Reviewerly/exHarmony huggingface dataset:
Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition

The dichotomy between the challenging nature of obtaining annotations for activities, and the more straightforward nature of data collection from wearables, has resulted in significant interest in the development of techniques that utilize large quantities of unlabeled data for learning representations. Contrastive Predictive Coding (CPC) is one such method, learning effective representations by leveraging properties of time-series data to setup a contrastive future timestep prediction task. In this work, we propose enhancements to CPC, by systematically investigating the encoder architecture, the aggregator network, and the future timestep prediction, resulting in a fully convolutional architecture, thereby improving parallelizability. Across sensor positions and activities, our method shows substantial improvements on four of six target datasets, demonstrating its ability to empower a wide range of application scenarios. Further, in the presence of very limited labeled data, our technique significantly outperforms both supervised and self-supervised baselines, positively impacting situations where collecting only a few seconds of labeled data may be possible. This is promising, as CPC does not require specialized data transformations or reconstructions for learning effective representations.
An Integrity-Focused Threat Model for Software Development Pipelines

In recent years, there has been a growing concern with software integrity, that is, the assurance that software has not been tampered with on the path between developers and users. This path is represented by a software development pipeline and plays a pivotal role in software supply chain security. While there have been efforts to improve the security of development pipelines, there is a lack of a comprehensive view of the threats affecting them. We develop a systematic threat model for a generic software development pipeline using the STRIDE framework and identify possible mitigations for each threat. The pipeline adopted as a reference comprises five stages (integration, continuous integration, infrastructure-as-code, deployment, and release), and we review vulnerabilities and attacks in all stages reported in the literature. We present a case study applying this threat model to a specific pipeline, showing that the adaptation is straightforward and produces a list of relevant threats.
A hybrid entity-centric approach to Persian pronoun resolution

Pronoun resolution is a challenging subset of an essential field in natural language processing called coreference resolution. Coreference resolution is about finding all entities in the text that refers to the same real-world entity. This paper presents a hybrid model combining multiple rulebased sieves with a machine-learning sieve for pronouns. For this purpose, seven high-precision rule-based sieves are designed for the Persian language. Then, a random forest classifier links pronouns to the previous partial clusters. The presented method demonstrates exemplary performance using pipeline design and combining the advantages of machine learning and rulebased methods. This method has solved some challenges in end-to-end models. In this paper, the authors develop a Persian coreference corpus called Mehr in the form of 400 documents. This corpus fixes some weaknesses of the previous corpora in the Persian language. Finally, the efficiency of the presented system compared to the earlier model in Persian is reported by evaluating the proposed method on the Mehr and Uppsala test sets.
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization

Advances in reinforcement learning (RL) often rely on massive compute resources and remain notoriously sample inefficient. In contrast, the human brain is able to efficiently learn effective control strategies using limited resources. This raises the question whether insights from neuroscience can be used to improve current RL methods. Predictive processing is a popular theoretical framework which maintains that the human brain is actively seeking to minimize surprise. We show that recurrent neural networks which predict their own sensory states can be leveraged to minimise surprise, yielding substantial gains in cumulative reward. Specifically, we present the Predictive Processing Proximal Policy Optimization (P4O) agent; an actor-critic reinforcement learning agent that applies predictive processing to a recurrent variant of the PPO algorithm by integrating a world model in its hidden state. Even without hyperparameter tuning, P4O significantly outperforms a baseline recurrent variant of the PPO algorithm on multiple Atari games using a single GPU. It also outperforms other state-of-the-art agents given the same wall-clock time and exceeds human gamer performance on multiple games including Seaquest, which is a particularly challenging environment in the Atari domain. Altogether, our work underscores how insights from the field of neuroscience may support the development of more capable and efficient artificial agents.
ALANNO: An Active Learning Annotation System for Mortals

Supervised machine learning has become the cornerstone of today's data-driven society, increasing the need for labeled data. However, the process of acquiring labels is often expensive and tedious. One possible remedy is to use active learning (AL) -- a special family of machine learning algorithms designed to reduce labeling costs. Although AL has been successful in practice, a number of practical challenges hinder its effectiveness and are often overlooked in existing AL annotation tools. To address these challenges, we developed ALANNO, an open-source annotation system for NLP tasks equipped with features to make AL effective in real-world annotation projects. ALANNO facilitates annotation management in a multi-annotator setup and supports a variety of AL methods and underlying models, which are easily configurable and extensible.
Can one hear the position of nodes?

