Miracle-dz/UniFAR-FTUs huggingface dataset:
Lift Pressure Colour MidLocation End Location Real or Fake Policy

We investigate using reinforcement learning agents as generative models of images (Ganin et al., 2018). A generative agent controls a simulated painting environment, and is trained with rewards provided by a discriminator network simultaneously trained to assess the realism of the agent’s samples, either unconditional or reconstructions. Compared to prior work, we make a number of improvements to the architectures of the agents and discriminators that lead to intriguing and at times surprising results. We find that when sufficiently constrained, generative agents can learn to produce images with a degree of visual abstraction, despite having only ever seen real photographs (no human brush strokes). And given enough time with the painting environment, they can produce images with considerable realism. These results show that, under the right circumstances, some aspects of human drawing can emerge from simulated embodiment, without the need for external supervision, imitation or social cues. Finally, we note the framework’s potential for use in creative applications. Pa rs in g Reconstruction Unsupervised Abstraction Emergent Styles Increased Realism Figure 1: Despite not seeing examples of human drawings, generative agents can produce images with a degree of visual abstraction, in a diverse array of styles, and they can scale to approach realistic results. Agents can also learn to reconstruct, for instance parsing Omniglot characters into strokes. See https://learning-to-paint.github. io for an interactive version of this paper, with videos. ar X iv :1 91 0. 01 00 7v 1 [ cs . C V ] 2 O ct 2 01 9
Sustainable Solutions for Energy and Environment , EENVIRO-YRC 2015 , 18-20 November 2015 , Bucharest , Romania Recovering hydrogen sulfide from sulfurous waters with PEM fuel cells

Hydrogen sulfide is an environmental pollutant and abundant in various sulfurous waters such as Black Sea. Hydrogen sulfide is in the same time a potential energetic materials as source for hydrogen production. In this respect, designing an appropriate fuel cell with proton exchange membranes (PEMFC) for hydrogen sulfide recovering from sulfurous water is challenge. This study reports a PEMFC with Pt/Pt-Ru catalysts and membrane with perflurosulfonic ionomer. PEMFC was run at different concentrations of hydrogen sulfide in water (50-150 mg/L). Even though power densities are in the range microwatts the experimental set-up shows a promising perspective for sulfurous water treatment. A short overview on the actual state of the art in hydrogen sulfide recovering and potential applications of the fuel cells is presented in introduction. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee EENVIRO 2015.
ON THE IMPACT OF ALCOHOL DEPENDENCY : IMPLICATIONS FOR FUTURE RESEARCH AND PRACTICE

Alcohol abuse has significant individual, familial and social costs. Long term and/or chronic alcohol use has been associated with liver cirrhosis, liver disease, lip, oral cavity and pharynx cancers and heart disease (WHO, 2004). Additionally, intoxication increases risk for road traffic accidents, poisoning and intentional and unintentional injury. Globally alcohol causes 3.2% of all deaths or 1.8 million deaths annually and accounts for 4.0% of disease burden (WHO 2007). This review examines what is known about the impact of alcohol on the health and lives of individuals and families in India. The present study was conducted to examine the prevalence and impacts of alcohol dependency. The study reviewed different studies that are conducted in re relation to alcohol prevalence and impacts. For this purpose, the researcher employed a qualitative approach mainly conventional content analysis. The researcher also used for basic themes that are thought to take the lion share in analyzing the review.Based on the present review, the conclusion made was alcohol greatly impacts in individuals specifically in affecting their health in general and injuries and violence in particular. Based on the conclusion, the recommendation forwarded were raising awareness through education regarding the problem and capacity building for action. Preventive efforts not only create awareness but can also help to de-stigmatize the problem and individuals affected by alcohol misuse,
2 4 M ar 2 01 7 Accretion of rotating fluids onto stationary solutions

