OpenRaiser/Intern-Atlas huggingface dataset:
Artificial Intelligence for Drug Discovery: Are We There Yet?
Venue: arXiv cs.AI 2023
Year: 2023

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
Agreement Tracking for Multi-Issue Negotiation Dialogues
Venue: arXiv cs.CL 2023
Year: 2023

Automated negotiation support systems aim to help human negotiators reach more favorable outcomes in multi-issue negotiations (e.g., an employer and a candidate negotiating over issues such as salary, hours, and promotions before a job offer). To be successful, these systems must accurately track agreements reached by participants in real-time. Existing approaches either focus on task-oriented dialogues or produce unstructured outputs, rendering them unsuitable for this objective. Our work introduces the novel task of agreement tracking for two-party multi-issue negotiations, which requires continuous monitoring of agreements within a structured state space. To address the scarcity of annotated corpora with realistic multi-issue negotiation dialogues, we use GPT-3 to build GPT-Negochat, a synthesized dataset that we make publicly available. We present a strong initial baseline for our task by transfer-learning a T5 model trained on the MultiWOZ 2.4 corpus. Pre-training T5-small and T5-base on MultiWOZ 2.4's DST task enhances results by 21% and 9% respectively over training solely on GPT-Negochat. We validate our method's sample-efficiency via smaller training subset experiments. By releasing GPT-Negochat and our baseline models, we aim to encourage further research in multi-issue negotiation dialogue agreement tracking.
What Exactly is an Insight? A Literature Review
Venue: arXiv cs.HC 2023
Year: 2023

Insights are often considered the ideal outcome of visual analysis sessions. However, there is no single definition of what an insight is. Some scholars define insights as correlations, while others define them as hypotheses or aha moments. This lack of a clear definition can make it difficult to build visualization tools that effectively support insight discovery. In this paper, we contribute a comprehensive literature review that maps the landscape of existing insight definitions. We summarize key themes regarding how insight is defined, with the goal of helping readers identify which definitions of insight align closely with their research and tool development goals. Based on our review, we also suggest interesting research directions, such as synthesizing a unified formalism for insight and connecting theories of insight to other critical concepts in visualization research.
Edge-Coloring Algorithms for Bounded Degree Multigraphs
Venue: arXiv cs.DS 2023
Year: 2023

In this paper, we consider algorithms for edge-coloring multigraphs $G$ of bounded maximum degree, i.e., $Δ(G) = O(1)$. Shannon's theorem states that any multigraph of maximum degree $Δ$ can be properly edge-colored with $\lfloor3Δ/2\rfloor$ colors. Our main results include algorithms for computing such colorings. We design deterministic and randomized sequential algorithms with running time $O(n\log n)$ and $O(n)$, respectively. This is the first improvement since the $O(n^2)$ algorithm in Shannon's original paper, and our randomized algorithm is optimal up to constant factors. We also develop distributed algorithms in the $\mathsf{LOCAL}$ model of computation. Namely, we design deterministic and randomized $\mathsf{LOCAL}$ algorithms with running time $\tilde O(\log^5 n)$ and $O(\log^2n)$, respectively. The deterministic sequential algorithm is a simplified extension of earlier work of Gabow et al. in edge-coloring simple graphs. The other algorithms apply the entropy compression method in a similar way to recent work by the author and Bernshteyn, where the authors design algorithms for Vizing's theorem for simple graphs. We also extend those results to Vizing's theorem for multigraphs.
Nowcasting Temporal Trends Using Indirect Surveys
Venue: arXiv cs.SI 2023
Year: 2023

Indirect surveys, in which respondents provide information about other people they know, have been proposed for estimating (nowcasting) the size of a \emph{hidden population} where privacy is important or the hidden population is hard to reach. Examples include estimating casualties in an earthquake, conditions among female sex workers, and the prevalence of drug use and infectious diseases. The Network Scale-up Method (NSUM) is the classical approach to developing estimates from indirect surveys, but it was designed for one-shot surveys. Further, it requires certain assumptions and asking for or estimating the number of individuals in each respondent's network. In recent years, surveys have been increasingly deployed online and can collect data continuously (e.g., COVID-19 surveys on Facebook during much of the pandemic). Conventional NSUM can be applied to these scenarios by analyzing the data independently at each point in time, but this misses the opportunity of leveraging the temporal dimension. We propose to use the responses from indirect surveys collected over time and develop analytical tools (i) to prove that indirect surveys can provide better estimates for the trends of the hidden population over time, as compared to direct surveys and (ii) to identify appropriate temporal aggregations to improve the estimates. We demonstrate through extensive simulations that our approach outperforms traditional NSUM and direct surveying methods. We also empirically demonstrate the superiority of our approach on a real indirect survey dataset of COVID-19 cases.
Pair Programming Practiced in Hybrid Work
Venue: arXiv cs.SE 2023
Year: 2023

