Reviewerly/exHarmony huggingface dataset:
SSN: Learning Sparse Switchable Normalization via SparsestMax

Normalization methods improve both optimization and generalization of ConvNets. To further boost performance, the recently-proposed switchable normalization (SN) provides a new perspective for deep learning: it learns to select different normalizers for different convolution layers of a ConvNet. However, SN uses softmax function to learn importance ratios to combine normalizers, leading to redundant computations compared to a single normalizer. This work addresses this issue by presenting Sparse Switchable Normalization (SSN) where the importance ratios are constrained to be sparse. Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax. SSN has several appealing properties. (1) It inherits all benefits from SN such as applicability in various tasks and robustness to a wide range of batch sizes. (2) It is guaranteed to select only one normalizer for each normalization layer, avoiding redundant computations. (3) SSN can be transferred to various tasks in an end-to-end manner. Extensive experiments show that SSN outperforms its counterparts on various challenging benchmarks such as ImageNet, Cityscapes, ADE20K, and Kinetics. Code is available at \url{https://github.com/switchablenorms/Sparse_SwitchNorm}.
Unsupervised and Semi-Supervised Image Classification With Weak Semantic Consistency

Supervised methods have been widely used for image classifications. Although great progress has been made, existing supervised methods rely on well-labeled samples for classification. However, we often have large quantities of images with few or no labels. To cope with this problem, in this paper, we propose a novel weak semantic consistency constrained image classification method. We start from an extreme circumstance by viewing each image as one class. We train exemplar classifiers to separate each image from other images. For each image, we use the learned exemplar classifiers to predict the weak semantic correlations with the exemplar classifiers. When no labeled information is available, we cluster images using the weak semantic correlations and assign images within one cluster to the same mid-level class. When partially labeled images are available, we can use them to constrain the clustering process by assigning images of varied semantics to different mid-level classes. We use the newly assigned images for classifier training and new image representations, which can then be used for similar image assignments. The classifier training, image representation, and assignment processes are repeated until convergence. We conduct both unsupervised and semi-supervised image classification experiments on several datasets. The experimental results show the effectiveness of the proposed unsupervised and semi-supervised weak semantic consistency image classification method.
Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges

Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data. AI has already been applied successfully to such combined datasets, with applications such as improving the insurance claim processing pipeline and reducing estimation biases in retrospective studies. Nevertheless, there is still the potential to do much more. The identification of complex patterns within high dimensional datasets may find new predictors for early onset of diseases or lead to a more proactive offering of personalized preventive services. While there are potential risks and challenges associated with the use of AI, these are not insurmountable. As with the introduction of any innovation, it will be necessary to be thoughtful and responsible as we increasingly apply AI methods in healthcare.
Feedback-Induced Quantum Phase Transitions Using Weak Measurements

We show that applying feedback and weak measurements to a quantum system induces phase transitions beyond the dissipative ones. Feedback enables controlling essentially quantum properties of the transition, i.e., its critical exponent, as it is driven by the fundamental quantum fluctuations due to measurement. Feedback provides the non-Markovianity and nonlinearity to the hybrid quantum-classical system, and enables simulating effects similar to spin-bath problems and Floquet time crystals with tunable long-range (long-memory) interactions.
Critical Success Factors of Small and Medium-Sized Enterprises in Saudi Arabia: Insights from Sustainability Perspective

: The aim of this study is to explore the critical success factors (CSFs) of small and medium-sized enterprises (SMEs) in Saudi Arabia. A questionnaire was developed using 28 factors/indicators identified from the previous researches. From 500 respondents, a total of 347 questionnaires were returned. By conducting exploratory factors analysis, these indicators were categorized into six factors, namely: Individual factors, business characteristics, management factors, business support, capital availability and business environment. Using IBM SPSS and AMOS, the results indicated that business support was the most critical factor that significantly affects the success of SMEs in Saudi Arabia, followed by individual factors, capital availability, and management factors. They also indicated that business characteristics and business environment factors had no significant impacts on the success of these enterprises.
A survey on association rules mining using heuristics

Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area. This article is categorized under: Algorithmic Development &gt; Association Rules Technologies &gt; Association Rules Fundamental Concepts of Data and Knowledge &gt; Motivation and Emergence of Data Mining
TracTrac: A fast multi-object tracking algorithm for motion estimation

