Miracle-dz/UniFAR-FTUs huggingface dataset:
Structuring instruction-sets with higher-order functions

Structuring instruction-sets with higher-order functions John Byron Cook Ph.D., OGI School of Science & Engineering at Oregon Health & Science University January 2005 Thesis Advisor: Dr. John Launchbury In an effort to improve microprocessor performance, each generation of a microprocessor family's instruction-set architecture is typically extended with new features. For example, many modern microprocessors now support parallelism annotations, predication, speculative memory access, and SIMD-based multimedia instructions. These extensions allow a compiler or programmer to directly express instruction-level parallelism that is difficult for the microprocessor to find alone. This dissertation focuses on the modeling and formal verification of microprocessor designs with instruction-set extensions. Inspired by ARM and IA-64, we develop several elementary instruction-set architectures that employ extensions. We also construct microarchitectural implementation of the instruction sets. The specification and microarchitectural model in this dissertation are represented in a novel way: as the composition of functions between transition systems. We call these functions transition system transformers. In isolation, transformers can be used to model instruction-set extensions. Together, they can be used to model an entire machine. This dissertation demonstrates that the extra structure available in transformer-based specifications and models can be used to help decompose a proof that the model implements a specification. We develop several proof strategies that make use of the transformer structure in this way. The contribution of this dissertation is the modeling and verification method that facilitates the decomposition of microarchitectural correctness proofs using instruction-set extensions.
A Comparison of Forecasting Building Material Inventory between Backpropagation Neural Network And Arima

The demand for forecasting task is very important to determine the number of stocks efficiently. This process should accommodate the demand for a company’s product or service and control the inventory level. Especially for products such as building materials that needed capitals to buy and wide space to keep it safe. This research has objective to minimize the excessive amount of product in inventory and minimize loss in sales. This study was compared between a method named Back Propagation Neural Network (BPNN) that known as one of the most accurate and widely used forecasting model and ARIMA as a time series model to find the most accurate in forecasting of inventory. In this case, the model of BPNN used 6 input neurons as a monthly period of sale, the price of the product, amount of historical selling, an approximation of project renovation, an approximation of new project building and number of a competitor. And for Arima method we have three trials of tentative models. To compare the accuracy between them, we used the performance criteria such as MAD, MAE, RMSE, RRSE and RAE. In this research, we obtained that forecasting with BPNN is more accurate than ARIMA with error prediction of 19.6, 19.6, 30.4, 0.6, 0.5 for those performance criteria consecutively.
ANFIS and Deep Learning based missing sensor data prediction in IoT

Internet of Things (IoT) consists of billions of devices that generate big data which is characterized by the large volume, velocity, and heterogeneity. In the heterogeneous IoT ecosystem, it is not so surprising that these sensor‐generated data are considered to be noisy, uncertain, erroneous, and missing due to the lack of battery power, communication errors, and malfunctioning devices. This paper presents Deep Learning (DL) and Adaptive‐Network based Fuzzy Inference System (ANFIS) based prediction models for missing sensor data problem in IoT ecosystem. First, we build ANFIS based models and optimize their parameters. Then, we construct DL based models by using Long Short Term Memory (LSTM) network structure and optimize its parameters by applying the grid search method. Finally, we evaluate all the proposed models with Intel Berkeley Lab dataset. Experimental results demonstrate that the proposed models can significantly improve the prediction accuracy and may be promising for missing sensor data prediction.
Reinforced Training Data Selection for Domain Adaptation

Supervised models suffer from the problem of domain shifting where distribution mismatch in the data across domains greatly affect model performance. To solve the problem, training data selection (TDS) has been proven to be a prospective solution for domain adaptation in leveraging appropriate data. However, conventional TDS methods normally requires a predefined threshold which is neither easy to set nor can be applied across tasks, and models are trained separately with the TDS process. To make TDS self-adapted to data and task, and to combine it with model training, in this paper, we propose a reinforcement learning (RL) framework that synchronously searches for training instances relevant to the target domain and learns better representations for them. A selection distribution generator (SDG) is designed to perform the selection and is updated according to the rewards computed from the selected data, where a predictor is included in the framework to ensure a task-specific model can be trained on the selected data and provides feedback to rewards. Experimental results from part-of-speech tagging, dependency parsing, and sentiment analysis, as well as ablation studies, illustrate that the proposed framework is not only effective in data selection and representation, but also generalized to accommodate different NLP tasks.
Detection of defects on fruits by machine vision and unsupervised segmentation