We consider a general stationary solution and derive the general laws for accretion of rotating perfect fluids. For non-degenerate and degenerate Fermi and Bose fluids we derive new effects that mimic the center-of-mass-energy effect of two colliding particle in the vicinity of horizons. Nondegenerate fluids see their chemical potential grow arbitrarily and ultra-relativistic Fermi fluids see their specific enthalpy and Fermi momentum grow arbitrarily too while the latter vanishes gradually for non-relativistic Fermi fluids. For degenerate Bose fluids two scenarios remain possible as the fluid approaches a horizon: a) The Bose-Einstein condensation ceases or b) the temperature drops gradually down to zero. The critical flow is also investigated.
Analysis Identifying Antecedents of the Strategic Game Frame : A Longitudinal

Although election news framing is a burgeoning area of research, empirical studies of what factors influence frame building remain rare, especially in non-Western countries. This study investigates the use of the strategic game frame and the relationship between that frame and system-level and organizational-level factors. The analysis focuses on the coverage of campaign news in six elite Bulgarian newspapers between 1990 and 2009. Results show that the type of electoral system, number of parties in government, and newspaper specialization are significant predictors of game frame use. The results are discussed in relation to framing research in Western Europe and the United States.
Encoding N-party ManIn-Middle Attack for Diffie – Hellman Algorithm in a Client-Server Paradigm

Diffie–Hellman key exchange (D–H) is a specific method of exchanging cryptographic keys. It is one of the earliest practical methods of key exchange implemented within the field of cryptography. In the original description, the Diffie–Hellman exchange by itself does not provide authentication of the communicating parties and is thus vulnerable to a man-in-the-middle attack. There may be many attackers between sender and receiver. This paper mainly focuses on generation of N-Party Man-in-Middle Attack in Diffie–Hellman Key Exchange Protocol. We have analyzed how this method can work in Diffie-Hellman Key-Exchange Protocol for sharing secret key between users when more than one attacker are present between sender and receiver. Keywords— Diffie-Hellman Algorithm, encryption, decryption, asymmetric, cryptography.
Cloud Agnostic Virtual Machine Image Service

Facing the Cloud Computing paradigm and the role of virtualization, the management of virtual machine images is becoming increasingly important. Images are delivered by Infrastructure-as-a-Service frameworks to hypervisors that create virtual machines based on them. Most infrastructure frameworks save images in a storage system that serves as image repository. However, problems arise when managing environments with different cloud infrastructures, as their image repositories are tied to their own requirements. This article presents and evaluates an image server comprised in VISOR, a cloud agnostic virtual machine images management service.
Color QR Code with Pseudo Quantum Steganography and M-band Wavelet and Patch Group Prior based Denoising

As the barcode becomes more widely used, its applications and data capacity demands grow, increasing the need for barcodes with greater data density. Utilizing the quick response (QR) code–one of the many types of barcodes–we developed two algorithms. The first algorithm creates a color QR code that stores more information than a standard QR code and embeds extra data with limited access privilege. The second algorithm denoises a noisy color QR code. These algorithms consist of three techniques: (1) enlarging the data capacity of a compact QR code image by stacking multiple classical QR codes to form a color barcode, (2) embedding information into the color QR code using pseudo quantum signals in an M-band wavelet domain and selecting the discrete 4-band wavelet transforms to compress the QR images, and (3) applying Discrete M-band Wavelet Transform (DMWT) and Patch Group Prior based Denoising (PGPD) methods to denoise noisy QR code images. The peak-signal-to-noise-ratio (PSNR) summary indicates that information in a color QR code can be efficiently stored and retrieved with these methods. Moreover, it shows that our denoising algorithm effectively removes heavy noise from the noisy color QR code. Our algorithms are implemented in a flexible framework, which allows for further modifications to improve both the data capacity of a color QR code and the effectiveness of signal extraction from noisy data to meet future demands.
Neuromorphic computation of optic flow data Bio-inspired landing using biomorphic vision sensors Final Report

