Reviewerly/exHarmony huggingface dataset:
V-SVR+: Support Vector Regression With Variational Privileged Information

Many regression tasks encounter an asymmetric distribution of information between training and testing phases where the additional information available in training, the so-called privileged information (PI), is often inaccessible in testing. In practice, the privileged information in training data might be expressed in different formats, such as continuous, ordinal, or binary values. However, most the existing learning using privileged information (LUPI) paradigms primarily deal with the continuous form of PI, preventing them from managing variational PI, which motivates this research. Therefore, in this paper, we propose a unified framework to systematically address the aforementioned three forms of privileged information. The proposed V-SVR+ method integrates continuous, ordinal, and binary PI into the learning process of support vector regression (SVR) via three losses. For continuous privileged information, we define a linear correcting (slack) function in the privileged information space to estimate slack variables in the standard SVR method using privileged information. For the ordinal relations of privileged information, we first rank the privileged information and then, regard this ordinal privileged information as auxiliary information used in the learning process of the SVR model. For the binary or Boolean privileged information, we infer a probabilistic dependency between the privileged information and labels from the summarized privileged information knowledge. Then, we transfer the privileged information knowledge to constraints and form a constrained optimization problem. We evaluate the proposed method in three applications: music emotion recognition from songs with the help of implicit information about music elements judged by composers; multiple object recognition from images with the help of implicit information about the object&#x2019;s importance conveyed by the list of manually annotated image tags; and photo aesthetic assessment enhanced by high-level aesthetic attributes hidden in photos. Experiment results demonstrate that the proposed methods are superior to the classic learning paradigm when solving practical problems.
Video Super-Resolution Based on Generative Adversarial Network and Edge Enhancement

With the help of deep neural networks, video super-resolution (VSR) has made a huge breakthrough. However, these deep learning-based methods are rarely used in specific situations. In addition, training sets may not be suitable because many methods only assume that under ideal circumstances, low-resolution (LR) datasets are downgraded from high-resolution (HR) datasets in a fixed manner. In this paper, we proposed a model based on Generative Adversarial Network (GAN) and edge enhancement to perform super-resolution (SR) reconstruction for LR and blur videos, such as closed-circuit television (CCTV). The adversarial loss allows discriminators to be trained to distinguish between SR frames and ground truth (GT) frames, which is helpful to produce realistic and highly detailed results. The edge enhancement function uses the Laplacian edge module to perform edge enhancement on the intermediate result, which helps further improve the final results. In addition, we add the perceptual loss to the loss function to obtain a higher visual experience. At the same time, we also tried training network on different datasets. A large number of experiments show that our method has advantages in the Vid4 dataset and other LR videos.
Explaining monotonic ranking functions

Ranking functions are commonly used to assist in decision-making in a wide variety of applications. As the general public realizes the significant societal impacts of the widespread use of algorithms in decision-making, there has been a push towards explainability and transparency in decision processes and results, as well as demands to justify the fairness of the processes. In this paper, we focus on providing metrics towards explainability and transparency of ranking functions, with a focus towards making the ranking process understandable, a priori , so that decision-makers can make informed choices when designing their ranking selection process. We propose transparent participation metrics to clarify the ranking process, by assessing the contribution of each parameter used in the ranking function in the creation of the final ranked outcome, using information about the ranking functions themselves, as well as observations of the underlying distributions of the parameter values involved in the ranking. To evaluate the outcome of the ranking process, we propose diversity and disparity metrics to measure how similar the selected objects are to each other, and to the underlying data distribution. We evaluate the behavior of our metrics on synthetic data, as well as on data and ranking functions on two real-world scenarios: high school admissions and decathlon scoring.
Multi-target UAV path planning based on improved RRT algorithm

