Reviewerly/exHarmony huggingface dataset:
Together in music

Looking back at the diverse chapters of the “Together in music” volume, three main themes are identified that reoccur. These relate to the relevance of embodied in-the-moment interaction between musicians for the creative processes to develop, the rich multi-dimensionality of the group music-making experience at a micro-, meso-, and macro-level, and the close relationships between social and musical coordination. These themes highlight the need to advance research by investigating ensemble performance and creativity at multiple analytical levels, e.g. taking microtiming, social coordination, and identity into account, and by explicitly considering developments and emergence over time. Furthermore, these themes promote the advancement of methods and techniques to investigate ensemble music-making processes, several of which are identified and illustrated in the book, including pattern detection in behavioral interaction, visualization of relationships between musicians, and innovations in the measurement and analysis of entrainment behavior in timing and intonation.
Hard-Attention for Scalable Image Classification

Deep neural networks (DNNs) are typically optimized for a specific input resolution (e.g. $224 \times 224$ px) and their adoption to inputs of higher resolution (e.g., satellite or medical images) remains challenging, as it leads to excessive computation and memory overhead, and may require substantial engineering effort (e.g., streaming). We show that multi-scale hard-attention can be an effective solution to this problem. We propose a novel architecture, TNet, which traverses an image pyramid in a top-down fashion, visiting only the most informative regions along the way. We compare our model against strong hard-attention baselines, achieving a better trade-off between resources and accuracy on ImageNet. We further verify the efficacy of our model on satellite images (fMoW dataset) of size up to $896 \times 896$ px. In addition, our hard-attention mechanism guarantees predictions with a degree of interpretability, without extra cost beyond inference. We also show that we can reduce data acquisition and annotation cost, since our model attends only to a fraction of the highest resolution content, while using only image-level labels without bounding boxes.
Ranking Policy Decisions

Policies trained via Reinforcement Learning (RL) are often needlessly complex, making them more difficult to analyse and interpret. In a run with $n$ time steps, a policy will decide $n$ times on an action to take, even when only a tiny subset of these decisions deliver value over selecting a simple default action. Given a pre-trained policy, we propose a black-box method based on statistical fault localisation that ranks the states of the environment according to the importance of decisions made in those states. We evaluate our ranking method by creating new, simpler policies by pruning decisions identified as unimportant, and measure the impact on performance. Our experimental results on a diverse set of standard benchmarks (gridworld, CartPole, Atari games) show that in some cases less than half of the decisions made contribute to the expected reward. We furthermore show that the decisions made in the most frequently visited states are not the most important for the expected reward.
Fisheye vs Rubber Sheet: Supporting Visual Search and Fine Motor Skills in Augmented Reality

Fisheye and rubber sheet are common magnifying methods in information visualization. In this paper, we present two experiments about their performance in AR scenarios: visual search and fine motor manipulation. We also included baseline conditions without any magnification. For all magnified views, the size and shape of the magnified region were constant and we evaluated performance based on completion time and accuracy. Our results show that visual distortions of our magnifiers do not significantly decrease performance; our insights will be a reference for projects that require magnified AR views.
Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess

This paper introduces deep synoptic Monte Carlo planning (DSMCP) for large imperfect information games. The algorithm constructs a belief state with an unweighted particle filter and plans via playouts that start at samples drawn from the belief state. The algorithm accounts for uncertainty by performing inference on synopses, a novel stochastic abstraction of information states. DSMCP is the basis of the program Penumbra, which won the official 2020 reconnaissance blind chess competition versus 33 other programs. This paper also evaluates algorithm variants that incorporate caution, paranoia, and a novel bandit algorithm. Furthermore, it audits the synopsis features used in Penumbra with per-bit saliency statistics.
Study on Ownership Identification Using Discrete Wavelet Transform (DWT)-Based LOGO Watermarking

An efficient DWT-based watermarking technique is proposed in this research to insert gray-scale logos as a watermark in images to attest the owner identify and discourage unauthorised copying. To hide a higher energy hidden watermark in conspicuous picture components, the method converts both the host image and the watermark into the discrete wavelet domain, where their coefficients are fused adaptively based on the Human Visual System (HVS) model. The method merges the watermark coefficients at various resolution levels of the host image, resulting in simultaneous spatial localisation and frequency spread of the watermark, as well as robustness against various attacks. According to the simulation results, the suggested method is robust to a wide range of attacks, including image compression, linear and non-linear filtering, noise addition, image resizing, cropping, and enhancement, among others.
Vendor Selection through Review Analysis

