Exploring User Acceptance of a Text-message Base Health Intervention among Young African Americans

Information technology has been used in diverse ways. It has been used in both the public and private sectors to reduce costs and increase satisfaction. Technology may also be instrumental in improving individuals’ healthy behaviors. For instance, statistics suggest that technology-based interventions may promote healthy sexual behaviors; however, few studies have explored willingness to participate in technology-mediated interventions. In this study, we use the diffusion of innovation theory to identify factors that influence one’s intention to use a text-message service to receive sexual health information. The results indicate that technology diffusion factors rather than risk beliefs and privacy concerns impacted participant\u27s intention to use a text-message intervention. The findings of this study have significant implications for innovative uses of technology to promote health. Mobile-health interventions that are easy to use and that provide more benefits than other interventions are most likely to be adopted. However, these interventions should seek to maximize privacy protections and communicate clearly about these protections

Occupancy Detection using Wireless Sensor Network in Indoor Environment

Occupancy detection plays an important role in many smart buildings such as reducing building energy usage by controlling heating, ventilation and air conditioning (HVAC) systems, monitoring systems and managing lighting systems, tracking people in hospitals for medical issues, advertising to people in malls, and to search and rescue missions. The global positioning system (GPS) is used most widely as a localization system but highly inaccurate for indoor applications. The indoor environment is difficult to handle because along with the loss of signals, privacy is a major concern. Indoor tracking has many aspects in common with sensor localization in Wireless Sensor Networks (WSN). The contribution of this work is the demonstration of a nonintrusive approach to detect an occupancy in a building using wireless sensor networks to detect energy from cell phones in a secure facility and perform indoor localization based on the minimum mean square error (MMSE). To estimate the occupancy, the detected cellular signals information such as signal amplitude, frequency, power and detection time is sent to a fusion server, matched the detected signals by time and channel information, performed localization to estimate a location, and finally estimated the occupancy of rooms in a building from the estimated locations

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Wearables at work:preferences from an employee’s perspective

This exploratory study aims to obtain a first impression of the wishes and needs of employees on the use of wearables at work for health promotion. 76 employ-ees with a mean age of 40 years old (SD ±11.7) filled in a survey after trying out a wearable. Most employees see the potential of using wearable devices for workplace health promotion. However, according to employees, some negative aspects should be overcome before wearables can effectively contribute to health promotion. The most mentioned negative aspects were poor visualization and un-pleasantness of wearing. Specifically for the workplace, employees were con-cerned about the privacy of data collection

Mobile Message Design: A Mix-Methods Study of a Maternal Health Project in Northern Ghana

Mobile health (mHealth) message design strategies for low and middle-income countries (LMICs) have quickly gained acceptance in the field of health education. mHealth initiatives focusing on maternal health are frequently implemented with the aim of providing access to information while improving maternal health practices. Within Ghana, access to relevant health information and hospital care within rural settings remain scarce for the majority of citizens (WHO, 2011). However, with the rapid rate of mobile phone adoption, delivering learning opportunities in conjunction with mobile devices may be promising for many individuals in Ghana. The purpose of this study was to examine message design inputs influencing expecting mothers’ maternal health activity. McGuire’s communication-persuasion theoretical framework informed the mix-methods study. I used participatory rapid appraisal techniques while carrying out the study with research team members. I employed surveys to collect quantitative data. To gather qualitative data I engaged in open-ended survey questions, interviews (one-on-one and focus groups), a journal and team reflections. The findings revealed that participants from two communities in Northern Ghana in rural settings had several inputs in the message design which may influence expecting mothers. These include; information source, design and delivery, power dynamics and personal circumstances, and perceived gains. The findings highlight that for many mHealth projects in LMIC\u27s, there is an urgent need to reexamining the culture attributes of the users\u27 local environment. These findings also address critical aspects of a real world problem with intent to support rural community development in Northern Ghana with goals to alleviate the lack of academic knowledge by providing an insider’s perspectives regarding community insights

Out-of-school literacy practices - the case of Sesotho-speaking learners in Cape Town

