Reviewerly/exHarmony huggingface dataset:
Design of Divide-and-Conquer Noise Decontamination Strategy for <i>M</i>-Ary DCSK: From Remodulation to Denoising

In this letter, an effective divide-and-conquer noise decontamination strategy is proposed to reduce the noise corrupting the reference signals of Hilbert-transform-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary differential chaos shift keying ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary DCSK), thereby enhancing the system robustness. An <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary DCSK with discrete Hartley transform (MDCSK-DHT) is first proposed, which is taken as an example to show the effectiveness of the proposed noise decontamination strategy. Then, a noise-decontamination-driven MDCSK-DHT (ND-MDCSK-DHT) scheme is presented. In this design, the discrete Hartley transform enables the reference and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary information-bearing signals to be transmitted in the same time slot, which improves the spectral efficiency and peak to average power ratio (PAPR) performance. Computer simulations show that the bit error rate (BER) performance of MDCSK-DHT can be significantly enhanced by the proposed noise decontamination strategy, and ND-MDCSK-DHT can achieve better BER performance than its competitors.
DLRG@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil using Multilingual Transformer Models

Online Social Network has let people connect and interact with each other.It does, however, also provide a platform for online abusers to propagate abusive content.The majority of these abusive remarks are written in a multilingual style, which allows them to easily slip past internet inspection.This paper presents a system developed for the Shared Task on Abusive Comment Detection (Misogyny, Misandry, Homophobia, Transphobic, Xenophobia, Coun-terSpeech, Hope Speech) in Tamil Dravidi-anLangTech@ACL 2022 to detect the abusive category of each comment.We approach the task with three methodologies -Machine Learning, Deep Learning and Transformerbased modeling, for two sets of data -Tamil and Tamil+English language dataset.The dataset used in our system can be accessed from the competition on CodaLab.For Machine Learning, eight algorithms were implemented, among which Random Forest gave the best result with Tamil+English dataset, with a weighted average F1-score of 0.78.For Deep Learning, Bi-Directional LSTM gave best result with pre-trained word embeddings.In Transformer-based modeling, we used In-dicBERT and mBERT with fine-tuning, among which mBERT gave the best result for Tamil dataset with a weighted average F1-score of 0.7.
Few-Shot and Weakly Supervised Repetition Counting With Body-Worn Accelerometers

This study investigates few-shot weakly supervised repetition counting of a human action such as workout using a wearable inertial sensor. We present WeakCounterF that leverages few weakly labeled segments containing occurrences of a target action from a target user to achieve precise repetition counting. Here, a weak label is defined to specify only the number of repetitions of an action included in an input data segment in this study, facilitating preparation of datasets for repetition counting. First, WeakCounterF leverages data augmentation and label diversification techniques to generate augmented diverse training data from weakly labeled data from users other than a target user, i.e., source users. Then, WeakCounterF generates diverse weakly labeled training data from few weakly labeled training data from the target user. Finally, WeakCounterF trains its repetition counting model composed of an attention mechanism on the augmented diversified data from the source users, and then fine-tunes the model on the diversified data from the target user.
Investigating the Impact of Remote Working on Employee Productivity and Work-life Balance: A Study on the Business Consultancy Industry in Dubai, UAE

The advancements in digitalization have sparked the way traditional work takes place.COVID-19 pandemic has also prompted a drastic change for modification of the usual work setting.These give rise to remote working, also known as telework, hot desking, work from home, hoteling, and a flexible work plan through which a worker achieves his/her tasks in a place remote from the customary office.Studies have found that remote working brings several challenges and benefits to workers.People who practiced remote working have shown an increase in their job performance and job satisfaction.However, other workers have expressed concerns on other aspects such as pressures in time management, loneliness due to less or absence of interaction among workers, and the feeling of being left out.This study aims to examine remote working and its impact on the productivity and work-life balance of the employees in the business consultancy industry in Dubai, UAE.This is quantitative research wherein descriptive and correlation research designs employing regression analysis were utilized.96 employees working in the administration and office from different organizations in the business setup division were sampled using purposive sampling.Analysis of the responses demonstrated that remote working has a strong positive relation to employees' productivity.However, results also showed that remote working has no significant impact on the employees' work-life balance.On these bases, employers should take responsibility for the health and safety of the workers under their care whether or not the work is being done remotely.Similarly, employees should also take responsibility for themselves by carefully following the guidelines and procedures enforced by their employers.Further research is needed to identify other factors or variables that could bring fresh perspectives to elucidate the impact of remote working further.
New MRD codes from linear cutting blocking sets

