Reviewerly/exHarmony huggingface dataset:
Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis

Network-monitoring data commonly arrives in the form of fast and changing data streams. Continuous and dynamic learning is an effective learning strategy when dealing with such data, where concept drifts constantly occur. We propose different stream-based, adaptive learning approaches to analyze network-traffic streams on the fly. We address two major challenges associated to stream-based machine learning and online network monitoring: ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) how to dynamically learn from and adapt to non-stationary data changing over time, and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) how to deal with the limited availability of labeled data to continuously tune a supervised-learning model. We introduce ADAM & RAL, two stream-based machine-learning techniques to tackle these challenges. ADAM relies on adaptive memory strategies to dynamically tune stream-based learning models to changes in the input data distribution. RAL combines reinforcement learning with stream-based active-learning to reduce the amount of labeled data needed for continual learning, dynamically deciding on the most informative samples to learn from. We apply ADAM & RAL to the real-time detection of network attacks in Internet network traffic, and show that it is possible to continuously achieve high detection accuracy even under the occurrence of concept drifts, limiting the amount of labeled data needed for learning.
Quantum Programming Made Easy

We present IQu, namely a quantum programming language that extends Reynold's Idealized Algol, the paradigmatic core of Algol-like languages. IQu combines imperative programming with high-order features, mediated by a simple type theory. IQu mildly merges its quantum features with the classical programming style that we can experiment through Idealized Algol, the aim being to ease a transition towards the quantum programming world. The proposed extension is done along two main directions. First, IQu makes the access to quantum co-processors by means of quantum stores. Second, IQu includes some support for the direct manipulation of quantum circuits, in accordance with recent trends in the development of quantum programming languages. Finally, we show that IQu is quite effective in expressing well-known quantum algorithms.
A classification of Markovian fermionic Gaussian master equations

We introduce a classification scheme for the generators of open fermionic Gaussian dynamics. We simultaneously partition the dynamics along the following four lines: (1) unitary versus non-unitary, (2) active versus passive, (3) state-dependent versus state-independent, and (4) single-mode versus multi-mode. We find that only nine of these 16 types of dynamics are possible. Using this partition we discuss the consequences of imposing complete positivity on fermionic Gaussian dynamics. In particular, we show that completely positive dynamics must be either unitary (and so can be implemented without a quantized environment) or active (and so must involve particle exchange with an environment).
Ohmic reservoir-based non-Markovianity and Quantum Speed Limit Time

We study the non-Markovianity and quantum speedup of a two-level atom (quantum system of interest) in a dissipative Jaynes-Cumming model, where the atom is embedded in a single-mode cavity, which is leaky being coupled to an external reservoir with Ohmic spectral density. We obtain the non-Markovianity characterized by using the probability of the atomic excited state and the negative decoherence rate in the time-local master equation. We also calculate the quantum speed limit time (QSLT) of the evolution process of the atom. The results show that, the atom-cavity coupling is the main physical reasons of the transition from Markovian to non-Markovian dynamics and the transition from no speedup to speedup process, and the critical value of this sudden transition only depends on the Ohmicity parameter. The atom-cavity coupling and the appropriate reservoir parameters can effectively improve the non-Markovianity in the dynamics process and speed up the evolution of the atom. Moreover, the initial non-Markovian dynamics first turns into Markovian and then back to non-Markovian with increasing the atom-cavity coupling under certain condition. Finally, the physical interpretation is provided.
Real World Games Look Like Spinning Tops

This paper investigates the geometrical properties of real world games (e.g. Tic-Tac-Toe, Go, StarCraft II). We hypothesise that their geometrical structure resemble a spinning top, with the upright axis representing transitive strength, and the radial axis, which corresponds to the number of cycles that exist at a particular transitive strength, representing the non-transitive dimension. We prove the existence of this geometry for a wide class of real world games, exposing their temporal nature. Additionally, we show that this unique structure also has consequences for learning - it clarifies why populations of strategies are necessary for training of agents, and how population size relates to the structure of the game. Finally, we empirically validate these claims by using a selection of nine real world two-player zero-sum symmetric games, showing 1) the spinning top structure is revealed and can be easily re-constructed by using a new method of Nash clustering to measure the interaction between transitive and cyclical strategy behaviour, and 2) the effect that population size has on the convergence in these games.
Can learned frame prediction compete with block motion compensation for video coding?

