Reviewerly/exHarmony huggingface dataset:
Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation

Recent advances in unsupervised domain adaptation for semantic segmentation have shown great potentials to relieve the demand of expensive per-pixel annotations. However, most existing works address the domain discrepancy by aligning the data distributions of two domains at a global image level whereas the local consistencies are largely neglected. This paper presents an innovative local contextual-relation consistent domain adaptation (CrCDA) technique that aims to achieve local-level consistencies during the global-level alignment. The idea is to take a closer look at region-wise feature representations and align them for local-level consistencies. Specifically, CrCDA learns and enforces the prototypical local contextual-relations explicitly in the feature space of a labelled source domain while transferring them to an unlabelled target domain via backpropagation-based adversarial learning. An adaptive entropy max-min adversarial learning scheme is designed to optimally align these hundreds of local contextual-relations across domain without requiring discriminator or extra computation overhead. The proposed CrCDA has been evaluated extensively over two challenging domain adaptive segmentation tasks (e.g., GTA5 to Cityscapes and SYNTHIA to Cityscapes), and experiments demonstrate its superior segmentation performance as compared with state-of-the-art methods.
Delay Minimization for Federated Learning Over Wireless Communication Networks

In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model parameters to a base station (BS) which aggregates the local FL models and broadcasts the aggregated FL model back to all the users. Since FL involves learning model exchanges between the users and the BS, both computation and communication latencies are determined by the required learning accuracy level, which affects the convergence rate of the FL algorithm. This joint learning and communication problem is formulated as a delay minimization problem, where it is proved that the objective function is a convex function of the learning accuracy. Then, a bisection search algorithm is proposed to obtain the optimal solution. Simulation results show that the proposed algorithm can reduce delay by up to 27.3% compared to conventional FL methods.
BOSH: Bayesian Optimization by Sampling Hierarchically

Deployments of Bayesian Optimization (BO) for functions with stochastic evaluations, such as parameter tuning via cross validation and simulation optimization, typically optimize an average of a fixed set of noisy realizations of the objective function. However, disregarding the true objective function in this manner finds a high-precision optimum of the wrong function. To solve this problem, we propose Bayesian Optimization by Sampling Hierarchically (BOSH), a novel BO routine pairing a hierarchical Gaussian process with an information-theoretic framework to generate a growing pool of realizations as the optimization progresses. We demonstrate that BOSH provides more efficient and higher-precision optimization than standard BO across synthetic benchmarks, simulation optimization, reinforcement learning and hyper-parameter tuning tasks.
Mastery Learning Heuristics and Their Hidden Models

Mastery learning algorithms are used in many adaptive learning technologies to assess when a student has learned a particular concept or skill. To assess mastery, some technologies utilize data-driven models while others use simple heuristics. Prior work has suggested that heuristics may often perform comparably to model-based algorithms. But is there any reason we should expect these heuristics to be reasonable? In this paper, we show that two prominent mastery learning heuristics can be reinterpreted as model-based algorithms. In particular, we show that the N-Consecutive Correct in a Row heuristic and a simplified version of ALEKS’ mastery learning heuristic are both optimal policies for variants of the Bayesian knowledge tracing model. By putting mastery learning heuristics on the same playing field as model-based algorithms, we can gain insights on their hidden assumptions about learning and why they might perform well in practice.
PENERAPANA METODE NATIONAL INSTITUTE OF STANDARS AND TECHNOLOGY (NIST) DALAM ANALISIS FORENSIK DIGITAL UNTUK PENANGANAN CYBER CRIME

The development of technology is currently developing very quickly, this is directly proportional to releasing cyber crime. One crime that often occurs is the case of Pornography. This application is done using one of the very popular Instant Messenger (IM) applications, the WhatsApp application. But after the crime is committed next or sending evidence in the form of conversations, video recordings, images and others committed by the suspect using the WhatsApp application. Therefore, this study proposes to find evidence related to digital pornography. This research produced a forensic procedure in conducting an investigation of the WhatsApp application to obtain previously published evidence containing a conversation session, a list of contact numbers, a profile photo of the victim and others. This research was conducted by reading a backup file of the encrypted WhatsApp application database that stores the conversation session that has been released. This research uses the method (National Institute of Standards and Technology (NIST). This digital evidence can be obtained using one of the forensic tools namely WhtasApp Viewer. The results obtained in this study contain WhatsApp contents that have been available which can be found digitally in uncovering the crime of pornography that is happen.
Impact of Conversational Formality on the Quality and Formality of Written Summaries

