Reviewerly/exHarmony huggingface dataset:
Vector Quantized Semantic Communication System

Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems. In this paper, we develop a deep learning (DL)-enabled vector quantized (VQ) semantic communication system for image transmission, named VQ-DeepSC. Specifically, we propose a convolutional neural network (CNN)-based transceiver to extract multi-scale semantic features of images and introduce multi-scale semantic embedding spaces to perform semantic feature quantization, rendering the data compatible with digital communication systems. Furthermore, we employ adversarial training to improve the quality of received images by introducing a PatchGAN discriminator. Experimental results demonstrate that the proposed VQ-DeepSC is more robustness than BPG in digital communication systems and has comparable MS-SSIM performance to the DeepJSCC method.
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models

Variational inference with Gaussian mixture models (GMMs) enables learning of highly tractable yet multi-modal approximations of intractable target distributions with up to a few hundred dimensions. The two currently most effective methods for GMM-based variational inference, VIPS and iBayes-GMM, both employ independent natural gradient updates for the individual components and their weights. We show for the first time, that their derived updates are equivalent, although their practical implementations and theoretical guarantees differ. We identify several design choices that distinguish both approaches, namely with respect to sample selection, natural gradient estimation, stepsize adaptation, and whether trust regions are enforced or the number of components adapted. We argue that for both approaches, the quality of the learned approximations can heavily suffer from the respective design choices: By updating the individual components using samples from the mixture model, iBayes-GMM often fails to produce meaningful updates to low-weight components, and by using a zero-order method for estimating the natural gradient, VIPS scales badly to higher-dimensional problems. Furthermore, we show that information-geometric trust-regions (used by VIPS) are effective even when using first-order natural gradient estimates, and often outperform the improved Bayesian learning rule (iBLR) update used by iBayes-GMM. We systematically evaluate the effects of design choices and show that a hybrid approach significantly outperforms both prior works. Along with this work, we publish our highly modular and efficient implementation for natural gradient variational inference with Gaussian mixture models, which supports 432 different combinations of design choices, facilitates the reproduction of all our experiments, and may prove valuable for the practitioner.
High-performance state-vector emulator of a gate-based quantum processor implemented in the Rust programming language

We propose a high-performance state-vector emulator of a gate-based quantum processors developed in the Rust programming language. It supports OpenQASM 2.0 programming language for quantum circuits specification and has a user-friendly Python based API. We present a wide range of numerical benchmarks of the emulator. We expect that our emulator will be used for design and validation of new quantum algorithms.
MetaPrompting: Learning to Learn Better Prompts

Prompting method is regarded as one of the crucial progress for few-shot nature language processing. Recent research on prompting moves from discrete tokens based ``hard prompts'' to continuous ``soft prompts'', which employ learnable vectors as pseudo prompt tokens and achieve better performance. Though showing promising prospects, these soft-prompting methods are observed to rely heavily on good initialization to take effect. Unfortunately, obtaining a perfect initialization for soft prompts requires understanding of inner language models working and elaborate design, which is no easy task and has to restart from scratch for each new task. To remedy this, we propose a generalized soft prompting method called MetaPrompting, which adopts the well-recognized model-agnostic meta-learning algorithm to automatically find better prompt initialization that facilitates fast adaptation to new prompting tasks.Extensive experiments show MetaPrompting tackles soft prompt initialization problem and brings significant improvement on four different datasets (over 6 points improvement in accuracy for 1-shot setting), achieving new state-of-the-art performance.
Optimizing Class Distribution in Memory for Multi-Label Online Continual Learning

Online continual learning, especially when task identities and task boundaries are unavailable, is a challenging continual learning setting. One representative kind of methods for online continual learning is replay-based methods, in which a replay buffer called memory is maintained to keep a small part of past samples for overcoming catastrophic forgetting. When tackling with online continual learning, most existing replay-based methods focus on single-label problems in which each sample in the data stream has only one label. But multi-label problems may also happen in the online continual learning setting in which each sample may have more than one label. In the online setting with multi-label samples, the class distribution in data stream is typically highly imbalanced, and it is challenging to control class distribution in memory since changing the number of samples belonging to one class may affect the number of samples belonging to other classes. But class distribution in memory is critical for replay-based memory to get good performance, especially when the class distribution in data stream is highly imbalanced. In this paper, we propose a simple but effective method, called optimizing class distribution in memory (OCDM), for multi-label online continual learning. OCDM formulates the memory update mechanism as an optimization problem and updates the memory by solving this problem. Experiments on two widely used multi-label datasets show that OCDM can control the class distribution in memory well and can outperform other state-of-the-art methods.
T2FPV: Dataset and Method for Correcting First-Person View Errors in Pedestrian Trajectory Prediction

