Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey of Machine Learning for Sarcasm Detection

—Sarcasm is a very unique feature of human expression. It is heavily context dependent, and most humans understand it by means of tone and other visual cues in spoken language. This is particularly difficult in online discussions where these cues are not present, and even humans may fail to understand a post as being sarcastic. Renewed interest in using Artificial Intelligence for natural language processing, and increasing communication via text has many researchers intrigued by this problem. In this paper, some of the recent research on sarcasm detection are studied and compared.
When do people rely on algorithms

Author(s): Logg, Jennifer Marie | Advisor(s): Moore, Don A | Abstract: Algorithms, scripts for sequences of mathematical calculations or procedural steps, are powerful. Even though algorithms often outperform human judgment, people appear resistant to allowing a numerical formula to make decisions for them (Dawes, 1979). Nevertheless, people are increasingly dependent on algorithms to inform their decisions on a day-to-day basis. In eight experiments, I tested whether aversion to algorithms is as straightforward a story as past work suggests. The results shed light on the important questions of when people rely on algorithmic advice over advice from people and have implications for the use of algorithms within organizations.
Management the Development and Testing of Systems of Forecasting Changes of the Chemical Composition of the Atmosphere

Living standards in the industrial cities is determined by many parameters, one of which is the environmental situation of human living space. When evaluating the ecological state of the environment can be called the dominant change in the chemical composition of the atmosphere. Providing a healthy ecological conditions related research priorities of interdisciplinary specialists. To do this, you want to create human-machine interfaces for interoperability specialists from different direction: the developer of mathematical models, tracking and analysis of heterogeneous information from the field of meteorology, atmospheric chemistry, medicine and health services for assessment of the situation in the industrial areas and making decisions not favorable situations. A functioning system of ecological monitoring of the industrial city of Ust-Kamenogorsk consists of a set of application with different formats, which requires the solution of the problem for the development of the format of the application of the basic elements of the container system and the organization of the database and access it from a problem-oriented application. To solve this problem in the way decisions are given, a comparative analysis of possible approaches and methods, as well as modern principles of application. We describe the classification of containerization and overview of modern technologies to improve the efficiency of interaction of interdisciplinary teams to solve the tasks of monitoring and forecasting chemical weather. Application results can be used to control the development and testing of the system predict the chemical composition of the atmosphere changes.
Enhanced PEGASIS using Dynamic Programming for Data Gathering in Wireless Sensor Network

A number of routing protocol algorithms such as Low-Energy Adaptive Clustering Hierarchy (LEACH) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS) have been proposed to overcome the problem of energy consumption in Wireless Sensor Network (WSN) technology. PEGASIS is a development of the LEACH protocol, where within PEGASIS all nodes are active during data transfer rounds thus limiting the lifetime of the WSN. This study aims to propose improvements from the previous PEGASIS version by giving the name Enhanced PEGASIS using Dynamic Programming (EPDP). EPDP uses the Dominating Set (DS) concept in selecting a subset of nodes to be activated and using dynamic programming based optimization in forming chains from each node. There are 2 topology nodes that we use, namely random and static. Then for the Base Station (BS), it will also be divided into several scenarios, namely the BS is placed outside the network, in the corner of the network, and in the middle of the network. Whereas to determine the performance between EPDP, PEGASIS and LEACH, an analysis of the number of die nodes, number of alive nodes, and remaining of energy were analyzed. From the experiment result, it was found that the EPDP protocol had better performance compared to the LEACH and PEGASIS protocols in terms of number of die nodes, number of alive nodes, and remaining of energy. Whereas the best BS placement is in the middle of the network and uses static node distribution topologies to save more energy.
Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data

