Users Gain Research Articles

BLMDP: A new bi-level Markov decision process approach to joint bidding andtask-scheduling in cloud spot market

In the cloud computing market (CCM), computing services are traded between cloud providers and consumers in the form of the computing capacity of virtual machines (VMs). The Amazon spot market is one of the most well-known markets in which the surplus capacity of data centers is auctioned off in the form of VMs at relatively low prices. For each submitted task, the user can offer a price that is higher than the current price. However, uncertainty in the market environment confronts the user with challenges such as the variable price of VMs and the variable number of users. An appropriate strategy should both maximize the user?s utility and determine the best bid for him/her. In this paper, we aim to solve the problem of joint minimizing the cost of processing tasks and maximizing user satisfaction. Our proposed method, which we call bi-level Markov decision-making process (BLMDP), works on two levels. At the top level, it selects the most appropriate user bids and adjusts the spot price to minimize the cost of VMs on the cloud provider side. At a low level, it decides to admit tasks in such a way as to maximize the user side satisfaction. Performance evaluation based on real data collected from Amazon web services shows that BLMDP manages to minimize cloud provider's costs and maximize user gain more effectively compared to heuristic methods.

Cooperative SWIPT MIMO-NOMA for Reliable THz 6G Communications

In this paper, cooperative simultaneous wireless information and power transfer (SWIPT) terahertz (THz) multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) are considered. The aim is to improve wireless connectivity, resource management, scalability, and user fairness, as well as to enhance the overall performance of wireless communications and reliability. We optimized the current wireless communication systems by utilizing MIMO-NOMA technology and THz frequencies, exploring the performance and gains obtained. Hence, we developed a path-selection mechanism for the far user to enhance the system performance. The EH SWIPT approach used to improve THz communications performance was investigated. Moreover, we proposed a reliable transmission mechanism with a non-LoS (NLoS) line of THz communications for open areas or any location where the intelligent reflecting surface (IRS) cannot be deployed, in addition to using the cheap decode-forward (DF) relaying instead of IRS. The performance and scalability of the upgradeable system were examined, using adjustable parameters and the simplest modulation scheme. The system presents a noticeable improvement in energy efficiency (EE) and spectral efficiency (SE), in addition to reliability. Accordingly, the outcome showed an improvement in the overall reliability, SE, EE, and outage probability as compared to the conventional cooperative networks of the recent related work (e.g., cooperative MIMO-NOMA with THz) by multiple times with a simpler design, whereas it outperformed our previous work, i.e., cooperative SWIPT SISO-NOMA with THz, by more than 50%, with a doubled individual user gain. This system reduces the transceiver hardware and improves reliability with increasing transmission rates.

Farmers’ Access to Information on Improved Wheat Technology Package: The Case of Digalu Tijo of Arsi Zone, Ethiopia

Wheat is one of the most important staple crops of the country in terms of production and consumption. The study was conducted to investigate farmers’ access to information on improved wheat technology package of Digalu Tijo of Arsi Zone, Ethiopia. The study went further to examine the level of information dissemination for farmers and the benefits information users gain. Focus group discussion guide, interview checklist and observation checklist were employed to gather information required. The collected data analyzed using descriptive analysis. A large number 91.7% of the respondents were those who can read and write. The finding of the study indicated that men have more access to extension education programs than women. Similarly, the primary sources of information for respondents were development agents (70%). The study finding revealed that respondents benefited more from the adoption of improved wheat technology. Further and regular study need to undertaken so that farmers’ information and technology needs can be identified in order to provide improved and up dated information and technology for overall improvement of livelihood of farming community the study areas.

