Miracle-dz/UniFAR-FTUs huggingface dataset:
On Activation Function Coresets for Network Pruning

Model compression provides a means to efficiently deploy deep neural networks (DNNs) on devices that limited computation resources and tight power budgets, such as mobile and IoT (Internet of Things) devices. Consequently, model compression is one of the most critical topics in modern deep learning. Typically, the state-of-the-art model compression methods suffer from a big limitation: they are only based on heuristics rather than theoretical foundation and thus offer no worst-case guarantees. To bridge this gap, Baykal et. al. [2018a] suggested using a coreset, a small weighted subset of the data that provably approximates the original data set, to sparsify the parameters of a trained fully-connected neural network by sampling a number of neural network parameters based on the importance of the data. However, the sampling procedure is data-dependent and can only be only be performed after an expensive training phase. We propose the use of data-independent coresets to perform provable model compression without the need for training. We first prove that there exists a coreset whose size is independent of the input size of the data for any neuron whose activation function is from a family of functions that includes variants of ReLU, sigmoid and others. We then provide a compression-based algorithm that constructs these coresets and explicitly applies neuron pruning for the underlying model. We demonstrate the effectiveness of our methods with experimental evaluations for both synthetic and real-world benchmark network compression. In particular, our framework provides up to 90% compression on the LeNet-300-100 architecture on MNIST and actually improves the accuracy.
Accelerated Proximal Gradient Method with Line Search for Large-Scale Nonconvex Penalty Problems

In this paper, we propose an accelerated proximal gradient method with line search for solving the large-scale nonconvex penalty problems. Compared with the classic proximal gradient method, the new method does not need to evaluate the Lipschitz constant, parameters are initialized by some aggressive values and updated adaptively at each iteration. Under some common assumptions, the global subsequential convergence is established. Moreover, some numerical results on large-scale SCAD and MCP nonconvex penalty problems are reported to indicate the efficiency and superiority of the proposed method.
E-Spion: A System-Level Intrusion Detection System for IoT Devices

As the Internet of Things (IoT) grows at a rapid pace, there is a need for an effective and efficient form of security tailored for IoT devices. In this paper, we introduce E-Spion, an anomaly-based system level Intrusion Detection System (IDS) for IoT devices. E-Spion profiles IoT devices according to their 'behavior' using system level information, like running process parameters and their system calls, in an autonomous, efficient, and scalable manner. These profiles are then used to detect anomalous behaviors indicative of intrusions. E-Spion provides three layers of detection with increasing detection efficiency but at the same time higher overhead costs on the devices. We have extensively evaluated E-Spion using a comprehensive dataset of 3973 IoT malware samples in our testbed. We observe a detection efficiency ranging from 78% to 100% depending on the layers of detection employed. We provide an analysis and comparison of the different layers of E-Spion in terms of detection accuracy and overhead costs. We also analyze the behavior of the malware samples in terms of our device logs at each layer.
Multi-Writer Searchable Encryption: An LWE-based Realization and Implementation

Multi-Writer Searchable Encryption, also known as public-key encryption with keyword search(PEKS), serves a wide spectrum of data sharing applications. It allows users to search over encrypted data encrypted via different keys. However, most of the existing PEKS schemes are built on classic security assumptions, which are proven to be untenable to overcome the threats of quantum computers. To address the above problem, in this paper, we propose a lattice-based searchable encryption scheme from the learning with errors (LWE) hardness assumption. Specifically, we observe that the keys of each user in a basic scheme are composed of large-sized matrices and basis of the lattice. To reduce the complexity of key management, our scheme is designed to enable users to directly use their identity for data encryption. We present several optimization techniques for implementation to make our design nearly practical. For completeness, we conduct rigorous security, complexity, and parameter analysis on our scheme, and perform comprehensive evaluations at a commodity machine. With a scenario of 100 users, the cost of key generation for each user is 125s, and the cost of searching a document with 1000 keywords is 13.4ms.
Factor analysis on variables effecting energy consumption for retrofitting initiative at highway rest and service areas Malaysia

