Miracle-dz/UniFAR-FTUs huggingface dataset:
Web Video Clustering Based on Emotion Category

Web video clustering is a fundamental task in the field of social media mining. Automatically web video categorization methods enable users to find video corresponding to their interests. However, all the previous studies are only conducted on the default categories given by the website, i.e., 15 categories of YouTube. To date, clustering based on emotion category has not been a factor considered in this area. Therefore, in this paper, we propose a method to cluster YouTube videos into six emotion categories (e.g., angry, disgust, happy, horror, sad, surprise) with expect to improve video search results. The YouTube data is collected. Word embedding is utilized for transforming the video document into vectors which are then used in a clustering task. Clustering ensemble is employed to obtain final results. We compare the performance of this method with a state-of-the-art technology, i.e., Term Frequency-Inverse Document Frequency based on Vector Space Model. The experiment is implemented on a benchmark dataset for web video analysis. The results show that the best performance is achieved by applying clustering ensemble which reflects the feasibility of clustering web videos into suitable emotion categories.
Pervasive Smart Contracts for Blockchains in IoT Systems

Thanks to its decentralized structure and immutability, blockchain technology has the potential to address relevant security and privacy challenges in the Internet of Things (IoT). In particular, by hosting and executing smart contracts, blockchain allows secure, flexible, and traceable message communication between IoT devices. The unique characteristics of IoT systems, such as heterogeneity and pervasiveness, however, pose challenges in designing smart contracts for such systems. In this paper, we study these challenges and propose a design approach for smart contracts used in IoT systems. The main goal of our design model is to enhance the development of IoT smart contracts based on the inherent pervasive attributes of IoT systems. In particular, the design model allows the smart contracts to encapsulate functionalities such as contractlevel communication between IoT devices, access to data-sources within contracts, and interoperability of heterogeneous IoT smart contracts. The essence of our approach is structuring the design of IoT smart contracts as self-contained software services, inspired by the microservice architecture model. The flexibility, scalability and modularity of this model make it an efficient approach for developing pervasive IoT smart contracts.
Scratchpad-Memory Management for Multi-Threaded Applications on Many-Core Architectures

Contemporary many-core architectures, such as Adapteva Epiphany and Sunway TaihuLight, employ per-core software-controlled Scratchpad Memory (SPM) rather than caches for better performance-per-watt and predictability. In these architectures, a core is allowed to access its own SPM as well as remote SPMs through the Network-On-Chip (NoC). However, the compiler/programmer is required to explicitly manage the movement of data between SPMs and off-chip memory. Utilizing SPMs for multi-threaded applications is even more challenging, as the shared variables across the threads need to be placed appropriately. Accessing variables from remote SPMs with higher access latency further complicates this problem as certain links in the NoC may be heavily contended by multiple threads. Therefore, certain variables may need to be replicated in multiple SPMs to reduce the contention delay and/or the overall access time. We present Coordinated Data Management (CDM), a compile-time framework that automatically identifies shared/private variables and places them with replication (if necessary) to suitable on-chip or off-chip memory, taking NoC contention into consideration. We develop both an exact Integer Linear Programming (ILP) formulation as well as an iterative, scalable algorithm for placing the data variables in multi-threaded applications on many-core SPMs. Experimental evaluation on the Parallella hardware platform confirms that our allocation strategy reduces the overall execution time and energy consumption by 1.84× and 1.83×, respectively, when compared to the existing approaches.
It Is About What They Could Do with the Data

Smart Meters are a key component of increasing the power efficiency of the Smart Grid. To help manage the grid effectively, these meters are designed to collect information on power consumption and send it to third parties. With Smart Metering, for the first time, these cloud-connected sensing devices are legally mandated to be installed in the homes of millions of people worldwide. Via a multi-staged empirical study that utilized an open-ended questionnaire, focus groups, and a design probe, we examined how people characterize the tension between the utility of Smart Metering and its impact on privacy. Our findings show that people seek to make abstract Smart Metering data accountable by connecting it to their everyday practices. Our insight can inform the design of usable privacy configuration tools that help Smart Metering consumers relate abstract data with the real-world implications of its disclosure.
Video Watermark Algorithm Study of Shot Segmentation Based on Motion Vector

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the IHIP 2018 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library. None of the published papers from this Proceeding should be cited in the literature because of the questionable integrity of the peer review process for this Conference.
Modeling and Analysis of Non-unique Behaviors in Multiple Frictional Impacts

