OpenRaiser/Intern-Atlas huggingface dataset:
Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector
Venue: arXiv cs.CV 2022
Year: 2022

Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image. However, because the patch shrinks during preprocessing, most existing approaches that employ adversarial patches to attack object detectors would diminish the attack success rate on small and medium targets. This paper proposes a Frequency Module(FRAN), a frequency-domain attention module for guiding patch generation. This is the first study to introduce frequency domain attention to optimize the attack capabilities of adversarial patches. Our method increases the attack success rates of small and medium targets by 4.18% and 3.89%, respectively, over the state-of-the-art attack method for fooling the human detector while assaulting YOLOv3 without reducing the attack success rate of big targets.
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective
Venue: arXiv cs.IR 2022
Year: 2022

Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture. In the meantime, sparse representation learning fueled by traditional inverted indexing techniques has seen a growing interest, inheriting from desirable IR priors such as explicit lexical matching. While some architectural variants have been proposed, a lesser effort has been put in the training of such models. In this work, we build on SPLADE -- a sparse expansion-based retriever -- and show to which extent it is able to benefit from the same training improvements as dense models, by studying the effect of distillation, hard-negative mining as well as the Pre-trained Language Model initialization. We furthermore study the link between effectiveness and efficiency, on in-domain and zero-shot settings, leading to state-of-the-art results in both scenarios for sufficiently expressive models.
Hybrid RIS and DMA Assisted Multiuser MIMO Uplink Transmission With Electromagnetic Exposure Constraints
Venue: arXiv cs.IT 2022
Year: 2022

In the fifth-generation and beyond era, reconfigurable intelligent surface (RIS) and dynamic metasurface antennas (DMAs) are emerging metamaterials keeping up with the demand for high-quality wireless communication services, which promote the diversification of portable wireless terminals. However, along with the rapid expansion of wireless devices, the electromagnetic (EM) radiation increases unceasingly and inevitably affects public health, which requires a limited exposure level in the transmission design. To reduce the EM radiation and preserve the quality of communication service, we investigate the spectral efficiency (SE) maximization with EM constraints for uplink transmission in hybrid RIS and DMA assisted multiuser multiple-input multiple-output systems. Specifically, alternating optimization is adopted to optimize the transmit covariance, RIS phase shift, and DMA weight matrices. We first figure out the water-filling solutions of transmit covariance matrices with given RIS and DMA parameters. Then, the RIS phase shift matrix is optimized via the weighted minimum mean square error, block coordinate descent and minorization-maximization methods. Furthermore, we solve the unconstrainted DMA weight matrix optimization problem in closed form and then design the DMA weight matrix to approach this performance under DMA constraints. Numerical results confirm the effectiveness of the EM aware SE maximization transmission scheme over the conventional baselines.
Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker
Venue: arXiv cs.NE 2022
Year: 2022

The human brain is the most powerful and efficient machine in existence today, surpassing in many ways the capabilities of modern computers. Currently, lines of research in neuromorphic engineering are trying to develop hardware that mimics the functioning of the brain to acquire these superior capabilities. One of the areas still under development is the design of bio-inspired memories, where the hippocampus plays an important role. This region of the brain acts as a short-term memory with the ability to store associations of information from different sensory streams in the brain and recall them later. This is possible thanks to the recurrent collateral network architecture that constitutes CA3, the main sub-region of the hippocampus. In this work, we developed two spike-based computational models of fully functional hippocampal bio-inspired memories for the storage and recall of complex patterns implemented with spiking neural networks on the SpiNNaker hardware platform. These models present different levels of biological abstraction, with the first model having a constant oscillatory activity closer to the biological model, and the second one having an energy-efficient regulated activity, which, although it is still bio-inspired, opts for a more functional approach. Different experiments were performed for each of the models, in order to test their learning/recalling capabilities. A comprehensive comparison between the functionality and the biological plausibility of the presented models was carried out, showing their strengths and weaknesses. The two models, which are publicly available for researchers, could pave the way for future spike-based implementations and applications.
Massive Enhanced Extracted Email Features Tailored for Cosine Distance
Venue: arXiv cs.IR 2022
Year: 2022