In this project, we demonstrated the feasibility of purely vision based landing strategies using analog VLSI sensors, in simulation. We simulated the operation of a neuromorphic vision sensor in realistic planetary landing scenarios, at four different levels. At the lowest level we carried out SPICE simulations to characterize the response properties of the sensor’s individual blocks; at the intermediate level, we used transistor equations to derive analytically the individual circuit block behavior and carry out full-chip simulations, by providing realistic inputs from planetary landing scenarios and at third level we implemented a simulink model of the sensor which reproduce its working principles. Our results are useful for evaluating the overall performance of the chip in this specific task. In parallel we explored different control strategies for landing and analyzed their performances at different levels of realism for the optic flow extraction, starting from a purely geometrically calculated one to a realistic planetary images based one. The simulations achieved their aim of enabling us to verify bio-inspired control strategies for planetary landing using different configurations of sensors.
Picking the Best Intercoder Reliability Statistic for Your Digital Activism Content Analysis

[UPDATED] Stories of digital activism, created by both citizen and professional journalists, are shared freely on the web. Content created by the activists themselves – webpages, video, Facebook groups – also often remain online and public long after their campaigns end. Because of the wide available of freely accessible content, content analysis has become a valuable method for studying global digital activism.
Compositional Hard Negatives for Visual Semantic Embeddings via an Adversary

Learning high-quality representations for multi-modal data with a shared underlying meaning is a key building block for cross-modal information retrieval. Further, hard negative mining has been shown effective in forcing models to learn discriminaitve features for accurate retrieval. In this paper, we present a new technique for hard negative mining for learning visual-semantic embeddings, with an adversary that is learned in a min-max game with the cross-modal embedding model. The adversary exploits compositionality of images and texts and is able to compose harder negatives through novel combination of objects and regions across different images for a given caption. We find that our approach leads to higher scores across-the-board for all R@K based metrics over the previous state of the art.
p 53 inactivation decreases dependence on estrogen / ERK signalling for proliferation but promotes EMT and susceptility to 3-bromopyruvate in ER a + breast cancer MCF-7 cells

Background: Most breast cancers express the estrogen receptor alpha (ERa), harbor wt TP53, depend on estrogen/ERK signalling for proliferation, and respond to anti-estrogens. However, concomittant activation of the epidermal growth factor receptor (EGFR)/MEK pathway promotes resistance by decreasing estrogen dependence. Previously, we showed that retroviral transduction of mutant p53 R175H into wt TP53 ERa MCF-7 cells induces epidermal growth factor (EGF)-independent proliferation, activation of the EGF receptor (p-EGFR) and some characteristics of epithelial-mesenchymal transition (EMT). Purpose: To investigate whether p53 inactivation augments ERa cell proliferation in response to restrictive estradiol, chemical MEK inhibition or metabolic inhibitors. Results: Introduction of mutant p53 R175H lowered expression of p53-dependent PUMA and p21WAF1, decreased E-cadherin and cytokeratin 18 associated with EMT, but increased the % of proliferating ERa/Ki67 cells, diminishing estrogen dependence. These cells also exhibited higher proliferation in the presence of MEK-inhibitor UO126, reciprocally correlating with preferential susceptibility to the pyruvate analog 3-bromopyruvate (3-BrPA) without a comparable response to 2-deoxyglucose. p53 siRNA silencing by electroporation in wt TP53 MCF-7 cells also decreased estrogen dependence and response to MEK inhibition, while also conferring susceptibility to 3-BrPA. Conclusions: (a) ERa breast cancer cells dysfunctional for TP53 which proliferate irrespective of low estrogen and chemical MEK inhibition are likely to increase metabolic consumption becoming increasingly susceptible to 3-BrPA; (b) targeting the pyruvate pathway may improve response to endocrine therapy in ERa breast cancer with p53 dysfunction. 2014 The Authors. Published by Elsevier Inc.
Real-Time Twitter Sentiment toward Thanksgiving and Christmas Holidays