Many fundamental challenges in robotics, based in manipulation or locomotion, require making and breaking contact with the environment. To represent the complexity of frictional contact events, impulsive impact models are especially popular, as they often lead to mathematically and computationally tractable approaches. However, when two or more impacts occur simultaneously, the precise sequencing of impact forces is generally unknown, leading to the potential for multiple possible outcomes. This simultaneity is far from pathological, and occurs in many common robotics applications. In this work, we propose an approach for resolving simultaneous frictional impacts, represented as a differential inclusion. Solutions to our model, an extension to multiple contacts of Routh's method, naturally capture the set of potential post-impact velocities.We prove that solutions to the presented model must terminate. This is, to the best of our knowledge, the first such guarantee for set-valued outcomes to simultaneous frictional impacts.
Detecting Permanent and Intermittent Purchase Hotspots via Computational Stigmergy

The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational stigmergy. By using computational stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only in certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015.
GNU Radio Implementation of MALIN: “Multi-Armed bandits Learning for Internet-of-things Networks”

We implement an IoT network the following way: one gateway, one or several intelligent (i.e., learning) objects, embedding the proposed solution, and a traffic generator that emulates radio interferences from many other objects. Intelligent objects communicate with the gateway with a wireless ALOHA-based protocol, which does not require any specific overhead for the learning. We model the network access as a discrete sequential decision making problem, and using the framework and algorithms from Multi-Armed Bandit (MAB) learning, we show that intelligent objects can improve their access to the network by using low complexity and decentralized algorithms, such as UCB1 and Thompson Sampling. This solution could be added in a straightforward and costless manner in LoRaWAN networks, just by adding this feature in some or all the devices, without any modification on the network side.
Metric Learning on Manifolds

Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geometrical operations on manifolds are generally more complicated than in Euclidean space, and thus many techniques for processing and analysis taken for granted in Euclidean space are difficult on manifolds. A priori, it is not obvious how we may generalize such methods to manifolds. We consider specifically the problem of distance metric learning, and present a framework that solves it on a large class of manifolds, such that similar data are located in closer proximity with respect to the manifold distance function. In particular, we extend the existing metric learning algorithms, and derive the corresponding sample complexity rates for the case of manifolds. Additionally, we demonstrate an improvement of performance in $k$-means clustering and $k$-nearest neighbor classification on real-world complex networks using our methods.
Literature Survey on Finding Influential Communities in Large Scale Networks

Community or modular structure is considered to be a significant property of large scale real-world graphs such as social or information networks. Detecting influential clusters or communities in these graphs is a problem of considerable interest as it often accounts for the functionality of the system. We aim to provide a thorough exposition of the topic, including the main elements of the problem, a brief introduction of the existing research for both disjoint and overlapping community search, the idea of influential communities, its implications and the current state of the art and finally provide some insight on possible directions for future research.
Partisanship, Propaganda and Post-Truth Politics: Quantifying Impact in Online Debate

The recent past has highlighted the influential role of social networks and online media in shaping public debate on current affairs and political issues. This paper is focused on studying the role of politically-motivated actors and their strategies for influencing and manipulating public opinion online: partisan media, state-backed propaganda, and post-truth politics. In particular, we present quantitative research on the presence and impact of these three `Ps' in online Twitter debates in two contexts: (i) the run up to the UK EU membership referendum (`Brexit'); and (ii) the information operations of Russia-backed online troll accounts. We first compare the impact of highly partisan versus mainstream media during the Brexit referendum, specifically comparing tweets by half a million `leave' and `remain' supporters. Next, online propaganda strategies are examined, specifically left- and right-wing troll accounts. Lastly, we study the impact of misleading claims made by the political leaders of the leave and remain campaigns. This is then compared to the impact of the Russia-backed partisan media and propaganda accounts during the referendum. In particular, just two of the many misleading claims made by politicians during the referendum were found to be cited in 4.6 times more tweets than the 7,103 tweets related to Russia Today and Sputnik and in 10.2 times more tweets than the 3,200 Brexit-related tweets by the Russian troll accounts.
Mixture Learning from Partial Observations and Its Application to Ranking

