Reviewerly/exHarmony huggingface dataset:
Consumer, Producer, Creator: The mixtape as creative form

The medium and genre of the mixtape, a form dependent upon borrowed, repurposed and re-contextualised sonic material, has recently re-emerged as a vital component of contemporary commercial and creative music culture. This article suggests that analysis of the mixtape’s history and ethos can provide useful insight into ongoing tensions between cultures of active and passive listening, questions of ownership with regard to recorded sound and between the roles of producer and consumer within contemporary audio culture. It also proposes, via reference to relevant contemporary works, that the mixtape may now be considered a vital hybrid creative form, part composition/part compilation, with which composers, producers and sonic artists may actively engage. It historically and culturally contextualises this form, placing it within a lineage of military, political, creative and commercial conflict, which, among recorded sound technologies, is unique to tape and shows that the mixtape may be seen as an expression or utilisation of these factors. Consistent commercial and industrial efforts to suppress tape’s subversive qualities are outlined as are creative methodologies which have been adopted to resist such efforts. Contemporary music industry strategies to channel and commodify the aura and ethos of the mixtape as forerunner of the curated playlists vital to its current business model are detailed and contemporary cassette culture is considered as an alternative inheritor of the mixtape’s legacy and as alternative model for future musical creation and distribution. Finally, a set of characteristics which distinguish the mixtape as a creative form are identified and their potential application and implications are discussed.
MDiET: Malware Detection in Encrypted Traffic

With the increasing adoption of end-to-end encryption in industrial systems, the risk of distributing hidden malware by exploiting encrypted channels gradually turns to a major concern.Due to encryption, the stateof-the-art, signature-based mechanisms might fail to detect malware sufficiently, thus new approaches are required.In this work, a method for malware detection in encrypted traffic based on Machine Learning is presented.A supervised learning approach is adopted and the efficiency of the solution is demonstrated by a set of exhaustive simulations.Further considerations for incorporating the proposed method in a reference industrial network are also discussed.
Two-Stage Guessing

Correlated memoryless sources produce a principal and an ancillary sequence. The exponential growth of the least expected total number of guesses required to guess the principal sequence is determined when, prior to guessing it, the guesser is allowed to produce guesses (not necessarily terminating with a correct one) of the ancillary.
Research of Uyghur-Chinese Machine Translation System Combination Based on Semantic Information

Uyghur-Chinese Machine Translation System Combination bears some drawbacks of not considering semantic information when doing the combination and the individual systems which participated in system combination lacking diversity. This paper tackles these problems by proposing a system combination method which was generated multiple new systems from a single Statistical Machine Translation (SMT) engine and combined together. These new systems are generated based on a bilingual phrase semantic representation model. Specifically, the Uyghur-Chinese bilingual phrase bilinear semantic similarity score and cosine semantic similarity score were firstly computed by a bilingual phrase semantic representation model and then several new systems were generated by adding features to the original feature set of the phrase-based translation model by static features and dynamic features. Finally, the newly generated system is combined with the baseline system to obtain the final combination results. Experimental results on the Uyghur-Chinese CWMT2013 test sets show that our approach significantly outperforms the baseline by 0.63 BLEU points respectively.
Optimal Use of Experience in First Person Shooter Environments

Although reinforcement learning has made great strides recently, a continuing limitation is that it requires an extremely high number of interactions with the environment. In this paper, we explore the effectiveness of reusing experience from the experience replay buffer in the Deep Q-Learning algorithm. We test the effectiveness of applying learning update steps multiple times per environmental step in the VizDoom environment and show first, this requires a change in the learning rate, and second that it does not improve the performance of the agent. Furthermore, we show that updating less frequently is effective up to a ratio of 4:1, after which performance degrades significantly. These results quantitatively confirm the widespread practice of performing learning updates every 4th environmental step.
Dialogue Act Classification in Reference Interview Using Convolutional Neural Network with Byte Pair Encoding

Dialogue act classification is an important component of dialogue management, which captures the user’s intention and chooses the appropriate response action. Kawano, Seiya In this paper, we focus on the dialogue act classification in reference interviews to model the behaviors of librarians in the information seeking dialogues. Yoshino, Koichiro Reference interviews sometimes include rare words and phrases. Suzuki, Yu Therefore, the existing approaches that use words as units of input often do not work well here. We used the byte pair encoding compression algorithm to build a new vocabulary for the inputs of the classifier. Nakamura, Satoshi By using this new unit as a feature of the convolutional neural network-based classifier, we improved the accuracy of the dialogue act classification while suppressing the size of vocabulary.
Locality-Sensitive Hashing Scheme Based on Heap Sort of Hash Bucket

