Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Audio Classification and Speaker Identification for Video Content Analysis

Recently, more literatures proposed to apply audio content analysis techniques in content-based video parsing. This paper presents our works on audio classification and speaker identification techniques for video content analysis. Firstly, soundtrack extracted from video stream is partitioned into homogeneous segments using rule and support vector machine(SVM) based classifier. Secondly, fixed-length speech clips randomly selected from speech segments are clustered into several clusters based on spectral clustering techniques. The clustered speech feature datasets initialize and train Gaussian mixture model(GMM) for each speaker. Finally, the trained GMMs accomplish speaker identification. Experimental results confirm the validity of the proposed scheme.
Fast learning methodology for GNU / Linux driver development

It is pretended to present an effective teaching system for programming of Linux device drivers is presented. With this system the basic skills to create Linux device drivers can be obtained in efficient manner during one hour of theoretical lesson and two hours of workshop with and hands on. In order to get the best learning results it's strongly recommended to use real hardware devices. The most of the current devices are too complex to learn in an easy way. For this reason it has been decided to create a low complexity and low cost hardware device, in order the students can practice with it, without affecting the rest of the system. The student will have a suitable peripheral for learning, that will allow they to have an absolute control of all the communication with the computer.
A new approach for asynchronous distributed rate control of elastic sessions in integrated packet networks

We develop a new class of asynchronous distributed algorithms for the explicit rate control of elastic sessions in an integrated packet network. Sessions can request for minimum guaranteed rate allocations (e.g., minimum cell rates in the ATM context), and, under this constraint, we seek to allocate the max-min fair rates to the sessions. We capture the integrated network context by permitting the link bandwidths available to elastic sessions to be stochastically time varying. The available capacity of each link is viewed as some statistic of this stochastic process [e.g., a fraction of the mean, or a large deviations-based equivalent service capacity (ESC)]. The ESC is obtained so as to satisfy an overflow probability constraint on the buffer length. For fixed available capacity at each link, we show that the vector of max-min fair rates can be computed from the root of a certain vector equation. A distributed asynchronous stochastic approximation technique is then used to develop a provably convergent distributed algorithm for obtaining the root of the equation, even when the link flows and the available capacities are obtained from on-line measurements. The switch algorithm does not require per connection monitoring, nor does it require per connection marking of control packets. A virtual buffer based approach for on-line estimation of the ESC is utilized. We also propose techniques for handling large variations in the available capacity owing to the arrivals or departures of CBR/VBR sessions. Finally, simulation results are provided to demonstrate the performance of this class of algorithms in the local and wide area network context.
Preprocessing Position Data of Mobile Objects

We present the design and implementation of a component for the preprocessing of position data taken from moving objects. The movement of mobile objects is represented by piece wise functions over time that approximate the real object movement and significantly reduce the initial data volume such that efficient storage and analysis of object trajectories can be achieved. The maximal acceptable deviation - an input parameter of our algorithms - of the approximations also includes the uncertainty of the position sensor measurements. We analyze and compare five different lossy preprocessing methods. Our results clearly indicate that even with simple approaches, a more than sufficient overall performance can be achieved.
Quantization with Lagrangian Distortion Measures

The Lagrangian formulations of fixedrate and variable-rate vector quantization can be combined into a single formulation involving constraints on both entropy and log codebook size. The approach leads to a Lloyd design algorithm and to results on high-rate or high-resolution quantization. It provides both a unified view of the traditional results and an approach to studying the implications of Gersho’s conjecure, including asymptotic quantizer point density functions. We here describe the approach, preliminary results, and open problems.
Changes in the Structure of Global Food Demand

A newly developed demand system is used to estimate the response of food and food product demand to per capita expenditure changes. The resulting Engel elasticities are then used to project food and food product demand in 2020 assuming per capita expenditure and population changes. Results suggest that while food expenditure is projected to grow, it accounts for a smaller proportion of total expenditure. Further analysis indicates change in the composition of food demand away from a grain and towards livestock is projected to occur in lower income countries in 2020.
A genome–wide screen to identify genes controlling the rate of entry into mitosis in fission yeast