Predicting pedestrian motion is essential for developing socially-aware robots that interact in a crowded environment. While the natural visual perspective for a social interaction setting is an egocentric view, the majority of existing work in trajectory prediction therein has been investigated purely in the top-down trajectory space. To support first-person view trajectory prediction research, we present T2FPV, a method for constructing high-fidelity first-person view (FPV) datasets given a real-world, top-down trajectory dataset; we showcase our approach on the ETH/UCY pedestrian dataset to generate the egocentric visual data of all interacting pedestrians, creating the T2FPV-ETH dataset. In this setting, FPV-specific errors arise due to imperfect detection and tracking, occlusions, and field-of-view (FOV) limitations of the camera. To address these errors, we propose CoFE, a module that further refines the imputation of missing data in an end-to-end manner with trajectory forecasting algorithms. Our method reduces the impact of such FPV errors on downstream prediction performance, decreasing displacement error by more than 10% on average. To facilitate research engagement, we release our T2FPV-ETH dataset and software tools.
Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses

Deep reinforcement learning (DRL) is vulnerable to adversarial perturbations. Adversaries can mislead the policies of DRL agents by perturbing the state of the environment observed by the agents. Existing attacks are feasible in principle, but face challenges in practice, either by being too slow to fool DRL policies in real time or by modifying past observations stored in the agent's memory. We show that Universal Adversarial Perturbations (UAP), independent of the individual inputs to which they are applied, can fool DRL policies effectively and in real time. We introduce three attack variants leveraging UAP. Via an extensive evaluation using three Atari 2600 games, we show that our attacks are effective, as they fully degrade the performance of three different DRL agents (up to 100%, even when the $l_\infty$ bound on the perturbation is as small as 0.01). It is faster than the frame rate (60 Hz) of image capture and considerably faster than prior attacks ($\approx 1.8$ms). Our attack technique is also efficient, incurring an online computational cost of $\approx 0.027$ms. Using two tasks involving robotic movement, we confirm that our results generalize to complex DRL tasks. Furthermore, we demonstrate that the effectiveness of known defenses diminishes against universal perturbations. We introduce an effective technique that detects all known adversarial perturbations against DRL policies, including all universal perturbations presented in this paper.
Adapting $k$-means algorithms for outliers

This paper shows how to adapt several simple and classical sampling-based algorithms for the $k$-means problem to the setting with outliers. Recently, Bhaskara et al. (NeurIPS 2019) showed how to adapt the classical $k$-means++ algorithm to the setting with outliers. However, their algorithm needs to output $O(\log (k) \cdot z)$ outliers, where $z$ is the number of true outliers, to match the $O(\log k)$-approximation guarantee of $k$-means++. In this paper, we build on their ideas and show how to adapt several sequential and distributed $k$-means algorithms to the setting with outliers, but with substantially stronger theoretical guarantees: our algorithms output $(1+\varepsilon)z$ outliers while achieving an $O(1 / \varepsilon)$-approximation to the objective function. In the sequential world, we achieve this by adapting a recent algorithm of Lattanzi and Sohler (ICML 2019). In the distributed setting, we adapt a simple algorithm of Guha et al. (IEEE Trans. Know. and Data Engineering 2003) and the popular $k$-means$\|$ of Bahmani et al. (PVLDB 2012). A theoretical application of our techniques is an algorithm with running time $\tilde{O}(nk^2/z)$ that achieves an $O(1)$-approximation to the objective function while outputting $O(z)$ outliers, assuming $k \ll z \ll n$. This is complemented with a matching lower bound of $\Omega(nk^2/z)$ for this problem in the oracle model.
Philosophical-Psychological, Pedagogical-Managerial Aspects of "Qualitative" Formation of Future Specialists of All Levels of Training and Specialties in the Conditions of Technical University

The study is devoted to such topical issues in the theory and methodology of education and pedagogy of higher education as the training of future leaders, namely the formation and development of leadership qualities of students in technical universities. On the topic of the study, the authors analyzed the literature of leading scientists in the field of philosophy, psychology and pedagogy, identified features of the formation of leadership in the student environment. The study reveals the seven most important criteria that form the core of the personality of the future specialist leader. The use of these seven criteria of personality development of the future specialist in technical universities requires a combination and adaptation of traditional approaches to education and the latest pedagogical technologies. This is aimed not just at introducing certain forms and methods of work into the educational process, but at creating a specially organized professional environment in which future professionals have opportunities for professional and personal growth through conscious change and transformation of both the environment and themselves. In the process of personal change, the student's historical "I" is as close as possible to his logical "I" and is held by the latter with the help of Onto In-Se. This is how the future specialist acquires leadership qualities, first of all: cognitive, communicative, organizational, moral and volitional and reflexive skills. It is established that through the development of conceptual and creative thinking of the student it is possible to influence the formation of his critical "I" as a necessary attribute of leadership: the ability to listen and hear, understand and act.
Parallel sampling of decomposable graphs using Markov chain on junction trees

Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable graphs, as they enjoy a rich set of properties making them amenable to high-dimensional problems. While parameter inference is straightforward in this setup, inferring the underlying graph is a challenge driven by the computational difficulty in exploring the space of decomposable graphs. This work makes two contributions to address this problem. First, we provide sufficient and necessary conditions for when multi-edge perturbations maintain decomposability of the graph. Using these, we characterize a simple class of partitions that efficiently classify all edge perturbations by whether they maintain decomposability. Second, we propose a novel parallel non-reversible Markov chain Monte Carlo sampler for distributions over junction tree representations of the graph. At every step, the parallel sampler executes simultaneously all edge perturbations within a partition. Through simulations, we demonstrate the efficiency of our new edge perturbation conditions and class of partitions. We find that our parallel sampler yields improved mixing properties in comparison to the single-move variate, and outperforms current state-of-the-arts methods in terms of accuracy and computational efficiency. The implementation of our work is available in the Python package parallelDG.
The Impact of Entertainment on Buyers’ Decisions via Facebook: Jordanian Youth Behavior of E-Fashion Stores Context

This study aims to determine the impact of social media entertainment on buying decisions via the Facebook platform. The research is quasi-experimental and is quantitative and descriptive. The sample size reached 500 active Facebook users from the Jordanian youth, and the snowball sample technique was employed. A questionnaire is the only tool used to collect the primary data and contains closed-ended questions. The Likert five-point scale measures the responses through the partial least squares (PLS) method. It was concluded that there is a positive statistical impact of social media-based entertainment on the buying decisions from e-fashion stores on the Facebook platform, and social media entertainment explains 28% of the variations in the buying decisions from e-fashion stores via Facebook. Social media is an effective and safe solution to cutting high unemployment rates in the Jordanian economy. The e-buying experience depends on entertainment, enjoyment, relaxation, and leisure. It is a radical solution for creating new jobs in the economy and decreasing the poverty rates among poor and unemployed people.
A Novel Online Action Detection Framework from Untrimmed Video Streams

Online temporal action localization from an untrimmed video stream is a challenging problem in computer vision. It is challenging because of i) in an untrimmed video stream, more than one action instance may appear, including background scenes, and ii) in online settings, only past and current information is available. Therefore, temporal priors, such as the average action duration of training data, which have been exploited by previous action detection methods, are not suitable for this task because of the high intra-class variation in human actions. We propose a novel online action detection framework that considers actions as a set of temporally ordered subclasses and leverages a future frame generation network to cope with the limited information issue associated with the problem outlined above. Additionally, we augment our data by varying the lengths of videos to allow the proposed method to learn about the high intra-class variation in human actions. We evaluate our method using two benchmark datasets, THUMOS'14 and ActivityNet, for an online temporal action localization scenario and demonstrate that the performance is comparable to state-of-the-art methods that have been proposed for offline settings.
Comparing the Accuracy of Sonoelastographic Ultrasound with Biopsy in the Assessment of Breast Masses among Nigerian Women in the Lagos University Teaching Hospital, Lagos, Nigeria

Background: Breast cancer is the commonest cause of cancer related deaths globally, particularly in Africa where late presentation is prevalent. Early diagnosis demands the evolution of non-invasive, highly sensitive and specific screening and diagnostic investigation. This study assessed the diagnostic properties of Sonoelastography ultrasonography, in classifying breast masses compared to biopsy among women receiving care at the Lagos University Teaching hospital (LUTH), Lagos.&#x0D; Methods: Using a cross-sectional, analytical study design, 100 women with breast masses were recruited consecutively as they presented to a multidisciplinary breast clinic in LUTH. They were assessed by history taking, clinical examination, and sonoelastography ultrasound was done to classify the breast masses. Breast masses were also biopsied to get a histological diagnosis. The outcomes were compared in a cross tabulation and the sensitivity, specificity, positive and negative predictive values, and accuracy were calculated for sonoelastography USS to assess its diagnostic properties. These indicators were also calculated for the 3 parameters (Elastography score, Strain and Lesion ratio) of sonoelastography USS.&#x0D; Results: The mean age of the women with breast mass was 39.4 years with a standard deviation of 13.4 years. Above half of the women were overweight (53.0%) with tertiary education (58.0%), Mean weight of breast mass was 368.7±627.6mg. Histology report and sonoelastography USS classified 46.0% and 47.0% of breast masses respectively, as malignant breast masses. The sensitivity, specificity, and accuracy of sonoelastography USS in relation to histology report was 87.0% in the study. The diagnostic performance of each parameter was good but Elastogram score was best with 69.6% sensitivity, 87.0% specificity and 79.0% accuracy.&#x0D; Conclusion: This study concluded that sonoelastography is useful in characterizing breast masses as either benign or malignant which can be compared to the use of histologic diagnosis in characterizing breast masses. The Elastography score was the elastography parameter that yielded the highest diagnostic accuracy, and the lesion ratio yielded the least diagnostic accuracy. More research may be needed for better standardization of the cut-off values to be applied for the elastography parameters (Elastography score, strain ratio and lesion ratio)to improve their diagnostic performance.
The Impact of Choosing Distribution Channels Factors During the Coronavirus Pandemic on the Wholesaler’s Satisfaction