This study investigated the impact of conversational agent formality on the quality of summaries and formality of written summaries during the training session and on posttest in a trialog-based intelligent tutoring system (ITS). During training, participants learned summarization strategies with the guidance of conversational agents who spoke one of the following three styles of language: (1) a formal language for both the teacher agent and the student agent, (2) an informal language for both agents, and (3) a mixed language with a formal language for the teacher agent and the informal language for the student agent. Results showed that participants wrote better quality summaries during training than pretest and/or posttest in each condition. Results also showed that agent informal language caused participants to write more informal summaries during training than on pretest. Implications are discussed for the potential application of adaptive design of conversational agents in the ITS.
Teaching Staff’s Salaries in Regions of the Far North: Economic and Statistical Analysis

This article covers selected results of economic and statistical analysis of the dynamics of teaching staff’s remuneration in 12 regions of the Russian Federation, the territory of which completely belongs to the Far North and equated areas as of 01.01.2019. The growth tendencies in remuneration of teaching staff at two levels of the educational system preschool and general education (including as compared to Russia as a whole) were revealed, as well as problems encountered in 2013-2018 in connection with the implementation of Presidential Executive Order No. 597 of May 7, 2012 «On Measures to Implement the State Social Policy». The authors argue the need for widespread use of the average annual absolute value of one percent growth, which allows to correctly validate the ﬁnal rating of the country’s regions by average wages level. The indicator «advance coefficient» is proposed, which allows to highlight the main factors of fulﬁllment or nonfulﬁllment of the 2012 May Decree of the President of the Russian Federation in certain regions of the country. The article states that the achievement of the salary targets for each year of the period under review is determined by a combination of two factors: the level of implementation of the target indicator in the base year and the advance coefficient, which is the ratio between the growth rate of teachers’ salaries and the growth rate of the Presidential Decree of 2012’s target indicator. According to the authors, improved methods of economic and statistical analysis shall improve forecast estimates accuracy, which in turn is necessary to outline concrete steps to implement social tasks of national importance concerning each of the regions under consideration.
AViD Dataset: Anonymized Videos from Diverse Countries

We introduce a new public video dataset for action recognition: Anonymized Videos from Diverse countries (AViD). Unlike existing public video datasets, AViD is a collection of action videos from many different countries. The motivation is to create a public dataset that would benefit training and pretraining of action recognition models for everybody, rather than making it useful for limited countries. Further, all the face identities in the AViD videos are properly anonymized to protect their privacy. It also is a static dataset where each video is licensed with the creative commons license. We confirm that most of the existing video datasets are statistically biased to only capture action videos from a limited number of countries. We experimentally illustrate that models trained with such biased datasets do not transfer perfectly to action videos from the other countries, and show that AViD addresses such problem. We also confirm that the new AViD dataset could serve as a good dataset for pretraining the models, performing comparably or better than prior datasets.
Discovering Fails in Software Projects Planning Based on Linguistic Summaries

Linguistic data summarization techniques help to discover complex relationships between variables and to present the information in natural language. There are some investigations associated to algorithms to build linguistic summaries. But the literature does no report investigations concerned with combination linguistic data summarization techniques and outliers' mining applied to planning of software project. In particular, outliers' mining is a datamining technique, useful in errors and fraud detection. In this work authors present new algorithms to build linguistic data summaries from outliers in software project planning context. Besides, authors compare different outliers' detection algorithms in software project planning context. The main motivation of this work is to detect planning errors in projects, to avoid high cost and time delays. Authors consider that the combination of outliers' mining and linguistic data summarization support project managers to decision-making process in the software project planning. Finally, authors present the interpretation of obtained summaries and comment about its impact.
Design of Network Precision Marketing Based on Big Data Analysis Technology

