Miracle-dz/UniFAR-FTUs huggingface dataset:
Feelings Detection System - A Proposal

The feelings detection is the study of the human feelings, attitudes and opinions and their classification about a specific topic. This paper consists of carry out the feelings (rever sentido da frase) analysis of a population, expressed in written comments from online newspapers about several subjects. The motivation for this project results from the local newspaper agents who suggested the development of this application for their use. After looking into several algorithms able of doing this analysis it was decided to use the algorithm SentiStrength, which allows words classification based on the punctuations stored in its dictionary as positives or negatives. “To feed” the algorithm authors used comments from online newspapers through a web spider, storing it in a text file that will be the algorithm input. The results will be achieved through the Media reach by the SentiStrength analysis in which the positive and negative feelings are expressed and presented in the extracted comments.
Relationship between High Temperature and Formation of Chalkiness and Their Effects on Quality of Rice

Occurrence of chalkiness in rice is attributed to genetic and environmental factors, especially high temperature (HT). The HT induces heat stress, which in turn compromises many grain qualities, especially transparency. Chalkiness in rice is commonly studied together with other quality traits such as amylose content, gel consistency, and protein storage. In addition to the fundamental QTLs, some other QTLs have been identified which accelerate chalkiness occurrence under HT condition. In this review, some of the relatively stable chalkiness, amylose content, and gel consistency related QTLs have been presented well. Genetically, HT effect on chalkiness is explained by the location of certain chalkiness gene in the vicinity of high-temperature-responsive genes. With regard to stable QTL distribution and availability of potential material resources, there is still feasibility to find out novel stable QTLs related to chalkiness under HT condition. A better understanding of those achievements is essential to develop new rice varieties with a reduced chalky grain percentage. Therefore, we propose the pyramiding of relatively stable and nonallelic QTLs controlling low chalkiness endosperm into adaptable rice varieties as pragmatic approach to mitigate HT effect.
Heme ameliorates dextran sodium sulfate-induced colitis through providing intestinal macrophages with noninflammatory profiles

Significance Following hemorrhage in damaged tissues, hemoglobin induces macrophages (Mϕs) possessing ability to protect against tissue inflammation. Hemorrhage-appearing mucosa is observed in patients with inflammatory bowel disease. However, heme-mediated modulation of intestinal Mϕ activity remains poorly understood. Here, we provide evidence that Spi-C induced by heme is a key molecule for providing noninflammatory gene expression patterns of intestinal CX3CR1high Mϕs. We found that the Spic deficiency in intestinal Mϕs resulted in increased sensitivity to dextran sodium sulfate-induced colitis. Heme-mediated Spi-C inhibited a subset of LPS-induced genes such as Il6 and Il1a by intestinal CX3CR1high Mϕs through inhibition of IRF5-NF-κB p65 complex formation. These results reveal a mechanism modulating the noninflammatory phenotype of intestinal Mϕs and may help identify targets for therapy of intestinal inflammation. The local environment is crucial for shaping the identities of tissue-resident macrophages (Mϕs). When hemorrhage occurs in damaged tissues, hemoglobin induces differentiation of anti-inflammatory Mϕs with reparative function. Mucosal bleeding is one of the pathological features of inflammatory bowel diseases. However, the heme-mediated mechanism modulating activation of intestinal innate immune cells remains poorly understood. Here, we show that heme regulates gut homeostasis through induction of Spi-C in intestinal CX3CR1high Mϕs. Intestinal CX3CR1high Mϕs highly expressed Spi-C in a heme-dependent manner, and myeloid lineage-specific Spic-deficient (Lyz2-cre; Spicflox/flox) mice showed severe intestinal inflammation with an increased number of Th17 cells during dextran sodium sulfate-induced colitis. Spi-C down-regulated the expression of a subset of Toll-like receptor (TLR)-inducible genes in intestinal CX3CR1high Mϕs to prevent colitis. LPS-induced production of IL-6 and IL-1α, but not IL-10 and TNF-α, by large intestinal Mϕs from Lyz2-cre; Spicflox/flox mice was markedly enhanced. The interaction of Spi-C with IRF5 was linked to disruption of the IRF5-NF-κB p65 complex formation, thereby abrogating recruitment of IRF5 and NF-κB p65 to the Il6 and Il1a promoters. Collectively, these results demonstrate that heme-mediated Spi-C is a key molecule for the noninflammatory signature of intestinal Mϕs by suppressing the induction of a subset of TLR-inducible genes through binding to IRF5.
Build systems à la carte

