Reviewerly/exHarmony huggingface dataset:
Clench Interface

People eat every day and biting is one of the most fundamental and natural actions that they perform on a daily basis. Existing work has explored tooth click location and jaw movement as input techniques, however clenching has the potential to add control to this input channel. We propose clench interaction that leverages clenching as an actively controlled physiological signal that can facilitate interactions. We conducted a user study to investigate users' ability to control their clench force. We found that users can easily discriminate three force levels, and that they can quickly confirm actions by unclenching (quick release). We developed a design space for clench interaction based on the results and investigated the usability of the clench interface. Participants preferred the clench over baselines and indicated a willingness to use clench-based interactions. This novel technique can provide an additional input method in cases where users' eyes or hands are busy, augment immersive experiences such as virtual/augmented reality, and assist individuals with disabilities.
Engaging High School Students in Cameroon with Exam Practice Quizzes via SMS and WhatsApp

We created a quiz-based intervention to help secondary school students in Cameroon with exam practice. We sent regularly-spaced, multiple-choice questions to students' own mobile devices and examined factors which influenced quiz participation. These quizzes were delivered via either SMS or WhatsApp per each student's preference. We conducted a 3-week deployment with 546 students at 3 schools during their month of independent study prior to their graduating exam. We found that participation rates were heavily impacted by trust in the intervening organization and perceptions of personal security in the socio-technical environment. Parents also played a key gate-keeping role on students' digital activities. We describe how this role - along with different perceptions of smartphones versus basic phones - may manifest in lower participation rates among WhatsApp-based users as compared to SMS. Finally, we discuss design implications for future educational interventions that target students' personal cellphones outside of the classroom.
Efficient feature selection using shrinkage estimators

Information theoretic feature selection methods quantify the importance of each feature by estimating mutual information terms to capture: the relevancy, the redundancy and the complementarity. These terms are commonly estimated by maximum likelihood, while an under-explored area of research is how to use shrinkage methods instead. Our work suggests a novel shrinkage method for data-efficient estimation of information theoretic terms. The small sample behaviour makes it particularly suitable for estimation of discrete distributions with large number of categories (bins). Using our novel estimators we derive a framework for generating feature selection criteria that capture any high-order feature interaction for redundancy and complementarity. We perform a thorough empirical study across datasets from diverse sources and using various evaluation measures. Our first finding is that our shrinkage based methods achieve better results, while they keep the same computational cost as the simple maximum likelihood based methods. Furthermore, under our framework we derive efficient novel high-order criteria that outperform state-of-the-art methods in various tasks.
Examining English and Welsh Detectives’ Views of Online Crime

The advancement of the Internet and digital technology has enabled individuals to commit crimes affecting both virtual and real targets at will. Criminological scholarship related to cybercrime and technology-enabled offending has primarily focused on offenders and victims, calling to question how police agencies effectively respond to these incidents. Limited empirical research has largely focused on line officers, who view these cyber offenses to be less serious in comparison to traditional crimes. Research examining the perceptions of detectives and inspectors is virtually nonexistent, though they are much more likely to have an investigative responsibility to pursue offenders and engage with citizens about the offense. Thus, there is a need to understand the attitudes inspectors hold toward online crime in order to improve our knowledge of police views and inform policy makers in building effective strategies to aid their response to unique and emerging crime types. This study examined the attitudinal factors associated with online crimes and incidents using a sample of 155 inspectors from 26 local agencies across England and Wales. The implications of this study for both police detectives and law enforcement agencies are discussed in detail.
Multiplexed readout of four qubits in 3D circuit QED architecture using a broadband Josephson parametric amplifier

We propose and demonstrate a frequency-multiplexed readout scheme in 3D circuit-quantum electrodynamics (cQED) architecture. We use four transmon qubits coupled to individual rectangular cavities which are aperture-coupled to a common rectangular waveguide feedline. A coaxial to waveguide transformer at the other end of the feedline allows one to launch and collect the multiplexed signal. The reflected readout signal is amplified by an impedance-engineered broadband parametric amplifier with 380 MHz bandwidth. This provides us high fidelity single-shot readout of multiple qubits using compact microwave circuitry, an efficient way for scaling up to more qubits in 3D cQED.
Explaining black box models by means of local rules

