Reviewerly/exHarmony huggingface dataset:
Memory-Guided Coordinate Encoding Network for Anomaly Detection

Video anomaly detection remains challenging due to the complexity of visual scenes. Video surveillance is often fixed lens, and existing approaches, either using autoencoder architectures or generative adversarial network models, lack encoding of dynamic and static information in feature extraction to emphasize information-rich features. Based on this, we improve the encoder architecture and propose a memory-guided coordinate encoding network based on extensive experiments to introduce a coordinate attention module to improve the U-Net network and enhance dynamic entity representation. Considering the diversity of abnormal events, we use the memory module to record the prototype patterns of normal features and propose feature discretization loss and feature aggregation loss to make a compact representation between features and separation between feature terms to improve the accessibility and prediction accuracy of the memory module. Our experimental results on three open standard datasets show that our model outperforms the state-of-the-art methods.
EgoFormer: Transformer-Based Motion Context Learning for Ego-Pose Estimation

Ego-pose estimation, i.e. predicting 3D pose of the camera wearer, has an essential value in AR and VR applications. First-person video has an ambiguity in that similar video frames may correspond to totally different body poses because of the invisible body part. However, exploiting the context of a video and establishing a long-term temporal relationship can alleviate this ambiguity. To this end, this paper proposes EgoFormer, a Transformer-based model, to learn the motion context from egocentric videos. Moreover, dynamic features commonly used to characterize first-person video do not provide sufficient temporal information to remove the ambiguity inherent in such videos. Therefore, we present a method that can effectively extract temporal features in first-person videos. Results on real-scene and synthetic datasets show that our method could estimate a sequence of human poses with high accuracy and coherence.
DDPM-SKDNet: A Deep Learning Method for ICG Image Classification

Over the past several years, deep learning technologies have made tremendous progress in medical image tasks including classification, segmentation, and object detection. However, there are two main limitations of indocyanine green (ICG) images which are often used in breast cancer related lymphedema (BCRL): insufficient sample numbers and low image quality. Consequently, the conventional deep learning based classification methods such as ResNet have faced challenges in achieving satisfactory results. To tackle the concern, this paper puts forward a deep learning method named Denoising Diffusion Probabilistic Model Self-supervised Knowledge Distillation Net (DDPM-SKDNet) for the ICG images classification task, by incorporating a contrastive learning based approach as the network architecture and using DDPM as the image generator in the contrastive module to expand the dataset size. Furthermore, a knowledge distillation approach is utilized to increase the effectiveness of the network. The proposed method was validated on ICG datasets and achieved a significant improvement in classification accuracy, increasing it from 66.7% in the baseline method to 82.1% in the proposed method.
FedGSync: Jointly Optimized Weak Synchronization and Gradient Transmission for Fast Distributed Machine Learning in Heterogeneous WAN

Due to privacy and cost reasons, distributed machine learning in Wide-Area Networks(DML-WAN) is becoming an emerging and popular collaborative learning paradigm. However, heterogeneity in computing power and data distribution among workers in different locations has a dramatic impact on training performance, including convergence speed and learning accuracy. Most of the existing works on distributed training mechanisms either focus on computing heterogeneity or data heterogeneity, and none of them can handle both well. In this paper, we propose FedGSync, a novel distributed training mechanism to improve the training performance for DML-WAN, where computing heterogeneity and data heterogeneity usually coexist. To speed up training and improve model accuracy, FedGSync clusters workers into groups according to the similarity of their data distribution and introduce group-based weak synchronization to minimize the synchronization delays waiting for slow workers and the accuracy loss by balancing the contributions of all data distributions. To preserve data privacy and improve efficiency, FedGSync only groups workers based on principal components of gradients and design an approximate grouping mechanism based on Kmeans. To further reduce synchronization time, FedGSync prioritizes packets and uses differential transmission for gradient packets between groups. Evaluation results demonstrate that FedGSync improves convergence speed and learning accuracy under the coexistence of computing heterogeneity and data heterogeneity compared with state-of-the-art distributed training mechanisms.
How Do Low-Level Image Features Affect CNN-Based Face Detector Accuracy?

