Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversity Checker: Toward Recommendations for Improving Journalism with Respect to Diversity

The Diversity Checker is a tool that aims to make it easier for journalists to author their texts with diversity in mind. To provide helpful hints for them in this respect, it is necessary to define how to quantify diversity so that this can be programmed into the tool. At this early stage in the development of the tool, we present a two-fold contribution. First, we offer an analysis on what we mean by "improving diversity". Second, we present the first version of the Diversity Checker, along with some analysis of its current performance.
MULTIPATH BROADCAST AND GOSSIP BASED APPROACH FOR VIDEO CIRCULATION

LIVE media streaming applications have become more and more popular. IP multicast is the most efficient mechanism but Due to the practical issues of routers, IP multicast has not been widely deployed in the wide-area network infrastructure. The application-level solution build a peer-to-peer (P2P) overlay network out of unicast tunnels across cooperative participating users. P2P media streaming has become a promising approach to broadcast non interactive live media from one source to a large number of receivers. Design of a live P2P streaming system faces many challenges. Therefore, no single application-level multicast stream can meet the requirements of everyone. The proposed architecture aims to provide higher streaming quality and to provide robustness. In the Proposed System the Parallel efforts have been exerted in the media streaming field and networking field to avoid the problem of distributing LIVE video. The tree-based approaches are vulnerable for dynamic group variation but the gossip based meshlike topology for overlay network systems allow peers to form multiple neighbors, so multilayered video contents are distributed among mesh-like network. Due to this multisource transmission scheme, packets can be exchanged among clients efficiently. In Proposed, The system can achieve improved performance on video delivery quality, bandwidth utilization, and service reliability when using the peer-assisted multipath transmission.
Degree Thesis in Microdata Analysis Level : Master of Science ( MSc ) in Business Intelligence Detecting Fake Reviews with Machine Learning

Many individuals and businesses make decisions based on freely and easily accessible online reviews. This provides incentives for the dissemination of fake reviews, which aim to deceive the reader into having undeserved positive or negative opinions about an establishment or service. With that in mind, this work proposes machine learning applications to detect fake online reviews from hotel, restaurant and doctor domains. In order to filter these deceptive reviews, Neural Networks and Support Vector Machines are used. Both algorithms’ parameters are optimized during training. Parameters that result in the highest accuracy for each data and feature set combination are selected for testing. As input features for both machine learning applications, unigrams, bigrams and the combination of both are used. The advantage of the proposed approach is that the models are simple yet yield results comparable with those found in the literature using more complex models. The highest accuracy achieved was with Support Vector Machine using the Laplacian kernel which obtained an accuracy of 82.92% for hotel, 80.83% for restaurant and 73.33% for doctor reviews.
Recognition of Poultry Disease in Real Time using Extreme Learning Machine

Disease recognition is very important for successful poultry management. This paper aims to focus on real time avian pox disease recognition in poultry. In this paper, two main soft computing approaches were employed such as Support Vector Machine with Gaussian Radial Basis Function (SVM (GRBF)) and Extreme learning Machine (ELM) to detect the disease. Texture features are extracted using Gray Level Co-Occurrence Matrix (GLCM) and mean, standard deviation, kurtosis and skewness are the statistical features. The confusion matrix and Root Mean Square Error (RMSE) were used to evaluate the performance of the SVM (GRBF) and ELM. The results showed that ELM classifier provides better accuracy than SVM (GRBF). KeywordsAvian pox disease, SVM (GRBF), ELM, GLCM, RMSE
When Augmented Reality Met Art: Lessons Learned from Researcher-Artist Interdisciplinary Work

Over the last few years, Augmented Reality (AR) technology has quickly evolved and today there is commercial software and hardware that allow the creation of AR experiences. However, it might still be cumbersome to create rich AR experiences without a deep knowledge of the technology. Artists have collaborated with IT experts during the last few years in order to enhance artistic pieces embedding AR technology, leading to the emergence of interesting collaborations between artists and engineers, computer scientists, architects, etc. The resulting works can be referred as AR Art. However, the interdisciplinary work behind these collaborations is usually not addressed. The aim of this paper is to review some of the reported AR Art work since the AR term was first coined in 1990, focusing on collaborations between different disciplines, especially during the early years, where art and technology became one. Additionally, the author reports her own experience in contributing to AR Art from an interdisciplinary perspective.
Implementing Virtual Machines for Dynamic Resource Allocation in Cloud Computing Environment