Thanksgiving and Christmas are two major holidays in the United States. Many people use social media to stay connected with their families and friends, including sharing their holiday experiences. This study utilized a stream of millions of tweets on Twitter to explore how people feel about these two holidays through real-time sentiment analysis. With help of Twitter Streaming API, we discovered the patterns of sentiment changes by hour before and after the two holidays in 2011, thus providing a unique peek into the celebration of these holidays that could not be accomplished with traditional methods. Our analysis suggested that in 2011 people had higher sentiment toward Christmas than Thanksgiving on average. The sentiment reached its maximum on the Thanksgiving Day and on The Christmas Eve and Christmas Day, highlighting stronger zeal for Christmas than Thanksgiving, while remained a stable and lower sentiment before and after the holidays. Typically there was a peak of sentiment toward Thanksgiving and Christmas in the morning of each day around 9:00 am (EST). On the Thanksgiving Day the number of tweets on shopping increased rapidly and monotonically to its maximum as time approaching the midnight when people thinking of shopping on the Black Friday, but unexpectedly the sentiment toward shopping dropped quickly and monotonically, displaying the exact opposite trend. We also investigated the shopping distraction on the theme of these two holidays. It was found that there were more people talking about thankfulness than shopping during the Thanksgiving season, but more people talking about shopping than Jesus during the Christmas season.
Audio-Visual Fusion for Sentiment Classiﬁcation using Cross-Modal Autoencoder

Humans often correlate information from multiple modalities, particularly audio and visual modalities, while learning as well as interacting with the external environment. In this learning mechanism, they also acquire the capability to interpret the information about missing modalities from the available modalities. Imparting these capabilities to machines might help build better Human-Machine interfaces and interactions. In this paper, we consider the task of multi-modal sentiment classiﬁcation, using the audio and visual modalities, under the scenario where both the modalities are available during training but only one modality is available during test. We propose a novel model combining deep canonical correlation analysis (DCCA) with cross-modal autoencoders. These autoencoders try to reconstruct the representations corresponding to the missing modality, using the DCCA transformed representations of the available input modalities. Experiments on the CMU-MOSI and CMU-MOSEI datasets for sentiment classiﬁcation on Youtube videos show the effectiveness of our proposed model.
O ct 2 01 9 Ward 2 ICU : A Vital Signs Dataset of Inpatients from the General Ward

We present a proxy dataset of vital signs with class labels indicating patient transitions from the ward to intensive care units called Ward2ICU. Patient privacy is protected using a Wasserstein Generative Adversarial Network to implicitly learn an approximation of the data distribution, allowing us to sample synthetic data. The quality of data generation is assessed directly on the binary classification task by comparing specificity and sensitivity of an LSTM classifier on proxy and original datasets. We initialize a discussion of unintentionally disclosing commercial sensitive information and propose a solution for a special case through class label balancing.
Chapter 40 DATA MINING FOR IMBALANCED DATASETS : AN OVERVIEW

A dataset is imbalanced if the classification categories are not approximately equally represented. Recent years brought increased interest in applying machine learning techniques to difficult "real-world" problems, many of which are characterized by imbalanced data. Additionally the distribution of the testing data may differ from that of the training data, and the true misclassification costs may be unknown at learning time. Predictive accuracy, a popular choice for evaluating performance of a classifier, might not be appropriate when the data is imbalanced andlor the costs of different errors vary markedly. In this Chapter, we discuss some of the sampling techniques used for balancing the datasets, and the performance measures more appropriate for mining imbalanced datasets.
Sliding Mode Impedance Control of a Hydraulic Artificial Muscle

Hydraulic artificial muscles offer unrivaled specific power and power density and are instrumental to the improved performance and success of soft robotics and lightweight mobile applications. This paper addresses the lack of model-based impedance control approaches for soft actuators such as hydraulic artificial muscles. Impedance control of actuators and robotic systems has been proven to be an effective approach for interacting with physical objects in the presence of uncertainty. A sliding mode impedance control approach based on Filippov’s principle of equivalent dynamics is introduced and applied to a hydraulic artificial muscle. A nonlinear lumped parameter model of the system is presented and a sliding mode impedance controller is derived. Experimental results show superior performance using model-based sliding mode impedance control versus a linear impedance control law in both tracking of position and stiffness when disturbances are introduced.
Probabilistic-Entropic Concept of Sustainable Development of the Example of Territories