Face detectors are a subset of object detectors that output, at a minimum, a set of locations in an image if and where human faces are present. Face detection is challenging, in part, due to low variance in the structural content of frontal-view faces (i.e., most faces have two eyes, a nose and a mouth) and high variance in visual appearance. This aspect of the domain skews detectors to higher false positive rates as a consequence of many patches of imagery containing features spatially consistent with frontal-view faces. In this study, we evaluate the performance of three state-of-the-art face detectors (BlazeFace, MTCNN, and SCRFD) on frontal-view face imagery in a novel human-labeled dataset of 64,104 images with reliable ground truth. We show evidence that modern CNN-based models rely heavily on low-level image features, in spite of their powerful capability to learn complex, discriminatory visual features and concepts. We do this by altering the spectral and color content of frontal-view face images. To gain a better understanding of detector failures, we apply the Deep Dream technique to enhance image features that lead models to false positives.
NCDFSA: Neural Cognitive Diagnostic Focusing on Students' Attention to Knowledge Concepts

The primary aim of cognitive diagnosis is to predict students' performance and knowledge structures by analyzing their learning behavior and answering results, thereby enabling educators can provide personalized instruction. Scholars have proposed many cognitive diagnostic models. However, most of the models do not fully extract and utilize the relevant data and parameters of cognitive diagnosis. Moreover, some models only rely on artificially designed simple functions to analyze the cognitive process of students, which cannot fully capture the complex relationship between students and the exercises. To address these limitations, this paper proposes a neural cognitive diagnostic model named NCDFSA, which focuses on students' attention to knowledge concepts. The model utilizes neural networks to diagnose students' knowledge and considers students' implicit relationships with knowledge concepts. We introduce the concept of attention matrix(AM) and define the importance of knowledge concepts by the frequency of usage of knowledge concepts to improve the prediction effect. This paper compares NCDFSA with existing classical models on four real datasets and finds that the model has higher accuracy and rationality in predicting student performance.
Development of a High-Definition Visualization System for Immersion into a Moving Miniature World

Free viewpoint video technology, which provides a high sense of presence and immersion by allowing users to view live video from any viewpoint, is increasingly demanded mainly in the entertainment field, as an application that provides new experiences for users. Although there have been many studies on free viewpoint video generation for large-scale spaces, they have focused on generating video for the entire wide-area space. For this reason, challenges still remain in terms of reproduction of object details and real-time video performance of image generation. In this study, for the purpose of developing a system which enables immersion into a moving miniature world, a method to generate high-definition free viewpoint video for small-scale spaces in real time was realized and evaluated. It can generate high-definition free viewpoint images by combining free viewpoint depth images applying noise reduction processing and the corresponding color images considering occlusion and the normal direction of the object's surface. The free viewpoint images generated by the proposed system was evaluated. The results confirmed that consideration of occlusion and the normal direction of the object's surface improved the quality of the generated images. In addition, the realized methods were able to generate clear free viewpoint images even when the free viewpoint approached the object.
A Text Classification Model Based on Virtual Adversarial Training and Bilateral Contrastive Learning

In unsupervised text classification, contrastive learning can effectively learn discriminative representations of text features, thereby improving the performance of the model. However, in the context of supervised text classification, contrastive learning methods cannot fully utilize the label information of samples in the text classification model, nor can they learn the relationship between labels and features among samples well. Therefore, how to improve the generalization and robustness of the supervised text classification model has been a problem that researchers have been exploring. In this paper, we propose a framework called VABCL (Virtual Adversarial and Bidirectional Contrastive Learning) based on virtual adversarial training and bidirectional contrastive learning. This framework combines virtual adversarial training with contrastive learning to train the model to establish better relationships between adversarial and real samples, thus improving the robustness and generalization performance of the classification model. Furthermore, we generate augmented samples using the label information of the samples, and use bidirectional contrastive learning to learn the relationship between sample labels and features, further improving the accuracy of the classification model. We experimentally verified the VABCL framework on three benchmark text classification datasets, and the results confirmed the improvement in the feature extraction ability and classification accuracy of VABCL, indicating that VABCL has good performance in the field of supervised text classification.
Multifunctionality in a Connectome-Based Reservoir Computer

