Reviewerly/exHarmony huggingface dataset:
The role of social-psychological factors of victimity on victimization of online fraud in China

The high incidence of online fraud in China poses a great threat to the social stability and property security of society. Victimity is the state or mindset of victims, referring to the various conditions related to being victims of crimes. Examining the association between the inherent and extrinsic factors of victimity and victimization of online fraud is of great importance for revealing the psychological mechanism of victimization and drawing up preventative measures.Through the questionnaire survey of 504 online fraud victims and 523 non-victims, univariate analysis and logistic regression analysis were used to investigate the risk factors correlated with victimization.Age, education, and social support are positively correlated with fraud victimization, while impulsiveness, trust tendency, smartphone usage, and negative life experiences are negatively correlated with fraud victimization. Subsequent regression analyses showed that all the factors above, except for smartphone usage, are significant predictors for online fraud victimization.Our conceptual model and empirical results demonstrate the important role of victimity in making an individual become a victim and help clarify the mixed findings of previous studies on the risk factors for online fraud.
Robust Multi-object Tracking by Marginal Inference

Multi-object tracking in videos requires to solve a fundamental problem of one-to-one assignment between objects in adjacent frames. Most methods address the problem by first discarding impossible pairs whose feature distances are larger than a threshold, followed by linking objects using Hungarian algorithm to minimize the overall distance. However, we find that the distribution of the distances computed from Re-ID features may vary significantly for different videos. So there isn’t a single optimal threshold which allows us to safely discard impossible pairs. To address the problem, we present an efficient approach to compute a marginal probability for each pair of objects in real time. The marginal probability can be regarded as a normalized distance which is significantly more stable than the original feature distance. As a result, we can use a single threshold for all videos. The approach is general and can be applied to the existing trackers to obtain about one point improvement in terms of IDF1 metric. It achieves competitive results on MOT17 and MOT20 benchmarks. In addition, the computed probability is more interpretable which facilitates subsequent post-processing operations.
Multimodal Transformer with Variable-Length Memory for Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions with the environment during moving. Recent Transformer-based VLN methods have made great progress benefiting from the direct connections between visual observations and language instructions via the multimodal cross-attention mechanism. However, these methods usually represent temporal context as a fixed-length vector by using an LSTM decoder or using manually designed hidden states to build a recurrent Transformer. Considering a single fixed-length vector is often insufficient to capture long-term temporal context, in this paper, we introduce Multimodal Transformer with Variable-length Memory (MTVM) for visually-grounded natural language navigation by modeling the temporal context explicitly. Specifically, MTVM enables the agent to keep track of the navigation trajectory by directly storing activations in the previous time step in a memory bank. To further boost the performance, we propose a memory-aware consistency loss to help learn a better joint representation of temporal context with random masked instructions. We evaluate MTVM on popular R2R and CVDN datasets. Our model improves Success Rate on R2R test set by 2% and reduces Goal Process by 1.5 m on CVDN test set. Code is available at: https://github.com/clin1223/MTVM .
NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition

It is challenging for artificial intelligence systems to achieve accurate video recognition under the scenario of low computation costs. Adaptive inference based efficient video recognition methods typically preview videos and focus on salient parts to reduce computation costs. Most existing works focus on complex networks learning with video classification based objectives. Taking all frames as positive samples, few of them pay attention to the discrimination between positive samples (salient frames) and negative samples (non-salient frames) in supervisions. To fill this gap, in this paper, we propose a novel Non-saliency Suppression Network (NSNet), which effectively suppresses the responses of non-salient frames. Specifically, on the frame level, effective pseudo labels that can distinguish between salient and non-salient frames are generated to guide the frame saliency learning. On the video level, a temporal attention module is learned under dual video-level supervisions on both the salient and the non-salient representations. Saliency measurements from both two levels are combined for exploitation of multi-granularity complementary information. Extensive experiments conducted on four well-known benchmarks verify our NSNet not only achieves the state-of-the-art accuracy-efficiency trade-off but also present a significantly faster ( $$2.4{\sim }4.3\times $$ ) practical inference speed than state-of-the-art methods. Our project page is at https://lawrencexia2008.github.io/projects/nsnet .
What do navigation agents learn about their environment?