Many high performance machine learning methods produce black box models, which do not disclose their internal logic yielding the prediction. However, in many application domains understanding the motivation of a prediction is becoming a requisite to trust the prediction itself. We propose a novel rule-based method that explains the prediction of any classifier on a specific instance by analyzing the joint effect of feature subsets on the classifier prediction. The relevant subsets are identified by learning a local rule-based model in the neighborhood of the prediction to explain. While local rules give a qualitative insight of the local behavior, their relevance is quantified by using the concept of prediction difference. Preliminary experiments show that, despite the approximation introduced by the local model, the explanations provided by our method are effective in detecting the effects of attribute correlation. Our method is model-agnostic. Hence, experts can compare explanations and local behaviors of the predictions for the same instance made by different classifiers.
Chats with Bots

Advances in AI are paving the way towards more natural interactions, blurring the line between bot and human. We present findings from a two-week diary study exploring users' interactions with the chatbot Replika. In particular, we focus on how users anthropomorphize chatbots and how this influences their engagement. We find that failing to adhere to social norms and glaring signs of humanity leads to decreased engagement unless balanced appropriately.
SRCMIMM: managing requirements change activities in global software development

Global software development (GSD) has been considered by the majority of firms to develop the quality product with low cost and time. However, there are many challenges face by the GSD firms mainly related to Requirements Change Management (RCM). The objective of this research is to develop a Software Requirements Change Management and Implementation Maturity Model (SRCMIMM) that can assist the GSD firms in modifying and improving their RCM activities. The development of SRCMIMM will be based on the existing RCM literature, an industrial empirical study with RCM practitioners, and an understanding of the factors that could impact the implementation of RCM activities in GSD environment.
"I Almost Fell in Love with a Machine"

Listening and speaking are tied to human experiences of closeness and trust. As voice interfaces gain mainstream popularity, we ask: is our relationship with technology that speaks with us fundamentally different from technology we use to read and type? In particular, will we disclose more about ourselves to computers that speak to us and listen to our answer? We examine this question through a controlled experiment where a conversational agent asked participants closeness-generating questions common in social psychology through either text-based or voice-based interfaces. We found that people skipped more invasive questions when reading-typing compared to speaking-listening. Surprisingly, typing in their answers seemed to increased the propensity for self-disclosure. This research has implications for the future design of voice-based conversational agents and deepens concerns of user privacy.
DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.
Multi-Window 3D Interaction for Collaborative Virtual Reality

We present a novel collaborative virtual reality system that offers multiple immersive 3D views at large 3D scenes. The physical setup consists of two synchronized multi-user 3D displays: a tabletop and a large vertical projection screen. These displays afford different presentations of the shared 3D scene. The wall display lends itself to the egocentric exploration at 1:1 scale, while the tabletop affords an allocentric overview. Additionally, handheld 3D portals facilitate the personal exploration of the scene, the comparison of views, and the exchange with others. Our developments enable seamless 3D interaction across these independent 3D views. This requires the simultaneous representation of user input in the different viewing contexts. However, the resulting interactions cannot be executed independently. The application must coordinate the interactions and resolve potential ambiguities to provide plausible effects. We analyze and document the challenges of seamless 3D interaction across multiple independent viewing windows, propose a high-level software design to realize the necessary functionality, and apply the design to a set of interaction tools. Our setup was tested in a formal user study, which revealed general advantages of collaborative 3D data exploration with multiple views in terms of user preference, comfort, and task performance.
Teaching Data Visualization and Storytelling with Data Comic Workshops

This paper presents a method for hands-on creation of data comics in a workshop context and includes a description of the results, lessons learned and future improvements. Data comics is a promising format for data-driven storytelling, leveraging the power of data visualization and visual storytelling with comics. However, authoring data comics requires a diverse range of skills that are both creative and analytical. Our workshop is aimed at developing a blue-print for future workshops and reflecting on challenges and potential improvements. Within a 3-week assignment for an illustration class, we ran three 3-hour sessions. Our design was informed by the experiences of previous data-comics workshops. Results show the creative potential of data comics. Challenges to learn from the workshop include the stages to introduce data visualizations and journalistic narratives, the structuring of stories and the method of developing iterations of comic drafts. We close by reflecting on these challenges and how they can inform future improvements and adaptations.
StakeQP: A semi-automated stakeholder quantification and prioritisation technique for requirement selection in software system projects