Wave propagation through nodes and links of a network forms the basis of spectral graph theory. Nevertheless, the sound emitted by nodes within the resonating chamber formed by a network are not well studied. The sound emitted by vibrations of individual nodes reflects the structure of the overall network topology but also the location of the node within the network. In this article, a sound recognition neural network is trained to infer centrality measures from the nodes' wave-forms. In addition to advancing network representation learning, sounds emitted by nodes are plausible in most cases. Auralization of the network topology may open new directions in arts, competing with network visualization.
Deterministic single-photon source in the ultrastrong coupling regime

Deterministic single-photon sources are important and ubiquitous in quantum information protocols. However, to the best of our knowledge, none of them work in the ultrastrong light-matter coupling regime, and each excitation process can only emit one photon. We propose a deterministic single-photon source in circuit QED which can work in the ultrastrong coupling regime. Here, two qubits are excited simultaneously in one process and two deterministic single photons can be sequentially emitted with an arbitrary time separation. This happens through two consecutive adiabatic transfers along the one-photon solutions of the two-qubit Rabi and Jaynes-Cummings model, which has constant eigenenergy in the whole coupling regime. Unlike the stimulated Raman adiabatic passage, the system goes back to the initial state of another period automatically after photon emission. Our scheme can approach unity single-photon efficiency, indistinguishability, and purity simultaneously. With the assistance of the Stark shift, a deterministic single photon can be generated within a time proportional to the inverse of the resonator frequency.
Mathematics and incentives in the slums

In response to COVID-19, a new project was started to allow students to solve computerised math drills outside of school. In 9 months it has gone from zero to one thousand students, in co-operation with ten community libraries in various slums and low-income regions in Kenya. The program uses the tutor-web as a study environment and access is provided by donating tablet computers to participating community libraries. Students are rewarded using the SmileyCoin cryptocurrency as they progress through the system and the libraries are free to sell for SMLY small food items, sanitary pads and the tablets themselves. The reward system is designed to put an emphasis on secondary school mathematics. Completion of the corresponding collection of drills gives SmileyCoin awards sufficient to purchase a tablet. Conclusions based on the first year indicate that the resulting effect on participation and performance is unprecedented: Eleven libraries with 1301 students opted for voluntary participation in 2021 causing the program to run at full financial capacity. In that year, 450 students earned enough SMLY to purchase the tablets, which involves completing a large collection of drills to a level of excellence. Status exam questions are dispersed within the drill collection. These independent measurements show learning which surpasses both rote learning and mechanical learning and demonstrate an increase in the general ability to address new mathematical problems not seen before.
An LSTM-Based Word Prediction in Bengali

In this paper, Bengali text information has been utilized for predicting the next word contingent based on the previous one. To do that, one should consider two key aspects such as the natural language processing (NLP) stage and the word predicting stage. When both work together, the system gets a new predicted word that is relevant to the previous word. For achieving such correct predicted words, long short-term memory (LSTM) has been used which is best known for its memory management. LSTM embeds the input words and fits them into the model, then after successful training of the model, it can predict the next word from a given sentence. The user can also initialize the number of predicted words. This paper gives an overview of word prediction for the Bengali language based on LSTM and describes the database integration and proposed approach obtained 97.60% accuracy.
Named-Entity Recognition in Medical Records Using Transfer Learning

Based on government regulations, eighteen attributes from medical records are classified as protected health information (PHI) which needs to be de-identified prior to sharing the records to a secondary source such as research. The de-identification task is classified under natural language processing task considering the obstacles in manual de-identification. Many pretrained models are implemented for de-identification task. But there is no comparison to determine which is the most efficient one. Hence, this is an attempt to compare efficiency (recall, precision and F1-score) of few potential transfer learning models by implementing them using same dataset—Informatics for Integrating Biology and Bedside (I2B2). Through this experiment, it is found that NeuroNER is the most efficient model identifying almost 20 categories with accuracy of 99.68%.
Enhanced Video Classification System with Convolutional Neural Networks Using Representative Frames as Input Data