Despite recent advances in rank aggregation and mixture learning, there has been a limited amount of success for learning a mixture model for ranking data. Motivated by the problem of learning a mixture of ranking models from pair-wise comparisons, we consider mixture learning from partial observations. The generic approaches for mixture learning do not generalize to this setting. Matrix estimation, however, provides a way to recover a structured underlying matrix from its partial, noisy observations. We utilize matrix estimation as a pre-processing step to extend the mixture learning problem to allow for partial observations. Instantiating our matrix estimation subroutine with singular value thresholding, we provide a bound on the estimation error with respect to $\|\cdot\|_{2,\infty}$-norm. In particular, we show that if $p$ (the fraction of observed entries) scales as $\tilde{\Omega}((\frac{r}{d})^{\frac{1}{3}})$, then the normalized $\|\cdot\|_{2,\infty}$ error vanishes to $0$ as long as the underlying $N \times d$ ($N\geq d$) matrix is rank $r$; this holds true even if the noise is correlated across columns. As an application, we argue if $\Gamma p=\tilde{\Omega}(\sqrt{r})$, then the mixture components can be correctly identified with $N=poly(d)$ samples; $\Gamma$ is the minimum gap between the mixture means. Further, we argue a large class of popular ranking models (e.g., Mallow, Multinomial Logit (MNL) Model) satisfy the sub-gaussian property when viewed through a pairwise embedding lens. Hence, our method provides a sufficient condition for efficiently recovering the mixture components for an important class of models. For example, mixtures of $r$ components can be clustered correctly using $\tilde{O}(rn^4)$ pair-wise comparisons when the components are well-separated and distributed as per either a Mallows, MNL, or any Random Utility Model over $n$ items.
Hierarchical Matrix Operations on GPUs

Hierarchical Matrix Operations on GPUs Wajih Halim Boukaram Large dense matrices are ubiquitous in scientific computing, arising from the discretization of integral operators associated with elliptic pdes, Schur complement methods, covariances in spatial statistics, kernel-based machine learning, and numerical optimization problems. Hierarchical matrices are an efficient way for storing the dense matrices of very large dimension that appear in these and related settings. They exploit the fact that the underlying matrices, while formally dense, are data sparse. They have a structure consisting of blocks many of which can be well-approximated by low rank factorizations. A hierarchical organization of the blocks avoids superlinear growth in memory requirements to store n× n dense matrices in a scalable manner, requiring O(n) units of storage with a constant depending on a representative rank k for the low rank blocks. The asymptotically optimal storage requirement of the resulting hierarchical matrices is a critical advantage, particularly in extreme computing environments, characterized by low memory per processing core. The challenge then becomes to develop the parallel linear algebra operations that can be performed directly on this compressed representation. In this dissertation, I implement a set of hierarchical basic linear algebra subroutines (HBLAS) optimized for GPUs, including hierarchical matrix vector multiplication, orthogonalization, compression, low rank updates, and matrix multiplication. I develop a library of open source batched kernel operations previously missing on GPUs for the high performance implementation of the H2 operations, while relying wherever possible on existing open source and vendor kernels to ride future improvements in the technology. Fast marshaling routines extract the batch operation data from an efficient representation of the trees 5 that compose the hierarchical matrices. The methods developed for GPUs extend to CPUs using the same code base with simple abstractions around the batched routine execution. To demonstrate the scalability of the hierarchical operations I implement a distributed memory multi-GPU hierarchical matrix vector product that focuses on reducing communication volume and hiding communication overhead and areas of low GPU utilization using low priority streams. Two demonstrations involving Hessians of inverse problems governed by pdes and space-fractional diffusion equations show the effectiveness of the hierarchical operations in realistic applications.
Joint Millimeter Wave and Microwave Wave Resource Allocation Design for Dual-Mode Base Stations

In this paper, we consider the design of joint resource blocks (RBs) and power allocation for dual-mode base stations operating over millimeter wave (mmW) band and microwave (µW) band. The resource allocation design aims to minimize the system energy consumption while taking into account the channel state information, maximum delay, load, and different types of user applications (UAs). To facilitate the design, we first propose a group-based algorithm to assign UAs to multiple groups. Within each group, low-power UAs, which often appear in short distance and experience less obstacles, are inclined to be served over mmW band. The allocation problem over mmW band can be solved by a greedy algorithm. Over µW band, we propose an estimation-optimal-descent algorithm. The rate of each UA at all RBs is estimated to initialize the allocation. Then, we keep altering RB's ownership until any altering makes power increases. Simulation results show that our proposed algorithm offers an excellent tradeoff between low energy consumption and fair transmission.
Consistent Risk Estimation in High-Dimensional Linear Regression

