Reviewerly/exHarmony huggingface dataset:
A Systemic Approach to Evaluating the Organizational Agility in Large-Scale Companies

This paper presents action research to analyze an approach for assessment of the alleged agile transformation.This approach was implemented at AK Bars Digital Technologies, an IT spin-off of one of the largest banks in Russia using the Scaled Agile Framework.The approach is based on the Goal-Question-Metric approach, non-invasive measurement collection, and systemic analysis.It uses data from several different sources, including interviews, code repositories, user ratings in the play stores, and templates for agile assessment.The effectiveness of the approach is subjectively validated by the adoption of the proposed recommendations by the banks' senior management.Details are provided on the approach, the required effort from the side of both those assessing and of the people being assessed and the results.The final part of the paper is devoted to the discussion of its generalizability and the plan for future experimentation and refinement.
Temporal Speciation Network for Few-Shot Object Detection

Recently, few-shot object detection (FSOD) has become an increasing research focus, which can largely alleviate the heavy dependency on expensive annotations in the traditional object detection task. However, existing FSOD approaches fail to generate sufficient high-quality positive region proposals which are the key to detection performance, due to the lack of informative knowledge from base classes and non-specific alteration for novel classes. To address the problem, this paper presents a simple yet effective few-shot object detection framework referred to as Temporal Speciation Network (TeSNet) with an evolving training, which improves the diversity and rationality of positive proposal generation. Our TeSNet, imitating the natural evolution which relies on inheritation and mutation, correspondingly consists of two key components: a Selective Recombination Module (SRM) for effectively inheriting from base classes and a Mutational Region Proposal Network (MRPN) for flexibly mutating according to the unique traits of novel samples. Specifically, SRM selects and reorganizes relevant base categories, and further instantiates diverse individuals to ensure the diversity of positive proposals. MRPN adapts the parameters trained on base classes aiming for accurately locating positive proposals. Extensive experiments are conducted on several commonly-used datasets, in which our TeSNet achieves state-of-the-art results and outperforms baselines by large margin.
Estimation of Magnitude and Epicentral Distance From Seismic Waves Using Deeper CRNN

Estimating earthquake parameters is an essential process for an earthquake analysis system. In particular, the magnitude and epicentral distance of an earthquake are the most basic parameters in earthquake analysis. To estimate these, the existing approaches require long waveform data from multiple stations. In this letter, we propose a novel estimation method based on multitasking deep learning and a convolutional recurrent neural network (CRNN) using only a single station. We also use the stream maximum of the input waveform to accurately estimate the earthquake magnitude. Based on the evaluation using the Stanford Earthquake dataset (STEAD) and the Kiban Kyoshin Network (KiK-net) dataset, we verify the high performance of the proposed method.
Traceable one-time address solution to the interactive blockchain for digital museum assets

Blockchain systems often use public keys or addresses as pseudonym accounts to protect the identity of users. However, as the blockchain system is transparent, an adversary can analyze all the public keys or addresses and obtain some real information about users, making the pseudonym mechanism ineffective. CryptoNote V2.0 is the first blockchain system to introduce a one-time address technology to enable the true anonymity of a user. But it does not have rigorous security proof. Moreover, due to application requirements, for one thing, users need anonymity. For another, administrators need to trace the identity of anonymous users. Therefore, a traceable one-time address scheme for the interactive system of digital museum items is proposed to fulfill these concerns. It allows the recipient to receive transactions anonymously, and a supervisor can trace the identity of a user efficiently. Two security models are defined and two corresponding instances are constructed. We prove that our instances are secure in IND-PK-CPA/IND-PK-CCA mode, respectively. Analysis and experiments show that the proposed schemes are effective in tracing the long-term identity of a recipient. Finally, the challenges of user privacy protection are enhanced with the advancement of the integration of digital museum asset interactions with blockchain. We build a blockchain system for Digital Museum assets and implement our two scheme instances in it. The system can integrate museum heritage resources around the world, share data based on blockchain, provide a large number of interactions and collections, and achieve large-scale exhibitions.
MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction

Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human's actions and intentions is critical for efficient and effective collaborative Human-Robot Interactions (HRI). Learning from Demonstration (LfD) methods from Human-Human Interactions (HHI) have shown promising results, especially when coupled with representation learning techniques. However, such methods for learning HRI either do not scale well to high dimensional data or cannot accurately adapt to changing via-poses of the interacting partner. We propose Multimodal Interactive Latent Dynamics (MILD), a method that couples deep representation learning and probabilistic machine learning to address the problem of two-party physical HRIs. We learn the interaction dynamics from demonstrations, using Hidden Semi-Markov Models (HSMMs) to model the joint distribution of the interacting agents in the latent space of a Variational Autoencoder (VAE). Our experimental evaluations for learning HRI from HHI demonstrations show that MILD effectively captures the multimodality in the latent representations of HRI tasks, allowing us to decode the varying dynamics occurring in such tasks. Compared to related work, MILD generates more accurate trajectories for the controlled agent (robot) when conditioned on the observed agent's (human) trajectory. Notably, MILD can learn directly from camera-based pose estimations to generate trajectories, which we then map to a humanoid robot without the need for any additional training.
Static Type Recommendation for Python

Recently, Python has adopted optional type annotation to support type checking and program documentation. However, to enjoy the benefits, developers have to manually write type annotations, which is recognized to be a time-consuming task. To alleviate human efforts on manual type annotation, machine-learning-based approaches have been proposed to recommend types based on code features. However, they suffer from the correctness problem, i.e., the recommended types cannot pass type checking. To address the correctness problem of the machine-learning-based approaches, in this paper, we present a static type recommendation approach, named Stray. Stray can recommend types correctly. We evaluate Stray by comparing it against four state-of-art type recommendation approaches, and find that Stray outperforms these baselines by over 30% absolute improvement in both precision and recall.
A Hybrid Approach for Inference between Behavioral Exception API Documentation and Implementations, and Its Applications

Automatically producing behavioral exception (BE) API documentation helps developers correctly use the libraries. The state-of-the-art approaches are either rule-based, which is too restrictive in its applicability, or deep learning (DL)-based, which requires large training dataset. To address that, we propose StatGen, a novel hybrid approach between statistical machine translation (SMT) and tree-structured translation to generate the BE documentation for any code and vice versa. We consider the documentation and source code of an API method as the two abstraction levels of the same intent. StatGen is specifically designed for this two-way inference, and takes advantage of their structures for higher accuracy.
Indoor Activity Recognition Using a Hybrid Generative-Discriminative Approach with Hidden Markov Models and Support Vector Machines

Human activity recognition is used for many practical applications such as context modeling in smart cities, surveillance and assisted living. In this paper, we apply a hybrid generative-discriminative approach using Fisher kernels with inverted Dirichlet-based and inverted Beta-Liouville-based hidden Markov models (HMMs) to improve the recognition performance. We propose a method that combines HMMs as a generative approach, with the discriminative approach of Support Vector Machine (SVM). This strategy allows us to deal with Spatio-temporal motion data, and at the same time use the special focus on the classification task that SVM could provide us. Experiments on the challenging activity recognition benchmark UCF101, demonstrate an effective improvement of the recognition performance compared to the standard generative and Gaussian-based HMM approaches.
Predicting future mooring line tension of floating structure by machine learning

The prediction of mooring line tension is helpful for the station-keeping of floating structures. In this study, we propose a machine learning approach based on the reservoir computing (RC) model to predict the mooring line tension of a barge and the KVLCC2 ship, respectively. The datasets were collected from the model tests carried out in the deep-water wave basin. The RC model was trained using the low-frequency and wave-frequency parts of the mooring line tension combined with its main relevant vessel motion. The prediction was obtained by superposing these two components of tension. Finally, the trained model could extend the predictions of mooring line tension 80 s into the future with high accuracy, which was validated by the corresponding experimental results. This strategy has been verified to improve the prediction accuracy significantly. Besides, compared with the long short-term memory (LSTM) model, the RC model was found to be more accurate and efficient to predict the mooring line tension. This work provides some guidelines and shows the great potential of the proposed method to forecast mooring tensions for offshore vessels and platforms.
ZigZag transform with Durstenfeld shuffle for fast and secure image encryption