Today's state of the art visual navigation agents typically consist of large deep learning models trained end to end. Such models offer little to no interpretability about the learned skills or the actions of the agent taken in response to its environment. While past works have explored interpreting deep learning models, little attention has been devoted to interpreting embodied AI systems, which often involve reasoning about the structure of the environment, target characteristics and the outcome of one's actions. In this paper, we introduce the Interpretability System for Embodied agEnts (iSEE) for Point Goal and Object Goal navigation agents. We use iS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EE</inf> to probe the dynamic representations produced by these agents for the presence of information about the agent as well as the environment. We demonstrate interesting insights about navigation agents using iSEE, including the ability to encode reachable locations (to avoid obstacles), visibility of the target, progress from the initial spawn location as well as the dramatic effect on the behaviors of agents when we mask out critical individual neurons.
Webly Supervised Concept Expansion for General Purpose Vision Models

General Purpose Vision (GPV) systems are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and concepts from large fully supervised datasets. Scaling GPVs to tens of thousands of concepts by acquiring data to learn each concept for every skill quickly becomes prohibitive. This work presents an effective and inexpensive alternative: learn skills from supervised datasets, learn concepts from web image search, and leverage a key characteristic of GPVs: the ability to transfer visual knowledge across skills. We use a dataset of 1M+ images spanning 10k+ visual concepts to demonstrate webly-supervised concept expansion for two existing GPVs (GPV-1 and VL-T5) on 3 benchmarks: 5 Coco-based datasets (80 primary concepts), a newly curated series of 5 datasets based on the OpenImages and VisualGenome repositories ( $$\sim $$ 500 concepts), and the Web-derived dataset (10k+ concepts). We also propose a new architecture, GPV-2 that supports a variety of tasks — from vision tasks like classification and localization to vision+language tasks like QA and captioning, to more niche ones like human-object interaction detection. GPV-2 benefits hugely from web data and outperforms GPV-1 and VL-T5 across these benchmarks. Our data, code, and web demo are available at https://prior.allenai.org/projects/gpv2 .
G-tran

Graph transaction processing poses unique challenges such as random data access due to the irregularity of graph structures, low throughput and high abort rate due to the relatively large read/write sets in graph transactions. To address these challenges, we present G-Tran, a remote direct memory access (RDMA)-enabled distributed in-memory graph database with serializable and snapshot isolation support. First, we propose a graph-native data store to achieve good data locality and fast data access for transactional updates and queries. Second, G-Tran adopts a fully decentralized architecture that leverages RDMA to process distributed transactions with the massively parallel processing (MPP) model, which can achieve high performance by utilizing all computing resources. In addition, we propose a new multi-version optimistic concurrency control (MV-OCC) protocol with two optimizations to address the issue of large read/write sets in graph transactions. Extensive experiments show that G-Tran achieves competitive performance compared with other popular graph databases on benchmark workloads.
MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration

Multimodal video-audio-text understanding and generation can benefit from datasets that are narrow but rich. The narrowness allows bite-sized challenges that the research community can make progress on. The richness ensures we are making progress along the core challenges. To this end, we present a large-scale video-audio-text dataset MUGEN, collected using the open-sourced platform game CoinRun. We made substantial modifications to make the game richer by introducing audio and enabling new interactions. We trained RL agents with different objectives to navigate the game and interact with 13 objects and characters. This allows us to automatically extract a large collection of diverse videos and associated audio. We sample 375K video clips (3.2 s each) and collect text descriptions from human annotators. Each video has additional annotations that are extracted automatically from the game engine, such as accurate semantic maps for each frame and templated textual descriptions. Altogether, MUGEN can help progress research in many tasks in multimodal understanding and generation. We benchmark representative approaches on tasks involving video-audio-text retrieval and generation. Our dataset and code are released at: https://mugen-org.github.io/ .
RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics

Recurrent Neural Networks (RNNs) are used in applications that learn dependencies in data sequences, such as speech recognition, human activity recognition, and anomaly detection. In recent years, newer RNN variants, such as GRUs and LSTMs, have been used for implementing these applications. As many of these applications are employed in real-time scenarios, accelerating RNN/LSTM/GRU inference is crucial. In this paper, we propose a novel photonic hardware accelerator called RecLight for accelerating simple RNNs, GRUs, and LSTMs. Simulation results indicate that RecLight achieves 37x lower energy-per-bit and 10% better throughput compared to the state-of-the-art.
PalGAN: Image Colorization with Palette Generative Adversarial Networks

