Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-Time Audio Monitoring Framework with Limited Data for Constrained Devices

An effective and non-invasive audio monitoring system needs to be capable of simultaneous real-time detection of multiple audio events in many different environments, and locally executable on resource constrained devices, such as, smart microphones. A major challenge in this research domain is having limited available annotated data. This paper presents a novel framework to generate robust detection models of environmental and human audio events with limited available data. The framework presents the generation of a large synthetic dataset using limited data for any audio event, a novel computationally efficient feature modeling technique, named Audio2Vec, that is robust against environmental variations, and identifies and exploits the syntactic relation between audio states represented by the features and the targeted audio events. The presented framework achieves 10.3% higher F-1 scores compared to the best baseline approaches. To demonstrate the effectiveness of the framework we implemented a real-time audio monitoring system that simultaneously detects 10 audio events on a Raspberry Pi 3B and evaluate it in real home and in-car settings, that achieve F-1 scores of 0.96 and 0.956, respectively.
A Comparative Study of the Efficiency of Chinese and American Housing Markets——Based on the Hurst Index from Fractal Market Theory

Based on the fractal structure theory of stock market, this paper applied Hurst index as a measure and then explored the rationality of housing prices and the effectiveness of real estate markets in China and the United States. by using data from several large and medium-sized cities in China and each state in America. Statistical analysis shows that the real estate markets of China and the United States didn’t reach weak effective, that is, the information response of real estate markets in both countries is inadequate. Therefore, it’s urgent for government to solve the problem that how to strengthen the information transparency of the real estate industry so as to improve the efficiency of this market.
Robot Packing With Known Items and Nondeterministic Arrival Order

This article formulates two variants of packing problems in which the set of items is known, but the arrival order is unknown. The goal is to certify that the items can be packed in a given container and/or to optimize the size or cost of a container so that that the items are guaranteed to be packable, regardless of arrival order. The nondeterministically ordered packing (NDOP) variant asks to generate a certificate that a packing plan exists for every ordering of items. Quasi-online packing (QOP) asks to generate a partially observable packing policy that chooses the location of each item as their arrival order is revealed one-by-one, with all of the remaining items certified to be packable regardless of their future arrival order. Theoretical analysis demonstrates that even the simple subproblem of verifying the feasibility of a packing policy is NP-complete. Despite this worst case complexity, practical solvers for both NDOP and QOP are developed. Multiple extensions to the basic nondeterministic problem are presented, including packing with a fixed-capacity buffer and packing with equivalent objects. Experiments demonstrate that these algorithms can be applied to packing irregular 3-D shapes with stability and manipulator loading constraints. Note to Practitioners—Automatic packing of objects into containers, such as a shipping box, has applications in warehouse automation for e-commerce applications and less-than-truckload shipping. In many scenarios, a container needs to be preselected for a set of objects before they arrive, and the order of the objects cannot be controlled. This article studies the theoretical foundations of packing problems, in which the set of items is known, but the arrival order is unknown. The algorithms presented in this article can certify whether a container can hold a set of objects in the case where the arrival order is revealed at once, one-by-one, and where a limited set of objects can be put aside in a buffer before packing. These algorithms can be used to choose optimal containers and packing strategies, both for robot and human packers.
On Realizing Distributed Deep Neural Networks: An Astrophysics Case Study

Deep Learning architectures are extensively adopted as the core machine learning framework in both industry and academia. With large amounts of data at their disposal, these architectures can autonomously extract highly descriptive features for any type of input signals. However, the extensive volume of data combined with the demand for high computational resources, are introducing new challenges in terms of computing platforms. The work herein presented explores the performance of Deep Learning in the field of astrophysics, when conducted on a distributed environment. To set up such an environment, we capitalize on TensorFlowOnSpark, which combines both TensorFlow’s dataflow graphs and Spark’s cluster management. We report on the performance of a CPU cluster, considering both the number of training nodes and data distribution, while quantifying their effects via the metrics of training accuracy and training loss. Our results indicate that distribution has a positive impact on Deep Learning, since it accelerates our network’s convergence for a given number of epochs. However, network traffic adds a significant amount of overhead, rendering it suitable for mostly very deep models or in big Data Analytics.
Multi-Party Media Partisanship Attention Score. Estimating Partisan Attention of News Media Sources Using Twitter Data in the Lead-up to 2018 Italian Election