Rest and service areas are located along the highway which its function is to provide 24 hours' service for passengers traveling along the highway. Reducing the amount of end use energy and enhance energy efficiency in buildings are the essential strategies to inaugurates the impact to the environment. The aim of this paper is to indicate the potential factors affecting the energy consumption for a future retrofitting initiative of Rest and Service Areas called Ayer Keroh which located at west part of peninsular Malaysia. Preliminary study started by identifies the sources of end use energy consumption and identifies the Building End Use Energy Intensity of the Building [BEI]. Survey questionnaire is distributed to experts and engineers who working under all highway contractor companies to measure the level of agreement on factors effecting consumption. Then factor analysis will be performed to reduce numbers of variables on energy consumption and to prioritize the level of its importance with a factor loading 0.5 and above. The outcome from this analysis of this paper is an assisting roadmap to the future retrofitting initiative for Rest and Service Areas Ayer Keroh and provides result in energy saving approaches to improve building energy performance.
Quantitative cryptocurrency trading: exploring the use of machine learning techniques

Machine learning techniques have found application in the study and development of quantitative trading systems. These systems usually exploit supervised models trained on historical data in order to automatically generate buy/sell signals on the financial markets. Although in this context a deep exploration of the Stock, Forex, and Future exchange markets has already been made, a more limited effort has been devoted to the application of machine learning techniques to the emerging cryptocurrency exchange market. This paper explores the potential of the most established classification and time series forecasting models in cryptocurrency trading by backtesting model performance over a eight year period. The results show that, due to the heterogeneity and volatility of the underlying financial instruments, prediction models based on series forecasting perform better than classification techniques. Furthermore, trading multiple cryptocurrencies at the same time significantly increases the overall returns compared to baseline strategies exclusively based on Bitcoin trading.
Making Computer Science Education Mandatory: Exploring a Demographic Shift in Switzerland.

A promising approach to make K-12 Computer Science education more systemic could arise from a strategy focusing mostly on pre-service teachers educated through mandatory courses instead of self-selected in-service teachers. When employing mandatory courses, schools of education can reach all future teachers, but what are potential consequences resulting from this demographic shift? Pre-service teachers may not expect to acquire programming skills and may not be convinced of the relevance of Computer Science. In 2017, one of the first mandatory Computer Science education courses for pre-service K-12 teachers was introduced at the School of Education of northwestern Switzerland (PH FHNW). The mandatory nature of the course was possible because of the introduction of Computer Science as a subject in a new national curriculum. The course, based on Scalable Game Design, was taken by over 600, mostly female (75%), pre-service elementary school teachers. This paper explores the characteristics of this new audience and investigates the consequences of mandatory pre-service teacher Computer Science education. While our research shows that the course was successful, with regards to improving the students' skills, it reveals significant gender effects concerning attitudes towards Computer Science and self-efficacy.
Utilising Game Design to Create Engaging Education: A framework for Gameful Learning

If educators were to be skilled game designers, then they would be better equipped to design engaging education scenarios. This research engages in studying commercial design of engagement in games, so educators may be able to better facilitate engaging educational design, facilitated through quantified engagement metrics (SCEQ) and game design principles (GameFlow).
Towards a Common Instrument for Measuring Prior Programming Knowledge

Computing education researchers and educators use a wide range of approaches for measuring students' prior knowledge in programming. Such measurement can help adapt the learning goals and assessment tools for groups of learners at different skills levels and backgrounds. There seems to be no consensus on if and how prior programming knowledge should be measured. Traditional background surveys are often ad-hoc or non-standard, which do not allow comparison of results between different course contexts, levels, and learner groups. Moreover, surveys may yield inaccurate information and may not be useful due to lack of detail. In contrast, tests can provide much higher detail and accuracy than surveys about student knowledge or skills, but large-scale tests are typically very time-consuming or impractical to arrange. To bridge the gap between ad-hoc surveys and standardized tests, we propose and evaluate a novel self-evaluation instrument for measuring prior programming knowledge in introductory programming courses. This instrument investigates in higher detail typical course concepts in programming education considering the different levels of proficiency. Based on a sample of two thousand introductory programming course students, our analysis shows that the instrument is internally consistent, correlates with traditional background information metrics and identifies students of varying programming backgrounds.
Decoding the Human Brain Activity and Predicting the Visual Stimuli from Magnetoencephalography (MEG) Recordings