Stakeholder quantification and prioritisation (SQP) is performed on the basis of the stakeholder's influence. The involvement of adequate stakeholders plays a crucial role in identifying and selecting the most essential requirements to produce a successful system. However, the current SQP techniques still face serious limitations, such as having insufficient low-level implementation details, being time-consuming, lacking an automation level, heavily relying on professional expertise and having no attribute measurement criteria (AMC) for stakeholder evaluation. These key issues serve as the motivation of the present study. Hence, this study is aimed at proposing a new semi-automated technique called StakeQP to address the reported key limitations. StakeQP introduces new low-level implementation details to perform SQP automatically on the basis of the newly proposed AMC using the multi-attribute decision-making method, namely, the technique of order preference similarity to the ideal solution (TOPSIS), to achieve an efficient StakeQP. The effectiveness of StakeQP is evaluated using a benchmark dataset of the actual software project. The findings show that StakeQP can produce more accurate results with less time consumption and is more effective in addressing the defined key limitations compared with other alternative techniques.
Identifying malicious social media contents using multi-view Context-Aware active learning

This paper presents a semi-supervised, multi-view, active learning method, which uses an optimized set of most informative samples and utilizes domain specific context information to efficiently and effectively identify malicious forum content in web-based social media platforms. As research shows, the task of automated identification of malicious forum posts, which also helps in detecting their associated key suspects in web forums, faces numerous challenges: (1) Online data, particularly social media data originate from diverse and heterogeneous sources and are largely unstructured; (2) Online data characteristics evolve quickly; and, (3) There are limited amounts of ground truth data to support the development of effective classification technologies in a strictly supervised scenario. In order to address the above challenges, the proposed human–machine collaborative, semi-supervised learning method is designed to efficiently and effectively identify harmful, provocative, or fabricated forum content by observing only a small number of annotated samples. Our learning framework is initiated by modeling initial view-dependent classifiers from a limited labeled data collection and allows each, in an interactive manner, to evolve dynamically into a sophisticated model by observing data patterns from a shared shortlist of most informative samples, identified via a graph-based optimization method and solved by a maximum flow algorithm. By designing a context rich metric definition in a data-driven manner, the proposed framework is able to learn a sufficiently robust classification model, that utilizes only a small number of human annotated samples, typically 1–2 orders of magnitude fewer as compared to a fully supervised solution. We validate our method using a large collection of flagged words with a wide range of origins, words frequently appearing in web-based forums and manually verified by multiple experienced, independent domain experts.
Ghost loads

Speculative execution is necessary for achieving high performance on modern general-purpose CPUs but, starting with Spectre and Meltdown, it has also been proven to cause severe security flaws. In case of a misspeculation, the architectural state is restored to assure functional correctness but a multitude of microarchitectural changes (e.g., cache updates), caused by the speculatively executed instructions, are commonly left in the system. These changes can be used to leak sensitive information, which has led to a frantic search for solutions that can eliminate such security flaws. The contribution of this work is an evaluation of the cost of hiding speculative side-effects in the cache hierarchy, making them visible only after the speculation has been resolved. For this, we compare (for the first time) two broad approaches: i) waiting for loads to become non-speculative before issuing them to the memory system, and ii) eliminating the side-effects of speculation, a solution consisting of invisible loads (Ghost loads) and performance optimizations (Ghost Buffer and Materialization). While previous work, InvisiSpec, has proposed a similar solution to our latter approach, it has done so with only a minimal evaluation and at a significant performance cost. The detailed evaluation of our solutions shows that: i) waiting for loads to become non-speculative is no more costly than the previously proposed InvisiSpec solution, albeit much simpler, non-invasive in the memory system, and stronger security-wise; ii) hiding speculation with Ghost loads (in the context of a relaxed memory model) can be achieved at the cost of 12% performance degradation and 9% energy increase, which is significantly better that the previous state-of-the-art solution.
Fine-Tuning ResNet for Breast Cancer Classification from Mammography

