Reviewerly/exHarmony huggingface dataset:
MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks

As the development of neural networks, more and more deep neural networks are adopted in various tasks, such as image classification. However, as the huge computational overhead, these networks could not be applied on mobile devices or other low latency scenes. To address this dilemma, multi-classifier convolutional network is proposed to allow faster inference via early classifiers with the corresponding classifiers. These networks utilize sophisticated designing to increase the early classifier accuracy. However, naively training the multi-classifier network could hurt the performance (accuracy) of deep neural networks as early classifiers throughout interfere with the feature generation process. In this paper, we propose a general training framework named multi-self-distillation learning (MSD), which mining knowledge of different classifiers within the same network and increase every classifier accuracy. Our approach can be applied not only to multi-classifier networks, but also modern CNNs (e.g., ResNet Series) augmented with additional side branch classifiers. We use sampling-based branch augmentation technique to transform a single-classifier network into a multi-classifier network. This reduces the gap of capacity between different classifiers, and improves the effectiveness of applying MSD. Our experiments show that MSD improves the accuracy of various networks: enhancing the accuracy of every classifier significantly for existing multi-classifier network (MSDNet), improving vanilla single-classifier networks with internal classifiers with high accuracy, while also improving the final accuracy.
A Micro-Differential Evolution Algorithm for Continuous Complex Functions

In this paper, we incorporate a local search procedure into a micro differential evolution algorithm MED with the aim of solving the HappyCat function. Our purpose is to find out if our proposal is more competitive than a Ray-ES algorithm. We test our micro Differential Evolution algorithm (μDE) on HappyCat and HGBat functions. The results that we obtained with micro-DE are better compared with the results the original RayES reference algorithm. This analysis supports our conjecture that a reduced population DE hybridized with a local search (Ray search) is a key combination in dealing with this function. Our results support the hypothesis that a well-focused micro population is more accurate and efficient than existing techniques, representing (that of micro-algorithms) a serious competitor because of its efficiency and accuracy. In fact, the proposed (but never solved) HGBat function can be dealt with, showing the scalability and potential future uses of our technique.
Information Sciences in the Schedules of Library Bibliographic Classification

Library bibliographic classification (LBC) is the national classification system of Russia. The article briefly covers the history of the LBC and presents the structure and content of new sections of the LBC schedules, reflecting the modern literature on information science, information technology and cybernetics. The author considers specific features of delimitation of literature within the sections and between other divisions of the LBC medium schedules. Cybernetics section gives the most general and modern definition of cybernetics as the science of systems organization and control. The article studies the principles of literature delimitation between cybernetics, social and economic sciences. The author determines the main place for general literature on mathematical modelling and other methods of process control simulation. The article considers the principles of delimitation from the literature using mathematical modelling as a research method. The author presents the control system as a complex of interrelated and interdependent elements that form an ordered integrity and unity. In the construction of “Informatics and information technology” section the priority is given to the literature for users. The author analyses the principles of delimitation between informatics, mathematics, technology and scientific-information activity. The sources on preparation of text and graphic documents, technical and accounting documentation are considered as information technology, and literature on study and use of computer programs for these purposes is collected in the relevant divisions on computer technology. The article studies the principles of delimitation of literature on information processes in computer networks, network equipment, etc. The author determines the main place for the literature on Internet technologies and analyses the principles of delimitation from the literature on information search as scientific-information activity, creation and programming of web sites. There is fixed the place for literature on information systems and databases, the main areas of research in the field of artificial intelligence, virtual reality and virtualization technology. “Information security” completes the section. Delimitation is carried out with the legal basis of information security and engineering protection of information. New sections of the LBC schedules have improved the classification of literature and made it more convenient to search the catalogues.
Performance Analysis of Vegetation Evolution

We focus on analyzing the impact of operations of a proposed Vegetation evolution (VEGE) algorithm on its performance rather than compare it with other EC algorithms, i.e., investigate the impact of each component of the VEGE algorithm on its performance. To further analyze the performance of VEGE algorithm, we design a series of controlled experiments to investigate the contribution of each VEGE component by running them on 28 benchmark functions of 3 different dimensions. Subsequently, we summarize some our experiences on setting VEGE parameters to apply the VEGE to optimization tasks. The experimental results reveal that the maturity operation has a critical impact on performance and the number of growth operations of an individual is set as small as possible, while the number of generated seed individuals is not an important factor. Besides, we discover that population size should be gradually increased as the dimension increases. Finally, we point out several potential research directions.
Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