Recently, mass spectrometry (MS) has become a viable method for elucidation of protein structure. Surface-induced dissociation (SID), colliding multiply charged protein complexes or other ions with a surface, has been paired with native MS to provide useful structural information such as connectivity and topology for many different protein complexes. We recently showed that SID gives information not only on connectivity and topology but also on relative interface strengths. However, SID has not yet been coupled with computational structure prediction methods that could use the sparse information from SID to improve the prediction of quaternary structures, i.e., how protein subunits interact with each other to form complexes. Protein–protein docking, a computational method to predict the quaternary structure of protein complexes, can be used in combination with subunit structures from X-ray crystallography and NMR in situations where it is difficult to obtain an experimental structure of an entire complex. While de novo structure prediction can be successful, many studies have shown that inclusion of experimental data can greatly increase prediction accuracy. In this study, we show that the appearance energy (AE, defined as 10% fragmentation) extracted from SID can be used in combination with Rosetta to successfully evaluate protein–protein docking poses. We developed an improved model to predict measured SID AEs and incorporated this model into a scoring function that combines the RosettaDock scoring function with a novel SID scoring term, which quantifies agreement between experiments and structures generated from RosettaDock. As a proof of principle, we tested the effectiveness of these restraints on 57 systems using ideal SID AE data (AE determined from crystal structures using the predictive model). When theoretical AEs were used, the RMSD of the selected structure improved or stayed the same in 95% of cases. When experimental SID data were incorporated on a different set of systems, the method predicted near-native structures (less than 2 Å root-mean-square deviation, RMSD, from native) for 6/9 tested cases, while unrestrained RosettaDock (without SID data) only predicted 3/9 such cases. Score versus RMSD funnel profiles were also improved when SID data were included. Additionally, we developed a confidence measure to evaluate predicted model quality in the absence of a crystal structure.
Design of an Air-Cooled Sabatier Reactor for Thermocatalytic Hydrogenation of CO2: Experimental Proof-of-Concept and Model-Based Feasibility Analysis

This study assesses the techno-economic feasibility of using a single-pass, air-cooled Sabatier reactor for the thermocatalytic conversion of CO2 into renewable natural gas (RNG). The reactor was first analyzed numerically using a dynamic mathematical model. Effects of the feed rate, coolant type (compressed air vs molten salt), and cooling rate on the reactor performance were investigated. Next, the experimental proof-of-concept was provided using an autonomous Sabatier reactor (62 g of Ni/Al2O3 catalyst), including stability tests up to 100 h time-on-stream. Both simulations and experimental investigation have shown that, with a proper selection of operating parameters, it is possible to achieve CO2 conversions higher than 90%, while keeping the selectivity to CH4 production at 100%. On the basis of these results, a large-scale system for RNG generation from landfill gas has been designed, simulated, and analyzed, resulting in the RNG production cost as low as $15/GJ for the electricity price of $0.05/kWh.
Automated Cryptocurrencies Prices Prediction Using Machine Learning

Currently, Cryptocurrency is one of the trending areas of research among researchers. Many researchers may analyze the cryptocurrency features in several ways such as market price prediction, the impact of cryptocurrency in real life and so on. In this paper, we focus on market price prediction of the number of cryptocurrencies based on their historical trend. For our study, we tried to understand and identify the daily trends in the cryptocurrency market which analyzing the features related to the price of cryptocurrency. Our dataset consists of over nine features relating to the cryptocurrency price recorded daily over the period of 6 months. We applied some machine-learning algorithms to predict the daily price change of cryptocurrencies.
RIPS, a Statistical Method for Characterizing the Limiting Scenario in a BEPU Approach

Abstract Combined approaches are now applied in safety analyses of design-basis accidents. They consist of using best-estimate models in the computer codes together with their estimated uncertainties, and require the most unfavorable initial and boundary conditions (IBCs) to be found with respect to the plant operating conditions. This implies determining first the worst-case scenarios, then predicting the figures of merit (FOMs) that must fulfill safety criteria. Such scenarios can be identified by sensitivity studies on IBCs resulting in an input vector of fixed values to realize a deterministic bounding calculation. However, it is a difficult and time-consuming iterative task especially for complex transients with interactions between parameters. Alternatively, the RIPS (Reduction of the Interval of variation of the Parameters of the Scenario) method has been developed in a best-estimate plus uncertainty approach to find the worst IBCs as a set of reduced ranges of variation of the related inputs, rather than by a vector of discrete values. It defines a critical zone for which the FOM is maximized (or minimized). To this end the RIPS method provides quantitative and graphical outcomes enabling identification of the detrimental (or favorable) ranges of variation of the preponderant IBC parameters. This is done through a statistical analysis of a large set of calculations in which all the input parameters and code model uncertainties are randomly sampled. The RIPS method analyzes the higher (or lower) quantiles of the FOM cumulative density function and determines for each input parameter the critical zone within its variation interval, i.e., where it is the most influential. Correlations between parameters are also detected. This paper describes the RIPS method and demonstrates with several examples its ability to adequately identify the critical zone of the IBC configuration space.
CodeShovel : constructing robust source code history

Source code histories are valuable resources for developers, and development tools, to reason about the evolution of their software systems. Through a survey with 42 professional software developers, we gained insight in how they use the history of their projects and what challenges they face while doing so. We discovered significant mismatches between the output provided by developers’ existing approaches and what they need to successfully complete their tasks. To address these shortcomings, we created CodeShovel, a tool for navigating method histories that is able to quickly produce complete method histories in 90% of the cases. CodeShovel enables developers to navigate the entire history of source code methods quickly and reliably, regardless of the transformations and refactorings the method has undergone over its lifetime, helping developers build a robust understanding of its evolution. A field study with 16 industrial developers confirmed our empirical findings of CodeShovel’s correctness and efficiency and additionally showed that our approach can be useful for a wide range of industrial development tasks.
Iron oxide nanoparticles: a platform for biomolecule conjugation