Decoding the human brain activity and predicting the visual stimuli is about retrieving and translating the signals of the human brain by applying the signal processing techniques in order to know the concurrent activities of the human brain. The topic has gotten the attention of many scientific communities in recent years as it has many medical applications. Magnetoencephalography is a non-invasive emerging medical technique and signal processing approach that utilizes the magnetic field of the human brain to decode its internal activities. Consequently, it is difficult to analyze and recognize how the human brain reacts to different visual stimulus by analyzing the signals of MEG without using the progressive techniques of signal processing such as classification. The features that are extracted from the human brain can be classified using different algorithms such as k-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), and Artificial neural network (ANN) in order to finalize the process of decoding human brain. But, these methods did not produce satisfying results because of the great number of features. In this paper, the problem of classifying MEG signals is presented and Least Square Regression (LSR) classifier is proposed to classify the signals of MEG for two different types of the visual stimuli: face and scrambled face. Our experimental results show more accurate classification performance over other opposing methodologies.
TweetScore: Scoring Tweets via Social Attribute Relationships for Twitter Spammer Detection

The spammers have been grossly detrimental since the inception of Twitter social networks and keep polluting social environments by hiding themselves among a large amount of normal users. In this paper, we aim to address two challenges existing in the spammer detection problem: 1) monitoring tweets that have a higher probability of including spam messages; 2) providing an accurate solution for spam classification. To address these two challenges, we first propose a pseudo-honeypot framework for efficient tweets monitoring and collection. By taking advantage of users' diversity and selecting normal users as the parasitic body, the pseudo-honeypot can harness normal users with features having much more potentials of attracting spammers. This lets the pseudo-honeypot collect tweets that are far more likely to include spam messages. Furthermore, we design a novel spam classification solution called TweetScore by exploring both the intrinsic attributes' and users' relationships in social networks. TweetScore quantifies such relationships into a vector of numerical values to represent each tweet's score, reflecting the associated user's behaviors. The neural network is then employed to take these vectors as input to classify spams and spammers. Through extensive experiments, we demonstrate the efficiency of the pseudo-honeypot system on spam monitoring and the accuracy of TweetScore on spam classification. Specifically, the spam and spammer ratios collected by our pseudo-honeypot system are four times as much as those of a non pseudo-honeypot counterpart while the TweetScore can achieve, on an average, 93.5% accuracy, 93.71% precision, and 1.52% false positive in online spam classification.
Impact of Spatial Constraints when Signing in Uncontrolled Mobile Conditions

In this paper, we study the impact of uncontrolled mobile conditions on signatures, when signing on two touch-screen devices of different size. 74 persons captured their signatures on both an iPad and an iPhone with different signing areas. The study exploited several quantitative indicators: an intra-personal variability measure, a statistical quality measure, and verification performance with two classifiers. We show that for 69% of writers, their signatures are more complex when made on the large surface of the iPad. This result reflects that the majority of writers are less comfortable with signing on a small handheld device with a very constrained signing surface; besides, the used rubber-tipped stylus does not offer an optimal writing precision regarding the constrained signing surface. For the remaining writers, results show that when signing on the iPad, writers tend to fill out, spontaneously, the whole available space; this leads to information loss reflected by a reduced signature complexity and stability. Performance assessment shows a significant degradation when test and reference signatures are not captured on the same platform. When test and reference signatures are captured on the same platform, performance is better when the writer signs on the small surface available on the iPhone.
Big Prime Field FFT on Multi-core Processors

We report on a multi-threaded implementation of Fast Fourier Transforms over generalized Fermat prime fields. This work extends a previous study realized on graphics processing units to multi-core processors. In this new context, we overcome the less fine control of hardware resources by successively using FFT in support of the multiplication in those fields. We obtain favorable speedup factors (up to 6.9x on a 6-core, 12 threads node, and 4.3x on a 4-core, 8 threads node) of our parallel implementation compared to the serial implementation for the overall application thanks to the low memory footprint and the sharp control of arithmetic instructions of our implementation of generalized Fermat prime fields.
The Peanut Butter and Jelly Sandwich Challenge as an Approach to Improve Students Abilities in Test Case Writing

Test cases are a set of test inputs, execution conditions, and expected results developed for a particular objective, such as to exercise a particular program path or to verify compliance with a specific requirement. Test cases that are written by students typically have recurring errors that make difficult to find hidden bugs or lack essential details about the data to be used and the instructions that must be followed. As an attempt to improve the quality of students test cases, we used a "Peanut butter and jelly sandwich challenge", typically used to teach algorithmic thinking, to show students the difficulties one may have repeating a simple set of instructions when they are not precisely described. That activity was adapted to cover contents focused on test case writing.
IPGOD: An Integrated Visualization Platform Based on Big Data Mining and Cloud Computing

