Reviewerly/exHarmony huggingface dataset:
Agile UX: Integrating good UX development practices in Agile

This paper explores a framework for the integration of User Experience (UX) in Agile within the South African context. For this study, grounded theory was applied as a suitable qualitative method to determine how the integration of UX into Agile is carried out in practice. The data was collected using semi-structured interviews with professionals working in an Agile/UX environment in South Africa. Further data was collected using questionnaires in order to promote validity and reliability with more information from more participants. The themes that emerged from the study shows that there is an increasing need for UX in software development amongst Agile teams. The findings were used to design a framework to facilitate the integration of UX into Agile.
SociaBowl

In this paper, we introduce SociaBowl, a dynamic table centerpiece to promote positive social dynamics in 2-way cooperative conversations. A centerpiece such as a bowl of food, a decorative flower arrangement, or a container of writing tools, is commonly placed on a table around which people have conversations. We explore the design space for an augmented table and centerpiece to influence how people may interact with one another. We present an initial functional prototype to explore different choices in materiality of feedback, interaction styles, and animation and motion patterns. These aspects are discussed with respect to how it may impact people's awareness of their turn taking dynamics as well as provide an additional channel for expression. Potential enhancements for future iterations in its design are then outlined based on these findings.
A conceptual model of information security compliant behaviour based on the self-determination theory

The increase in threats to information systems resources in organisations has been attributed to the failure by employees to adhere to information security policies. Research into the information security behaviour of employees is still predominantly based on the extrinsic model, while the intrinsic model has not received as much attention. Therefore, this paper aims to contribute to the understanding of the intrinsic motivations that lead to information security compliant behaviour. To this end, a review of literature on this topic was conducted to understand what other researchers have found in this area. The results show that intrinsic motivational factors could provide alternative explanations for information security compliant behaviour. This paper proposes a conceptual framework for compliant behaviour based on the self-determination theory.
Affordable Smart Wheelchair

Power wheelchairs (PW) are an example of an assistive technology in that they are used to increase, maintain, or improve the functional capabilities of persons with disabilities [6]. As seen in [5] [3], the commercially available products do not provide any assistance beyond enhanced mobility. Furthermore, existing PW research fails at comprehensively noting an individual's challenges, such as navigating through narrow passages or fixing their broken wheelchairs. Instead, they focus mostly on novel interaction methods such as BCI, head and gaze control [9] [15]. In this paper, we explore these individual needs and show that PWs have the novel potential to become a smart wheelchair at an affordable price. Our research follows the double diamond (DD) process [2] and relies on the participatory design (PD) methodology [7], which addresses all stakeholders involved. Namely, we consider individuals with wheelchairs, the assistive technology research community, and the PW industry. For further insight we also contacted medical doctors, healthcare professionals, and non- profit organizations. We spent time getting to know these communities through interviews, surveys, demonstrations, and continuous user inputs, aligning our work to the PD tools. We found that individuals using wheelchairs overall desire safety, accessibility, and a durable design. Guided by these results, we designed a proof of concept (POC) system called the Affordable Smart Wheelchair (ASW) for indoor use. This kit implements full-autonomy in the form of indoor navigation from one room to another and to predetermined docking locations through voice control. It also has semi-autonomous functions in the form of manual joystick control augmented with real-time collision avoidance and staircase detection.
Multimodal Speech-based Dialogue for the Mini-Mental State Examination

We present a system-initiative multimodal speech-based dialogue system for the Mini-Mental State Examination (MMSE). The MMSE is a questionnaire-based cognitive test, which is traditionally administered by a trained expert using pen and paper and afterwards scored manually to measure cognitive impairment. By using a digital pen and speech dialogue, we implement a multimodal system for the automatic execution and evaluation of the MMSE. User input is evaluated and scored in real-time. We present a user experience study with 15 participants and compare the usability of the proposed system with the traditional approach. Our experiment suggests that both modes perform equally well in terms of usability, but the proposed system has higher novelty ratings. We compare assessment scorings produced by our system with manual scorings made by domain experts.
Robust and Efficient Approach to Diagnose Sickle Cell Anemia in Blood

