OpenRaiser/Intern-Atlas huggingface dataset:
Energy Balancing Algorithm for Wireless Sensor Network
Venue: arXiv cs.NI 2022
Year: 2022

A Wireless Sensor Network (WSN) is made up of a large number of nodes that are spread randomly or on a regular basis to detect the surrounding environment and transfer data to a base station (BS) over the Internet to the user. It is widely used in a variety of civil and military concerns. Because the sensor has limited battery capacity, energy efficiency is a critical issue with WSNs. As a result, developing a routing protocol that decreases energy consumption in sensor nodes to extend the lifetime of the WSN using an intelligence algorithm has become difficult. LEACH is the first hierarchical routing protocol that divides the WSN into clusters to reduce energy usage. However, it has reached its limit in selecting a suitable cluster head and the sensor nodes to be joined, as well as their quantity. Thus, this research proposes an algorithm called Wireless Energy Balancing algorithm (WEB) that works on energy balancing distribution by identifying a suitable cluster head with minimum distance and high energy. Then it uses the knapsack-problem as a novel algorithm to design the cluster members. The simulation results demonstrate that the WEB algorithm outperforms LEACH by 31% in terms of energy conservation and WSN lifetime extension.
Adaptive Learning with Artificial Barriers Yielding Nash Equilibria in General Games
Venue: arXiv cs.GT 2022
Year: 2022

Artificial barriers in Learning Automata (LA) is a powerful and yet under-explored concept although it was first proposed in the 1980s. Introducing artificial non-absorbing barriers makes the LA schemes resilient to being trapped in absorbing barriers, a phenomenon which is often referred to as lock in probability leading to an exclusive choice of one action after convergence. Within the field of LA and reinforcement learning in general, there is a sacristy of theoretical works and applications of schemes with artificial barriers. In this paper, we devise a LA with artificial barriers for solving a general form of stochastic bimatrix game. Classical LA systems possess properties of absorbing barriers and they are a powerful tool in game theory and were shown to converge to game's of Nash equilibrium under limited information. However, the stream of works in LA for solving game theoretical problems can merely solve the case where the Saddle Point of the game exists in a pure strategy and fail to reach mixed Nash equilibrium when no Saddle Point exists for a pure strategy. In this paper, by resorting to the powerful concept of artificial barriers, we suggest a LA that converges to an optimal mixed Nash equilibrium even though there may be no Saddle Point when a pure strategy is invoked. Our deployed scheme is of Linear Reward-Inaction ($L_{R-I}$) flavor which is originally an absorbing LA scheme, however, we render it non-absorbing by introducing artificial barriers in an elegant and natural manner, in the sense that that the well-known legacy $L_{R-I}$ scheme can be seen as an instance of our proposed algorithm for a particular choice of the barrier. Furthermore, we present an $S$ Learning version of our LA with absorbing barriers that is able to handle $S$-Learning environment in which the feedback is continuous and not binary as in the case of the $L_{R-I}$.
Revisiting Neighborhood-based Link Prediction for Collaborative Filtering
Venue: arXiv cs.IR 2022
Year: 2022

Collaborative filtering (CF) is one of the most successful and fundamental techniques in recommendation systems. In recent years, Graph Neural Network (GNN)-based CF models, such as NGCF [31], LightGCN [10] and GTN [9] have achieved tremendous success and significantly advanced the state-of-the-art. While there is a rich literature of such works using advanced models for learning user and item representations separately, item recommendation is essentially a link prediction problem between users and items. Furthermore, while there have been early works employing link prediction for collaborative filtering [5, 6], this trend has largely given way to works focused on aggregating information from user and item nodes, rather than modeling links directly. In this paper, we propose a new linkage (connectivity) score for bipartite graphs, generalizing multiple standard link prediction methods. We combine this new score with an iterative degree update process in the user-item interaction bipartite graph to exploit local graph structures without any node modeling. The result is a simple, non-deep learning model with only six learnable parameters. Despite its simplicity, we demonstrate our approach significantly outperforms existing state-of-the-art GNN-based CF approaches on four widely used benchmarks. In particular, on Amazon-Book, we demonstrate an over 60% improvement for both Recall and NDCG. We hope our work would invite the community to revisit the link prediction aspect of collaborative filtering, where significant performance gains could be achieved through aligning link prediction with item recommendations.
OdontoAI: A human-in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning has remarkably advanced in the last few years, supported by large labeled data sets. These data sets are precious yet scarce because of the time-consuming labeling procedures, discouraging researchers from producing them. This scarcity is especially true in dentistry, where deep learning applications are still in an embryonic stage. Motivated by this background, we address in this study the construction of a public data set of dental panoramic radiographs. Our objects of interest are the teeth, which are segmented and numbered, as they are the primary targets for dentists when screening a panoramic radiograph. We benefited from the human-in-the-loop (HITL) concept to expedite the labeling procedure, using predictions from deep neural networks as provisional labels, later verified by human annotators. All the gathering and labeling procedures of this novel data set is thoroughly analyzed. The results were consistent and behaved as expected: At each HITL iteration, the model predictions improved. Our results demonstrated a 51% labeling time reduction using HITL, saving us more than 390 continuous working hours. In a novel online platform, called OdontoAI, created to work as task central for this novel data set, we released 4,000 images, from which 2,000 have their labels publicly available for model fitting. The labels of the other 2,000 images are private and used for model evaluation considering instance and semantic segmentation and numbering. To the best of our knowledge, this is the largest-scale publicly available data set for panoramic radiographs, and the OdontoAI is the first platform of its kind in dentistry.
Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification
Venue: arXiv cs.LG 2022
Year: 2022