Risk estimation is at the core of many learning systems. The importance of this problem has motivated researchers to propose different schemes, such as cross validation, generalized cross validation, and Bootstrap. The theoretical properties of such estimates have been extensively studied in the low-dimensional settings, where the number of predictors $p$ is much smaller than the number of observations $n$. However, a unifying methodology accompanied with a rigorous theory is lacking in high-dimensional settings. This paper studies the problem of risk estimation under the high-dimensional asymptotic setting $n,p \rightarrow \infty$ and $n/p \rightarrow \delta$ ($\delta$ is a fixed number), and proves the consistency of three risk estimates that have been successful in numerical studies, i.e., leave-one-out cross validation (LOOCV), approximate leave-one-out (ALO), and approximate message passing (AMP)-based techniques. A corner stone of our analysis is a bound that we obtain on the discrepancy of the `residuals' obtained from AMP and LOOCV. This connection not only enables us to obtain a more refined information on the estimates of AMP, ALO, and LOOCV, but also offers an upper bound on the convergence rate of each estimate.
Suppression and facilitation of motion perception in humans

In a study that investigated the putative neuronal origin of suppression and facilitation in human motion perception Schallmo et al. (2018) used various techniques to investigate how motion perception is shaped by excitatory and inhibitory interactions within hMT+ or earlier areas. They further proposed that neuronal normalization is a sufficient account of the behavioural results, discounting accepted and precise neuronal mechanisms of excitation or inhibition. In this Research Advance, it is shown (1) that once the full computational model that predicts the psychophysical results is defined, it is not “divisive normalization” but actually excitatory and inhibitory processes that are the neuronal mechanisms shaping facilitation and suppression in the behavioural domain, then (2) that the experimental design they used allows a quantitative comparison and usage of such “contrast–size tuning” data.
Chemoorganotrophic Bacteria From Lake Fryxell, Antarctica, Including Pseudomonas Strain LFY10, a Cold-Adapted, Halotolerant Bacterium Useful in Teaching Labs

Lake Fryxell, situated in the McMurdo Dry Valleys of Antarctica, is an intriguing aquatic ecosystem because of its perennial ice cover, highly stratified water column, and extreme physicochemical conditions, which collectively restrict lake biodiversity to solely microbial forms. To expand our current understanding of the cultivable biodiversity of Lake Fryxell, water samples were collected from depths of 10 and 17 m, and pure cultures of eight diverse strains of aerobic, chemoorganotrophic bacteria were obtained. Despite having high 16S rRNA gene sequence similarity to mesophilic bacteria inhabiting various temperate environments, all Lake Fryxell isolates were psychrotolerant, with growth occurring at 0°C and optimal growth from 18–24°C for all isolates. Phylogenetic analyses showed the isolates to be members of six taxonomic groups, including the genera Brevundimonas, Arthrobacter, Sphingobium, Leifsonia, and Pseudomonas, as well as the family Microbacteriaceae (one strain could not reliably be assigned to a specific genus based on our analysis). Pseudomonas strain LFY10 stood out as a useful tool for teaching laboratory activities because of its substantial cold adaptation (visible growth is evident in 1–2 days at 4°C), beta-hemolytic activity, and halotolerance to 8.5% (w/v) NaCl. These cold-adapted bacteria likely play a role in carbon mineralization and other nutrient cycling in Lake Fryxell, and their characterization broadens our understanding of microbial biodiversity in aquatic polar ecosystems.
On the Other Side: Manipulating the Immune Checkpoint Landscape of Dendritic Cells to Enhance Cancer Immunotherapy

Monoclonal antibodies targeting co-inhibitory immune checkpoint molecules have been successful in clinical trials of both solid and hematological malignancies as acknowledged by the 2018 Nobel Prize in Medicine, however improving clinical response rates is now key to expanding their efficacy in areas of unmet medical need. Antibodies to checkpoint inhibitors target molecules on either T cells or tumor cells to stimulate T cells or remove tumor mediated immunosuppression, respectively. However, many of the well-characterized T cell immune checkpoint receptors have their ligands on antigen presenting cells or exert direct effects on those cells. Dendritic cells are the most powerful antigen presenting cells; they possess the ability to elicit antigen-specific responses and have important roles in regulation of immune tolerance. Despite their theoretical benefits in cancer immunotherapy, the translation of DC therapies into the clinic is yet to be fully realized and combining DC-based immunotherapy with immune checkpoint inhibitors is an attractive strategy. This combination takes advantage of the antigen presenting capability of DC to maximize specific immune responses to tumor antigens whilst removing tumor-associated immune inhibitory mechanisms with immune checkpoint inhibition. Here we review the expression and functional effects of immune checkpoint molecules on DC and identify rational combinations for DC vaccination to enhance antigen-specific T cell responses, cytokine production, and promotion of long-lasting immunological memory.