Multimodal ambiguity and color bleeding remain challenging in colorization. To tackle these problems, we propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. To circumvent the multimodality issue, we present a new colorization formulation that estimates a probabilistic palette from the input gray image first, then conducts color assignment conditioned on the palette through a generative model. Further, we handle color bleeding with chromatic attention. It studies color affinities by considering both semantic and intensity correlation. In extensive experiments, PalGAN outperforms state-of-the-arts in quantitative evaluation and visual comparison, delivering notable diverse, contrastive, and edge-preserving appearances. With the palette design, our method enables color transfer between images even with irrelevant contexts.
Security Analysis of a User Authentication Scheme for IoT-Based Healthcare

Very recently, Masud et al. (2022) proposed a lightweight and anonymity-preserving user authentication scheme to establish secure communication between the doctor, the gateway, and sensor nodes in IoT-based healthcare, aiming to ensure the privacy of the patients’ physiological data. In this article, however, we carefully revisit their scheme and first point out that the scheme is not practically implementable in its current form, and second we show that it is vulnerable to session key disclosure attacks, off-line password guessing attacks, and traceability attacks, under the assumption that the attacker can gain access to the sensor nodes and the doctor’s device. We also propose fixes for each of these issues or vulnerabilities.
ROI-Fuzzy Based Medical Data Authentication Scheme for Smart Healthcare System

During the current pandemic, telemedicine applications encourage radiologists to consult patients remotely. Medical images are typically sent over the internet for easy access when diagnosing a patient. Intruders can manipulate these images during communication. Therefore, we require a method of authentication for these images that is capable of authenticating these images before diagnosis. To address these issues, a watermarking scheme has been developed within the scope of this paper. The foundation of anticipated watermarking system is Regions of Interest (ROI), using FRT to RONI, and converted into blocks; afterward, Hessenberg decomposition is applied on blocks selected by means of fuzzy logic. The binary watermark is quantized into the medical image. It has made the watermarking system non-fragil and secure.The proposed scheme’s performance is evaluated on various attack situations, including noise and scale assaults. Watermarked pictures with enhanced PSNR ranging from 56.9902 to 57.2356 have been produced using the proposed technique. In particular, we achieved high NC values of 0.9801 on applying image processing attacks. A series of tests are performed on the system to determine its sensitivity, and the results are compared to protocols. The simulation results demonstrate that the suggested solution offers a high level of security while being imperceptible and robust to various attacks.
The Covid 19 Crisis and The Future of Bitcoin in E-Commerce

The spread of the coronavirus has devalued stock indices, lowered commodity prices such as oil, and chaos in financial markets around the world. Fiat currencies have plummeted as central banks injected too much money into affected economies, and an unprecedented recession has hit the world that central banks and financial institutions are unable to cope with. Bitcoin is a virtual currency whose production is the result of a time-consuming computational activity called exploration. This internati
Secure and Scalable Collaborative Edge Computing using Decision Tree

Edge computing is an essential step of an IoT-Edge workflow, where the Internet of Things (IoT) devices sense or collect the data, and data are processed and evaluated at the edge data centers (EDCs) for near real-time evaluation. Further, data are processed in the resource-rich Cloud environment. It is always challenging to filter out the corrupted data at the resource-constrained EDCs. To address this challenge, we proposed a method to create a collaborative edge computing environment and apply the Decision Tree (DT) to filter out the corrupted data and make the intact data available for data processing. Secondly, we developed a Decision Tree-based authentication mechanism to strengthen the Proof-of-Authentication (PoAh) process for seamless integration of Blockchain in IoT-Edge secure end-to-end data transmission. Finally, we have experimented in a real-time testbed to validate both the proposed approaches.
A Pseudo-Random Number Generator Based on New Hybrid LFSR and LCG Algorithm

