Miracle-dz/UniFAR-FTUs huggingface dataset:
Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories

Background Online Cancer Support Groups (OCSG) are becoming an increasingly vital source of information, experiences and empowerment for patients with cancer. Despite significant contributions to physical, psychological and emotional wellbeing of patients, OCSG are yet to be formally recognised and used in multidisciplinary cancer support programs. This study highlights the opportunity of using Artificial Intelligence (AI) in OCSG to address psychological morbidity, with supporting empirical evidence from prostate cancer (PCa) patients. Methods A validated framework of AI techniques and Natural Language Processing (NLP) methods, was used to investigate PCa patient activities based on conversations in ten international OCSG (18,496 patients- 277,805 conversations). The specific focus was on activities that indicate psychological morbidity; the reasons for joining OCSG, deep emotions and the variation from joining through to milestones in the cancer trajectory. Comparative analyses were conducted using t-tests, One-way ANOVA and Tukey-Kramer post-hoc analysis. Findings PCa patients joined OCSG at four key phases of psychological distress; diagnosis, treatment, side-effects, and recurrence, the majority group was ‘treatment’ (61.72%). The four groups varied in expression of the intense emotional burden of cancer. The ‘side-effects’ group expressed increased negative emotions during the first month compared to other groups (p<0.01). A comparison of pre-treatment vs post-treatment emotions showed that joining pre-treatment had significantly lower negative emotions after 12-months compared to post-treatment (p<0.05). Long-term deep emotion analysis reveals that all groups except ‘recurrence’ improved in emotional wellbeing. Conclusion This is the first empirical study of psychological morbidity and deep emotions expressed by men with a new diagnosis of cancer, using AI. PCa patients joining pre-treatment had improved emotions, and long-term participation in OCSG led to an increase in emotional wellbeing, indicating a decrease in psychological distress. It is opportune to further investigate AI in OCSG for early psychological intervention as an adjunct to conventional intervention programs.
Quickly Generating Diverse Valid Test Inputs with Reinforcement Learning

Property-based testing is a popular approach for validating the logic of a program. An effective property-based test quickly generates many diverse valid test inputs and runs them through a parameterized test driver. However, when the test driver requires strict validity constraints on the inputs, completely random input generation fails to generate enough valid inputs. Existing approaches to solving this problem rely on whitebox or greybox information collected by instrumenting the input generator and/or test driver. However, collecting such information reduces the speed at which tests can be executed. In this paper, we propose and study a black-box approach for generating valid test inputs. We first formalize the problem of guiding random input generators towards producing a diverse set of valid inputs. This formalization highlights the role of a guide which governs the space of choices within a random input generator. We then propose a solution based on reinforcement learning (RL), using a tabular, on-policy RL approach to guide the generator. We evaluate this approach, RLCheck, against pure random input generation as well as a state-of-the-art greybox evolutionary algorithm, on four real-world benchmarks. We find that in the same time budget, RLCheck generates an order of magnitude more diverse valid inputs than the baselines.
Acute Hospital Admission for Stroke Is Characterised by Inactivity

Background. Measuring physical activity behaviours of stroke survivors in the inpatient setting is challenging. Authors of observational studies of early poststroke report that stroke survivors are “inactive and alone”. Using activity monitoring devices may help refine clinical practice and inform therapeutic activity targets. Aim. To measure the physical activity behaviour of stroke survivors during acute inpatient hospitalisation. We hypothesized that greater levels of inactivity would be positively associated with stroke severity and age. Method. Using a cross-sectional study design, consecutive stroke patients admitted to an acute stroke unit (Geelong, Australia) and recruited within 48 hours of admission had their physical activity recorded using an ActivPAL™ accelerometer device over a minimum of 3 days. Activity was categorised as time spent inactive (lying or sitting), standing, or stepping. The number of steps per day was recorded. Demographic and ActivPal™ data are described. Results. Seventy-eight stroke survivors were recruited of whom 54 had complete data for 3 days, all starting within 2 days poststroke. Of the 54 participants, 32 had a mild stroke, 17 moderate, and five severe stroke. Nine were able to walk independently at admission. The median age was 82.5 years (interquartile range (IQR) 74-86), 26 were female. On average, during their inpatient stay, participants spent a median of 98% of their admission inactive. A median of 18 minutes per day was spent standing and less than a minute per day was spent walking amounting to a median of 169 steps taken per day. Conclusion. The ActivePal™ device was feasible to use in an acute stroke setting. We observed high levels of inactivity in the first days post-stroke, highlighting the ongoing challenge of promoting activity in the acute stroke period. To our knowledge, this is the largest study to provide objective data on time spent upright, walking, and sedentary using accelerometer data in an acute stroke setting.
Computing Shrub-Depth Decompositions

