Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .
Choice of Foraging Habitat by Northern Flickers Reflects Changes in Availability of their Ant Prey Linked To Ambient Temperature1

Abstract Foraging theory suggests animals should prefer habitats with a greater density of prey, but few have investigated whether birds change foraging habitats according to short-term changes in prey abundance caused by weather. We studied a woodpecker, the northern flicker (Colaptes auratus), in which the diet is composed mainly of ants collected on the ground surface. We measured the surface density of the ant prey in 1-m2 quadrats placed in 2 habitat types that had different thermal properties: open grassland and forest. The density of ants varied according to year of the study, habitat type, date during the summer, and time of day and was strongly associated with ambient temperature. In the shaded forest habitat, ant density increased linearly with air temperatures between 6 and 28 °C In contrast, the surface activity of ants in the open habitat exposed to sun began to decline once ground surface temperatures reached 26 °C Ant densities were higher in the open habitat than in the shade in relatively cold conditions but were higher in the shaded forest habitat when it was hot. Using radio telemetry, we recorded the habitat use of foraging flickers and found they shifted from foraging in the open when it was cold to foraging in shaded habitats when it was hot. Flickers tracked the density of their main prey on fine spatial and short temporal scales consistent with foraging theory.
ROMANet: Fine-Grained Reuse-Driven Off-Chip Memory Access Management and Data Organization for Deep Neural Network Accelerators

Enabling high energy efficiency is crucial for embedded implementations of deep learning. Several studies have shown that the DRAM-based off-chip memory accesses are one of the most energy-consuming operations in deep neural network (DNN) accelerators and, thereby, limit the designs from achieving efficiency gains at the full potential. DRAM access energy varies depending upon the number of accesses required and the energy consumed per-access. Therefore, searching for a solution toward the minimum DRAM access energy is an important optimization problem. Toward this, we propose the ROMANet methodology that aims at reducing the number of memory accesses, by searching for the appropriate data partitioning and scheduling for each layer of a network using a design space exploration, based on the knowledge of the available on-chip memory and the data reuse factors. Moreover, ROMANet also targets decreasing the number of DRAM row buffer conflicts and misses by exploiting the DRAM multibank burst feature to improve the energy-per-access. Besides providing the energy benefits, our proposed DRAM data mapping also results in an increased effective DRAM throughput, which is useful for latency-constraint scenarios. Our experimental results show that the ROMANet saves DRAM access energy by 12% for the AlexNet, 36% for the VGG-16, 46% for the MobileNet, and 45% for the SqueezeNet while improving the DRAM throughput by 10% on average across different networks compared to the state of the art, i.e., bus-width aware (BWA) technique.
How Different is Test Case Prioritization for Open and Closed Source Projects?

Improved test case prioritization means that software developers can detect and fix more software faults sooner than usual. But is there one “best” prioritization algorithm? Or do different kinds of projects deserve special kinds of prioritization? To answer these questions, this article applies nine prioritization schemes to 31 projects that range from (a) highly rated open-source Github projects to (b) computational science software to (c) a closed-source project. We find that prioritization approaches that work best for open-source projects can work worst for the closed-source project (and vice versa). From these experiments, we conclude that (a) it is ill-advised to always apply one prioritization scheme to all projects since (b) prioritization requires tuning to different project types.
Randomized Gossiping With Effective Resistance Weights: Performance Guarantees and Applications

The effective resistance (ER) between a pair of nodes in a weighted undirected graph is defined as the potential difference induced when a unit current is injected at one node and extracted from the other, treating edge weights as the conductance values of edges. The ER is a key quantity of interest in many applications, e.g., solving linear systems, Markov chains, and continuous-time averaging networks. In this article, we consider ERs in the context of designing randomized gossiping methods for the consensus problem, where the aim is to compute the average of node values in a distributed manner through iteratively computing weighted averages among randomly chosen neighbors. For barbell graphs, we prove that choosing wake-up and communication probabilities proportional to ER weights improves the averaging time corresponding to the traditional choice of uniform weights. For $c$-barbell graphs, we show that ER weights admit lower and upper bounds on the averaging time that improves upon the lower and upper bounds available for uniform weights. Furthermore, for graphs with a small diameter, we can show that ER weights can improve upon the existing bounds for Metropolis weights by a constant factor under some assumptions. We illustrate these results through numerical experiments, where we showcase the efficiency of our approach on several graph topologies, including barbell and small-world graphs. We also present an application of ER gossiping to distributed optimization: we numerically verify that using ER gossiping within EXTRA and DPGA-W methods improves their practical performance in terms of communication efficiency.
A Novel Physics-Based Channel Model for Reconfigurable Intelligent Surface-Assisted Multi-User Communication Systems

