Reviewerly/exHarmony huggingface dataset:
Reservoir Dynamic Interpretability for Time Series Prediction: A Permutation Entropy View

An echo state network (ESN) is an efficient recurrent neural network (RNN) that is widely used in time series prediction tasks due to its simplicity and low training cost. However, the "black-box" nature of reservoirs hinders the development of ESN. Although a large number of studies have concentrated on reservoir interpretability, the perspective of reservoir modeling is relatively single, and the relationship between reservoir richness and reservoir projection capacity has not been effectively established. To tackle this problem, a novel reservoir interpretability framework based on permutation entropy (PE) theory is proposed in this paper. In structure, this framework consists of reservoir state extraction, PE modeling, and PE analysis. Based on these, the instantaneous reservoir states and neuronal time-varying states are extracted, which are followed by phase space reconstruction, sorting, and entropy calculation. Firstly, the obtained instantaneous state entropy (ISE) and global state entropy (GSE) can measure reservoir richness for interpreting good reservoir projection capacity. On the other hand, the multiscale complexity-entropy analysis of global and neuron-level reservoir states is performed to reveal more detailed dynamics. Finally, the relationships between ESN performance and reservoir dynamic are investigated via Pearson correlation, considering different prediction steps and time scales. Experimental evaluations on several benchmarks and real-world datasets demonstrate the effectiveness and superiority of the proposed reservoir interpretability framework.
Color Image Enhancement Focused on Limited Hues

Some color images primarily comprise specific hues, for example, food images predominantly contain a warm hue. Therefore, these hues are essential for creating delicious impressions of food images. This paper proposes a color image enhancement method that can select hues to be enhanced arbitrarily. The current chroma is considered such that near achromatic colors are not over-enhanced. The effectiveness of the proposed method was confirmed through experiments using several food images.
Ensuring privacy and confidentiality of data on the cloud using an enhanced homomorphism scheme

Cloud computing is one of the widest phenomena embraced in information technology. This result from numerous advantages associated with it making many organizations and individuals offload their data to the cloud. Encryption schemes restrict access to data from unauthorized clients, helping attain confidentiality and privacy. The modification of the ciphertext of clients’ data on the cloud demand downloading, deciphering, editing, and finally uploading back to the cloud by sharing their private key with the cloud service provider making it tedious. The application of homomorphism, allows computation to be performed on ciphertext with no decipher activity which helps to avoid the surfacing of sensitive client data stored on the cloud. In this paper, an Enhanced Homomorphism Scheme (EHS) is proposed based on Good Prime Numbers (GPN), Linear Congruential Generator (LCG), Fixed Sliding Window Algorithm (FSWA), and Gentry’s homomorphism scheme. A dataset from the Kaggle database was used to test the proposed algorithm. A variety of tests were conducted using the proposed algorithm such as the Uniqueness of ciphertext, addition and multiplication property of full homomorphism, and the execution times using 2^n (n ∈ 2,3,4,5) data sizes. A comparison of the execution time of the proposed EHS was conducted with the New Fully Homomorphism Scheme (NFHS), and the Enhanced Homomorphism Encryption Scheme (EHES). From the comparison, the proposed EHS algorithm had the lowest encryption time when a data size of 24kb was executed but with a higher decryption time of 567.6667 ± 96.38911when a data size of 8kb was used. On the other hand, with a data size of 32kb, EHES had the highest decryption time of 1274ms with the proposed EHS having the lowest decryption time of 551.2222 ± 82.68746 indicating a decryption percentage decrease of 56.73%. This confirms that execution times are dependent on the size of the encryption key but not on data size.
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models

In recent years, much progress has been made in learning robotic manipulation policies that follow natural language instructions. Such methods typically learn from corpora of robot-language data that was either collected with specific tasks in mind or expensively re-labelled by humans with rich language descriptions in hindsight. Recently, large-scale pretrained vision-language models (VLMs) like CLIP or ViLD have been applied to robotics for learning representations and scene descriptors. Can these pretrained models serve as automatic labelers for robot data, effectively importing Internet-scale knowledge into existing datasets to make them useful even for tasks that are not reflected in their ground truth annotations? To accomplish this, we introduce Data-driven Instruction Augmentation for Language-conditioned control (DIAL): we utilize semi-supervised language labels leveraging the semantic understanding of CLIP to propagate knowledge onto large datasets of unlabelled demonstration data and then train language-conditioned policies on the augmented datasets. This method enables cheaper acquisition of useful language descriptions compared to expensive human labels, allowing for more efficient label coverage of large-scale datasets. We apply DIAL to a challenging real-world robotic manipulation domain where 96.5% of the 80,000 demonstrations do not contain crowd-sourced language annotations. DIAL enables imitation learning policies to acquire new capabilities and generalize to 60 novel instructions unseen in the original dataset.
Creating Secure File Systems in Open-Source Operating Systems