INTRODUCTION Defect detection on fruits by machine vision is a complex task. Indeed, the sound tissue colour is not uniform and the defects present a wide variability in colour, shape and texture. Mostly often, images are acquired by conventional RGB cameras and defect segmentation is performed by algorithms based on Bayes’ rules. The efficiency of these methods can be improved firstly by acquiring images with a dedicated vision system and secondly by implementing unsupervised segmentation methods. The present study concerns the results obtained on ‘Jonagold’ apples.
Tool of Spies: Leaking your IP by Altering the 3D Printer Compiler

In cyber-physical additive manufacturing systems, side-channel attacks have been used to reconstruct the G/M-code (which are instructions given to a manufacturing system) of 3D objects being produced. This method is effective for stealing intellectual property from an organization, through least expected means, during prototyping stage before the product goes through a large-scale fabrication and comes out in the market. However, an attacker can be far from being able to completely reconstruct the G/M-code due to lack of enough information leakage through the side-channels. In this paper, we propose a novel way to amplify the information leakage and thus boost the chances of recovery of G/M-code by surreptitiously altering the compiler. By using this compiler, an adversary may easily control various parameters to magnify the leakage of information from a 3D printer while still producing the desired object, thus remaining hidden from the authentic users. This type of attack may be implemented by strong attackers having access to the tool chain and seeking high level of stealth. We have implemented such a compiler and have demonstrated that it increases the success rate of recovering G/M-codes from the four side-channels (acoustic, power, vibration, and electromagnetic) by up to 39 percent compared to previously proposed attacks.
Joint Management of Wireless and Computing Resources for Computation Offloading in Mobile Edge Clouds

We consider the computation offloading problem in an edge computing system in which an operator jointly manages wireless and computing resources across devices that make their offloading decisions autonomously with the objective to minimize their own completion times. We develop a game theoretical model of the interaction between the devices and an operator that can implement one of two resource allocation policies, a cost minimizing or a time fair resource allocation policy. We express the optimal cost minimizing resource allocation policy in closed form and prove the existence of Stackelberg equilibria for both resource allocation policies. We propose two efficient decentralized algorithms that devices can use for computing equilibria of offloading decisions under the cost minimizing and the time fair resource allocation policies. We establish bounds on the price of anarchy of the games played by the devices and by doing so we show that the proposed algorithms have bounded approximation ratios. Our simulation results show that the cost minimizing resource allocation policy can achieve significantly lower completion times than the time fair allocation policy. At the same time, the convergence time of the proposed algorithms is approximately linear in the number of devices, and thus they could be effectively implemented for edge computing resource management.
Cognitive Imagery Classification of EEG Signals using CSP-based Feature Selection Method

This paper presents a novel approach of spectral feature selection using spatial filters for the classification of four cognitive imagery tasks. The input dataset consists of electroencephalogram (EEG) signals acquired through a commercial wireless headset. The spectral features included mel frequency (MF) components extracted from the low frequency bands of EEG signal. A spatial projection filter was used for the selection of the most relevant features before classification. The popular method of multiclass common spatial pattern (CSP) and regularized CSP (RCSP) are investigated for a subject dependent (intra) and subject independent (inter) generation of spatial projection filter, respectively. Based upon this, present study used two different algorithmic approaches namely MF-CSP and MF-RCSP. The developed algorithm successfully classified four imagery actions with the reported prediction accuracy of 46.23% and 64.01% and standard deviation of 11.60% and 8.67% for MF-CSP and MF-RCSP, respectively.
Zero-shot Word Sense Disambiguation using Sense Definition Embeddings

Word Sense Disambiguation (WSD) is a long-standing but open problem in Natural Language Processing (NLP). WSD corpora are typically small in size, owing to an expensive annotation process. Current supervised WSD methods treat senses as discrete labels and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen during training. This leads to poor performance on rare and unseen senses. To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space. This allows EWISE to generalize over both seen and unseen senses, thus achieving generalized zero-shot learning. To obtain target sense embeddings, EWISE utilizes sense definitions. EWISE learns a novel sentence encoder for sense definitions by using WordNet relations and also ConvE, a recently proposed knowledge graph embedding method. We also compare EWISE against other sentence encoders pretrained on large corpora to generate definition embeddings. EWISE achieves new state-of-the-art WSD performance.
Application of Blind Adaptive LMS Algorithm for Detection of CDMA Signals in Frequency Selective Fading Channels