This study investigates the out-of-school multilingual literacy practices of four Grade Seven learners aged between 13 and 14 years at Lehlohonolo Primary School (henceforth LPS) in Gugulethu, Cape Town. They come from lower-income Sesotho speaking households and live in residential areas where isiXhosa is the predominant language of interaction. LPS is one of only two primary schools in the area that cater for these Sesotho speaking learners. The Language of Learning and Teaching is Sesotho from Grade R to Three, and then changes to English from Grade Four onwards for all subjects besides Sesotho. Located within the broader New Literacy Studies framework, this study approaches literacy as a historically and socially situated practice. It examines the learners‟ exposure and engagements with formal and informal texts by identifying the diverse communicative resources they have access to, and employ in, especially, out-of-school contexts. One central aim is to specify the roles of the various languages with a particular focus on Sesotho. Using an ethnographic approach, data was gathered primarily through observations and conversations. This was complemented by the photographic documentation of literacy artefacts and semi-structured interviews with the learners, their teachers, caregivers and other household members. To gain a better understanding of their multilingual repertoires and communication networks, the learners were asked to participate in language portrait and social network communication exercises. The core research question that informs the study is: What communicative resources do participants use in different out-of-school literacy events? The study‟s main findings are as follows: (a) the learners have unique language and literacy histories with varying degrees of digital access and competence in Sesotho, English and isiXhosa; (b) standard varieties of Sesotho and English are used for academic purposes; (c) the scarcity of Sesotho literacy is highlighted by the dominant English and isiXhosa literacy practices in out-of-school contexts, including online spaces and (d) Sesotho is used in spoken interactions at home and does not feature in leisure reading and writing

Examining the Anomalies, Explaining the Value: Should the USA FREEDOM Act’s Metadata Program be Extended?

Edward Snowden’s disclosure of National Security Agency (“NSA”) bulk collection of communications metadata was a highly disturbing shock to the American public. The intelligence community was surprised by the response, as it had largely not anticipated a strong negative public reaction to this surveillance program. Controversy over the bulk metadata collection led to the 2015 passage of the USA FREEDOM Act. The law mandated that the intelligence community would collect the Call Detail Records (“CDR”) from telephone service providers in strictly limited ways, not in bulk, and only under order from the Foreign Intelligence Surveillance Court. The new program initially seemed to be working well, although the fact that from 40 court orders in both 2016 and 2017, the NSA collected hundreds of millions of CDRs created public concern. Then in June 2018 the NSA announced it had purged three years’ worth of CDRs due to “technical irregularities”; later the agency made clear that it would not seek the program’s renewal. This Article demystifies these situations, analyzing how forty orders might lead to the collection of several million CDRs and providing the first explanation that fits the facts of what might have caused the “technical irregularities” leading to the purge of records. This Article also exposes a rather remarkable lacuna in Congressional oversight: even at the time of the passage of the USA FREEDOM Act a changing terrorist threat environment and changing communications technologies had effectively eliminated value of the CDR collection. We conclude with recommendations on conducting intelligence oversight

User identification and community exploration via mining big personal data in online platforms