Nowadays the problem of sustainable development became one of the topical issues. However, many authors point to conceptual complexity, which is that the concept “ sustainable development ” includes two terms “ development ” and “ sustainability. ” At the same time, each of these terms is treated not unambiguously. It leads to the emergence of different interpretations of sustainable development in relation to specific systems. A new concept of sustainable development of systems is proposed. According to this concept, the sustainable development of a complex multidimensional system will be understood as the dynamics consisting in the presence of a trend of balanced change in the entropies of randomness and self-organization while maintaining an acceptable risk level in multidimensional systems. The proposed concept is approved on practical examples. Dynamics of vector entropy and multidimensional risk of Yekaterinburg and Sverdlovsk regions in 1992 – 2017 is given.
Rearing of Codling Moth , Cydia pomonella ( L . ) ( Lepidoptera : Tortricidae ) on Its Natural Host “ Green Immature Apple ” Malus domestica ( Borkh ) ( Rosales : Rosaceae )

The codling moth, Cydia pomonella Linnaeus, 1758 (Lepidoptera: Tortricidae) is an economically important pest of apple trees in Turkey and also in worldwide. The aim of this study was to report a continuous rearing method on green immature apples (Malus domestica cv. 'Gala') for C. pomonella and fundamental information of its biology. The experiments were conducted from 2017 to 2019 under the laboratory conditions. Eggs were laid individually or disorderly in small masses on the oviposition substrate in the adult cages. Adult food had a positive effect on the number of eggs laid per female and egg viability. Larvae developed through five instars based on head capsule measurements. Larval development was completed in 15.89±0.25 days. Survival rate of larvae ranged from 24.1% and 92.6% based on larval density per apple. The duration of pupal development was 8.90±0.10 days with 87.5% adult emergence. The life cycle was completed on average in 29.5±0.3 days. Adult longevity was 12.2±0.60 days for females and 16.7±1.90 days for males. A single female laid about 12.3 eggs/day and a total of 85-200 eggs with 83.9% viability. The codling moth has been reared continuously in the laboratory for two years with no evidence of disease in the colony.
Cooperative Medium Access Protocols in Wireless Networks A Survey

Cooperative communication is a new paradigm in the area of wireless communication. It promises spatial diversity by virtual antenna array. The efficient designing of Medium Access Control (MAC) protocol is necessary for gaining advantage of the cooperation gained at physical layer. Cooperative MAC layer protocol are designed considering three important features : Benefit of Cooperation, Selection of Best Relay(s) and Relay Selection Scheme. Cooperative MAC layer can provide benefits like increased data rate and throughput, reduced delay, improved energy efficiency, improved coverage area and improved transmission reliability. At the same time it has to deal with continuously changing wireless channels, overhead due to implementation of protocol, node mobility and interference due to cooperation. In this survey, a broad overview of existing protocols is given in accordance with the networks they are used and common issues involved in designing of cooperative MAC protocol.
Online Learning of Feed-Forward Models for Task-Space Variable Impedance Control

During the initial trials of a manipulation task, humans tend to keep their arms stiff in order to reduce the effects of any unforeseen disturbances. After a few repetitions, humans perform the task accurately with much lower stiffness. Research in human motor control indicates that this behavior is supported by learning and continuously revising internal models of the manipulation task. These internal models predict future states of the task, anticipate necessary control actions, and adapt impedance quickly to match task requirements. Drawing inspiration from these findings, we propose a framework for online learning of a time-independent forward model of a manipulation task from a small number of examples. The measured inaccuracies in the predictions of this model dynamically update the forward model and modify the impedance parameters of a feedback controller during task execution. Furthermore, our framework includes a hybrid force-motion controller that provides compliance in particular directions while adapting the impedance in other directions. These capabilities are evaluated on continuous contact tasks such as pulling non-linear springs, polishing a board, and stirring porridge.