Miracle-dz/UniFAR-FTUs huggingface dataset:
VR Application for Technology Education in a Public Library

In this video paper we report on our experiences of deploying a VR point at a public library for the purpose of educating library patrons on VR technology. The primary application offered at the VR point is called Virtual Library, a VR application developed specifically for the Oulu City Library. The Virtual Library application provides an immersive virtual interface into selected library services, as well as allows library patrons to browse literature recommendations inside genre-specific fantasy realms. Based on the observations of the first week weeks of deployment, the VR point and the Virtual Library are successful approaches for technology education in a public library.
Interactive Narrative in Virtual Reality

Interactive fiction is a literary genre that is rapidly gaining popularity. In this genre, readers are able to explicitly take actions in order to guide the course of the story. With the recent popularity of narrative focused games, we propose to design and develop an interactive narrative tool for content creators. In this extended abstract, we show how we leverage this interactive medium to present a tool for interactive storytelling in virtual reality. Using a simple markup language, content creators and researchers are now able to create interactive narratives in a virtual reality environment. We further discuss the potential future directions for a virtual reality storytelling engine.
Preliminary Investigation of Object-based Activity Recognition Using Egocentric Video Based on Web Knowledge

This study shows a preliminary investigation of daily activity recognition based on a wearable camera without using training data prepared by a user in her environment. Recently, deep learning frameworks have been publicly available, and we can now easily use deep convolutional neural networks (CNNs) pre-trained on a large image data set. In our method, we first detect objects used in the user's activity from her first-person images using a pre-trained CNN for object recognition. We then estimate an activity of the user using the object detection result because objects used in an activity strongly relate to the activity. To estimate the activity without using training data, we utilize knowledge on the Web because the Web is a repository of knowledge that reflects real-world events and common sense. Specifically, we compute semantic similarity between a list of the detected object names and a name of each activity class based on the Web knowledge. The activity class with the largest similarity value is the estimated activity of the user.
Quantitative Methods for the Evaluation of Institutional Work Performance Based on the Example of Cleaning Services Market in Russia and in Poland

Summary Subject and purpose of work: The article compares cleaning services markets in Warsaw and Moscow. The productivity of work is the subject of the research, but not in the traditional neoclassical interpretation, but within the institutional economy. The aim of the article is to show new ways of analyzing work productivity using institutional methodology. Materials and methods: The article uses a conventional method, which is a combination of institutional and neoclassical methodology. It covers sociological research tools and statistical methods of data processing used for quantitative analysis as part of the institutional approach. Results: Similar parameters of human capital indicators were obtained in the employees of both markets. The indicators of opportunism in the representatives of the employees of the Moscow market are much higher, which significantly reduces the quantitative values of the institutional work productivity in Eastern markets. Conclusions: In order to increase institutional work productivity in Eastern European markets (Russia) and to reduce opportunistic behavior of transaction participants, it is necessary to modernize institutional environment in these countries.
Internet of Things Search Engine: Concepts, Classification, and Open Issues

This article focuses on the complicated yet still relatively immature area of the Internet of Things Search Engines (IoTSE). It introduces related concepts of IoTSE and a model called meta-path to describe and classify IoTSE systems based on their functionality. Based on these concepts, we have organized the research and development efforts on IoTSE into eight groups and presented the representative works in each group. The concepts and ideas presented in this article are generated from an extensive structured study on over 200 works spanning over one decade of IoTSE research and development.
Dichromatic Gray Pixel for Camera-agnostic Color Constancy

We propose a novel statistical color constancy method, especially suitable for the Camera-agnostic Color Constancy, i.e. the scenario where nothing is known a priori about the capturing devices. The method, called Dichromatic Gray Pixel, or DGP, relies on a novel gray pixel detection algorithm derived using the Dichromatic Reflection Model. DGP is suitable for camera-agnostic color constancy since varying devices are set to make achromatic pixels look gray under standard neutral illumination. In the camera-agnostic scenario, the proposed method outperforms on standard benchmarks, both state-of-the-art learning-based and statistical methods. DGP is simple, literally dozens of lines of code, and fast, processing a 1080p image in 0.4 seconds with unoptimized MATLAB code running in a CPU Intel i7 2.5 GHz.
Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phase

We present a practical method for protecting data during the inference phase of deep learning based on bipartite topology threat modeling and an interactive adversarial deep network construction. We term this approach \emph{Privacy Partitioning}. In the proposed framework, we split the machine learning models and deploy a few layers into users' local devices, and the rest of the layers into a remote server. We propose an approach to protect user's data during the inference phase, while still achieve good classification accuracy. We conduct an experimental evaluation of this approach on benchmark datasets of three computer vision tasks. The experimental results indicate that this approach can be used to significantly attenuate the capacity for an adversary with access to the state-of-the-art deep network's intermediate states to learn privacy-sensitive inputs to the network. For example, we demonstrate that our approach can prevent attackers from inferring the private attributes such as gender from the Face image dataset without sacrificing the classification accuracy of the original machine learning task such as Face Identification.
Retinoblastoma Binding Protein 5 Correlates with the Progression in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancy tumors with insidious onset, rapid development and metastasis, and poor prognosis. Therefore, it is necessary to understand molecular mechanisms of HCC and identify clinically useful biomarkers for it. This study aimed to investigate the role of retinoblastoma binding protein 5 (RBBP5) in HCC. The expression level of RBBP5 was examined by immunohistochemistry and western blot. The effect of RBBP5 on cell cycle, proliferation, apoptosis, and drug sensitivity was analyzed. RBBP5 was significantly upregulated in HCC tissues and cells. High RBBP5 expression was significantly associated with elevated level of AFP, advanced TNM stage, high Ki-67 expression, larger tumor size, and poor prognosis. Knockdown of RBBP5 significantly inhibited proliferation of HCC cells through cell cycle arrest. In addition, inhibition of RBBP5 increased the sensitivity of HCC cells to doxorubicin. In conclusion, our findings suggest that RBBP5 plays an important role in the progression of HCC and may serve as a novel biomarker and potential therapeutic target for HCC.
Cognitive Behavioural Therapy through PowerPoint: Efficacy in an Adolescent Clinical Population with Depression and Anxiety

Background Limited help-seeking behaviours, among adolescents with mental health concerns and many barriers to accessing mental health services, make innovative approaches to administering mental health therapies crucial. Therefore, this study evaluated the efficacy of e-CBT given via PowerPoint slides to treat adolescents with anxiety and/or depression. Method 15 adolescents referred to an outpatient adolescent psychiatry clinic to treat a primary DSM-IV diagnosis of anxiety and/or depression chose between 8 weeks of e-CBT (n=7) or 7 weeks of live CBT (n=8). The e-CBT modules were presented using PowerPoint delivered weekly through email by either a senior psychiatry resident or an attending physician. Within each session, participants in both groups had personalized feedback on their mandatory weekly homework assignment from the previous week's module. BYIs were completed before treatment and and after final treatment within both groups to assess changes in depression, anxiety, anger, disruption, and self-concept. Findings Before treatment, BYI scores did not sig. differ between groups. After treatment, e-CBT participants reported sig. improved depression, anger, anxiety, and self-concept BYI scores while live CBT participants did not report any sig. changes. Only the Beck Anxiety Inventory sig. differed between groups after CBT. Conclusion Despite the low sample size within this study, using email to deliver e-CBT PowerPoint slides and individualized homework feedback shows promise as an alternate method of CBT delivery that reduces barriers to receiving mental health treatment that occur internationally.
Integrating Selection Mapping With Genetic Mapping and Functional Genomics

Genomic scans for signatures of selection allow us to, in principle, detect variants and genes that underlie recent adaptations. By combining selection mapping with genetic mapping of traits known to be relevant to adaptation, we can simultaneously investigate whether genes and variants show signals of recent selection and whether they impact traits that have likely been selected. There are three ways to integrate selection mapping with genetic mapping or functional genomics: (1) To use genetic mapping data from other populations as a form of genome annotation. (2) To perform experimental evolution or artificial selection to be able to study selected variants when they segregate, either by performing genetic mapping before selection or by crossing the selected individuals to some reference population. (3) To perform a comparative study of related populations facing different selection regimes. This short review discusses these different ways of integrating selection mapping with genetic mapping and functional genomics, with examples of how each has been done.
Direct Comparison of Herbicidal or Biological Treatment on Myriophyllum spicatum Control and Biochemistry

Myriophyllum spicatum or Eurasian watermilfoil (hereafter, milfoil) is among the most problematic invasive aquatic plant species throughout much of North America. M. spicatum infestations can result in reduced diversity and abundance of native plant populations. Control of the invader is essential to promoting healthy ecosystems. Several treatment alternatives are available for milfoil control, although cost and efficacy vary significantly, with some treatments resulting in more harm to the native population than no treatment at all. A series of field-based microcosms containing actively growing milfoil were constructed in order to directly compare the impact of two herbicides (2,4-dichlorophenoxyacetic acid and fluridone) and the milfoil weevil (Euhrychiopsis lecontei) on weed control and plant biochemistry. Herbicide concentrations in water, plants, and sediments were monitored, as were weevil population dynamics and resulting invertebrate damage to milfoil stems. The impact of the different treatments on levels of polyphenols, carbohydrates, ash, and overall carbon and nitrogen levels in the milfoil were determined. Total biomass of the untreated milfoil increased by more than 2.7-fold during the 53-day experimental period. Conversely, the biomass of milfoil subjected to chemical or biological treatment either remained constant or decreased significantly during the experiment. The herbicide 2,4-D resulted in nearly 100% milfoil mortality by day 20, whereas fluridone toxicity was significantly slower but reached 75% by the end of the trial. Similarly, milfoil growth in the weevil-amended tanks was somewhat erratic but by the end of the trial, the total plant biomass was 71% less than that of un-amended controls. Although the total biomass remaining at the end of the fluridone and weevil treatments was similar, the carbohydrate and starch content of the shoots in the insect treatment were nearly 4.6- and 4.8-fold greater, respectively, than that of the herbicide treated plants. The higher starch content in insect-treated plants could lead to increased autofragmentation and spread of M. spicatum. However, herbicide treatments are frequently required for several years. Therefore, integrated pest management, which combines the long-term benefits of biological controls with the short-term benefits of herbicides may provide the best solution to the control of M. spicatum and the conservation of native plants.
Vertex-Connectivity Measures for Node Failure Identification in Boolean Network Tomography

We study a few problems in Boolean Network Tomography, related to maximal vertex identifiability, i.e. the maximal number of failing nodes simultaneously identifiable in a network. First, we further our understanding of the relationship between identifiability and $\kappa(G)$, the vertex connectivity of the host network $G$. Second, we look at tradeoffs between the number of monitors and the maximal number of identifiable failures in various classes of random networks. We show that the maximal identifiability of so called Line-of-Sight networks is exactly equal to $\kappa(G)-1$. We then give a tight characterization of the maximal identifiability of any graph $G$ on $n$ nodes with vertex connectivity $\kappa(G) \in \{2, \ldots, n/3\}$. Using Menger's theorem, we prove that the maximal identifiability is always either $\kappa(G)-2$ or $\kappa(G)-1$. Finally we initiate the study of maximal identifiability for random networks. We focus on two models: the classical Erdős-Renyi model, and that of Random Regular graphs. The framework proposed in the paper allows a probabilistic analysis of the identifiability in random networks giving a tradeoff between the number of monitors to place and the maximal identifiability.
A Systematic Review of the Psychological Implications of Genetic Testing: A Comparative Analysis Among Cardiovascular, Neurodegenerative and Cancer Diseases

Background: Genetic testing is performed for different purposes, such as identifying carriers, predicting a disease onset in presymptomatic individuals or confirming a diagnosis. However, these tests may have notable psychological effects, such as generating anxiety and depression. These effects may depend on people's perception of risk, severity, and controllability of the disease; and the availability of treatments. To date, there are no reports that analyze these factors specifically, and their role in influencing genetic test users' experience. Methods: We performed a systematic review of the psychological implication of undergoing genetic testing for cardiovascular, neurodegenerative and cancer diseases. Articles were searched on PubMed, Google Scholar, and PsychInfo. Results: 47 studies were included, 9 concerning cardiovascular disease, 18 neurodegenerative disorders, and 20 for cancer disease. According to the reviewed studies, people experience no significant increase in distress and anxiety, or adverse impacts on quality of life, except the Huntington disease, which is characterized by depressive symptoms, suicidal ideations, and hopelessness in gene carriers. People tend to consider genetic tests as valid information to take important preventive decisions. Genetic risk for cardiovascular disease is perceived to be manageable; genetic analysis for some neurodegenerative diseases (e.g., Alzheimer) or cancer (breast cancer in particular) is considered useful because the problem could be addressed in advance with preventive behaviors. Conclusions: Genetic tests should be proposed along with proper psychological support and counseling focused on users' genetic health literacy; perception of risk, beliefs about disease controllability, in order to foster fruitful medical decisions.
Intent Detection for code-mix utterances in task oriented dialogue systems

Intent detection is an essential component of task oriented dialogue systems. Over the years, extensive research has been conducted resulting in many state of the art models directed towards resolving user's intents in dialogue. A variety of vector representation for user utterances have been explored for the same. However, these models and vectorization approaches have more so been evaluated in a single language environment. Dialogue systems generally have to deal with queries in different languages and most importantly Code-Mix form of writing. Since Code-Mix texts are not bounded by a formal structure they are difficult to handle. We thus conduct experiments across combinations of models and various vector representations for Code-Mix as well as multi-language utterances and evaluate how these models scale to a multi-language environment. Our aim is to find the best suitable combination of vector representation and models for the process of intent detection for code-mix utterances. We have evaluated the experiments on two different dataset consisting of only Code-Mix utterances and the other dataset consisting of English, Hindi, and Code-Mix(English-Hindi) utterances.
Real-time Indoor Scene Reconstruction with RGBD and Inertial Input

Camera motion estimation is a key technique for 3D scene reconstruction. Previous works usually assume slow camera motions, which limit the usage in many real cases. We propose an end-to-end 3D reconstruction system which combines color, depth and inertial measurements to achieve robust reconstruction with fast sensor motions. Our framework utilizes extended Kalman filter to fuse the three kinds of information and involve an iterative method to jointly optimize feature correspondences, camera poses and scene geometry. We also propose a novel geometry-aware patch deformation technique to adapt the feature appearance in image domain, leading to a more accurate feature matching under fast camera motions. Experiments show that our patch deformation method improves the accuracy of feature tracking, and our 3D reconstruction framework outperforms the state-of-the-art solutions under fast camera motions.
Engineered Streptomyces lividans Strains for Optimal Identification and Expression of Cryptic Biosynthetic Gene Clusters