Pair programming (PP) has been a widespread practice for decades and is known for facilitating knowledge exchange and improving the quality of software. Many agilists advocated the importance of collocation, face-to-face interaction, and physical artifacts incorporated in the shared workspace when pairing. After a long period of forced work-from-home, many knowledge workers prefer to work remotely two or three days per week, which is affecting practices such as PP. In this revelatory single-case study, we aimed to understand how PP is practiced during hybrid work when team members alternate between on-site days and working from home. We collected qualitative and quantitative data through 11 semi-structured interviews, observations, feedback sessions, and self-reported surveys. The interviewees were members of an agile software development team in a Norwegian fintech company. The results presented in this paper indicate that PP can be practiced through on-site, remote, and mixed sessions, where the mixed mode seems to be the least advantageous. The findings highlight the importance of adapting the work environment to suit individual work mode preferences when it comes to PP. In the future, we will build on these findings to explore PP in other teams and organizations practicing hybrid work.
A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems
Venue: arXiv cs.CR 2023
Year: 2023

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.
DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classification
Venue: arXiv cs.CV 2023
Year: 2023

Deep neural networks face many problems in the field of hyperspectral image classification, lack of effective utilization of spatial spectral information, gradient disappearance and overfitting as the model depth increases. In order to accelerate the deployment of the model on edge devices with strict latency requirements and limited computing power, we introduce a lightweight model based on the improved 3D-Densenet model and designs DGCNet. It improves the disadvantage of group convolution. Referring to the idea of dynamic network, dynamic group convolution(DGC) is designed on 3d convolution kernel. DGC introduces small feature selectors for each grouping to dynamically decide which part of the input channel to connect based on the activations of all input channels. Multiple groups can capture different and complementary visual and semantic features of input images, allowing convolution neural network(CNN) to learn rich features. 3D convolution extracts high-dimensional and redundant hyperspectral data, and there is also a lot of redundant information between convolution kernels. DGC module allows 3D-Densenet to select channel information with richer semantic features and discard inactive regions. The 3D-CNN passing through the DGC module can be regarded as a pruned network. DGC not only allows 3D-CNN to complete sufficient feature extraction, but also takes into account the requirements of speed and calculation amount. The inference speed and accuracy have been improved, with outstanding performance on the IN, Pavia and KSC datasets, ahead of the mainstream hyperspectral image classification methods.
Unsupervised Calibration through Prior Adaptation for Text Classification using Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

A wide variety of natural language tasks are currently being addressed with large-scale language models (LLMs). These models are usually trained with a very large amount of unsupervised text data and adapted to perform a downstream natural language task using methods like fine-tuning, calibration or in-context learning. In this work, we propose an approach to adapt the prior class distribution to perform text classification tasks without the need for labelled samples and only few in-domain sample queries. The proposed approach treats the LLM as a black box, adding a stage where the model posteriors are calibrated to the task. Results show that these methods outperform the un-adapted model for different number of training shots in the prompt and a previous approach were calibration is performed without using any adaptation data.
Experimental Demonstration of 3D Reflected Beamforming at sub6GHz thanks to Varactor Based Reconfigurable Intelligent Surface
Venue: arXiv cs.IT 2023
Year: 2023

Reconfigurable intelligent surface (RIS) is a promising solution to boost coverage sustainably by reflecting waves from a transmitter to a receiver and acting as a low-power and passive relay. In this paper, for the first time, we demonstrate experimentally that a reconfigurable intelligent surface designed for sub6GHz, and using varactor technology, can perform three-dimensional reflective beamforming. This result is achieved with a RIS prototype of 984 unit-cells, thanks to a compact control circuit individually addressing and configuring the voltage of each unit-cell, with a distinct voltage. To our knowledge, this prototype configures 17 to 70 times more distinct voltages than in the state-of-the-art. The experimental results in an indoor environment show a 10 dB gain. They also show, for the first time, that producing such a new prototype is feasible with minimal energy footprint and environmental impact, thanks to refurbishing. Indeed, a reflectarray antenna originally designed for three-dimensional beamforming has been turned into a RIS.
Negated Complementary Commonsense using Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Larger language models, such as GPT-3, have shown to be excellent in many tasks. However, we demonstrate that out-of-ordinary questions can throw the model off guard. This work focuses on finding answers to negated complementary questions in commonsense scenarios. We illustrate how such questions adversely affect the model responses. We propose a model-agnostic methodology to improve the performance in negated complementary scenarios. Our method outperforms few-shot generation from GPT-3 (by more than 11 points) and, more importantly, highlights the significance of studying the response of large language models in negated complementary questions. The code, data, and experiments are available under: https://github.com/navidre/negated_complementary_commonsense.
Dynamic Capacity Enhancement using Air Computing: An Earthquake Case
Venue: arXiv cs.NI 2023
Year: 2023

