Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting and Measuring Human Walking in Laser Scans

This paper presents work on detecting and tracking human movement in planar range data. Our method stacks multiple planar scans into a 3D frame where time serves as the third dimension. This representation simultaneously informs about the size and shape of the objects in the scene and about their movement, so that no explicit motion models are necessary. The scene is then segmented into 3D spatio-temporal objects which are classified as 'pairs of walking legs' using methods from machine vision. Our main contribution is a novel pre-processing step which aligns the spatio-temporal objects, so that information about the direction and speed of movement is factored out of the representation. The advantage is that the subsequent feature extraction and classification steps are only exposed to movement patterns without reference to direction and speed which are not relevant to recognizing human walking. The method is empirically evaluated and found to significantly increase classification accuracy.
Cost-Efficient Orchestration of Containers in Clouds: A Vision, Architectural Elements, and Future Directions

This paper proposes an architectural framework for the efficient orchestration of containers in cloud environments. It centres around resource scheduling and rescheduling policies as well as autoscaling algorithms that enable the creation of elastic virtual clusters. In this way, the proposed framework enables the sharing of a computing environment between differing client applications packaged in containers, including web services, offline analytics jobs, and backend pre-processing tasks. The devised resource management algorithms and policies will improve utilization of the available virtual resources to reduce operational cost for the provider while satisfying the resource needs of various types of applications. The proposed algorithms will take factors that are previously omitted by other solutions into consideration, including 1) the pricing models of the acquired resources, 2) and the fault-tolerability of the applications, and 3) the QoS requirements of the running applications, such as the latencies and throughputs of the web services and the deadline of the analytical and pre-processing jobs. The proposed solutions will be evaluated by developing a prototype platform based on one of the existing container orchestration platforms.
Investigation of kernel functions in EDA-GK

We have proposed EDA-GK, Estimation of Distribution Algorithms with Graph Kernels. The EDA-GK is designed for solving graph-related problems, where individuals can be represented by graphs. By using graph kernels, the EDA-GK can be solved for graph-related problems well. The EDA-GK uses the graph kernels as probabilistic models in EDA. In this study, we examine the Weisfeiler-Lehman Kernel, and the mixture of two kernels. Experimental results on Graph Isomorphism problems showed the effectiveness of the pro- posed method:
Effect of heat treatments on total phenols, flavonoids and antioxidant activity of Clove ( Syzygium aromaticumL.) and Turmeric ( Curcuma longaL.)

Title of Thesis : Effect of heat treatments on total phenols, flavonoids and antioxidant activity of Clove (Syzygium aromaticum L.) and Turmeric (Curcuma longa L.) Name of Degree holder : Dusyant Admission number : 2010BS52D Title of Degree : Doctor of Philosophy (Chemistry) Name and address of Major Advisor : Dr. Vinod Kumar Madan Principal Scientist (Phytochemistry) Medicinal, Aromatic and Potential Crops Section CCS HAU, Hisar, Haryana-125 004 Degree awarding University : CCS Haryana Agricultural University, Hisar -125004 Year of award of Degree : 2015 Major Subject : Chemistry Total number of pages in Thesis : 84+xiii Number of words in the Abstract : 462
IGLOO: Slicing the Features Space to Represent Long Sequences

We introduce a new neural network architecture, IGLOO, which aims at providing a representation for long sequences where RNNs fail to converge. The structure uses the relationships between random patches sliced out of the features space of some backbone 1 dimensional CNN to find a representation. This paper explains the implementation of the method and provides benchmark results commonly used for RNNs and compare IGLOO to other structures recently published. It is found that IGLOO can deal with sequences of up to 25,000 time steps. For shorter sequences it is also found to be effective and we find that it achieves the highest score in the literature for the permuted MNIST task. Benchmarks also show that IGLOO can run at the speed of the CuDNN optimized GRU or LSTM or faster for most of the tasks presented.
Empath-D: VR-based Empathetic App Design for Accessibility

With app-based interaction increasingly permeating all aspects of daily living, it is essential to ensure that apps are designed to be inclusive and are usable by a wider audience such as the elderly, with various impairments (e.g., visual, audio and motor). We propose Empath-D, a system that fosters empathetic design, by allowing app designers, in-situ, to rapidly evaluate the usability of their apps, from the perspective of impaired users. To provide a truly authentic experience, Empath-D carefully orchestrates the interaction between a smartphone and a VR device, allowing the user to experience simulated impairments in a virtual world while interacting naturally with the app, using a real smartphone. By carefully orchestrating the VR-smartphone interaction, Empath-D tackles challenges such as preserving low-latency app interaction, accurate visualization of hand movement and low-overhead perturbation of I/O streams. Experimental results show that user interaction with Empath-D is comparable (both in accuracy and user perception) to real-world app usage, and that it can simulate impairment effects as effectively as a custom hardware simulator.
Two-Phase Dynamic Analysis of Message-Passing Go Programs Based on Vector Clocks