Given recent advances in learned video prediction, we investigate whether a simple video codec using a pre-trained deep model for next frame prediction based on previously encoded/decoded frames without sending any motion side information can compete with standard video codecs based on block-motion compensation. Frame differences given learned frame predictions are encoded by a standard still-image (intra) codec. Experimental results show that the rate-distortion performance of the simple codec with symmetric complexity is on average better than that of x264 codec on 10 MPEG test videos, but does not yet reach the level of x265 codec. This result demonstrates the power of learned frame prediction (LFP), since unlike motion compensation, LFP does not use information from the current picture. The implications of training with L1, L2, or combined L2 and adversarial loss on prediction performance and compression efficiency are analyzed.
Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never been systematically investigated before. Here, we study 9M tweets related to stocks of the five main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice—referred to as cashtag piggybacking—perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot-detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam- and bot-detection techniques in all studies and applications that exploit user-generated content for predicting the stock market.
Neural network agent playing spin Hamiltonian games on a quantum computer

Quantum computing is expected to provide new promising approaches for solving the most challenging problems in material science, communication, search, machine learning and other domains. However, due to the decoherence and gate imperfection errors modern quantum computer systems are characterized by a very complex, dynamical, uncertain and fluctuating computational environment. We develop an autonomous agent effectively interacting with such an environment to solve magnetism problems. By using the reinforcement learning the agent is trained to find the best-possible approximation of a spin Hamiltonian ground state from self-play on quantum devices. We show that the agent can learn the entanglement to imitate the ground state of the quantum spin dimer. The experiments were conducted on quantum computers provided by IBM. To compensate the decoherence we use local spin correction procedure derived from a general sum rule for spin-spin correlation functions of a quantum system with even number of antiferromagnetically-coupled spins in the ground state. Our study paves a way to create a new family of the neural network eigensolvers for quantum computers.
MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model

Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BNs) with First-Order Logic (FOL). MEBN has sufficient expressive power for general-purpose knowledge representation and reasoning, and is the logical basis of Probabilistic Web Ontology Language (PR-OWL), a representation language for probabilistic ontologies. Developing an MEBN model to support a given application is a challenge, requiring definition of entities, relationships, random variables, conditional dependence relationships, and probability distributions. When available, data can be invaluable both to improve performance and to streamline development. By far the most common format for available data is the relational database (RDB). Relational databases describe and organize data according to the Relational Model (RM). Developing an MEBN model from data stored in an RDB therefore requires mapping between the two formalisms. This paper presents MEBN-RM, a set of mapping rules between key elements of MEBN and RM. We identify links between the two languages (RM and MEBN) and define four levels of mapping from elements of RM to elements of MEBN. These definitions are implemented in the MEBN-RM algorithm, which converts a relational schema in RM to a partial MEBN model. Through this research, the software has been released as an MEBN-RM open-source software tool. The method is illustrated through two example use cases using MEBN-RM to develop MEBN models: a Critical Infrastructure Defense System and a Smart Manufacturing System. Both systems are proof-of-concept systems used for situation awareness, where data coming from various sensors are stored in RDBs and converted into MEBN models through the MEBN-RM algorithm. In these use cases, we evaluate the performance of the MEBN-RM algorithm in terms of mapping speed and quality to show its efficiency in MEBN modeling.
Security Challenges in Universities: Implications for Safe School Environment

This study researched on security challenges in universities and the implications for safe school environment. Three hundred and twenty five (325) samples, constituting 50% of a population of 650 security personnel in two universities in the study area participated in the study. The study was based on three research questions and one hypothesis. Data for the study was gathered through a 42-item questionnaire. The questionnaire was validated and tested for reliability using Cronbach Alpha with a reliability index of 0.87. Data collected were analyzed using means, standard deviation and independent t-test. Results indicated that cult-related activities, kidnapping, drug abuse/offences, illegal possession of fire arms by students, students’ restiveness/demonstrations, students’ union election crisis, room break-in, stealing and pilfering and sexual assaults were the most prevalent security challenges. Findings also showed that inadequate security personnel, insufficient patrol vehicles, poor lighting of university environments, non-availability of modern security facilities, poor funding of security departments, porous campus environments, lack of security-awareness education, non-availability of electronic gadgets among others were the factors militating against effective security checks in universities. Implications for safe school environment were discussed, and one of the recommendations was that the security unit should be well funded and equipped with modern technology by the university management for a better performance.
Sound Synthesis, Propagation, and Rendering: A Survey