This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC). By carefully utilizing unlabeled signal data with a self-supervised contrastive-learning pre-training step, our framework achieves higher performance given smaller amounts of labeled data, thereby largely reducing the labeling burden of deep learning. We evaluate the performance of our semi-supervised framework on a public dataset. The evaluation results demonstrate that our semi-supervised approach significantly outperforms supervised frameworks thereby substantially enhancing our ability to train deep neural networks for automatic modulation classification in a manner that leverages unlabeled data.
Deeply Interleaved Two-Stream Encoder for Referring Video Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Referring video segmentation aims to segment the corresponding video object described by the language expression. To address this task, we first design a two-stream encoder to extract CNN-based visual features and transformer-based linguistic features hierarchically, and a vision-language mutual guidance (VLMG) module is inserted into the encoder multiple times to promote the hierarchical and progressive fusion of multi-modal features. Compared with the existing multi-modal fusion methods, this two-stream encoder takes into account the multi-granularity linguistic context, and realizes the deep interleaving between modalities with the help of VLGM. In order to promote the temporal alignment between frames, we further propose a language-guided multi-scale dynamic filtering (LMDF) module to strengthen the temporal coherence, which uses the language-guided spatial-temporal features to generate a set of position-specific dynamic filters to more flexibly and effectively update the feature of current frame. Extensive experiments on four datasets verify the effectiveness of the proposed model.
DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation
Venue: arXiv cs.LG 2022
Year: 2022

The further development of deep neural networks is hampered by the limited GPU memory resource. Therefore, the optimization of GPU memory resources is highly demanded. Swapping and recomputation are commonly applied to make better use of GPU memory in deep learning. However, as an emerging domain, several challenges remain:1)The efficiency of recomputation is limited for both static and dynamic methods. 2)Swapping requires offloading parameters manually, which incurs a great time cost. 3) There is no such dynamic and fine-grained method that involves tensor swapping together with tensor recomputation nowadays. To remedy the above issues, we propose a novel scheduler manager named DELTA(Dynamic tEnsor offLoad and recompuTAtion). To the best of our knowledge, we are the first to make a reasonable dynamic runtime scheduler on the combination of tensor swapping and tensor recomputation without user oversight. In DELTA, we propose a filter algorithm to select the optimal tensors to be released out of GPU memory and present a director algorithm to select a proper action for each of these tensors. Furthermore, prefetching and overlapping are deliberately considered to overcome the time cost caused by swapping and recomputing tensors. Experimental results show that DELTA not only saves 40%-70% of GPU memory, surpassing the state-of-the-art method to a great extent but also gets comparable convergence results as the baseline with acceptable time delay. Also, DELTA gains 2.04$\times$ maximum batchsize when training ResNet-50 and 2.25$\times$ when training ResNet-101 compared with the baseline. Besides, comparisons between the swapping cost and recomputation cost in our experiments demonstrate the importance of making a reasonable dynamic scheduler on tensor swapping and tensor recomputation, which refutes the arguments in some related work that swapping should be the first and best choice.
ESNI: Domestic Robots Design for Elderly and Disabled People
Venue: arXiv cs.RO 2022
Year: 2022

Our paper focuses on the research of the possibility for speech recognition intelligent agents to assist the elderly and disabled people's lives, to improve their life quality by utilizing cutting-edge technologies. After researching the attitude of elderly and disabled people toward the household agent, we propose a design framework: ESNI(Exploration, Segmentation, Navigation, Instruction) that apply to mobile agent, achieve some functionalities such as processing human commands, picking up a specified object, and moving an object to another location. The agent starts the exploration in an unseen environment, stores each item's information in the grid cells to his memory and analyzes the corresponding features for each section. We divided our indoor environment into 6 sections: Kitchen, Living room, Bedroom, Studio, Bathroom, Balcony. The agent uses algorithms to assign sections for each grid cell then generates a navigation trajectory base on the section segmentation. When the user gives a command to the agent, feature words will be extracted and processed into a sequence of sub-tasks.
Computing critical points for algebraic systems defined by hyperoctahedral invariant polynomials
Venue: arXiv cs.SC 2022
Year: 2022