The ongoing radical transformations in communication ecosystems have brought up concerns about the risks of partisan selective exposure and ideological polarization. Traditionally, partisan selective exposure is measured by cross-tabulating survey responses to questions on vote intentions and media consumption. This process is expensive, limits the number of news outlets taken into account and is prone to the typical biases of self-reported data. Building upon previous works and with a specific focus on the online media environment, we introduce a new method to measure partisan media attention in a multi-party political system using Twitter data from 2018 Italian general election. Our first research question addresses the effectiveness of this method by measuring the extent to which our estimates correlate with partisan newspaper consumption measured by the latest Italian National Election Studies (ITANES) survey. Once established the reliability of our method, we employ these scores and measures to analyze the Italian digital media ecosystem in the lead-up to March 2018 election. The traditionally high level of political parallelism that characterizes both the Italian press and TV sectors is only partially reflected in a digital media ecosystem where partisan news sources seem to coexist with cross-partisan outlets. Results also point out that certain online partisan communities tend to rely more on exclusive news media sources.
Mining on Twitter Data of Movie Reviews using R * Ms

Supposition investigation is essentially worried with the examination of feelings and assessments from the content. We can allude supposition examination as conclusion mining. Conclusion investigation finds and legitimizes the opinion of the individual regarding a given wellspring of substance. Web-based social networking contain a gigantic measure of the opinion information as tweets, websites, and updates on the status, posts, and so forth. Estimation examination of this to a great extent created information is exceptionally helpful to express the feeling of the mass. Twitter feeling investigation is dubious when contrasted with wide slant examination due to the slang words and incorrect spellings and rehashed characters. We realize that the greatest length of each tweet in Twitter is 140 characters. So it is essential to distinguish amend notion of each word. In our venture, we are proposing an exceedingly exact model of feeling investigation of tweets as for most recent r eviews of forthcoming Tollywood or Bollywood or Hollywood films utilizing Internet Movie Database (IMDb). With the assistance of highlight vector and classifiers, for example, Naïve Bayes, we are effectively characterizing these tweets as positive, negative and impartial to give feeling of each tweet. Keyword: Naïve Bayes, Machine Learning, Twitter, Sentiment analysis, IMDb, Unigram. -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 07-07-2017 Date of acceptance: 28-07-2017 -------------------------------------------------------------------------------------------------------------------------------------
Mining Chinese comparative sentences and relations based on CRF algorithm

Comparative sentences are a common kind of expression to indicate the relations of different objects. They are valuable for text mining, especially for opinion mining. It is a novel research to identify Chinese comparative sentences and extract comparative relations. To identify Chinese comparative sentences, this paper took SVM as classifier and regarded keywords and class sequential rule as feature based on the previous research, and then used CRF algorithm to identify entity and also took the entity’s information as feature. Finally, remarkably improve the precision, recall and F-measure for identifying comparative sentences and got the result up to 96.55%, 88.63% and 92.43% respectively. To mine comparative relations, extracted comparative subject and objected by defining some rules together with the result of CRF algorithm for identifying entity, and obtained good result. And the result to extract comparative subject is better than comparative object.
Predict futures using video prediction model Training Time Test Time Collect Kinesthetic Demonstrations Autonomous Random Data

Machine learning has enabled robots to perform complex tasks in narrowly-scoped settings, and to perform simple tasks with high generalization. However, learning a model that can both perform complex tasks and generalize to previously unseen objects and goals remains a significant challenge. We study this challenge in the context of “improvisational” tool use: a robot is presented with novel objects and a user-specified goal (e.g., sweep some clutter into the dustpan), and must figure out, using only raw image observations, how to accomplish the goal using the available objects as tools. We approach this problem by training a model with both a visual and physical understanding of multi-object interactions, and develop a sampling-based optimizer that can leverage these interactions to accomplish tasks. We do so by combining diverse demonstration data with selfsupervised interaction data, aiming to leverage the interaction data to build generalizable models and the demonstration data to guide the model-based RL planner to solve complex tasks. Our experiments show that our approach can solve a variety of complex tool use tasks from raw pixel inputs, outperforming both imitation learning and self-supervised learning individually. Furthermore, we show that the robot can perceive and use novel objects as tools, including objects that are not conventional tools, while also choosing dynamically to use or not use tools depending on whether or not they are required. Videos of the results are available online.
Performance Evaluation of MIMO Schemes in 5 MHz Bandwidth LTE System