The conventional matched filter (MF) receiver is considered the optimum filter to recover the CDMA signals. One of its problems is that its performance is significantly degraded due to the channel impairments and the increase of the multiple access interference (MAI). Parallel interference cancellation (PIC) is considered a simple yet effective multi-user detector for direct-sequence code-division multiple-access (DSCDMA) systems. However, its performance may deteriorate due to unreliable interference cancellation in the early stages. Thus, a partial PIC detector, in which partial cancellation factors (PCFs) are introduced to control the interference cancellation level, it has been developed as a remedy. Recently, an interesting adaptive multistage PIC algorithm was proposed. In this paper, an application of the least mean square (LMS) adaptive algorithm is presented by training the adaptive coefficients blindly to optimize the values of the PCFs. The performance of the presented receiver is measured in terms of bit error rate (BER) and compared with other receivers over frequency selective fading channel. It is found that the performance of the adaptive PPIC receiver is better than the performance of the other receivers in the frequency selective fading channel especially in the last stages.
The Effect of Friends’ Churn on Consumer Behavior in Mobile Networks

Short bio : Pedro Ferreira’s work focuses on how people use technology to consume experience goods and influence others to do so. These are inextricably linked to how firms behave and how public policies affect market structures. Ferreira’s work focuses on the application of robust empirical identification methods to analyze large datasets obtained from organic in-vivo large-scale network-centric randomized experiments. His research spans two interrelated applied areas -- the impact of information and communication technologies on education and peer-influence and consumption in the media industry. The bulk of his work is on medialytics – using big data analytics to understand the future of the media industry. In addition, Ferreira has also been studying competition, consumer churn and switching costs in telecommunications.
Developing Internet Online Procurement Frameworks for Construction Firms

Electronic purchasing or e-procurement saves millions of dollars yearly in transaction costs. E-procurement helps to cut down the supplier base, promotes paperless transactions, and increases transparency and accountability in the procurement process. Nonetheless, studies report that around 80% of e-procurement initiatives have met with failure and failed to achieve the desired results. Although studies to better understand the Critical Success Factors (CSFs) of e-procurement implementation involving various industries have been on the rise, little is known about architecture engineering and construction (AEC) practices, which has led to limited development of pragmatic frameworks to uncover the factors. Thus, this study aims to identify those CSFs (predicting variables) which significantly contribute to e-procurement implementation success in the construction sector and to put forward for better implementation. Results from multiple regression analysis revealed five factors to be statistically significant predictors of success. Three factors were determined to be predictors of user satisfaction. Finally, internet online procurement frameworks were developed for the success of e-procurement implementation in the construction sector.
A QRD-LSL smoothing algorithm for adaptive equalization

This paper introduces order-recursive QRD-based least-squares lattice (QRD-LSL) smoothers for adaptive equalization. The QRD-LSL smoothers use past, present and future received data sequences to estimate the present value of the transmitted data sequence. Except for an overall delay needed for physical realization, QRD-LSL smoothers can substantially outperform conventional QRD-LSL filters in adaptive equalization while retaining all the advantages of an order-recursive structure.
Analyzing Linguistic Differences between Owner and Staff Attributed Tweets

Research on social media has to date assumed that all posts from an account are authored by the same person. In this study, we challenge this assumption and study the linguistic differences between posts signed by the account owner or attributed to their staff. We introduce a novel data set of tweets posted by U.S. politicians who self-reported their tweets using a signature. We analyze the linguistic topics and style features that distinguish the two types of tweets. Predictive results show that we are able to predict owner and staff attributed tweets with good accuracy, even when not using any training data from that account.
Semi-Automted IoT Vehicles

Developing countries are the most vulnerable to road accidents because of inefficient implementation of traffic rules. There is either lack of awareness about the road safety rules to the automobilist or they simply choose to ignore them. An intelligent system that can automatically respond to road safety rules is proposed in this paper. The system proposed first identifies all the road safety rules to be implemented in the current location of the vehicle upon recognition of the rules, the system alerts the driver. In case of no response by the driver in the way of following the rule, an automated imposition of rules is done. The recognition of the rules is done by sensing the current location through GPS and scanning through the Virtual Traffic Management System for all the rules that are applicable in that region which are predefined. Upon having known all the rules to be followed, the driver is alerted about the rules. Following which, the control signals are sent to control system to orient the functioning along the lines of following the traffic rules thereby automating the response directly by the vehicles.
Pseudolinearization of the acrobot using spline functions