With the dawn of shopping websites it is much easier for small businesses and individual consumers to buy goods. These websites also have reviews and ratings of the available products. Many people also like to use blogging websites like twitter to give feedback on products. But with the wide range of products that are now available, the decision-making process has become much more difficult. This is why we propose a vendor selection tool that can be used by small businesses or consumers trying to find the perfect product. It aggregates reviews and ratings from e-shops as well as the sentiment people have expressed towards the products on blogging websites such as Twitter. It uses web scraping technique to extract reviews and ratings of the people given under each product. Python’s beautifulsoup library is used to scrape reviews and store them in a .csv file. Contents of this .csv file can be accessed and displayed as per requirement. It also uses sentiment analysis technique to extract people’s opinions on the microblogging website Twitter. A Support Vector Machine is trained to analyse tweets that have been manually labelled. Then, this SVM is used to classify product sentiment.
Image Retrieval Based on Edge Histogram Descriptor of MPEG-7

A major research area in computer vision is content-based image retrieval.MPEG-7 sets up a list of descriptions of the structured image content.We examine the weakness and lack of retrieval approaches based on global characteristics in this study by incorporating the commonly used feature descriptors of MPEG-7.In the meantime, to satisfy user requirements for assessing spatial information similarities, an image retrieval approach based on texture region features for MPEG-7 is recommended.Retrieval tests show the validity and efficiency of our approach.This paper also defines our approach to color quantization, extraction, and matching processes of features and so on in depth.
Emulating the Provisions of the Budapest Convention on Cybercrime in the Nigerian Cybercrime (Prohibition, Prevention etc) Act 2015: Contextual Implications

Cybercrime is a global phenomenon. The context of cybercrime must focus on the regulations and this is the concern of the nations across the globe. The global community strives in providing the machineries meant to combat the crimes while using the Budapest Convention (International Convention) as the guiding tool. Therefore, countries must take measures as a matter of international and national concern to combat the spread of the crimes. The aim of paper is to examine the rise and the challenges of cybercimes in Nigeria. It further examines the real occurrences of computer crimes as a major setback to the Nigerian government and, in addition, it explores some salient provisions of the Nigeria Cybercrime (Probition, Prevention etc) Act 2015. Finally, it analyses the important provisions of the Articles of the Budapest Convention of Cybercrime through the relevant provisions of the Nigeria Cybercrime (Probition, Prevention etc) Act 2015.
Linear logic in normed cones: probabilistic coherence spaces and beyond

Abstract Ehrhard et al. (2018. Proceedings of the ACM on Programming Languages , POPL 2, Article 59.) proposed a model of probabilistic functional programming in a category of normed positive cones and stable measurable cone maps, which can be seen as a coordinate-free generalization of probabilistic coherence spaces (PCSs). However, unlike the case of PCSs, it remained unclear if the model could be refined to a model of classical linear logic. In this work, we consider a somewhat similar category which gives indeed a coordinate-free model of full propositional linear logic with nondegenerate interpretation of additives and sound interpretation of exponentials. Objects are dual pairs of normed cones satisfying certain specific completeness properties, such as existence of norm-bounded monotone weak limits, and morphisms are bounded (adjointable) positive maps. Norms allow us a distinct interpretation of dual additive connectives as product and coproduct. Exponential connectives are modeled using real analytic functions and distributions that have representations as power series with positive coefficients. Unlike the familiar case of PCSs, there is no reference or need for a preferred basis; in this sense the model is invariant. PCSs form a full subcategory, whose objects, seen as posets, are lattices. Thus, we get a model fitting in the tradition of interpreting linear logic in a linear algebraic setting, which arguably is free from the drawbacks of its predecessors.
Cloud Computing and Its Features: An Advanced Approach

Cloud Computing is a technology that utilizes internet for storing and managing data on remote servers. They can be classified as Public cloud – it’s owned and operated by a third party cloud service providers, Private cloud – It refers to computing resources used exclusively by a single business or organization, and Hybrid cloud – It combines public and private clouds, bound together that allows data and applications to be shared between them. Few applications are discussed for Cloud Computing.
Effect Analysis of Language Education Based on Computer and Network Sensors