In this paper, we study the performance of the MIMO Schemes in the 3GPP Long Term Evolution (LTE) system with 5 MHz bandwidth. As performances metrics, the Block Error Rate (BLER) and the Data Throughput are evaluated in terms of Signal to Noise Ratio (SNR) for three different MultiInput Multi-Output (MIMO) schemes as defined in LTE standard. Two transmit diversity schemes known as Space Frequency Block Codes (SFBC) and Frequency Switched Transmit Diversity (FSTD) as well as one Open Loop Spatial Multiplexing (OLSM) scheme are considered in the evaluation. The performance of the three MIMO schemes are compared to the performance of Single Input Single Output (SISO) scheme to evaluate the improvement in BLER and data throughput of the system. The ITU pedestrian B channel with high order modulation and coding scheme is considered for the evaluation. KeywordsMulti-antenna MIMO system, LTE, BLER, Data Throughput
1 Defining Driver Distraction

This paper discusses the various issues concerned with the precise definition of the term driver distraction. There has been growing interest in driver distraction in the research community due to the development of information and communication technologies for concurrent use whilst driving. Concern has been raised that such technologies may lead to an increase in accidents. In order to reliably monitor the driver distraction problem it is essential that cross study comparisons can be made. This is made difficult due to the lack of a comprehensive definition of the term. This paper reviews those definitions available in the literature and examines the necessary components of a reliable definition with support from accident statistics in a database of work-related road traffic accidents in the Midlands area of the UK. It is found that driver distraction should be discussed in terms of four components: the difference between distraction and inattention; the recognition that distraction can be internal and external to the vehicle; that distraction can be categorised into four types; and, the effect of distraction on the driving task. Finally, a proposed, comprehensive definition for driver distraction is provided along with other conclusions.
Semi-parametric instrument-free demand estimation : relaxing optimality and equilibrium assumptions ∗

In most markets, consumer demand results from a compound arrival/choice process: consumers arrive to a market stochastically and make independent discrete choices over which item to purchase, including the “outside good”. Market demand results from an aggregation of individual consumer choices, and in general is more accurately modeled a a price-dependent probability distribution rather than a linear demand curve that is traditionally studied in the literature on demand estimation. We consider the problem of identification of consumer preferences and arrivals when market prices are endogeneously determined but the implied distribution of demand (including mean demand) is nonlinear in prices and there are no relevant instrumental variables to deal with price endogeneity. In addition, most data sets are subject to censoring: we typically do not observe the number of arriving customers, or those who chose the outside good. Recent studies have shown that the hypotheses of a) optimality, and b) equilibrium constitute powerful identifying restrictions that enable demand estimation in the presence of a variety of endogeneity and censoring problems. In this paper we focus whether it is possible to identify demand when assumptions a) and b) are relaxed. We establish the global, non-parametric identification of preferences and consumer arrival probabilities in a simplified static setting but show via examples that the identification of unobserved types of consumers is very challenging, in contrast to the more optimistic conclusions from theoretical analyses that prove that the random coefficient logit model (which is a component of our overall model of demand) is non-parametrically identified.
Anomaly Detection and Fault Localization Using Runtime State Models

Software systems are impacting every aspect of our daily lives, making software failures expensive, even life endangering. Despite rigorous testing, software bugs inevitably exist, especially in complex systems. Existing tools to aid debugging, such as tracing, profiling, and logging facilities, reveal the behavior of a program’s execution; however, they require the developers to manually correlate the data to diagnose faults. This work is the first to introduce the Runtime State Model, a summarization of a program’s behavior, for software anomaly detection and fault localization. A Runtime State Model is constructed from variables’ value change events of an execution. It consists of a set of states, and state transitions, where a state is a set of variables with their current values, and a state transition is induced by a variable’s value change. Comparisons between states from difference executions can be conducted to detect software anomalies. Deviations from the healthy states also help explain and locate faults in the source code. To automate this process, we implement Xtract, a facility that automatically extracts runtime traces from the Java Virtual Machines and constructs Runtime State Models for multiple simultaneous Java applications. Our evaluation provides evidence that Runtime State Models might be effective in detecting and locating injected faults to a RUBiS server with Xtract.
jmbReview Characterization and Genomic Analysis of Novel Bacteriophage Φ CS 01 Targeting Cronobacter sakazakii