Understanding the runtime behavior of concurrent programs is a challenging task. A popular approach is to establish a happens-before relation via vector clocks. Thus, we can identify bugs and performance bottlenecks, for example, by checking if two conflicting events may happen concurrently. We employ a two-phase method to derive vector clock information for a wide range of concurrency features that includes all of the message-passing features in Go. The first phase (instrumentation and tracing) yields a runtime trace that records all events related to message-passing concurrency that took place. The second phase (trace replay) is carried out of fline and replays the recorded traces to infer vector clock information. Trace replay operates on thread-local traces. Thus, we can observe behavior that might result from some alternative schedule. Our approach is not tied to any specific language. We have built a prototype for the Go programming language and provide empirical evidence of the usefulness of our method.
Diversifying Search Engine Results

Nowadays1, that the use. of search engines has been expanded due to user requirements, there is a great need to diversify their results in order to cover as many informational needs as possible and not to repeat similar content for a given query. In this context, this paper was initiated, which, using textual commenting techniques on static and dynamic ranking algorithms, applies cutting and merging of unnecessary information, aiming at differentiating the results without decreasing their relevance.
Interaction of Tamoxifen Analogs With the Pocket Site of Some Hormone Receptors. A Molecular Docking and Density Functional Theory Study

In this paper, the antiestrogenic properties of Tamoxifen analogs have been investigated and a theoretical report of its analogs interaction with the pocket site of some hormone receptors are presented. Analogs were generated by modification of the hydrophilic functional group of Tamoxifen by hydroxyl, amide, carboxyl, and sulfhydryl functional groups, in an attempt to improve their activity and selectivity. The analogs exhibit a negative binding energy in the estrogen and progesterone receptors, which indicates a spontaneous interaction between the analogs and the pocket site in the hormone receptors. The values of the molecular polar surface area indicate that the analogs have good permeability and are strong electrophiles. The couplings showed electrostatic interactions such as hydrogen bond and π-π interactions. According with the Lipinsky Rule of Five, the four analogs presented a good biodistribution, permeability, and pharmacological action on the hormone receptors. The analysis of the charge transfer suggests a limited enhanced oxidative damage in the estrogen receptor that not takes place with the progesterone receptor.
Hybrid swarm of particle swarm with firefly for complex function optimization

Swarm intelligence is rather a simple implementation but has a good performance in function optimization. There are a variety of instances of swarm model and has its inherent dynamic property. In this study we consider a hybrid swarm model where agents complement each other using its native property. Employing popular swarm intelligence model Particle swarm and Firefly we consider hybridization methods in this study. This paper presents a hybridization that agents in Particle swarm selected by a simple rule or a random choice are changing its property to Firefly. Numerical studies are carried out by using complex function optimization benchmarks, the proposed method gives better performance compared with standard PSO.
Continuous Time-Dependent kNN Join by Binary Sketches

An important functionality of current social applications is real-time recommendation, which is responsible for suggesting relevant published data to the users based on their preferences. By representing the users and the published data in a metric space, each user can be recommended with their k nearest neighbors among the published data. We consider the scenario when the relevance of a published data item to a user decreases as the data gets older, i.e., a time-dependent distance function is applied. We define the problem as the continuous time-dependent kNN join and provide a solution to a broad range of time-dependent functions. In addition, we propose a binary sketch-based approximation technique used to speed up the join evaluation by replacing expensive metric distance computations with cheap Hamming distances.
Datalog-Based Scalable Semantic Diffing of Concurrent Programs

