Reviewerly/exHarmony huggingface dataset:
Voice-Based Quizzes for Measuring Knowledge Retention in Under-Connected Populations

Information dissemination using automated phone calls allows reaching low-literate and tech-naive populations. Open challenges include rapid verification of expected knowledge gaps in the community, dissemination of specific information to address these gaps, and follow-up measurement of knowledge retention. We report Sawaal, a voice-based telephone service that uses audio-quizzes to address these challenges. Sawaal allows its open community of users to post and attempt multiple-choice questions and to vote and comment on them. Sawaal spreads virally as users challenge friends to quiz competitions. Administrator-posted questions allow confirming specific knowledge gaps, spreading correct information and measuring knowledge retention via rephrased, repeated questions. In 14 weeks and with no advertisement, Sawaal reached 3,433 users (120,119 calls) in Pakistan, who contributed 13,276 questions that were attempted 455,158 times by 2,027 users. Knowledge retention remained significant for up to two weeks. Surveys revealed that 71% of the mostly low-literate, young, male users were blind.
Frequency-Based Design of Smart Textiles

Despite the increasing amount of smart textile design practitioners, the methods and tools commonly available have not progressed to the same scale. Most smart textile interaction designs today rely on detecting changes in resistance. The tools and sensors for this are generally limited to DC-voltage-divider based sensors and multimeters. Furthermore, the textiles and the materials used in smart textile design can exhibit behaviour making it difficult to identify even simple interactions using those means. For instance, steel-based textiles exhibit intrinsic semiconductive properties that are difficult to identify with current methods. In this paper, we show an alternative way to measure interaction with smart textiles. By relying on visualisation known as Lissajous-figures and frequency-based signals, we can detect even subtle and varied forms of interaction with smart textiles. We also show an approach to measuring frequency-based signals and present an Arduino-based system called Teksig to support this type of textile practice.
Toward a Unified Performance Metric for Benchmarking Steganography Systems

Recent advances in network speeds for exchanging multimedia data over insecure networks has resulted in an increased interest in steganography techniques. These techniques are usually evaluated based on their performance in three attributes; namely, capacity, imperceptibility, and robustness. Each of these attributes has its own measurement metric. Hence, comparing two different steganography schemes based on these individual metric tools becomes inconsistent. In this paper, a novel measurement metric tool is introduced for benchmarking steganography schemes. This new tool, named the “Combined Capacity-Quality-Robustness Effectiveness” (CCQRE) metric, combines the three opposing attributes of a steganography system into one conglomerate performance measure. Comparative results demonstrate the effectiveness of the proposed CCQRE metric for benchmarking various steganography schemes based on the researcher’s interest in capacity, imperceptibility, or robustness.
Could LSA become a “Bifactor” model? Towards a model with general and group factors

One insufficiently grounded criticism made against Latent Semantic Analysis is that it is impossible to semantically interpret its dimensions. This is not true, as several studies have transformed the latent semantic space to interpret them, by means of some methods. One of them is the Inbuilt-Rubric method. Rather than grouping concepts around dimensions, as in Exploratory Factor Analysis based rotation methods, the Inbuilt-Rubric is a method that perform an “a priori” imposition of concepts onto the latent semantic space. It uses a confirmatory strategy. This study seeks to propose solutions for two limitations found in the current Inbuilt-Rubric methodology: one solution is inspired by Bifactor Models and the management of common variance of the concepts involved; and the other one is based in randomizing the sequence to perform the process. Both methods outperform the current Inbuilt-Rubric version in relevant content detection. The reported improvements can be incorporated into expert systems that use Latent Semantic Analysis and Inbuilt-Rubric in relevant content detection or text classification tasks.
Consensus variable structure multiple model filtering for distributed maneuvering tracking

This paper considers the problem of maneuvering target tracking for distributed sensor networks. Consensus-based multiple model (MM) filters are effective methods to deal with this problem. However, the model set of these filters is fixed. When the scene is complicated and the model set for MM methods is large, these methods may become inefficient and performance may decrease while the variable structure multiple model (VSMM) method can handle it well. The VSMM version of consensus filter based on the expected mode augmentation (EMA) is presented in this paper to track maneuvering target for distributed sensor networks. The distributed consensus filter for VSMM is presented first and it runs parallel consensus on both the mode probability and the mode-matched probability density function (PDF). Then the EMA algorithm is used to determine the model set utilized at each time step. Simulation results demonstrate the superiority of the proposed method.
Our Story

