Miracle-dz/UniFAR-FTUs huggingface dataset:
Affective analysis of videos: detecting emotional content in real-life scenarios

As the amount of available multimedia content becomes more and more abundant, the use of automatic multimedia analysis solutions in order to find relevant semantic search results or to identify illegal content present on the World Wide Web has reached a critical importance. In addition, the advances in digital media management techniques have facilitated delivering digital videos to consumers. Therefore, accessing online video content has become extremely easy. As emotions play an important role for multimedia content selection and consumption in peoples’ daily life, analyzing the emotional content (i.e., affective content) of videos in order to structure mostly unstructured or ill-structured data is of high value. This thesis focuses on the high-level research question of discriminative feature representations and modeling methods for affective content (including violence) analysis of videos, while keeping the domain knowledge about the problem or application at hand to a minimum. Towards addressing this research question, analysis frameworks which let the video data itself construct mid-level steps to narrow the “affective gap” between low-level audio visual elements and high-level semantics are presented. In the first part of the thesis, we first address the issue of feature engineering in the field of video affective content analysis. We present a deep learning architecture to construct audio and static visual higher level representations from raw data instead of handcrafting such higher level representations. Second, a comprehensive analysis of supervised machine learning algorithms for emotional content modeling is performed. Finally, the importance of temporal information for the generation of discriminative analysis models is investigated. In the second part of the thesis, we concentrate on a special case of video affective content analysis: Violence detection. A comprehensive analysis on the discriminative power of different modalities including audio, static and dynamic visual is performed. A “divide-et-impera” approach is presented to model a complex
Findings of a User Study of Automatically Generated Personas

We report findings and implications from a semi-naturalistic user study of a system for Automatic Persona Generation (APG) using large-scale audience data of an organization's social media channels conducted at the workplace of a major international corporation. Thirteen participants from a range of positions within the company engaged with the system in a use case scenario. We employed a variety of data collection methods, including mouse tracking and survey data, analyzing the data with a mixed method approach. Results show that having an interactive system may aid in keeping personas at the forefront while making customer-centric decisions and indicate that data-driven personas fulfill information needs of decision makers by mixing personas and numerical data. The findings have implications for the design of persona systems and the use of online analytics data to better understand users and customers.
Subgoal Discovery for Hierarchical Dialogue Policy Learning

Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given successful example dialogues, we propose the Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion. We then use these subgoals to learn a multi-level policy by hierarchical reinforcement learning. We demonstrate our method by building a dialogue agent for the composite task of travel planning. Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals. Moreover, we show that the learned subgoals are often human comprehensible.
A New Channel Boosted Convolutional Neural Network using Transfer Learning

We present a novel architectural enhancement of Channel Boosting in deep convolutional neural network (CNN). This idea of Channel Boosting exploits both the channel dimension of CNN (learning from multiple input channels) and Transfer learning (TL). TL is utilized at two different stages; channel generation and channel exploitation. In the proposed methodology, a deep CNN is boosted by various channels available through TL from already trained Deep Neural Networks, in addition to its own original channel. The deep architecture of CNN then exploits the original and boosted channels down the stream for learning discriminative patterns. Churn prediction in telecom is a challenging task due to high dimensionality and imbalanced nature of the data and it is therefore used to evaluate the performance of the proposed Channel Boosted CNN (CB CNN). In the first phase, discriminative informative features are being extracted using a staked autoencoder, and then in the second phase, these features are combined with the original features to form Channel Boosted images. Finally, the knowledge gained by a pre trained CNN is exploited by employing TL. The results are promising and show the ability of the Channel Boosting concept in learning complex classification problem by discerning even minute differences in churners and non churners. The proposed work validates the concept observed from the evolution of recent CNN architectures that the innovative restructuring of a CNN architecture may increase the representative capacity of the network.
Pointing at a Distance with Everyday Smart Devices

Large displays are becoming commonplace at work, at home, or in public areas. However, interaction at a distance -- anything greater than arms-length -- remains cumbersome, restricts simultaneous use, and requires specific hardware augmentations of the display: touch layers, cameras, or dedicated input devices. Yet a rapidly increasing number of people carry smartphones and smartwatches, devices with rich input capabilities that can easily be used as input devices to control interactive systems. We contribute (1) the results of a survey on possession and use of smart devices, and (2) the results of a controlled experiment comparing seven distal pointing techniques on phone or watch, one- and two-handed, and using different input channels and mappings. Our results favor using a smartphone as a trackpad, but also explore performance tradeoffs that can inform the choice and design of distal pointing techniques for different contexts of use.
An analysis of sampling based techniques in bearing-only tracking