When an evolving program is modified to address issues related to thread synchronization, there is a need to confirm the change is correct, i.e., it does not introduce unexpected behavior. However, manually comparing two programs to identify the semantic difference is labor intensive and error prone, whereas techniques based on model checking are computationally expensive. To fill the gap, we develop a fast and approximate static analysis for computing synchronization differences of two programs. The method is fast because, instead of relying on heavy-weight model checking techniques, it leverages a polynomial-time Datalog-based program analysis framework to compute differentiating data-flow edges, i.e., edges allowed by one program but not the other. Although approximation is used our method is sufficiently accurate due to careful design of the Datalog inference rules and iterative increase of the required data-flow edges for representing a difference. We have implemented our method and evaluated it on a large number of multithreaded C programs to confirm its ability to produce, often within seconds, the same differences obtained by human; in contrast, prior techniques based on model checking take minutes or even hours and thus can be 10x to 1000x slower.
Detecting Levels of Depression in Text Based on Metrics

Depression is one of the most common and a major concern for society. Proper monitoring using devices that can aid in its detection could be helpful to prevent it all together. The Distress Analysis Interview Corpus (DAIC) is used to build a metric-based depression detection. We have designed a metric to describe the level of depression using negative sentences and classify the participant accordingly. The score generated from the algorithm is then levelled up to denote the intensity of depression. The results show that measuring depression is very complex to using text alone as other factors are not taken into consideration. Further, In the paper, the limitations of measuring depression using text are described, and future suggestions are made.
Multi-scale imaging of anticancer platinum(iv) compounds in murine tumor and kidney

Nano-scale secondary ion mass spectrometry (NanoSIMS) enables trace element and isotope analyses with high spatial resolution. This unique capability has recently been exploited in several studies analyzing the subcellular distribution of Au and Pt anticancer compounds. However, these studies were restricted to cell culture systems. To explore the applicability to the in vivo setting, we developed a combined imaging approach consisting of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), NanoSIMS and transmission electron microscopy (TEM) suitable for multi-scale detection of the platinum distribution in tissues. Applying this approach to kidney and tumor samples upon administration of selected platinum(iv) anticancer prodrugs revealed uneven platinum distributions on both the organ and subcellular scales. Spatial platinum accumulation patterns were quantitatively assessed by LA-ICP-MS in histologically heterogeneous organs (e.g., higher platinum accumulation in kidney cortex than in medulla) and used to select regions of interest for subcellular-scale imaging with NanoSIMS. These analyses revealed cytoplasmic sulfur-rich organelles accumulating platinum in both kidney and malignant cells. Those in the tumor were subsequently identified as organelles of lysosomal origin, demonstrating the potential of the combinatorial approach for investigating therapeutically relevant drug concentrations on a submicrometer scale.
A Step forward for Spatial Skyline Queries for a Group of Users: Semantic in the Evidence Theory Setting

Cities are the main poles of human and economic activity. Analyzing cities data is very important to improve the city economy as well as the life quality of the citizens. Since location based services and GPS devices can easily connect users located in different positions, it is worthwhile to optimize the efficiency of their shifting to a common location according to their preferences. For this reason, the support of advanced analysis queries such as the skyline operator has become important. This later finds the interesting objects according to a user preferences. However, data in such application can be uncertain, imprecise and incomplete. In this paper, we propose an imperfect spatial skyline query for users located in different positions. Detailed experimental analysis are reported. In addition, the theoretical properties developed in this paper help to devise efficient techniques to compute the spatial skyline over uncertain data fora set of users. Our extensive experiments show that the proposed algorithms provide quick initial response time.
Does the Public Still Look at Public Displays?

Public displays are widely used for displaying information in public space, such as shopping centres. They are typically programmed to display advertisements or general information about the space in which they are situated. Due to recent advances in technology, public displays are becoming ubiquitous in space around cities and can potentially enable new interactions with public space. However, despite these advances, research reports that public displays are often found to be: (1) generally irrelevant to the space in which they are situated; and (2) ignored by passers-by. Although much research has focused on tackling these issues, a gap remains regarding knowledge about how public displays in the wild are currently being used at a time when people are increasingly relying on their smartphones as a main source for accessing information and for connecting with others. The study reported in this article aims to address this gap by presenting new insights about the current practices of non-research public displays and their role in a hyperconnected society. To achieve this, we provide results from a field observation study of non-research public displays and contextualise our findings within an analysis of related work. This article makes three main contributions: (1) identifying how user engagement with public displays has changed over the past 10 years; (2) understanding how the pervasiveness of smartphones and other connected devices has modified whether users notice public displays and their interactions with public displays; and (3) outlining design recommendations and opportunities towards making public displays more relevant in a hyperconnected society.
Real Time Location Based Sentiment Analysis on Twitter: The AirSent System