In this paper, the process of converting the Enron email dataset (the version cited in the preprint) to thousands of features per email for a selected set of 2400 labelled emails is explained and evaluated. The final features are tailored for Cosine distance so that the Cosine distance invertly reflect the number of top indicative words of each email that are common between the two emails in an explainable normalized fashion. The labelling is based on the leaf folder name in the Enron email dataset (the version cited in the preprint) folders tree and the 2400 emails selected consist 300 emails for each of the 8 labels. The evaluation is based on the accuracy of a k nearest neighbours majority voting classification using Cosine distance. In addition to KNN majority voting classification accuracy and confusion matrix, some statistics for the process is reported. The KNN majority voting classification accuracy using Cosine distance is 76.75% which shows at least some level of success given the 8 labels involved. The result of conversion is 48557 features per selected email out of which exactly 40 features per email are non-zero. The result of conversion is a data set named MeeefTCD (Massive Enhanced Extracted Email Features Tailored for Cosine Distance) available at https://web.cs.dal.ca/~barahimi/data-sets/meeeftcd/ and on a github repository mentioned in this paper.
Universal Caching
Venue: arXiv cs.IT 2022
Year: 2022

In learning theory, the performance of an online policy is commonly measured in terms of the static regret metric, which compares the cumulative loss of an online policy to that of an optimal benchmark in hindsight. In the definition of static regret, the action of the benchmark policy remains fixed throughout the time horizon. Naturally, the resulting regret bounds become loose in non-stationary settings where fixed actions often suffer from poor performance. In this paper, we investigate a stronger notion of regret minimization in the context of online caching. In particular, we allow the action of the benchmark at any round to be decided by a finite state machine containing any number of states. Popular caching policies, such as LRU and FIFO, belong to this class. Using ideas from the universal prediction literature in information theory, we propose an efficient online caching policy with a sub-linear regret bound. To the best of our knowledge, this is the first data-dependent regret bound known for the caching problem in the universal setting. We establish this result by combining a recently-proposed online caching policy with an incremental parsing algorithm, namely Lempel-Ziv '78. Our methods also yield a simpler learning-theoretic proof of the improved regret bound as opposed to the involved problem-specific combinatorial arguments used in the earlier works.
Strong Equivalence of Logic Programs with Ordered Disjunction: a Logical Perspective
Venue: arXiv cs.LO 2022
Year: 2022

Logic Programs with Ordered Disjunction (LPODs) extend classical logic programs with the capability of expressing preferential disjunctions in the heads of program rules. The initial semantics of LPODs, although simple and quite intuitive, is not purely model-theoretic. A consequence of this is that certain properties of programs appear non-trivial to formalize in purely logical terms. An example of this state of affairs is the characterization of the notion of strong equivalence for LPODs. Although the results of Faber et al. (2008) are accurately developed, they fall short of characterizing strong equivalence of LPODs as logical equivalence in some specific logic. This comes in sharp contrast with the well-known characterization of strong equivalence for classical logic programs, which, as proved by Lifschitz et al. (2001), coincides with logical equivalence in the logic of here-and-there. In this paper we obtain a purely logical characterization of strong equivalence of LPODs as logical equivalence in a four-valued logic. Moreover, we provide a new proof of the coNP-completeness of strong equivalence for LPODs, which has an interest in its own right since it relies on the special structure of such programs. Our results are based on the recent logical semantics of LPODs introduced by Charalambidis et al. (2021), a fact which we believe indicates that this new semantics may prove to be a useful tool in the further study of LPODs.
Rich Screen Reader Experiences for Accessible Data Visualization
Venue: arXiv cs.HC 2022
Year: 2022

Current web accessibility guidelines ask visualization designers to support screen readers via basic non-visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables; they afford interactive data exploration at varying levels of granularity -- from fine-grained datum-by-datum reading to skimming and surfacing high-level trends. In response to the lack of comparable non-visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co-design process, we identify three key design dimensions for expressive screen reader accessibility: structure, or how chart entities should be organized for a screen reader to traverse; navigation, or the structural, spatial, and targeted operations a user might perform to step through the structure; and, description, or the semantic content, composition, and verbosity of the screen reader's narration. We operationalize these dimensions to prototype screen-reader-accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed-methods study with 13 blind and low vision readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process. An accessible HTML version of this paper is available at: http://vis.csail.mit.edu/pubs/rich-screen-reader-vis-experiences.
On the Value of Project Productivity for Early Effort Estimation
Venue: arXiv cs.SE 2022
Year: 2022