Let $\mathbb{K}$ be a field of characteristic zero and $\mathbb{K}[x_1, \dots, x_n]$ the corresponding multivariate polynomial ring. Given a sequence of $s$ polynomials $\mathbf{f} = (f_1, \dots, f_s)$ and a polynomial $ϕ$, all in $\mathbb{K}[x_1, \dots, x_n]$ with $s<n$, we consider the problem of computing the set $W(ϕ, \mathbf{f})$ of points at which $\mathbf{f}$ vanishes and the Jacobian matrix of $\mathbf{f}, ϕ$ with respect to $x_1, \dots, x_n$ does not have full rank. This problem plays an essential role in many application areas. In this paper we focus on a case where the polynomials are all invariant under the action of the signed symmetric group $B_n$. We introduce a notion called {\em hyperoctahedral representation} to describe $B_n$-invariant sets. We study the invariance properties of the input polynomials to split $W(ϕ, \mathbf{f})$ according to the orbits of $B_n$ and then design an algorithm whose output is a {hyperoctahedral representation} of $W(ϕ, \mathbf{f})$. The runtime of our algorithm is polynomial in the total number of points described by the output.
Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version
Venue: arXiv cs.LG 2022
Year: 2022

In step with the digitalization of transportation, we are witnessing a growing range of path-based smart-city applications, e.g., travel-time estimation and travel path ranking. A temporal path(TP) that includes temporal information, e.g., departure time, into the path is fundamental to enable such applications. In this setting, it is essential to learn generic temporal path representations(TPRs) that consider spatial and temporal correlations simultaneously and that can be used in different applications, i.e., downstream tasks. Existing methods fail to achieve the goal since (i) supervised methods require large amounts of task-specific labels when training and thus fail to generalize the obtained TPRs to other tasks; (ii) through unsupervised methods can learn generic representations, they disregard the temporal aspect, leading to sub-optimal results. To contend with the limitations of existing solutions, we propose a Weakly-Supervised Contrastive (WSC) learning model. We first propose a temporal path encoder that encodes both the spatial and temporal information of a temporal path into a TPR. To train the encoder, we introduce weak labels that are easy and inexpensive to obtain and are relevant to different tasks, e.g., temporal labels indicating peak vs. off-peak hours from departure times. Based on the weak labels, we construct meaningful positive and negative temporal path samples by considering both spatial and temporal information, which facilities training the encoder using contrastive learning by pulling closer to the positive samples' representations while pushing away the negative samples' representations. To better guide contrastive learning, we propose a learning strategy based on Curriculum Learning such that the learning performs from easy to hard training instances. Experiments studies verify the effectiveness of the proposed method.
Eff the ineffable: on the uncommunicability of a conceptually simple contribution to HCI methodology
Venue: arXiv cs.HC 2022
Year: 2022

Given a simple concept that has the potential for a methodological contribution to the field of HCI, the constraints of submission conventions within the field may make it impossible to communicate the concept in a manner that is intelligible to the reader.
Rabbit, toad, and the Moon: Can machine categorize them into one class?
Venue: arXiv cs.CV 2022
Year: 2022

Recent machine learning algorithms such as neural networks can classify objects and actions in video frames with high accuracy. Here, I discuss a classification of objects based on basal dynamic patterns referencing one tradition, the link between rabbit, toad, and the Moon, which can be seen in several cultures. In order for them to be classified into one class, a basic pattern of behavior (cyclic appearance and disappearance) works as a feature point. A static character such as the shape and time scale of the behavior are not essential for this classification. In cognitive semantics, image schemas are introduced to describe basal patterns of events. If learning of these image schemas is attained, a machine may be able to categorize rabbit, toad, and the Moon as the same class. For learning, video frames that show boundary boxes or segmentation may be helpful. Although this discussion is preliminary and many tasks remain to be solved, the classification based on basal behaviors can be an important topic for cognitive processes and computer science.
Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications
Venue: arXiv cs.SE 2022
Year: 2022

