OpenRaiser/Intern-Atlas huggingface dataset:
PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++
Venue: arXiv cs.LG 2022
Year: 2022

Standard deep learning algorithms are implemented using floating-point real numbers. This presents an obstacle for implementing them on low-end devices which may not have dedicated floating-point units (FPUs). As a result, researchers in tinyML have considered machine learning algorithms that can train and run a deep neural network (DNN) on a low-end device using integer operations only. In this paper we propose PocketNN, a light and self-contained proof-of-concept framework in pure C++ for the training and inference of DNNs using only integers. Unlike other approaches, PocketNN directly operates on integers without requiring any explicit quantization algorithms or customized fixed-point formats. This was made possible by pocket activations, which are a family of activation functions devised for integer-only DNNs, and an emerging DNN training algorithm called direct feedback alignment (DFA). Unlike the standard backpropagation (BP), DFA trains each layer independently, thus avoiding integer overflow which is a key problem when using BP with integer-only operations. We used PocketNN to train some DNNs on two well-known datasets, MNIST and Fashion-MNIST. Our experiments show that the DNNs trained with our PocketNN achieved 96.98% and 87.7% accuracies on MNIST and Fashion-MNIST datasets, respectively. The accuracies are very close to the equivalent DNNs trained using BP with floating-point real number operations, such that accuracy degradations were just 1.02%p and 2.09%p, respectively. Finally, our PocketNN has high compatibility and portability for low-end devices as it is open source and implemented in pure C++ without any dependencies.
Attention-based Random Forest and Contamination Model
Venue: arXiv cs.LG 2022
Year: 2022

A new approach called ABRF (the attention-based random forest) and its modifications for applying the attention mechanism to the random forest (RF) for regression and classification are proposed. The main idea behind the proposed ABRF models is to assign attention weights with trainable parameters to decision trees in a specific way. The weights depend on the distance between an instance, which falls into a corresponding leaf of a tree, and instances, which fall in the same leaf. This idea stems from representation of the Nadaraya-Watson kernel regression in the form of a RF. Three modifications of the general approach are proposed. The first one is based on applying the Huber's contamination model and on computing the attention weights by solving quadratic or linear optimization problems. The second and the third modifications use the gradient-based algorithms for computing trainable parameters. Numerical experiments with various regression and classification datasets illustrate the proposed method.
Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision
Venue: arXiv cs.CV 2022
Year: 2022

UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on two real-world datasets. One dataset is recorded for observation of Cercospora leaf spot - a fungal disease - in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers large-scale spatio-temporal image dataset that in turn can be applied to train further machine learning models including various data layers. The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
λ-Scaled-Attention: A Novel Fast Attention Mechanism for Efficient Modeling of Protein Sequences
Venue: arXiv cs.LG 2022
Year: 2022

Attention-based deep networks have been successfully applied on textual data in the field of NLP. However, their application on protein sequences poses additional challenges due to the weak semantics of the protein words, unlike the plain text words. These unexplored challenges faced by the standard attention technique include (i) vanishing attention score problem and (ii) high variations in the attention distribution. In this regard, we introduce a novel λ-scaled attention technique for fast and efficient modeling of the protein sequences that addresses both the above problems. This is used to develop the λ-scaled attention network and is evaluated for the task of protein function prediction implemented at the protein sub-sequence level. Experiments on the datasets for biological process (BP) and molecular function (MF) showed significant improvements in the F1 score values for the proposed λ-scaled attention technique over its counterpart approach based on the standard attention technique (+2.01% for BP and +4.67% for MF) and state-of-the-art ProtVecGen-Plus approach (+2.61% for BP and +4.20% for MF). Further, fast convergence (converging in half the number of epochs) and efficient learning (in terms of very low difference between the training and validation losses) were also observed during the training process.
Intelligent Reflecting Surface-Aided LEO Satellite Communication: Cooperative Passive Beamforming and Distributed Channel Estimation
Venue: arXiv cs.IT 2022
Year: 2022

