Miracle-dz/UniFAR-FTUs huggingface dataset:
Process mining in programming game logs to differentiate between skill levels

People have different skill levels in a certain expertise because some people have a talent for it or have worked hard to master a certain procedure. This paper will try to use process mining to see if skill level can be correctly assessed by letting a person play a game where a certain underlying knowledge and expertise is required. The game used in this paper is called Blockly Games by Google. This game teaches young children how to program and to think like a programmer using visual blocks. The way a person plays this game can say a lot about his or her skill in programming.
Micro Agent and Neural Network based Model for Data Error Detection in a Real Time Data Stream

In this paper, we present a model for learning and detecting the presence of data type errors in a real time big data stream processing context. The proposed approach is based on a collection of micro-agents. Each micro-agent is trained to detect a specific type of error using an atomic neural network based on a sample multilayer perceptron. The supervised learning process is based on a binary classifier where the training data inputs are represented by data types and data values. The Micro-Agent for Error Detection (MAED) is deployed at several instances depending on the number of error types to be handled. The orchestration mechanism of data streams to be examined is performed by a special Host Micro Agent (HMA). This later receives in real time a data stream, splits the current record into several elementary fields. Each field value is streamed to an instance of MAED Agent which responds with a signal of presence or not of a specific data type error of the corresponding data field. For each detected data type error, the HMA Agent selects and performs the appropriate cleaning algorithm from a repository to correct the present errors of the data stream. To validate this approach, we propose an implementation based on Framework Deep Learning 4j for the Machines Learning part and JADE as a Multi Agent System (MAS) platform. The used dataset is generated by an events generator for smart highways.
Expert Systems With Applications

In this paper, an adaptive network intrusion detection method using fuzzy rough set-based feature selection and GA-GOGMM-based pattern learning is presented. Based on the fuzzy rough set theory, the optimal attribute subset of network connection records is achieved by the information gain ratio criterion in advance. A greedy algorithm-based global optimal Gaussian mixture model (GMM) clustering method, termed GA-GOGMM, is introduced, to extract the intrinsic structure of network instances to achieve highly-discernable and stable normal and intrusion pattern libraries for the subsequent network intrusion detection (NID). GA-GOGMM-based pattern learning can achieve the optimal GMM of network traﬃc instances for the pattern clustering while avoiding the negative effect of the empirical initialization of clustering numbers and random initialization of clustering centers with a low computational complexity. An adaptive model updating mechanism is further introduced for the online updating of normal and intrusion pattern libraries to ensure the adaptability of the NID model. Extensive validation and comparative experiments, conducted on a benchmark dataset NSL-KDD and a self-built Nidsbench-based network simulation platform, show that the proposed ANID approach leads to a signiﬁcant improvement in detection accuracies with low false alarms and missing reports on both known and unknown attacks. It can effectively adapt to the dynamic changing network environments with high detection accuracy and low false alarm rate as well as low missing reporting rate, which has signiﬁcant application prospects.
Integrating Ethics within Machine Learning Courses

This article establishes and addresses opportunities for ethics integration into Machine-learning (ML) courses. Following a survey of the history of computing ethics and the current need for ethical consideration within ML, we consider the current state of ML ethics education via an exploratory analysis of course syllabi in computing programs. The results reveal that though ethics is part of the overall educational landscape in these programs, it is not frequently a part of core technical ML courses. To help address this gap, we offer a preliminary framework, developed via a systematic literature review, of relevant ethics questions that should be addressed within an ML project. A pilot study with 85 students confirms that this framework helped them identify and articulate key ethical considerations within their ML projects. Building from this work, we also provide three example ML course modules that bring ethical thinking directly into learning core ML content. Collectively, this research demonstrates: (1) the need for ethics to be taught as integrated within ML coursework, (2) a structured set of questions useful for identifying and addressing potential issues within an ML project, and (3) novel course models that provide examples for how to practically teach ML ethics without sacrificing core course content. An additional by-product of this research is the collection and integration of recent publications in the emerging field of ML ethics education.
Channel Estimation and Self-Interference Cancellation in Full-Duplex Communication Systems