Abstract When the Rapidly-exploring Random Tree (RRT) algorithm in the global path planning algorithm is used for autonomous online path planning of UAVs, there is a problem that the optimal path cannot be obtained. An improved RRT algorithm is proposed, including The dynamic constraints, heuristic search strategy, dynamic step fusion strategy, and the improvement of the selection method of changing new nodes under specific conditions. The simulation results show that the improved RRT algorithm can meet the premise of UAV dynamic constraints. Next, the optimal path planning for multiple goals is realized, especially in reducing the nodes in the random tree.
Paralinguistic and linguistic fluency features for Alzheimer's disease detection

Alzheimer’s disease (AD) is one of the most common forms of dementia in the world. The Mini-Mental State Examination, a tool developed to detect AD, is composed of various tests that evaluate functional performance in several fields, one of which is language. Several symptoms are manifested in voices as a result of language and speech problems caused by AD, including frequent involuntary pauses during conversations and diction and vocabulary difficulties. Speech fluency is considered a key feature for AD detection in this research, for which two algorithms are proposed. The first algorithm is a paralinguistic system that is independent of the language and task and whose low-dimension feature vectors facilitate the training stage. This algorithm is tested on two databases (AcceXible and ADReSS), on two languages (Spanish and English) and on several tests. The second algorithm is based on analysing temporal patterns of silence between words and errors in spoken words. This approach, based on verbal fluency tests, is tested on the AcceXible database. To benchmark these algorithms, two baseline algorithms are used: the i-vector framework, a speaker modelling algorithm that has been effectively used for speech-related tasks such as speaker recognition, language identification, speaker diarization and speech-related health tasks; and a classic counting-terms algorithm, which processes transcriptions of speech. The paralinguistic system yields promising results for different tests and languages, while the silence-based system achieves high accuracy in verbal fluency tests.
An evolutionary/heuristic-based proof searching framework for interactive theorem prover

The proof development process in interactive theorem provers (ITPs) requires the users to manually search for proofs by interacting with proof assistants. The activity of finding the correct proofs can become quite cumbersome and time consuming for users. To make the proof searching process easier in proof assistants, we provide an evolutionary/heuristic-based framework. The basic idea for the framework is to first generate random proof sequences from a population of frequently occurring proof steps that are discovered with sequential pattern mining. Generated proof sequences are then evolved till their fitness match the fitness of the target (or original) proof sequences. Three algorithms based on the proposed framework are developed using the Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Extensive experiments are performed to investigate the performance of the proposed algorithms using the HOL4 proof assistant. Results have shown that the proposed algorithms can efficiently evolve the random sequences to obtain the target sequences. In comparison, PSO performed better than SA and SA performed better than GA. In general, the experimental results suggest that combining evolutionary/heuristic algorithms with proof assistants allow efficient support for proof finding/optimization.
Blockchain based audit trailing of XAI decisions: Storing on IPFS and Ethereum Blockchain

Explainable Artificial Intelligence (XAI) generates explanations which are used by regulators to audit the responsibility in case of any catastrophic failure. These explanations are currently stored in centralized systems. However, due to lack of security and traceability in centralized systems, the respective owner may temper the explanations for his convenience in order to avoid any penalty. Nowadays, Blockchain has emerged as one of the promising technologies that might overcome the security limitations. Hence, in this paper, we propose a novel Blockchain based framework for proof-of-authenticity pertaining to XAI decisions. The framework stores the explanations in InterPlanetary File System (IPFS) due to storage limitations of Ethereum Blockchain. Further, a Smart Contract is designed and deployed in order to supervise the storage and retrieval of explanations from Ethereum Blockchain. Furthermore, to induce cryptographic security in the network, an explanation's hash is calculated and stored in Blockchain too. Lastly, we perform the cost and security analysis of our proposed system.
Path planning for mobile manipulator robots under non-holonomic and task constraints

This paper presents a path planner, which enables a nonholonomic mobile manipulator to move its end-effector on an observed surface with a constrained orientation, given start and destination points. A partial point cloud of the environment is captured using a vision-based sensor, but no prior knowledge of the surface shape is assumed. We consider the multi-objective optimisation problem of finding robot paths which account for the nonholonomic constraints of the base, maximise the robot's manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of rough robotic cutting, e.g. demolition of legacy nuclear plants, where dismantling does not require a precise path. We show how our approach embeds the nonholonomic constraints of the mobile platform into an extended Jacobian, while additionally encoding the constraint that the end-effector must remain in contact with the cut surface throughout the motion. We use this constrained Jacobian to plan a time-series of robot configurations. Additionally, we show how our novel cost function is suitable for combining with a variety of well-known path planners, such as RRT*. We present several empirical experiments in different scenarios, where a simulated non-holonomic mobile manipulator follows a trajectory, which is generated on noisy point clouds derived from real depth-camera images of real objects. Our planner (RRT*-CRMM) enables successful task completion by optimising the path over the travelled distance, the manipulability of the arm, and the movements of the mobile base.
Image Classification Algorithm Based on Improved AlexNet in Cloud Computing Environment

In the cloud computing environment, the traditional classification algorithms often ignore the feature relationship between images, which leads to unstable classification process, poor accuracy of classification results and other problems, which can not achieve the ideal classification effect. An image classification algorithm based on improved AlexNet is proposed and designed. After preprocessing the collected images, such as normalization, mean value and standardization, the convolutional nerve is introduced to train the features of the standard images. On this basis, the image classification algorithm model based on improved AlexNet is established. Through the optimization training of the classification model, the high-level semantic features of the image are extracted, and the process of image classification and calculation is realized. The experiments show that the improved AlexNet image classification algorithm improves the accuracy and stability of image classification, and has good effectiveness and robustness, which provides a good reference for the development of network technology.
An efficient data structure for distributed ledger in blockchain systems

The storage structure on the data block of the current blockchain systems is still a linked list. This traditional structure is one-way and is not efficient for query operation. In view of this, we proposed a novel searching structure based on a height balanced Binary Search Tree (BST). The data structure we proposed not only retains the characteristics of the traditional ledgers but also adds the function of quick query. Through the new data structure, we can quickly find the starting position of the search and start searching for all transaction records within a specified range of time from this position. We also made an analysis and comparisons in the final.
Development of an open peer review system using blockchain and reviewer recommendation technologies

In order to create a transparent and sound academic communication ecosystem centered on researchers, we developed a system that applied blockchain technology to an open peer review system. In this study, an open peer review system was developed based on Hyperledger Fabric, which is a private blockchain. The system can be operated in connection with the reviewer recommendation module of the existing submission management system. In the reviewer recommendation module, reviewers are recommended by excluding co-authors and colleagues after an expertise test. The blockchain system performs an open peer review process based on smart contracts, while the submission management system selects reviewers for peer review. A service broker intervenes between these two systems for data interchange. The system developed herein is expected to be used as a researcher-centered scholarly communication model in the open science era, in which the intervention of publishers is minimized, and authors and reviewers (as researchers) are centered.
Anomaly Detection on MIL-STD-1553 Dataset using Machine Learning Algorithms

This paper evaluates the ability of several machine learning algorithms to detect attacks that emulate normal nonperiodical messages in the MIL-STD-1553 communication traffic. The dataset for this research is highly imbalanced and most algorithms fail or simply produce poor results when classifying the data. We conduct several experiments with different machine learning algorithms to correctly classify MIL-STD-1553 dataset. We identify appropriate metrics to judge the performance of the models applied to this dataset. Using these metrics, we compare the performance of different machine learning models that we generated.
Video Face Recognition of Virtual Currency Trading System Based on Deep Learning Algorithms