ABSTRACT We have carried out a haploinsufficiency (HI) screen in fission yeast using heterozygous deletion diploid mutants of a genome-wide set of cell cycle genes to identify genes encoding products whose level determines the rate of progression through the cell cycle. Cell size at division was used as a measure of advancement or delay of the G2-M transition of rod-shaped fission yeast cells. We found that 13 mutants were significantly longer or shorter (greater than 10%) than control cells at cell division. These included mutants of the cdc2, cdc25, wee1 and pom1 genes, which have previously been shown to play a role in the timing of entry into mitosis, and which validate this approach. Seven of these genes are involved in regulation of the G2-M transition, 5 for nuclear transport and one for nucleotide metabolism. In addition we identified 4 more genes that were 8–10% longer or shorter than the control that also had roles in regulation of the G2-M transition or in nuclear transport. The genes identified here are all conserved in human cells, suggesting that this dataset will be useful as a basis for further studies to identify rate-limiting steps for progression through the cell cycle in other eukaryotes.
Magnetless Nonreciprocal Metamaterial (MNM) Technology: Application to Microwave Components

A magnetless nonreciprocal metamaterial (MNM), consisting of traveling-wave resonant ring particles loaded by transistor and exhibiting the gyromagnetic properties as ferrites, without their size, weight, cost, and monolithic microwave integrated circuit incompatibility drawbacks, was recently introduced in 2011 by Kodera et al. This paper presents the first extensive investigation of the applicability of MNM technology to nonreciprocal microwave components. It recalls the key principle of the MNM, provides basic MNM design guidelines, explains coupling mechanism between a microstrip line and MNM rings, and demonstrates two nonreciprocal MNM components based on a microstrip-ring configuration, an isolator, and a circulator. Although these components have not been fully optimized, they already exhibit attractive performance and provide a proof-of-concept that MNM technology has a potential for microwave nonreciprocal microwave components with substantial benefits compared to their ferrite and active-circuit counterparts.
Study on the composition rules for Chinese Jiangnan ditty

Rule-based system and genetic algorithm are artificial intelligence methods for algorithmic composition. Rule-based system is aimed at isolating the aesthetic code of a certain musical genre and using this code to create new similar compositions. In genetic algorithm, the chromosomes are evaluated by utilizing fitness function and how to define the fitness function is the most important key. This paper took typical Chinese traditional music-Jiangnan ditty as an object of study. Rule-based systems of Jiangnan ditty were established based on two methods. Firstly, acoustical features were extracted to characterize the musical genre of Jiangnan ditty. Secondly, a set of melodic features were established through statistical analyzing each piece of melody as a linear sequence of notes to describe pitch range, melodic interval, mode, note duration, timbre, rhythm pattern, and intervallic motive of Jiangnan ditty. According to the results, fitness function was defined, which would provide the judging basis for the subsequent genetic algorithm.
Balancing Dependability Quality Attributes Relationships for Increased Embedded Systems Dependability

Embedded systems are used in many critical applications of our daily life. The increased complexity of embedded systems and the tightened safety regulations posed on them and the scope of the environment in which they operate are driving the need for more dependable embedded systems. Therefore, achieving a high level of dependability to embedded systems is an ultimate goal. In order to achieve this goal we are in need of understanding the interrelationships between the different dependability quality attributes and other embedded systems’ quality attributes. This research study provides indicators of the relationship between the dependability quality attributes and other quality attributes for embedded systems by identifying the impact of architectural tactics as the candidate solutions to construct dependable embedded systems.
Least-squares approach to risk parity in portfolio selection