We consider in this paper a new intelligent reflecting surface (IRS)-aided LEO satellite communication system, by utilizing the controllable phase shifts of massive passive reflecting elements to achieve flexible beamforming, which copes with the time-varying channel between the high-mobility satellite (SAT) and ground node (GN) cost-effectively. In particular, we propose a new architecture for IRS-aided LEO satellite communication where IRSs are deployed at both sides of the SAT and GN, and study their cooperative passive beamforming (CPB) design over line-of-sight (LoS)-dominant single-reflection and double-reflection channels. Specifically, we jointly optimize the active transmit/receive beamforming at the SAT/GN as well as the CPB at two-sided IRSs to maximize the overall channel gain from the SAT to each GN. Interestingly, we show that under LoS channel conditions, the high-dimensional SAT-GN channel can be decomposed into the outer product of two low-dimensional vectors. By exploiting the decomposed SAT-GN channel, we decouple the original beamforming optimization problem into two simpler subproblems corresponding to the SAT and GN sides, respectively, which are both solved in closed-form. Furthermore, we propose an efficient transmission protocol to conduct channel estimation and beam tracking, which only requires independent processing of the SAT and GN in a distributed manner, thus substantially reducing the implementation complexity. Simulation results validate the performance advantages of the proposed IRS-aided LEO satellite communication system with two-sided cooperative IRSs, as compared to various baseline schemes such as the conventional reflect-array and one-sided IRS.
Video Source Characterization Using Encoding and Encapsulation Characteristics
Venue: arXiv cs.CR 2022
Year: 2022

We introduce a new method for camera-model identification. Our approach combines two independent aspects of video file generation corresponding to video coding and media data encapsulation. To this end, a joint representation of the overall file metadata is developed and used in conjunction with a two-level hierarchical classification method. At the first level, our method groups videos into metaclasses considering several abstractions that represent high-level structural properties of file metadata. This is followed by a more nuanced classification of classes that comprise each metaclass. The method is evaluated on more than 20K videos obtained by combining four public video datasets. Tests show that a balanced accuracy of 91% is achieved in correctly identifying the class of a video among 119 video classes. This corresponds to an improvement of 6.5% over the conventional approach based on video file encapsulation characteristics. Furthermore, we investigate a setting relevant to forensic file recovery operations where file metadata cannot be located or are missing but video data is partially available. By estimating a partial list of encoding parameters from coded video data, we demonstrate that an identification accuracy of 57% can be achieved in camera-model identification in the absence of any other file metadata.
Arguments about Highly Reliable Agent Designs as a Useful Path to Artificial Intelligence Safety
Venue: arXiv cs.AI 2022
Year: 2022

Several different approaches exist for ensuring the safety of future Transformative Artificial Intelligence (TAI) or Artificial Superintelligence (ASI) systems, and proponents of different approaches have made different and debated claims about the importance or usefulness of their work in the near term, and for future systems. Highly Reliable Agent Designs (HRAD) is one of the most controversial and ambitious approaches, championed by the Machine Intelligence Research Institute, among others, and various arguments have been made about whether and how it reduces risks from future AI systems. In order to reduce confusion in the debate about AI safety, here we build on a previous discussion by Rice which collects and presents four central arguments which are used to justify HRAD as a path towards safety of AI systems. We have titled the arguments (1) incidental utility,(2) deconfusion, (3) precise specification, and (4) prediction. Each of these makes different, partly conflicting claims about how future AI systems can be risky. We have explained the assumptions and claims based on a review of published and informal literature, along with consultation with experts who have stated positions on the topic. Finally, we have briefly outlined arguments against each approach and against the agenda overall.
Indian Language Wordnets and their Linkages with Princeton WordNet
Venue: arXiv cs.CL 2022
Year: 2022

Wordnets are rich lexico-semantic resources. Linked wordnets are extensions of wordnets, which link similar concepts in wordnets of different languages. Such resources are extremely useful in many Natural Language Processing (NLP) applications, primarily those based on knowledge-based approaches. In such approaches, these resources are considered as gold standard/oracle. Thus, it is crucial that these resources hold correct information. Thereby, they are created by human experts. However, human experts in multiple languages are hard to come by. Thus, the community would benefit from sharing of such manually created resources. In this paper, we release mappings of 18 Indian language wordnets linked with Princeton WordNet. We believe that availability of such resources will have a direct impact on the progress in NLP for these languages.
A Survey on Face Recognition Systems
Venue: arXiv cs.CV 2022
Year: 2022