With big data analytics and open data mining becoming increasingly important in this information explosion era, a highly efficient approach to providing an integrated service is by combining these two topics. Therefore, to maximize the convenience of Taiwan's open data utilization and to enrich users' experiences with big data analytics, this paper proposes the Integrated Platform for Government Open Data (IPGOD). The platform consists of a "Data System" based on a cloud data warehouse and an "Analytics System" based on machine learning utilities; these two systems can work individually or in an integrated manner. Moreover, we leverage the Apache Spark cloud platform to enhance low latency response and high performance. The experimental results demonstrate that the proposed IPGOD realizes the open data warehouse effectively and derives machine learning visualization in a user-friendly and intelligent way.
Exact Optimization via Sums of Nonnegative Circuits and Arithmetic-geometric-mean-exponentials

We provide two hybrid numeric-symbolic optimization algorithms, computing exact sums of nonnegative circuits (SONC) and sums of arithmetic-geometric-exponentials (SAGE) decompositions. Moreover, we provide a hybrid numeric-symbolic decision algorithm for polynomials lying in the interior of the SAGE cone. Each framework, inspired by previous contributions of Parrilo and Peyrl, is a rounding-projection procedure. For a polynomial lying in the interior of the SAGE cone, we prove that the decision algorithm terminates within a number of arithmetic operations, which is polynomial in the degree and number of terms of the input, and singly exponential in the number of variables. We also provide experimental comparisons regarding the implementation of the two optimization algorithms.
Unveiling Systematic Biases in Decisional Processes: An Application to Discrimination Discovery

Decisional processes are at the basis of several security and privacy applications. However, they are often not transparent and can be affected by human or algorithmic biases that may lead to systematically misleading or unfair outcomes. To unveil these biases, one has to identify which information was used to make the decision and to quantify to what extent such information has influenced the process outcome. Two classes of techniques are widely used to determine possible correlation between variables within decisional processes from observational data: (i) econometric techniques, in particular regression analysis, and (ii) knowledge discovery techniques, in particular association rules mining. However, these techniques, taken individually, have intrinsic drawbacks that limit their applicability. In this work, we propose an approach for unveiling biases in decisional processes, which leverages association rule mining for systematic hypothesis generation and regression analysis for model selection and recommendation extraction. We demonstrate the proposed approach in the context of discrimination detection, showing that not only it provides 'statistically significant' evidence of discrimination but it also allows for a more efficient operationalization of the recommendations extracted, upon which the decision maker can operate.
Qualitative Observations of Student Reasoning: Coding in the Wild

Understanding student thinking and identifying student misconceptions are important precursors to developing high quality pedagogical materials and approaches. Prior work has used conceptual assessment surveys and task-based interviews to pursue this knowledge, typically having students evaluate existing code or predict or explain code behavior. However, features of student thinking captured under these conditions may differ from features of student thinking that occurs "in the wild. " We present the results of a study conducted with 10 introductory CS students at a large, engineering-focused US university. In this work, students were asked to "Please, think aloud" as they interacted in a development environment, attempting to reverse engineer a solution to a defined task based on a provided executable. We captured and analyzed video recordings of the screen and audio recordings of their utterances to characterize their actions, current task, the relevant CS concept involved, current problem-solving phase, and expressed level of certainty. We also took note of the nature of their uncertainties and if and how these uncertainties were resolved. We found that students showed uncertainty regardless of success at task completion. Students who were successful at the task engaged in live experimentation to address these uncertainties in their thinking and coding. However, the ability to produce a program that behaved correctly did not guarantee that the students fully grasped the underlying concepts: some students submitted erroneous code that coincidentally worked, while expressing doubts about the correctness of their implementation.
Eye Tracking to Evaluate Comprehension of Computational Thinking

concepts (CTCs), specifically in block-based programming environments like Scratch, has been challenging, and assessment instruments developed and presented in prior studies either do not provide a complete picture about students' cognitive processes or they are not easy-to-administer, subjective, or requires lengthy analysis of qualitative data. Eye-tracking has become one of the most popular methods to study what individuals view, in what order, for how long, and how often, and it is a technology that could give us a window into students' cognitive processes and could be used as an assessment tool for comprehension of CTCs [1]. This study presents eye-tracking technology as an objective, non-intrusive, and easy-to-administer assessment instrument to measure students' comprehension of core CTCs including sequences, loops, events, parallelism, conditionals, operators, data, motion, looks, sound, pen, and sensing. It explores gradual changes in students' comprehension of CTCs as reflected on three eye-gaze data as a proxy; FD (Fixation Duration), SA (Saccade Amplitudes), and RF (Regression Frequency).