Shrub-depth is a width measure of graphs which, roughly speaking, corresponds to the smallest depth of a tree into which a graph can be encoded. It can be thought of as a low-depth variant of clique-width (or rank-width), similarly as treedepth is a low-depth variant of treewidth. We present an fpt algorithm for computing decompositions of graphs of bounded shrub-depth. To the best of our knowledge, this is the ﬁrst algorithm which computes the decomposition directly, without use of rank-width decompositions and FO or MSO logic. 2012
In or Out? Perceptions of Inclusion and Exclusion among AIS Members

People want a sense of community, a benefit that a professional association such as the Association for Information Systems (AIS) can provide to members. When attempts to create a shared experience fall short and we feel excluded, we disengage and stop further attempts to participate. In this paper, we lay a foundation for individual and association inclusion practices in the AIS. First, we describe the current state of inclusion practices in the academy and in the AIS. Then, we describe findings from a survey of AIS members that measured their perceptions about inclusion and exclusion and factors that cultivated these perceptions. In doing so, we establish a baseline against which we can measure future change. Our data yields key insights about diversity and inclusion in the AIS, and we offer recommendations for all individuals in various roles and positions in the AIS.
Deconvolutional Pixel Layer Model for Road segmentation without Human Assistance

The autonomous vehicle is the evolutionary goal of designing Advanced Driver Assistance System, (ADAS) to the point where human assistance is not needed anymore. To create a broad drivable geographical area and mapping for route planning we need robust and eﬃ-cient semantic segmentation algorithm. Convolutional Neural Networks (CNN) have been able to achieve state of the art performance for tasks such as Image classiﬁcation, Face recognition and Detection. However semantic segmentation has remained a challenging problem in the ﬁeld of computer vision. With the help of convolution neural networks, we have witnessed proliﬁc results over time. We propose a convolutional neural network model which uses Fully Convolutional Neural Network (FCN) with deconvolutional pixel layers. The goal is to create a hierarchy of features while the fully convolutional model does the primary learning and later deconvolutional model visually segments the target image. The proposed approach creates a direct link among the several adjacent pixels in the resulting feature maps. It also preserves the spatial features such as corners and edges in images and hence adding more accuracy to the resulting outputs. We test our algorithm on the Karlsruhe Institute of Technology and Toyota Technologies Institute (KITTI) street view datasets. Our method achieves a mIoU accuracy of 92.04 percent.
Effects of Visual Attention on Tactile P300 BCI

Objective. Tactile P300 brain-computer interfaces (BCIs) can be manipulated by users who only need to focus their attention on a single-target stimulus within a stream of tactile stimuli. To date, a multitude of tactile P300 BCIs have been proposed. In this study, our main purpose is to explore and investigate the effects of visual attention on a tactile P300 BCI. Approach. We designed a conventional tactile P300 BCI where vibration stimuli were provided by five stimulators and two of them were fixed on target locations on the participant's left and right wrists. Two conditions (one condition with visual attention and the other condition without visual attention) were tested by eleven healthy participants. Main Results. Our results showed that, when participants visually attended to the location of target stimulus, significantly higher classification accuracies and information transfer rates were obtained (both for p < 0.05). Furthermore, participants reported that visually attending to the stimulus made it easier to identify the target stimulus in random sequences of vibration stimuli. Significance. These findings suggest that visual attention has positive effects on both tactile P300 BCI performance and user-evaluation.
Waste Management System Using IoT-Based Machine Learning in University

