Reviewerly/exHarmony huggingface dataset:
Error Correcting Codes that Achieve BSC Capacity Against Channels that are Poly-Size Circuits

Guruswami and Smith (J. ACM 2016) considered codes for channels that are poly-size circuits which modify at most a p-fraction of the bits of the codeword. This class of channels is significantly stronger than Shannon’s binary symmetric channel (BSC), but weaker than Hamming’s channels which are computationally unbounded. Guruswami and Smith gave an explicit Monte-Carlo construction of codes with optimal rate of R(p) = 1 − H(p) that achieve list-decoding in this scenario. Here, “explicit Monte-Carlo” means that both encoding and decoding algorithms run in polynomial time. However, the encoding and decoding algorithms also receive a uniformly chosen string of polynomial length (which is chosen and published, once and for all, in a pre-processing stage) and their correctness is guaranteed w.h.p. over this random choice. Guruswami and Smith asked whether it is possible to obtain uniquely decodable codes for poly-size channels with rate that beats the Gilbert-Varshamov bound $R^{GV}(p)=1-H(2p)$. We give an affirmative answer, Specifically:•For every $0\leq p\lt\frac{1}{4}$, we give an explicit Monte-Carlo construction of uniquely-decodable codes with optimal rate R(p) = 1 − H(p). This matches the rate achieved by Guruswami and Smith for the easier task of list-decoding, and also matches the capacity of binary symmetric channels. Moreover, this rate is strictly larger than that of codes for the standard coding scenario (namely, uniquely-decodable codes for Hamming channels).•Even ignoring explicitness, our result implies a characterization of the capacity of poly-size channels, which was not previously understood.Our technique builds on the earlier list-decodable codes of Guruswami and Smith, achieving unique-decoding by extending and modifying the construction so that we can identify the correct message in the list. For this purpose we use ideas from coding theory and pseudorandomness, specifically:•We construct codes for binary symmetric channels that beat the Gilbert-Varshamov bound, and are “evasive” in the sense that a poly-size circuit that receives a random (or actually pseudorandom) string, cannot find a codeword within relative distance 2p. This notion of evasiveness is inspired by the recent work of Shaltiel and Silbak (STOC 2021) on codes for space bounded channels.•We develop a methodology (that is inspired by proofs of t-wise independent tail inequalities, and may be of independent interest) to analyze random codes, in scenarios where the success of the channel is measured in an additional random experiment (as in the evasiveness experiment above).•We introduce a new notion of “small-set non-malleable codes” that is tailored for our application, and may be of independent interest.
Analysis of Layer-Wise Training in Direct Speech to Speech Translation Using BI-LSTM

Speech-to-speech translation (S2ST) is the process of translation of speech from one language to another. Traditional S2ST systems follow a cascaded approach, where three modules automatics speech recognition (ASR), machine translation (MT), and text-to-speech translation (TTS) are concatenated to obtain the final translated speech utterance. The cascaded nature of the system results in the propagation of errors from one module to another. This, in turn, leads to the degradation in the overall performance of the S2ST task. With the evolution of the deep learning approaches to speech processing, many attempts have been made to perform end-to-end and direct speech-to-speech translation (DS2ST). But most of these approaches rely on language transcripts in one way or the other. In this work, we aim to perform the DS2ST task without using language transcripts. In this direction we have performed three experiments: First, we have investigated the direct learning of mapping function from source to target language with the increase in the number of utterances. Second, we have analyzed how the learning function improves with an increase in the number of Bi-LSTM layers. Third, we have observed how the system behaves with the unknown speakers (not used during training) during inference. From the experiments, it has been observed that with the increase in the number of utterances and layers, the quality of translation improves. And also, with a speaker and text-dependent training of approximately 4.4 hrs of speech, the model can generate the target language utterance even for unknown speakers. Though the generated utterance quality is not that good, but intelligent to some extent to be perceived.
Experimentation Of Various Preprocessing Pipelines For Sentiment Analysis On Twitter Data About New Indonesia’s Capital City Using SVM And CNN

Selecting the sequence of pre-processing stages to obtain the best data for subsequent processes is still challenging. We have conducted a series of experiments with various sequences of pre-processing methods to acquire the best data. For the experiments, we used 12. 5K Indonesian Twitter on the new capital city of Indonesia (IKN) and 16. 2M monolingual text. We compared the manual labeling results with the outputs of SVM and CNN, which use the word-embedding feature generated by Word2Vec. The proposed pre-processing pipeline can be used as a reference for sentiment analysis research using Indonesian social media data. The best experimental results show a better F-1 value in SVM with B.2 pipeline (normalized word, stop word) is 69.32% and CNN with B.3 pipeline (normalized word, stop word, stemming) is 73.03%.
FCPP+Miosix: Scaling Aggregate Programming to Embedded Systems