Full-duplex (FD) wireless communications, along with millimeter wave (mmWave), and massive multiple-input multiple-output (MIMO) are key technologies for future communication networks, known as 5G networks. The main challenge in exploiting the full potential of FD communication systems lies in cancellation of strong self-interference (SI) signal. In particular, since SI cancellation requires accurate knowledge of both SI and communication channels, bandwidth efficient channel estimation techniques are of practical interest. Furthermore, SI cancellation encounters new challenges, once FD technology is combined with mmWave or massive MIMO technologies. This is because FD communication at mmWave frequencies needs to be able to deal with fast phase noise (PN) variation, and FD massive MIMO base station (BS) requires simultaneous cancellation of SI and multi-user interference (MUI). The first half of this thesis investigates channel estimation techniques to simultaneously estimate both SI and communication channels for FD communication at microwave and mmWave frequencies. We first consider FD communication at microwave frequencies and inspired by superimposed signalling, we propose a novel bandwidth efficient channel estimation technique for estimating the SI and communication channels. To evaluate the performance of the proposed estimator, we derive the lower bound for the estimation error, and show that the proposed estimator reaches the performance of the bound. In contrast to microwave frequencies, at mmWave frequencies the challenge lies in jointly estimating the channels and tracking the fast varying PN process. We address this problem by proposing an Extended Kalman filter to jointly estimate the channels and track the PN process. We derive a lower bound for the estimation error of PN at mmWave, and numerically show that the mean square error performance of the proposed estimator approaches the lower bound. The second half of this thesis focuses on the SI cancellation and data detection
Sentiment classification using tree‐based gated recurrent units

Natural Language Processing is one of the most challenging fields of Artificial Intelligence. The past 10 years, this field has witnessed a fascinating progress due to Deep Learning. Despite that, we haven’t achieved to build an architecture of models that can understand natural language as humans do. Many architectures have been proposed, each of them having its own strengths and weaknesses. In this report, we will cover the tree based architectures and in particular we will propose a different tree based architecture that is very similar to the Tree-Based LSTM, proposed by Tai(2015). In this work, we aim to make a critical comparison between the proposed architecture -Tree-Based GRUwith Tree-based LSTM for sentiment classification tasks, both binary and fine-grained.
Network Embedding via Coupled Kernelized Multi-Dimensional Array Factorization

Network embedding has been widely employed in networked data mining applications as it can learn low-dimensional and dense node representations from the high-dimensional and sparse network structure. While most existing network embedding methods only model the proximity between two nodes regardless of the order of the proximity, this paper proposes to explicitly model multi-node proximities which can be widely observed in practice, e.g., multiple researchers coauthor a paper, and multiple genes co-express a protein. Explicitly modeling multi-node proximities is important because some two-node interactions may not come into existence without a third node. By proving that LINE(1st), a recent network embedding method, is equivalent to kernelized matrix factorization, this paper proposes coupled kernelized multi-dimensional array factorization (Cetera) which jointly factorizes multiple multi-dimensional arrays by enforcing a consensus representation for each node. In this way, node representations can be more comprehensive and effective, which is demonstrated on three real-world networks through link prediction and multi-label classification.
Determining Trunk Lines in Call Centers with Nonstationary Arrivals and Lognormal Service Times

Two important resources in a call center are the number of staff and the number of trunk lines required. In this paper, we focus on the decision of the number of trunk lines that a call center should have. The current practice is to use the Erlang B or the M/M/s/0 queueing model which assumes Poisson arrivals, exponential service times, s servers and no places in queue, i.e. no customers can wait. In this paper, we improve on the state of practice in determining the required number of trunk lines, by including two realistic features present in call centers. The first realistic feature is to consider nonstationarity of arrivals. The second feature is to consider the lognormal service time distribution instead of the exponential distribution. There is extensive empirical evidence for both features. In order to carry out our computations we use the results of a paper by Massey and Whitt, Operations Research, 44(6), 1996. We have two main findings. Firstly, we find numerically that in our nonstationary Erlang loss model, Mt/G/s/0, an insensitivity result holds. The blocking probability of arrivals at the call center depends only on the mean of the lognormal service time distribution and not on its variance. Our second finding is that current practice is quite robust. In particular, we find the number of trunk lines required using a stationary Poisson approximation. This approximation assumes stationary Poisson arrivals with an appropriately chosen arrival rate and exponential service times. The approximation does quite well in predicting the number of trunk lines required.
Neural embeddings: accurate and readable inferences based on semantic kernels