The reconfigurable intelligent surface (RIS) is one of the promising technologies contributing to the next generation smart radio environment. A novel physics-based RIS channel model is proposed. In the model, the signal reflected through the scatters and the RIS elements are jointly studied as multipath components of the overall received envelope. This novel strategy simplifies the mathematical structure of the channel gain and is able to compactly derive the distribution of the overall channel. For the case of continuous phase shifts, the distribution depends on the number of elements of the RIS and the observing direction of the receiver. For the case of discrete phase shifts, the distribution further depends on the number of phase quantization levels. The scaling law of the average received power is obtained from the scale factor of the distribution. For the application scenarios where RIS functions as an anomalous reflector, we investigate the performance of single RIS-assisted multiple access networks for time-division multiple access (TDMA), frequency-division multiple access (FDMA), and non-orthogonal multiple access (NOMA). Closed-form expressions for the outage probability of the proposed channel model are derived. It is proved that a constant diversity order exists, which is independent of the number of RIS elements. Simulation results are presented to confirm that the proposed model applies effectively to the element-based RISs.
DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis

Memes have become an ubiquitous social media entity and the processing and analysis of such multimodal data is currently an active area of research. This paper presents our work on the Memotion Analysis shared task of SemEval 2020, which involves the sentiment and humor analysis of memes. We propose a system which uses different bimodal fusion techniques to leverage the inter-modal dependency for sentiment and humor classification tasks. Out of all our experiments, the best system improved the baseline with macro F1 scores of 0.357 on Sentiment Classification (Task A), 0.510 on Humor Classification (Task B) and 0.312 on Scales of Semantic Classes (Task C).
Insights on drying and precipitation dynamics of respiratory droplets from the perspective of COVID-19

We isolate a nano-colloidal droplet of surrogate mucosalivary fluid to gain fundamental insights into airborne nuclei’s infectivity and viral load distribution during the COVID-19 pandemic. The salt-water solution containing particles at reported viral loads is acoustically trapped in a contactless environment to emulate the drying, flow, and precipitation dynamics of real airborne droplets. Similar experiments validate observations with the surrogate fluid with samples of human saliva samples from a healthy subject. A unique feature emerges regarding the final crystallite dimension; it is always 20%–30% of the initial droplet diameter for different sizes and ambient conditions. Airborne-precipitates nearly enclose the viral load within its bulk while the substrate precipitates exhibit a high percentage (∼80–90%) of exposed virions (depending on the surface). This work demonstrates the leveraging of an inert nano-colloidal system to gain insights into an equivalent biological system.
Bilateral Simultaneous Endoscopic Dacryocystorhinostomy: Outcome and Impact on the Quality of Life of the Patients

Abstract Introduction Bilateral simultaneous endoscopic dacryocystorhinostomy (endo-DCR) has received little attention in the literature, thus many surgeons continue to address bilateral nasolacrimal duct obstruction at two stages, rather than in the same setting. Objective To evaluate the feasibility and the outcome of simultaneous bilateral Endo-DCR and its impact on the quality of life of the patients. Methods We have conducted a retrospective analysis of patients who underwent bilateral simultaneous endo-DCR between March 2013 and February 2017 at our tertiary care institution. The reviewed data included clinical presentation; operative details; success rate; pre and postoperative evaluation of the symptoms of the patients, using the Nasolacrimal Duct Obstruction Symptom Score Questionnaire; satisfaction of the patients, and improvement in the quality of life, assessed by the Glasgow Benefit Inventory (GBI) questionnaire. Results Out of 128 cases in which endo-DCRs were performed, 13 were bilateral (26 sides). Postoperative success was documented in 24 of the 26 sides (92.3%), with a mean follow-up duration of 16.2 months. The two failed sides were reported in the same case. The preoperative symptom score ranged between 12 and 80 (mean ± standard deviation [SD]: 38.23 ± 15.7). The postoperative symptom score was significantly lower (mean ± SD: 5.4 ± 12.9). The success rates in unilateral and bilateral cases were comparable, with no statistically significant difference. A notable improvement in the quality of life of the patients was also reported, with a mean GBI score of 81.38 ± 12.37. Conclusion Our results support that a simultaneous bilateral endo-DCR is a safe procedure that offers a high success rate, spares the patient from the stress of a second surgery, provides the patient with a bilateral resolution of the symptoms, and confers an immediate improvement in the quality of life of the patients.
Enhancing autonomy transparency: an option-centric rationale approach

