Reviewerly/exHarmony huggingface dataset:
Insight from a Docker Container Introspection

Large-scale adoption of virtual containers has stimulated concerns by practitioners and academics about the viability of data acquisition and reliability due to the decreasing window to gather relevant data points. These concerns prompted the idea that introspection tools, which are able to acquire data from a system as it is running, can be utilized as both an early warning system to protect that system and as a data capture system that collects data that would be valuable from a digital forensic perspective. An exploratory case study was conducted utilizing a Docker engine and Prometheus as the introspection tool. The research contribution of this research is two-fold. First, it provides empirical support for the idea that introspection tools can be utilized to ascertain differences between pristine and infected containers. Second, it provides the ground work for future research conducting an analysis of large-scale containerized applications in a virtual cloud.
Data Link Layer Processor for 100 Gbps Terahertz Wireless Communications in 28 nm CMOS Technology

In this paper, we show our 165 Gbps data link layer processor for wireless communication in the terahertz band. The design utilizes interleaved Reed-Solomon codes with dedicated link adaptation, fragmentation, aggregation, and hybrid-automatic-repeat-request. The main advantage is the low-chip area required to fabricate the processor, which is at least two times lower than the area of low-density parity-check decoders. Surprisingly, our solution loses only ~1 dB gain when compared to high-speed low-density parity-check decoders. Moreover, with only 2.38 pJ/bit of energy consumption at 0.8 V, one of the best results in the class of comparable implementations has been achieved. Alongside, we show our vision of a complete 100 Gbps wireless transceiver, including radio frequency frontend and baseband processing. For the baseband realization, we propose a parallel sequence spread spectrum and channel combining at the baseband level. Challenges to high-speed wireless transmission at the terahertz band are addressed as well. To the authors' best knowledge, it is one of the first data link layer implementations that deal with a data rate of ≥ 100 Gbps.
Developing a Course Recommender by Combining Clustering and Fuzzy Association Rules

Each semester, students go through the process of selecting appropriate courses. It is difficult to find information about each course and ultimately make decisions. The objective of this paper is to design a course recommender model which takes student characteristics into account to recommend appropriate courses. The model uses clustering to identify students with similar interests and skills. Once similar students are found, dependencies between student course selections are examined using fuzzy association rules mining. The application of clustering and fuzzy association rules results in appropriate recommendations and a predicted score. In this study, a collection of data on undergraduate students at the Management and Accounting Faculty of College of Farabi in University of Tehran is used. The records are from 2004 to 2015. The students are divided into two clusters according to Educational background and demographics. Finally, recommended courses and predicted scores are given to students. The mined rules facilitate decision-making regarding course selection.
Exploring the Interfaces Between Big Data and Intellectual Property Law

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Monte-Carlo Tree Search for Persona Based Player Modeling

Is it possible to conduct player modeling without any players? In this paper we use Monte-Carlo Tree Search-controlled procedural personas to simulate a range of decision making styles in the puzzle game MiniDungeons 2. The purpose is to provide a method for synthetic play testing of game levels with synthetic players based on designer intuition and experience. Five personas are constructed, representing five different decision making styles archetypal for the game. The personas vary solely in the weights of decision-making utilities that describe their valuation of a set affordances in MiniDungeons 2. By configuring these weights using designer expert knowledge, and passing the configurations directly to the MCTS algorithm, we make the personas exhibit a number of distinct decision making and play styles.
Music recognition algorithm based on T-S cognitive neural network

Abstract The main task of music recognition is to acquire relevant information of music content through processing and feature extraction of audio signals, and then used for comparison, classification, and automatic recording. The cognitive neural network based on T-S model is used to train the network weights with improved genetic algorithm in the paper. The strategy of membership function parameter adjustment is combined with the combination of momentum method and learning rate adaptive adjustment. The new proposed algorithm can be used in the music recognition algorithm by adding a compensation factor related to the input dimension on the membership degree, and the experimental result of the rule disaster caused by the excessive input dimension shows that the new proposed method can be applied to the music recognition system. At the same time, it shows that the accuracy rate of the recognition network is more accurate than that of the other algorithms, and its robustness is better.
The concept of authorship and inventorship under pressure: Does artificial intelligence shift paradigms?