Abstract Sentence embeddings are the suitable input vectors for the neural learning of a number of inferences about content and meaning. Similarity estimation, classification, emotional characterization of sentences as well as pragmatic tasks, such as question answering or dialogue, have largely demonstrated the effectiveness of vector embeddings to model semantics. Unfortunately, most of the above decisions are epistemologically opaque as for the limited interpretability of the acquired neural models based on the involved embeddings. We think that any effective approach to meaning representation should be at least epistemologically coherent. In this paper, we concentrate on the readability of neural models, as a core property of any embedding technique consistent and effective in representing sentence meaning. In this perspective, this paper discusses a novel embedding technique (the Nyström methodology) that corresponds to the reconstruction of a sentence in a kernel space, inspired by rich semantic similarity metrics (a semantic kernel) rather than by a language model. In addition to being based on a kernel that captures grammatical and lexical semantic information, the proposed embedding can be used as the input vector of an effective neural learning architecture, called Kernel-based deep architectures (KDA). Finally, it also characterizes by design the KDA explanatory capability, as the proposed embedding is derived from examples that are both human readable and labeled. This property is obtained by the integration of KDAs with an explanation methodology, called layer-wise relevance propagation (LRP), already proposed in image processing. The Nyström embeddings support here the automatic compilation of argumentations in favor or against a KDA inference, in form of an explanation: each decision can in fact be linked through LRP back to the real examples, that is, the landmarks linguistically related to the input instance. The KDA network output is explained via the analogy with the activated landmarks. Quantitative evaluation of the explanations shows that richer explanations based on semantic and syntagmatic structures characterize convincing arguments, as they effectively help the user in assessing whether or not to trust the machine decisions in different tasks, for example, Question Classification or Semantic Role Labeling. This confirms the epistemological benefit that Nyström embeddings may bring, as linguistically rich and meaningful representations for a variety of inference tasks.
Learning under Ambiguity through Multiple Predictions

Nearly all real-world image understanding problems in computer vision are inherently ambiguous. Often, predictive systems do not model this ambiguity and do not consider the possibility that there can be more than just a single outcome for a given problem. This leads to sub-par performance on ambiguous tasks as the model has to account for all possibilities with one answer. We define three typical sources of confusion that render tasks not optimally solvable with a single unique prediction. First, the fact that we are using 2D images means that the input to our models is a non-reversible projection of the 3D real world. Thus, information about sizes, distances and shapes is lost and has to be inferred by the algorithm from cues such as shading, texture or relative size. Sometimes the ambiguities cannot be resolved by the image context alone. For example, objects of interest can be occluded or too small and do not provide sufficient details to accurately solve a task. The second source of ambiguity are the annotations in our datasets. In supervised or semi-supervised learning scenarios, predictive systems are typically trained using labeled data. However, labeling large amounts of data is tedious and expensive. Thus, often not everything is fully annotated or the annotations can be wrong and inconsistent if they come from unreliable sources, such as non-experts, crowd-sourcing or automatically from the internet. Finally, and most importantly, the problem itself could be ambiguous. For example, predicting the future is inherently uncertain and, in most cases, it cannot be precisely foreseen. In this dissertation we describe two principled and general approaches of dealing with ambiguity. First, we elaborate on a method that allows the algorithm to predict multiple instead of one single answer. This is a pragmatic way of dealing with ambiguity: instead of deciding for an exclusive outcome for a given problem, we enable the system to list several possibilities. This approach has already proven to be useful in several exciting downstream applications. We show that the general framework of multiple hypothesis prediction can be used for image classification, segmentation, future prediction, conditional image generation, reinforcement learning, object pose estimation and robotic grasp point estimation. The second part describes an alternative way to deal with uncertain predictions. Often human perception can provide additional information about a task or application that an intelligent system might have not recognized. Building on the paradigm of human-machine interaction, we show how enabling interaction between the system and a user can improve predictions on the example of semantic segmentation. We describe a novel guiding mechanism that can be seamlessly integrated into the system and shows great potential beyond the demonstrated tasks for several further applications.
Sequential set-point control of thermostatic loads using extended Markov chain abstraction to improve future renewable energy integration