In the process of big data processing, big data analysis is the core work content. After obtaining a large amount of data, we use related analysis technology to perform data processing and analysis to obtain knowledge. Its related content includes visual analysis, data mining, predictive analysis, semantic analysis and data quality management. How to obtain big data, classify and store according to data types, mine valuable information from big data, and effectively apply big data in the field of precision marketing are hot topics of research. On the basis of researching the key technologies of big data analysis, this paper uses Hadoop big data to analyze and store the massive online logs generated by users' mobile terminals, and calculates and builds user characteristic databases. We use relevant analysis technology to analyze the user's location information, browsing and usage habits, hobbies, and focus content. At the same time, a precise marketing model is established according to user behavior characteristics and attributes, thereby improving the marketing effect of the enterprise.
Behavioral Repertoire via Generative Adversarial Policy Networks

Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpected changes in the environment or in robot morphology may require using different behaviors to achieve the same effect, for instance, to reach and grasp an object in changing clutter. An emerging paradigm addressing this robustness issue is to learn a diverse set of successful behaviors for a given task, from which a robot can select the most suitable policy when faced with a new environment. In this article, we explore a novel realization of this vision by learning a generative model over policies. Rather than learning a single policy, or a small fixed repertoire, our generative model for policies compactly encodes an unbounded number of policies and allows novel controller variants to be sampled. Leveraging our generative policy network, a robot can sample novel behaviors until it finds one that works for a new scenario. We demonstrate this idea with an application of robust ball throwing in the presence of obstacles, as well as joint-damage-robust throwing. We show that this approach achieves a greater diversity of behaviors than an existing evolutionary approach, while maintaining good efficacy of sampled behaviors, allowing a Baxter robot to hit targets more often when ball throwing in the presence of varying obstacles or joint impediments.
Analysis of Predictive Coding Models for Phonemic Representation Learning in Small Datasets

Neural network models using predictive coding are interesting from the viewpoint of computational modelling of human language acquisition, where the objective is to understand how linguistic units could be learned from speech without any labels. Even though several promising predictive coding -based learning algorithms have been proposed in the literature, it is currently unclear how well they generalise to different languages and training dataset sizes. In addition, despite that such models have shown to be effective phonemic feature learners, it is unclear whether minimisation of the predictive loss functions of these models also leads to optimal phoneme-like representations. The present study investigates the behaviour of two predictive coding models, Autoregressive Predictive Coding and Contrastive Predictive Coding, in a phoneme discrimination task (ABX task) for two languages with different dataset sizes. Our experiments show a strong correlation between the autoregressive loss and the phoneme discrimination scores with the two datasets. However, to our surprise, the CPC model shows rapid convergence already after one pass over the training data, and, on average, its representations outperform those of APC on both languages.
BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy

Brain storm optimization algorithms (BSO) have shown great potential in many global black-box optimization problems. However, the existing BSO variants can suffer from three problems: (1) large-scale optimization problem; (2) hyperparameter optimization problem; (3) high computational cost of the clustering operations. To address these problems, in this paper, we propose a simple yet effective BSO variant named Brain Storm Optimization Algorithm with Cooperative Learning Strategy (BSO-CLS). It is inspired by the new ideas generating process of brain storm in which the participators propose their own ideas by cooperatively learning other participators’ ideas. Thus, BSO-CLS iteratively updates the candidate solutions by linearly combining other solutions with the weights deriving from the fitness values of other solutions. To validate the effectiveness of the proposed method, we test it on 6 benchmark functions with the 1000 dimensions. The experimental results show that BSO-CLS can outperform the vanilla BSO and the other BSO variant with the learning strategy.
QoS to QoE Mapping Function for Iptv Quality Assessement Based on Kohonen Map: A Pilot Study

Abstract This paper deals with an analysis of Kohonen map usage possibility for real-time evaluation of end-user video quality perception. The Quality of Service framework (QoS) describes how the network impairments (network utilization or packet loss) influence the picture quality, but it does not reflect precisely on customer subjective perceived quality of received video stream. There are several objective video assessment metrics based on mathematical models trying to simulate human visual system but each of them has its own evaluation scale. This causes a serious problem for service providers to identify a critical point when intervention into the network behaviour is needed. On the other hand, subjective tests (Quality of Experience concept) are time-consuming and costly and of course, cannot be performed in real-time. Therefore, we proposed a mapping function able to predict subjective end-user quality perception based on the situation in a network, video stream features and results obtained from the objective video assessment method.
A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research

Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature.
A New Local Search Adaptive Genetic Algorithm for the Pseudo-Coloring Problem

Several applications result in a gray level image partitioned into different regions of interest. However, the human brain has difficulty in recognizing many levels of gray. In some cases, this problem is alleviated with the attribution of artificial colors to these regions, thus configuring an application in the area of visualization and graphic processing responsible for categorizing samples using colors. However, the task of making a set of distinct colors for these regions stand out is a problem of the NP-hard class, known as the pseudo-coloring problem (PsCP). In this work, it is proposed to use the well-known meta-heuristic Genetic Algorithm together with operators specialized in the local search for solutions as well as self-adjusting operators responsible for guiding the parameterization of the technique during the resolution of PsCPs. The proposed methodology was evaluated in two different scenarios of color assignment, having obtained the best results in comparison to the techniques that configure the state of the art.
Blockchain Software Selection for a Maritime Organization with MCDM Method

Technological development has begun with the Mainframe in 1970’s and the blockchain appears in near 2010s. The blockchain is known as a distributed ledger system that keeps records of the transactions between various parties. These records are unalterable and immutable. Maritime industry is one of the most preferable method of transport due to pricing and it plays a huge role in the global supply chain, however, due to the customs operations and the slowness of the ship, it lags behind in terms of time compared to its competitors. This situation also questions its efficiency. With the help of the blockchain, maritime industry can become smarter, more efficient and trustworthy. This study aims to help a maritime organization to choose Blockchain software with an MCDM Method such as technique for order of preference by similarity to ideal solution (TOPSIS). All the selection criteria are taken from the literature and evaluated based on the opinions of the experts of the industry. A real case study is applied in a maritime enterprise and the results are explicated in the last section.
COMPARISON OF BLOCKCHAIN TECHNOLOGY AND DIRECTED ACYCLIC GRAPH DURING DATA STORAGE AND PROCESSING IN A DISTRIBUTED REGISTRY

The technology of distributed registry, which revolutionary approach to recording and exchanging data between multiple data warehouses, is considered. The key features of distributed registry technology are highlighted and described. Particular attention is paid to blockchain technology and a directed acyclic graph. The principle of the operation of blockchain technology is described. All stages that go through transactions before they are added to the blockchain are graphically presented and described. All the main advantages of blockchain technology are described, and key network disadvantages are also presented. In describing the limitations of blockchain technology, real information was presented that confirms the existing flaws of this technology. Thanks to the optimized consensus mechanism, high scalability and the ability to carry out microtransactions, the technology of a directed acyclic graph is rapidly developing. The principle of operation of a directed acyclic graph is described and how the digraph differs from blockchain technology. The main advantages of a directed acyclic graph are described, which the digraph inherited from the blockchain and improved due to its structure. When comparing registers based on a directed acyclic graph and blockchain, interesting aspects of platforms can be identified. Despite the obvious similarities between paradigms, differences also persist. Based on the results, it becomes apparent that the future of distributed ledger technology is huge. Since a directed acyclic graph, due to its structure and advantages, has already overshadowed the blockchain architecture. The appearance of a directed acyclic graph will allow distributed registry technology to be introduced into all areas of activity on a global scale.
Gated Recurrent Context: Softmax-Free Attention for Online Encoder-Decoder Speech Recognition

Recently, attention-based encoder-decoder (AED) models have shown state-of-the-art performance in automatic speech recognition (ASR). As the original AED models with global attentions are not capable of online inference, various online attention schemes have been developed to reduce ASR latency for better user experience. However, a common limitation of the conventional softmax-based online attention approaches is that they introduce an additional hyperparameter related to the length of the attention window, requiring multiple trials of model training for tuning the hyperparameter. In order to deal with this problem, we propose a novel softmax-free attention method and its modified formulation for online attention, which does not need any additional hyperparameter at the training phase. Through a number of ASR experiments, we demonstrate the tradeoff between the latency and performance of the proposed online attention technique can be controlled by merely adjusting a threshold at the test phase. Furthermore, the proposed methods showed competitive performance to the conventional global and online attentions in terms of word-error-rates (WERs).