The risk parity portfolio selection problem aims to find such portfolios for which the contributions of risk from all assets are equally weighted. Portfolios constructed using the risk parity approach are a compromise between two well-known diversification techniques: minimum variance optimization and the equal weighting approach. In this paper, we discuss the problem of finding portfolios that satisfy risk parity over either individual assets or groups of assets. We describe the set of all risk parity solutions by using convex optimization techniques over orthants and we show that this set may contain an exponential number of solutions. We then propose an alternative non-convex least-squares model whose set of optimal solutions includes all risk parity solutions, and propose a modified formulation which aims at selecting the most desirable risk parity solution according to a given criterion. When general bounds are considered, a risk parity solution may not exist. In this case, the non-convex least-squares model seeks a feasible portfolio which is as close to risk parity as possible. Furthermore, we propose an alternating linearization framework to solve this non-convex model. Numerical experiments indicate the effectiveness of our technique in terms of both speed and accuracy.
Enhancing Spatial Locality using Data Layout Optimizations

This paper aims to improve locality of references by suitably choosing array layouts. We use a new definition of spatial reuse vectors that takes into account memory layout of arrays. This capability creates two opportunities. First, it allows us to develop an array restructuring framework based on a combination of hyperplane theory and reuse vectors. Second, it allows us to observe the effect of different array layout optimizations on spatial reuse vectors. Since the iteration space based locality optimizations also change the spatial reuse vectors, our approach allows us to compare the iteration-space based and data-space based approaches in terms of their effects on spatial reuse vectors. We illustrate the effectiveness of our technique using two benchmark examples on two distributed shared-memory machines, the Convex Exemplar and the SGI Origin.
Wireless ECG for Telemedicine using Wireless Sensor Network and TinyOS

Telemedicine is a future trend of medical care due to the increasing cost of treatment and the aging of population around the World. Several telecommunication technologies are proposed for transferring medical data between a point of care and a hospital. However, it does not provide full mobility where a patient still needs to be attached with wired monitoring devices. In this paper, we proposed a wireless three-lead electrocardiogram (ECG) device using a mote-based Wireless Sensor Network (WSN) to transfer heart signals from a patient to a personal computer for monitoring and storing. Our proposed device augments the mote-based Telos node and TinyOS. Our device is designed using low-cost, low-power components, and it provides full mobility to a patient by eliminating noise due to patient’s movement. The experiment results show the similarity of the ECG signals between our three-lead wireless ECG device and a 12-lead wired commercial ECG device.
General Framework for the Relationship between Social Segregation and Workplace Segregation

The relationship between social segregation and wo rkplace segregation had been traditionally studied as a one -way causal relationship mediated by referral hiring. In this paper we intro duce an alternative framework which describes the dynamic relationships between s ocial segregation, workplace segregation, individuals’ homophily level s, and referral hiring. An agent-based simulation model was developed based on this framework. The model describes the process of continuous change in composition of workplaces and social networks of agents, and how this process affects levels of workplace segregation and segregation of social networks of t he agents (people). The results of the simulation model include: (1) social segregation and workplace segregation may co-evolve even when hiring of worke rs occurs mainly through formal channels and population is initially integra ted (2) majority groups tend to be more homophilous than minority groups, and (3 ) referral hiring may be beneficial for minority groups when the population s highly segregated.
PROCEEDINGS OF THE 2014 SCHEME AND FUNCTIONAL PROGRAMMING WORKSHOP

The Scheme language has three major standards; Revised Report on the Algorithmic language Scheme (R5RS) standardised in February 1998, the Revised Report on Algorithmic language Scheme (R6RS) standardised in September 2007 and the Revised Report on the Algorithmic language Scheme (R7RS) standardised in July 2013. R7RS, the latest standard of Scheme focuses on the R5RS compatibility thus making R5RS implementations compliant with it would not be so difficult. For R6RS implementations it would be much more difficult; R7RS clearly says it is not a successor of the R6RS. This paper describes the major differences between these two Scheme standards and how we made our Scheme system, Sagittarius, compliant with both R6RS and R7RS, and made it able to use both standards’ libraries seamlessly.
New Old Algorithms for Stochastic Scheduling Extended