Build systems are awesome, terrifying -- and unloved. They are used by every developer around the world, but are rarely the object of study. In this paper we offer a systematic, and executable, framework for developing and comparing build systems, viewing them as related points in landscape rather than as isolated phenomena. By teasing apart existing build systems, we can recombine their components, allowing us to prototype new build systems with desired properties.
Hunting High and Low: Visualising Shifting Correlations in Financial Markets

The analysis of financial assets’ correlations is fundamental to many aspects of finance theory and practice, especially modern portfolio theory and the study of risk. In order to manage investment risk, in‐depth analysis of changing correlations is needed, with both high and low correlations between financial assets (and groups thereof) important to identify. In this paper, we propose a visual analytics framework for the interactive analysis of relations and structures in dynamic, high‐dimensional correlation data. We conduct a series of interviews and review the financial correlation analysis literature to guide our design. Our solution combines concepts from multi‐dimensional scaling, weighted complete graphs and threshold networks to present interactive, animated displays which use proximity as a visual metaphor for correlation and animation stability to encode correlation stability. We devise interaction techniques coupled with context‐sensitive auxiliary views to support the analysis of subsets of correlation networks. As part of our contribution, we also present behaviour profiles to help guide future users of our approach. We evaluate our approach by checking the validity of the layouts produced, presenting a number of analysis stories, and through a user study. We observe that our solutions help unravel complex behaviours and resonate well with study participants in addressing their needs in the context of correlation analysis in finance.
Randomized Block Cubic Newton Method

We study the problem of minimizing the sum of three convex functions: a differentiable, twice-differentiable and a non-smooth term in a high dimensional setting. To this effect we propose and analyze a randomized block cubic Newton (RBCN) method, which in each iteration builds a model of the objective function formed as the sum of the natural models of its three components: a linear model with a quadratic regularizer for the differentiable term, a quadratic model with a cubic regularizer for the twice differentiable term, and perfect (proximal) model for the nonsmooth term. Our method in each iteration minimizes the model over a random subset of blocks of the search variable. RBCN is the first algorithm with these properties, generalizing several existing methods, matching the best known bounds in all special cases. We establish ${\cal O}(1/\epsilon)$, ${\cal O}(1/\sqrt{\epsilon})$ and ${\cal O}(\log (1/\epsilon))$ rates under different assumptions on the component functions. Lastly, we show numerically that our method outperforms the state-of-the-art on a variety of machine learning problems, including cubically regularized least-squares, logistic regression with constraints, and Poisson regression.
Regulation of Energy Metabolism during Early B Lymphocyte Development

The most important feature of humoral immunity is the adaptation of the diversity of newly generated B cell receptors, that is, the antigen receptor repertoire, to the body’s own and foreign structures. This includes the transient propagation of B progenitor cells and B cells, which possess receptors that are positively selected via anabolic signalling pathways under highly competitive conditions. The metabolic regulation of early B-cell development thus has important consequences for the expansion of normal or malignant pre-B cell clones. In addition, cellular senescence programs based on the expression of B cell identity factors, such as Pax5, act to prevent excessive proliferation and cellular deviation. Here, we review the basic mechanisms underlying the regulation of glycolysis and oxidative phosphorylation during early B cell development in bone marrow. We focus on the regulation of glycolysis and mitochondrial oxidative phosphorylation at the transition from non-transformed pro- to pre-B cells and discuss some ongoing issues. We introduce Swiprosin-2/EFhd1 as a potential regulator of glycolysis in pro-B cells that has also been linked to Ca2+-mediated mitoflashes. Mitoflashes are bioenergetic mitochondrial events that control mitochondrial metabolism and signalling in both healthy and disease states. We discuss how Ca2+ fluctuations in pro- and pre-B cells may translate into mitoflashes in early B cells and speculate about the consequences of these changes.
On the Optimal Management of Public Debt: a Singular Stochastic Control Problem

Consider the problem of a government that wants to reduce the debt-to-GDP (gross domestic product) ratio of a country. The government aims at choosing a debt reduction policy which minimises the total expected cost of having debt, plus the total expected cost of interventions on the debt ratio. We model this problem as a singular stochastic control problem over an infinite time-horizon. In a general not necessarily Markovian framework, we first show by probabilistic arguments that the optimal debt reduction policy can be expressed in terms of the optimal stopping rule of an auxiliary optimal stopping problem. We then exploit such link to characterise the optimal control in a two-dimensional Markovian setting in which the state variables are the level of the debt-to-GDP ratio and the current inflation rate of the country. The latter follows uncontrolled Ornstein-Uhlenbeck dynamics and affects the growth rate of the debt ratio. We show that it is optimal for the government to adopt a policy that keeps the debt-to-GDP ratio under an inflation-dependent ceiling. This curve is given in terms of the solution of a nonlinear integral equation arising in the study of a fully two-dimensional optimal stopping problem.
Making Personalized Recommendation through Conversation: Architecture Design and Recommendation Methods