As the density of nodes capable of sensing, computing and actuation increases, it becomes increasingly useful to model an entire network of physical devices as a single, continuous space-time computing machine. The emergent behaviour of the whole software system is then induced by local computations deployed within each node and by the dynamics of the information diffusion. A relevant example of this distribution model is given by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aggregate programming</i> and its minimal set of functional constructs used to manipulate distributed data structures evolving over space and time, and resulting in robustness to changes. In this paper, we propose the first implementation of the aggregate computing paradigm targeting microcontrollers, by integrating FCPP, a C++ implementation of the paradigm, with Miosix, a modern operating system for microcontrollers with full C++ support. To the best of the author's knowledge, we are the first to present results on the effectiveness of FCPP in an embedded operating system setting as opposed to a simulation environment, thus considering tight memory and computational constraints and accounting for packet losses due to nonidealities of the radio channel. We implemented and tested on a network of WandStem nodes two benchmark applications: a network connectivity checker for network planning and preventive maintenance, and a decentralised contact tracing application. Additionally, we show that common problems in sensor networks such as neighbour discovery, construction of a graph of the network topology, coarse grain clock synchronisation as well as network monitoring and the collection of statistics (such as memory occupation data) can be easily performed thanks to the expressive semantics of aggregate programming.
Reducing Spatial Labeling Redundancy for Active Semi-Supervised Crowd Counting

Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to reduce the labeling efforts. Given a limited labeling budget, they typically select a few crowd images and densely label all individuals in each of them. Despite the promising results, we argue the None-or-All labeling strategy is suboptimal as the densely labeled individuals in each crowd image usually appear similar while the massive unlabeled crowd images may contain entirely diverse individuals. To this end, we propose to break the labeling chain of previous methods and make the first attempt to reduce spatial labeling redundancy for semi-supervised crowd counting. First, instead of annotating all the regions in each crowd image, we propose to annotate the representative ones only. We analyze the region representativeness from both vertical and horizontal directions of initially estimated density maps, and formulate them as cluster centers of Gaussian Mixture Models. Additionally, to leverage the rich unlabeled regions, we exploit the similarities among individuals in each crowd image to directly supervise the unlabeled regions via feature propagation instead of the error-prone label propagation employed in the previous methods. In this way, we can transfer the original spatial labeling redundancy caused by individual similarities to effective supervision signals on the unlabeled regions. Extensive experiments on the widely-used benchmarks demonstrate that our method can outperform previous best approaches by a large margin.
Symmetric text encryption scheme based Karhunen Loeve transform

AbstractAbstractRecently cryptography is one of the essence areas of study. There are two types of cryptography, this paper is concerned with the simplest and fastest which is “Symmetric Key Cryptography”. Symmetric encryption text information is widely known as an effective and secure method for the purpose of secure transmission. In this paper, we present a new symmetric encryption scheme for text encryption and decryption based on Karhunen Loeve transform, where Karhunen Loeve transform is a reversible linear transform. The time complexity of the proposed scheme is calculated for both encryption and decryption. The proposed scheme has an has efficiency according to the security measures.Subject Classification: 11A15Keywords: Symmetric key cryptographyText encryption and KarhunenLoeve transform
PL-kNN: A Python-based implementation of a parameterless <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e43" altimg="si5.svg"><mml:mi>k</mml:mi></mml:math>-Nearest Neighbors classifier

<h2>Abstract</h2> This paper presents an open-source implementation of PL-kNN, a parameterless version of the <mml:math><mml:mi>k</mml:mi></mml:math>-Nearest Neighbors algorithm. The proposed model, developed in Python 3.6, was designed to avoid the choice of the <mml:math><mml:mi>k</mml:mi></mml:math> parameter required by the standard <mml:math><mml:mi>k</mml:mi></mml:math>-Nearest Neighbors technique. Essentially, the model computes the number of nearest neighbors of a target sample using the data distribution of the training set. The source code provides functions resembling the Scikit-learn methods for fitting the model and predicting the classes of the new samples. The source code is available in the GitHub repository with instructions for installation and examples for usage.
Approximating global illumination with ambient occlusion and environment light via generative adversarial networks