Participatory Video (PV) is emerging as a rich and valuable method for monitoring and evaluating (M & E) projects in the International Development sector. Although shown to be useful for engaging communities within short-term monitoring exercises or promotion, PV in these contexts presents significant complexity and logistical challenges for sustained uptake by Development organizations. In this paper, we present Our Story, a digitally mediated work flow iteratively designed and deployed on initiatives in Indonesia and Namibia. Developed in collaboration with the International Federation of Red Cross and Red Crescent (IFRC), it supports end-to-end PV production in the field, and was specifically developed to make PV a more sustainable tool for monitoring. We discuss and evaluate Our Story, reporting on how by lowering skills barriers for facilitators and leveraging consumer technology, PV can be delivered at scale.
Blockchain Assemblages

In this paper we unpack empirical data from two domains within the Blockchain information infrastructure: The cryptocurrency trading domain, and the energy domain. Through these accounts we introduce the relational concepts of Blockchain Assemblages and Whiteboxing. Blockchain assemblages comprise configurations of digital and analogue artefacts that are entangled with imaginaries about the current and future state of the Blockchain information infrastructure. Rather than being a black box, Blockchain assemblages alternate between being dynamic and stable entities. We propose Whiteboxing as the sociomaterial process which drives blockchain assemblages in their dynamic state to be (re)configured, while related artefacts and imaginaries are simultaneously transformed, creating dynamic representations. Whiteboxing is triggered during disconfirming events when representations are discovered as problematic. Complementing existing historical accounts demonstrating technologies in the making, the contribution of this paper, proposes whiteboxing as an analytical concept which allows us to unpack how contemporary technologies are created through entrepreneurial activities.
Threats and Security Issues in Smart City Devices

The main objective of this chapter is to discuss various security and privacy issues in smart cities. The development of smart cities involves both the private and public sectors. The theoretical background is also discussed in future growth of smart city devices. Thus, the literature survey part discusses different smart devices and their working principle is elaborated. Cyber security and internet security play a major role in smart cities. The primary solution of smart city security issues is to find some encryption methods. The symmetric and asymmetric encryption algorithm is analyzed and given some comparative statement. The final section discusses some possible ways to solve smart city security issues. This chapter showcases the security issues and solutions for smart city devices.
VLSI Architectures for 8 Bit Data Comparators for Rank Ordering Image Applications

The main objective of the proposed work is to develop a new Data comparator which gives an economical solution for sorting / Rank ordering networks on the basis of speed, power, and area. The proposed work comprises a design of six different comparators for 8 -bit comparison. The performances of these six different comparators were targeted for XCV1000-4bg560 using Xilinx 7.1i compiler tool using VHDL. It was found that Carry select logic based data comparator requires less area and suitable for reduced area applications. The Conventional bit wise logic based data comparator operated with less delay. Hence this architecture can be used for high speed application. The Twos complement using binary to excess one data converter consumes less power. Hence a twos complement based implementation is suitable for low power implementations. The Different architecture for data comparators were applied on parallel and pipelined architecture of modified shear sorting. These three architectures were compared with other existing median finding architectures. It was found that the CSLA based parallel architecture, CBC based pipelined architecture and 2BEC based pipelined architecture were stand out in area, speed and power when compared with conventional median finding algorithms.
Probabilistic Analysis of Abnormal Behaviour Detection in Activities of Daily Living

This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from sensor data collected from 30 participants. The ADLs considered are: (i) preparing and drinking tea, and (ii) preparing and drinking coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal aspect of the sequences of actions that are part of each ADL and that vary between participants. The average and standard deviation for the durations of each action were calculated to define an average time and a range in which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) was used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity. The data analysis show that CDF can provide accurate and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute. Finally, this approach could be used to train machine learning algorithms for the abnormal behaviour detection.
Position Exchange Workshops

Existing methods for researching and designing to support relationships between parents and their adult children tend to lead to designs that respect the differences between them. We conducted 14 Position Exchange Workshops with parents and their adult children, where the child has left home in recent years, aiming to explicate and confront their positions in creative and supportive ways. We designed three co-design methods (Card Sort for Me & You, Would I Lie to You? and A Magic Machine for You) to support participants to explore, understand, empathize, and design for each other. The findings show that the methods facilitated understanding, renegotiating, and reimagining their current positions. We discuss how positions can help consider both perspectives in the design process. This paper seeks to contribute (1) how the notion of positions enables generating understandings of the relationship, and (2) a set of methods influenced by position exchange, empathy, and playful engagement that help explore human relationships.
Performance Analysis of Fuzzy Logic Controlled DC-DC Converters