Coproducing Sea Ice Predictions with Stakeholders Using Simulation

Abstract Forecasts of sea ice evolution in the Arctic region for several months ahead can be of considerable socioeconomic value for a diverse range of marine sectors and for local community supply logistics. However, subseasonal-to-seasonal (S2S) forecasts represent a significant technical challenge, and translating user needs into scientifically manageable procedures and robust user confidence requires collaboration among a range of stakeholders. We developed and tested a novel, transdisciplinary coproduction approach that combined socioeconomic scenarios and participatory, research-driven simulation gaming to test a new S2S sea ice forecast system with experienced mariners in the cruise tourism sector. Our custom-developed computerized simulation game known as “ICEWISE” integrated sea ice parameters, forecast technology, and human factors as a participatory environment for stakeholder engagement. We explored the value of applications-relevant S2S sea ice prediction and linked uncertainty information. Results suggest that the usefulness of S2S services is currently most evident in schedule-dependent sectors but is expected to increase as a result of anticipated changes in the physical environment and continued growth in Arctic operations. Reliable communication of uncertainty information in sea ice forecasts must be demonstrated and trialed before users gain confidence in emerging services and technologies. Mariners’ own intuition, experience, and familiarity with forecast service provider reputation impact the extent to which sea ice information may reduce uncertainties and risks for Arctic mariners. Our insights into the performance of the combined foresight/simulation coproduction model in brokering knowledge across a range of domains demonstrates promise. We conclude with an overview of the potential contributions from S2S sea ice predictions and from experiential coproduction models to the development of decision-driven and science-informed climate services.

Delegation of Moral Tasks to Automated Agents—The Impact of Risk and Context on Trusting a Machine to Perform a Task

The rapid development of automation has led to machines increasingly taking over tasks previously reserved for human operators, especially those involving high-risk settings and moral decision making. To best benefit from the advantages of automation, these systems must be integrated into work environments, and into the society as a whole. Successful integration requires understanding how users gain acceptance of technology by learning to trust in its reliability. It is, thus, essential to examine factors that influence the integration, acceptance, and use of automated technologies. As such, this study investigated the conditions under which human operators were willing to relinquish control, and delegate tasks to automated agents by examining risk and context factors experimentally. In a decision task, participants ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=43$ </tex-math></inline-formula> , 27 female) were placed in different situations in which they could choose to delegate a task to an automated agent or manual execution. The results of our experiment indicated that both, context and risk, significantly influenced people’s decisions. While it was unsurprising that the reliability of an automated agent seemed to strongly influence trust in automation, the different types of decision support systems did not appear to impact participant compliance. Our findings suggest that contextual factors should be considered when designing automated systems that navigate moral norms and individual preferences.

World's first 16:4:1 triple conversion gain sensor with all-pixel AF for 82.4dB single exposure HDR

A Triple Conversion Gain (TCG) sensor with all-pixel auto focus based on 2PD of 1.4 um-pitch has been demonstrated for mobile applications. TCG was implemented by sharing adjacent Floating Diffusion (FD) without adding other capacitor. TCG helps to reduce the noise gap or slow the noise increase as user gain increases. An image with a Dynamic Range (DR) of 82.4 dB through a single exposure can be obtained through intra-scene TCG (i-TCG). Through this, a wider range of illuminance environments can be captured in the image. In addition, a more natural image can be obtained by reducing the SNR dip in one image by using TCG.

Efficient User Pairing for Performance Enhancement of Downlink NOMA Systems

In this paper, the resource allocation problem for user pairing (UP) in downlink non-orthogonal multiple access (NOMA) systems is investigated. NOMA allows the use of one subcarrier for more than one user at the same time, thus increases the total capacity of the wireless communication system. However, users pairing is a challenging task in the NOMA systems, because a good channel quality subcarrier should be selected and allocated for the user to enhance the performance of NOMA systems. The proposed UP algorithm aims to enhance the sum rate of the paired users per subcarrier and consequently enhance the total sum rate of downlink NOMA systems. Moreover, the proposed UP algorithm target to improve the fairness of the users. The proposed UP algorithm is based on a simple search for the subcarrier with the minimum average channel gains to be assigned its paired users and then excluding it from the next searching process. The proposed scheme ensures the higher channel gain for users by giving the priority to the subcarrier with the minimum average channel gains during the user pairing process. The simulation results demonstrate that the proposed UP algorithm can not only enhance the total sum rate compared with the random UP and conventional UP but also can enhance the fairness of the users. Moreover, it is clearly seen that the proposed UP algorithm provides the lowest outage probability.