Sound, as a crucial sensory channel, plays a vital role in improving the reality and immersiveness of a virtual environment, following only vision in importance. Sound can provide important clues such as sound directionality and spatial size. This paper gives a broad overview of research works on sound simulation in virtual reality, games, multimedia, computer-aided design. We first survey various sound synthesis methods, including harmonic synthesis, texture synthesis, spectral analysis, and physics-based synthesis. Then, we summarize popular sound propagation techniques, namely wave-based methods, geometric-based methods, and hybrid methods. Next, the sound rendering methods are reviewed. We further demonstrate the latest deep learning based sound simulation approaches. Finally, we point to some future directions of this field. To the best of our knowledge, this is the first attempt to provide a comprehensive summary of sound research in the field of computer graphics.
Enriching Word Embeddings with Temporal and Spatial Information

The meaning of a word is closely linked to sociocultural factors that can change over time and location, resulting in corresponding meaning changes.Taking a global view of words and their meanings in a widely used language, such as English, may require us to capture more refined semantics for use in time-specific or location-aware situations, such as the study of cultural trends or language use.However, popular vector representations for words do not adequately include temporal or spatial information.In this work, we present a model for learning word representation conditioned on time and location.In addition to capturing meaning changes over time and location, we require that the resulting word embeddings retain salient semantic and geometric properties.We train our model on time-and locationstamped corpora, and show using both quantitative and qualitative evaluations that it can capture semantics across time and locations.We note that our model compares favorably with the state-of-the-art for time-specific embedding, and serves as a new benchmark for location-specific embeddings.
Performance Comparison and Analysis for Large-Scale Crowd Counting Based on Convolutional Neural Networks

Nowadays, crowd analysis is one of the most important concepts that needs be relied upon, it contributes to decision making and ensuring the safety and security of the crowd. There are a variety of interesting research problems within the scope of crowd analysis including crowd tracking, crowd behaviour recognition and crowd counting. Crowd counting based on images and videos has been studied in past years. Nonetheless, estimating and detecting the number of human heads remains a challenging task due to occlusions, resolution, and lighting changes. This paper provides an overview and performance comparison of crowd counting techniques using convolutional neural networks (CNN) based on density map estimation. In this paper, we present a comprehensive analysis and benchmarking of crowd counting based on the UCF-QNRF dataset that contains the largest number of crowd count images and head annotations available in the public domain. We also show the density maps generation and their empirical evaluation along with performance comparison.
Sentiment Analysis of Czech Texts: An Algorithmic Survey

In the area of online communication, commerce and transactions, analyzing sentiment polarity of texts written in various natural languages has become crucial.While there have been a lot of contributions in resources and studies for the English language, "smaller" languages like Czech have not received much attention.In this survey, we explore the effectiveness of many existing machine learning algorithms for sentiment analysis of Czech Facebook posts and product reviews.We report the sets of optimal parameter values for each algorithm and the scores in both datasets.We finally observe that support vector machines are the best classifier and efforts to increase performance even more with bagging, boosting or voting ensemble schemes fail to do so.
Examining How GDPR Challenges Emerging Technologies

Abstract Emerging technologies, particularly cloud computing, blockchain, Internet of Things, and artificial intelligence, have received noticeable attention from research and industry. These technologies contribute to innovation in public and private organizations, but threaten the privacy of individuals. The natural characteristics of these technologies are challenged by the new general data protection regulation (GDPR). In this article, we examine the compliance challenges between these technologies' characteristics and GDPR both individually and when combined. We identified compliance opportunities related to the characteristics of these technologies. We discuss possible approaches to address the compliance challenges identified and raise questions for further research in the area.
ChangeBeadsThreader: An Interactive Environment for Tailoring Automatically Untangled Changes