TracTrac is an open-source Matlab/Python implementation of a robust and efficient object tracking algorithm capable of simultaneously tracking several thousands of objects in very short time. Originally developed as an alternative to particle image velocimetry algorithms for estimating fluid flow velocities, its versatility and robustness makes it relevant to many other dynamic sceneries encountered in geophysics such as granular flows and drone videography. In this article, the structure of the algorithm is detailed and its capacity to resolve strongly variable and intermittent object motions is tested against three examples of geophysical interest.
Atrous convolutions spatial pyramid network for crowd counting and density estimation

Scale variation because of perspective distortion is still a challenge for crowd analysis. To address this problem, an atrous convolutions spatial pyramid network (ACSPNet) is proposed to perform crowd counts and density maps for both sparse and congested scenarios. Atrous Convolutions sequenced with increasing atrous rates are utilized to exaggerate the receptive field and maintain the resolution of extracted features. Different rates of atrous convolution blocks in the pyramid are skip-connected to integrate multi-scale information and extent scale perception ability. Atrous Spatial Pyramid Pooling (ASPP) is employed to resample information at different scales and contain global context. We evaluate our ACSPNet on five challenging benchmark crowd counting datasets and our method achieves state-of-the-art mean absolute error (MAE) and mean squared error (MSE) performances.
Chatbots and Gender Stereotyping

Abstract Chatbots are very much an emerging technology, and there is still much to learn about how conversational user interfaces will affect the way in which humans communicate not only with computers but also with one another. Further studies on anthropomorphic agents and the projection of human characteristics onto a system are required to further develop this area. Gender stereotypes operate a profound effect on human behaviour. The application of gender to a conversational agent brings along with it the projection of user biases and preconceptions. These feelings and perceptions about an agent can be used to develop mental models of a system. Users can be inclined to measure the success of a system based on their biases and emotional connections with the agent rather than that of the system’s performance. There have been many studies that show how gender affects human perceptions of a conversational agent. However, there is limited research on the effect of gender when applied to a chatbot system. This chapter presents early results from a research study which indicate that chatbot gender does have an effect on users overall satisfaction and gender-stereotypical perception. Subsequent studies could focus on examining the ethical implications of the results and further expanding the research by increasing the sample size to validate statistical significance, as well as recruiting a more diverse sample size from various backgrounds and experiences. RESEARCH HIGHLIGHTS Many studies have indicated how gender affects human perceptions of a conversational agent. However, there is limited research on the effect of gender when applied to a chatbot system. This research study presents early results which indicate that chatbot gender does have an effect on users overall satisfaction and gender-stereotypical perception. Users are more likely to apply gender stereotypes when a chatbot system operates within a gender-stereotypical subject domain, such as mechanics, and when the chatbot gender does not conform to gender stereotypes. This study raise ethical issues. Should we exploit this result and perpetuate the bias and stereotyping? Should we really have a male chatbot for technical advice bots? Is this perpetuating stereotyping, the dilemma being that a male version would elicit more trust?
Mobi3DSketch

Mid-air 3D sketching has been mainly explored in Virtual Reality (VR) and typically requires special hardware for motion capture and immersive, stereoscopic displays. The recently developed motion tracking algorithms allow real-time tracking of mobile devices, and have enabled a few mobile applications for 3D sketching in Augmented Reality (AR). However, they are more suitable for making simple drawings only, since they do not consider special challenges with mobile AR 3D sketching, including the lack of stereo display, narrow field of view, and the coupling of 2D input, 3D input and display. To address these issues, we present Mobi3DSketch, which integrates multiple sources of inputs with tools, mainly different versions of 3D snapping and planar/curves surface proxies. Our multimodal interface supports both absolute and relative drawing, allowing easy creation of 3D concept designs in situ. The effectiveness and expressiveness of Mobi3DSketch are demonstrated via a pilot study.
Fog-Embedded Deep Learning for the Internet of Things

In current deep learning models, centralized architecture forces participants to pool their data to the central Cloud to train a global model, while distributed architecture requires a parameter server to mediate the training process. However, privacy issues, response delays, and computation and communication bottlenecks prevent these architectures from working well at the scale of Internet of Things devices. To counter these problems, in this paper we build a Fog-embedded privacy-preserving deep learning framework (FPPDL), which moves computation from the centralized Cloud to Fog nodes near the end devices. The experimental results on benchmark image datasets under different settings demonstrate that FPPDL achieves comparable accuracy to the centralized stochastic gradient descent (SGD) framework, and delivers better accuracy than the standalone SGD framework. Our evaluations also show that both computation and communication cost are greatly reduced by FPPDL, hence achieving the desired tradeoff between privacy and performance.
How to Design Voice Based Navigation for How-To Videos