To scale up and down the resource usage of stake holders such as customers, the cloud computing environment is used. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands, the green computing is supported by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Bluetooth to Bluetooth RSSI estimation Using Smart Phones

Mobile computing is one of the most advanced computing models used in scientific research applications. Although the foundation of the proximity estimation model was laid by past generations only the recently advances opened on expanding proximity estimation application range and its research implementation. Existing approaches used such as GPS and WIFI triangulation are more complicated to find the accurate extraction of proximate location and it insufficient to meet the requirements of flexibility and accuracy. In nowadays the Bluetooth which is commonly available on most modern Smartphone’s. And it finds the exact location of any Bluetooth users only in a certain limit. By pairing the key in their mobile of one Bluetooth users to any another Bluetooth users. This paper proposes a proximity estimation model to identify the distance based on the RSSI values of Bluetooth and light Sensor data in different environments. And also state Bluetooth proximity estimation model on Android with respect to accuracy and power consumption with a several real world scenarios.
Analyzing P300 Distractors for Target Reconstruction

P300-based brain-computer interfaces (BCIs) are often trained per-user and per-application space. Training such models requires ground truth knowledge of target and nontarget stimulus categories during model training, which imparts bias into the model. Additionally, not all non-targets are created equal; some may contain visual features that resemble targets or may otherwise be visually salient. Current research has indicated that non-target distractors may elicit attenuated P300 responses based on the perceptual similarity of these distractors to the target category. To minimize this bias, and enable a more nuanced analysis, we use a generalized BCI approach that is fit to neither user nor task. We do not seek to improve the overall accuracy of the BCI with our generalized approach; we instead demonstrate the utility of our approach for identifying targetrelated image features. When combined with other intelligent agents, such as computer vision systems, the performance of the generalized model equals that of the user-specific models, without any user specific data.
Using Fault Injection to weaken RSA public key verification

Embedded devices can be susceptible to Voltage Fault Injection (V-FI) attacks, which can cause data corruption. Many embedded devices also rely on RSA for their security. For example, using a signature to verify the image they boot from. When fault injection is used to modify the public modulus of an RSA key, the resulting modulus can often be more easily factored. This factorization can be used to compute a private key corresponding to the modified modulus. We study the effect of V-FI on RSA by introducing voltage glitches to an embedded device, showing which types of faults can be expected to occur. A fault suitable for attacking RSA public moduli is then selected. We simulate the selected fault in randomly-generated RSA public keys and attempt to factor them. For every key that was tested, at least one fault yielded a successful factorization within only 60 seconds. Repeating the same fault on the target is possible within minutes, allowing use of the private key obtained from the factorization to sign messages which will verify on the target.
Three Phase Grid Interfaced with Renewable Energy Source using Active Power Filter

Renewable energy sources are a reliable alternative to the conventional energy sources. This paper presented an capable of expandable grid-connected distribution generation interconnection of Three phase Three wire system for reactive power compensation. The proposed system not only, can control the active power flow but also mitigate the load unbalance and harmonics, and manage reactive power. All of these functions may be accomplished either individually or simultaneously. So the performing an active power balance by controlling the RES voltage level for the whole system. With the proposed approach, to shape the grid currents into sinusoidal currents in phase with the grid voltage waveforms and with an amplitude depending on the power available from renewable sources. On the other hand the load harmonics current will reduced by injecting into the alternating current system harmonic currents with an opposite phase. Index Terms- Renewable Energy Sources (RES), Active Power Filter (APF), Power Quality (PQ), Grid Interconnection
Ownership Structure, Reputation Crises and Recovery: Theory and Experiment

We model the repair of damaged corporate reputations through organizational structure reform. In a rational-choice framework our model explains the effects of the emergence and growth of the professional reputation-crisis management industry. The model produces two key conclusions: (a) Although, ex post, reputation repair can increase firm value, ex ante, the option to repair reputation dilutes the incentive to maintain reputation. (b) Separating ownership and control by delegating management to professionals can ameliorate this dilution. An experimental implementation of the model supports these conclusions and shows that they are robust to behavioral deviations from rational-choice behavior.
Improving Real Time Tuning on YARN by Vı́ctor

Big data is becoming a significant part of the operations of modern organizations. Performing analysis of large amounts of data requires computer clusters to run the calculations and analysis. YARN is an internal framework that is responsible for coordinating big data jobs for some popular distributed storage and processing frameworks like Hadoop and Spark. Running YARN with the correct configuration parameters is critical for the good performance of a cluster. KERMIT is an online tuner of YARN configuration parameters that aims to improve cluster performance. The first KERMIT implementation proved the feasibility of the concept. In this study we modified the tuning algorithms inside the KERMIT components; by doing so, we achieved a reduction in the execution time as compared to industry benchmarks for a shallow tuning technique. We also verified that KERMIT can tune Spark; this suggests that Kermit could be used in other YARN-based frameworks.
Why Young Patients with Cancer Require Different Coping Tools

Calderon C1*, Jimenez-Fonseca P2, Carmona-Bayonas A3 and Jara C4 1Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Spain 2Medical Oncology Department, Hospital Universitario Central de Asturias, Oviedo, Spain 3Medical Oncology Department, Hospital Universitario Morales Meseguer, Murcia, Spain 4Medical Oncology Department, Hospital Universitario Fundación Alcorcón, Madrid, Spain *Corresponding author: Calderon C, Professor of Psychology, Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Spain, Tel: +34933125107; Fax +34934021362; E-mail: ccalderon@ub.edu
Embedded Software Component Technologies for Real time Systems - An Industrial Perspective

In this paper , we compare existing software component technologies for embedded systems with respect to requirements captured from the vehicle industry. The vehicular industry wants to make use of the advantages with component based design ; however they also need to address non-functional properties of their products, such as reliability and timeliness. Several component technologies addressing such properties have recently been proposed. In this paper, we present initial findings from an ongoing evaluation concerning some of these technologies with respect to the requirements stated by industrial actors. We conclude that none of the studied technologies is a perfect match for the industrial requirements. Furthermore, no single technology stands out as being a significantly better choice than the others; each technology has its own pros and cons
Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression

To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks. This new construction transforms multinomial regression into regression analysis of stick-specific binary random variables that are mutually independent given their covariate-dependent stick success probabilities, which are parameterized by the regression coefficients of their corresponding categories. The paSB construction allows transforming an arbitrary cross-entropy-loss binary classifier into a Bayesian multinomial one. Specifically, we parameterize the negative logarithms of the stick failure probabilities with a family of covariate-dependent softplus functions to construct nonparametric Bayesian multinomial softplus regression, and transform Bayesian support vector machine (SVM) into Bayesian multinomial SVM. These Bayesian multinomial regression models are not only capable of providing probability estimates, quantifying uncertainty, increasing robustness, and producing nonlinear classification decision boundaries, but also amenable to posterior simulation. Example results demonstrate their attractive properties and performance.
Eosinophils suppress Th1 responses and restrict bacterially induced gastrointestinal inflammation

Eosinophils are predominantly known for their contribution to allergy. Here, we have examined the function and regulation of gastrointestinal eosinophils in the steady-state and during infection with Helicobacter pylori or Citrobacter rodentium. We find that eosinophils are recruited to sites of infection, directly encounter live bacteria, and activate a signature transcriptional program; this applies also to human gastrointestinal eosinophils in humanized mice. The genetic or anti–IL-5–mediated depletion of eosinophils results in improved control of the infection, increased inflammation, and more pronounced Th1 responses. Eosinophils control Th1 responses via the IFN-&ggr;–dependent up-regulation of PD-L1. Furthermore, we find that the conditional loss of IFN-&ggr;R in eosinophils phenocopies the effects of eosinophil depletion. Eosinophils further possess bactericidal properties that require their degranulation and the deployment of extracellular traps. Our results highlight two novel functions of this elusive cell type and link it to gastrointestinal homeostasis and anti-bacterial defense.
Episodic memory and executive functions in cognitively healthy individuals display distinct neuroanatomical correlates which are differentially modulated by aging

The neuroanatomical bases of episodic memory (EM) and executive functions (EFs) have been widely addressed in patients with brain damage and in individuals with neurologic disorders. These studies reported that larger brain structures support better outcomes in both cognitive domains, thereby supporting the “bigger is better” account. However, relatively few studies have explored the cerebral morphological properties underlying EM and EFs in cognitively healthy individuals and current findings indicate no unitary theoretical explanation for the structure–function relationship. Moreover, existing studies have typically restricted the analyses to a priori defined regions of interest. Here we conducted unbiased voxel‐wise analysis of the associations between regional gray as well as white matter volumes (GMv; WMv) and performance in both cognitive domains in a sample of 463 cognitively intact individuals. We found that efficiency in EM was predicted by lower GMv in brain areas belonging to the default‐mode network (DMN). By contrast, EFs performance was predicted by larger GMv in a distributed set of regions, which overlapped with the executive control network (ECN). Volume of white matter bundles supporting both cross‐cortical and interhemispheric connections was positively related to processing speed. Furthermore, aging modulated the relationship between regional volumes and cognitive performance in several areas including the hippocampus and frontal cortex. Our data extend the critical role of the DMN and ECN by showing that variability in their morphological properties, and not only their activation patterns, affects EM and EFs, respectively. Moreover, our finding that aging reverts these associations supports previously advanced theories of cognitive neurodevelopment.
Tangible User Interface for Social Interactions for the Elderly: A Review of Literature

The global population is ageing rapidly. The ageing population faces not only the risk of health-related problems but also the challenge of social isolation and loneliness. While mainstream technology is designed to improve daily life, elderly people’s unique needs are often neglected. These technology designs can be difficult for older adults to learn and use. Tangible user interface (TUI) gives physical form to digital information, with the aim of bridging the gap between the digital world and the physical world. Thus, it can be a more natural and intuitive interface for the older adults. The objective of this research is to review the existing research on TUI for enhancing the social interactions of elderly people. Results show that very little research has been published, given that the TUI concept was introduced 20 years ago. Our systematic literature review also resulted in several recommendations for future research, which includes getting elderly people involved in the process, from designing to evaluating the prototype and investigating the effect of TUI on older adults’ social interactions and health.
Condensin Depletion Causes Genome Decompaction Without Altering the Level of Global Gene Expression in Saccharomyces cerevisiae

Gene expression occurs in the context of chromatin organization, but the extent to which higher-order chromatin compaction affects gene expression remains unknown. Here, Paul et al. show that gene expression and genome compaction can be. Condensins are broadly conserved chromosome organizers that function in chromatin compaction and transcriptional regulation, but to what extent these two functions are linked has remained unclear. Here, we analyzed the effect of condensin inactivation on genome compaction and global gene expression in the yeast Saccharomyces cerevisiae by performing spike-in-controlled genome-wide chromosome conformation capture (3C-seq) and mRNA-sequencing analysis. 3C-seq analysis shows that acute condensin inactivation leads to a global decrease in close-range intrachromosomal interactions as well as more specific losses of interchromosomal tRNA gene clustering. In addition, a condensin-rich interaction domain between the ribosomal DNA and the centromere on chromosome XII is lost upon condensin inactivation. Unexpectedly, these large-scale changes in chromosome architecture are not associated with global changes in mRNA levels. Our data suggest that the global transcriptional program of proliferating S. cerevisiae is resistant to condensin inactivation and the associated profound changes in genome organization.
Meta-analysis of randomized controlled trials only exploring the role of single incision laparoscopic surgery versus conventional multiport laparoscopic surgery for colorectal resections.

Background The objective of this article is to evaluate the surgical outcomes in patients undergoing single incision laparoscopic surgery (SILS) versus conventional multi-incision laparoscopic surgery (MILS) for colorectal resections. Methods The data retrieved from the published randomized controlled trials (RCTs) reporting the surgical outcomes in patients undergoing SILS versus MILS for colorectal resections was analysed using the principles of meta-analysis. The combined outcome of dichotomous data was represented as risk ratio (RR) and continuous data was shown as standardized mean difference (SMD). Results Five RCTs on 525 patients reported the colorectal resections by SILS versus MILS technique. In the random effects model analysis using the statistical software Review Manager 5.3, the operation time (SMD, 0.20; 95% CI, -0.11 to 0.52; z=1.28; P=0.20), length of in-patient stay (SMD, -0.18; 95% CI, -0.51 to 0.14; z=1.10; P=0.27) and lymph node harvesting (SMD, 0.09; 95% CI, -0.14 to 0.33; z=0.76; P=0.45) were comparable between both techniques. Furthermore, post-operative complications (RR, 1.00; 95% CI, 0.65-1.54; z=0.02; P=0.99), post-operative mortality, surgical site infection rate (RR, 3.00; 95% CI, 0.13-70.92; z=0.68; P=0.50), anastomotic leak rate (RR, 0.43; 95% CI, 0.11-1.63; z=1.24; P=0.21), conversion rate (P=0.13) and re-operation rate (P=0.43) were also statistically similar following SILS and MILS. Conclusions SILS failed to demonstrate any superiority over MILS for colorectal resections in all post-operative surgical outcomes.