Bearing only Tracking (BoT) has been of particular interest in RADAR / SONAR based passive surveillance. There are various methods and algorithms for localizing a target based on bearing measurements alone. This range from classic least square method developed by Gauss to Bayesian estimation techniques like Kalman filtering, Particle filtering etc. A review of nonlinear Bayesian filtering methods including deterministic sampling techniques like unscented filtering to random sampling techniques like Particle filters is included in this paper. Bayesian framework for nonlinear estimation is about evaluating multidimensional integrals to find out posterior and prior probability density function (pdf) of the state vector. This can be achieved via deterministic or random sampling in the state space. The main scope of this work is to compare various sampling based BoT algorithms like sigma point Alter, particle filters and cubature kalman filters in Bayesian estimation problem.
Volta: Performance and Programmability

GV100 is NVIDIAs latest flagship GPU. It has been designed with many features to improve performance and programmability. It features enhancements to NVLink, a redesigned streaming microprocessor (SM), and independent thread scheduling enhancements to the single instruction, multiple threads (SIMT) model. GV100 also adds new tensor cores for an order of magnitude throughput improvement for deep-learning kernels.
Deterministic and Randomized Diffusion based Iterative Generalized Hard Thresholding (DiFIGHT) for Distributed Sparse Signal Recovery

In this paper, we propose a distributed iterated hard thresholding algorithm termed DiFIGHT over a network that is built on the diffusion mechanism and also propose a modification of the proposed algorithm termed MoDiFIGHT, that has low complexity in terms of communication in the network. We additionally propose four different strategies termed RP, RNP, RGPr, and RGNPr that are used to randomly select a subset of nodes that are subsequently activated to take part in the distributed algorithm, so as to reduce the mean number of communications during the run of the distributed algorithm. We present theoretical estimates of the long run communication per unit time for these different strategies, when used by the two proposed algorithms. Also, we present an analysis of the two proposed algorithms and provide provable bounds on their recovery performance with or without using the random node selection strategies. Finally, we use numerical studies to show that both when the random strategies are used as well as when they are not used, the proposed algorithms display performances far superior to distributed IHT algorithm using consensus mechanism.
An Algebraic Approach to Unital Quantities and their Measurement

Abstract The goals of this paper fall into two closely related areas. First, we develop a formal framework for deterministic unital quantities in which measurement unitization is understood to be a built-in feature of quantities rather than a mere annotation of their numerical values with convenient units. We introduce this idea within the setting of certain ordered semigroups of physical-geometric states of classical physical systems. States are assumed to serve as truth makers of metrological statements about quantity values. A unital quantity is presented as an isomorphism from the target system’s ordered semigroup of states to that of positive reals. This framework allows us to include various derived and variable quantities, encountered in engineering and the natural sciences. For illustration and ease of presentation, we use the classical notions of length, time, electric current and mean velocity as primordial examples. The most important application of the resulting unital quantity calculus is in dimensional analysis. Second, in evaluating measurement uncertainty due to the analog-to-digital conversion of the measured quantity’s value into its measuring instrument’s pointer quantity value, we employ an ordered semigroup framework of pointer states. Pointer states encode the measuring instrument’s indiscernibility relation, manifested by not being able to distinguish the measured system’s topologically proximal states. Once again, we focus mainly on the measurement of length and electric current quantities as our motivating examples. Our approach to quantities and their measurement is strictly state-based and algebraic in flavor, rather than that of a representationalist-style structure-preserving numerical assignment.
Dual band band-pass filter using cascaded split ring resonator

In this paper, a simple and different method is used to design a dual band band-pass filter by using split ring resonator (SRR). In wireless communication filters are the most commonly used device. Dual band band-pass filter is used for passing a desired frequency band in RF communication system. Most of the research areas, size and bandwidth of the filter are focused. In this paper two desired center frequency band pass filter is implemented. The center frequencies of these filters are 1346MHz and 2352MH. Fractional bandwidths of proposed filter are 6.8%and 3.2% for 1346MHz and 2352MHz center frequencies respectively. The most common applications of dual band (1346MHz & 2352MHz) band-pass filter in RADAR, global positioning system (GPS) etc.
Road Traffic Forecasting: Recent Advances and New Challenges