Red Blood Corpuscles (RBCs) form the main cellular component of human blood. RBCs in common physiological status has a circular form in the front view and bi-concave form in side view. In case of a person is infected with anemia RBCs form as sickle-shaped cells which drive to blood vessel obstruction joined by painful episodes and even death. Precise and robust cells classification and counting are essential in evaluating the level of anemia disease danger. The classification and counting of red blood cells (normal and abnormal RBC cells) are challenging due to the complex and heterogeneous shapes and overlapped cells. In this paper, we propose a new robust approach to classify red blood cells to two groups: Normal and Abnormal RBC cells based on area and Eccentricity of each cell, then count the total number of normal and abnormal RBC cells individually. For the sake of comparison, we also implement the latest Sickle cell research, which uses circular Hough transform. We compare our approach to circular Hough transform in the same execution environment. Our new approach sets the state-of-the-art performance in term of effectiveness (cell counting) and efficiency (execution time).
Privacy and Ethical Challenges in Big Data

The advent of Big Data coupled with the profiling of users has lead to the development of services and decision-making processes that are highly personalized, but also raise fundamental privacy and ethical issues. In particular, the absence of transparency has lead to the loss of control of individuals on the collection and use on their personal information while making it impossible for an individual to question the decision taken by the algorithm and to make it accountable for it. Nonetheless, transparency is only a prerequisite to be able to analyze the possible biases that personalized algorithms could have (e.g., discriminating against a particular group in the population) and then potentially correct them. In this position paper, I will review in a non-exhaustive manner some of the main privacy and ethical challenges associated with Big Data that have emerged in recent years before highlighting a few approaches that are currently investigated to address these challenges.
Kirigami Keyboard

We propose a DIY process to produce customized paper keyboards with kinesthetic feedback that interact with touchscreens. The process is built using two techniques: kirigami and printable double-layered circuits. Our goal is to improve the extensibility and usability of various interfaces made with 2D paper substrates. First, Our kirigami structures provide kinesthetic sensations whose z-directional key stroke is comparable to that of traditional keyboards. In order to design keys with appropriate stroke and reaction force, we adopted the Rotational Erection System (RES). Second, printable double-layered circuits allow users to easily adjust input layouts. This easy-to-customize keyboard can be especially useful for those who have specific requirements for input devices.
Do We Need Natural Language?

Natural language interfaces (NLIs) that aim to understand arbitrary language are not only difficult to engineer; they can also create unrealistic expectations of the capabilities of the system, resulting in user confusion and disappointment. We use an interactive language learning game in a 3D blocks world to examine whether limiting a user's communication to a small set of artificial utterances is an acceptable alternative to the much harder task of accepting unrestricted language. We find that such a restricted language interface provides same or better performance on this task while improving user experience indices. This suggests that some NLIs can restrict user languages without sacrificing user experience and highlights the importance of conveying NLI limitations to users.
An Algorithm for Producing Fuzzy Negations via Conical Sections

In this paper we introduced a new class of strong negations, which were generated via conical sections. This paper focuses on the fact that simple mathematical and computational processes generate new strong fuzzy negations, through purely geometrical concepts such as the ellipse and the hyperbola. Well-known negations like the classical negation, Sugeno negation, etc., were produced via the suggested conical sections. The strong negations were a structural element in the production of fuzzy implications. Thus, we have a machine for producing fuzzy implications, which can be useful in many areas, as in artificial intelligence, neural networks, etc. Strong Fuzzy Negations refers to the discrepancy between the degree of difficulty of the effort and the significance of its results. Innovative results may, therefore, derive for use in literature in the specific field of mathematics. These data are, moreover, generated in an effortless, concise, as well as self-evident manner.
Identification of alteration zones using a Landsat 8 image of densely vegetated areas of the Wayang Windu Geothermal field, West Java, Indonesia

Remote sensing is a method usually used for conducting geothermal reconnaissance surveys by mapping surface alteration anomalies, which are captured by a satellite sensor. This study analyzes the characteristics of the surface alteration zones in the Wayang Windu geothermal field (WWGF). It employs a remote sensing method using multispectral Landsat 8 imagery and is validated by ground truth data from field surveys. The WWGF is located in Pangalengan, West Java with an elevation of 1500-2600 m.a.s.l., and lies in a quaternary volcanic arc. The rock types in Wayang Windu consist of andesite, basalt, tuff, breccia, and pumice. Fractures and faults are identified as lineaments in this area and based on their structures, were directed to be oriented northwest-southeast and northeast-southwest. This research combined a field survey and remote sensing methods to enhance the spatial data. Field surveys yield 18 spots for obtaining soil samples and laboratory analyses were performed. Spectral reflectance analysis was performed to determine the reflectance and mineral composition of the samples, X-ray diffraction was performed to determine the mineral composition, and X-ray fluorescence was performed to determine the abundance of elements. A scene from Landsat 8 image acquired on September 10, 2013 was evaluated using a principal component analysis-based method. The surface alteration zones in the WWGF correlated with joints and faults. Based on the mineral composition, the surface alteration zones in WWGF were identified as advanced argillic zone with the occurrence of secondary minerals such as cristobalite and halloysite, and a propylitic zone with the occurrence of secondary minerals such as epidote and chlorite.
Impedance Control based Assistive Mobility Aid through Online Classification of User’s State