When it comes to the impact of AI on the concept of authorship and inventorship, opinions may differ widely. While some believe that IP will cope with the forthcoming challenges without major adjustments or alterations, others see the system as a whole at stake. The truth may lie in-between, depending on whether one is looking at patent law or at copyright law.
An Improved Attack on the Basic Merkle–Hellman Knapsack Cryptosystem

Knapsack problem is a famous NP-complete problem, which is believed to be difficult to be solved even by a quantum computer. Hence, this type of cryptosystem is a good candidate for post-quantum cryptography. Recently, many new knapsack-based cryptosystems were proposed. The basic operations of all these cryptosystems are superincreasing sequences and modular multiplications, which is the same as the basic Merkle-Hellman cryptosystem. In this paper, we revisit and present an improved version of Shamir's attack on the basic Merkle-Hellman cryptosystem, this new idea would be helpful to estimate the security of the new knapsack-based cryptosystems. The main tool of our attack is the orthogonal lattice technique. More precisely, we first obtain a sublattice containing the private key vector by calculating the orthogonal lattice of the public key vector. Combining with the necessary conditions of the equivalent keys, we can easily recover several groups of equivalent keys. The time complexity of our new attack is lower than Shamir's. The feasibility of our attack is validated by the experimental data.
Functional linguistic variation in Twitter trolling

Trolling is a multifunctional phenomenon, which varies considerably, not only in terms of the behaviours it displays and the perceptions of those behaviours, but also with respect to the platform and the community in which it resides. From a forensic perspective, trolling also varies in terms of that which is prosecutable to that which is not. Despite trolling being a linguistic act, little is known about how trolling varies linguistically. This article examines the functional linguistic variation within a corpus of Twitter trolling as a step towards distinguishing forensically significant trolling from the rest. The analysis reveals two major dimensions of linguistic variation, namely ‘interactive versus non-interactive' and ‘challenging versus non-challenging'. This second dimension reflects previous descriptions of trolling behaviours, specifically that they can be hostile and challenging, and that they post content that is not challenging but provocative. While no distinct types of trolling Tweets are found in this corpus, the findings provide a framework for quantifying the degree of a communicative function exhibited by a trolling tweet, which arguably could inform prosecuting decisions.
Longitudinal Evaluation of Software Quality Metrics in Open-Source Applications

Assessment of software quality remains the focus of important research efforts, with several proposed quality models and assessment methodologies. ISO 25010 describes software quality in terms of characteristics such as reliability, security or maintainability. In turn, these characteristics can be evaluated in terms of software metric values, establishing a relation between software metrics and quality. However, a general metric-based model for software quality does not yet exist. The diversity of software applications, metric definitions and differences between proposed quality models all contribute to this. Our paper proposes a longitudinal evaluation of the metric values and their relations in the context of three complex, open-source applications. We cover the entire 18 year development history of the targeted applications. We explore typical values for metrics associated with software product quality and explore their evolution in the context of software development. We identify dependant metrics and explore the effect class size has on the strength of dependencies. At each step, we compare the obtained results with relevant related work in order to contribute to a growing pool of evidence towards our goal - a metric-based evaluation of software quality characteristics.
Disentangling feature and lazy learning in deep neural networks: an empirical study.

Two distinct limits for deep learning as the net width $h\to\infty$ have beenproposed, depending on how the weights of the last layer scale with $h$. In thelazy-learning regime, the dynamics becomes linear in the weights and isdescribed by a Neural Tangent Kernel $\Theta$. By contrast, in thefeature-learning regime, the dynamics can be expressed in terms of thedensity distribution of the weights. Understanding which regime describesaccurately practical architectures and which one leads to better performanceremains a challenge. We answer these questions and produce newcharacterizations of these regimes for the MNIST data set, by considering deepnets $f$ whose last layer of weights scales as $\frac{\alpha}{\sqrt{h}}$ atinitialization, where $\alpha$ is a parameter we vary. We performed systematicexperiments on two setups (A) fully-connected Softplus momentum full batch and(B) convolutional ReLU momentum stochastic. We find that (1)$\alpha^*=\frac{1}{\sqrt{h}}$ separates the two regimes. (2) for (A) and (B)feature learning outperforms lazy learning, a difference in performance thatdecreases with $h$ and becomes hardly detectable asymptotically for (A) but isvery significant for (B). (3) In both regimes, the fluctuations $\delta f$induced by initial conditions on the learned function follow $\deltaf\sim1/\sqrt{h}$, leading to a performance that increases with $h$. Thisimprovement can be instead obtained at intermediate $h$ values by ensembleaveraging different networks. (4) In the feature regime there exists a timescale $t_1\sim\alpha\sqrt{h}$, such that for $t\ll t_1$ the dynamics is linear.At $t\sim t_1$, the output has grown by a magnitude $\sqrt{h}$ and the changesof the tangent kernel $\|\Delta\Theta\|$ become significant. Ultimately, itfollows $\|\Delta\Theta\|\sim(\sqrt{h}\alpha)^{-a}$ for ReLU and Softplusactivation, with $a<2$ & $a\to2$ when depth grows.
Quantum Realism