User-generated big data mining is vital important for large online platforms in terms of security, profits improvement, products recommendation and system management. Personal attributes recognition, user behavior prediction, user identification, and community detection are the most critical and interesting issues that remain as challenges in many real applications in terms of accuracy, efficiency and data security. For an online platform with tens of thousands of users, it is always vulnerable to malicious users who pose a threat to other innocent users and consume unnecessary resources, where accurate user identification is urgently required to prevent corresponding malicious attempts. Meanwhile, accurate prediction of user behavior will help large platforms provide satisfactory recommendations to users and efficiently allocate different amounts of resources to different users. In addition to individual identification, community exploration of large social networks that formed by online databases could also help managers gain knowledge of how a community evolves. And such large scale and diverse social networks can be used to validate network theories, which are previously developed from synthetic networks or small real networks. In this thesis, we study several specific cases to address some key challenges that remain in different types of large online platforms, such as user behavior prediction for cold-start users, privacy protection for user-generated data, and large scale and diverse social community analysis. In the first case, as an emerging business, online education has attracted tens of thousands users as it can provide diverse courses that can exactly satisfy whatever demands of the students. Due to the limitation of public school systems, many students pursue private supplementary tutoring for improving their academic performance. Similar to online shopping platform, online education system is also a user-product based service, where users usually have to select and purchase the courses that meet their demands. It is important to construct a course recommendation and user behavior prediction system based on user attributes or user-generated data. Item recommendation in current online shopping systems is usually based on the interactions between users and products, since most of the personal attributes are unnecessary for online shopping services, and users often provide false information during registration. Therefore, it is not possible to recommend items based on personal attributes by exploiting the similarity of attributes among users, such as education level, age, school, gender, etc. Different from most online shopping platforms, online education platforms have access to a large number of credible personal attributes since accurate personal information is important in education service, and user behaviors could be predicted with just user attribute. Moreover, previous works on learning individual attributes are based primarily on panel survey data, which ensures its credibility but lacks efficiency. Therefore, most works simply include hundreds or thousands of users in the study. With more than 200,000 anonymous K-12 students' 3-year learning data from one of the world's largest online extra-curricular education platforms, we uncover students' online learning behaviors and infer the impact of students' home location, family socioeconomic situation and attended school's reputation/rank on the students' private tutoring course participation and learning outcomes. Further analysis suggests that such impact may be largely attributed to the inequality of access to educational resources in different cities and the inequality in family socioeconomic status. Finally, we study the predictability of students' performance and behaviors using machine learning algorithms with different groups of features, showing students' online learning performance can be predicted based on personal attributes and user-generated data with MAE$<10\%$. As mentioned above, user attributes are usually fake information in most online platforms, and online platforms are usually vulnerable of malicious users. It is very important to identify the users or verify their attributes. Many researches have used user-generated mobile phone data (which includes sensitive information) to identify diverse user attributes, such as social economic status, ages, education level, professions, etc. Most of these approaches leverage original sensitive user data to build feature-rich models that take private information as input, such as exact locations, App usages and call detailed records. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g. GDPR). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the user attributes without privacy concern. Typically, identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Due to limited data protection of various popular online services in some countries such as taxi hailing or takeouts ordering, many users nowadays encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, bringing threats to the user individuals as well as the society. Additionally, more and more people suffer from excessive digital marketing and fraud phone calls because of personal information leakage. Therefore, a real time identification of unfamiliar caller is urgently needed. We explore the feasibility of user identification with privacy-preserved user-generated mobile, and we develop CPFinder, a system which implements automatic user identification callers on end devices. The system could mainly identify four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City shows that the CPFinder can achieve an accuracy of 75+\% for multi-class classification and 92.35+\% for binary classification. In addition to the mining of personal attributes and behaviors, the community mining of a large group of people based on online big data also attracts lots of attention due to the accessibility of large scale social network in online platforms. As one of the very important branch of social network, scientific collaboration network has been studied for decades as online big publication databases are easy to access and many user attribute are available. Academic collaborations become regular and the connections among researchers become closer due to the prosperity of globalized academic communications. It has been found that many computer science conferences are closed communities in terms of the acceptance of newcomers' papers, especially are the well-regarded conferences~\cite{cabot2018cs}. However, an in-depth study on the difference in the closeness and structural features of different conferences and what caused these differences is still missing. %Also, reviewing the strong and weak tie theories, there are multifaceted influences exerted by the combination of this two types of ties in different context. More analysis is needed to determine whether the network is closed or has other properties. We envision that social connections play an increasing role in the academic society and influence the paper selection process. The influences are not only restricted within visible links, but also extended to weak ties that connect two distanced node. Previous studies of coauthor networks did not adequately consider the central role of some authors in the publication venues, such as \ac{PC} chairs of the conferences. Such people could influence the evolutionary patterns of coauthor networks due to their authorities and trust for members to select accepted papers and their core positions in the community. Thus, in addition to the ratio of newcomers' papers it would be interesting if the PC chairs' relevant metrics could be quantified to measure the closure of a conference from the perspective of old authors' papers. Additionally, the analysis of the differences among different conferences in terms of the evolution of coauthor networks and degree of closeness may disclose the formation of closed communities. Therefore, we will introduce several different outcomes due to the various structural characteristics of several typical conferences. In this paper, using the DBLP dataset of computer science publications and a PC chair dataset, we show the evidence of the existence of strong and weak ties in coauthor networks and the PC chairs' influences are also confirmed to be related with the tie strength and network structural properties. Several PC chair relevant metrics based on coauthor networks are introduced to measure the closure and efficiency of a conference.2021-10-2