The relevance of this study is determined by insecure data storage on personal computers, as it is the main operating system that performs authentication and file access control. Bypassing these security rules is possible in case of using another open-source operating system on the same personal computer. The aim of this work is the research and development of file encryptors, disk encryptors and file system encryptors. Each of them has its shortcomings which manifest themselves during development. Combining the advantages of file encryptors and file system encryptors helped to overcome those shortcomings. The userspace filesystem library was used for this purpose. The study involved the methods aimed at designing and developing the Udev daemon file system for Linux using the OpenSSL library. The file system design was mathematically modelled and formally verified through a test parser. The file system also has its own authentication and authorization procedures to provide uniform access across multiple operating systems. The Udev daemon file system is the result of this work. Each file is encrypted with a separate key to protect against cryptanalysis. This key is encrypted with the owner’s private key, thereby enabling him/her to change the ownership. The passphrase is used to decrypt the user’s private key. The developed file system has passed authentication and access control testing successfully. The file system shows best performance with file sizes 1 KB to 256 MB. Encryption-caused performance degradation was also measured and found to be within acceptable limits. This Udev daemon stackable file system is available for all Unix clones with OpenSSL libraries. The prospects for further work are the development of a file system using several combined methods from a list of existing design and development methods for file systems.
Coreference Resolution through a seq2seq Transition-Based System

Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution system that uses a text-to-text (seq2seq) paradigm to predict mentions and links jointly. We implement the coreference system as a transition system and use multilingual T5 as an underlying language model. We obtain state-of-the-art accuracy on the CoNLL-2012 datasets with 83.3 F1-score for English (a 2.3 higher F1-score than previous work (Dobrovolskii, 2021)) using only CoNLL data for training, 68.5 F1-score for Arabic (+4.1 higher than previous work) and 74.3 F1-score for Chinese (+5.3). In addition we use the SemEval-2010 data sets for experiments in the zero-shot setting, a few-shot setting, and supervised setting using all available training data. We get substantially higher zero-shot F1-scores for 3 out of 4 languages than previous approaches and significantly exceed previous supervised state-of-the-art results for all five tested languages.
Agile Lossless Compression Algorithm for Big Data of Solar Energy Harvesting Wireless Sensor Network

Time series data are collected through most of the applications that permeate our lives today.Internet of Things (IoT) sensor data are generated through smart applications and stored in databases.Time series databases require huge storage spaces, as over time they consume a large amount of memory.In this paper, we propose an enhanced compression algorithm for time series data generated by IoT systems that monitor the production of electrical energy by solar panels.The best way to ensure that solar energy systems have high efficiency is to continuously monitor all electrical and environmental factors.However, this requires the collection of enormous quantities of data that can be used to detect defects in the generation of electric energy or in solar panels.As the data must be available for analysis, a lossless compression algorithm is needed.In addition, the compressed data must be in a format that can be queried to perform analysis operations dependent on speed; this means that the decompression of data should not be timeconsuming.Our results showed the high speed of the compression process along with good compression rate (16.6%) after applying the proposed compression algorithm.
Retracted: Effect of Respiratory Training Combined with Core Muscle Training on the Overall Motor Function and Activities of Daily Living of Patients with Early and Midterm Stroke

[This retracts the article DOI: 10.1155/2022/2830711.].
Combining Variable Neighbourhood with Simulated Annealing for Learning to Rank Problem

Abstract Variable Neighbourhood Search (VNS) is a problem-solving technique that improves heuristic solutions. The solutions are built around incremental adjustments to neighboring solutions. Changes are done during the climbing phase to get local optimal solutions, followed by a stochastic phase to achieve global optimum solutions. A range of mutation step-sizes are used in the exploration and exploitation methods. The function's purpose is to choose the best offspring to pass on to the next developing generation. As a consequence, this paper changes the Offspring solution in the following iteration applying a version of VNS based on four random probability distributions. Once the cooling temperature is met, it is capable of accepting a bad solution. Variable Neighborhood Annealing is a novel approach in Learning to Rank (LTR) (VNA). Each Offspring ranking model solution is built from a single probability distribution during the mutation phase (all mutation step-sizes made by only one probability distribution for each Neighbourhood candidate). Based on the results, we may infer that the VNA approach outperformed contemporary research on Evolutionary and Machine Learning methodologies. In the studies, we used datasets from Yahoo, Microsoft Bing Search (MSLR-WEB10K), and LETOR 4 (MQ2008, MQ2007).
Ball Position Feature Embedded Group Activity Recognition Model for Team Sport Games