With electricity representing around 20% of the global energy demand, and increasing support for renewable sources of electricity, there is also an escalating need to improve solar forecasts to support power management. While considerable research has been directed to statistical methods to improve solar power forecasting, few have employed finite mixture distributions. A statistically-objective classification of the overall sky condition may lead to improved forecasts. Combining information from the synoptic driving conditions for daily variability with local processes controlling subdaily fluctuations could assist with forecast validation and enhancement where few observations are available. Gaussian mixture models provide a statistical learning approach to automatically identify prevalent sky conditions (clear, semi-cloudy, and cloudy) and explore associated weather patterns. Here a first stage in the development of such a model is presented: examining whether there is sufficient information in the large-scale environment to identify days with clear, semi-cloudy, or cloudy conditions. A three-component Gaussian distribution is developed that reproduces the observed multimodal peaks in sky clearness indices, and their temporal distribution. Posterior probabilities from the fitted mixture distributions are used to identify periods of clear, partially-cloudy, and cloudy skies. Composites of low-level (850 hPa) humidity and winds for each of the mixture components reveal three patterns associated with the typical synoptic conditions governing the sky clarity, and hence, potential solar power.
Social and welfare training in Denmark (from the experience of NVGU and UC SYD academic mobility)

Social work as a sphere of professional activity had formed in Europe by the middle of the XX century, but the experience accumulated in different countries did not result in uniform standard for the social welfare work and professional training. In Russia, social and welfare training is a relatively new training programme (since 1991), which needs to be substantially detailed by theoretical experience and practical recommendations from colleagues from around the world. In these conditions, academic mobility provides an opportunity to enrich the modern system of qualified social and welfare personnel training and expand our understanding of the structure of foreign professional education, as well as technologies, approaches and forms of student training. The authors show that various aspects of student social and welfare professional training are in the focus of domestic and foreign academic interest; two main sources of this problem are identified. The article dwells on the practical experience the students and teachers of Nizhnevartovsk State University and Aabenraa University College South Denmark accumulated through academic exchange. Regulations and statistical data analysis, observations, interviews, discussions and seminars helped to provide the necessary insight into the system of social and welfare training in Denmark. Research material may be of interest to students of Social Work programmes when studying such subjects as ‘Foreign Experience of Social Work’, ‘Social Education Fundamentals’; to teachers when developing bachelor training curriculum; to specialists of institutions when organizing student practical training.
Modelling with Non-stratified Chain Event Graphs

Chain Event Graphs (CEGs) are recent probabilisticShenvi, Aditi graphical modelling tools that haveSmith, Jim Q. proved successful in modelling scenarios withWalton, Robert context-specific independencies. Although theEldridge, Sandra theory underlying CEGs supports appropriate representation of structural zeroes, the literature so far does not provide an adaptation of the vanilla CEG methods for a real-world application presenting structural zeroes also known as the non-stratified CEG class. To illustrate these methods, we present a non-stratified CEG representing a public health intervention designed to reduce the risk and rate of falling in the elderly. We then compare the CEG model to the more conventional Bayesian Network model when applied to this setting.
incom.py – A Toolbox for Calculating Linguistic Distances and Asymmetries between Related Languages

Languages may be differently distant from each other and their mutual intelligibility may be asymmetric.In this paper we introduce incom.py, a toolbox for calculating linguistic distances and asymmetries between related languages.incom.pyallows linguist experts to quickly and easily perform statistical analyses and compare those with experimental results.We demonstrate the efficacy of incom.py in an incomprehension experiment on two Slavic languages: Bulgarian and Russian.Using incom.pywe were able to validate three methods to measure linguistic distances and asymmetries: Levenshtein distance, word adaptation surprisal, and conditional entropy as predictors of success in a reading intercomprehension experiment.
$α^α$-Rank: Practically Scaling $α$-Rank through Stochastic Optimisation