This paper presents an approach for a comparative study on different types of dc-dc converters. The design of a fuzzy logic controller using output voltage as feedback for a remarkable improvement. The dynamic performance of various types of DC-DC converters by using MATLAB Simulink software is described in this paper. Here, closed loop Fuzzy logic controlled Buck, Boost, Flyback and Buck-Boost converters are developed. The outputs have been compared by MATLAB simulation between the open loop and PID controlled counterpart with same parameters. Comparing these results it has been noticed that the Fuzzy Logic Controlled Converters perform remarkably better (No overshoot, less ripple, settling time reduced) than the PID and open loop counterparts.
Look Who’s Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog

We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners. The corpus consists of half a million instant messages, across several messaging platforms. We focus our analyses on seven speaker attributes, each of which partitions the set of speakers, namely: gender; relative age; family member; romantic partner; classmate; co-worker; and native to the same country. In addition to the content of the messages, we examine conversational aspects such as the time messages are sent, messaging frequency, psycholinguistic word categories, linguistic mirroring, and graph-based features reflecting how people in the corpus mention each other. We present two sets of experiments predicting each attribute using (1) short context windows; and (2) a larger set of messages. We find that using all features leads to gains of 9–14% over using message text only.
Gender bias at scale: Evidence from the usage of personal names

Recent research within the computational social sciences has shown that when computational models of lexical semantics are trained on standard natural-language corpora, they embody many of the implicit biases that are seen in human behavior (Caliskan, Bryson, & Narayanan, 2017). In the present study, we aimed to build on this work and demonstrate that there is a large and systematic bias in the use of personal names in the natural-language environment, such that male names are much more prevalent than female names. This bias holds over an analysis of billions of words of text, subcategorized into different genres within fiction novels, nonfiction books, and subtitles from television and film. Additionally, we showed that this bias holds across time, with more recent work displaying the same patterns as work published tens or hundreds of years previously. Finally, we showed that the main cause of the bias comes from male authors perpetuating the bias toward male names, with female authors showing a much smaller bias. This work demonstrates the potential of big-data analyses to shed light on large-scale trends in human behavior and to elucidate their causes.
Convergence of Enterprise IT and Embedded Systems

Convergence is currently melding entire disciplines to fully new business models and technologies across industries. What used to be embedded systems, on the one hand, with their specific constraints from the physical environments, and IT, on the other hand, are combining.<sup>1,2</sup> This will impact education programs as well as classic industry boundaries.
Context-aware composite SaaS using feature model

Software as a Service (SaaS) in cloud computing is delivered in a composite form to effectively address complex levels of user’s requirements. Composite SaaS runs in a dynamic distributed cloud environment where the quality of service (QoS) properties of the constituents may get violated at runtime. To face such dynamism and volatility, it is vital to support an online adaptation of composite SaaS. Recent research focused on centralized adaptation approaches based on the closed world assumption that the boundary between SaaS and the cloud environment is known. This is impractical for dynamic composition that requires distributed settings in the open world. To address these challenges, this paper proposes a distributed approach for composite SaaS adaptation applying the master/slave pattern. Slaves locally monitor and adapt the distributed SaaS constituents and send performance information to the master, which adapts the composite service to provide the global expected QoS and monitors the overall performance. To support dynamic adaptation by the master, we propose a solution based on the feature model that captures the variability of the composite SaaS. The activation and deactivation of nodes in the feature model reconfigure the workflow of the composition. Since the reconfiguration task is complex, we apply a meta-heuristic search technique to solve this problem while minimizing the adaptation cost (i.e., resource consumption and violation penalties). Furthermore, we propose an adaption approach for SaaS constituents that substitutes the failed ones promptly to avoid costly global SLA violations. Finally, we present a Kalman-based on-line QoS prediction approach for making decisions regarding the adaptation actions to be taken. Experimental results show that our approach is efficient in distributed and large-scale cloud environments compared to the centralized and off-line approaches.
Efficient Noise Level Estimation Based on Principal Image Texture