As the knowledge about the interferences of nanomaterials on human staminal cells are scarce and contradictory, we undertook a comparative multidisciplinary study based on the size effect of zero-valent iron, cobalt, and nickel microparticles (MPs) and nanoparticles (NPs) using human adipose stem cells (hASCs) as a model, and evaluating cytotoxicity, morphology, cellular uptake, and gene expression. Our results suggested that the medium did not influence the cell sensitivity but, surprisingly, the iron microparticles (FeMPs) resulted in being toxic. These data were supported by modifications in mRNA expression of some genes implicated in the inflammatory response. Microscopic analysis confirmed that NPs, mainly internalized by endocytosis, persist in the vesicles without any apparent cell damage. Conversely, MPs are not internalized, and the effects on hASCs have to be ascribed to the release of ions in the culture medium, or to the reduced oxygen and nutrient exchange efficiency due to the presence of MP agglomerating around the cells. Notwithstanding the results depicting a heterogeneous scene that does not allow drawing a general conclusion, this work reiterates the importance of comparative investigations on MPs, NPs, and corresponding ions, and the need to continue the thorough verification of NP and MP innocuousness to ensure unaffected stem cell physiology and differentiation.
Aplikasi Simulasi Tes Toefl Berbasis Android di Fakultas Keguruan dan Ilmu Pendidikan Universitas Hamzanwadi

This study aims to develop and appropriateness discover of the Android-based TOEFL test simulation application at FKIP of Hamzanwadi University and discover the response of students to the Android-based TOEFL test simulation application at FKIP of Hamzanwadi University. This research is Research and Development with ADDIE development model. The results of this study are the Android-based TOEFL Test Simulation Application at FKIP of Hamzanwadi University, with the display in the form of a main menu page consisting of six navigation buttons: navigation structure material, listening material, reading material, tips and trick, simulation test, and developer profile. The results of the media expert feasibility test are based on two aspects of assessment, namely the software engineering aspect and the display aspect are in the very feasible category with a total percentage of eligibility of 91%. And the results of the feasibility test from material experts are based on two aspects of assessment, namely the aspect of material content and language & communication, which is in the feasible category with a total percentage of eligibility of 79%. Meanwhile, based on student responses tested to 30 students on the developed application is positive with a total percentage of 78%.
Impact of ferrous iron dosing on iron and phosphorus solids speciation and transformation in a pilot scale membrane bioreactor

Dosing of a pilot scale MBR with a ferrous salt for P removal results in a mix of FePO4(s)and P adsorbed to amorphous iron oxyhydroxides. Increase in the proportion of FePO4(s)is desired given the higher ratio of P : Fe in the biosolids produced.
Comparing Supervised Learning Methods for Classifying Spanish Tweets Comparación de Métodos de Aprendizaje Supervisado para la Clasificación de Tweets en Español

This paper presents a set of experiments to address the global polarity classification task of Spanish Tweets of TASS 2015. In this work, we compare the main supervised classification algorithms for Sentiment Analysis: Support Vector Machines, Naive Bayes, Maximum Entropy and Decision Trees. We propose to improve the performance of these classifiers using a class reduction technique and then a voting algorithm called Naive Voting. Results show that our proposal outperforms the other machine learning methods proposed in this work.
The Modelling of Biological Growth:: a Pattern Theoretic Approach

