative strategy that establishes a one-to-one interaction between the user agent and the service provider is also included in the suggested design. In this case, user agents can use their fingerprint to access various cloud application services and seek registration. The highlights of the suggested architecture have been offered based on comparison analysis with a number of existing architectures

Outlier detection in wireless sensor network based on time series approach

Sensory data inWireless Sensor Network (WSN) is not always reliable because of open environmental factors such as noise, weak received signal strength or intrusion attacks. The process of detecting highly noisy data and noisy sensor node is called outlier detection. Outlier detection is one of the fundamental tasks of time series analysis that relates to predictive modeling, cluster analysis and association analysis. It has been widely researched in various disciplines besides WSN. The challenge of noise detection in WSN is when it has to be done inside a sensor with limited computational and communication capabilities. Furthermore, there are only a few outlier detection techniques in WSNs and there are no algorithms to detect outliers on real data with high level of accuracy locally and select the most effective neighbors for collaborative detection globally. Hence, this research designed a local and global time series outlier detection in WSN. The Local Outlier Detection Algorithm (LODA) as a decentralized noise detection algorithm runs on each sensor node by identifying intrinsic features, determining the memory size of data histogram to accomplish effective available memory, and making classification for predicting outlier data was developed. Next, the Global Outlier Detection Algorithm (GODA)was developed using adaptive Gray Coding and Entropy techniques for best neighbor selection for spatial correlation amongst sensor nodes. Beside GODA also adopts Adaptive Random Forest algorithm for best results. Finally, this research developed a Compromised SensorNode Detection Algorithm (CSDA) as a centralized algorithm processed at the base station for detecting compromised sensor nodes regardless of specific cause of the anomalies. To measure the effectiveness and accuracy of these algorithms, a comprehensive scenario was simulated. Noisy data were injected into the data randomly and the sensor nodes. The results showed that LODA achieved 89% accuracy in the prediction of the outliers, GODA detected anomalies up to 99% accurately and CSDA identified accurately up to 80% of the sensor nodes that have been compromised. In conclusion, the proposed algorithms have proven the anomaly detection locally and globally, and compromised sensor node detection in WSN

An Intelligent System for Automatic Fingerprint Identification using Feature Fusion by Gabor Filter and Deep Learning

This paper introduces an intelligent computational approach to automatically authenticate fingerprint for personal identification and verification. The feature vector is formed using combined features obtained from Gabor filtering technique and deep learning technique such as Convolutional Neural Network (CNN). Principle Component Analysis (PCA) has been performed on the feature vectors to reduce the overfitting problems in order to make the classification results more accurate and reliable. A multiclass classifier has been trained using the extracted features. Experiments performed using standard public databases demonstrated that the proposed approach showed better performance with regard to accuracy (99.87%) compared to the more recent classification techniques such as Support Vector Machine (97.86%) or Random Forest (95.47%). However, the proposed method also showed higher accuracy compared to other validation approaches such as K-fold (98.89%) and generalization (97.75%). Furthermore, these results were supported by confusion matrix results where only 10 failures were found when tested with 5000 images

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

Deep Learning Based Methods for Outdoor Robot Localization and Navigation

The number of elderly people is increasing around the globe. In order to support the growing of ageing society, mobile robot is one of viable choices for assisting the elders in their daily activities. These activities happen in any places, either indoor or outdoor. Although outdoor activities benefit the elders in many ways, outdoor environments contain difficulties from their unpredictable natures. Mobile robots for supporting humans in outdoor environments must automatically traverse through various difficulties in the environments using suitable navigation systems.Core components of mobile robots always include the navigation segments. Navigation system helps guiding the robot to its destination where it can perform its designated tasks. There are various tools to be chosen for navigation systems. Outdoor environments are mostly open for conventional navigation tools such as Global Positioning System (GPS) devices. In this thesis three systems for localization and navigation of mobile robots based on visual data and deep learning algorithms are proposed. The first localization system is based on landmark detection. The Faster Regional-Convolutional Neural Network (Faster R-CNN) detects landmarks and signs in the captured image. A Feed-Forward Neural Network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The second system is the navigation system based on visual data and a deep reinforcement learning algorithm called Deep Q Network (DQN). The employed DQN automatically guides the mobile robot with visual data in the form of images, which received from the only Universal Serial Bus (USB) camera that attached to the robot. DQN consists of a deep neural network called convolutional neural network (CNN), and a reinforcement learning algorithm named Q-Learning. It can make decisions with visual data as input, using experiences from consequences of trial-and-error attempts. Our DQN agents are trained in the simulation environments provided by a platform based on a First-Person Shooter (FPS) game named ViZDoom. Simulation is implemented for training to avoid any possible damage on the real robot during trial-and-error process. Perspective from the simulation is the same as if a camera is attached to the front of the mobile robot. There are many differences between the simulation and the real world. We applied a markerbased Augmented Reality (AR) algorithm to reduce differences between the simulation and the world by altering visual data from the camera with resources from the simulation.The second system is assigned the task of simple navigation to the robot, in which the starting location is fixed but the goal location is random in the designated zone. The robot must be able to detect and track the goal object using a USB camera as its only sensor. Once started, the robot must move from its starting location to the designated goal object. Our DQN navigation method is tested in the simulation and on the real robot. Performances of our DQN are measured quantitatively via average total scores and the number of success navigation attempts. The results show that our DQN can effectively guide a mobile robot to the goal object of the simple navigation tasks, for both the simulation and the real world.The third system employs a Transfer Learning (TL) strategy to reduce training time and resources required for the training of newly added tasks of DQN agents. The new task is the task of reaching the goal while also avoiding obstacles at the same time. Additionally, the starting and the goal locations are all random within the specified areas. The employed transfer learning strategy uses the whole network of the DQN agent trained for the first simple navigation task as the base for training the DQN agent for the second task. The training in our TL strategy decrease the exploration factor, which cause the agent to rely on the existing knowledge from the base network more than randomly selecting actions during the training. It results in the decreased training time, in which optimal solutions can be found faster than training from scratch.We evaluate performances of our TL strategy by comparing the DQN agents trained with our TL at different exploration factor values and the DQN agent trained from scratch. Additionally, agents trained from our TL are trained with the decreased number of episodes to extensively display performances of our TL agents. All DQN agents for the second navigation task are tested in the simulation to avoid any possible and uncontrollable damages from the obstacles. Performances are measured through success attempts and average total scores, same as in the first navigation task. Results show that DQN agents trained via the TL strategy can greatly outperform the agent trained from scratch, despite the lower number of training episodes.博士(工学)法政大学 (Hosei University

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 24th International Conference on Fundamental Approaches to Software Engineering, FASE 2021, which took place during March 27–April 1, 2021, and was held as part of the Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg but changed to an online format due to the COVID-19 pandemic. The 16 full papers presented in this volume were carefully reviewed and selected from 52 submissions. The book also contains 4 Test-Comp contributions