We consider the stochastic identical parallel machine scheduling problem and its online extension, when the objective is to minimize the expected total weighted completion time of a set of jobs that are released over time. We give randomized as well as deterministic online and offline algorithms that have the best known performance guarantees in either setting, online or offline and deterministic or randomized. Our analysis is based on a novel linear programming relaxation for stochastic scheduling problems that can be solved online.
CUDAP: A Novel Clustering Algorithm for Uncertain Data Based on Approximate Backbone

Clustering for uncertain data is an interesting research topic in data mining. Researchers prefer to define uncertain data clustering problem by using combinatorial optimization model. Heuristic clustering algorithm is an efficient way to deal with this kind of clustering problem, but initialization sensitivity is one of inevitable drawbacks. In this paper, we propose a novel clustering algorithm named CUDAP (Clustering algorithm for Uncertain Data based on Approximate backbone). In CUDAP, we (1) make M times random sampling on the original uncertain data set D m to generate M sampled data sets DS= { Ds 1 ,Ds 2 ,…,Ds M }; (2) capture the M local optimal clustering results P ={ C 1 ,C 2 ,…,C M } from DS by running UK-Medoids algorithm on each sample data set Ds i , i=1,…M ; (3) design a greedy search algorithm to find out the approximate backbone( APB ) from P ; (4) run UK-Medoids again on the original uncertain data set D m guided by new initialization which was generated from APB . Experimental results on synthetic and real world data sets demonstrate the superiority of the proposed approach in terms of clustering quality measures.
Worm Proteins of Schistosoma mansoni Reduce the Severity of Experimental Chronic Colitis in Mice by Suppressing Colonic Proinflammatory Immune Responses

Although helminthic therapy as a possible new option to treat inflammatory bowel disease is a well-established concept by now, the search for immunomodulatory helminth-derived compounds and their mechanisms of action is still ongoing. We investigated the therapeutic potential and the underlying immunological mechanisms of Schistosoma mansoni soluble worm proteins (SmSWP) in an adoptive T cell transfer mouse model of chronic colitis. Both a curative and a preventive treatment protocol were included in this study. The curative administration of SmSWP (started when colitis was established), resulted in a significant improvement of the clinical disease score, colonoscopy, macroscopic and microscopic inflammation score, colon length and myeloperoxidase activity. The therapeutic potential of the preventive SmSWP treatment (started before colitis was established), was less pronounced compared with the curative SmSWP treatment but still resulted in an improved clinical disease score, body weight loss, colon length and microscopic inflammation score. Both the curative and preventive SmSWP treatment downregulated the mRNA expression of the proinflammatory cytokines IFN-γ and IL-17A and upregulated the mRNA expression of the anti-inflammatory cytokine IL-4 in the colon at the end of the experiment. This colonic immunomodulatory effect of SmSWP could not be confirmed at the protein level. Moreover, the effect of SmSWP appeared to be a local colonic phenomenon, since the flow cytometric T cell characterization of the mesenteric lymph nodes and the cytokine measurements in the serum did not reveal any effect of SmSWP treatment. In conclusion, SmSWP treatment reduced the severity of colitis in the adoptive transfer mouse model via the suppression of proinflammatory cytokines and the induction of an anti-inflammatory response in the colon.
Comprehension processes during large scale maintenance

We present results of observing professional maintenance engineers working with industrial code at actual maintenance tasks. Protocol analysis is used to explore how code understanding might differ for small versus large scale code. The experiment confirms that cognition processes work at all levels of abstraction simultaneously as programmers build a mental model of the code. Cognition processes emerged at three levels of aggregation representing lower and higher level strategies of understanding. They show differences in what triggers them and how they achieve their goals. Results are useful for defining core competencies which maintenance engineers need for their work and for documentation and development standards. <<ETX>>
Remember When It Rained : Gender Discrimination in Elementary School Enrollment in India