Additional flexible resources are required to achieve resilience and sustainable power systems. Challenges emerged due to the increasing amounts of renewable generation penetrations at both the bulk power system and the distribution sides. System operators are required to deal with higher levels of variable and uncertain power outputs for various time-scales. Moreover, replacing existing thermal units with other inertial-less technologies, make the system sensitive to even small contingencies. Demand-side control is becoming an ingredient part of our future power system operation. Effective utilization of demand-side resources can make the system more elastic to integrate the future renewable plans. To help in resolving these challenges, this work develops a demand-side control framework on the Thermostatically Controlled Loads (TCLs) to support the grid with minimal impacts on customers’ comfort and devices’ integrity. The Markov chain abstraction method is used to aggregate the TCLs and describe their collective dynamics. Statistical learning techniques of hidden Markov chain analysis is used to identify the parameters of the resulting Markov chains at fixed temperature set-points. Various sensitivities are conducted to reveal the optimal Markov chain representation. To allow extracting or storing additional thermal energy, this thesis develops an Extended Markov Model(EMM) which describes devices’ transition when a new set-point is instructed. The results have shown that the EMM is able to capture both devices’ transient and steady-state behaviors under small and large set-point adjustments. Parameters heterogeneity affects the accuracy of the EMM model. In contrast to what proposed in the literature, more comprehensive heterogeneous parameters are defined and considered. The Kmean clustering approach is proposed in our analysis to minimize the heterogeneity error. Devices are divided into multiple clusters based on the power ratings and cycling characteristics. The results
Monitoring network data by considering the correlation of network feature statistics

Network monitoring has wide applications in computer and social network surveillance ， pathological diagnosis in neuroscience and bioscience among others. Motivated by a real example of brain networks, we focus our interests on monitoring networks by considering correlations of feature statistics. Structural statistics of numbers of edges, stars and triangles, are adopted to summarize the main features of a network - density, degree variability, and transitivity. A multivariate chart is proposed to monitor the multiple statistics simultaneously, which has not been paid much attention to in previous studies. A simulation study is conducted to compare the performances of the multivariate chart and individual charts for the structural statistics as well as a model-based approach as a benchmark. The results show that the multivariate chart for the structural statistics perform well in most scenarios. In particular, it is more advantageous in timely detecting large shifts of connection propensity and degree variability locally and globally. A real case of monitoring Enron email networks is analyzed as an illustration.
Cross Layer Optimization of Wireless Control Links in the Software-Defined LEO Satellite Network

The low earth orbit (LEO) satellite network can benefit from software-defined networking (SDN) by lightening forwarding devices and improving service diversity. In order to apply SDN into the network, however, reliable SDN control links should be associated from satellite gateways to satellites, with the wireless and mobile properties of the network taken into account. Since these characteristics affect both control link association and gateway power allocation, we define a new cross layer SDN control link problem. To the best of our knowledge, this is the first attempt to explore the cross layer control link problem for the software-defined satellite network. A logically centralized SDN control framework constrained by maximum total power is introduced to enhance gateway power efficiency for control link setup. Based on the power control analysis of the problem, a power-efficient control link algorithm is developed, which establishes low latency control links with reduced power consumption. Along with the sensitivity analysis of the proposed control link algorithm, numerical results demonstrate low latency and high reliability of control links established by the algorithm, ultimately suggesting the feasibility, both technical and economical, of the software-defined LEO satellite network.
Reframing the Design Process: Integrating Goals, Methods and Manifestation into the Co-Evolution Model

In their early years of education, design students may experience difficulties in reframing design problems. Since reframing is linked to creativity, this may be problematic. While there are some models available to describe the reframing process, it is yet unclear how they are supporting design students. This study concerned the development and test of a framing model based on co-evolution transitions, through a two-part study: interviews with expert designers and workshops followed by group interviews with novices. The resulting model offers a way of thinking and a way of working, based on the fluidity of the design process. This study yielded two major insights. Firstly, students tend to perceive the problem space to be fixed once they defined it, even if they discovered disparate information along the way. Secondly, the developed model provides students with guidance and confidence in dealing with complex problems. Our results have a considerable impact on design education, as it is important to reinforce to design students that both the problem and solution understanding are fluid, and this model provides initial steps to help designers structuring their process.
Design of Compressed Sensing System With Probability-Based Prior Information

This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing systems. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived that requires low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of video streaming are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed compressed sensing (CS) approach outperforms existing CS systems.