Nearest neighbor search (NNS) is one of the current popular research directions, which widely used in machine learning, pattern recognition, image detection and so on. In the low dimension data, based on tree search method can get good results. But when the data dimension goes up, that will produce a curse of dimensional. The proposed Locality-Sensitive Hashing algorithm (LSH) greatly improves the efficiency of nearest neighbor query for high dimensional data. But the algorithm relies on the building a large number of hash table, which makes the space complexity very high. C2LSH based on dynamic collision improves the disadvantage of LSH, but its disadvantage is that it needs to detect the collision times of a large number of data points which Increased query time. Therefore, Based on LSH algorithm, later researchers put forward many improved algorithms, but still not ideal.In this paper, we put forward Locality-Sensitive Hashing Scheme Based on Heap Sort of Hash Bucket (HSLSH) algorithm aiming at the shortcomings of LSH and C2LSH. Its main idea is to take advantage of the efficiency of heapsort in massive data sorting to improve the efficiency of nearest neighbor query. It only needs to rely on a small number of hash functions can not only overcome the shortcoming of LSH need to build a large number of hash table, and avoids defects of C2LSH. Experiments show that our algorithm is more than 20% better than C2LSH in query accuracy and 40% percent lower in query time.
Improving the Performance of Chat-Oriented Dialogue Systems via Dialogue Breakdown Detection

Dialogue breakdown detectionInaba, Michimasa is a techniqueTakahashi, Kenichi used for identifying inappropriate utterances in dialogue systems that has attracted increased attention, especially in chat-oriented dialogue systems. Although it is generally assumed that dialogue breakdown detection avoids generating system responses that then cause difficulties in continuing the given dialogue, this has yet to be verified experimentally or theoretically. In this paper, we apply the dialogue breakdown detection technique to generate responses for a chat-oriented dialogue system and experimentally verify that performance is improved by measuring the extent to which dialogue breakdown is avoided. Our experimental results show that dialogue breakdown detection indeed is able to improve the appropriateness of system responses; however, short, simple, and dull responses tend to increase when using this technique.
Predictive Modeling and Sentiment Classification of Social Media Through Extreme Learning Machine

Social media platform has revolutionized all sections of the society. The popularity of social or community netwroking platform in the last decade has created new opening to analyze and study public opinions and sentiments for use in financial and social behavioral studies. On the other hand, machine-learning techniques have also laid a significant impact. Machine learning approaches are widely implemented in processing and analyzing sentiments. Extreme Learning Machine is the most favoured machine leaning classifier, which shows better results apart from support vector machine classifier. The working principle of extreme learning machine can represent results in categorical form. However, one-to-one sentiment classification may not disclose too much information, which could have been beneficial for research purpose. So multi-class sentiment has been discussed here with the help of extreme learning machine. The experimental results show that extreme leaning machine achieves better accuracy and performance in comparison to other machine learning classifiers.
Creating an Affective Fighting Game AI System with Gamygdala

State-of-the-art fighting game AI technologies are already able to play at the level of professional human players. Thus, the challenge is not only to create artificial opponents that can play on a par with people, but also to make them enjoyable to play against. This can be done to a certain extent by implementing traits of human-like behaviour, such as affective (emotional) decision-making. In this work-in-progress report, we show how the task can be accomplished. We rely on Gamygdala, an emotional appraisal engine, to model emotional states of the computer-controlled opponent, and use findings from psychology to translate emotions into actions.
School-Enterprise Cooperation on Python Data Analysis Teaching

To meet the needs of enterprises for data analysis talents, school-enterprise cooperative course Python Data Analysis introduces the basic theories and methods for data analysis by using Python programming language. The teaching content is organized around practical cases designed according to the actual demand of the enterprise. Exploratory teaching method is conducted to cultivate the communication, collaboration, critical thinking and creativity ability of students. Students fully participate in the teaching process by discussing, analyzing and programming the cases. The implementation scheme, organization of exploratory teaching and the design of teaching cases are introduced in this paper.
On the Effects of Simulating Human Decisions in Game Analysis

The analysis of games or game content, e.g. in the context of AI-assisted game design and search-based PCG, is often based on playthroughs that are generated by simulating human decisions using AI players. With this paper, we hope to encourage more systematic analyses of uncertainties and errors potentially introduced by simulating human decisions in this context. To this end, we conduct a case study on StarCraft II to demonstrate the potential effects of these uncertainties. We construct two usecases from an analysis of existing approaches in research that employ simulations of human decisions through game AI. We are able to demonstrate large impacts of the simulations and finally discuss how the resulting uncertainties can be controlled in future work.
Provable Subspace Tracking with Missing Entries