Most group activity recognition models focus mainly on spatio-temporal features from the players in sports games. Often they do not pay enough attention to the game object, which heavily affects not only individual action but also a group activity. We propose a new group activity recognition model for sports games that incorporates players’ motion information and game object positional information. The proposed method uses a transformer encoder for temporal feature extraction and a ’simple’ conventional convolutional neural network for extracting spatial features and fusing them with the relative ball position-embedded features. The experimental results show that our model achieved comparable results to state-of-the-art methods on the Volleyball dataset by using only one transformer encoder block and the ball position.
SD-Depth: Light-Weight Monocular Depth Estimation Using Space Depth CNN for Real-Time Applications

With the help of the space-to-depth and depth-to-space modules, we provide a convolutional neural network design for depth estimation. We show designs that down sample the spatial information of the picture utilizing space-to-depth (SD) as opposed to the widely used pooling methods (Max-pooling and Average-pooling). The space-to-depth module may shrink the image while maintaining the spatial information of the image in the form of additional depth information. This technique is far superior to Max-pooling, which diminishes the image’s information and features. We also suggest a lightweight decoder step that builds a high-resolution depth map out of many low-resolution feature maps using the depth-to-space (DS) module. The suggested architecture effectively learns depth estimation with high processing speed and accuracy. We trained and evaluated our suggested model on NYU-depthV2 dataset and attained low error values (RMSE=0.342) and high delta accuracies (δ3=0.996) at a fast-processing speed (25Fps).
Hybrid TDOA/AOA Hypocenter Localization Using the Constrained Least Squares Method with Deep Learning P-Onset Picking

In this study, we propose a hypocenter localization algorithm that uses the time difference of arrival (TDOA) and angle of arrival (AOA) as a hybrid model. The hypocenter measurements are detected by the accelerator sensors of the four separate observatories that are closest to the origin of an earthquake. The measurements are calibrated by the proposed deep learning P-onset picking system with short-time Fourier transform (STFT) signal analysis because the accurate detection of Primary waves (P-waves) is limited by seismic environmental noise. The revised measurements are used to calculate the precise distances between the observatories and hypocenters. The proposed hybrid TDOA/AOA is represented by a linear matrix equation that includes the unknowns of the precise distances, coordinates, and arrival angles to the observatories. We estimate a hypocenter using the constrained least squares method (CLS) under the constraints of the TDOA/AOA. The objective function with the constraints is optimized using the Lagrange function, and the asymptotic optimum is obtained by specifying the optimal Lagrange multipliers. Simulations show the performance of the proposed hypocenter localization method.
Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?

Recent state-of-the-art open-domain QA models are typically based on a two stage retriever-reader approach in which the retriever first finds the relevant knowledge/passages and the reader then leverages that to predict the answer. Prior work has shown that the performance of the reader usually tends to improve with the increase in the number of these passages. Thus, state-of-the-art models use a large number of passages (e.g. 100) for inference. While the reader in this approach achieves high prediction performance, its inference is computationally very expensive. We humans, on the other hand, use a more efficient strategy while answering: firstly, if we can confidently answer the question using our already acquired knowledge then we do not even use the external knowledge, and in the case when we do require external knowledge, we don't read the entire knowledge at once, instead, we only read that much knowledge that is sufficient to find the answer. Motivated by this procedure, we ask a research question "Can the open-domain QA reader utilize external knowledge efficiently like humans without sacrificing the prediction performance?" Driven by this question, we explore an approach that utilizes both 'closed-book' (leveraging knowledge already present in the model parameters) and 'open-book' inference (leveraging external knowledge). Furthermore, instead of using a large fixed number of passages for open-book inference, we dynamically read the external knowledge in multiple 'knowledge iterations'. Through comprehensive experiments on NQ and TriviaQA datasets, we demonstrate that this dynamic reading approach improves both the 'inference efficiency' and the 'prediction accuracy' of the reader. Comparing with the FiD reader, this approach matches its accuracy by utilizing just 18.32% of its reader inference cost and also outperforms it by achieving up to 55.10% accuracy on NQ Open.
Depicting deterministic variables within directed acyclic graphs (DAGs): An aid for identifying and interpreting causal effects involving tautological associations, compositional data, and composite variables

Deterministic variables are variables that are fully explained by one or more parent variables. They commonly arise when a variable has been algebraically constructed from one or more parent variables, as with composite variables, and in compositional data, where the 'whole' variable is determined from its 'parts'. This article introduces how deterministic variables may be depicted within directed acyclic graphs (DAGs) to help with identifying and interpreting causal effects involving tautological associations, compositional data, and composite variables. We propose a two-step approach in which all variables are initially considered, and an explicit choice is then made whether to focus on the deterministic variable(s) or the determining parents. Depicting deterministic variables within DAGs bring several benefits. It is easier to identify and avoid misinterpreting tautological associations, i.e., self-fulfilling associations between variables with shared algebraic parent variables. In compositional data, it is easier to understand the consequences of conditioning on the 'whole' variable, and correctly identify total and relative causal effects. For composite variables, it encourages greater consideration of the target estimand and greater scrutiny of the consistency and exchangeability assumptions. DAGs with deterministic variables are a useful aid for planning and interpreting analyses involving tautological associations, compositional data, and/or composite variables.
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning

Computer vision models suffer from a phenomenon known as catastrophic forgetting when learning novel concepts from continuously shifting training data. Typical solutions for this continual learning problem require extensive rehearsal of previously seen data, which increases memory costs and may violate data privacy. Recently, the emergence of large-scale pre-trained vision transformer models has enabled prompting approaches as an alternative to data-rehearsal. These approaches rely on a key-query mechanism to generate prompts and have been found to be highly resistant to catastrophic forgetting in the well-established rehearsal-free continual learning setting. However, the key mechanism of these methods is not trained end-to-end with the task sequence. Our experiments show that this leads to a reduction in their plasticity, hence sacrificing new task accuracy, and inability to benefit from expanded parameter capacity. We instead propose to learn a set of prompt components which are assembled with input-conditioned weights to produce input-conditioned prompts, resulting in a novel attention-based end-to-end key-query scheme. Our experiments show that we outperform the current SOTA method DualPrompt on established benchmarks by as much as 4.5% in average final accuracy. We also outperform the state of art by as much as 4.4% accuracy on a continual learning benchmark which contains both class-incremental and domain-incremental task shifts, corresponding to many practical settings. Our code is available at https://github.com/GT-RIPL/CODA-Prompt
The Quantified Argument Calculus with Two- and Three-valued Truth-valuational Semantics

Abstract We introduce a two-valued and a three-valued truth-valuational substitutional semantics for the Quantified Argument Calculus (Quarc). We then prove that the 2-valid arguments are identical to the 3-valid ones with strict-to-tolerant validity. Next, we introduce a Lemmon-style Natural Deduction system and prove the completeness of Quarc on both two- and three-valued versions, adapting Lindenbaum’s Lemma to truth-valuational semantics. We proceed to investigate the relations of three-valued Quarc and the Predicate Calculus (PC). Adding a logical predicate T to Quarc, true of all singular arguments, allows us to represent PC quantification in Quarc and translate PC into Quarc, preserving validity. Introducing a weak existential quantifier into PC allows us to translate Quarc into PC, also preserving validity. However, unlike the translated systems, neither extended system can have a sound and complete proof system with Cut, supporting the claim that these are basically different calculi.
Analysis of Students’ Reading Comprehension of Descriptive Text at SMK Negeri 2 Pematangsiantar

This research aims to find out how is the students’ Reading Comprehension of Descriptive Text. And what are their difficulties in Reading Comprehension.The method used in this research is a qualitative method, the research was conducted at SMK N2 PEMATANGSIANTAR with the samples were 35 students The researcher used five stories of descriptive text and fifty questions about the text,and there searcher used interview test too. The data collection was taken from the sudents’ score. The result of the research showed that there is 0 studentor 0% who was categorized exellent,there were 2 students or 5,7%who were categorized as very good,there were 7 studentsor 20% who were categorized as good, there were 14 students or 40 % who were categorized as fair, there were 8 students or 22,8 % who were categorized as poor, there were 4 students or 11,4 % who were categorized as very poor. And the researcher found the average by the score was 61,8 % . So, fromthe result the researcher can conclude that the students were still in the fair category
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization

Facial action unit recognition has many applications from market research to psychotherapy and from image captioning to entertainment. Despite its recent progress, deployment of these models has been impeded due to their limited generalization to unseen people and demographics. This work conducts an in-depth analysis of performance across several dimensions: individuals(40 subjects), genders (male and female), skin types (darker and lighter), and databases (BP4D and DISFA). To help suppress the variance in data, we use the notion of self-supervised denoising autoencoders to design a method for deep face normalization(DeepFN) that transfers facial expressions of different people onto a common facial template which is then used to train and evaluate facial action recognition models. We show that person-independent models yield significantly lower performance (55% average F1 and accuracy across 40 subjects) than person-dependent models (60.3%), leading to a generalization gap of 5.3%. However, normalizing the data with the newly introduced DeepFN significantly increased the performance of person-independent models (59.6%), effectively reducing the gap. Similarly, we observed generalization gaps when considering gender (2.4%), skin type (5.3%), and dataset (9.4%), which were significantly reduced with the use of DeepFN. These findings represent an important step towards the creation of more generalizable facial action unit recognition systems.