Recently, $\alpha$-Rank, a graph-based algorithm, has been proposed as a solution to ranking joint policy profiles in large scale multi-agent systems. $\alpha$-Rank claimed tractability through a polynomial time implementation with respect to the total number of pure strategy profiles. Here, we note that inputs to the algorithm were not clearly specified in the original presentation; as such, we deem complexity claims as not grounded, and conjecture solving $\alpha$-Rank is NP-hard. The authors of $\alpha$-Rank suggested that the input to $\alpha$-Rank can be an exponentially-sized payoff matrix; a claim promised to be clarified in subsequent manuscripts. Even though $\alpha$-Rank exhibits a polynomial-time solution with respect to such an input, we further reflect additional critical problems. We demonstrate that due to the need of constructing an exponentially large Markov chain, $\alpha$-Rank is infeasible beyond a small finite number of agents. We ground these claims by adopting amount of dollars spent as a non-refutable evaluation metric. Realising such scalability issue, we present a stochastic implementation of $\alpha$-Rank with a double oracle mechanism allowing for reductions in joint strategy spaces. Our method, $\alpha^\alpha$-Rank, does not need to save exponentially-large transition matrix, and can terminate early under required precision. Although theoretically our method exhibits similar worst-case complexity guarantees compared to $\alpha$-Rank, it allows us, for the first time, to practically conduct large-scale multi-agent evaluations. On $10^4 \times 10^4$ random matrices, we achieve $1000x$ speed reduction. Furthermore, we also show successful results on large joint strategy profiles with a maximum size in the order of $\mathcal{O}(2^{25})$ ($\approx 33$ million joint strategies) -- a setting not evaluable using $\alpha$-Rank with reasonable computational budget.
A Discrete CVAE for Response Generation on Short-Text Conversation

Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder(CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled la-tent variables, the model is expected to generate diverse responses. Although the CVAE-based models have shown tremendous potential, their improvement of generating high-quality responses is still unsatisfactory. In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation. A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables. Accordingly, we pro-pose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task. Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and in-formative responses.
Exploring website gist through rapid serial visual presentation

Users can make judgments about web pages in a glance. Little research has explored what semantic information can be extracted from a web page within a single fixation or what mental representations users have of web pages, but the scene perception literature provides a framework for understanding how viewers can extract and represent diverse semantic information from scenes in a glance. The purpose of this research was (1) to explore whether semantic information about a web page could be extracted within a single fixation and (2) to explore the effects of size and resolution on extracting this information. Using a rapid serial visual presentation (RSVP) paradigm, Experiment 1 explored whether certain semantic categories of websites (i.e., news, search, shopping, and social networks/blogs) could be detected within a RSVP stream of web page stimuli. Natural scenes, which have been shown to be detectable within a single fixation in the literature, served as a baseline for comparison. Experiment 2 examined the effects of stimulus size and resolution on observers' ability to detect the presence of website categories using similar methods.Findings from this research demonstrate that users have conceptual models of websites that allow detection of web pages from a fixation's worth of stimulus exposure, when provided additional time for processing. For website categories other than search, detection performance decreased significantly when web elements were no longer discernible due to decreases in size and/or resolution. The implications of this research are that website conceptual models rely more on page elements and less on the spatial relationship between these elements.Participants can detect websites accurately when they were displayed for less than a fixation and when the participants were allowed additional processing time. Subjective comments and stimulus onset asynchrony data suggested that participants likely relied on local features for the detection of website targets for several website categories. This notion was supported when the size and/or resolution of stimuli were decreased to the extent that web elements were indistinguishable. This demonstrates that schemas or conceptualizations of websites provided information sufficient to detect websites from approximately 140 ms of stimulus exposure.
Cyber security in Africa: The boring technology story that matters

Cybersecurity involves protection of computer systems from theft, damage, or manipulation to the hardware, software, or the data contained on them. A limitless cyberspace, little to no boundaries, and eroding national borders is making Africa vulnerable to cyber threats and potential harms. Cybersecurity represents serious economic and national security challenges, which need to be properly defined and contextualized.
A Cybersecurity Control Framework for Blockchain Ecosystems

This paper proposes a cybersecurity control framework for blockchain ecosystems, drawing from risks identified in the practitioner and academic literature.The framework identifies thirteen risks for blockchain implementations, ten common to other information systems and three risks specific to blockchains: centralization of computing power, transaction malleability, and flawed or malicious smart contracts.It also proposes controls to mitigate the risks identified; some were identified in the literature and some are new.Controls that apply to all types of information systems are adapted to the different components of the blockchain ecosystem.
Fault diagnosis method of gear based on lifting wavelet packet and combined optimization BP neural network