Along with the development of the Internet of Things (IoT), waste management has appeared as a serious issue. Waste management is a daily task in urban areas, which requires a large amount of labour resources and affects natural, budgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, such as using the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods. However, the results are still too vague and cannot be applied in real systems, such as in universities or cities. Recently, there has been a trend of combining optimal waste management strategies with low-cost IoT architectures. In this paper, we propose a novel method that vigorously and efficiently achieves waste management by predicting the probability of the waste level in trash bins. By using machine learning and graph theory, the system can optimize the collection of waste with the shortest path. This article presents an investigation case implemented at the real campus of Ton Duc Thang University (Vietnam) to evaluate the performance and practicability of the system’s implementation. We examine data transfer on the LoRa module and demonstrate the advantages of the proposed system, which is implemented through a simple circuit designed with low cost, ease of use, and replace ability. Our system saves time by finding the best route in the management of waste collection.
Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks against Two Objective Functions Using a Novel Dataset

One of the important features of routing protocol for low-power and lossy networks (RPLs) is objective function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the contrary, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyberattacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models.
Relay Selection Joint Consecutive Packet Routing Scheme to Improve Performance for Wake-Up Radio-Enabled WSNs

Reducing energy consumption, increasing network throughput, and reducing delay are the pivot issues for wake-up radio- (WuR-) enabled wireless sensor networks (WSNs). In this paper, a relay selection joint consecutive packet routing (RS-CPR) scheme is proposed to reduce channel competition conflicts and energy consumption, increase network throughput, and then reduce end-to-end delay in data transmission for WuR-enabled WSNs. The main innovations of the RS-CPR scheme are as follows: (1) Relay selection: when selecting a relay node for routing, the sender will select the node with the highest evaluation weight from its forwarding node set (FNS). The weight of the node is weighted by the distance from the node to sink, the number of packets in the queue, and the residual energy of the node. (2) The node sends consecutive packets once it accesses the channel successfully, and it gives up the channel after sending all packets. Nodes that fail the competition sleep during the consecutive packet transmission of the winner to reduce collisions and energy consumption. (3) Every node sets two thresholds: the packet queue length threshold and the packet maximum waiting time threshold . When the corresponding value of the node is greater than the threshold, the node begins to contend for the channel. Besides, to make full use of energy and reduce delay, the threshold of nodes which are far from sink is small while that of nodes which are close to sink is large. In such a way, nodes in RS-CPR scheme will select those with much residual energy, a large number of packets, and a short distance from sink as relay nodes. As a result, the probability that a node with no packets to transmit becomes a relay is very small, and the probability that a node with many data packets in the queue becomes a relay is large. In this strategy, only a few nodes in routing need to contend for the channel to send packets, thereby reducing channel contention conflicts. Since the relay node has a large number of data packets, it can send many packets continuously after a successful competition. It also reduces the spending of channel competition and improves the network throughput. In summary, RS-CPR scheme combines the selection of relay nodes with consecutive packet routing strategy, which greatly improves the performance of the network. As is shown in our theoretical analysis and experimental results, compared with the receiver-initiated consecutive packet transmission WuR (RI-CPT-WuR) scheme and RI-WuR protocol, the RS-CPR scheme reduces end-to-end delay by 45.92% and 65.99%, respectively, and reduces channel collisions by 51.92% and 76.41%. Besides, it reduces energy consumption by 61.24% and 70.40%. At the same time, RS-CPR scheme improves network throughput by 47.37% and 75.02%.
Android Malware Detection through Machine Learning Techniques: A Review

