Miracle-dz/UniFAR-FTUs huggingface dataset:
INCREASING THE BIOLOGICAL VALUE OF BREAD THROUGH THE APPLICATION OF PUMPKIN PUREE

with additives. It has been found that the introduction of pump­ kin puree has practically no effect on the amount of washed gluten. However, at a dosage of pumpkin puree from 5 to 25 %, the compres­ sion strain of raw gluten increases from 68.5 to 94.7 units instrument. This makes it possible to regulate the desired final properties of bread and the deformation of gluten in the dough
Master Computer Science Modelling Startup Growth and Spending by using Markov Decision Processes

Startups, which are companies of a scalable business model, often operate in an uncertain and volatile environment. Because of its business model, a startup has the potential to grow exponentially rather than linearly. However, taking a wrong decision at a time could lead to the end of the ﬁrm. In 2021, 9 out of 10 startups fail. In order to grow, startups often resort to data analytics. In this research, we study whether more spending on data analytics can foster startup growth and what type of data analytics capability should a ﬁrm purchase at each time. We use Markov decision processes to model growth and spending and derive the optimal policy. We apply our method to a dataset from Amazon Web Services, which contains billing information from 10s of thousands of startups. We ﬁnd that increasing data analytics capabilities can facilitate the growth of a startup. We also compare our optimal policy with the realized spendings of the best, average, and worst-performing ﬁrms and ﬁnd that the actions of the best-performing ﬁrm highly correspond with the optimal policy, whereas both the actions of the average-performing ﬁrm and those of the worse-performing ﬁrm are far o ↵ from the optimal policy. Based on our results, we provide practical suggestions to ﬁrms on purchasing data analytics capability.
A Blockchain-based System for Secure and Distributed Virtual Network Functions Orchestration

Service provisioning in next-generation networks, such as 5G and 6G, relies on virtualization to carry out multi-domain and multi-tenant connections. In these scenarios, virtual network functions (VNF) orchestration becomes susceptible to security threats once trust between peers cannot be assumed. This paper1 proposes a blockchain-based system for an agile, secure, and distributed provisioning of virtual network functions in scenarios with multiple administrative domains. Our proposal employs smart contracts to deliver all stages of a service-level-agreement management life cycle automatically. We develop, implement, and evaluate a prototype of the proposed system using smart contracts running on Hyperledger Fabric. The performance evaluation results show that the system guarantees high-rate VNF provisioning, reaching hundreds of slice requests per second in a trustful way.
Quantum Information Physics II

We study quantum entropy, a measure of randomness over the degrees of freedom of a quantum state and quantiﬁed in quantum phase spaces. We show that it is dimensionless, a relativistic scalar, and it is invariant under coordinate and CPT transformations. We show that the entropy evolution of a coherent state is increasing with time. We augment time reversal with time translation and show that CPT with time translation can transform particles with decreasing entropy evolution for a ﬁnite time interval into antiparticles with increasing entropy evolution for the same ﬁnite time interval. We revisit transition probabilities of a two state Hamiltonian and show how they relate to entropy oscillations. We also explore the possibility that entropy oscillations trigger the annihilations and the creations of particles.
Poster : PhisherCop - An Automated Tool Using ML Classiﬁers for Phishing Detection

—Phishing poses a signiﬁcant security risk to organizations and individuals, leading to the loss of billions of dollars yearly. While risk communication serves as a tool to mitigate phishing attempts, it is imperative to create automated phishing detection tools. Numerous Natural Language Processing (NLP) and Machine Learning (ML) approaches have been deployed to tackle phishing. However, phishing emails and SMS continue to increase exponentially, reiterating the need for more effective approaches. To address this, we have developed an anti-phishing tool called PhisherCop. PhisherCop is built upon a Stochastic Gradient Descent classiﬁer (SGD) and a Support Vector Classiﬁer (SVC) which showed an average accuracy of 96 % performing better than six other popular classiﬁers, including Decision Tree, Logistics Regression, Random Forest, Gradient Boosting Classi-ﬁer, K-Nearest Neighbors and Multinomial Naive Bayes. With a high level of accuracy, our tool distinguishes between phishing and legitimate content both over emails and text messages based on a user-centered approach.
Cyber Threat Observatory: Design and Evaluation of an Interactive Dashboard for Computer Emergency Response Teams