In this study, we propose an image encryption algorithm based on ZigZag transform with Durstenfeld Shuffling Algorithm (DSA), where we adopt confusion–diffusion architecture. Our encryption method is simpler to implement than high-dimensional chaos-based approaches, and its computational complexity is very low, which make it very fast and suitable for real-world applications. Additionally, its dependence on plaintext image makes it adaptive and hence very robust against brute force and differential attacks. Experimental analyses using histogram, correlation, NIST, NPCR, UACI and PSNR values suggest that our proposed algorithm is strong and secure against statistical and differential attacks.
GEAR: A General Inference Engine for Automated MultiStrategy Reasoning

The pervasive use of AI today caused an urgent need for human-compliant AI approaches and solutions that can explain their behavior and decisions in human-understandable terms, especially in critical domains, so as to enforce trustworthiness and support accountability. The symbolic/logic approach to AI supports this need because it aims at reproducing human reasoning mechanisms. While much research has been carried out on single inference strategies, an overall approach to combine them is still missing. This paper claims the need for a new overall approach that merges all the single strategies, named MultiStrategy Reasoning. Based on an analysis of research on automated inference in AI, it selects a suitable setting for this approach, reviews the most promising approaches proposed for single inference strategies, and proposes a possible combination of deduction, abduction, abstraction, induction, argumentation, uncertainty and analogy. It also introduces the GEAR (General Engine for Automated Reasoning) inference engine, that has been developed to implement this vision.
An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.
Controlling the Difficulty of Combinatorial Optimization Problems for Fair Proof-of-Useful-Work-Based Blockchain Consensus Protocol

The wide range of Blockchain (BC) applications and BC’s ubiquity come from the fact that BC, as a collection of records linked to each other, is strongly resistant to alteration, protected using cryptography, and maintained autonomously. All these benefits come with a cost, which in BC is expressed by a very high use of energy needed to execute consensus protocols. Traditionally, consensus protocols based on Proof-of-Work (PoW) ensure fairness, but are not very useful. The paradigm proposed in the recent literature, known as Proof-of-Useful-Work (PoUW), assumes the completion of additional useful work for the same amount of resources (energy) used. However, the majority of the proposed PoUW approaches do not adequately consider fairness in balancing and controlling the difficulty of the work miners need to perform. A minority of the studies that do address fairness in miners’ work utilize PoW as a tool to ensure it. Therefore, a general framework to provide a structure for understanding the difficulty of useful work and how it can be used to fine-tune the complexity of miners’ effort in PoUW-based consensus protocols is proposed in this paper. The main characteristic of the proposed framework is that controlling the difficulty and fairness of miners’ work in PoUW-based consensus protocols is achieved exclusively through the useful work. The modules of the framework are discussed, and many research challenges and opportunities are articulated. The benefits of the proposed approach are illustrated taking as an example two optimization algorithms for a variant of the scheduling problem. In addition, the steps that should be taken to make this general framework applicable to any PoUW-based consensus protocols are identified.
Does trade openness improve the quality of domestic institutions? Evidence from Africa

Research background: The research article deals with impacts of international trade openness on institutions in less developed economies, namely in Africa. Purpose of the article: It investigates the impacts of international trade openness on institutional quality in Africa measured by twelve various variables. Methods: It applies generalized methods of moments to a dynamic panel data of 34 African countries in the period of 1988?2012. Institutional quality data come from International Country Risk Guide, the rest from World Development Indicators and UNESCO databases. Findings &amp; value added: Our results indicate that in the case of Africa, trade openness seems to be a positive and significant determinant of institutional quality, however, it differs across various institutional variables. Trade openness brings a positive impact on government stability, bureaucracy and law and order, we also identify its conflict-mitigating effects. This happens, unfortunately, with an exemption for natural resources exports. Trade openness also positively influences security and socioeconomic conditions, although we find the link much weaker. Regarding our control variables, we find two interesting results. First, GDP per capita has a strong and positive association with institutional quality when measured by one bundled indicator, however, individual variables show different intensities. Second, we find a strong and positive association of net foreign direct investment inflows and government stability. The added value of this research lies not only in focus on the less researched relationship, i.e., how trade impacts the institutional quality, but dominantly in focus on many institutional variables at once and comparison of their effects. Other empirical studies usually focus on selected variables only, or on selected trade items (natural resources).
HIFGAN: A High-Frequency Information-Based Generative Adversarial Network for Image Super-Resolution

Since the neural network was introduced into the super-resolution (SR) field, many SR deep models have been proposed and have achieved excellent results. However, there are two main drawbacks: one is that the methods based on the best peak-signal-to-noise ratio (PSNR) do not have enough comfortable visual quality; the other is that although the SR models based on generative adversarial network (GAN) have satisfactory visual quality, the structure of the reconstructed image has apparent defects. Therefore, according to the characteristics that human eyes are sensitive to high-frequency components in images, this article proposes an improved image SRGAN model based on high-frequency information fusion (HIFGAN). It builds a feature extraction network for high-frequency information fusion by designing a lightweight spatial attention module and improving the network architecture of enhanced super-resolution GAN (ESRGAN). It makes the generator in the GAN network have better feature recovery ability, reduces the dependence of the later training on the decider and loss function, and makes the generated image structure more consistent with the real situation. In addition, we build a high-frequency loss function to optimize the training of the generator network. Detailed experimental results show that HIFGAN performs excellently in both objective criterion evaluation and subjective visual effect. Compared with the state-of-the-art GAN-based SR networks, the reconstructed image by our model is more precise and complete in texture details.
HOP+: History-Enhanced and Order-Aware Pre-Training for Vision-and-Language Navigation

Recent works attempt to employ pre-training in Vision-and-Language Navigation (VLN). However, these methods neglect the importance of historical contexts or ignore predicting future actions during pre-training, limiting the learning of visual-textual correspondence and the capability of decision-making. To address these problems, we present a history-enhanced and order-aware pre-training with the complementing fine-tuning paradigm (HOP+) for VLN. Specifically, besides the common Masked Language Modeling (MLM) and Trajectory-Instruction Matching (TIM) tasks, we design three novel VLN-specific proxy tasks: Action Prediction with History (APH) task, Trajectory Order Modeling (TOM) task and Group Order Modeling (GOM) task. APH task takes into account the visual perception trajectory to enhance the learning of historical knowledge as well as action prediction. The two temporal visual-textual alignment tasks, TOM and GOM further improve the agent's ability to order reasoning. Moreover, we design a memory network to address the representation inconsistency of history context between the pre-training and the fine-tuning stages. The memory network effectively selects and summarizes historical information for action prediction during fine-tuning, without costing huge extra computation consumption for downstream VLN tasks. HOP+ achieves new state-of-the-art performance on four downstream VLN tasks (R2R, REVERIE, RxR, and NDH), which demonstrates the effectiveness of our proposed method.
Deep continual hashing with gradient-aware memory for cross-modal retrieval

Cross-modal hashing (CMH) has become widely used for large-scale multimedia retrieval. However, most current CMH methods focus on the closed retrieval scenario, not the real-world environments, i.e., complex and changing semantics. When data containing new class objects emerge, the current CMH has to retrain the model on all history training data, not the new data, to accommodate new semantics, but the never-stop upload of data on the Internet makes this impractical. In this paper, we devise a deep hashing method called Continual Cross-Modal Hashing with Gradient Aware Memory (CCMH-GAM) for learning binary codes of multi-label cross-modal data with increasing categories. CCMH-GAM is a two-step hashing architecture, one hashing network learns to hash the increasing semantics of data, i.e., label, into the semantic codes, and other modality-specific hashing networks learn to map data into the corresponding semantic codes. Specifically, to keep the encoding ability for old semantics, a regularization based on accumulating low-storage label-code pairs is designed for the former network. For the modality-specific networks, we propose a memory construction method via approximating the full episodic gradients of all data by some exemplars and derive its fast implementation with the upper bound of approximation error. Based on this memory, we propose a gradient projection method to theoretically improve the probability of old data’s code being unchanged after updating the model. Extensive experiments on three datasets demonstrate that CCMH-GAM can continually learn hash functions and yield state-of-the-art retrieval performance.