To improve the usability of a revision history, change untangling, which reconstructs the history to ensure that changes in each commit belong to one intentional task, is important. Although there are several untangling approaches based on the clustering of fine-grained editing operations of source code, they often produce unsuitable result for a developer, and manual tailoring of the result is necessary. In this paper, we propose ChangeBeadsThreader (CBT), an interactive environment for splitting and merging change clusters to support the manual tailoring of untangled changes. CBT provides two features: 1) a two-dimensional space where fine-grained change history is visualized to help users find the clusters to be merged and 2) an augmented diff view that enables users to confirm the consistency of the changes in a specific cluster for finding those to be split. These features allow users to easily tailor automatically untangled changes.
A link between symmetries of critical states and the structure of SLOCC classes in multipartite systems

Central in entanglement theory is the characterization of local transformations among pure multipartite states. As a first step towards such a characterization, one needs to identify those states which can be transformed into each other via local operations with a non-vanishing probability. The classes obtained in this way are called SLOCC classes. They can be categorized into three disjoint types: the null-cone, the polystable states and strictly semistable states. Whereas the former two are well characterized, not much is known about strictly semistable states. We derive a criterion for the existence of the latter. In particular, we show that there exists a strictly semistable state if and only if there exist two polystable states whose orbits have different dimensions. We illustrate the usefulness of this criterion by applying it to tripartite states where one of the systems is a qubit. Moreover, we scrutinize all SLOCC classes of these systems and derive a complete characterization of the corresponding orbit types. We present representatives of strictly semistable classes and show to which polystable state they converge via local regular operators.
Inner For-Loop for Speeding Up Blockchain Mining

In this paper, the authors propose to increase the efficiency of blockchain mining by using a population-based approach. Blockchain relies on solving difficult mathematical problems as proof-of-work within a network before blocks are added to the chain. Brute force approach, advocated by some as the fastest algorithm for solving partial hash collisions and implemented in Bitcoin blockchain, implies exhaustive, sequential search. It involves incrementing the nonce (number) of the header by one, then taking a double SHA-256 hash at each instance and comparing it with a target value to ascertain if lower than that target. It excessively consumes both time and power. In this paper, the authors, therefore, suggest using an inner for-loop for the population-based approach. Comparison shows that it's a slightly faster approach than brute force, with an average speed advantage of about 1.67% or 3,420 iterations per second and 73% of the time performing better. Also, we observed that the more the total particles deployed, the better the performance until a pivotal point. Furthermore, a recommendation on taming the excessive use of power by networks, like Bitcoin's, by using penalty by consensus is suggested.
Wasserstein-based fairness interpretability framework for machine learning models

Abstract The objective of this article is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a distribution. In our work, we measure the model bias across sub-population distributions in the model output using the Wasserstein metric. To properly quantify the contributions of predictors, we take into account favorability of both the model and predictors with respect to the non-protected class. The quantification is accomplished by the use of transport theory, which gives rise to the decomposition of the model bias and bias explanations to positive and negative contributions. To gain more insight into the role of favorability and allow for additivity of bias explanations, we adapt techniques from cooperative game theory.
Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills

Knowledge Tracing (KT) is to trace the knowledge of students as they solve a sequence of problems represented by their related skills. This involves abstract concepts of students' states of knowledge and the interactions between those states and skills. Therefore, a KT model is designed to predict whether students will give correct answers and to describe such abstract concepts. However, existing methods either give relatively low prediction accuracy or fail to explain those concepts intuitively. In this paper, we propose a new model called Knowledge Query Network (KQN) to solve these problems. KQN uses neural networks to encode student learning activities into knowledge state and skill vectors, and models the interactions between the two types of vectors with the dot product. Through this, we introduce a novel concept called probabilistic skill similarity that relates the pairwise cosine and Euclidean distances between skill vectors to the odds ratios of the corresponding skills, which makes KQN interpretable and intuitive. On four public datasets, we have carried out experiments to show the following: 1. KQN outperforms all the existing KT models based on prediction accuracy. 2. The interaction between the knowledge state and skills can be visualized for interpretation. 3. Based on probabilistic skill similarity, a skill domain can be analyzed with clustering using the distances between the skill vectors of KQN. 4. For different values of the vector space dimensionality, KQN consistently exhibits high prediction accuracy and a strong positive correlation between the distance matrices of the skill vectors.