In this paper, we present our effort on the development of an assistive mobility aid for daily elderly care through human-robot interaction. The focus of this work is to provide the user with physical movement support in their daily life such as movement transition, e.g., sit-to-stand and stand-to-sit, and walking using a mobile assistive robot. We propose an approach to providing desirable physical support through an appropriately designed impedance control strategy with online recognition of the user's current movement states. In the proposed framework, the desired set of impedance parameters are determined based on the online estimation of the probabilities of the user's state with an LSTM-RNN, and the robot provides suitable mobility aid accordingly. In order to demonstrate the effectiveness of the proposed approach, we first present a performance evaluation of online recognition of user's movement state and transition. Then, we report experimental results of our proposed assistive mobility aid for sit-to-stand/stand-to-sit and walking movement support with a human support robot.
Design and Analysis of a Lightweight Context Fusion CNN Scheme for Crowd Counting

Crowd counting, which is widely used in disaster management, traffic monitoring, and other fields of urban security, is a challenging task that is attracting increasing interest from researchers. For better accuracy, most methods have attempted to handle the scale variation explicitly. which results in huge scale changes of the object size. However, earlier methods based on convolutional neural networks (CNN) have focused primarily on improving accuracy while ignoring the complexity of the model. This paper proposes a novel method based on a lightweight CNN-based network for estimating crowd counting and generating density maps under resource constraints. The network is composed of three components: a basic feature extractor (BFE), a stacked à trous convolution module (SACM), and a context fusion module (CFM). The BFE encodes basic feature information with reduced spatial resolution for further refining. Various pieces of contextual information are generated through a short pipeline in SACM. To generate a context fusion density map, CFM distills feature maps from the above components. The whole network is trained in an end-to-end fashion and uses a compression factor to restrict its size. Experiments on three highly-challenging datasets demonstrate that the proposed method delivers attractive performance.
Analyzing Hate Speech Toward Players from the MENA in League of Legends

We analyze hate speech toward the MENA players as a form of toxic behavior in League of Legends in-game and forum chats. We find that this kind of toxicity: (1) is initiated by one or two players; (2) sparks from criticizing the skills of team members; (3) can be elevated by frustration with game elements and hardware; and (4) can turn into personal clashes. There is also non-toxic use of abusive language, which stresses the importance of context-aware analysis (i.e., interpreting what is actually toxic). Finally, we find evidence that the type of toxicity varies by server location, advising gaming companies to consider the location of players when setting up policies to mitigate hate speech.
Reconstruction of Complex Dynamical Systems from Time Series using Reservoir Computing

We investigate the capacity of reservoir computers to reconstruct the dynamics of a network of chaotic oscillators via the observation of its multivariate time series.The reservoir is itself a structured echo-state network which receives the current observations as inputs, and is trained to produce the next observations as outputs.We study the performance of this scheme and its dependence on the separation of the inputs, modularity of the reservoir network, and observability of the system.We observe optimal performance with a segregated input structure and extremely modular network.
The Impact of "Cosmetic" Changes on the Usability of Error Messages

Programmatic errors are often difficult to resolve due to poor usability of error messages. Applying theories of visual perception and techniques in visual design, we created three visual variants of a representative error message in a modern UI framework. In an online experiment, we found that the visual variants led to substantial improvements over the original error message in both error comprehension and resolution. Our results demonstrate that seemingly cosmetic changes to the presentation of an error message can have an oversized impact on its usability.
Privacy-Aware Controllable Compressed Data Publishing Against Sparse Estimation Attack in IoT