Due to its paramount relevance in transport planning and logistics, road traffic forecasting has been a subject of active research within the engineering community for more than 40 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. More recently, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. This paper aims to summarize the efforts made to date in previous related surveys towards extracting the main comparing criteria and challenges in this field. A review of the latest technical achievements in this field is also provided, along with an insightful update of the main technical challenges that remain unsolved. The ultimate goal of this work is to set an updated, thorough, rigorous compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.
NEUTRON ACTIVATION TECHNIQUE: A RELIABLE TOOL TO DETERMINE THE MINERAL COMPOSITION IN AGRO-INDUSTRIAL PRODUCTS

Mineral composition analysis in agro industrial products is necessary for several reasons, such as the determination of nutritious value, assessment of product quality, detection of adulteration, compliance with legal and labelling requirements, food forensic, research and development. It is important to enhance that a variety of toxic elements in food has been increasing as a consequence of new agricultural practices, industrial development, and environmental pollution. For that, analytical techniques are expected to play a crucial role on chemical elemental concentration determination. In this paper, the neutron activation technique was applied by means of k 0 standardized method. This method uses neutron flux monitors, spectral parameters of the nuclear reactor, gamma system counter absolutely calibrated and k 0 constants and not standards of chemical elements, as in relative method. Several agro-industrial samples and one soil sample were analysed using the 100 kW TRIGA Mark I research reactor. Therefore, the objective of this paper was to show the versatility and the efficiency of this technique on multielemental concentration determination in diversified matrices, showing that several chemical elements were determined with a large range of elemental concentration. Reference materials were also analysed and evaluated statistically, pointing out the reliability of the technique.
Panel: Voice Assistants, UX Design and Research

In this panel, we discuss the challenges that are faced by HCI practitioners and researchers as they study how voice assistants (VA) are used on a daily basis. Voice has become a widespread and commercially viable interaction mechanism with the introduction of VAs such as Amazon's Alexa, Apple's Siri, the Google Assistant, and Microsoft's Cortana. Despite their prevalence, the design of VAs and their embeddedness with other personal technologies and daily routines have yet to be studied in detail. Making use of a roundtable, we will discuss these issues by providing a number of VA use scenarios that panel members will discuss. Some of the issues that researchers will discuss in this panel include: (1) obtaining VA data & privacy concerns around the processing and storage of user data; (2) the personalization of VAs and the user value derived from this interaction; and (3) the relevant UX work that reflects on the design of VAs?
Charrette: Supporting In-Person Discussions around Iterations in User Interface Design

As a rule, user interface designers work iteratively. Over the course of a project, they repeatedly gather feedback, typically through in-person meetings, and update their designs accordingly. Through formative work, we find that design software tools do not support designers in managing meeting notes and previous design iterations as a cohesive whole. This causes designers to rely on ad-hoc practices for organizing work, which makes it hard for them to keep track of relevant feedback and explain their design decisions. To address this problem, we present Charrette, a system that allows designers to curate design iterations, attach meeting notes to the relevant content, and navigate sequences of design iterations with the associated notes to facilitate in-person discussions. In an exploratory user study, we evaluate how Charrette affects designers' self-reported ease in handling feedback during face-to-face discussions, compared with using their own tools. We find that using Charrette correlates with increased confidence and recall in discussing previous design decisions.
The Role of Flavonoids in Inhibiting Th17 Responses in Inflammatory Arthritis

Flavonoids have been considered powerful anti-inflammatory agents, and their exact immunomodulatory action as therapeutic agents in autoimmune diseases has started to emerge. Their role in the manipulation of immunoregulation is less understood. Several studies attempted to investigate the role of various flavonoids mainly in experimental models of autoimmune diseases, especially in the context of their potential effect on the increase of regulatory T cells (Tregs) and their ability to stimulate an overexpression of anti-inflammatory cytokines, in particular that of IL-10. The emergence of IL-17, a cytokine largely produced by Th17 cells, as a powerful proinflammatory stimulus which attenuates the induction of Tregs has prompted a series of studies investigating the role of flavonoids on Th17 cells in experimental models as well as human autoimmune diseases. This review thoroughly discusses accumulated data on the role of flavonoids on Th17 in rheumatoid arthritis and experimental autoimmune arthritis.
A Functional Optimization Based Approach for Continuous 3D Retargeted Touch of Arbitrary, Complex Boundaries in Haptic Virtual Reality