Breast cancer classification from mammography is significant for treatment decisions and assessments of prognosis. However, the traditional classification method is not efficient due to the need for professional domain knowledge, time-consuming, and difficult in extracting high-quality features. Therefore, this paper proposed an automatic classification method based on convolutional neural network (CNN). In this paper, the fine-tuning residual network (ResNet) has been introduced to have good performance, reduce training time, and automatically extract features. Then, a data augmentation policy was adopted to expand training data which can reduce the probability of overfitting caused by small training set. The main contribution of this paper is to introduce transfer learning and data augmentation to construct an automatic mammography classification, which has high prediction performance. Experiments were conducted on a public data set CBIS-DDSM which contains 2620 scanned film mammography studies. The proposed method obtains desirable performances on accuracy, specificity, sensitivity, AUC, and loss, corresponding to 93.15, 92.17, 93.83%, 0.95, and 0.15. The proposed method is of good robustness and generalization.
Remittances, the diffusion of information and industrialisation in Africa

This study examines the role of information and communication technology (ICT) on remittances for industrialisation in a panel of 49 African countries for the period 1980–2014. The empirical evidence is based on three simultaneity-robust estimation techniques, namely: (i) Instrumental Fixed Effects (FE) in order to control for the unobserved heterogeneity; (ii) Generalised Method of Moments (GMM) to account for persistence in industrialisation; and (iii) Instrumental Quantile Regressions (QR) to control for initial levels of industrialisation. Our best estimators are from FE and QR estimations because the GMM regression outputs largely fail post-estimation diagnostic tests. The following findings are established: (i) There are positive marginal effects from the interaction between remittances and ICT in the FE regressions whereas there are negative marginal impacts from the interaction between remittances and ICT; (ii) Interactions between remittances and mobile phone penetration are positive in the bottom and 90th quantile whereas the interaction between internet penetration and remittances is positive in the bottom and top quantiles of the industrialisation distribution. Overall, the role of ICT in remittances for industrialisation is much more apparent when existing levels of industrialisation are accounted for.
Mitigating Phishing Attacks

Social engineering is the process of getting a person to provide a service or complete a task that may give away private or confidential information. Phishing is the most common type of social engineering. In phishing, an attacker poses as a trustworthy source in an attempt to have the victim release personal or private information. Spear phishing is a popular type of phishing attack where the attacker provides information retaining to a select few targets rather than generic information for a mass amount of targets. This paper provides an overview of social engineering attacks, the detection methods of social engineering and phishing attacks, the education and training techniques for preventing social engineering and phishing attacks, as well as the susceptibility of users to social engineering and phishing attacks.
Stealing Passwords by Observing Hands Movement

The use of mobile phones in public places opens up the possibilities of remote side channel attacks on these devices. We present a video-based side channel attack to decipher passwords on mobile devices. Our method uses short video clips ranging from 5 to 10 s each, which can be taken unobtrusively from a distance and do not require the keyboard or the screen of the phone to be visible. By relating the spatiotemporal movements of the user's hand during typing and an anchor point on any visible part of the phone, we predict the typed password with high accuracy. The results on a dataset of 375 short videos of password entry process on a Samsung Galaxy S4 phone show an exponential reduction in the search space compared to a random guess. For each key-press corresponding to a character in the passwords, our method was able to reduce the search space to an average of 2-3 keys compared to ~30 keys if one has to guess the key randomly. Thus, this paper reaffirms threats to smartphone users' conventional login in public places and highlights the threats in scenarios such as hiding the screen that otherwise gives the impression of being safe to the users.
SEEMless

End-to-end encrypted messaging (E2E) is only secure if participants have a way to retrieve the correct public key for the desired recipient. However, to make these systems usable, users must be able to replace their keys (e.g. when they lose or reset their devices, or reinstall their app), and we cannot assume any cryptographic means of authenticating the new keys. In the current E2E systems, the service provider manages the directory of public keys of its registered users; this allows a compromised or coerced service provider to introduce their own keys and execute a man in the middle attack. Building on the approach of CONIKS (Melara et al, USENIX Security '15), we formalize the notion of a Privacy-Preserving Verifiable Key Directory (VKD): a system which allows users to monitor the keys that the service is distributing on their behalf. We then propose a new VKD scheme which we call SEEMless, which improves on prior work in terms of privacy and scalability. In particular, our new approach allows key changes to take effect almost immediately; we show experimentally that our scheme easily supports delays less than a minute, in contrast to previous work which proposes a delay of one hour.