Computer emergency response teams (CERTs) of the public sector provide preventive and reactive cybersecurity services for authorities, citizens, and enterprises. However, their tasks of monitoring, analyzing, and communicating threats to establish cyber situational awareness are getting more complex due to the increasing information volume disseminated through public channels. Besides the time-consuming data collection for incident handling and daily reporting, CERTs are often confronted with irrelevant, redundant, or incredible information, exacerbating the time-critical prevention of and response to cyber threats. Thus, this design science research paper presents the user-centered design and evaluation of the Cyber Threat Observatory, which is an automatic, cross-platform and real-time cybersecurity dashboard. Based on expert scenario-based walkthroughs and semi-structured interviews (N=12), it discusses six design implications, including customizability and filtering, data source modularity, cross-platform interrelations, content assessment algorithms, integration with existing software, as well as export and communication capabilities.
A Bayesian Approach to Context-based Recognition of Human Intention for Context-Adaptive Robot Assistance in Space Missions

Long-duration space exploration (LDSE) missions with a high demand for intelligent and adaptable robotic assistance are expected to start in the next decades. Robot assistants deployed on these missions should be able to interpret the intentions of astronaut(s) and provide assistance accordingly, under continuously changing interaction context. This necessitates the development of robust and context-adaptive models for inferring human intentions. However, current state-of-the-art artificial intelligence (AI) algorithms rely on expensive data-driven machine learning (ML) models that provide inferences assuming a set of known contexts. In this paper, we present a context-adaptive hybrid architecture for intelligent and adaptable Human Computer Interaction (HCI). This architecture uses state-of-the-art machine learning models for the recognition of specific contexts within the robot’s environment, and a two-layer Bayesian network to infer human intentions based on the current context. The Bayesian network enables the integration of human expert knowledge about the mission goals within the inference process and can also deal with missing or uncertain information about context. Thus, the proposed framework increases robustness and reduces the need for large and complex (high dimensional) human-robot interaction data, which is unavailable in space applications. Context-adaptive interpretation of human intention would promote an intuitive interaction with robots, which in turn could reduce the overall stress in astronauts while interacting with robot assistants in isolated, confined or extreme (ICE) conditions.
ReChan: An Automated Analysis of Android App Release Notes to Report Inconsistencies

“What's new? ” This is what users wonder when they see the notification that a mobile app has just been updated on their device. New releases may involve simple bug fixes, or may include new features that users are eager to try. Regardless of the change, users do want to know what are the differences with respect to the release that have been using so far. The Google Play store has a visible section for each Android app that clearly describes the changes that affect the latest release. This description, however, is curated by developers, and may not match the actual changes in the binary code. This paper presents ReChan, a novel technique aiming to automatically detect mismatches between release notes of Android applications and the actual changes in the code. We define a taxonomy of 9 release categories by manually tagging 1,200 real samples, and we present our solution to automatically classify release notes written in English. ReChan then implements specific analyses to detect such changes in the code, and compares the analyses outcome to detect mismatches. ReChan achieves a precision, recall and f-score of 84.9% on the manually crafted ground truth of three open source apps. Experiments on a dataset of 12,706 closed source Android apps show that developers tend to correctly report changes due to bug fixes and new features, but omit changes that affect the list of requested permissions, the UI and other content that the app uses.
Unbiased inverse volume rendering with differential trackers

Volumetric representations are popular in inverse rendering because they have a simple parameterization, are smoothly varying, and transparently handle topology changes. However, incorporating the full volumetric transport of light is costly and challenging, often leading practitioners to implement simplified models, such as purely emissive and absorbing volumes with "baked" lighting. One such challenge is the efficient estimation of the gradients of the volume's appearance with respect to its scattering and absorption parameters. We show that the straightforward approach---differentiating a volumetric free-flight sampler---can lead to biased and high-variance gradients, hindering optimization. Instead, we propose using a new sampling strategy: differential ratio tracking, which is unbiased, yields low-variance gradients, and runs in linear time. Differential ratio tracking combines ratio tracking and reservoir sampling to estimate gradients by sampling distances proportional to the unweighted transmittance rather than the usual extinction-weighted transmittance. In addition, we observe local minima when optimizing scattering parameters to reproduce dense volumes or surfaces. We show that these local minima can be overcome by bootstrapping the optimization from nonphysical emissive volumes that are easily optimized.
AN ELECTRONIC CONTROLLER FOR BIO MEDICAL APPLICATIONS USING LVDT CHARACTERISTICS

This research paper deals with the designing of an electronic controller using LVDT transducerscharacteristics for the better and accurate pressure measurement for a given liquid flow which could be used in case of different bio medical applications. Further the study includes the monitoring of flow of pressure with respect to a middle acceptable range set such that the controller can work at three different conditions for the pressure value increasing beyond the acceptable range, decreasing below the acceptable range and value equal to acceptable range.
Do the Institutional Factors Affect the Accounting information quality Post IFRS Adoption: Asian Countries Study