Face Recognition has proven to be one of the most successful technology and has impacted heterogeneous domains. Deep learning has proven to be the most successful at computer vision tasks because of its convolution-based architecture. Since the advent of deep learning, face recognition technology has had a substantial increase in its accuracy. In this paper, some of the most impactful face recognition systems were surveyed. Firstly, the paper gives an overview of a general face recognition system. Secondly, the survey covers various network architectures and training losses that have had a substantial impact. Finally, the paper talks about various databases that are used to evaluate the capabilities of a face recognition system.
Emotional Speaker Identification using a Novel Capsule Nets Model
Venue: arXiv cs.SD 2022
Year: 2022

Speaker recognition systems are widely used in various applications to identify a person by their voice; however, the high degree of variability in speech signals makes this a challenging task. Dealing with emotional variations is very difficult because emotions alter the voice characteristics of a person; thus, the acoustic features differ from those used to train models in a neutral environment. Therefore, speaker recognition models trained on neutral speech fail to correctly identify speakers under emotional stress. Although considerable advancements in speaker identification have been made using convolutional neural networks (CNN), CNNs cannot exploit the spatial association between low-level features. Inspired by the recent introduction of capsule networks (CapsNets), which are based on deep learning to overcome the inadequacy of CNNs in preserving the pose relationship between low-level features with their pooling technique, this study investigates the performance of using CapsNets in identifying speakers from emotional speech recordings. A CapsNet-based speaker identification model is proposed and evaluated using three distinct speech databases, i.e., the Emirati Speech Database, SUSAS Dataset, and RAVDESS (open-access). The proposed model is also compared to baseline systems. Experimental results demonstrate that the novel proposed CapsNet model trains faster and provides better results over current state-of-the-art schemes. The effect of the routing algorithm on speaker identification performance was also studied by varying the number of iterations, both with and without a decoder network.
Quantum Distributed Algorithms for Detection of Cliques
Venue: arXiv cs.DS 2022
Year: 2022

The possibilities offered by quantum computing have drawn attention in the distributed computing community recently, with several breakthrough results showing quantum distributed algorithms that run faster than the fastest known classical counterparts, and even separations between the two models. A prime example is the result by Izumi, Le Gall, and Magniez [STACS 2020], who showed that triangle detection by quantum distributed algorithms is easier than triangle listing, while an analogous result is not known in the classical case. In this paper we present a framework for fast quantum distributed clique detection. This improves upon the state-of-the-art for the triangle case, and is also more general, applying to larger clique sizes. Our main technical contribution is a new approach for detecting cliques by encapsulating this as a search task for nodes that can be added to smaller cliques. To extract the best complexities out of our approach, we develop a framework for nested distributed quantum searches, which employ checking procedures that are quantum themselves. Moreover, we show a circuit-complexity barrier on proving a lower bound of the form $Ω(n^{3/5+ε})$ for $K_p$-detection for any $p \geq 4$, even in the classical (non-quantum) distributed CONGEST setting.
Arithmetic Network Coding for Secret Sum Computation
Venue: arXiv cs.IT 2022
Year: 2022

We consider a network coding problem where the destination wants to recover the sum of the signals (Gaussian random variables or random finite field elements) at all the source nodes, but the sum must be kept secret from an eavesdropper that can wiretap on a subset of edges. This setting arises naturally in sensor networks and federated learning, where the secrecy of the sum of the signals (e.g. weights, gradients) may be desired. While the case for finite field can be solved, the case for Gaussian random variables is surprisingly difficult. We give a simple conjecture on the necessary and sufficient condition under which such secret computation is possible for the Gaussian case, and prove the conjecture when the number of wiretapped edges is at most 2.
Medication Error Detection Using Contextual Language Models
Venue: arXiv cs.CL 2022
Year: 2022

Medication errors most commonly occur at the ordering or prescribing stage, potentially leading to medical complications and poor health outcomes. While it is possible to catch these errors using different techniques; the focus of this work is on textual and contextual analysis of prescription information to detect and prevent potential medication errors. In this paper, we demonstrate how to use BERT-based contextual language models to detect anomalies in written or spoken text based on a data set extracted from real-world medical data of thousands of patient records. The proposed models are able to learn patterns of text dependency and predict erroneous output based on contextual information such as patient data. The experimental results yield accuracy up to 96.63% for text input and up to 79.55% for speech input, which is satisfactory for most real-world applications.
Near-Field Spatial Correlation for Extremely Large-Scale Array Communications
Venue: arXiv cs.IT 2022
Year: 2022