While the advances in artificial intelligence and machine learning empower a new generation of autonomous systems for assisting human performance, one major concern arises from the human factors perspective: Humans have difficulty deciphering autonomy-generated solutions and increasingly perceive autonomy as a mysterious black box. The lack of transparency contributes to the lack of trust in autonomy and sub-optimal team performance. To enhance autonomy transparency, this study proposed an option-centric rationale display and evaluated its effectiveness. We developed a game Treasure Hunter wherein a human uncovers a map for treasures with the help from an intelligent assistant, and conducted a human-in-the-loop experiment with 34 participants. Results indicated that by conveying the intelligent assistant's decision-making rationale via the option-centric rationale display, participants had higher trust in the system and calibrated their trust faster. Additionally, higher trust led to higher acceptance of recommendations from the intelligent assistant, and in turn higher task performance.
Testing Semi-Strong Form Efficiency of the Prewar Japanese Stock Market

This paper examines Fama's (1970) semi-strong form efficient market hypothesis (EMH) in the prewar Japanese stock market using a new dataset. We particularly focus on the relationship between the prewar Japanese stock market and several government policy interventions to explore whether the semi-strong form stock market efficiency evolves over time. To capture the long-run impact of government policy interventions against stock markets, we measure the time-varying joint degree of market efficiency and the time-varying impulse responses based on Ito et al.'s (2014; 2017) generalized least squares-based time-varying vector autoregressive model. The empirical results reveal that (1) the joint degree of market efficiency in the prewar Japanese stock market fluctuated over time because of external events such as policy changes and wars, (2) the semi-strong form EMH is almost supported in the prewar Japanese stock market, and (3) the markets rapidly reflect the information of the external events through time. Therefore, we conclude that Lo's (2004) adaptive market hypothesis is supported in the prewar Japanese stock market even if we consider that the public information affects the stock market.
From Design Draft to Real Attire: Unaligned Fashion Image Translation

Fashion manipulation has attracted growing interest due to its great application value, which inspires many researches towards fashion images. However, little attention has been paid to fashion design draft. In this paper, we study a new unaligned translation problem between design drafts and real fashion items, whose main challenge lies in the huge misalignment between the two modalities. We first collect paired design drafts and real fashion item images without pixel-wise alignment. To solve the misalignment problem, our main idea is to train a sampling network to adaptively adjust the input to an intermediate state with structure alignment to the output. Moreover, built upon the sampling network, we present design draft to real fashion item translation network (D2RNet), where two separate translation streams that focus on texture and shape, respectively, are combined tactfully to get both benefits. D2RNet is able to generate realistic garments with both texture and shape consistency to their design drafts. We show that this idea can be effectively applied to the reverse translation problem and present R2DNet accordingly. Extensive experiments on unaligned fashion design translation demonstrate the superiority of our method over state-of-the-art methods. Our project website is available at: https://victoriahy.github. io/MM2020/.
A spectral clustering approach for the evolution of the COVID-19 pandemic in the state of Rio Grande do Sul, Brazil

The aim of this paper is to analyse the evolution of the COVID-19 pandemic in Rio Grande do Sul by applying graph-theoretical tools, particularly spectral clustering techniques, on weighted graphs defined on the set of 167 municipalities in the state with population 10,000 or more, which are based on data provided by government agencies and other sources. To respond to this outbreak, the state has adopted a system by which pre-determined regions are assigned flags on a weekly basis, and different measures go into effect according to the flag assigned. Our results suggest that considering a flexible approach to the regions themselves might be a useful additional tool to give more leeway to cities with lower incidence rates, while keeping the focus on public safety. Moreover, simulations show the dampening effect of isolation on the dissemination of the disease.
Two-sample Testing on Latent Distance Graphs With Unknown Link Functions