Aiming at the problem of weak signal signature recognition of gear faults, a gear fault diagnosis method based on lifting wavelet packet and combined optimization BP neural network is proposed. The initial non-sampling prediction and update operators are calculated by Lagrange interpolation subdivision based on the principle of lifting wavelet, and the adaptive redundancy lifting wavelet packet decomposition and reconstruction algorithm is constructed. The network parameters of the number of hidden layers and the quantity of nodes, initial weights and thresholds of BP neural network are optimized by genetic algorithm (GA). The Levenberg-Marquardt (LM) algorithm is used to improve the search space of the network. Through experimental analysis, the results show that the gear fault diagnosis method proposed in this paper not only has high diagnostic accuracy, but also increase efficiency.
TracKlinic: Diagnosis of Challenge Factors in Visual Tracking

Generic visual tracking is difficult due to many challenge factors (e.g., occlusion, blur, etc.). Each of these factors may cause serious problems for a tracking algorithm, and when they work together can make things even more complicated. Despite a great amount of efforts devoted to understanding the behavior of tracking algorithms, reliable and quantifiable ways for studying the per factor tracking behavior remain barely available. Addressing this issue, in this paper we contribute to the community a tracking diagnosis toolkit, TracKlinic, for diagnosis of challenge factors of tracking algorithms. TracKlinic consists of two novel components focusing on the data and analysis aspects, respectively. For the data component, we carefully prepare a set of 2,390 annotated videos, each involving one and only one major challenge factor. When analyzing an algorithm for a specific challenge factor, such one-factor-per-sequence rule greatly inhibits the disturbance from other factors and consequently leads to more faithful analysis. For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically. With TracKlinic, a thorough study is conducted on ten state-of-the-art trackers on nine challenge factors (including two compound ones). The results suggest that, heavy shape variation and occlusion are the two most challenging factors faced by most trackers. Besides, out-of-view, though does not happen frequently, is often fatal. By sharing TracKlinic, we expect to make it much easier for diagnosing tracking algorithms, and to thus facilitate developing better ones.
Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation

Dialogue response generation (DRG) is a critical component of task-oriented dialogue systems (TDSs). Its purpose is to generate proper natural language responses given some context, e.g., historical utterances, system states, etc. State-of-the-art work focuses on how to better tackle DRG in an end-to-end way. Typically, such studies assume that each token is drawn from a single distribution over the output vocabulary, which may not always be optimal. Responses vary greatly with different intents, e.g., domains, system actions. We propose a novel mixture-of-generators network (MoGNet) for DRG, where we assume that each token of a response is drawn from a mixture of distributions. MoGNet consists of a chair generator and several expert generators. Each expert is specialized for DRG w.r.t. a particular intent. The chair coordinates multiple experts and combines the output they have generated to produce more appropriate responses. We propose two strategies to help the chair make better decisions, namely, a retrospective mixture-of-generators (RMoG) and prospective mixture-of-generators (PMoG). The former only considers the historical expert-generated responses until the current time step while the latter also considers possible expert-generated responses in the future by encouraging exploration. In order to differentiate experts, we also devise a global-and-local (GL) learning scheme that forces each expert to be specialized towards a particular intent using a local loss and trains the chair and all experts to coordinate using a global loss. We carry out extensive experiments on the MultiWOZ benchmark dataset. MoGNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, demonstrating its effectiveness for DRG.
SEERL: Sample Efficient Ensemble Reinforcement Learning

Ensemble learning is a very prevalent method employed in machine learning. The relative success of ensemble methods is attributed to their ability to tackle a wide range of instances and complex problems that require different low-level approaches. However, ensemble methods are relatively less popular in reinforcement learning owing to the high sample complexity and computational expense involved in obtaining a diverse ensemble. We present a novel training and model selection framework for model-free reinforcement algorithms that use ensembles of policies obtained from a single training run. These policies are diverse in nature and are learned through directed perturbation of the model parameters at regular intervals. We show that learning and selecting an adequately diverse set of policies is required for a good ensemble while extreme diversity can prove detrimental to overall performance. Selection of an adequately diverse set of policies is done through our novel policy selection framework. We evaluate our approach on challenging discrete and continuous control tasks and also discuss various ensembling strategies. Our framework is substantially sample efficient, computationally inexpensive and is seen to outperform state-of-the-art (SOTA) scores in Atari 2600 and Mujoco.