In general, estimating software effort using a Use Case Point (UCP) size requires the use of productivity as a second prediction factor. However, there are three drawbacks to this approach: (1) there is no clear procedure for predicting productivity in the early stages, (2) the use of fixed or limited productivity ratios does not allow research to reflect the realities of the software industry, and (3) productivity from historical data is often challenging. The new UCP datasets now available allow us to perform further empirical investigations of the productivity variable in order to estimate the UCP effort. Accordingly, four different prediction models based on productivity were used. The results showed that learning productivity from historical data is more efficient than using classical approaches that rely on default or limited productivity values. In addition, predicting productivity from historical environmental factors is not often accurate. From here we conclude that productivity is an effective factor for estimating the software effort based on the UCP in the presence and absence of previous historical data. Moreover, productivity measurement should be flexible and adjustable when historical data is available
Learning to Answer Visual Questions from Web Videos
Venue: arXiv cs.CV 2022
Year: 2022

Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision. We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations. Given narrated videos, we then automatically generate the HowToVQA69M dataset with 69M video-question-answer triplets. To handle the open vocabulary of diverse answers in this dataset, we propose a training procedure based on a contrastive loss between a video-question multi-modal transformer and an answer transformer. We introduce the zero-shot VideoQA task and the VideoQA feature probe evaluation setting and show excellent results, in particular for rare answers. Furthermore, our method achieves competitive results on MSRVTT-QA, ActivityNet-QA, MSVD-QA and How2QA datasets. We also show that our VideoQA dataset generation approach generalizes to another source of web video and text data. We use our method to generate the WebVidVQA3M dataset from the WebVid dataset, i.e., videos with alt-text annotations, and show its benefits for training VideoQA models. Finally, for a detailed evaluation we introduce iVQA, a new VideoQA dataset with reduced language bias and high-quality manual annotations. Code, datasets and trained models are available at https://antoyang.github.io/just-ask.html
Environmental Sensing Options for Robot Teams: A Computational Complexity Perspective
Venue: arXiv cs.MA 2022
Year: 2022

Visual and scalar-field (e.g., chemical) sensing are two of the options robot teams can use to perceive their environments when performing tasks. We give the first comparison of the computational characteristic of visual and scalar-field sensing, phrased in terms of the computational complexities of verifying and designing teams of robots to efficiently and robustly perform distributed construction tasks. This is done relative a basic model in which teams of robots with deterministic finite-state controllers operate in a synchronous error-free manner in 2D grid-based environments. Our results show that for both types of sensing, all of our problems are polynomial-time intractable in general and remain intractable under a variety of restrictions on parameters characterizing robot controllers, teams, and environments. That being said, these results also include restricted situations for each of our problems in which those problems are effectively polynomial-time tractable. Though there are some differences, our results suggest that (at least in this stage of our investigation) verification and design problems relative to visual and scalar-field sensing have roughly the same patterns and types of tractability and intractability results.
Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks
Venue: arXiv cs.NI 2022
Year: 2022

The 6G network enables a subnetwork-wide evolution, resulting in a "network of subnetworks". However, due to the dynamic mobility of wireless subnetworks, the data transmission of intra-subnetwork and inter-subnetwork will inevitably interfere with each other, which poses a great challenge to radio resource management. Moreover, most of the existing approaches require the instantaneous channel gain between subnetworks, which are usually difficult to be collected. To tackle these issues, in this paper we propose a novel effective intelligent radio resource management method using multi-agent deep reinforcement learning (MARL), which only needs the sum of received power, named received signal strength indicator (RSSI), on each channel instead of channel gains. However, to directly separate individual interference from RSSI is an almost impossible thing. To this end, we further propose a novel MARL architecture, named GA-Net, which integrates a hard attention layer to model the importance distribution of inter-subnetwork relationships based on RSSI and exclude the impact of unrelated subnetworks, and employs a graph attention network with a multi-head attention layer to exact the features and calculate their weights that will impact individual throughput. Experimental results prove that our proposed framework significantly outperforms both traditional and MARL-based methods in various aspects.
Richer Countries and Richer Representations
Venue: arXiv cs.CL 2022
Year: 2022