The widespread use of Social Media creates great opportunities for businesses to take advantage of. By combining clever techniques, companies can develop powerful data analysis systems to understand their customers. This paper presents a real time sentiment analysis and location inference system, showcased via AirSent, an R-based application designed to assist airline carriers in measuring their passengers' satisfaction. AirSent can download, classify and locate tweets within seconds, presenting the results in interactive maps.
Enabling relational leadership in primary healthcare settings: lessons from the DIALHS collaboration

Abstract Strong management and leadership competencies have been identified as critical in enhancing health system performance. While the need for strong health system leadership has been raised, an important undertaking for health policy and systems researchers is to generate lessons about how to support leadership development (LD), particularly within the crisis-prone, resource poor contexts that are characteristic of Low- and Middle-Income health systems. As part of the broader DIALHS (District Innovation and Action Learning for Health Systems Development) collaboration, this article reflects on 5 years of action learning and engagement around leadership and LD within primary healthcare (PHC) services. Working in one sub-district in Cape Town, we co-created LD processes with managers from nine PHC facilities and with the six members of the sub-district management team. Within this article, we seek to provide insights into how leadership is currently practiced and to highlight lessons about whether and how our approach to LD enabled a strengthening of leadership within this setting. Findings suggest that the sub-district is located within a hierarchical governance context, with performance monitored through the use of multiple accountability mechanisms including standard operating procedures, facility audits and target setting processes. This context presents an important constraint to the development of a more distributed, relational leadership. While our data suggest that gains in leadership were emerging, our experience is of a system struggling to shift from a hierarchical to a more relational understanding of how to enable improvements in performance, and to implement these changes in practice.
DLOPT: Deep Learning Optimization Library

Deep learning hyper-parameter optimization is a tough task. Finding an appropriate network configuration is a key to success, however most of the times this labor is roughly done. In this work we introduce a novel library to tackle this problem, the Deep Learning Optimization Library: DLOPT. We briefly describe its architecture and present a set of use examples. This is an open source project developed under the GNU GPL v3 license and it is freely available at this https URL
Jellyfish Life Stages Shape Associated Microbial Communities, While a Core Microbiome Is Maintained Across All

The key to 650 million years of evolutionary success in jellyfish is adaptability: with alternating benthic and pelagic generations, sexual and asexual reproductive modes, multitudes of body forms and a cosmopolitan distribution, jellyfish are likely to have established a plenitude of microbial associations. Here we explored bacterial assemblages in the scyphozoan jellyfish Chrysaora plocamia (Lesson 1832). Life stages involved in propagation through cyst formation, i.e., the mother polyp, its dormant cysts (podocysts), and polyps recently excysted (excysts) from podocysts – were investigated. Associated bacterial assemblages were assessed using MiSeq Illumina paired-end tag sequencing of the V1V2 region of the 16S rRNA gene. A microbial core-community was identified as present through all investigated life stages, including bacteria with closest relatives known to be key drivers of carbon, nitrogen, phosphorus, and sulfur cycling. Moreover, the fact that half of C. plocamia’s core bacteria were also present in life stages of the jellyfish Aurelia aurita, suggests that this bacterial community might represent an intrinsic characteristic of scyphozoan jellyfish, contributing to their evolutionary success.
Towards a Knowledge Graph for Science

The document-centric workflows in science have reached (or already exceeded) the limits of adequacy. This is emphasized by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. This presents an opportunity to rethink the dominant paradigm of document-centric scholarly information communication and transform it into knowledge-based information flows by representing and expressing information through semantically rich, interlinked knowledge graphs. At the core of knowledge-based information flows is the creation and evolution of information models that establish a common understanding of information communicated between stakeholders as well as the integration of these technologies into the infrastructure and processes of search and information exchange in the research library of the future. By integrating these models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work as information and research results can be seamlessly interlinked with each other and better matched to complex information needs. Furthermore, research results become directly comparable and easier to reuse. As our main contribution, we propose the vision of a knowledge graph for science, present a possible infrastructure for such a knowledge graph as well as our early attempts towards an implementation of the infrastructure.
Personalized Recommendation of Socially Relevant Images

We present a social image recommender system that offers a hybrid filtering approach, combining content- and knowledge-based filtering with a novel social-based filtering, that selects images of social interest to the user, by e.g. being posted by close friends or family. User activity on social media is used when generating a user profile reflecting user interests and social context, and images are recommended according to a combination of social relevance and topic of interest. Our system handles both cross-source user profiling and image recommendation across social media, currently focusing on images from Facebook and Flickr.