Calculating global illumination in computer graphics is difficult, especially for complex scenes. This is due to the interreflections of the rays of light and the interactions with the materials of the objects composing the scene. Solutions based on ambient light approximations have been implemented. However, these are computationally intensive and produce images with less precision, as these solutions ignore the ambient lighting component by adopting coarse approximations. In this paper, we propose a method capable of approximating the global illumination effect. Our idea is to compute the global illumination by adding three images (direct illumination, environmental light, and ambient occlusion). Direct illumination is calculated by a reference method. Environmental illumination is computed using an adversarial neural network from a single 2D image. Ambient occlusion is generated using conditional adversarial neural networks with an attention mechanism to pay more attention to the relevant image features during the training step. We use two image masks to keep the object’s position in the screen space, which allows efficient reconstruction of the final result. Our solution produces quality images compared to reference images and does not require any computation in the 3D scene or screen space.
Artificially intelligent differential diagnosis of enlarged lymph nodes with random vector functional link network plus

Differential diagnosis of enlarged lymph nodes (ELNs) is essential for the treatment of related patients. Though multi-modal ultrasound including B-mode, Doppler ultrasound, elastography and contrast-enhanced ultrasound (CEUS) can enhance diagnostic performance for ELNs, the scenario of having only single or dual modal data is often encountered. In this study, an artificially intelligent diagnosis model based on the learning using privileged information was proposed to aid in differential diagnosis of ELNs in the case of single or dual modal images. In our model, B-mode, or combined with another modality, was used as the standard information (SI) and other modalities were used as the privileged information (PI). The model was constructed through the combination of the SI and PI in the training stage. By learning from the training samples, a random vector functional link network with privileged information (RVFL+) was obtained, which was used to classify the testing samples of solely the SI. Results showed that the accuracy, precision and Youden's index of the RVFL+ model, using B-mode with elastography as the SI and CEUS as the PI, reached 78.4%, 92.4% and 54.9%, increased by 14.0%, 8.4% and 24.5% compared with the model using B-mode as the SI without the PI. The method based on the LUPI can improve the diagnostic performance for ELNs.
A class of general type-2 fuzzy controller based on adaptive alpha-plane for nonlinear systems

This study introduces a general type-2 Takagi–Sugeno–Kang fuzzy controller (GT2-TSKFC) for controlling uncertain systems. The proposed GT2-TSKFC uses equidistant type-2 triangular membership functions (MFs) for the antecedents, Larsen's implication method, type-1 fuzzy sets for the consequent parameters, and a direct defuzzification method. The analytical structure of the proposed controller indicates that the alpha-plane and apex of the secondary MFs have a noticeable effect on calculating the control signal. Adaptation of the alpha-plane and apex of the secondary MFs is performed using the Lyapunov function to achieve the stability of the controlled system. The proposed controller is applied to an uncertain nonlinear inverted pendulum system. The results of the proposed control algorithm are compared with those of a general type-2 fuzzy controller with a specific number of alpha-planes, a quasi type-2 fuzzy controller, an interval type-2 fuzzy controller, and a type-1 fuzzy controller to demonstrate the robustness and effectiveness of the proposed scheme.
Towards Malay named entity recognition: an open-source dataset and a multi-task framework

Named entity recognition (NER) is a key component of many natural language processing (NLP) applications. The majority of advanced research, however, has not been widely applied to low-resource languages represented by Malay due to the data-hungry problem. In this paper, we present a system for building a Malay NER dataset (MS-NER) of 20,146 sentences through labelled datasets of homologous languages and iterative optimisation. Additionally, we propose a Multi-Task framework, namely MTBR, to integrate boundary information more effectively for NER. Specifically, boundary detection is treated as an auxiliary task and an enhanced Bidirectional Revision module with a gated ignoring mechanism is proposed to undertake conditional label transfer. This can reduce error propagation by the auxiliary task. We conduct extensive experiments on Malay, Indonesian, and English. Experimental results show that MTBR could achieve competitive performance and tends to outperform multiple baselines. The constructed dataset and model would be made available to the public as a new, reliable benchmark for Malay NER.
Is technology gender neutral? A systematic literature review on gender stereotypes attached to artificial intelligence

Artificial Intelligence implies computer systems capable of mimicking human-like intelligence and competencies. In the nowadays society it is an exciting topic, thus, technology’s gender features and roles are of great interest as well. As the literature is still scarce and inconsistent, the present paper aims to develop a systematic literature review on gender stereotypes attached to technology (virtual assistants and robots). The main goals are to emphasize the labels given to technology from a gender perspective, the perceived competencies of the gendered technology, the most relevant variables responsible for the way gender issues are perceived in connection with technology, and the proposed solutions for diminishing the technology gender stereotypes. Forty-five scientific papers have been selected and analyzed. Findings suggest that the most intelligent technologies are designed as females, male-gendered technology performs better in task-solving, and users’ age and technology’s visual representation are important variables in perception.
Explicit Lower Bounds Against Ω(n)-Rounds of Sum-of-Squares