We study the problem of subspace tracking (ST) in the presence of missing data (ST-miss). In recent work, we have studied the Robust ST (RST) problem. In this work, we show that a simple modification of our solution approach for RST also provably solves ST-miss under weaker assumptions. To our knowledge, our result is the first complete guarantee for ST-miss. This means we are able to show that, under assumptions on only the algorithm inputs (input data and/or initialization), the output subspace estimates are close to the true data subspaces at all times. Our guarantees hold under mild and easily interpretable assumptions and handle time-varying subspaces (unlike all previous work). We also show that our algorithm and its extensions are fast and have competitive experimental performance when compared with existing methods.
A Novel Behavior Steganography Model Based on Secret Sharing

This article proposes a novel behavior steganography model based on secret sharing, the main idea of which is to use secret messages as random elements in the secret sharing process to generate shadow images. Based on the introduced model and analyzing two secret image sharing algorithms — threshold secret image sharing (SIS) and threshold visual secret sharing (VSS), two specific behavior steganography schemes are presented, which are implemented by utilizing secret sharing behavior. In the embedding phase, the random selection behavior is employed to hide secret messages. In the extraction phase, when the secret image is recovered from shadow images, secret messages can also be extracted successfully. The contribution of the authors model is that two secret information transmission channels are opened, which provides a large amount of hidden capacity and has loss tolerance and so on. Experimental results and analyses demonstrate the effectiveness of the proposed scheme. It has both good imperceptibility and large capacity, but the robustness of their scheme is poor.
Semantic Segmentation in Compressed Videos

Existing approaches for semantic segmentation in videos usually extract each frame as an RGB image, then apply standard image-based semantic segmentation models on each frame. This is time-consuming. In this paper, we tackle this problem by exploring the nature of video compression techniques. A compressed video contains three types of frames, I-frames, P-frames, and B-frames. I-frames are represented as regular images, P-frames are represented as motion vectors and residual errors, and B-frames are bidirectionally frames that can be regarded as a special case of a P frame. We propose a method that directly operates on I-frames (as RGB images) and P-frames (motion vectors and residual errors) in a video. Our proposed model uses a ConvLSTM model to capture the temporal information in the video required for producing the semantic segmentation on P-frames. Our experimental results show that our method performs much faster than other alternatives while achieveing similar performance in terms of accuracies.
Measuring the Significance of Writing Style for Recommending Where to Publish – A Case Study

Abstract Writing style plays a role in publication venue recommendation. However, such finding should be observed further; it is concluded from an arbitrary dataset which contains various topics and writing quality. This paper aims to observe that style's impact in a more controlled environment. A dataset with the same specific topic and writing quality was used and analysed. In our case, the dataset is comprised of papers published on reputable software engineering publication venues with natural language generation as the specific topic. According to our observation, writing style only affects significantly on paper title wherein the impact is proportional to n in n-gram. Moreover, the style’s impact becomes more salient when the venues are grouped per publisher or only a specific publication type is considered.
Neural Learning to Rank using TensorFlow Ranking

A number of open source packages harnessing the power of deep learning have emerged in recent years and are under active development, including TensorFlow, PyTorch and others. Supervised learning is one of the main use cases of deep learning packages. However, compared with the comprehensive support for classification or regression in open-source deep learning packages, there is a paucity of support for ranking problems. To address this gap, we developed TensorFlow Ranking: an open-source library for training large scale learning-to-rank models using deep learning in TensorFlow. The library is flexible and highly configurable: it provides an easy-to-use API to support different scoring mechanisms, loss functions, example weights, and evaluation metrics.
NORMALIZATION AND DETERMINATION OF THE EDUCATIONAL LOAD OF TEACHERS AS A METHOD FOR OPTIMIZING THE CALCULATION OF PAYMENT OF TS

This article discusses the problems associated with the effectiveness of standardization and determination of all types of work of the teaching staff. The problem in this case lies not only in determining the optimal amount of funding for the university, but also in the search for approaches to its justification. Moreover, the literacy of these decisions directly affects the innovative development of higher education institutions and the calculation of salaries of faculty members. The influence of this factor on the payroll calculation of teaching staff, as well as the optimization of this process, is shown.&#x0D; The calculation of wages is influenced by a lot of factors that must be considered. Work on improving the system of standardization of the teaching load of a teacher should continue and develop.
A Convolution Neural Networks Based Character and Word Recognition System for Similar Script Languages Kannada and Telugu