Streptomyces lividans is a suitable host for the heterologous expression of biosynthetic gene clusters (BGCs) from actinomycetes to discover “cryptic” secondary metabolites. To improve the heterologous expression of BGCs, herein we optimized S. lividans strain SBT5 via the stepwise integration of three global regulatory genes and two codon-optimized multi-drug efflux pump genes and deletion of a negative regulatory gene, yielding four engineered strains. All optimization steps were observed to promote the heterologous production of polyketides, non-ribosomal peptides, and hybrid antibiotics. The production increments of these optimization steps were additional, so that the antibiotic yields were several times or even dozens of times higher than the parent strain SBT5 when the final optimized strain, S. lividans LJ1018, was used as the heterologous expression host. The heterologous production of these antibiotics in S. lividans LJ1018 and GX28 was also much higher than in the strains from which the BGCs were isolated. S. lividans LJ1018 and GX28 markedly promoted the heterologous production of secondary metabolites, without requiring manipulation of gene expression components such as promoters on individual gene clusters. Therefore, these strains are well-suited as heterologous expression hosts for secondary metabolic BGCs. In addition, we successfully conducted high-throughput library expression and functional screening (LEXAS) of one bacterial artificial chromosome library and two cosmid libraries of three Streptomyces genomes using S. lividans GX28 as the library-expression host. The LEXAS experiments identified clones carrying intact BGCs sufficient for the heterologous production of piericidin A1, murayaquinone, actinomycin D, and dehydrorabelomycin. Notably, due to lower antibiotic production, the piericidin A1 BGC had been overlooked in a previous LEXAS screening using S. lividans SBT5 as the expression host. These results demonstrate the feasibility and superiority of S. lividans GX28 as a host for high-throughput screening of genomic libraries to mine cryptic BGCs and bioactive compounds.
Boosting Action Observation and Motor Imagery to Promote Plasticity and Learning

Department of Experimental Medicine, Section of Human Physiology, Centro Polifunzionale di Scienze Motorie, University of Genoa, Genoa, Italy Center for Neuroprosthetics, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva and Campus SUVA, Sion, Switzerland Laboratory of Cognitive Neuroscience, Brain Mind Institute, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva, Switzerland INSERM, U1093, Cognition Action Plasticité Sensorimotrice, Université de Bourgogne, Dijon, France Fondazione Istituto Italiano di Tecnologia, Centro di Neurofisiologia Traslazionale c/o Sezione Fisiologia Umana, Ferrara, Italy Neural Control of Movement Laboratory, ETH Zurich, Zurich, Switzerland
Choriocarcinoma Syndrome as an Initial Presentation of Testicular Cancer

Choriocarcinoma syndrome (CS) is a rare clinical entity within the spectrum of nonseminomatous germ-cell tumors (NSGCT). It is characterized by the abrupt establishment of rapidly progressive and hemorrhagic tumors associated with very high levels of the beta fraction of human chorionic gonadotropin (β-hCG) and with a very poor prognosis, particularly in patients with β-hCG values above 50,000 IU/L. We present the case of a 17-year-old man with a sudden onset nonmassive hemoptysis. Physical examination revealed a right testicular mass. Imaging studies showed metastatic lung, bone, and retroperitoneal disease. β-hCG serum levels were 222,493.21 IU/L, AFP 1.56 ng/mL, and DHL 457 IU/L. Histopathological study after right radical orchiectomy showed a mixed germ-cell tumor. Based on poor-risk characteristics, chemotherapy was started with an adequate clinical response. Physicians should be aware of the potential complications of CS in the treatment of testicular cancer with high β-hCG levels since they could be associated with a rapidly progressive and high-volume disease. Patients in this category should be referred to the centers experienced in the treatment of advanced germ-cell tumors. Due to the severity of the presentation, hemodynamic monitoring, ideally in an intensive care unit, is essential as well as timely administration of cytotoxic treatment.
Embedding-reparameterization procedure for manifold-valued latent variables in generative models

Conventional prior for Variational Auto-Encoder (VAE) is a Gaussian distribution. Recent works demonstrated that choice of prior distribution affects learning capacity of VAE models. We propose a general technique (embedding-reparameterization procedure, or ER) for introducing arbitrary manifold-valued variables in VAE model. We compare our technique with a conventional VAE on a toy benchmark problem. This is work in progress.