Passive or actuated physical props can provide haptic feedback, leading to a satisfying sense of presence and realism in virtual reality. However, the mismatch between the physical and virtual surfaces (boundaries) can diminish user experience. Haptic retargeting can overcome this limitation by utilizing visio-haptic effects. Previous investigations in haptic retargeting have focused on methods for point based position retargeting and techniques for remapping 2D shapes or simple 3D shape changes. Our approach extends haptic retargeting to complex, arbitrary shapes that provide a continuous mapping across all points on a boundary. This new approach also allows for multi-finger interaction. We describe a functional optimization to find the ideal spatial warping function with different goals: a maximum mapping smoothness, a minimum mismatch between the real and virtual world, or the combination of the two. We report on a preliminary user study of different optimization goals and elaborate potential applications through a set of demonstrations.
Remote monitoring of leaf turgor pressure of grapevines subjected to different irrigation treatments using the leaf patch clamp pressure probe

Background and Aims: Effects of four irrigation treatments on leaf turgor pressure of grapevines were studied using the novel leaf patch clamp pressure (LPCP) probe. Data were correlated with yield and yield components. Methods and Results: The LPCP probe measures leaf water status by monitoring the attenuation of an external pressure applied magnetically to a leaf patch. The output pressure signals, P p, are inversely correlated with cell turgor pressure. Measurements showed that changes in transpiration and stomatal conductance induced by environmental parameters were reflected nearly immediately in Pp. Ongoing non-irrigation resulted in a continuous increase of Pp, in the occurrence of stomatal oscillations and in an increased turgor pressure recovery phase during afternoon. Interestingly, analysis of the numerous diurnal Pp data sets showed that east-directed leaves responded more sensitively to water stress than west-directed leaves. Conclusions: For the cultivar and conditions used in this study, the probe data as well as the yield data support irrigation on a 3-day basis with relatively small amounts of water. Significance of the Study: The results show that the LPCP probe is a user-friendly, high precision instrument for online-monitoring of leaf turgor pressure in dependency on changes in microclimate and irrigation, thus helping growers to increase yield while simultaneously saving water.
Multi-view topic model learning to generate audience metadata automatically

In this paper, we propose a study on multi-view topic model learning to generate automatically audience metadata for clips. We use closed caption of broadcasting contents, user's subscription information and viewing history. An existing topic model has limits to being utilized for user targeted services by learning topics based on subtitles or scripts without user data. To overcome this limitation, this paper proposes a multi-view topic model learning technique using multi domain data such as closed caption of broadcast contents and viewing rating of audience groups.
Tracing studies in cohorts with attrition: Selection models for efficient sampling

Cohort studies of chronic diseases involve recruitment and longitudinal follow‐up of affected individuals with a view to studying the effect of risk factors on disease progression and death. When the time to withdrawal from the cohort is conditionally independent of the disease process the primary consequence is a loss of information on the parameters of interest. This loss can sometimes be mitigated through the conduct of tracing studies in which a subsample of those lost to follow up are contacted and some information is obtained on their disease and survival status. We describe the use of selection models to sample individuals for tracing who will yield more efficient estimators than those obtained by simple random sampling. Efficient sampling schemes featuring cost constraints are also developed and shown to perform well. An application to data from the University of Toronto Psoriatic Arthritis Cohort illustrates how to apply the method in a real setting.
Designing the Desirable Smart Home: A Study of Household Experiences and Energy Consumption Impacts

Research has shown that desirable designs shape the use and experiences people have when interacting with technology. Nevertheless, how desirability influences energy consumption is often overlooked, particularly in HCI studies evaluating the sustainability benefits of smart home technology. In this paper, we present a qualitative study with 23 Australian households who reflect on their experiences of living with smart home devices. Drawing on Nelson and Stolterman's concept of desiderata we develop a typology of householders' desires for the smart home and their energy implications. We structure these desires as three smart home personas: the helper, optimiser and hedonist, which align with desiderata's three approaches to desire (reason, ethics and aesthetics). We use these insights to discuss how desirability can be used within HCI for steering design of the smart home towards sustainability.