Girls in India have significantly lower school enrollment rates than boys. Anecdotal evidence suggests that intra-household gender discrimination is the most important reason for this gap, but empirical support in most of the previous literature is rare and seemingly inconsistent with patterns in related economic research. I propose that this may be due to a combination of endogeneity issues and inadequate attention to age-specific forms of discrimination. I analyze school enrollment using a plausibly exogenous source of income variation for rural households: rainfall shocks. The results show that girls’ school enrollment is more vulnerable to rainfall shocks than that of boys, with 6-10 year old children driving these effects. This empirical pattern is consistent with young girls being out of school because of credit constraints, and older girls being out of school because of low perceived net benefits of education. ∗email:lvzimmer@umich.edu. I thank Dean Yang, David Lam, Raj Arunachalam, Ashwini Deshpande, Suresh Tendulkar, Miguel Urquiola and participants of the University of Michigan Informal Development Seminar for valuable comments and feedback. Sulabha Parasuraman provided important information on district analysis in the NFHS datasets.
Different Approaches to Improve Thermostability of Whey Proteins: A Review

Whey proteins constitute 20% of the proteins in milk and include β-lactoglobulin (β-Lg; approximately 3.2 g/L), αlactalbumin (α-La; approximately 1.2 g/L), bovine serum albumin (BSA; approximately 0.4 g/L), and immunoglobulins (approximately 0.7 g/L) (Raikos, 2010). Whey protein is considered as high quality protein because of its branch chain amino acid. Growing awareness about the nutritional benefits of whey proteins and technological advancements in improving functional properties has led to an ever increasing demand of whey proteins in the form of commercial whey protein ingredients, such as whey protein concentrates (WPC) and whey protein isolates (WPI). Whey protein ingredients are widely used in a variety of products, such as whey protein fortified sports beverages, nutritional and meal replacement as well as medical and clinical nutrition beverages (Suresh and Hasmukh, 2017). Whey protein isolates (WPI) and whey protein concentrate (WPC) are used as food ingredients due to their commercially important functional properties such as solubility, viscosity, water-holding capacity, International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 04 (2019) Journal homepage: http://www.ijcmas. com
A 7/8-approximation algorithm for MAX 3SAT?

We describe a randomized approximation algorithm which takes an instance of MAX 3SAT as input. If the instance-a collection of clauses each of length at most three-is satisfiable, then the expected weight of the assignment found is at least 7/8 of optimal. We provide strong evidence (but not a proof) that the algorithm performs equally well on arbitrary MAX 3SAT instances. Our algorithm uses semidefinite programming and may be seen as a sequel to the MAX CUT algorithm of Goemans and Williamson (1995) and the MAX 2SAT algorithm of Feige and Goemans (1995). Though the algorithm itself is fairly simple, its analysis is quite complicated as it involves the computation of volumes of spherical tetrahedra. Hastad has recently shown that, assuming P/spl ne/NP, no polynomial-time algorithm for MAX 3SAT can achieve a performance ratio exceeding 7/8, even when restricted to satisfiable instances of the problem. Our algorithm is therefore optimal in this sense. We also describe a method of obtaining direct semidefinite relaxations of any constraint satisfaction problem of the form MAX CSP(F), where F is a finite family of Boolean functions. Our relaxations are the strongest possible within a natural class of semidefinite relaxations.
Modeling and Veriflcation of Marine Equipment Systems Using a Model Checker

We discuss the modeling and veriflcation of marine equipment systems that are implemented on a real-time OS. We construct the framework that provides primary functions, such as tasks with prior- ities, a scheduler, and an interrupt handler. Using this framework, we construct a behavioral model for two modules of simplifled flshflnder, and verify the requirements such as deadlock-freeness using a model checker SPIN.