The open source nature of Android Operating System has attracted wider adoption of the system by multiple types of developers. This phenomenon has further fostered an exponential proliferation of devices running the Android OS into different sectors of the economy. Although this development has brought about great technological advancements and ease of doing businesses (e-commerce) and social interactions, they have however become strong mediums for the uncontrolled rising cyberattacks and espionage against business infrastructures and the individual users of these mobile devices. Different cyberattacks techniques exist but attacks through malicious applications have taken the lead aside other attack methods like social engineering. Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based. Machine learning techniques have risen to become a more competent choice for combating the kind of sophistications and novelty deployed by emerging Android malwares. The models created via machine learning methods work by first learning the existing patterns of malware behaviour and then use this knowledge to separate or identify any such similar behaviour from unknown attacks. This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.
Efficient Privacy-Preserving Fingerprint-Based Authentication System Using Fully Homomorphic Encryption

To help smartphone users protect their phone, fingerprint-based authentication systems (e.g., Apple’s Touch ID) have increasingly become popular in smartphones. In web applications, however, fingerprint-based authentication is still rarely used. One of the most serious concerns is the lack of technology for securely storing fingerprint data used for authentication. Because scanned fingerprint data are not exactly the same each time, the use of a traditional cryptographic hash function (e.g., SHA-256) is infeasible to protect raw fingerprint data. In this paper, we present an efficient privacy-preserving fingerprint authentication system using a fully homomorphic encryption scheme in which fingerprint data are always stored and processed in an encrypted form. We implement a fully working fingerprint authentication system with a fingerprint database (containing 4,000 samples) using the Fast Fully Homomorphic Encryption over the Torus (TFHE) library. The proposed system can perform the fingerprint matching process within about 166 seconds (±0.564 seconds) on average.
TAM as a Model to Understand the Intention of Using a Mobile-based Cancer Early Detection Learning Application

Technology Acceptance Model (TAM) framework was utilized in this study. Its purpose was to determine the correlation between independent variables consisting of Perceived Ease of Use (PEU), Perceived Usefulness (PU), Attitude toward Using (AU) with dependent variable Behavioral Intention to Use (BIU). Data collection techniques were carried out by distributing questionnaires through group discussion forums. Respondents consisted of medical workers and health cadres both in Jakarta and Yogyakarta. Data were analyzed using correlation test and t-test. The results of the correlation test state that the correlation between PEU and AU is 0.30, which shows a weak correlation. Meanwhile, the correlation of PU and AU is 0.56, PEU and BIU is 0.41, and PU and BIU is 0.47, which are considered as moderate correlations. Finally, a strong correlation exists between AU and BIU. T-test results show that the effect of PU on AU is statistically significant with CI = 95%. Likewise, the effects of PEU on AU, AU towards BIU, PU towards BIU, and PEU towards BIU are significant (p < 0.05).
Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson's Disease

The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson’s disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD.
Hazard Contribution Modes of Machine Learning Components

Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs)—i.e., software components that use machine learning (ML)—are to analyze and understand the contribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, ﬁrst, introducing the notion of hazard contribution modes (HCMs)—a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i ) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii ) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing LEC safety analysis towards eventually automating it in a tool.
Business and Leisure “Bleisure” in Organization: Antecedents and Outcomes

The purpose of this study is to explore the concept of bleisure (business and leisure) in an organization along with its antecedents and outcomes. Bleisure is a combination of completing work and entertainment or a pleasant situation at the same time. The sample used in this study were 106 respondents who work in Event Organizer (EO). Data collection methods in this study used questionnaires. Data was analyzed by using regression test. The results of this study indicate that if people work in an organization that combines business and leisure (bleisure), they will have the creativity and high innovation performance, and the application of business and leisure (bleisure) can be achieved through mobile working style and gamification. Bleisure or a combination of fun activities and work can enhance creative intelligence and innovation performance for individuals in completing work, every person will be more creative and innovative. Leisure and work are related things, when individuals are burdened with assignments, the application of leisure in an organization is one of the effective things.