When watching how-to videos related to physical tasks, users' hands are often occupied by the task, making voice input a natural fit. To better understand the design space of voice interactions for how-to video navigation, we conducted three think-aloud studies using: 1) a traditional video interface, 2) a research probe providing a voice controlled video interface, and 3) a wizard-of-oz interface. From the studies, we distill seven navigation objectives and their underlying intents: pace control pause, content alignment pause, video control pause, reference jump, replay jump, skip jump, and peek jump. Our analysis found that users' navigation objectives and intents affect the choice of referent type and referencing approach in command utterances. Based on our findings, we recommend to 1) support conversational strategies like sequence expansions and command queues, 2) allow users to identify and refine their navigation objectives explicitly, and 3) support the seven interaction intents.
A Translational Science Model for HCI

Using scientific discoveries to inform design practice is an important, but difficult, objective in HCI. In this paper, we provide an overview of Translational Science in HCI by triangulating literature related to the research-practice gap with interview data from many parties engaged (or not) in translating HCI knowledge. We propose a model for Translational Science in HCI based on the concept of a continuum to describe how knowledge progresses (or stalls) through multiple steps and translations until it can influence design practice. The model offers a conceptual framework that can be used by researchers and practitioners to visualize and describe the progression of HCI knowledge through a sequence of translations. Additionally, the model may facilitate a precise identification of translational barriers, which allows devising more effective strategies to increase the use of scientific findings in design practice.
Phishing page detection via learning classifiers from page layout feature

The web technology has become the cornerstone of a wide range of platforms, such as mobile services and smart Internet-of-things (IoT) systems. In such platforms, users’ data are aggregated to a cloud-based platform, where web applications are used as a key interface to access and configure user data. Securing the web interface requires solutions to deal with threats from both technical vulnerabilities and social factors. Phishing attacks are one of the most commonly exploited vectors in social engineering attacks. The attackers use web pages visually mimicking legitimate web sites, such as banking and government services, to collect users’ sensitive information. Existing phishing defense mechanisms based on URLs or page contents are often evaded by attackers. Recent research has demonstrated that visual layout similarity can be used as a robust basis to detect phishing attacks. In particular, features extracted from CSS layout files can be used to measure page similarity. However, it needs human expertise in specifying how to measure page similarity based on such features. In this paper, we aim to enable automated page-layout-based phishing detection techniques using machine learning techniques. We propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. We prototype our solution and evaluate four popular machine learning classifiers on their accuracy and the factors affecting their results.
Comparing Effectiveness and Engagement of Data Comics and Infographics

This paper compares the effectiveness of data comics and infographics for data-driven storytelling. While infographics are widely used, comics are increasingly popular for explaining complex and scientific concepts. However, empirical evidence comparing the effectiveness and engagement of infographics, comics and illustrated texts is still lacking. We report on the results of two complementary studies, one in a controlled setting and one in the wild. Our results suggest participants largely prefer data comics in terms of enjoyment, focus, and overall engagement and that comics improve understanding and recall of information in the stories. Our findings help to understand the respective roles of the investigated formats as well as inform the design of more effective data comics and infographics.
Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Abstract In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOSSASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the MOSCA, MOPSO, MOEAD, and NSGA-II and the MOSSASCA are 0.53, 1.31, 1.36, and 1.44, respectively. Additionally, the MOSSASCA has higher MTBHS value than MOSCA, MOPSO, MOEAD, and NSGA-II by 362.49, 274.70, 585.73 and 672.94, respectively, and the proposed method has lower SLAV values than MOPSO, MOEAD, and NSGA-II by 0.41, 0.28, and 1.27, respectively.
Grasping Microgestures

Single-hand microgestures have been recognized for their potential to support direct and subtle interactions. While pioneering work has investigated sensing techniques and presented first sets of intuitive gestures, we still lack a systematic understanding of the complex relationship between microgestures and various types of grasps. This paper presents results from a user elicitation study of microgestures that are performed while the user is holding an object. We present an analysis of over 2,400 microgestures performed by 20 participants, using six different types of grasp and a total of 12 representative handheld objects of varied geometries and size. We expand the existing elicitation method by proposing statistical clustering on the elicited gestures. We contribute detailed results on how grasps and object geometries affect single-hand microgestures, preferred locations, and fingers used. We also present consolidated gesture sets for different grasps and object size. From our findings, we derive recommendations for the design of microgestures compatible with a large variety of handheld objects.
Assessment of Features and Classifiers for Bluetooth RF Fingerprinting

Recently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals' energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals' transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks.