Reviewerly/exHarmony huggingface dataset:
Semi-supervised 3D Object Detection via Temporal Graph Neural Networks

3D object detection plays an important role in autonomous driving and other robotics applications. However, these detectors usually require training on large amounts of annotated data that is expensive and time-consuming to collect. Instead, we propose leveraging large amounts of unlabeled point cloud videos by semi-supervised learning of 3D object detectors via temporal graph neural networks. Our insight is that temporal smoothing can create more accurate detection results on unlabeled data, and these smoothed detections can then be used to retrain the detector. We learn to perform this temporal reasoning with a graph neural network, where edges represent the relationship between candidate detections in different time frames. After semi-supervised learning, our method achieves state-of-the-art detection performance on the challenging nuScenes [3] and H3D [19] benchmarks, compared to baselines trained on the same amount of labeled data. Project and code are released at https://www.jianrenw.com/SOD-TGNN/.
Fast and Low-Power Quantized Fixed Posit High-Accuracy DNN Implementation

This brief compares quantized float-point representation in posit and fixed-posit formats for a wide variety of pre-trained deep neural networks (DNNs). We observe that fixed-posit representation is far more suitable for DNNs as it results in a faster and low-power computation circuit. We show that accuracy remains within the range of 0.3&#x0025; and 0.57&#x0025; of top-1 accuracy for posit and fixed-posit quantization. We further show that the posit-based multiplier requires higher power-delay-product (PDP) and area, whereas fixed-posit reduces PDP and area consumption by 71&#x0025; and 36&#x0025;, respectively, compared to (Devnath <i>et al.</i>, 2020) for the same bit-width.
Memory-efficient Transformers via Top-k Attention

Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length.While these variants are memory and compute efficient, it is not possible to directly use them with popular pre-trained language models trained using vanilla attention, without an expensive corrective pre-training stage.In this work, we propose a simple yet highly accurate approximation for vanilla attention.We process the queries in chunks, and for each query, compute the top-k scores with respect to the keys.Our approach offers several advantages: (a) its memory usage is linear in the input size, similar to linear attention variants, such as Performer and RFA (b) it is a drop-in replacement for vanilla attention that does not require any corrective pre-training, and (c) it can also lead to significant memory savings in the feed-forward layers after casting them into the familiar query-key-value framework.We evaluate the quality of top-k approximation for multi-head attention layers on the Long Range Arena Benchmark, and for feedforward layers of T5 and UnifiedQA on multiple QA datasets.We show our approach leads to accuracy that is nearly-identical to vanilla attention in multiple setups including training from scratch, fine-tuning, and zero-shot inference.
A Two-Phase ASP Encoding for Solving Rehabilitation Scheduling

The rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several requirements and optimizations, e.g., patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation’s needs.In this paper, we present a solution to rehabilitation scheduling based on Answer Set Programming (ASP), which proved to be an effective tool for solving practical scheduling problems. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution.
Selectively Linkable Group Signatures—Stronger Security and Preserved Verifiability

Group signatures allow group members to sign on behalf of the group anonymously. They are therefore well suited to storing data in a way that preserves the users’ privacy, while guaranteeing its authenticity. Garms and Lehmann (PKC’19) introduced a new type of group signatures that balance privacy with utility by allowing to selectively link subsets of the group signatures via an oblivious entity, the converter. The conversion takes a batch of group signatures and blindly transforms signatures originating from the same user into a consistent representation. Their scheme essentially targets a setting where the entity receiving fully unlinkable signatures and the converted ones is the same: only pseudonyms but not full signatures are converted, and the input to the converter is assumed to be well-formed. Thus, the converted outputs are merely linkable pseudonyms but no longer signatures.In this work we extend and strengthen such convertibly linkable group signatures. Conversion can now be triggered by malicious entities too, and the converted outputs can be publicly verified. This preserves the authentication of data during the conversion process. We define the security of this scheme and give a provably secure instantiation. Our scheme makes use of controlled-malleable NIZKs, which allow proofs to be mauled in a controlled manner. This allows signatures to be blinded, while still ensuring they can be verified during conversions.
Applying 3DES to Chaotic Synchronization Cryptosystems

The triple data encryption standard (3DES) has played a key role in the field of data encryption over the past few decades. However, the ciphertext&#x2019;s security is decreased by the meet-in-the-middle attack. Accordingly, we propose a systematic design methodology combining chaotic synchronization with 3DES to conduct double encryption. The encryption method not only increases the strength of the communications system but also effectively protects the encrypted message (ciphertext). The conventional genetic algorithm (GA) has flaws in premature convergence and local search. While the methods of improving the GA performance have been explored, we adopted an improved genetic algorithm (IGA) in this work due to its better performance in the light of globalization and convergence rate. The IGA-based observer not only realizes the exponential synchronization but also minimizes the disturbance attenuation level to get optimal <inline-formula> <tex-math notation="LaTeX">$H^{\infty }$ </tex-math></inline-formula> performance simultaneously. In the end, an example with numerical simulations is provided to demonstrate the effectiveness of this study.
A Convolutional Neural Networks-Based Approach for Texture Directionality Detection

The perceived texture directionality is an important, not fully explored image characteristic. In many applications texture directionality detection is of fundamental importance. Several approaches have been proposed, such as the fast Fourier-based method. We recently proposed a method based on the interpolated grey-level co-occurrence matrix (iGLCM), robust to image blur and noise but slower than the Fourier-based method. Here we test the applicability of convolutional neural networks (CNNs) to texture directionality detection. To obtain the large amount of training data required, we built a training dataset consisting of synthetic textures with known directionality and varying perturbation levels. Subsequently, we defined and tested shallow and deep CNN architectures. We present the test results focusing on the CNN architectures and their robustness with respect to image perturbations. We identify the best performing CNN architecture, and compare it with the iGLCM, the Fourier and the local gradient orientation methods. We find that the accuracy of CNN is lower, yet comparable to the iGLCM, and it outperforms the other two methods. As expected, the CNN method shows the highest computing speed. Finally, we demonstrate the best performing CNN on real-life images. Visual analysis suggests that the learned patterns generalize to real-life image data. Hence, CNNs represent a promising approach for texture directionality detection, warranting further investigation.
The Political Economy of Industrialization in Sub-Saharan Africa: Evaluating the Role of Institutions

Sub-Saharan Africa has recently witnessed rising growth rates, but the continent is still largely not industrialised. Mainstream empirical diagnosis has identified the paucity of physical and human capital as the main culprit. However, with the increasing inflow of capital into the continent, such arguments have become hackneyed. A possible culprit identified in the evolving development literature is the quality of institutions. How much has the quality of institutions, structured largely by the prevailing political economy of individual states, influenced Africa’s industrial performance? This study deploys descriptive and analytical methodologies to proffer answers to these questions. The estimates obtained from the Pool Mean Group Panel Autoregressive Distributed Lag (PMG-ARDL) as well as the Augmented Mean Group (AMG) panel estimators point strongly to the fact that institutions are bane of industrialization in Sub-Saharan Africa (SSA). Specifically, we find evidence that in the long run, regulatory quality, rule of law and control of corruption all impact the manufacturing subsector negatively and significantly. The panacea is not only within the matrix of optimal resource allocation, but must integrate the entire political and sociological process, involving governments at all levels, non-governmental organisations (NGOs) and faith-based groups.
University Development Strategy: Sociological Aspect

Students are active participants in the educational process. The events of recent years have affected seriously the activities of the University. The authors conducted a sociological study in 2 areas: the pandemic situation and its impact on the educational process; strategic direction of the university development. The article presents the results of a sociological study of students of Baikal State University (BSU) with a detailed description of issues related to satisfaction with the educational process, including the use of an online study mode. A set of issues related to the proposals of students on the strategy for the development of the university is considered. Students are satisfied with the education in general and in the relevant areas, as well as the high quality of training specialists at BSU. Objective and subjective difficulties encountered by students when switching to online learning are analyzed. The advantages and disadvantages of distance learning according to respondents are described. The examples of the responses are given, the grouping of which allows to adjust the direction of the university development. Social aspects of communication of students with teachers and employees are distinguished. It was concluded that students of BSU have preferences for obtaining applied education as well as oriented to the development of modern professional skills. The results of sociological research influence the formation of new approaches to learning and the development direction of the university, in general.
VD-PCR: Improving visual dialog with pronoun coreference resolution