Earthquakes are one of the most destructive natural disasters harming life and the infrastructure of cities. After an earthquake, functioning communication and computational capacity are crucial for rescue teams and healthcare of victims. Therefore, an earthquake can be investigated for dynamic capacity enhancement in which additional resources are deployed since the surviving portion of the infrastructure may not meet the demand of the users. In this study, we propose a new computation paradigm, air computing, which is the air vehicle assisted next generation edge computing through different air platforms, in order to enhance the capacity of the areas affected by an earthquake. To this end, we put forward a novel paradigm that presents a dynamic, responsive, and high-resolution computation environment by explaining its corresponding components, air layers, and essential advantages. Moreover, we focus on the unmanned aerial vehicle (UAV) deployment problem and apply three different methods including the emergency method, the load balancing method, and the location selection index (LSI) method in which we take the delay requirements of applications into account. To test and compare their performance in terms of the task success rate, we developed an earthquake scenario in which three towns are affected with different severity. The experimental results showed that each method can be beneficial considering the circumstances, and goal of the rescue.
LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions
Venue: arXiv cs.CV 2023
Year: 2023

Lane detection plays a pivotal role in the field of autonomous vehicles and advanced driving assistant systems (ADAS). Despite advances from image processing to deep learning based models, algorithm performance is highly dependent on training data matching the local challenges such as extreme lighting conditions, partially visible lane markings, and sparse lane markings like Botts' dots. To address this, we present an end-to-end lane detection and classification system based on deep learning methodologies. In our study, we introduce a unique dataset meticulously curated to encompass scenarios that pose significant challenges for state-of-the-art (SOTA) lane localization models. Moreover, we propose a CNN-based classification branch, seamlessly integrated with the detector, facilitating the identification of distinct lane types. This architecture enables informed lane-changing decisions and empowers more resilient ADAS capabilities. We also investigate the effect of using mixed precision training and testing on different models and batch sizes. Experimental evaluations conducted on the widely-used TuSimple dataset, Caltech Lane dataset, and our LVLane dataset demonstrate the effectiveness of our model in accurately detecting and classifying lanes amidst challenging scenarios. Our method achieves state-of-the-art classification results on the TuSimple dataset. The code of the work can be found on www.github.com/zillur-av/LVLane.
The Human Blockage Impact on ARIS Assisted D2D Communication Systems
Venue: arXiv cs.NI 2023
Year: 2023

Aerial reconfigurable intelligent surface (ARIS), is an intelligent reflecting surface (IRS) mounted by unmanned aerial vehicle (UAV), represent a promising candidate for assisting device to device (D2D) millimeter wave (mmWave) communication in temporal and urgent situations, e.g., open-air events. IRS can efficiently mitigate the high blockage impact on mmWave propagation signal in base station to device use case. But, the scenario of D2D communication is different as both the transmitter (TX) to ARIS and the ARIS to receiver (RX) links are highly susceptible to be blocked due to the low height of the TX and RX. Consequently, in this paper, the impact of human bodies blockage on ARIS aided D2D mmWave communication is studied. Firstly, we assure the effectiveness of using ARIS in this network to significantly enhance its performance, then, the effect of ARIS height on the blockage occurrence and system performance is investigated to find out the optimum height. Our results proves that ARIS highly mitigates the blockage, reduces it by 85%, comparable to the case without it. Moreover, a high increase in system spectral efficiency, 1.2 bps/Hz, can be guaranteed, if ARIS is configured at optimum height.
Proof Systems for the Modal $μ$-Calculus Obtained by Determinizing Automata
Venue: arXiv cs.LO 2023
Year: 2023