Multifunctionality describes the capacity for a neural network to perform multiple mutually exclusive tasks without altering its network connections; and is an emerging area of interest in the reservoir computing machine learning paradigm. Multifunctionality has been observed in the brains of humans and other animals: particularly, in the lateral horn of the fruit fly. In this work, we transplant the connectome of the fruit fly lateral horn to a reservoir computer (RC), and investigate the extent to which this ‘fruit fly RC’ (FFRC) exhibits multifunctionality using the ‘seeing double’ problem as a benchmark test. We furthermore explore the dynamics of how this FFRC achieves multifunctionality while varying the network's spectral radius. Compared to the widely-used Erdös-Renyi Reservoir Computer (ERRC), we report that the FFRC exhibits a greater capacity for multifunctionality; is multifunctional across a broader hyperparameter range; and solves the seeing double problem far beyond the previously observed spectral radius limit, wherein the ERRC's dynamics become chaotic.
Prompt-assisted Relation Fusion in Knowledge Graph Acquisition

This paper investigated how prompt-based learning techniques can assist with relation fusion in Knowledge Graph (KG) acquisition. We created a unsupervised framework to generate a KG from a real-world dataset. The framework incorporates prompting with knowledge entity metadata and generating predicate embeddings with the pretrained Masked Language Model (MLM) RoBERTa. Predicate embeddings were clustered to form conceptual groups and feature tokens were used to derive relation labels. In addition, we conducted a comparative study on the effects of different prompting templates. The resulting relation labels were evaluated by human annotators, which indicated that prompt-based learning, if applied appropriately, can help with deducing conceptualized relations. Our framework proposed a way to improve the quality of KGs acquired using traditional Relation Extraction (RE). It can also assist human experts effectively in semi-automated knowledge acquisition.
Finance and Development in Tanzania Post Cold War

This study set out to investigate how financial reforms embarked upon after the change in economic policy affected economic development in Tanzania by analysing the finance and development nexus. The study utilized autoregressive distributive lag technique to evaluate data on how gross domestic product was used to measure economic development and seven variables proxied finance. The study established that there is a long-run relationship between finance and development in Tanzania for the period studied and that this relationship is able to return to equilibrium at an above average speed in the event of volatility. The study concluded that all the dependent variables except financial openness are leading indicators of economic development in Tanzania. Consequently, it is recommended that the switch in economic policy is in the right direction and the trajectory is sustained because it is creating the desired turnaround in the economy.
CL-SML: Secure Certificateless Signature with Message Linkages for IoT-Based Fog Computing Environments

A certificateless signature with message linkages (CL-SML) is a technique of asymmetric key systems, which combines the traditional digital signature schemes with message linkages and the certificateless public key systems. A certificateless cryptographic scheme eliminates the need to address the challenges associated with key escrow and certificate management. In this type of mechanism, the private key of a signer is composed of two components. The first part is a partial private key provided by the KGC, while the second part is a secret integer selected by the signer. It is not possible for a malicious KGC to impersonate any legitimate user without knowledge of the user's secret number. When designing a CL-S scheme for the environments of fog computing supported by IoT devices, we must consider the practical scenario in which the data message is usually composed of several message blocks gathered from different targets. Therefore, current existing protocols might not be well-suited in the IoT-based applications. In this research, we present an efficient CL-S scheme with message linkages (CL-SML) for IoT-based applications. In particular, a verifier is capable of examining the correctness of decrypted message blocks as well as its CL-S simultaneously. Additionally, the security requirement of existential unforgeability for the proposed work under random oracle models will be formally proven. We also demonstrate that the constructed mechanism has better efficiency compared with related mechanisms.
Integrating Dual-Stream Cross Fusion and Ambiguous Exclude Contrastive Learning for Enhanced Human Action Recognition

In the field of semi-supervised human action recognition, the effective utilization of both labeled and unlabeled data remains a central and challenging pursuit. To address this issue, we present an innovative framework (DSCF-AEC) that combines a Dual-stream Cross Fusion network (DSCF) with an Ambiguous Exclude Contrastive Learning (AEC) module. Specifically, our Dual-stream Cross Fusion network utilizes the ST-GCN as encoder, independently encoding two augmented versions of the joint and bone streams, which are subsequently cross-fused to achieve enhanced representation. To further bolster the performance, we designed the AEC module. This module constructs a memory bank capable of distinguishing reliable positive and negative samples, while ambiguous samples are excluded. This strategic approach ensures that, through contrastive learning, the model is trained solely on meaningful and trustworthy samples. Extensive experiments on NTU RGB+D and NW-UCLA datasets validate the effectiveness of our approach. The results indicate that, our proposed method significantly outperforms other existing methods.
Finance and Nigeria's Economic Development Post First Cold War