This study aims to investigate the effect of institutional factors (i.e., differences in national accounting standards with IFRS, enforcement of accounting and auditing standards, investor protection, and corporate governance) on the accounting information quality. We tested the hypotheses by multiple linear regression analysis, estimated on three measures of accounting information quality (i.e., accruals quality, value relevance, and earnings management). Statistically, the results show that H1 is supported on the dependent variable of earnings management but not for accrual quality and value relevance. H2 is not supported in all measures of accounting information quality (accrual quality, value relevance, and earnings management). The results show that statistically, H3 is supported for the dependent variable accruals quality and earnings management but not significant for the dependent variable value relevance. Finally, the results show that statistically, H4 is supported for the dependent variable of accrual quality but not for the other two dependent variables. The test results H1, H2, H3, and H4, are robust by controlling for country and company level variables (i.e., capital market developments, classification of developed/developing countries, legal system, company size, and sales growth.
Longitudinal Event-Level Analysis of Gay and Bisexual Men’s Anal Sex Versatility: Roles, Behaviour, and Substance Use

Gay and bisexual Men Who Have Sex with Men (GBM) are sexually unique in that they can practice penile-anal sex versatility, i.e. engage in insertive and receptive anal sex. Individual-level versatility is extensively researched both as a sexual behavior linked to HIV/STI transmission, and as a GBM identity that can change over time. However, there is a dearth of research on event-level versatility (ELV), defined as taking the receptive and insertive role in the same sexual encounter. We analyzed event-level data from 644 GBM in the Momentum Health Study from February 2012-February 2017 to identify factors associated with ELV prevalence, the relationship between ELV and anal sex role preference, and sero-adaptive and sexualized drug use strategies. Univariate analysis revealed ELV prevalence rates between 15-20%. A multivariate generalized linear mixed model indicated ELV significantly (p<0.05) associated with versatile role preference and condomless sex. However, the majority of ELV came from GBM reporting insertive or receptive role preferences, and there was significantly higher condom use among sero-discordant partners, indicating sero-adaptation. Multivariate log-linear modeling identified multiple polysubstance combinations significantly associated with ELV. Results provide insights into GBM sexual behavior and constitute empirical data useful for future HIV/STI transmission pattern modeling.
Triggered Placeholder An Object Creational Pattern for Delaying Object Creation

There are times when delaying an object’s creation is desirable to avoid untimely side effects or the use of resources before they are actually needed. It is non-trivial to receive a request to create an object and delay that object’s creation until a later time. The Triggered Placeholder pattern decouples a request for an object’s creation from its actual creation. An example usage is shown along with the benefits and liabilities of using the pattern. An implementation outline is also provided along with some sample code. Finally, patterns related to the Triggered Placeholder pattern are listed.
Classification of the Radif of Mirza Abdollah a canonic repertoire of Persian music using SVM method

Automatic music classification is very useful to music indexing, content-based retrieval and on-line music distribution, but it is a challenge to extract the most common and salient themes from unstructured raw music data. In this paper, a novel approach is proposed to automatically classify the Radif of Mirza Abdollah a canonic repertoire of Persian music. The Radif is made up essentially of non-measured pieces or free rhythm which provide a generative model or pattern for the creation of new composition. Music Segments are decomposed according to time segments obtained from the beginning parts of the original music signal into segments of 3 sec . In order to better classify pure and vocal music, a number of features including inharmonicity, mel-frequency cepstral coefficient, pitch, mean and standard deviation of spectral centroid are extracted to characterize the music content which are mainly related to frequency domain. Experimental results are carried out on a novel database, which contains 250 gushe of the repertoire played by the four most famous Iranian masters and performed on two stringed instruments the Tar & The Setar. Classical machine learning algorithms such as MLP neural networks, KNN and SVM are employed. Finally, SVM shows a better performance in music classification than the others. Key words: EEG, Migraine, Spectral Analysis, Welch, Burg, Covariance, Modified Covariance.
Analysis of Renewable Energy Power Systems: Reliability and Flexibility during Unbalanced Network Fault

Due to different concerns, renewable energies stand out as an opportunity for a sustainable future. Wind/ Photovoltaic Hybrid Systems are one important type of renewable energy power systems. In order to increase renewable energies integration rate, this chapter proposes a utility interactive grid-connected wind-PV hybrid system with storage batteries. Facing modern electricity industries, the hybrid system considers the dispatch-ability and the quality of its power injection into the grid. The adopted modeling approach has been based on energy exchange considerations taking into account the converters structures. Particular attention has been given to cases where the hybrid system is connected to an unbalanced grid. This chapter develops a new control strategy, which aims to isolate the hybrid system of the adverse impact of the grid fault. The results point out thoroughly the applicability of the proposed control scheme under unbalanced grid conditions. Analysis of Renewable Energy Power Systems: Reliability and Flexibility during Unbalanced Network Fault
Online News Detection on Twitter