Blind noise-level estimation (NLE) is a fundamental issue in digital image processing. This paper provides a noise-level estimator for additive white Gaussian noise and multiplicative Gaussian noise using principal texture patches (PTPs). The proposed algorithm first identifies the principal texture of the noisy image by using the principal component analysis, and then, it chooses PTPs to calculate the noise level. The major contributions of this paper toward addressing the challenges in the NLE literature include: 1) analyzing the nonlinear relationship between the smallest eigenvalue, true noise level, number of chosen patches, and block size; 2) extracting PTPs; and 3) computing noise level using PTPs. The abundant experimental results show that the proposed method works well for various natural images over a large range of noise levels and it performs well for multiplicative noise. Compared with some state-of-the-art approaches, our method has the best performance and a faster running speed.
Video Anomaly Detection and Localization in Crowded Scenes

Nowadays, the analysis of abnormal events becomes more and more exhausting due to the divine use of surveillance cameras. This paper proposes a novel approach to predict and localize anomaly events. In this paper, a new framework for motion extraction called BQM is proposed. Then, the regions of interest are extracted and a filtering process is applied to eliminate the non-significant ones. However, for more precision, the HFG descriptor is applied for each region already divided into non-overlapping cells, Finally, we have evaluated our method using UCSD and Avenues datasets. The Sparse Auto-encoder, an instance of a deep learning strategy is presented for efficient abnormal activity detection and the Softmax for the classification.
Forecasting Solar Power Generation Using Fuzzy Logic and Artificial Neural Network

This paper presents the study done on forecasting solar photovoltaic (PV) plant output power using fuzzy logic and artificial neural network methods. Fuzzy logic aided by high performance computer processors enables acceptable accuracy predictions of solar plants outputs and enables system flexibility to consider natural circumstances. Artificial neural network (ANN) technique has capabilities of machine learning and pattern recognition; and is widely used for forecasting purposes. The main objective of the study presented in this paper is to conduct production forecasting of a solar PV plant which will be useful in effective load management and in studying the reliability of the system supplying electrical power to a distribution network. The fuzzy logic method used to mimic the solar PV plant was successfully developed as the average percentage error obtained is 1.9 %. Similarly for ANN method the obtained average error was 2.6 %. The study has been conducted using MATLAB.
Fuzzy-genetic approach to context-aware recommender systems based on the hybridization of collaborative filtering and reclusive method techniques

Recent advancements in web personalization techniques facilitate enhanced web-based services that allow recommender systems (RSs) to incorporate contextual knowledge about users and items as an additional dimension into recommendation process. Context-awareness is one of the important aspects of ub iquitous computing to support cognitive environment and provide services in various e-commerce recommendation applications. Tracking each user’s preferences over various contextual dimensions from their past transactions and providing personalized recommendations to them are the essence of context-aware recommender systems (CARSs). Conventional paradigms for incorporating context in recommendation process cannot fully cover the challenges on several levels of a context-aware system. Our proposed scheme is based on the hybridization of two complementary techniques, collaborative filtering (CF) and reclusive method (RM) to make context valuable at each level of users’ preferences and improve predictive capability of CARSs. Further, a fuzzy real-coded genetic algorithm (Fuzzy-RCGA) approach is incorporated for identifying the influential contextual situations and handling the uncertainty of users’ preferences under various contextual situations. Furthermore, users’ demographic features are utilized for alleviating the problem of data sparsity. The empirical results on two real-world benchmark datasets clearly demonstrate the effectiveness of our proposed schemes for CARS framework.
<i>iScream!</i>

In this demonstration, we present iScream!, a novel gustosonic experience that generates unique digital sounds as a result of eating ice cream. The system uses capacitive sensing to detect eating actions and based on these actions, it plays out six different playful sounds to facilitate a playful eating experience. Our aim is to support a playful way of eating because we believe that interactive technology offers unique opportunities to facilitate novel engaging eating experiences. Ultimately, with this work, we aim to inspire and guide designers working with interactive playful gustosonic experiences, which open up new interaction possibilities to experience eating as play.
BlockSubPay - A Blockchain Framework for Subscription-Based Payment in Cloud Service

The payment of a subscription-based model for the cloud service is regularly operated with the association of the bank as a centralized trustful third party. This system taxes the parties involved by requiring them to pay high transaction costs to the bank. In this paper, we propose a protocol to enable Fixed Subscription and Pay As You Go Subscription payments in the cloud service without the use of the third party. We achieve this goal by decentralizing the payment with blockchain. In return, this decentralized system generates low transaction costs. We implement our idea in the form of two smart contracts in Ethereum network. We also evaluate the security properties of our protocol. Based on the performance analysis, we can argue that all of the operations in the smart contract produces reasonable gas costs and the contracts are usable in the real network.