The openness and inclusiveness of Internet of Things (IoT) inevitably result in security risks. Privacy protection in data publishing in IoT has become one of the most important topics. For data owners, besides the privacy of released information, they are increasingly concerned about the privacy of unreleased data because data users are particularly keen to analyze results from the released information. Our experimental results demonstrate that a small number of known data could be successfully utilized to estimate the unknowns. To address the unreleased data privacy while guaranteeing the utility of released data, we propose a privacy-aware controllable compressed data publishing strategy that could resist sparse estimation attacks efficiently. Specifically, by introducing compressive sensing (CS) technology to achieve compressed data publishing, communication overhead is reduced greatly. With the help of CS reconstruction error, the privacy protection capability of unreleased data is enhanced under the premise of the utility of released data. What is more, users are divided into the data user and the authorized user to realize controllability of the data owner. For the same compression data, the data user with general permissions is limited to the released data from the data owner; while the authorized user is entitled to access the whole data. Furthermore, we analyze the upper bound of the released data number that could satisfy the privacy requirement of the unreleased data. The experimental results demonstrate that the utility of the released data, the privacy of the unreleased data, and the compressibility all achieve desirable effect.
Super-Resolution Convolutional Neural Networks Using Modified and Bilateral ReLU

Super-resolution convolutional neural network (SRCNN) is a deep learning method that reconstructs high-resolution image from low-resolution image. In this paper, we propose to change the ReLU which is the activation function of existing SRCNN, as modified and bilateral ReLU. This modification gives increased number of active nodes and results in improved image quality.
Camera-identification and common-source identification: The correlation values of mismatches

A robust and well-known way to identify the source of an image is the use of Photo Response Non-Uniformity. The ability to be able to extract the PRNU-pattern from images is both used for camera-identification (linking one or more images to a camera) and common-source identification (linking images to images). In this paper we focus on the correlation values of mismatches, i.e. the correlation between images made with different cameras. Although the correlation values of mismatches are close to zero, they are never exactly zero. In this paper we show that it is possible with an extremely simple formula to a priori estimate the typical range for mismatch correlation values. This simple formula can be used as a decision rule in digital camera identification to either perform a complete investigation including reference recordings (which is time consuming) or not. In common-source identification this simple formula can be used to provide a well-educated guess for the threshold value to the cluster algorithm instead of just arbitrarily trying a range of threshold values.
Profiling the scheduling decisions for handling critical paths in deadline-constrained cloud workflows

In this paper, we study the scheduling decisions for handling deadline-constrained workflows in the context of planning customized virtual infrastructures in the cloud. We specifically focus on the effects of using different types of greediness in selecting cost-effective virtual machines for the tasks in an application's workflow graph. The profiling procedure followed demonstrates that for the widely used approach of the partial critical path algorithm a greedy version is preferred to a more stringent version under different stress conditions, from tight to loose deadlines. Representative topologies of workflow applications are used to generate sets of task graph scheduling problems. Monitoring the performance of the partial critical path algorithm with different types of greediness reveals which of the topologies tested are difficult to solve under various stress conditions. It turns out that an invalid outcome of a greedy version of the partial critical path algorithm is more susceptible to become valid via a final refinement cycle than a less greedy version. The procedure outlined in this paper will allow for a systematic study of a specific heuristic in a workflow scheduling method to increase its success in infrastructure planning under different deadline conditions and is proposed to be part of a general profiling framework.
Design of robust phase-shifting algorithms using N-step formulas as building blocks

We present a general theory that allows us to easily design robust phase-shifting algorithms (PSA) using the frequency transfer function (FTF) formalism and N-step PSAs as building blocks. The resulting algorithms require M(N–1)+1 phase-shifted samples to be taken from M temporal periods, with M ≥ 2. By design, our proposed PSAs are robust against noise and harmonics even under detuned conditions, and tolerate (bandlimited) slow temporal variations of the bias component and fringes' contrast. We support our conclusions with numerical simulations.
A comparative analysis of right-wing radical and Islamist communities’ strategies for survival in social networks (evidence from the Russian social network <i>VKontakte</i>)

This article presents a comparative analysis of online communities of right-wing radicals and Islamists, who are considered to be numerous and dangerous extremist groups in Russian society. The online communities were selected based on the content posted on the largest Russian social networking site VKontakte. The goal of this article is to determine the strategy and tactics employed by extremist online communities for survival on social networking sites. The authors discovered that both right-wing radical and Islamist groups employ similar behavioural techniques, with the mimicry of ideologically neutral content as the most common. In addition, every extremist community also applies some unique methods. For example, if there is a risk of being blocked, right-wing radicals tend to shift their activity and communication to the other internet-based platforms that are not under state control; however, Islamists prefer to suddenly change the content of their communities (i.e. by using secondary mimicry).