Protecting Location Privacy by Multiquery: A Dynamic Bayesian Game Theoretic Approach

When using location-based services (LBSs), a user obtains points-of-interest (PoI) information by providing the LBS platform with his current geo-location. Such a search leads to potential privacy leakage if an adversary has access to his geo-data. Traditional <i>k</i>-anonymity mechanisms instruct a user to bear the overhead to report his current location together with <i>k</i> - 1 dummy locations to confuse the adversary, which only work well given a large number <i>k</i>. Aware of the common practices that a user is actually flexible in service requirement (e.g., as long as the searched PoIs are within his walking distance), we propose a novel approach to help the user gain location privacy from service flexibility for the challenging case of a small number <i>k</i>. By analyzing the strategic interaction between the user and the adversary in a dynamic Bayesian game, we prove that the user&#x2019;s equilibrium strategy depends on the adversary&#x2019;s capability of accessing geo-data. Take <i>k</i> = 2 for example, we find that if the adversary is not likely to access both geo-data, the user should report the two dummy locations at two different directions of his real location, and otherwise at the same direction. Perhaps surprisingly, the user may benefit from the adversary&#x2019;s access to more geo-data. Furthermore, we extend the game-theoretic approach for multi-query and arbitrary user location distributions. Numerical results show that our approach obviously outperforms <i>k</i>-anonymity mechanisms especially under a small number <i>k</i>.

Towards Improved Visualization and Optimization of Aquaculture Production Process

Aquaculture is one of the largest, and fastest growing industries in Norway. Recently, the industry has experienced significant development in the daily operations acquiring new technologies and systems that capture data and automate the different processes. These emerging technologies enable the generation of enormous amounts of data from sensors in the fish cages, cameras, boats, and feeding control rooms. Additional information relevant to the aquaculture industry is based on e-mails, manual notes, or intrinsic experiences and knowledge exchanges. One of the critical aspects of successful fish farming operation management, which is yet not achieved, is to allow domain experts to gain insight into the interconnection between the broad spectrum of heterogeneous data currently realized.This paper describes a framework for storing and retrieving critical information connected to fish farming based on a graph database approach. The overall architecture is presented with detailed illustrations of how data is visualized and interpreted through a user-friendly interface. Accordingly, this work demonstrates how aquaculture users can benefit from the system to identify possible connections in the data and reveal previously undiscovered causalities and correlations that suggest optimal actions. Further, studies and evaluations of the querying system are conducted, evaluating the capability of the proposed design to process complex relationships. This work showcases that the system helps fish farmers and aquaculture users gain knowledge, reveal hidden links in the data, and improve aquaculture operations.

M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis.

Multimodal sentiment analysis aims to recognize people's attitudes from multiple communication channels such as verbal content (i.e., text), voice, and facial expressions. It has become a vibrant and important research topic in natural language processing. Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels. However, current multimodal models with strong performance are often deep-learning-based techniques and work like black boxes. It is not clear how models utilize multimodal information for sentiment predictions. Despite recent advances in techniques for enhancing the explainability of machine learning models, they often target unimodal scenarios (e.g., images, sentences), and little research has been done on explaining multimodal models. In this paper, we present an interactive visual analytics system, M2 Lens, to visualize and explain multimodal models for sentiment analysis. M2 Lens provides explanations on intra- and inter-modal interactions at the global, subset, and local levels. Specifically, it summarizes the influence of three typical interaction types (i.e., dominance, complement, and conflict) on the model predictions. Moreover, M2 Lens identifies frequent and influential multimodal features and supports the multi-faceted exploration of model behaviors from language, acoustic, and visual modalities. Through two case studies and expert interviews, we demonstrate our system can help users gain deep insights into the multimodal models for sentiment analysis.