There is a growing interest in industry and academia in machine learning (ML) testing. We believe that industry and academia need to learn together to produce rigorous and relevant knowledge. In this study, we initiate a collaboration between stakeholders from one case company, one research institute, and one university. To establish a common view of the problem domain, we applied an interactive rapid review of the state of the art. Four researchers from Lund University and RISE Research Institutes and four practitioners from Axis Communications reviewed a set of 180 primary studies on ML testing. We developed a taxonomy for the communication around ML testing challenges and results and identified a list of 12 review questions relevant for Axis Communications. The three most important questions (data testing, metrics for assessment, and test generation) were mapped to the literature, and an in-depth analysis of the 35 primary studies matching the most important question (data testing) was made. A final set of the five best matches were analysed and we reflect on the criteria for applicability and relevance for the industry. The taxonomies are helpful for communication but not final. Furthermore, there was no perfect match to the case company's investigated review question (data testing). However, we extracted relevant approaches from the five studies on a conceptual level to support later context-specific improvements. We found the interactive rapid review approach useful for triggering and aligning communication between the different stakeholders.
Stack operation of tensor networks
Venue: arXiv cs.LG 2022
Year: 2022

The tensor network, as a facterization of tensors, aims at performing the operations that are common for normal tensors, such as addition, contraction and stacking. However, due to its non-unique network structure, only the tensor network contraction is so far well defined. In this paper, we propose a mathematically rigorous definition for the tensor network stack approach, that compress a large amount of tensor networks into a single one without changing their structures and configurations. We illustrate the main ideas with the matrix product states based machine learning as an example. Our results are compared with the for loop and the efficient coding method on both CPU and GPU.
The Block-based Mobile PDE Systems Are Not Secure -- Experimental Attacks
Venue: arXiv cs.CR 2022
Year: 2022

Nowadays, mobile devices have been used broadly to store and process sensitive data. To ensure confidentiality of the sensitive data, Full Disk Encryption (FDE) is often integrated in mainstream mobile operating systems like Android and iOS. FDE however cannot defend against coercive attacks in which the adversary can force the device owner to disclose the decryption key. To combat the coercive attacks, Plausibly Deniable Encryption (PDE) is leveraged to plausibly deny the very existence of sensitive data. However, most of the existing PDE systems for mobile devices are deployed at the block layer and suffer from deniability compromises. Having observed that none of existing works in the literature have experimentally demonstrated the aforementioned compromises, our work bridges this gap by experimentally confirming the deniability compromises of the block-layer mobile PDE systems. We have built a mobile device testbed, which consists of a host computing device and a flash storage device. Additionally, we have deployed both the hidden volume PDE and the steganographic file system at the block layer of the testbed and performed disk forensics to assess potential compromises on the raw NAND flash. Our experimental results confirm it is indeed possible for the adversary to compromise the block-layer PDE systems by accessing the raw NAND flash in practice. We also discuss potential issues when performing such attacks in real world.
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis
Venue: arXiv cs.CL 2022
Year: 2022

Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence. While pre-trained language models such as BERT have achieved great success, incorporating dynamic semantic changes into ABSA remains challenging. To this end, in this paper, we propose to address this problem by Dynamic Re-weighting BERT (DR-BERT), a novel method designed to learn dynamic aspect-oriented semantics for ABSA. Specifically, we first take the Stack-BERT layers as a primary encoder to grasp the overall semantic of the sentence and then fine-tune it by incorporating a lightweight Dynamic Re-weighting Adapter (DRA). Note that the DRA can pay close attention to a small region of the sentences at each step and re-weigh the vitally important words for better aspect-aware sentiment understanding. Finally, experimental results on three benchmark datasets demonstrate the effectiveness and the rationality of our proposed model and provide good interpretable insights for future semantic modeling.
Long-term Dynamics of Fairness Intervention in Connection Recommender Systems
Venue: arXiv cs.CY 2022
Year: 2022

Recommender system fairness has been studied from the perspectives of a variety of stakeholders including content producers, the content itself and recipients of recommendations. Regardless of which type of stakeholders are considered, most works in this area assess the efficacy of fairness intervention by evaluating a single fixed fairness criterion through the lens of a one-shot, static setting. Yet recommender systems constitute dynamical systems with feedback loops from the recommendations to the underlying population distributions which could lead to unforeseen and adverse consequences if not taken into account. In this paper, we study a connection recommender system patterned after the systems employed by web-scale social networks and analyze the long-term effects of intervening on fairness in the recommendations. We find that, although seemingly fair in aggregate, common exposure and utility parity interventions fail to mitigate amplification of biases in the long term. We theoretically characterize how certain fairness interventions impact the bias amplification dynamics in a stylized Pólya urn model.
L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors
Venue: arXiv cs.CV 2022
Year: 2022

In this research, we propose a tiny image segmentation model, L^3U-net, that works on low-resource edge devices in real-time. We introduce a data folding technique that reduces inference latency by leveraging the parallel convolutional layer processing capability of the CNN accelerators. We also deploy the proposed model to such a device, MAX78000, and the results show that L^3U-net achieves more than 90% accuracy over two different segmentation datasets with 10 fps.