Due to popularity in texting and messaging, a recent advancement of deep learning technologies, a conversation-based interaction becomes an emerging user interface. While todays conversation platforms offer basic conversation capabilities such as natural language understanding, entity extraction and simple dialogue management , there are still challenges in developing practical applications to support complex use cases using a dialogue system. In this paper, we highlight such challenges and share practical knowledge learned from our experiences on developing a leisure travel shopping application that combines a personalized recommendation system and a conversation system. Such efforts include a conversation de-sign, extraction of user intents, communication of variables between a dialogue system and analytics engines, and dynamic user interface designs. In particular, we introduce our approach to overcome the unique challenges, understanding user’s intent , when dialogue system met personalized recommendation system. Furthermore, we propose a semantic mapping as a novel method to utilize undeﬁned user’s preferences when producing recommended items. Finally, examples of recommendations based on natural language conversations are provided in order to exhibit how components in the overall architecture are seamlessly orchestrated. In general, our framework provides guiding principles and best practices on the implementation of task-oriented dialogue system connected with other components in the overall architecture.
Gathering Behavior of Groups of People in a City

The city's social structure is mainly based on the activities and behavior of small groups of people whose cohesion is preserved by their tight social relations. The interactions that people have among themselves and with the places/services of the city ensure strength and duration to these social ties. Among the many media at disposal today to carry on social activities, on-phone and off-line interactions are those mainly involved in the creation of the strongest social links. In this paper we leverage mobile data to detect groups of people with strong ties and to identify the urban places where they are used to gather. We apply this methodology to the city of Milan, by means of a large anonymized dataset of Call Detail Records (CDRs) collecting phone activities (calls, texts, and Internet traffic) of about 1 million mobile users. The paper shows that mobile phone data enables to detect socially cohesive groups gathering in city's places.
Distributed learning-based state prediction for multi-agent systems with reduced communication effort

A novel distributed event-triggered communication for multi-agent systems is presented. Each agent predicts its future states via an artificial neural network, where the prediction is solely based on own past states. The approach is therefore scalable with the number of agents. A communication is triggered if the discrepancy between actual and predicted state exceeds a threshold. Numerical results show that this approach reduces the communication effort remarkably compared to existing methods.
Goal Reasoning: Foundations, Emerging Applications, and Prospects

Goal reasoning (GR) has a bright future as a foundation for the research and development of intelligent agents. GR is the study of agents that can deliberate on and self-select their goals/objectives, which is a desirable capability for some applications of deliberative autonomy. While studied in diverse AI sub-communities for multiple applications, our group has focused on how GR can play a key role for controlling autonomous systems. Thus, its importance is rapidly growing and it merits increased attention, particularly from the perspective of research on AI safety. In this article, I introduce GR, briefly relate it to other AI topics, summarize some of our group’s work on GR foundations and emerging applications, and describe some current and future research directions.
Uplink Interference Analysis with RF Switching for Lens-Based Millimeter-Wave Systems

In this paper, we take a fundamental look at the interference characteristics of a lens-based millimeter-wave (mmWave) multiuser multiple-input multiple-output system (MU-MIMO) system. We consider a hybrid architecture, implemented via a bank of radio-frequency (RF) switches which perform beam selection followed by low-complexity uplink maximum-ratio combining (MRC) at baseband. Considering a Rotman lens antenna array in line- of-sight (LoS) propagation, we derive tight analytical approximations to the average (expected) interference power of an arbitrary terminal, with and without the presence of RF switching. The analytical expressions show that without RF switching, the Rotman lens losses its benefits and collapses to a conventional uniform linear array. Our numerical results demonstrate that the expected interference power to a given terminal decreases significantly with RF switching, due to beam selection separating the uplink direction-of-arrivals (DoAs), in contrast to the case without RF switching, which relaxes the beam selection constraint and thus allows very similar DoAs. Overall, the results in this paper emphasize the necessity of RF switching in order to obtain superior performance with lens arrays, over conventional phased arrays.
Towards a Solution to the Red Wedding Problem

Edge computing promises lower latency interactions for clients operating at the edge by shifting computation away from Data Centers to Points of Presence which are more abundant and located geographically closer to end users. However, most commercially available infrastructure for edge computing focuses on applications without shared state. In this paper, we present the Red Wedding Problem , a real-world scenario motivating the need for stateful computations at the edge. We sketch the design and implementation of a prototype database for operation at the edge that addresses the issues presented in the Red Wedding Problem and present issues around implementing our solution on commercial edge infrastructure due to limitations in these offerings
MLBench: Benchmarking Machine Learning Services Against Human Experts