Language talent education is an essential education in education, but today’s language talent education needs to be improved because of the influence of teaching methods and other factors. This paper puts forward an idea of sensor-assisted education. The sensor is connected to the computer to help improve the language ability and information acquisition ability of the educated by means of network sensing and try to intelligently classify the relevant languages in language education, so as to reduce the time for the educated to process information, so as to realize the matching between language and reception. At the same time, the research also found that the sensors based on computers and networks have the function of intelligently strengthening the language, and the meaning of expression is simpler and more accurate. By studying the improvement of the effect of language education based on computer and network sensors, this paper provides a reference for the application of network sensors in the future.
A Conceptual Model: Securing Resources Through a Decentralized Access Control Using Blockchain Technology for Smart Farming

Securing high number of IoT devices and sensors in smart farming ecosystem became a challenge especially when it comes to access control. Traditional approaches for access control mostly rely on a central authority which is a centralised server that is risky as it possesses a single point of failure and could be compromised with malicious attacks. Hence having a centralised model for access control does not seem to be an ideal solution. Meanwhile, blockchain technology, a decentralised Distributed Ledger Technology potentially can be applied to manage the access control. This study aims to propose a secure decentralised access control model using blockchain technology to secure the resources in smart farming ecosystem with a multi-tenant deployment model.
LMdiff: A Visual Diff Tool to Compare Language Models

While different language models are ubiquitous in NLP, it is hard to contrast their outputs and identify which contexts one can handle better than the other. To address this question, we introduce LMdiff, a tool that visually compares probability distributions of two models that differ, e.g., through finetuning, distillation, or simply training with different parameter sizes. LMdiff allows the generation of hypotheses about model behavior by investigating text instances token by token and further assists in choosing these interesting text instances by identifying the most interesting phrases from large corpora. We showcase the applicability of LMdiff for hypothesis generation across multiple case studies. A demo is available at http://lmdiff.net .
Perancangan Webgis Kelurahan Baratajaya Kota Surabaya

The Baratajaya Urban Spatial Data Base is an online database system application that stores and processes geospatial data and information on spatial planning and thematic areas into a publication that is more informative, easy to understand and easily accessible to the wider community as well as an instrument in the process of sharing data. The webgis database embodiment process is an evolutionary process consisting of several stages or phases of development. The development of this webgis will be continuous and requires improvement so that it requires continuous efforts in updating and developing program applications based on contemporary technology. The Webgis of Kelurahan Baratajawa is a WebGIS (Web-Geographic Information System) that has the ability to perform data processing, display and analyze data. all parties and parties. The target of the planned findings is the compilation of an E-base map which is a WebGIS (Web-Geographic Information System) Baratajaya Village, Surabaya City which has the ability to perform data processing, display and analyze data.
Compositional Reinforcement Learning from Logical Specifications

We study the problem of learning control policies for complex tasks given by logical specifications. Recent approaches automatically generate a reward function from a given specification and use a suitable reinforcement learning algorithm to learn a policy that maximizes the expected reward. These approaches, however, scale poorly to complex tasks that require high-level planning. In this work, we develop a compositional learning approach, called DiRL, that interleaves high-level planning and reinforcement learning. First, DiRL encodes the specification as an abstract graph; intuitively, vertices and edges of the graph correspond to regions of the state space and simpler sub-tasks, respectively. Our approach then incorporates reinforcement learning to learn neural network policies for each edge (sub-task) within a Dijkstra-style planning algorithm to compute a high-level plan in the graph. An evaluation of the proposed approach on a set of challenging control benchmarks with continuous state and action spaces demonstrates that it outperforms state-of-the-art baselines.
TECHNOLOGY OF TRAINING DOCTORS OF PHILOSOPHY: TENDENCIES, ADVANTAGES, DISADVANTAGES

Third education level: quality control over scientific-professional training of a PhD: individual issues : Collective monograph / edited by Myroslava Filipovych. Riga, Latvia : Izdevnieciba “Baltija Publishing”, 2021. 227 p.
An Adaptive Deep Learning Optimization Method Based on Radius of Curvature

An adaptive clamping method (SGD-MS) based on the radius of curvature is designed to alleviate the local optimal oscillation problem in deep neural network, which combines the radius of curvature of the objective function and the gradient descent of the optimizer. The radius of curvature is considered as the threshold to separate the momentum term or the future gradient moving average term adaptively. In addition, on this basis, we propose an accelerated version (SGD-MA), which further improves the convergence speed by using the method of aggregated momentum. Experimental results on several datasets show that the proposed methods effectively alleviate the local optimal oscillation problem and greatly improve the convergence speed and accuracy. A novel parameter updating algorithm is also provided in this paper for deep neural network.
SNR Improvement in Voice Activity Detection