Virtual tryvertising. Marketing strategies for empowered customers

The aim of this study is to look at the experiential marketing strategy of virtual tryvertising, by analysing a specific case study: the augmented reality application Ikea Place. It provides customers with a set of tools to prefigure a virtual representation of a piece of a furniture within a physical space, in order to appraise its fitness in a prior time to its actual purchase, and hence to provide a benefit for him/her. The semiotic analysis will be carried out by taking into account the use practices prescribed by the application and the promotional discourses of the company. In particular it will look at two advertising videos which accompanied the launch of the application in 2013 and 2017. It will also consider the added value and the experiential gain for users that the adoption of this type of technology entails, in order to understand AR media not only as a mere strategy for commercial purposes, but as a tool for empowering the users’ appraising skills traditionally used in the context of a dressing room of a physical store.

Moving with the Times: Investigating the Alt-Right Network Gab with Temporal Interaction Graphs

Gab is an online social network often associated with the alt-right political movement and users barred from other networks. It presents an interesting opportunity for research because near-complete data is available from day one of the network's creation. In this paper, we investigate the evolution of the user interaction graph, that is the graph where a link represents a user interacting with another user at a given time. We view this graph both at different times and at different timescales. The latter is achieved by using sliding windows on the graph which gives a novel perspective on social network data. The Gab network is relatively slowly growing over the period of months but subject to large bursts of arrivals over hours and days. We identify plausible events that are of interest to the Gab community associated with the most obvious such bursts. The network is characterised by interactions between 'strangers' rather than by reinforcing links between 'friends'. Gab usage follows the diurnal cycle of the predominantly US and Europe based users. At off-peak hours the Gab interaction network fragments into sub-networks with absolutely no interaction between them. A small group of users are highly influential across larger timescales, but a substantial number of users gain influence for short periods of time. Temporal analysis at different timescales gives new insights above and beyond what could be found on static graphs.

Learning Through Social Networks: How Foreign Workers Optimize the Use of Fintech to Send Remittances

We use data from a leading Korean Fintech to study the use of a financial technology for remittance payments. Low-income workers in our sample improve the timing of the exchange rate applied to their remittance transactions by 1.7% through learning to take advantage of a cancellation feature. Using referral information to identify social networks, we find that workers improve the timing of payments by 2.3% if the feature is used more by other members of their social network. Overall, we find that users gain both from personal learning-by-doing as well as from the experience of their social networks.

Sun Exposure and Physical Activity: The Valuable Role of UV Wearables

Skin cancers are one of the most common types of cancer in the United States [1], and melanoma has steadily increased over the last 20 years. Solar ultraviolet radiation (UVR) is known as the main cause of damaging effects to the skin [1], [2], yet also the most preventable risk factor. Outdoor physical activity, an important health behavior, exposes the body to UVR and is thus ostensibly linked to increases the risk of sunburn, melanoma, and non-melanoma skin cancers [3], [4]. Simultaneously, new UVR sensors can play an essential role in ensuring users gain enough sunlight for Vitamin D synthesis for optimal health. However, accurately monitoring personal UVR exposure is historically complex and costly, and thus minimal research exists at the intersection of personal UVR exposure during physical activity.

An Analysis Framework for Examination Timetabling

An examination timetabling problem taken from real world universities was proposed at the International Timetabling Competition (ITC2007). The aim was to establish a common base for comparing different solution approaches. This paper presents new preprocessing methods that disclose hidden constraints and significantly increase the number of new edges that can be added to the conflict graph. Results show that the size of the maximum clique of the obtained conflict graph has been more than doubled for two instances as a result of our preprocessing. These larger cliques mean that instances can be analyzed in advance of a solution and end users gain useful information for making decisions. In addition, we have looked at the different criteria that compose the objective function, in order to provide more useful insights into the difficulty of problems in practice. We propose new integer programming formulations using clique inequalities to compute optimal solutions for 4 criteria and to obtain lower bounds for the 3 others. Results are presented and discussed for all the benchmark instances.