In this thesis, we seek to find suitable methods for detecting news on Twitter within the fields of artificial intelligence, information retrieval, computational linguistics, and natural language processing. We combine these fields to find newsworthy tweets, cluster them based on time and similarity and find the most representative tweet for a news event. We compare different methods within the field of topic modeling to find news topics and tweets related to them in an online setting. Then we have a look at an online clustering approach to be able to detect news while they are being clustered based on time of arrival and similar content. One of the greatest challenges is to find the tweets we can characterize as news in the ocean of tweets. Many tweets are about personal matters or two-way communication among friends. We call these uninteresting tweets “chatter”. There are many tweets that contain abbreviations, misspellings, and lack of proper sentence structure. This makes it difficult for otherwise good natural language processing systems to evaluate the content and proper language in tweets. Our study shows that finding news using topic modeling is difficult. Training a proper model is time consuming, and even when a working model is obtained, it is unclear how to effectively use it to detect news. While developing the online news detection system for Twitter, we have found that the clustering approach elaborated on in this thesis works well for tweets. The system clusters similar tweets based on time and content, and it performs well doing so. Due to the information entropy and tuning of parameters in the clustering algorithm we were able to achieve a higher precision than the baseline clustering algorithm. Lastly, we have found that the task of finding the most representative tweet for a news event is simple when the tweets have been clustered well, that is when the clusters contain mostly news relevant twets.
Are Indian Language Teachers Prepared for the Web World? A Case Study

Dr. Deepti Gupta holds an M.A. (English Literature and Linguistics), an M.Phil. (Stylistics) and a PhD (ELT). Her publications are varied and has been recently published in ELT (Oxford University Press) on bringing context and methodology together. Present Position: Professor, Dept. of English, Punjab University, Chandigarh ( India ).
I NTRODUCING C OORDINATION IN C ONCURRENT R EIN - FORCEMENT L EARNING

Research on exploration in reinforcement learning has mostly focused on problems with a single agent interacting with an environment. However many problems are better addressed by the concurrent reinforcement learning paradigm, where multiple agents operate in a common environment. Recent work has tackled the challenge of exploration in this particular setting (Dimakopoulou & Van Roy, 2018; Dimakopoulou et al., 2018). Nonetheless, they do not completely leverage the characteristics of this framework and agents end up behaving independently from each other. In this work we argue that coordination among concurrent agents is crucial for efficient exploration. We introduce coordination in Thompson Sampling based methods by drawing correlated samples from an agent’s posterior. We apply this idea to extend existing exploration schemes such as randomized least squares value iteration (RLSVI). Empirical results on simple toy tasks emphasize the merits of our approach and call attention to coordination as a key objective for efficient exploration in concurrent reinforcement learning.
Time evolution of polarization-mode dispersion for aerial and buried cables

Summary form only given. In this paper, fibre cable measurements were performed with a commercial test set that uses the interferometric method. The test set uses an LED with a center wavelength near 1550 nm and a spectral width of 70 nm. The PMD measured is the average differential group delay (DGD) over the 70-nm wavelength range.
Mixed prototyping environment with different video tracking techniques

Mixed Prototyping has been recently proposed as an interesting design tool able to combine virtual and real objects, in order to allow both designers and final users to evaluate and assess multiple design choices. This paper describes a Mixed Reality environment that deals with several issues that usually arise in Mixed Prototyping: the reliable simulation of the product behaviour; the mutual occlusion between real and virtual objects; the interpretation of the user's gesture while she/he is interacting with the elements of the product interface; the composition of virtual graphics within the real scene through a fast and robust tracking methodology. In particular we focus on the tracking methodology considering two different solutions based on fiducial markers and natural features, respectively. We have considered different scenarios of use, referring to four typologies of products. Each one presents its intrinsic challenges: small hand held products (e.g. mobile phones, remote controllers, etc.), medium sized products (e.g. small appliances), large interactive products (e.g. major appliances) and large non-interactive products (e.g. furnishings). We propose a comparative study that aims to identify the tracking system that better fits each application.
Network-Embedded Management and Applications: Understanding Programmable Networking Infrastructure

Despite the explosion of networking services and applications in the past decades, the basic technological underpinnings of the Internet have remained largely unchanged. At its heart are special-purpose appliances that connect us to the digital world, commonly known as switches and routers. Now, however, the traditional framework is being increasingly challenged by new methods that are jostling for a position in the next-generation Internet. The concept of a network that is becoming more programmable is one of the aspects that are taking center stage. This opens new possibilities to embed software applications inside the network itself and to manage networks and communications services with unprecedented ease and efficiency. In this edited volume, distinguished experts take the reader on a tour of different facets of programmable network infrastructure and applications that exploit it. Presenting the state of the art in network embedded management and applications and programmable network infrastructure, the book conveys fundamental concepts and provides a glimpse into various facets of the latest technology in the field.