Automata operating on infinite objects feature prominently in the theory of the modal $μ$-calculus. One such application concerns the tableau games introduced by Niwiński & Walukiewicz, of which the winning condition for infinite plays can be naturally checked by a nondeterministic parity stream automaton. Inspired by work of Jungteerapanich and Stirling we show how determinization constructions of this automaton may be used to directly obtain proof systems for the $μ$-calculus. More concretely, we introduce a binary tree construction for determinizing nondeterministic parity stream automata. Using this construction we define the annotated cyclic proof system $\mathsf{BT}$, where formulas are annotated by tuples of binary strings. Soundness and Completeness of this system follow almost immediately from the correctness of the determinization method.
Crucible: Graphical Test Cases for Alloy Models
Venue: arXiv cs.SE 2023
Year: 2023

Alloy is a declarative modeling language that is well suited for verifying system designs. Alloy models are automatically analyzed using the Analyzer, a toolset that helps the user understand their system by displaying the consequences of their properties, helping identify any missing or incorrect properties, and exploring the impact of modifications to those properties. To achieve this, the Analyzer invokes off-the-shelf SAT solvers to search for scenarios, which are assignments to the sets and relations of the model such that all executed formulas hold. To help write more accurate software models, Alloy has a unit testing framework, AUnit, which allows users to outline specific scenarios and check if those scenarios are correctly generated or prevented by their model. Unfortunately, AUnit currently only supports textual specifications of scenarios. This paper introduces Crucible, which allows users to graphically create AUnit test cases. In addition, Crucible provides automated guidance to users to ensure they are creating well structured, valuable test cases. As a result, Crucible eases the burden of adopting AUnit and brings AUnit test case creation more in line with how Alloy scenarios are commonly interacted with, which is graphically.
PHOENI2X -- A European Cyber Resilience Framework With Artificial-Intelligence-Assisted Orchestration, Automation and Response Capabilities for Business Continuity and Recovery, Incident Response, and Information Exchange
Venue: arXiv cs.CR 2023
Year: 2023

As digital technologies become more pervasive in society and the economy, cybersecurity incidents become more frequent and impactful. According to the NIS and NIS2 Directives, EU Member States and their Operators of Essential Services must establish a minimum baseline set of cybersecurity capabilities and engage in cross-border coordination and cooperation. However, this is only a small step towards European cyber resilience. In this landscape, preparedness, shared situational awareness, and coordinated incident response are essential for effective cyber crisis management and resilience. Motivated by the above, this paper presents PHOENI2X, an EU-funded project aiming to design, develop, and deliver a Cyber Resilience Framework providing Artificial-Intelligence-assisted orchestration, automation and response capabilities for business continuity and recovery, incident response, and information exchange, tailored to the needs of Operators of Essential Services and the EU Member State authorities entrusted with cybersecurity.
FDAPT: Federated Domain-adaptive Pre-training for Language Models
Venue: arXiv cs.LG 2023
Year: 2023

Foundation models (FMs) have shown prominent success in a wide range of tasks. Their applicability to specific domain-task pairings relies on the availability of, both, high-quality data and significant computational resources. These challenges are not new to the field and, indeed, Federated Learning (FL) has been shown to be a promising solution in similar setups. This paper tackles the specific case of Domain-Adaptive Pre-Training (DAPT), a key step in the application of FMs. We conduct the first comprehensive empirical study to evaluate the performance of Federated Domain-Adaptive Pre-Training (FDAPT). We demonstrate that FDAPT can maintain competitive downstream task performance to the centralized baseline in both IID and non-IID situations. Finally, we propose a novel algorithm, Frozen Federated Domain-Adaptive Pre-Training (FFDAPT). FFDAPT improves the computational efficiency by 12.1% on average and exhibits similar downstream task performance to vanilla FDAPT, with general performance fluctuations remaining less than 1%.
Is Task-Agnostic Explainable AI a Myth?
Venue: arXiv cs.AI 2023
Year: 2023

Our work serves as a framework for unifying the challenges of contemporary explainable AI (XAI). We demonstrate that while XAI methods provide supplementary and potentially useful output for machine learning models, researchers and decision-makers should be mindful of their conceptual and technical limitations, which frequently result in these methods themselves becoming black boxes. We examine three XAI research avenues spanning image, textual, and graph data, covering saliency, attention, and graph-type explainers. Despite the varying contexts and timeframes of the mentioned cases, the same persistent roadblocks emerge, highlighting the need for a conceptual breakthrough in the field to address the challenge of compatibility between XAI methods and application tasks.