In this issue, we take a look at quantum computing: what has been accomplished and what is its future. We present a collection of views about the realistic challenges facing the future of quantum computing.
What Does Time Tell? Tracing the Forgetting Curve Using Deep Knowledge Tracing

Recurrent Neural Network (RNN) based Deep Knowledge Tracing (DKT) can extract a complex representation of student knowledge just using the historical time series of correct-incorrect responses given as input and can predict the student's performance on the next problem. funtoot is a personalized and adaptive learning system used by students to practice problems in school and at home. Our analysis of students' interaction with funtoot showed a time-gap as high as 1 h, 1 day and also 1 week between two problems attempted by a student in a task. In this work, along with the time series of previous correct-incorrect responses, we also encode the time-gap as a feature to investigate its effect on predictions. We call this variant of DKT as DKT-t. We test these models on our dataset and two major publicly available datasets from - Assistments and Carnegie Learning's Cognitive Tutor and analyze the predicted student knowledge by both the models and report our findings. We also show that DKT-t can help us trace the forgetting curve given various response sequences and their knowledge states.
Random Fault Attacks on a Class of Stream Ciphers

In this paper, we show that stream ciphers with a particular form of ciphertext output function are vulnerable to differential fault attacks using random faults. The CAESAR competition candidates Tiaoxin-346 and AEGIS-128L both fall into this category, and we show that our attack can be used to recover the secret key of Tiaoxin-346 and the entire state of AEGIS-128L with practical complexity. In the case of AEGIS-128L, the attack can be applied in a ciphertext-only scenario. Our attacks are more practical than previous fault attacks on these ciphers, which assumed bit-flipping faults. Although we also consider other ways of mitigating our attacks, we recommend that cipher designers avoid the form of ciphertext output function that we have identified.
Unleashing the Scalability Potential of Power-Constrained Data Center in the Microservice Era

Recent scale-out cloud services have undergone a shift from monolithic applications to microservices by putting each functionality into lightweight software containers. Although traditional data center power optimization frameworks excel at per-server or per-rack management, they can hardly make informed decisions when facing microservices that have different QoS requirements on a per-service basis. In a power-constrained data center, blindly budgeting power usage could lead to a power unbalance issue: microservices on the critical path may not receive adequate power budget. This unavoidably hinders the growth of cloud productivity.
Teaching AI Agents Ethical Values Using Reinforcement Learning and Policy Orchestration

Autonomous cyber-physical agents play an increasingly large role in our lives. To ensure that they behave in ways aligned with the values of society, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society. We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations and reinforcement learning to learn to maximize environmental rewards. A contextual bandit-based orchestrator then picks between the two policies: constraint-based and environment reward-based. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward-maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using Pac-Man and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.
Particulate control devices in Kemper County IGCC Project

Southern Company has commissioned a 2 × 1 Integrated Gasification Combined Cycle (IGCC) facility in Kemper County, Mississippi. The plant demonstrated the Transport Integrated Gasification (TRIGTM) technology developed by Southern Company, KBR, and the U.S. Department of Energy (DOE). With a capability of generating up to 524 MW net, the plant also captures CO2 from syngas produced from locally mined Mississippi lignite. Particulate Control Devices (PCDs), designed by Pall Corporation, are a critical component of the TRIGTM gasification process. The PCDs use dry filtration technology to remove essentially all the fine particulates from the syngas before it is sent to downstream processes for further gas clean-up, CO2 capture, and, finally for use in combustion turbines to generate power. The Kemper PCDs are designed to accommodate different types of filter elements for future optimization. The initial installation features the 2.5-meter long iron aluminide (FEAL) filter elements based on the extensive testing experience gained at the Power Systems Development Facility (PSDF). The PCD back-pulse system is equipped with the Coupled Pressure Pulse (CPPTM) blowback technology developed jointly by Pall's Schumacher Division and Karlsruhe Institute of Technology for online filter element cleaning. It is the first CPPTM application of this scale used for high-pressure, high-temperature filtration in a gasification process. The Kemper facility has achieved integrated operation with production of electricity, CO2, and other by-products from syngas per design specifications. This paper describes the operations, performance, and technical issues of both the PCD and the fines cooling and handling system that were encountered during the installation, unit commissioning, and extended operation with lignite.
On polycyclic codes over a finite chain ring