In many areas, such as simulation, numerical analysis, computer programming, decision-making, entertainment, and coding, a random number input is required. The pseudo-random number uses its seed value. In this paper, a hybrid method for pseudo number generation is proposed using Linear Feedback Shift Registers (LFSR) and Linear Congruential Generator (LCG). The hybrid method for generating keys is proposed by merging technologies. In each method, a new large in key-space group of numbers were generated separately. Also, a higher level of secrecy is gained such that the internal numbers generated from LFSR are combined with LCG (The adoption of roots in non-linear iteration loops). LCG and LFSR are linear structures and outputs of these Random Number Generators (RNGs) are predictable, while the proposal avoids this predictable nature. The results were tested in terms of randomness, in terms of the correlation between the keys and the effect of changing the initial state on the generated keys and the results of the tests showed that they had successfully passed the tests and resist brute force and differential attack.
Generalized Target Assignment and Path Finding Using Answer Set Programming

In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications. We address this limitation by generalizing TAPF to allow for (1) unequal number of agents and tasks; (2) tasks to have deadlines by which they must be completed; (3) ordering of groups of tasks to be completed; and (4) tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple -- one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based methods can be efficient and can scale up to solve practical applications.
Long-Term Video QoE Assessment Studies: A Systematic Review

Although longitudinal studies are common in other disciplines, such as psychology, medicine, or User Experience, they are rarely used in Quality of Experience (QoE). However, observing users over time can provide useful information on the QoE and help to better understand its influencing factors. Here, we present a systematic review of the methodologies of longitudinal studies in the QoE domain. We review papers selected through a systematic search and discuss various aspects of described studies, such as methods of gathering subjective assessment or length of the studies. Our work recognizes common practices that can be used to reproduce and extend the proposed long-term study designs. Additionally, based on our review, we propose future work directions for the longitudinal QoE studies.
Towards QR Code Health Systems Amid COVID-19: Lessons Learnt from Other QR Code Digital Technologies

The novel coronavirus disease (COVID-19) continuously crippling healthcare systems globally and disrupting movements of people which led to the temporary closure of schools, colleges, universities, industries, and businesses. To reduce the catastrophic impact of the new variant of COVID-19, governments in collaboration with World Health Organization (WHO) emphasize on vaccination of populations. However, several countries witnessed a rapid increase of new infections and deaths which are linked to relaxation of regulations, fake COVID-19 certificates, resistance to adoption of health digital technologies, overburdened health system, lack of personal protective equipment, social risk behaviors, poor policies and standards for immigrants and lack of standardized and synchronized regional and international health information system that facilitates the regular sharing of COVID-19 data, test results and vaccination certificates. Also, accessing COVID-19 data and patient health history data remains a challenge for many health systems. Therefore, we propose the use of secure regional and international quick response code-based health systems to monitor the migration patterns, validate COVID-19 test results and vaccination certificates to facilitate safe regional and international movement of people during the pandemic.
Fully privacy-preserving distributed optimization in power systems based on secret sharing

With the increasing development of smart grid, multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multiparty collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation (SMPC) with secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.
Semi-Supervised Video Semantic Segmentation with Inter-Frame Feature Reconstruction

One major challenge for semantic segmentation in realworld scenarios is only limited pixel-level labels available due to high expense of human labor though a vast volume of video data is provided. Existing semi-supervised methods attempt to exploit unlabeled data in model training, but they just regard video as a set of independent images. To better explore semi-supervised segmentation problem with video data, we formulate a semi-supervised video semantic segmentation task in this paper. For this task, we observe that the overfitting is surprisingly severe between labeled and unlabeled frames within a training video although they are very similar in style and contents. This is called inner-video overfitting, and it would actually lead to inferior performance. To tackle this issue, we propose a novel interframe feature reconstruction (IFR) technique to leverage the ground-truth labels to supervise the model training on unlabeled frames. IFR is essentially to utilize the internal relevance of different frames within a video. During training, IFR would enforce the feature distributions between labeled and unlabeled frames to be narrowed. Consequently, the inner-video overfitting issue can be effectively alleviated. We conduct extensive experiments on Cityscapes and CamVid, and the results demonstrate the superiority of our proposed method to previous state-of-the-art methods. The code is available at https://github.com/jfzhuang/IFR.