We examine whether some countries are more richly represented in embedding space than others. We find that countries whose names occur with low frequency in training corpora are more likely to be tokenized into subwords, are less semantically distinct in embedding space, and are less likely to be correctly predicted: e.g., Ghana (the correct answer and in-vocabulary) is not predicted for, "The country producing the most cocoa is [MASK].". Although these performance discrepancies and representational harms are due to frequency, we find that frequency is highly correlated with a country's GDP; thus perpetuating historic power and wealth inequalities. We analyze the effectiveness of mitigation strategies; recommend that researchers report training word frequencies; and recommend future work for the community to define and design representational guarantees.
Design and Implementation of a Secure RISC-V Microprocessor
Venue: arXiv cs.CR 2022
Year: 2022

Secret keys can be extracted from the power consumption or electromagnetic emanations of unprotected devices. Traditional counter-measures have limited scope of protection, and impose several restrictions on how sensitive data must be manipulated. We demonstrate a bit-serial RISC-V microprocessor implementation with no plain-text data. All values are protected using Boolean masking. Software can run with little to no counter-measures, reducing code size and performance overheads. Unlike previous literature, our methodology is fully automated and can be applied to designs of arbitrary size or complexity. We also provide details on other key components such as clock randomizer, memory protection, and random number generator. The microprocessor was implemented in 65 nm CMOS technology. Its implementation was evaluated using NIST tests as well as side channel attacks. Random numbers generated with our RNG pass on all NIST tests. Side-channel analysis on the baseline implementation extracted the AES key using only 375 traces, while our secure microprocessor was able to withstand attacks using 20 M traces.
Detecting Phishing sites Without Visiting them
Venue: arXiv cs.CR 2022
Year: 2022

Now-a-days, cyberattacks are increasing at an unprecedented rate. Phishing is a social engineering attack which has a massive global impact, destroying the financial and economic value of corporations, government sectors and individuals. In phishing, attackers steal users personal information such as username, passwords, debit card information and so on. In order to detect zero-hour attacks and protect end-users from these attacks, various anti-phishing techniques are developed, but the end-users have to visit the websites to know whether they are safe or not, which may lead to infecting their system. In this paper, we propose a method where end-users can detect the genuineness of the sites without visiting them. The proposed method collects legitimate and phishing URLs and extract features from them. The extracted features are given as input to six different classifiers for training and constructing the model. The classifiers used are Naive-Bayes, Logistic Regression, Random Forest,CatBoost, XGBoost and Multilayer perceptron. The method is tested by developing into an extension so that the end-users can use it when browsing. In the browser extension when the user takes the cursor over any link, a pop-up appears showing the nature of the website i.e., safe site or deceptive site and then a confirm box shows up asking the user whether they want to visit or not. The performance of the approach is tested using a dataset consisting of 2000 phishing and legitimate website URLs and the method is able to detect the sites correctly in very little time. Random-Forest is chosen for constructing the model as it gives the highest accuracy of 95%.
Multichannel Optimal Tree-Decodable Codes are Not Always Optimal Prefix Codes
Venue: arXiv cs.IT 2022
Year: 2022

The theory of multichannel prefix codes aims to generalize the classical theory of prefix codes. Although single- and two-channel prefix codes always have decoding trees, the same cannot be said when there are more than two channels. One question is of theoretical interest: Do there exist optimal tree-decodable codes that are not optimal prefix codes? Existing literature, which focused on generalizing single-channel results, covered little about non-tree-decodable prefix codes since they have no single-channel counterparts. In this work, we study the fundamental reason behind the non-tree-decodability of prefix codes. By investigating the simplest non-tree-decodable structure, we obtain a general sufficient condition on the channel alphabets for the existence of optimal tree-decodable codes that are not optimal prefix codes.
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making
Venue: arXiv cs.HC 2022
Year: 2022

Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and without techniques from the field of explainable AI (XAI) on human decision-making performance. However, as tasks and experimental setups vary due to different objectives, some studies report improved user decision-making performance through XAI, while others report only negligible effects. Therefore, in this article, we present an initial synthesis of existing research on XAI studies using a statistical meta-analysis to derive implications across existing research. We observe a statistically positive impact of XAI on users' performance. Additionally, the first results indicate that human-AI decision-making tends to yield better task performance on text data. However, we find no effect of explanations on users' performance compared to sole AI predictions. Our initial synthesis gives rise to future research investigating the underlying causes and contributes to further developing algorithms that effectively benefit human decision-makers by providing meaningful explanations.