Extremely large-scale array (XL-array) communications correspond to systems whose antenna sizes are so large that the scatterers and/or users may no longer be located in the far-field region. By discarding the conventional far-field uniform plane wave (UPW) assumption, this letter studies the near-field spatial correlation of XL-array communications, by taking into account the more generic non-uniform spherical wave (NUSW) characteristics. It is revealed that different from the far-field channel spatial correlation which only depends on the power angular spectrum (PAS), the near-field spatial correlation depends on the scattered power distribution not just characterized by their arriving angles, but also by the scatterers' distances, which is termed as power location spectrum (PLS). A novel integral expression is derived for the near-field spatial correlation in terms of the scatterers' location distribution, which includes the far-field spatial correlation as a special case. The result shows that different from the far-field case, the near-field spatial correlation no longer exhibits spatial stationarity in general, since the correlation coefficient for each pair of antennas depends on their specific positions, rather than their relative distance only. To gain further insights, we propose a generalized one-ring model for scatterer distribution, by allowing the ring center to be flexibly located rather than coinciding with the array center as in the conventional one-ring model. Numerical results are provided to show the necessity of the near-field spatial correlation modelling for XL-array communications.
A systematic literature review on counterexample explanation
Venue: arXiv cs.SE 2022
Year: 2022

Context: Safety is of paramount importance for cyber-physical systems in domains such as automotive, robotics, and avionics. Formal methods such as model checking are one way to ensure the safety of cyber-physical systems. However, adoption of formal methods in industry is hindered by usability issues, particularly the difficulty of understanding model checking results. Objective: We want to provide an overview of the state of the art for counterexample explanation by investigating the contexts, techniques, and evaluation of research approaches in this field. This overview shall provide an understanding of current and guide future research. Method: To provide this overview, we conducted a systematic literature review. The survey comprises 116 publications that address counterexample explanations for model checking. Results: Most primary studies provide counterexample explanations graphically or as traces, minimize counterexamples to reduce complexity, localize errors in the models expressed in the input formats of model checkers, support linear temporal logic or computation tree logic specifications, and use model checkers of the Symbolic Model Verifier family. Several studies evaluate their approaches in safety-critical domains with industrial applications. Conclusion: We notably see a lack of research on counterexample explanation that targets probabilistic and real-time systems, leverages the explanations to domain-specific models, and evaluates approaches in user studies. We conclude by discussing the adequacy of different types of explanations for users with varying domain and formal methods expertise, showing the need to support laypersons in understanding model checking results to increase adoption of formal methods in industry.
Projection: A Mixed-Initiative Research Process
Venue: arXiv cs.CL 2022
Year: 2022

Communication of dense information between humans and machines is relatively low bandwidth. Many modern search and recommender systems operate as machine learning black boxes, giving little insight as to how they represent information or why they take certain actions. We present Projection, a mixed-initiative interface that aims to increase the bandwidth of communication between humans and machines throughout the research process. The interface supports adding context to searches and visualizing information in multiple dimensions with techniques such as hierarchical clustering and spatial projections. Potential customers have shown interest in the application integrating their research outlining and search processes, enabling them to structure their searches in hierarchies, and helping them visualize related spaces of knowledge.
Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework
Venue: arXiv cs.CL 2022
Year: 2022

It is well known that translations of songs and poems not only break rhythm and rhyming patterns, but can also result in loss of semantic information. The Bhagavad Gita is an ancient Hindu philosophical text originally written in Sanskrit that features a conversation between Lord Krishna and Arjuna prior to the Mahabharata war. The Bhagavad Gita is also one of the key sacred texts in Hinduism and is known as the forefront of the Vedic corpus of Hinduism. In the last two centuries, there has been a lot of interest in Hindu philosophy from western scholars; hence, the Bhagavad Gita has been translated in a number of languages. However, there is not much work that validates the quality of the English translations. Recent progress of language models powered by deep learning has enabled not only translations but a better understanding of language and texts with semantic and sentiment analysis. Our work is motivated by the recent progress of language models powered by deep learning methods. In this paper, we present a framework that compares selected translations (from Sanskrit to English) of the Bhagavad Gita using semantic and sentiment analyses. We use hand-labelled sentiment dataset for tuning state-of-art deep learning-based language model known as bidirectional encoder representations from transformers (BERT). We provide sentiment and semantic analysis for selected chapters and verses across translations. Our results show that although the style and vocabulary in the respective translations vary widely, the sentiment analysis and semantic similarity shows that the message conveyed are mostly similar.