Development of a questionnaire to determine the case detection delay of leprosy: A mixed-methods cultural validation study

Background Delay in case detection is a risk factor for developing leprosy-related impairments, leading to disability and stigma. The objective of this study was to develop a questionnaire to determine the leprosy case detection delay, defined as the period between the first signs of the disease and the moment of diagnosis, calculated in total number of months. The instrument was developed as part of the PEP4LEP project, a large-scale intervention study which determines the most effective way to implement integrated skin screening and leprosy post-exposure prophylaxis with a single-dose of rifampicin (SDR-PEP) administration in Ethiopia, Mozambique and Tanzania. Methodology/Principal findings A literature review was conducted and leprosy experts were consulted. The first draft of the questionnaire was developed in Ethiopia by exploring conceptual understanding, item relevance and operational suitability. Then, the first draft of the tool was piloted in Ethiopia, Mozambique and Tanzania. The outcome is a questionnaire comprising nine questions to determine the case detection delay and two annexes for ease of administration: a local calendar to translate the patient’s indication of time to number of months and a set of pictures of the signs of leprosy. In addition, a body map was included to locate the signs. A ‘Question-by-Question Guide’ was added to the package, to provide support in the administration of the questionnaire. The materials will be made available in English, Oromiffa (Afaan Oromo), Portuguese and Swahili via https://www.infolep.org. Conclusions/Significance It was concluded that the developed case detection delay questionnaire can be administered quickly and easily by health workers, while not inconveniencing the patient. The instrument has promising potential for use in future leprosy research. It is recommended that the tool is further validated, also in other regions or countries, to ensure cultural validity and to examine psychometric properties like test-retest reliability and interrater reliability.

Missile guidance with assisted deep reinforcement learning for head-on interception of maneuvering target

In missile guidance, pursuit performance is seriously degraded due to the uncertainty and randomness in target maneuverability, detection delay, and environmental noise. In many methods, accurately estimating the acceleration of the target or the time-to-go is needed to intercept the maneuvering target, which is hard in an environment with uncertainty. In this paper, we propose an assisted deep reinforcement learning (ARL) algorithm to optimize the neural network-based missile guidance controller for head-on interception. Based on the relative velocity, distance, and angle, ARL can control the missile to intercept the maneuvering target and achieve large terminal intercept angle. To reduce the influence of environmental uncertainty, ARL predicts the target’s acceleration as an auxiliary supervised task. The supervised learning task improves the ability of the agent to extract information from observations. To exploit the agent’s good trajectories, ARL presents the Gaussian self-imitation learning to make the mean of action distribution approach the agent’s good actions. Compared with vanilla self-imitation learning, Gaussian self-imitation learning improves the exploration in continuous control. Simulation results validate that ARL outperforms traditional methods and proximal policy optimization algorithm with higher hit rate and larger terminal intercept angle in the simulation environment with noise, delay, and maneuverable target.

Synthesis of Line of Sight Angle Coordinate Filter on the Basis of Interactive Multi-Model Evaluation Algorithm

On the basis of the tracking multi-loop target angle coordinate system, the article has selected and proposed a interactive multi-model adaptive filter algorithm to improve the quality of the target phase coordinate filter. In which, the 3 models selected to design the line of sight angle coordinate filter; Constant velocity (CV) model, Singer model and constant acceleration model, characterizing 3 different levels of maneuverability of the target. As a result, the evaluation quality of the target phase coordinates is improved because the evaluation process has redistribution of the probabilities of each model to suit the actual maneuvering of the target. The structure of the filters is simple, the evaluation error is small and the maneuvering detection delay is significantly reduced. The results are verified through simulation, ensuring that in all cases the target is maneuvering with different intensity and frequency, the line of sight angle coordinate filter always accurately determines the target angle coordinates.

Evaluating Temporal Bias in Time Series Event Detection Methods

The detection of events in time series is an important task in several areas of knowledge where operations monitoring is essential. Experts often have to deal with choosing the most appropriate event detection method for a time series, which can be a complex task. There is a demand for benchmarking different methods in order to guide this choice. For this, standard classification accuracy metrics are usually adopted. However, they are insufficient for a qualitative analysis of the tendency of a method to precede or delay event detections. Such analysis is interesting for applications in which tolerance for "close" detections is important rather than focusing only on accurate ones. In this context, this paper proposes a more comprehensive event detection benchmark process, including an analysis of temporal bias of detection methods. For that, metrics based on the time distance between event detections and identified events (detection delay) are adopted. Computational experiments were conducted using real-world and synthetic datasets from Yahoo Labs and resources from the Harbinger framework for event detection. Adopting the proposed detection delay-based metrics helped obtain a complete overview of the performance and general behavior of detection methods.