Over the years, the aim of error-free continuous speech recognition has remained controversial.Environmental robustness has acquired widespread acceptance as one of the key topics of research in the voice recognition field in recent years. Several methods have been investigated. Window framing, speech feature extractions, feature filtering techniques, and other algorithms have all been examined and shown to improve recognition accuracy. One of the neuron connectivity strategies for artificial intelligence applications is to distinguish between sound signals in form of voice and unvoiced. The device first applies fixed weights to these audios before providing output for each format and high speed. Communication through Voice over Internet Protocol (VoIP) in modern-age speech is attracting many researchers. Voice Activity Detector (VAD) is a technique of distinguishing the articulated portion of speech and the silence component in the original voice signal. In many applications such as speech recognition, voice compression systems, mobile communication, this approach plays an important role for obtaining better speech quality and reducing bandwidth complexity.
Conditional Poisson Stochastic Beams

Beam search is the default decoding strategy for many sequence generation tasks in NLP. The set of approximate K-best items returned by the algorithm is a useful summary of the distribution for many applications; however, the candidates typically exhibit high overlap and may give a highly biased estimate for expectations under our model. These problems can be addressed by instead using stochastic decoding strategies. In this work, we propose a new method for turning beam search into a stochastic process: Conditional Poisson stochastic beam search. Rather than taking the maximizing set at each iteration, we sample K candidates without replacement according to the conditional Poisson sampling design. We view this as a more natural alternative to Kool et al. (2019)’s stochastic beam search (SBS). Furthermore, we show how samples generated under the CPSBS design can be used to build consistent estimators and sample diverse sets from sequence models. In our experiments, we observe CPSBS produces lower variance and more efficient estimators than SBS, even showing improvements in high entropy settings.
Case Studies/Success Stories on Machine Learning and Data Mining for Cancer Prediction

Cancer has become a primary cause of death all over the world. Every year, many people are diagnosed by pathologists with cancer. For decades, these pathologists diagnosed cancer with only 60% accuracy. According to the International Agency for Research on Cancer (IARC), cancer has become a major cause of approximately 8 million deaths, and around 14 million new cancer cases arise every year. Nowadays, artificial intelligence, i.e., machine learning (ML), is being used to predict cancer with a maximum accuracy rate from every other method used earlier. ML is a procedure in which data are given as an input. Then, patterns are identified, and after that, training is done using these data for the desired outcomes. Due to its high analyzing and categorizing capability, various data mining techniques have done tremendous work in the domain of medical science. The primary objective of the present chapter is to focus on various ML methods like support vector machines, Bayesian networks, decision trees, artificial neural networks, and many others with various data mining techniques for predicting different types of cancers like skin cancer, oral cancer, breast cancer, lung cancer, brain tumor, etc. It further highlights which method has achieved how much accuracy in cancer prediction, followed by a discussion on various issues and challenges faced with ML and data mining. At the end, a comparison is drawn between the ML method and other techniques for detection of cancer.
Application of Wireless Audio Real-time Transmission Model In Body Area Network

In order to achieve real-time compression of audio signals and establish a more stable wireless network transmission mode, this study designed a wireless audio real- time transmission model based on body area network technology. First, with the support of wireless body area network technology, a complete information aggregation processing system is built, and then the body area network environment is built by configuring the downloaded audio resources. Based on this, the digital signal processor is selected, and a complete data structure of multiple audio frames is established, so as to realize the real-time transmission of wireless audio. Experimental results show that compared with the traditional multi-channel audio transmission technology, the proposed model has higher effectiveness and stronger signal matching ability, which proves that it can compress audio signals in real time and establish a more stable wireless network transmission environment at the same time.
Video Instance Segmentation using Inter-Frame Communication Transformers

We propose a novel end-to-end solution for video instance segmentation (VIS) based on transformers. Recently, the per-clip pipeline shows superior performance over per-frame methods leveraging richer information from multiple frames. However, previous per-clip models require heavy computation and memory usage to achieve frame-to-frame communications, limiting practicality. In this work, we propose Inter-frame Communication Transformers (IFC), which significantly reduces the overhead for information-passing between frames by efficiently encoding the context within the input clip. Specifically, we propose to utilize concise memory tokens as a mean of conveying information as well as summarizing each frame scene. The features of each frame are enriched and correlated with other frames through exchange of information between the precisely encoded memory tokens. We validate our method on the latest benchmark sets and achieved the state-of-the-art performance (AP 44.6 on YouTube-VIS 2019 val set using the offline inference) while having a considerably fast runtime (89.4 FPS). Our method can also be applied to near-online inference for processing a video in real-time with only a small delay. The code will be made available.