This chapter x-rayed Nigeria's political and economic history from independence to date. Nigeria has been managed under four different paradigms: free market 1960-1972; socialist inclined; mixed economy, posture, structural adjustment, 1986-1994; and quasi-capitalist approach. Based on 39 multivariate time series data, econometric analyses reveal that foreign direct investment, domestic savings (GFCF) capital expenditure, private consumption, and economic openness made a positive and significant impact on the Nigerian economy. On the other hand, import, exchange rate, foreign debt, monetary management indicators, and reinterest rates had negative and significant impacts on the Nigerian economy during the period. The study therefore recommends that Nigeria adopt export focused policy, institute FDI-friendly policies, and improve on her good governance image. In summary, the study concluded that the Nigerian economy could have done better.
Finance and Economic Development Post Cold War

This chapter examined the effect of finance in the development of the Ethiopian economy post-Cold War. The result of the autoregressive distributed lag (ARDL) estimates shows that human development is positive and statistically significant both in the short-run and in the long-run while GDPPC, real interest rate, GDPPC, and exchange rates had significant but negative impacts on the Ethiopian economy. Finally, the result also shows that the gross fixed capital formation (GFCF) is statistically significant but negative in the short-run and positive in the long-run. The study therefore recommended that real interest rate in Ethiopia should be stabilized by the monetary authority in order to enhance private investment and by extension improve the GDPPC. Ethiopian authorities should improve on the capital accumulation to enhance total factor productivity.
Synbit: synthesizing bidirectional programs using unidirectional sketches

Abstract We propose a technique for synthesizing bidirectional programs from the corresponding unidirectional code plus input/output examples. The core ideas are: (1) constructing a sketch using the given unidirectional program as a specification, and (2) filling the sketch in a modular fashion by exploiting the properties of bidirectional programs. These ideas are enabled by our choice of programming language, HOBiT, which is specifically designed to maintain the unidirectional program structure in bidirectional programming, and keep the parts that control bidirectional behavior modular. To evaluate our approach, we implemented it in a tool called Synbit and used it to generate bidirectional programs for intricate microbenchmarks, as well as for a few larger, more realistic problems. We also compared Synbit to a state-of-the-art unidirectional synthesis tool on the task of synthesizing backward computations. This is an extended version of the paper “Synbit: Synthesizing Bidirectional Programs using Unidirectional Sketches”, published at OOPSLA 2021. In addition to the OOPSLA’21 paper, this journal will contain additional formalization and detailed examples.
Rozwój kompetencji interkulturowych na przykładzie podręcznika do nauki języka włoskiego Campionato d’italiano. Nowe tendencje w praktyce glottodydaktycznej?

The aim of this paper is to examine themethodology of teaching intercultural competencies in the innovative textbook for learning the Italian language, Campionato d’italiano. The use of the football theme – one of the cultural phenomena attracting the interest of the broad masses of society – as the main thematic pattern of a textbook for teaching L2 integrated skills fills a gap existing in the publishing market, but also prompts scientific reflection on the purposefulness and possible implications of such an approach in relation to the field of the development of intercultural competencies. Applying the evaluation criteria for the development of intercultural competencies proposed by E. Bandura, a content analysis of the didactic activities extracted from the analyzed material was made, showing significant glottodidactic differences in relation to previously available course books for teaching the Italian language.
On the Principle of Scientific Objectivity, “Incorrect Analysis and Interpretation of Statistical Data” and the New Librarianship: a Reply to the Critical Response of E. A. Pleshkevich

This article is the author’s answer to the critical response of E. A. Pleshkevich on an article devoted to reducing the quantity of libraries in the Moscow region. Reasoned answers are given to the opponent’s three key objections. The point of view on the initial cause of the crisis in librarianship, which is the transfer of information flows to the digital sphere, is confirmed and specified. E. A. Pleshkevich’s understanding of the nature of the library as a public institution designed to guide reading is refuted. In response to the criticism of “incorrect analysis and interpretation of statistical data reflecting library construction in the Moscow region,” additional statistics are provided that irrefutably prove the absolute priority of the Moscow region in the liquidation of libraries throughout the Russian Federation. In response to the accusation of a biased assessment of the situation in the Moscow region, in which model libraries are being opened and various projects with big names are being implemented, it is pointed out that the most powerful indicator of the real state of affairs is the closure of 151 public libraries in the region from 2018 to 2022. Optimization through liquidation gives grounds for the conclusion that library reform in the Moscow region has completely failed.