We propose a valid and consistent test for the hypothesis that two latent distance random graphs on the same vertex set have the same generating latent positions, up to some unidentifiable similarity transformations. Our test statistic is based on first estimating the edge probabilities matrices by truncating the singular value decompositions of the averaged adjacency matrices in each population and then computing a Spearman rank correlation coefficient between these estimates. Experimental results on simulated data indicate that the test procedure has power even when there is only one sample from each population, provided that the number of vertices is not too small. Application on a dataset of neural connectome graphs showed that we can distinguish between scans from different age groups while application on a dataset of epileptogenic recordings showed that we can discriminate between seizure and non-seizure events.
Colour–magnitude diagrams of transiting exoplanets – III. A public code, nine strange planets, and the role of phosphine

Colour–magnitude diagrams provide a convenient way of comparing populations of similar objects. When well populated with precise measurements, they allow quick inferences to be made about the bulk properties of an astronomic object simply from its proximity on a diagram to other objects. We present here a python toolkit that allows a user to produce colour–magnitude diagrams of transiting exoplanets, comparing planets to populations of ultra-cool dwarfs, of directly imaged exoplanets, to theoretical models of planetary atmospheres, and to other transiting exoplanets. Using a selection of near- and mid-infrared colour–magnitude diagrams, we show how outliers can be identified for further investigation, and how emerging subpopulations can be identified. Additionally, we present evidence that observed differences in the Spitzer’s 4.5 μm flux, between irradiated Jupiters and field brown dwarfs, might be attributed to phosphine, which is susceptible to photolysis. The presence of phosphine in low-irradiation environments may negate the need for thermal inversions to explain eclipse measurements. We speculate that the anomalously low 4.5 μm flux of the nightside of HD 189733b and the daysides of GJ 436b and GJ 3470b might be caused by phosphine absorption. Finally, we use our toolkit to include Hubble Wide Field Camera 3 spectra, creating a new photometric band called the ‘Water band’ (WJH band) in the process. We show that the colour index [WJH − H] can be used to constrain the C/O ratio of exoplanets, showing that future observations with James Webb Space Telescope and Ariel will be able to distinguish these populations if they exist, and select members for future follow-up.
DAMO: Deep Agile Mask Optimization for Full Chip Scale

Continuous scaling of the VLSI system leaves a great challenge on manufacturing, thus optical proximity correction (OPC) is widely applied in conventional design flow for manufacturability optimization. Traditional techniques conduct OPC by leveraging a lithography model but may suffer from prohibitive computational overhead. In addition, most of them focus on optimizing a single and local clip instead of addressing how to tackle the full-chip scale. In this paper, we present DAMO, a high performance and scalable deep learning-enabled OPC system for full-chip scale. It is an end-to-end mask optimization paradigm that contains a deep lithography simulator (DLS) for lithography modeling and a deep mask generator (DMG) for mask pattern generation. Moreover, a novel layout splitting algorithm customized for DAMO is proposed to handle full-chip OPC problem. Extensive experiments show that DAMO outperforms state-of-the-art OPC solutions in both academia and industrial commercial toolkit.
Dynamic Object Tracking and Masking for Visual SLAM

In dynamic environments, performance of visual SLAM techniques can be impaired by visual features taken from moving objects. One solution is to identify those objects so that their visual features can be removed for localization and mapping. This paper presents a simple and fast pipeline that uses deep neural networks, extended Kalman filters and visual SLAM to improve both localization and mapping in dynamic environments (around 14 fps on a GTX 1080). Results on the dynamic sequences from the TUM dataset using RTAB-Map as visual SLAM suggest that the approach achieves similar localization performance compared to other state-of-the-art methods, while also providing the position of the tracked dynamic objects, a 3D map free of those dynamic objects, better loop closure detection with the whole pipeline able to run on a robot moving at moderate speed.
Moral-hazard-free insurance contract design under rank-dependent utility theory

Bernard et al. (2015) studied an insurance contract design problem under rank-dependent utility (RDU) theory. Their results, however, suffer from a moral hazard problem, namely, providing incentives for the insured to falsely report the actual loss. Xu et al. (2019) investigated the same problem, but took the {incentive compatibility} constraint into consideration to avoid that moral hazard. Mathematically speaking, the model reduces to a quantile optimisation problem with a compatibility constraint. They solved the problem by imposing assumptions on the loss and the probability weighting function. This paper solves the problem completely by a new quantile optimisation approach under general setting. The optimal solution is expressed by the solution of an obstacle problem for a semilinear second-order elliptic operator with mixed boundary conditions. Surprisingly, it is shown that every reasonable moral-hazard-free contract is optimal for infinitely many RDU maximisers with different utility functions and probability weighting functions.