We construct an explicit family of 3-XOR instances hard for $\Omega(n)$-levels of the Sum-of-Squares (SoS) semi-definite programming hierarchy. Not only is this the first explicit construction to beat brute force search (beyond low-order improvements (Tulsiani 2021, Pratt 2021)), combined with standard gap amplification techniques it also matches the (optimal) hardness of random instances up to imperfect completeness (Grigoriev TCS 2001, Schoenebeck FOCS 2008).Our result is based on a new form of small-set high dimensional expansion (SS-HDX) inspired by recent breakthroughs in locally testable and quantum LDPC codes. Adapting the recent framework of Dinur, Filmus, Harsha, and Tulsiani (ITCS 2021) for SoS lower bounds from the Ramanujan complex to this setting, we show any (bounded-degree) SS-HDX can be transformed into a highly unsatisfiable 3-XOR instance that cannot be refuted by $\Omega(n)$-levels of SoS. We then show Leverrier and Zémor’s (Arxiv 2022) recent qLDPC construction gives the desired explicit family of bounded-degree SS-HDX. Incidentally, this gives the strongest known form of bi-directional high dimensional expansion to date.A full version of this paper is accessible at: https://arxiv.org/abs/2204.11469.
Abnormal Data Detection Based on Adaptive Sliding Window and Weighted Multiscale Local Outlier Factor for Machinery Health Monitoring

Identifying abnormal data to improve data quality is of great importance for machinery health monitoring (MHM). Existing abnormal data detection methods generally depend on appropriate parameter settings and prior knowledge of data distribution, which result in relatively low adaptability to MHM data. To obtain more reliable MHM results, this article proposed a novel method to detect abnormal data. First, the concept of adaptive sliding window (ASW) is defined and the advantages of ASW in avoiding data leakage and data redundancy are derived. The ASW can optimally divide the overall data into several segments. Next, statistical factors in time- and frequency-domain are extracted from these segments to generate corresponding research objects. Then, an improved weighted multiscale local outlier factor (WMLOF) algorithm is proposed herein to evaluate the data anomaly degree of each object. The WMLOF has the ability to assess and fuse the local outlier factor characteristics at multiple scales, and eventually yields a more comprehensive WMLOF value to evaluate the anomaly degree of the MHM data. Finally, the effectiveness and superiority of the ASW and WMLOF are validated by a synthetic simulation of a faulty rolling bearing and two engineering projects pertaining to a railway vehicle gearbox, and bench test data. Comparative analysis shows that the proposed abnormal data detection method based on the ASW and WMLOF strategies outperforms five reported algorithms in outlier detection.
Deep learning‐based multimodal fusion network for segmentation and classification of breast cancers using B‐mode and elastography ultrasound images

Ultrasonography is one of the key medical imaging modalities for evaluating breast lesions. For differentiating benign from malignant lesions, computer-aided diagnosis (CAD) systems have greatly assisted radiologists by automatically segmenting and identifying features of lesions. Here, we present deep learning (DL)-based methods to segment the lesions and then classify benign from malignant, utilizing both B-mode and strain elastography (SE-mode) images. We propose a weighted multimodal U-Net (W-MM-U-Net) model for segmenting lesions where optimum weight is assigned on different imaging modalities using a weighted-skip connection method to emphasize its importance. We design a multimodal fusion framework (MFF) on cropped B-mode and SE-mode ultrasound (US) lesion images to classify benign and malignant lesions. The MFF consists of an integrated feature network (IFN) and a decision network (DN). Unlike other recent fusion methods, the proposed MFF method can simultaneously learn complementary information from convolutional neural networks (CNNs) trained using B-mode and SE-mode US images. The features from the CNNs are ensembled using the multimodal EmbraceNet model and DN classifies the images using those features. The experimental results (sensitivity of 100 ± 0.00% and specificity of 94.28 ± 7.00%) on the real-world clinical data showed that the proposed method outperforms the existing single- and multimodal methods. The proposed method predicts seven benign patients as benign three times out of five trials and six malignant patients as malignant five out of five trials. The proposed method would potentially enhance the classification accuracy of radiologists for breast cancer detection in US images.