When virtual and real worlds coexist: Visualization and visual system affect spatial performance in augmented reality.

New visualization approaches are being actively developed aiming to mitigate the effect of vergence-accommodation conflict in stereoscopic augmented reality; however, high interindividual variability in spatial performance makes it difficult to predict user gain. To address this issue, we investigated the effects of consistent and inconsistent binocular and focus cues on perceptual matching in the stereoscopic environment of augmented reality using a head-mounted display that was driven in multifocal and single focal plane modes. Participants matched the distance of a real object with images projected at three viewing distances, concordant with the display focal planes when driven in the multifocal mode. As a result, consistency of depth cues facilitated faster perceptual judgments on spatial relations. Moreover, the individuals with mild binocular and accommodative disorders benefited from the visualization of information on the focal planes corresponding to image planes more than individuals with normal vision, which was reflected in performance accuracy. Because symptoms and complaints may be absent when the functionality of the sensorimotor system is reduced, the results indicate the need for a detailed assessment of visual functions in research on spatial performance. This study highlights that the development of a visualization system that reduces visual stress and improves user performance should be a priority for the successful implementation of augmented reality displays.

Task Specialization in Social Production Communities: The Case of Geographic Volunteer Work

In social production communities, users' individual and collective efforts lead to the creation of valuable resources — cf. Wikipedia, Open Street Map, and Reddit. Contributors to such communities often specialize in the tasks they choose to do. We found evidence for specialization by work type in Cyclopath, a geographic wiki for bicyclists -- most users edit a single type of map feature, such as points of interest or roads and trails. We also saw a user lifecycle effect: as users gain experience, they specialize in editing roads and trails. Our findings suggest more effective ways to organize social production interfaces, compose units of work, and match them to users who want to help.

Detecting and modelling real percolation and phase transitions of information on social media.

It is widely believed that information spread on social media is a percolation process, with parallels to phase transitions in theoretical physics. However, evidence for this hypothesis is limited, as phase transitions have not been directly observed in any social media. Here, through an analysis of 100 million Weibo and 40 million Twitter users, we identify percolation-like spread and find that it happens more readily than current theoretical models would predict. The lower percolation threshold can be explained by the existence of positive feedback in the coevolution between network structure and user activity level, such that more-active users gain more followers. Moreover, this coevolution induces an extreme imbalance in users' influence. Our findings indicate that the ability of information to spread across social networks is higher than expected, with implications for many information-spread problems.

Channel capacity analysis of non-orthogonal multiple access and massive multiple-input multiple-output wireless communication networks considering perfect and imperfect channel state information

In this paper, we investigate the non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (M-MIMO) techniques and through simulation, and a comparison is given between the NOMA and orthogonal multiple access techniques. Integrating NOMA with M-MIMO is a very challenging task. In this paper, for a single-cell system, NOMA is integrated with a M-MIMO system for better spectral and energy efficiency. Investigation of the multiple user gain is the focus of this work because the multiple user gain supports simultaneous transmission of multiple users in the case of the M-MIMO system. In this way, the M-MIMO will provide a 100 times channel capacity increase, which results in very high data transmission rate. In the modern communication system, achieving multiple user gain is a very difficult task when channel estimation error is present. The performance of the orthogonal multiple access as well as NOMA system significantly reduced in the presence of channel estimation error. However, most of the current schemes do not work well with imperfect perfect channel state information conditions. Simulation results closely agree with the theoretical outcomes.

Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive

BackgroundUnderstanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. ApproachWe introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing. ResultsGeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user’s data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive’s applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases. ConclusionGeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.