Cronobacter species, formerly known as Enterobacter sakazakii [1] were first isolated in 1980 [2] and are gramnegative, rod-shaped opportunistic pathogens of the family Enterobacteriacea [3]. These species are ubiquitous and have been isolated from food and environments, including water, soil, processed foods, and fresh produce [4]. Cronobacter sakazakii is associated with necrotizing enterocolitis and meningitis and is a contaminant in powdered infant formula (PIF) [5]. Contaminated PIF is a major cause of neonatal infection [6], and pathogenic bacterial contamination of PIF can be caused by improper product storage during the manufacture process [7]. Neonates and infants have weak immunity and C. sakazakii infection can cause more serious problems than in adults [8]. Therefore, pathogen-specific microbial control measures are required. At present, antibiotics are widely used to prevent infection by pathogenic C. sakazakii [9]. Nevertheless, the emergence of antibiotic-resistant bacteria has caused serious problems for both biotechnological and medical applications [10]. In addition, C. sakazakii demonstrated antibiotic resistance in antibiotic susceptibility tests [11]. Therefore, a safe biocontrol agent that can control C. sakazakii and replace antibiotics should be developed. In 2006, the US Food and Drug Administration (FDA) approved the use of purified bacteriophages as food additives [11]. In a previous study, the complete genomic sequence of a bacteriophage infecting C. sakazakii was reported, and phage genome analysis revealed no toxicityrelated genes encoding toxic agents and toxins [11-18]. The use of bacteriophages as biological agents is a potential alternative to the use of antibiotics [19]. A bacteriophage is a virus that uses bacterial cells as hosts and selectively infects target bacteria to destroy bacterial cells [19]. This lytic activity makes bacteriophages a promising substitute for antibiotics [20]. In this study, ΦCS01, a bacteriophage that infects the pathogenic bacterium C. sakazakii was isolated from swine Received: December 26, 2018 Revised: April 8, 2019 Accepted: April 9, 2019
Modeling anthropic factors as drivers of wildfire occurrence at the Monarch Butterfly Biosphere

Fire regimes in coniferous forests in Central Mexico have been severely disturbed by land use change and fire management activities. Hence, it is critical to assess the contribution of anthropic and environmental factors that drive the occurrence of fires in these forests. This information is essential for the effective planning of fire management and wildfire prevention policies. In this study, we identified the potential drivers of fire occurrence within the Monarch Butterfly Biosphere Reserve (MBBR) and modeled their spatial pattern through generalized linear mixed models. We employed fire event data for five years (2009-2013) and the spatial distribution of anthropic infrastructure and biophysical variables such as forest biomass and slope. We found fire occurrence increased with total population and forest edge density. The derived spatial model showed an acceptable accuracy (AUC = 0.71) for fire occurrence based on 2014 and 2015 fire events used to evaluate the model. To improve the model, we suggest the incorporation of direct fuel measurements. From our analyses, we suggest to develop fire management guidelines particularly in sites with high population density and close to forest fragments within the MBBR.
Anti Android Emulator Detection Accurate & Effective Dynamic Android Malware Analysis Final Report

Nowadays, mobile applications are highly demanded because of increasing smartphone users. Android, which is the biggest mobile market, attracts many hackers to develop malware to exploit users. To combat this situation, developing a trustworthy and fast malware analysis method is necessary. On the other hand, emulator detection in mobile allows malware to bypass automated malware analysis easily. If there is no solution to tackle emulator detection in malware, automated malware analysis will never be accurate since the malware is just ignoring the malware researcher. Cuckoodroid, which is an open source automated malware analysis framework, is very famous to many security companies and malware researchers. However, this framework development on Github is inactive, and according to latest update, the author was still improving the performance of multi-tasking. Inside this framework, anti-emulation solution was using Xposed framework. According to a previous research [21], the original anti-emulator solution is not enough to bypass newer malware. Another problem is the device has to be restarted to apply all the changes in the Xposed code. Therefore it is not convenient when users need to frequently update the anti-emulator module. This project built a more flexible and feasible anti-emulator module using Frida, in order to introduce an automated malware analysis environment, supported with a fast development environment for editing anti-emulator solution. This project had implemented some well-known anti-emulator detection methods by Frida to bypass the emulator checking in the malware. This project gained a new feature in Cuckoodroid, so users could make use of Frida to implement anti-emulator detection code, and run the dynamic analysis as usual, with additional logs and notification to show how the anti-emulator detection takes places during the dynamic analysis. Some root checking application available in Github, and real malware obtained from malware database were well-tested by the new Cuckoodroid. The result showed that the functionality of Cuckoodroid was not affected by the new implementation. Furthermore, it showed what emulator detection events which happened during the analysis in the output report. It is a useful information for researchers to know.