Mathematical and statistical modeling and analysis of biological growth using images collected over time are important for understanding of normal and abnormal development. In computational anatomy, changes in the shape of a growing anatomical structure have been modeled by means of diffeomorphic transformations in the background coordinate space. Various image and landmark matching algorithms have been developed for inference of large transformations that perform image registration consistent with the material properties of brain anatomy under study. However, from a biological perspective, it is not material constants that regulate growth, it is the genetic control system. A pattern theoretic model called the Growth as Random Iterated Diffeomorphisims (GRID) introduced by Ulf Grenander (Brown University) constructs growth-induced transformations according to fundamental biological principles of growth. They are governed by an underlying genetic control that is expressed in terms of probability laws governing the spatial-temporal patterns of elementary cell decisions (e.g., cell division/death). This thesis addresses computational and stochastic aspects of the GRID model and develops its application to image analysis of growth. The first part of the thesis introduces the original GRID view of growth-induced deformation on a fine time scale as a composition of several, elementary, local deformations each resulting from a random cell decision, a highly localized event in space-time called a seed. A formalization of the proposed model using theory of stochastic processes is presented, namely, an approximation of the GRID model by the diffusion process and the Fokker-Planck equation describing the evolution of the probability density of seed trajectories in space-time. Its time-dependent and stationary numerical solutions reveal bimodal distribution of a random seed trajectory in space-time. The second part of the thesis considers the growth pattern on a coarse time scale which underlies visible shape changes seen in images. It is shown that such a “macroscopic” growth pattern is a solution to a deterministic integro-differential equation in the form of a diffeomorphic flow dependent on the GRID growth variables such as the probability density of cell decisions and the rate of contraction/expansion. Since the GRID variables are unobserved, they have to be estimated from image data. Using the GRID macroscopic growth equation such an estimation problem is formulated as an optimal control problem. The estimated GRID variables are optimal controls that force the image of an initial organism to be continuously transformed into the image of a grown organism. The GRID-based inference method is implemented for inference of growth properties of the Drosophila wing disc directly from confocal micrographs of Wingless gene expression patterns.
Speculative Multithreading Architectures

With the conventional superscalar approach delivering diminishing returns, alternate designs that make optimal use of the increasing chip densities are actively being explored. Unlike the conventional superscalar that expends all its resources in exploiting instruction-level parallelism (ILP), there has been much emphasis on architectures that can also exploit thread-level parallelism from the application. Chip-multiprocessor architectures (CMP) are one promising approach in this direction that can also better exploit the increasing transistor count on a chip. In these architectures, speculation may be employed to execute applications that are either sequential or cannot be parallelized efficiently. Unfortunately, current approaches either use limited hardware support that permit a restricted communication mechanism between processors, which can occur only through memory, or use a large amount of speculation hardware that would remain unutilized when running a explicitly parallel program or a multiprogrammed workload. In our thesis, we show that wide-issue dynamic processors that will soon populate CMPs would make fast communication at the register level a requirement for high performance. Consequently, we propose an effective but quite modest hardware that supports communication and synchronization of registers between on-chip processors. Furthermore, we propose hardware support that handles true memory dependence violations when the application is run in a speculative execution mode. We also present the compiler support that enables automatic identification of threads from sequential binaries. We show how the software-hardware approach enables effective speculative execution of a sequential binary on a CMP architecture without source re-compilation. Overall, we augment the CMP with just enough support, while still maintaining the generic CMP architecture to a reasonable degree. Given that the amount of thread- and instruction-level parallelism of applications vary widely, the traditional CMP approach of statically partitioning the chip resources between threads may lead to wasted resources when one of the threads stalls due to hazards or when the application lacks threads. The Simultaneous Multithreading (SMT) architecture addresses this problem by allowing complete flexibility in resource sharing. Unfortunately, this approach like the conventional superscalar, is so centralized that it may not be a feasible architecture. In our thesis, we explore a hybrid approach, namely the clustered SMT architecture. We show that this restricted level of simultaneous multithreading is able to capture most of the performance benefits of the fully centralized approach while, at the same time, allowing the design to be decentralized. The final part of our thesis focuses on simulation methodology. Multiprocessor system evaluation has traditionally been based on direct-execution based Execution-Driven Simulations (EDS). In such environments, the proc
A detection framework for android financial malware

As attempts to thwart cybercrime have intensified, so have innovations in how cybercriminals provision their infrastructure to sustain their activities. Consequently, what motivates cybercriminals today has changed: from ego and status, to interest and money; the cybersecurity researchers today have turned their attention to financial-related malware, especially on the Android platform. There are some major issues faced by researchers in detecting Android financial malware. Firstly, what constitutes Android financial malware is still ambiguous. There is a disparity in labelling the type of malware where most of the current detection systems emphasize the recognition of generic malware types (e.g., Trojan, Worm) rather than indicating its capabilities (e.g., banking malware, ransomware). Without knowing what constitutes financial malware, the detection systems are not capable of providing an accurate recognition of an advanced and sophisticated financial-related malware. Secondly, most of the current anomaly-based detection systems via machine learning suffer from inaccurate evaluation and comparison due to the lack of adequate datasets, which result in unreliable outputs for real-world deployment. Due to time-consuming processes, most of the available datasets are crafted mainly for static analysis, and those created for dynamic analysis are installed on an emulator or sandbox. Sophisticated malware can bypass these approaches; malware authors have employed obfuscation methods and included a wide range of anti-emulator techniques, where the malware programs attempt to hide their malicious activities by detecting the emulator. These deficiencies are some of the major reasons why Android financial malware is able to avoid detection.