Modern machine learning services and systems are complicated data systems --- the process of designing such systems is an art of compromising between functionality , performance , and quality . Providing different levels of system supports for different functionalities, such as automatic feature engineering, model selection and ensemble, and hyperparameter tuning, could improve the quality, but also introduce additional cost and system complexity. In this paper, we try to facilitate the process of asking the following type of questions: How much will the users lose if we remove the support of functionality x from a machine learning service? Answering this type of questions using existing datasets, such as the UCI datasets, is challenging. The main contribution of this work is a novel dataset, MLBench, harvested from Kaggle competitions. Unlike existing datasets, MLBench contains not only the raw features for a machine learning task, but also those used by the winning teams of Kaggle competitions. The winning features serve as a baseline of best human effort that enables multiple ways to measure the quality of machine learning services that cannot be supported by existing datasets, such as relative ranking on Kaggle and relative accuracy compared with best-effort systems. We then conduct an empirical study using MLBench to understand example machine learning services from Amazon and Microsoft Azure, and showcase how MLBench enables a comparative study revealing the strength and weakness of these existing machine learning services quantitatively and systematically. The full version of this paper can be found at arxiv.org/abs/1707.09562
Empirical bounds for functions with weak interactions

We provide sharp empirical estimates of expectation, variance and normal approximation for a class of statistics whose variation in any argument does not change too much when another argument is modified. Examples of such weak interactions are furnished by U- and V-statistics, Lipschitz L-statistics and various error functionals of L2-regularized algorithms and Gibbs algorithms.
Spatio‐temporal query contextualization for microtext retrieval in social media

Generally, texts on the social media (eg, Twitter and Facebook) are too short (microtexts) to understand the meaning and to search for relevant texts. It is difficult for the conventional information retrieval systems to conduct the searching tasks. Thereby, in this paper, we propose a novel approach on query contextualization by integrating all possible microtexts by considering spatio‐temporal contexts. The proposed approach consists of two steps, which are (i) to understand and process microtexts in social media and (ii) to reformulate the queries for searching for relevant microtexts in these social media. To evaluate the performance of the query contextualization approach, microtexts from Twitter have been collected during 4 months.
A Novel Secure Scheme for Supporting Complex SQL Queries over Encrypted Databases in Cloud Computing

With the advance of database-as-a-service (DaaS) and cloud computing, increasingly more data owners are motivated to outsource their data to cloud database for great convenience and economic savings. Many encryption schemes have been proposed to process SQL queries over encrypted data in the database. In order to obtain the desired data, the SQL queries contain some statements to describe the requirement, e.g., arithmetic and comparison operators (+, -, ×, <, >, and =). However, to support different operators (+, -, ×, <, >, and =) in SQL queries over encrypted data, multiple encryption schemes need to be combined and adjusted to work together. Moreover, repeated encryptions will reduce the efficiency of execution. This paper presents a practical and secure homomorphic order-preserving encryption (FHOPE) scheme, which allows cloud server to perform complex SQL queries that contain different operators (such as addition, multiplication, order comparison, and equality checks) over encrypted data without repeated encryption. These operators are data interoperable, so they can be combined to formulate complex SQL queries. We conduct security analysis and efficiency evaluation of the proposed scheme FHOPE. The experiment results show that, compared with the existing approaches, the FHOPE scheme incurs less overhead on computation and communication. It is suitable for large batch complex SQL queries over encrypted data in cloud environment.
Emulating Smart City Sensors Using Soft Sensing and Machine Intelligence: A Case Study in Public Transportation

This paper proposes a new framework for emulating the functionality of a sensor by using multiple available soft sensors and machine intelligence algorithms. As a case study, the localization of city buses in a smart city setting is investigated by using the accelerometer and microphones of the passengers and a Support Vector Machine (SVM) running in the cloud; in this application, the GPS functionality is emulated by using these two soft sensors. What makes such an emulation feasible is the statistical dependence of the location data (which would normally be obtained from a GPS) on the accelerometer and microphone data; while accelerometers capture data that relate to the typical stop/start patterns of the buses, microphone capture enter/exit patterns of the passengers through the sound levels inside the bus. We evaluate our proposed scheme through simulations and show that the proposed framework can operate with more than 90\% accuracy in estimating the location of public buses while preserving the actual location privacy of the smartphone users. This approach results in smartphone battery energy savings of 38-46\% (as compared to GPS-based approaches) due to the elimination of the power-hungry GPS devices.