Convolutional neural networks are extensively used in video classifications systems, which uses video action recognition data set which consists of 13,320 videos grouped to 101 classes. In this proposed method, we are using a promising way of speeding up the training by using representative frames from each video for the entire video data set. The advantage is that the training time can be drastically decreased with the compact input video data. Here we are using the inceptionv3 pretrained model for classification where the weights and architecture can be used for similar types of inputs. Instead of giving the entire video frames for video classification, the proposed method, selects representative frames only, still producing comparable results. When used with ‘action recognition data set’ having 101 classes and 13,320 videos, while using full frames, it gives a F1-score of 0.717 and while using representative frames, it results with a 0.704 value which is comparable. When all the frames of the videos are given as input to the pretrained model, the performance is considerably high. However, there is a direct implication on the cost as far as time is concerned. The main objective of this research work is to enhance the computational efficiency of the video classifier by using the representative frames for speeding up the process of training the data set.
Combating Challenges in Health Care Using Blockchain

COVID-19 pandemic has enforced the launch of vaccination campaigns worldwide. Along with it, it is equally important to guarantee that the supply chain of vaccination is accessible to all stakeholders. In this research, we look at how the COVID-19 vaccination program can aid in a variety of ways. We propose a system that ensures data integrity and efficient vaccination for beneficiaries. To guarantee, the registration is done on a first-come-first-served basis, eliminating identity theft and other forms of impersonation. Our proposed system vaccination supply chain management (VSCM) uses the Blockchain technology, where smart contracts are developed to monitor and trace the distribution supply. The technique asserts safe shipping and handling of vaccinations. Also we focus on reviving the importance of existing frameworks in supply chain management systems, its wide range of applications and the challenges faced.
Enhancing and Adversarial: Improve ASR with Speaker Labels

ASR can be improved by multi-task learning (MTL) with domain enhancing or domain adversarial training, which are two opposite objectives with the aim to increase/decrease domain variance towards domain-aware/agnostic ASR, respectively. In this work, we study how to best apply these two opposite objectives with speaker labels to improve conformer-based ASR. We also propose a novel adaptive gradient reversal layer for stable and effective adversarial training without tuning effort. Detailed analysis and experimental verification are conducted to show the optimal positions in the ASR neural network (NN) to apply speaker enhancing and adversarial training. We also explore their combination for further improvement, achieving the same performance as i-vectors plus adversarial training. Our best speaker-based MTL achieves 7\% relative improvement on the Switchboard Hub5'00 set. We also investigate the effect of such speaker-based MTL w.r.t. cleaner dataset and weaker ASR NN.
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates

With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving model design has been rapid in research, we argue that many advances fail to translate into practice because of two limiting assumptions. First, most approaches focus on a transductive learning setting which cannot handle unseen users or items and second, many existing methods are developed for static settings that cannot incorporate new data as it becomes available. We argue that these are largely impractical assumptions on real-world platforms where new user interactions happen in real time. In this survey paper, we formalize both concepts and contextualize recommender systems work from the last six years. We then discuss why and how future work should move towards inductive learning and incremental updates for recommendation model design and evaluation. In addition, we present best practices and fundamental open challenges for future research.
STAR: A Session-Based Time-Aware Recommender System

Session-Based Recommenders (SBRs) aim to predict users' next preferences regard to their previous interactions in sessions while there is no historical information about them. Modern SBRs utilize deep neural networks to map users' current interest(s) during an ongoing session to a latent space so that their next preference can be predicted. Although state-of-art SBR models achieve satisfactory results, most focus on studying the sequence of events inside sessions while ignoring temporal details of those events. In this paper, we examine the potential of session temporal information in enhancing the performance of SBRs, conceivably by reflecting the momentary interests of anonymous users or their mindset shifts during sessions. We propose the STAR framework, which utilizes the time intervals between events within sessions to construct more informative representations for items and sessions. Our mechanism revises session representation by embedding time intervals without employing discretization. Empirical results on Yoochoose and Diginetica datasets show that the suggested method outperforms the state-of-the-art baseline models in Recall and MRR criteria.
Non-Parametric Online Learning from Human Feedback for Neural Machine Translation

We study the problem of online learning with human feedback in the human-in-the-loop machine translation, in which the human translators revise the machine-generated translations and then the corrected translations are used to improve the neural machine translation (NMT) system. However, previous methods require online model updating or additional translation memory networks to achieve high-quality performance, making them inflexible and inefficient in practice. In this paper, we propose a novel non-parametric online learning method without changing the model structure. This approach introduces two k-nearest-neighbor (knn) modules: one module memorizes the human feedback, which is the correct sentences provided by human translators, while the other balances the usage of the history human feedback and original NMT models adaptively. Experiments conducted on EMEA and JRC-Acquis benchmarks demonstrate that our proposed method obtains substantial improvements on translation accuracy and achieves better adaptation performance with less repeating human correction operations.
Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection

Deep learning-based approaches have shown remarkable performance in the 3D object detection task. However, they suffer from a catastrophic performance drop on the originally trained classes when incrementally learning new classes without revisiting the old data. This "catastrophic forgetting" phenomenon impedes the deployment of 3D object detection approaches in real-world scenarios, where continuous learning systems are needed. In this paper, we study the unexplored yet important class-incremental 3D object detection problem and present the first solution - SDCoT, a novel static-dynamic co-teaching method. Our SDCoT alleviates the catastrophic forgetting of old classes via a static teacher, which provides pseudo annotations for old classes in the new samples and regularizes the current model by extracting previous knowledge with a distillation loss. At the same time, SDCoT consistently learns the underlying knowledge from new data via a dynamic teacher. We conduct extensive experiments on two benchmark datasets and demonstrate the superior performance of our SDCoT over baseline approaches in several incremental learning scenarios.
SoK: A Framework for Unifying At-Risk User Research

At-risk users are people who experience elevated digital security, privacy, and safety threats because of what they do, who they are, where they are, or who they are with. In this systematization work, we present a framework for reasoning about at-risk users based on a wide-ranging meta-analysis of 85 papers. Across the varied populations that we examined (e.g., children, activists, women in developing regions), we identified 10 unifying contextual risk factors--such as oppression or stigmatization and access to a sensitive resource--which augment or amplify digital-safety threats and their resulting harms. We also identified technical and non-technical practices that at-risk users adopt to attempt to protect themselves from digital-safety threats. We use this framework to discuss barriers that limit at-risk users' ability or willingness to take protective actions. We believe that the security, privacy, and human-computer interaction research and practitioner communities can use our framework to identify and shape research investments to benefit at-risk users, and to guide technology design to better support at-risk users.
QuISP: a Quantum Internet Simulation Package

We present an event-driven simulation package called QuISP for large-scale quantum networks built on top of the OMNeT++ discrete event simulation framework. Although the behavior of quantum networking devices have been revealed by recent research, it is still an open question how they will work in networks of a practical size. QuISP is designed to simulate large-scale quantum networks to investigate their behavior under realistic, noisy and heterogeneous configurations. The protocol architecture we propose enables studies of different choices for error management and other key decisions. Our confidence in the simulator is supported by comparing its output to analytic results for a small network. A key reason for simulation is to look for emergent behavior when large numbers of individually characterized devices are combined. QuISP can handle thousands of qubits in dozens of nodes on a laptop computer, preparing for full Quantum Internet simulation. This simulator promotes the development of protocols for larger and more complex quantum networks.
A Comparative Study of Active Solo and Dual Inclined Compound Parabolic Concentrator Collector Solar Stills Based on Exergoeconomic and Enviroeconomic

Abstract: The Parabolic Concentrator (CPC) is a uniform photovoltaic thermal (PVT) compound linked to solar photos (N) of water collectors called PVT-CPC Active Solar Filtration System Analysis. New Delhi Analysis is done for a solar filter system for a given particle size under weather conditions. We assess efficiency, system productivity, and life cycle cost analysis. The Thermal Model Life cycle cost efficiency (LCCE), designed for LCCE analysis, is considered the only and double-doubled effective PVT-CPC system for filtering solar energy recovery time. In this work, we need to analyze the appropriate points of the collector and extract the bulk of the system. Tests were performed on dual-solar and dual-inclined PVT-CPC operating systems with a single basin size and a water depth of 0.14 m, with yield on yearly basis, factor of energy payback, and efficiency of life cycle cost conversion analysis of 5.0%, 12.63%. Moreover, 22.21% is two times higher than the solo inclined system. In addition, the water return, one PVT-CPC, and two turns have been found to have a recovery time (EPT) with an interest rate of 5%. The solar filter system is 10.89% and 17.99% higher than the solo inclined photovoltaic thermal compound parabolic concentrator activated solar filter system, respectively. The above analysis concluded, we can confirm that the two bends are better than the active PVT-CPC system for solar filtering, which is the only inclination of the depth of 0.14 m in water based on daily based analysis. If depth of water 0.14 m is more significant, for basin size provided the performance of one inline is improved and is better than curved solar-powered filtering systems. The upgraded system lasts longer and can meet potable water and DC electricity on sunny commercial days.