Galois images of polycyclic codes over a finite chain ring $ S $ and their annihilator dual are investigated. The case when a polycyclic code is Galois-disjoint over the ring $ S, $ is characterized and, the trace codes and restrictions of free polycyclic codes over $ S $ are also determined giving an analogue of Delsarte's theorem relating the trace code and the annihilator dual code.
On the Noise Complexity in an Optical Motion Capture Facility

Optical motion capture systems are state-of-the-art in motion acquisition; however, like any measurement system they are not error-free: noise is their intrinsic feature. The works so far mostly employ a simple noise model, expressing the uncertainty as a simple variance. In the work, we demonstrate that it might be not sufficient and we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. Such a knowledge is especially important for using optical motion capture to calibrate other techniques, and for applications requiring very fine quality of recording. For the automated readout of the noise coefficients, we solve the multidimensional regression problem using sophisticated metaheuristics in the exploration-exploitation scheme. We identified in the laboratory the notable contribution to the overall noise from white noise and random walk, and a minor contribution from blue noise and flicker, whereas the violet noise is absent. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion. We analyzed also how the noise types scale with an increasing number of cameras. We had also the opportunity to observe the influence of camera failure on the overall performance.
The Role of the Tutor in the Choice of Pedagogical Management Tools for Autonomous Work in Foreign Languages

Introduction. The paper presents the results a study of the implementation of individual educational routes in students’ autonomous foreign language study at a non-linguistic university. Factors determining student motivation and satisfaction with the autonomous study using the Rosetta Stone Advantage platform were considered. The purpose of the study was to find out how tutors manage autonomous work and what ICT tools are used in this process. The relevance of this study is determined by the importance of the implementation of electronic platforms in the process of teaching foreign languages, as well as the need to investigate new forms of management used by tutors in the process of monitoring individual learning activities in electronic educational environment. Materials and Methods.The following methods have been used in this research: a survey with Likert scale, data analysis, semantic interpretation of data and graphical representation of data. The empirical basis of this study includes the results of the survey of students of the Institute of Distance Learning and Open Education at the Financial University under the Government of the Russian Federation (Moscow). Results. In the present article it is shown that students can benefit from the integration of electronic platforms in foreign language learning since it provides them with excellent opportunities to choose their own educational content that takes both their professional interests and their present level of foreign language proficiency into account. However, although most students are motivated to use electronic educational platforms in their autonomous work, they need assistance. Tutors play an important role in the organisation of this type of educational activity in terms of explaining how to work with the platform, helping students to choose individual learning routes and providing motivation. Using various ICT tools of pedagogical management, tutors monitor the process of autonomous work and control its results; thus, the individual work of students becomes more ef ficient. Discussion and Conclusion. The results of the research and the discussion of these results with colleagues at scientific conferences and methodological seminars help to determine future perspectives for the successful use of pedagogical management tools in the process of autonomous study on electronic educational platforms. The results of the research have practical value for teachers of foreign languages working in the electronic educational environment.
Unsupervised Domain Adaptation with Sphere Retracting Transformation

Unsupervised domain adaptation aims to leverage the knowledge in training data (source domain) to improve the performance of tasks in the remaining unlabeled data (target domain) by mitigating the effect of the distribution discrepancy. Existing approaches resolve this problem mainly by 1) mapping data into a latent space where the distribution discrepancy between two domains is reduced; or 2) reducing the domain shift by weighting the source domain. However, most of these approaches share a common issue that they neglect inter-class margins while matching distributions, which has a significant impact on classification performance. In this paper, we analyze the issue from the theoretical aspect and propose a novel unsupervised domain adaptation approach: Sphere Retracting Transformation (SRT), which reduces the distribution discrepancy and increases inter-class margins. We implement SRT, according to our theoretical analysis by (1) assigning class-specific weights for data in the source domain, and (2) minimizing the intra-class variations. Experiments confirm that the SRT approach outperforms several competitive approaches for standard domain adaptation benchmarks.