The study aimed to clarify the impact of choosing distribution channel factors during the Coronavirus pandemic and the wholesaler’s satisfaction: internal control as a mediating variable (A case study of the Petra Investment and Trade Company/Jordan). The study population consisted (450) of wholesalers using the comprehensive survey method, (370) questionnaires were retrieved electronically, with a percentage of (82.2%), the dimensions of distribution channels were (efficiency in performance, speed, cost, length of the distribution channel). The study reached a set of results which are: there is a statistically significant impact of the factors of choosing distribution channels during Corona pandemic (performance efficiency, speed, cost, length of the distribution channel) on the internal control, there is an intermediary role of internal control over the relationship between the factors of choosing distribution channels (efficiency in performance, speed, cost, length of the distribution channel) on the satisfaction of wholesalers in the Petra Investment and Trade Company. The study recommended that it is necessary for the Petra Investment and Trade Company to continue to pay attention to the factors affecting the satisfaction of wholesalers (performance efficiency, speed, cost, and length of the distribution channel). This should happen through conducting training courses and establishing updated electronic systems that contribute to raising the effectiveness of the distribution channel. In addition, increasing the utilize in the internal control system of Petra Investment and Trade Company for its significant role in influencing the satisfaction of wholesalers. It has to happen through continuous follow-up of wholesalers’ business, updating information and establishing a system for exchanging the information with them to Raise the level of loyalty and manage the relationship more efficiently.
Differential Privacy Protection for Support Vector Machines for Nonlinear Classification

Currently, private data leakage and nonlinear classification are two challenges encountered in big data mining. In particular, few studies focus on these issues in support vector machines (SVMs). In this paper, to effectively solve them, we propose a novel framework based on the concepts of differential privacy (DP) and kernel functions. This framework can allocate privacy budgets and add artificial noise to different SVM locations simultaneously, which makes the perturbation process freer and more delicate. In addition, under this framework, we propose three algorithms, DP SVMs that perturb the training data set, perturb the kernel function, and utilize mixed perturbation (DPSVM-TDP, DPSVM-KFP, and DPSVM-MP, respectively), all of which can realize accurate classification while ensuring that the users’ privacy is not violated. Moreover, we conduct privacy analysis on these algorithms and prove that they all satisfy <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>ε</mi> <mo>,</mo> <mn>0</mn> </mrow> </mfenced> <mo>−</mo> </math> DP. Finally, we conduct experiments to evaluate the algorithms in terms of different aspects and compare them with the DPSVM with dual-variable perturbation (DVP) algorithm (DPSVM-DVP) to determine the optimal perturbation method. The results show that DPSVM-KFP can achieve the highest data utility and strictest privacy protection with the shortest running time.
Adopting a Blockchain-Based Algorithmic Model for Electronic Healthcare Records (EHR) in Nigeria

This work seeks to explore solutions to the challenges posed in sharing/integrating/transferring EHR data across heterogeneous healthcare institutions in developing nations, particularly in Nigeria. This blockchain-based algorithm model for EHR interoperability seeks to address EHR interoperability challenges such as semantics across heterogenous healthcare institutions, the proper infrastructure and consensus structure for the sharing of EHR across healthcare institutions, privacy and security of patients’ records, etc. Hence, this algorithm model, when adopted, is adjusted and contextualized to fit developing nations by addressing the underlining fundamental challenges. EHR interoperability would become a reality across the heterogeneous healthcare institutions in these nations. Future works on EHR will focus on the aggregate blockchain model. Also, the Artificial Intelligence (AI) model inculcating blockchain technology would be an attractive option to dive into future works on EHR.