This paper presents a cross language platform to recognize characters and words of low resource scripts i.e. scripts which do not have standard dataset and the datasets are not available for public access. Indic scripts come from common origin and some of the scripts have a common 3 zonal structure. Recognition of such scripts can be done with the help of other scripts having similar structure. To recognize these characters the model is trained with source language Kannada with zone-wise training and testing is done with both Kannada and the target language Telugu. An accuracy of 88% for Kannada and 62% for Telugu characters is achieved by using Inception Model which is built using Convolution Neural Networks (CNN) image classifier. The dataset consists of 10700 Kannada characters. The model is also tested for 100 words of Telugu and Kannada with an accuracy of 72% and 82% respectively.
A Cloud Based Entitlement Granting Engine

Cyber Sandbox Software Portal (CSSP) is a web application built on the Ruby on Rails framework which provides an entitlement granting engine to assign AWS Instances to different users on demand. The motivation behind this effort is to create an easy way to give users access to virtual machines while also monitoring their work. CSSP would be useful in a classroom setting that requires students to have their own machines to work on. It would also be useful in training for cybersecurity competitions such as the Mid-Atlantic Collegiate Cyber Defense Competition. By utilizing the cloud and cloud programming API’s, virtual instances can be allocated on demand which can not only save a huge amount of time, but also greatly reduce costs of setup, deployment, delegation, and usage of resources.
Empirical Study on Rain Classroom: A Tool for Blended Learning in Higher Education

An interactive learning tool named Rain Classroom was developed to facilitate teachers better implement blended learning and enhance communication between teachers and students. The paper presents its design principles, system architecture, and applications. An empirical study of 130 participants was conducted to evaluate the effectiveness of the tool. Positive results are shown both in students' learning performances and attitudes toward Rain Classroom.
Consumer Goods and Retail Industry

Blockchain technology has large-scale implications and the capacity to greatly improve efficiencies for a range of activities in the retail industry. As discussed earlier, the most logical advantage of blockchain is expediting the transfer of funds between two parties. Blockchain makes it possible for retailers to accept cryptocurrency payments. The technology could remove excessive fees from banks by acting as third parties during transactions. It would also drastically speed up lengthy processing and settlement times, especially in situations where a payment is moving across borders. With banks removed from the equation, most customer transactions processed over a blockchain can be settled within a matter of seconds saving time and money for retailers. Finally, using smart contracts with blockchain could also mean instant payments, automated refunds, and more (Hodges 2017).
PLFCN

In the field of remote sensing, the semantic segmentation network for orthophotos has received widely attention. However, it is usually impossible to achieve high accuracy and high efficiency at the same time. In this paper, we propose a novel pyramid loss reinforced fully convolutional network (PLFCN) to address this issue. By introducing deep pyramid supervisions, the network explores multi-scale spatial context information to improve performance of semantic segmentation. And the auxiliary pyramid loss structure can be ignored during testing, so that the network can inference as fast as FCN. The main contributions of this paper are as follows: 1) auxiliary pyramid loss structure is proposed to enhance the performance of FCN by multiscale and deep supervisions; 2) the advantages of multi scale structures and auxiliary loss is combined to improve the performance and maintain the efficiency at the same time. The results show that the semantic segmentation performance is significantly improved, while achieves the high effeciency as FCN.
Design tensions

User interfaces are continually evolving. Contemporary interfaces, particularly mobile interfaces, have developed their own design language as they have discarded skeuomorphs and metaphorical representations to support their design and introduced features not available before in desktop interfaces. Despite the pervasiveness of mobile interfaces, researchers have neglected the development of theory that accounts for how they still connect with or deviate from early interface design constructs. This paper adapts the notion of metaphorical tension to formulate and explore a new construct, design tension, which scopes an exercise of interaction criticism on Instagram's interface. As a result, this paper argues that design tension is useful to account for how interface components introduce features that are discordant to interface standards and best practices, things from the real world, and the user's belief system. It also suggests that both the method and construct can be applied to other communication design artifacts distinct from interfaces to produce intermediate-level knowledge.
Human-Computer Interaction Control of Snake-Like Robot Based on Gesture Recognition

Human-computer interaction has been a hot topic in the development of robots. Gesture recognition is a significant part of it. Existing snake-like robot is controlled by the handle and joystick. It is worth studying that use gesture to control robots. Existing main vision-based gesture recognition methods are based on a two-stage method: hand detection and segmentation, feature extraction and template matching process. Inspired by the YOLO[1] algorithm, this paper proposed a one-stage gesture recognition and snake-like robot control method, based on deep learning, combined image segmentation and classification, realized end-to-end recognition and robot control. The snake-like robot can react to four types of human's gestures with a rapid processing time.