Abstract Minimal rank-metric codes or, equivalently, linear cutting blocking sets are characterized in terms of the second generalized rank weight, via their connection with evasiveness properties of the associated q -system. Using this result, we provide the first construction of a family of $$\mathbb F_{q^m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mi>q</mml:mi> <mml:mi>m</mml:mi> </mml:msup> </mml:msub> </mml:math> -linear MRD codes of length 2 m that are not obtained as a direct sum of two smaller MRD codes. In addition, such a family has better parameters, since its codes possess generalized rank weights strictly larger than those of the previously known MRD codes. This shows that not all the MRD codes have the same generalized rank weights, in contrast to what happens in the Hamming metric setting.
Inverse Problems for Gradual Semantics

Gradual semantics with abstract argumentation provide each argument with a score reflecting its acceptability. Many different gradual semantics have been proposed in the literature, each following different principles and producing different argument rankings. A sub-class of such semantics, the so-called weighted semantics, takes, in addition to the graph structure, an initial set of weights over the arguments as input, with these weights affecting the resultant argument ranking. In this work, we consider the inverse problem over such weighted semantics. That is, given an argumentation framework and a desired argument ranking, we ask whether there exist initial weights such that a particular semantics produces the given ranking. The contribution of this paper are: (1) an algorithm to answer this problem, (2) a characterisation of the properties that a gradual semantics must satisfy for the algorithm to operate, and (3) an empirical evaluation of the proposed algorithm.
A Survey on Machine Learning Approaches for Modelling Intuitive Physics

Research in cognitive science has provided extensive evidence of human cognitive ability in performing physical reasoning of objects from noisy perceptual inputs. Such a cognitive ability is commonly known as intuitive physics. With advancements in deep learning, there is an increasing interest in building intelligent systems that are capable of performing physical reasoning from a given scene for the purpose of building better AI systems. As a result, many contemporary approaches in modelling intuitive physics for machine cognition have been inspired by literature from cognitive science. Despite the wide range of work in physical reasoning for machine cognition, there is a scarcity of reviews that organize and group these deep learning approaches. Especially at the intersection of intuitive physics and artificial intelligence, there is a need to make sense of the diverse range of ideas and approaches. Therefore, this paper presents a comprehensive survey of recent advances and techniques in intuitive physics-inspired deep learning approaches for physical reasoning. The survey will first categorize existing deep learning approaches into three facets of physical reasoning before organizing them into three general technical approaches and propose six categorical tasks of the field. Finally, we highlight the challenges of the current field and present some future research directions.
High-resource Language-specific Training for Multilingual Neural Machine Translation

Multilingual neural machine translation (MNMT) trained in multiple language pairs has attracted considerable attention due to fewer model parameters and lower training costs by sharing knowledge among multiple languages. Nonetheless, multilingual training is plagued by language interference degeneration in shared parameters because of the negative interference among different translation directions, especially on high-resource languages. In this paper, we propose the multilingual translation model with the high-resource language-specific training (HLT-MT) to alleviate the negative interference, which adopts the two-stage training with the language-specific selection mechanism. Specifically, we first train the multilingual model only with the high-resource pairs and select the language-specific modules at the top of the decoder to enhance the translation quality of high-resource directions. Next, the model is further trained on all available corpora to transfer knowledge from high-resource languages (HRLs) to low-resource languages (LRLs). Experimental results show that HLT-MT outperforms various strong baselines on WMT-10 and OPUS-100 benchmarks. Furthermore, the analytic experiments validate the effectiveness of our method in mitigating the negative interference in multilingual training.
RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training

Recently, self-supervised vision transformers have attracted unprecedented attention for their impressive representation learning ability. However, the dominant method, contrastive learning, mainly relies on an instance discrimination pretext task, which learns a global understanding of the image. This paper incorporates local feature learning into self-supervised vision transformers via Reconstructive Pre-training (RePre). Our RePre extends contrastive frameworks by adding a branch for reconstructing raw image pixels in parallel with the existing contrastive objective. RePre equips with a lightweight convolution-based decoder that fuses the multi-hierarchy features from the transformer encoder. The multi-hierarchy features provide rich supervisions from low to high semantic information, crucial for our RePre. Our RePre brings decent improvements on various contrastive frameworks with different vision transformer architectures. Transfer performance in downstream tasks outperforms supervised pre-training and state-of-the-art (SOTA) self-supervised counterparts.
Spiking Graph Convolutional Networks

Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices. In contrast, Spiking Neural Networks (SNNs), which perform a bio-fidelity inference process, offer an energy-efficient neural architecture. In this work, we propose SpikingGCN, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. The original graph data are encoded into spike trains based on the incorporation of graph convolution. We further model biological information processing by utilizing a fully connected layer combined with neuron nodes. In a wide range of scenarios (e.g., citation networks, image graph classification, and recommender systems), our experimental results show that the proposed method could gain competitive performance against state-of-the-art approaches. Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis, which demonstrates its great potential to construct environment-friendly machine learning models.
Global Inference with Explicit Syntactic and Discourse Structures for Dialogue-Level Relation Extraction

Recent research attention for relation extraction has been paid to the dialogue scenario, i.e., dialogue-level relation extraction (DiaRE). Existing DiaRE methods either simply concatenate the utterances in a dialogue into a long piece of text, or employ naive words, sentences or entities to build dialogue graphs, while the structural characteristics in dialogues have not been fully utilized. In this work, we investigate a novel dialogue-level mixed dependency graph (D2G) and an argument reasoning graph (ARG) for DiaRE with a global relation reasoning mechanism. First, we model the entire dialogue into a unified and coherent D2G by explicitly integrating both syntactic and discourse structures, which enables richer semantic and feature learning for relation extraction. Second, we stack an ARG graph on top of D2G to further focus on argument inter-dependency learning and argument representation refinement, for sufficient argument relation inference. In our global reasoning framework, D2G and ARG work collaboratively, iteratively performing lexical, syntactic and semantic information exchange and representation learning over the entire dialogue context. On two DiaRE benchmarks, our framework shows considerable improvements over the current state-of-the-art baselines. Further analyses show that the model effectively solves the long-range dependence issue, and meanwhile gives explainable predictions.
Convolutional Neural Networks and Heuristic Methods for Crowd Counting: A Systematic Review

The crowd counting task has become a pillar for crowd control as it provides information concerning the number of people in a scene. It is helpful in many scenarios such as video surveillance, public safety, and future event planning. To solve such tasks, researchers have proposed different solutions. In the beginning, researchers went with more traditional solutions, while recently the focus is on deep learning methods and, more specifically, on Convolutional Neural Networks (CNNs), because of their efficiency. This review explores these methods by focusing on their key differences, advantages, and disadvantages. We have systematically analyzed algorithms and works based on the different models suggested and the problems they are trying to solve. The main focus is on the shift made in the history of crowd counting methods, moving from the heuristic models to CNN models by identifying each category and discussing its different methods and architectures. After a deep study of the literature on crowd counting, the survey partitions current datasets into sparse and crowded ones. It discusses the reviewed methods by comparing their results on the different datasets. The findings suggest that the heuristic models could be even more effective than the CNN models in sparse scenarios.
A proposed model for text and image encryption using different ‎techniques

Data security and protection is one of the most common technologies used in the field of computer ‎ science. This is because of its great importance in all fields related to daily life, whether political, ‎ economic, and other aspects change. In this research paper, a new intelligent system ‎ that ‎ concentrates on image ‎ and text encryption and decryption is proposed. ‎ Furthermore, ‎ the image recognition rate is increased. In this paper, ‎ the Rivest Shamir Adleman 3 key (RSA3k) algorithm is used but ‎ in a new ‎ technique to encrypt and decrypt ‎ text. The proposed technique relies on using square root ‎ of public ‎ key as ‎ private key. XOR ‎ operation for colored images ‎ encryption and decryption is ‎ performed. By calculating signal to noise ratio (PSNR), mean square error (MSE), and bit error ‎ rate (BER), the most important things that we address how to provide data security ‎ for large data based on encryption techniques and increase the security of multimedia data and texture date and ‎ address the problem of time and accuracy in image matching. ‎ the results show that the quality of images after decryption is good.
An Efficient Face Gender Detector on a CPU with Multi-Perspective Convolution