The authors present an algorithm for the construction of approximation to both the nonlinear control and coordinate transformation under which a single-input nonlinear system has a linear tangent model independent of its operating point. Substituting spline approximations for functions that can be difficult to compute yields a stable closed-loop system with an arbitrarily small deterioration in performance. The approximate pseudolinearization control law is easy to calculate and inexpensive to implement, regardless of the complexity of the nonlinear model. Experimental results obtained by implementing the approximate pseudolinearization control law on a remotely driven acrobot verify the controller's ease of design and computational efficiency. <<ETX>>
Study on mutual information-based feature selection in text categorization

To solve the problem of the poor effect of mutual information-based feature selection on the unbalanced corpuswhich arise from not well combining positive feature and negative feature.The ratio of positive feature and negative featureis adjusted with balance factor to strengthen the effect of negative feature.And category strong related feature is distinctedwith feature distributed factor.The experimental results verify the efficiency and probability of the improved mutual informa-tion-based feature selection.
Comparison of multi-distance signal level difference Hjorth descriptor
 and its variations for lung sound classifications

A biological signal has the multi-scale and signals complexity properties. Many studies have used the signal complexity calculation methods and multi-scale analysis to analyze the biological signal, such as lung sound. Signal complexity methods used in the biological signal analysis include entropy, fractal analysis, and Hjorth descriptor. Meanwhile, the commonly used multi-scale methods include wavelet analysis, coarse-grained procedure, and empirical mode decomposition (EMD). One of the multi-scale methods in the biological signal analysis is the multi-distance signal level difference (MSLD), which calculates a difference between two signal samples at a specific distance. In previous studies, MSLD was combined with Hjorth descriptor for lung sound classification. MSLD has the potential to be developed by modifying the fundamental equation of MSLD. This study presents the comparison of MSLD and its variations combined with Hjorth descriptor for lung sound classification. The results showed that MSLD and its variations had the highest accuracy of 98.99% for five lung sound data classes. The results of this study provided several alternatives for multi-scale signal complexity analysis method for biological signals.
What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues

Predicting financial risk is an essential task in financial market. Prior research has shown that textual information in a firm’s financial statement can be used to predict its stock’s risk level. Nowadays, firm CEOs communicate information not only verbally through press releases and financial reports, but also nonverbally through investor meetings and earnings conference calls. There are anecdotal evidences that CEO’s vocal features, such as emotions and voice tones, can reveal the firm’s performance. However, how vocal features can be used to predict risk levels, and to what extent, is still unknown. To fill the gap, we obtain earnings call audio recordings and textual transcripts for S&P 500 companies in recent years. We propose a multimodal deep regression model (MDRM) that jointly model CEO’s verbal (from text) and vocal (from audio) information in a conference call. Empirical results show that our model that jointly considers verbal and vocal features achieves significant and substantial prediction error reduction. We also discuss several interesting findings and the implications to financial markets. The processed earnings conference calls data (text and audio) are released for readers who are interested in reproducing the results or designing trading strategy.
Time Series Predictive Analysis of Bitcoin with ARMA-GARCH model in Python and R

: Bitcoin arises recent years as a most successful and widely used cryptocurrency. Though it was first invented as a decentralized currency, Bitcoin has become more and more popular as a financial asset with a market capitalization of around $117 billion, as increasing number of investors and portfolio managers start to invest in and trade it. In this paper, we are interested in finding out the future course of Bitcoin prices and returns and examining the predictive power of the ARMA-GARCH model. The paper uses Python and R environment to analyze and model financial time series. The first part covers the preliminary analysis of the daily closing prices and returns of Bitcoin, and also the stationarity of the return series. The second part intends to fit an appropriate ARMA-GARCH model. The last part focuses on using fitted model to predict future returns and prices of Bitcoin and compare it to our validation dataset.
An Empirical Study of Span Representations in Argumentation Structure Parsing

For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established. With such basis, exploring task-dependent extensions for argumentation structure parsing (ASP) becomes an interesting research direction. This study investigates (i) span representation originally developed for other NLP tasks and (ii) a simple task-dependent extension for ASP. Our extensive experiments and analysis show that these representations yield high performance for ASP and provide some challenging types of instances to be parsed.
Multilingual Model Using Cross-Task Embedding Projection

We present a method for applying a neural network trained on one (resource-rich) language for a given task to other (resource-poor) languages. We accomplish this by inducing a mapping from pre-trained cross-lingual word embeddings to the embedding layer of the neural network trained on the resource-rich language. To perform element-wise cross-task embedding projection, we invent locally linear mapping which assumes and preserves the local topology across the semantic spaces before and after the projection. Experimental results on topic classification task and sentiment analysis task showed that the fully task-specific multilingual model obtained using our method outperformed the existing multilingual models with embedding layers fixed to pre-trained cross-lingual word embeddings.