The visual dialog task requires an AI agent to interact with humans in multi-round dialogs based on a visual environment. As a common linguistic phenomenon, pronouns are often used in dialogs to improve the communication efficiency. As a result, resolving pronouns (i.e., grounding pronouns to the noun phrases they refer to) is an essential step towards understanding dialogs. In this paper, we propose VD-PCR, a novel framework to improve Visual Dialog understanding with Pronoun Coreference Resolution in both implicit and explicit ways. First, to implicitly help models understand pronouns, we design novel methods to perform the joint training of the pronoun coreference resolution and visual dialog tasks. Second, after observing that the coreference relationship of pronouns and their referents indicates the relevance between dialog rounds, we propose to explicitly prune the irrelevant history rounds in visual dialog models' input. With pruned input, the models can focus on relevant dialog history and ignore the distraction in the irrelevant one. With the proposed implicit and explicit methods, VD-PCR achieves state-of-the-art experimental results on the VisDial dataset.
Embedding information onto a dynamical system

The celebrated Takens' embedding theorem concerns embedding an attractor of a dynamical system in a Euclidean space of appropriate dimension through a generic delay-observation map. The embedding also establishes a topological conjugacy. In this paper, we show how an arbitrary sequence can be mapped into another space as an attractive solution of a nonautonomous dynamical system. Such mapping also entails a topological conjugacy and an embedding between the sequence and the attractive solution spaces. This result is not a generalization of Takens embedding theorem but helps us understand what exactly is required by discrete-time state space models widely used in applications to embed an external stimulus onto its solution space. Our results settle another basic problem concerning the perturbation of an autonomous dynamical system. We describe what exactly happens to the dynamics when exogenous noise perturbs continuously a local irreducible attracting set (such as a stable fixed point) of a discrete-time autonomous dynamical system.
Blockchain technology empowers telecom network operation

Telecom operators are deploying the fifth generation (5G) networks around the world which promises high information transmission rate, wide network coverage, low communication delay, and easy access to a large number of devices. However, during the construction and operation of 5G, telecom operators face many challenges, such as insufficient frequency resources, low efficiency of network management, information opacity, risks of data interoperability, and network privacy vulnerabilities. As 5G is generally deployed in heterogeneous networks with massive ubiquitous devices, it is quite necessary to provide secure and decentralized solutions. Blockchain is a distributed system maintained by multiple parties with inherit features including decentralization, traceability and tamperproof. These features collectively contribute to a new application ecosystem where transactions are secure and trustworthy. Therefore, blockchain is expected to integrate with 5G networks to build safer and more reliable mobile network infrastructures. This paper firstly discusses the merits of blockchain technology and the benefits of integrating it with 5G networks. Then a variety of applications of blockchain technology in telecom network operation are reviewed, including spectrum sharing, international roaming settlement, network operation management, number selection management and supply chain management. In the end, we show our recently advance in integrating blockchain in 5G by introducing a multi framework based blockchain service network architecture deployed in real environment.
Informatisation of educational reform based on fractional differential equations

Abstract Information technology application ability is a necessary professional ability for teachers in the information society. The means to take effective measures to promote the acquisition and transfer of teacher information technology application ability involves undertaking measures to facilitate teachers to transform teaching methods, deepen basic education curriculum reform and promote teachers’ lifelong learning. It plays a vital role in effectively promoting the independent development of majors and further cracking the bottleneck problem of educational information development. This study uses the fractional differential equation method to select 220 elementary and middle school teachers in the Northeast Teacher Education Innovation Experimental Zone as the research objects. The research objects are measured by a scale and fractional differential equations are used to build a system that includes school factors, human factors, policy and institutional factors, training factors, self-efficacy factors and motivation factors of teacher information technology application ability transfer fractional differential equation influencing factor model; we hope to solve the problem of ‘learning and use disconnect’ in the process of teacher information technology application ability training, and to promote the effective transfer of teacher information technology application ability that can be used as an important reference.
Developing and Evaluating a University Recommender System