Intelligent digital signage demands software to work lightly in real-time on embedded devices. Smart digital signage requires gender detection to deliver relevant and targeted ads automatically. This study presents an efficient real-time face gender detector (FGenCPU) that can run smoothly on a CPU implemented in digital signage to decide immediately and provide relevant and targeted ads while the audience faces it. The proposed architecture contains Multi-Perspective Convolution (MPConvNet) consisting of two main modules: backbone and recognition. An efficient extractor module with a multi-perspective contextual track through the various kernel sizes is utilized to apprehend different feature areas of the object. This architecture employs few filters in every convolution layer and generates few parameters. It makes the detector operate at high speed for real-time. The training is conducted on the UTKFace dataset to generate efficiently weighted models. The MPConvNet achieves competitive performance with other common and light architectures on UTKFace, Adience, and LFW datasets. Furthermore, the detector can run 38 frames per second when performing on a CPU in real-time.
Gaze Estimation Approach Using Deep Differential Residual Network

Gaze estimation, which is a method to determine where a person is looking at given the person’s full face, is a valuable clue for understanding human intention. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain. However, there are still gaze calibration problems in the gaze estimation domain, thus preventing existing methods from further improving the performances. An effective solution is to directly predict the difference information of two human eyes, such as the differential network (Diff-Nn). However, this solution results in a loss of accuracy when using only one inference image. We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images. We treat the difference information as auxiliary information. We assess the proposed model (DRNet) mainly using two public datasets (1) MpiiGaze and (2) Eyediap. Considering only the eye features, DRNet outperforms the state-of-the-art gaze estimation methods with angular-error of 4.57 and 6.14 using MpiiGaze and Eyediap datasets, respectively. Furthermore, the experimental results also demonstrate that DRNet is extremely robust to noise images.
Envisioning multilingualism in source-based writing in L1, L2, and L3: The relation between source use and text quality

In this article, we report on a study that investigates how master's students consult external sources for reading-to-write integrated tasks in their L1 (Dutch), L2 (English), and L3 (French). Two hundred and eighty master's students in professional communication wrote synthesis texts based on three external sources, including a report, a web text, and a newspaper article in their L1 (Dutch), and in L2 (English), or L3 (French) at two moments of measurement, which were separated by an interval of 6 months. Their source use activities during the writing process were registered using Inputlog - a keylogging program. Inputlog enabled us to determine the amount of time the writers spent composing their main texts and consulting the sources (when the source consultation activities took place during the writing process, which sources were consulted most frequently, and how frequently the writers transitioned between the various sources). Final text quality was assessed holistically using pairwise comparisons (D-pac, now Comproved). Confirmatory factor analysis indicated three components that could describe source use processes in L1, L2, and L3 writing: (a) initial reading time, (b) source interaction, and (c) variance of source use throughout the writing process. Within-subject comparisons revealed that there were no improvements in the students' text quality in L1, L2, and L3 over an academic year. Structural equation modeling indicated that the source use approach, particularly source interaction, is related to text quality, but only in L1 and L3. We provide further explanations for this variation based on language proficiency, temporal distribution of writing process, and individual differences.
Hand Tracking and Gesture Recognition by Multiple Contactless Sensors: A Survey

Hand tracking and gesture recognition are fundamental in a multitude of applications. Various sensors have been used for this purpose, however, all monocular vision systems face limitations caused by occlusions. Wearable equipment overcome said limitations, although deemed impractical in some cases. Using more than one sensor provides a way to overcome this problem, but necessitates more complicated designs. In this work, we aim to highlight contemporary methods used for hand tracking and gesture recognition by collecting publications of systems developed in the last decade, that employ contactless devices as RGB cameras, IR, and depth sensors, along with some preceding pillar works. Additionally, we briefly present common steps, techniques, and basic algorithms used during the process of developing modern hand tracking and gesture recognition systems and, finally, we derive the trend for the next future.
FairCanary

Systems that offer continuous model monitoring have emerged in response to (1) well-documented failures of deployed Machine Learning (ML) and Artificial Intelligence (AI) models and (2) new regulatory requirements impacting these models. Existing monitoring systems continuously track the performance of deployed ML models and compute feature importance (a.k.a. explanations) for each prediction to help developers identify the root causes of emergent model performance problems.