Dynamic selective auditory attention detection using RNN and reinforcement learning

The cocktail party phenomenon describes the ability of the human brain to focus auditory attention on a particular stimulus while ignoring other acoustic events. Selective auditory attention detection (SAAD) is an important issue in the development of brain-computer interface systems and cocktail party processors. This paper proposes a new dynamic attention detection system to process the temporal evolution of the input signal. The proposed dynamic SAAD is modeled as a sequential decision-making problem, which is solved by recurrent neural network (RNN) and reinforcement learning methods of Q-learning and deep Q-learning. Among different dynamic learning approaches, the evaluation results show that the deep Q-learning approach with RNN as agent provides the highest classification accuracy (94.2%) with the least detection delay. The proposed SAAD system is advantageous, in the sense that the detection of attention is performed dynamically for the sequential inputs. Also, the system has the potential to be used in scenarios, where the attention of the listener might be switched in time in the presence of various acoustic events.

Adaptive Drift Detection Mechanism for Non-Stationary Data Stream

Mining is a challenging and important task in a non-stationary data stream. It is used in financial sectors, web log analysis, sensor networks, network traffic management, etc. In this environment, data distribution may change overtime and is called concept drift. So, it is necessary to identify the changes and address them to keep the model relevant to the incoming data. Many researchers have used Drift Detection Method (DDM). However, DDM is very sensitive to detect gradual drift where the detection delay is high. In this paper, we propose Adaptive Drift Detection Method (ADDM) which improves the performance of the drift detection mechanism. The ADDM uses a new parameter to detect the gradual drift in order to reduce the detection delay. The proposed method, ADDM, experiments with six synthetic datasets and four real-world datasets. Experimental results confirm that ADDM reduces the drift detection delay and false-positive rate (FPR) while preserving high classification accuracy.

Detection of DoS Attacks Using ARFIMA Modeling of GOOSE Communication in IEC 61850 Substations

Integration of Information and Communication Technology (ICT) in modern smart grids (SGs) offers many advantages including the use of renewables and an effective way to protect, control and monitor the energy transmission and distribution. To reach an optimal operation of future energy systems, availability, integrity and confidentiality of data should be guaranteed. Research on the cyber-physical security of electrical substations based on IEC 61850 is still at an early stage. In the present work, we first model the network traffic data in electrical substations, then, we present a statistical Anomaly Detection (AD) method to detect Denial of Service (DoS) attacks against the Generic Object Oriented Substation Event (GOOSE) network communication. According to interpretations on the self-similarity and the Long-Range Dependency (LRD) of the data, an Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model was shown to describe well the GOOSE communication in the substation process network. Based on this ARFIMA-model and in view of cyber-physical security, an effective model-based AD method is developed and analyzed. Two variants of the statistical AD considering statistical hypothesis testing based on the Generalized Likelihood Ratio Test (GLRT) and the cumulative sum (CUSUM) are presented to detect flooding attacks that might affect the availability of the data. Our work presents a novel AD method, with two different variants, tailored to the specific features of the GOOSE traffic in IEC 61850 substations. The statistical AD is capable of detecting anomalies at unknown change times under the realistic assumption of unknown model parameters. The performance of both variants of the AD method is validated and assessed using data collected from a simulation case study. We perform several Monte-Carlo simulations under different noise variances. The detection delay is provided for each detector and it represents the number of discrete time samples after which an anomaly is detected. In fact, our statistical AD method with both variants (CUSUM and GLRT) has around half the false positive rate and a smaller detection delay when compared with two of the closest works found in the literature. Our AD approach based on the GLRT detector has the smallest false positive rate among all considered approaches. Whereas, our AD approach based on the CUSUM test has the lowest false negative rate thus the best detection rate. Depending on the requirements as well as the costs of false alarms or missed anomalies, both variants of our statistical detection method can be used and are further analyzed using composite detection metrics.

A new adaptive XOR, hashing and encryption-based authentication protocol for secure transmission of the medical data in Internet of Things (IoT)

Internet of Things (IoT) plays a prominent role in health-care of patients, which assist the physicians and patients through the assistance in effective decision-making and additionally, in the medical field, IoT plays a significant role in real-time monitoring of the patients. Even though the data provided by the IoT devices ensure the effective decision-making, the data is susceptible to the network attacks. Thus, the paper proposes an authentication protocol for enabling the secure data transmission in IoT based on three functions, such as encryption function, hashing function, and adaptive XOR function. The proposed authentication protocol is named as, Adaptive XOR, hashing and Encryption Key Exchange (AXHE) protocol, which is the combination of the functions, such as encryption function, hashing function, and adaptive XOR function. The protocol ensures the security in the communication through two successive phases, such as registration and authentication of the user, where the user name, password, public keys, private keys, and security factor are employed. The authentication is progressed as seven levels and whenever the security factor matches, the user is authenticated and the communication continues. The analysis of the proposed AXHE is performed using 50 and 100 nodes in the presence of DOS and black hole attacks, which acquires the detection rate, throughput, and detection delay of 0.3859, 0.32, and 6.535 s, respectively.