A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.
Meaningless Divisions

In this article, we revisit a number of disputes regarding significance logics—that is, inferential frameworks capable of handling meaningless, although grammatical, sentences—that took place in a series of articles most of which appeared in the Australasian Journal of Philosophy between 1966 and 1978. These debates concern (i) the way in which logical consequence ought to be approached in the context of a significance logic, and (ii) the way in which the logical vocabulary has to be modified (either by restricting some notions, or by adding some vocabulary) to keep as much of classical logic as possible. Our aim is to show that the divisions arising from these disputes can be dissolved in the context of a novel and intuitive proposal that we put forward.
Routing Problem for Unmanned Aerial Vehicle Patrolling Missions - A Progressive Hedging Algorithm

The paper presents a two-stage stochastic program to model a routing problem involving an Unmanned Aerial Vehicle (UAV) in the context of patrolling missions. In particular, given a set of targets and a set of supplemental targets corresponding to each target, the first stage decisions involve finding the sequence in which the vehicle has to visit the set of targets. Upon reaching each target, the UAV collects information and if the operator of the UAV deems that the information collected is not of sufficient fidelity, then the UAV has to visit all the supplemental targets corresponding to that target to collect additional information before proceeding to visit the next target. The problem is solved using a progressive hedging algorithm and extensive computational results corroborating the effectiveness of the proposed model and the solution methodology is presented.
Toward Adversary-aware Non-iterative Model Pruning through <u>D</u> ynamic <u>N</u> etwork <u>R</u> ewiring of DNNs

We present a dynamic network rewiring (DNR) method to generate pruned deep neural network (DNN) models that both are robust against adversarially generated images and maintain high accuracy on clean images. In particular, the disclosed DNR training method is based on a unified constrained optimization formulation using a novel hybrid loss function that merges sparse learning with robust adversarial training. This training strategy dynamically adjusts inter-layer connectivity based on per-layer normalized momentum computed from the hybrid loss function. To further improve the robustness of the pruned models, we propose DNR++, an extension of the DNR method where we introduce the idea of sparse parametric Gaussian noise tensor that is added to the weight tensors to yield robust regularization. In contrast to existing robust pruning frameworks that require multiple training iterations, the proposed DNR and DNR++ achieve an overall target pruning ratio with only a single training iteration and can be tuned to support both irregular and structured channel pruning. To demonstrate the efficacy of the proposed method under the no-increased-training-time “free” adversarial training scenario, we finally present FDNR++, a simple yet effective training modification that can yield robust yet compressed models requiring training time comparable to that of an unpruned non-adversarial training. To evaluate the merits of our disclosed training methods, experiments were performed with two widely accepted models, namely VGG16 and ResNet18, on CIFAR-10 and CIFAR-100 as well as with VGG16 on Tiny-ImageNet. Compared to the baseline uncompressed models, our methods provide over 20× compression on all the datasets without any significant drop of either clean or adversarial classification performance. Moreover, extensive experiments show that our methods consistently find compressed models with better clean and adversarial image classification performance than what is achievable through state-of-the-art alternatives. We provide insightful observations to help make various model, parameter density, and prune-type selection choices and have open-sourced our saved models and test codes to ensure reproducibility of our results.
A Bidirectional Context Embedding Transformer for Automatic Speech Recognition

Transformers have become popular in building end-to-end automatic speech recognition (ASR) systems. However, transformer ASR systems are usually trained to give output sequences in the left-to-right order, disregarding the right-to-left context. Currently, the existing transformer-based ASR systems that employ two decoders for bidirectional decoding are complex in terms of computation and optimization. The existing ASR transformer with a single decoder for bidirectional decoding requires extra methods (such as a self-mask) to resolve the problem of information leakage in the attention mechanism This paper explores different options for the development of a speech transformer that utilizes a single decoder equipped with bidirectional context embedding (BCE) for bidirectional decoding. The decoding direction, which is set up at the input level, enables the model to attend to different directional contexts without extra decoders and also alleviates any information leakage. The effectiveness of this method was verified with a bidirectional beam search method that generates bidirectional output sequences and determines the best hypothesis according to the output score. We achieved a word error rate (WER) of 7.65%/18.97% on the clean/other LibriSpeech test set, outperforming the left-to-right decoding style in our work by 3.17%/3.47%. The results are also close to, or better than, other state-of-the-art end-to-end models.