Virtual currency trading develops rapidly, taking up a large proportion in the whole economy, and has become an important part of people's daily life. Firstly, a reliable virtual currency trading system model is proposed, which is composed of four participant platforms, providers and payers, mainly including the purchase of virtual currency, purchase of virtual goods or services, exchange of virtual currency and other trading activities. In order to monitor possible collusion fraud in virtual currency transactions, payers are introduced to prevent collusion attacks. Video face detection algorithm based on multi-task Cascaded Convolutional Networks is designed and implemented to solve the problems of pose, light and occlusion in the virtual currency trading system. The algorithm utilizes the inherent correlation between detection and calibration to improve the detection performance under the framework of deep cascading tasks. Furthermore, the paper utilizes the three-tiered architecture combined with the well-designed VNN algorithm to realize the face detection and the rough location of key points. Meanwhile, on the basis of in-depth study and analysis of traditional face detection technology, a face detection algorithm based on Harr features and AdaBoost is implemented for comparison and analysis with the aforementioned algorithm. The experimental results show that the method based on MTCNN deep learning network can better extract effective features about faces in videos and achieve more accurate detection. Compared with the traditional method based on AdaBoost and cascade structure, the positive detection rate is much improved.
Learning Domain Randomization Distributions for Training Robust Locomotion Policies

This paper considers the problem of learning behaviors in simulation without knowledge of the precise dynamical properties of the target robot platform(s). In this context, our learning goal is to mutually maximize task efficacy on each environment considered and generalization across the widest possible range of environmental conditions. The physical parameters of the simulator are modified by a component of our technique that learns the Domain Randomization (DR) that is appropriate at each learning epoch to maximally challenge the current behavior policy, without being overly challenging, which can hinder learning progress. This so-called sweet spot distribution is a selection of simulated domains with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution; and 2) The DR distribution made as wide as possible, to increase variability in the environments. These properties aim to ensure the trajectories encountered in the target system are close to those observed during training, as existing methods in machine learning are better suited for interpolation than extrapolation. We show how adapting the DR distribution while training context-conditioned policies results in improvements on jump-start and asymptotic performance when transferring a learned policy to the target environment <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Efficient Post-Processing for Physical-Layer Secure Key Distribution in Fiber

Post-processing is an essential step towards the practical deployment of physical-layer secure key distribution (SKD) in classical fiber channels. However, the comparison and combination of different post-processing methods are not explored yet. Here, we fully evaluate the post-processing protocol and analyze various combinations of quantization and information reconciliation (IR), and present the efficient solutions associated with the key entropy source between the legal parties. In addition, the SHA3-256 hash function-based privacy amplification is exploited to further enhance the robust key secrecy and randomness. The results show that, the proposed protocol significantly increases the achievable key generation rate (KGR) up to 130-205% using the combination of multi-bit adaptive quantizer and IR with syndrome-decoding LDPC, compared with the previous post-processing using the same original key waveforms extracted from physical-layer SKD.
AI in Software Engineering at Facebook

How can artificial intelligence help software engineers better do their jobs and advance the state of the practice? We describe three productivity tools that learn patterns from software artifacts: code search using natural language, code recommendation, and automatic bug fixing.
Picking Path Optimization of Mobile Robotic Arm Based on Differential Evolution and Improved A* Algorithm

With the development of artificial intelligence technology, robotic arm picking and path finding based on AI technology has attracted more and more attention. How to use the path search algorithm in a relatively short time is a huge challenge in the optimization of robot picking path. Most of the existing path finding methods based on the A* algorithm have the problems of long search time and non-optimal path. Most of them achieve poor results in the optimization of robotic arm picking paths. This paper proposes an AI intelligent path finding method based on differential evolution and improved A* algorithm. The method mainly achieves fast path search by accurately filtering abnormal conditions to achieve a higher convergence rate. The experimental results show that the method proposed in this paper has faster search speed and better path selection than the existing methods. The AI intelligent path finding method based on differential evolution and improved A* algorithm for robotic arm picking is feasible and effective.
A Novel Reputation System for Mobile App Stores Using Blockchain

With thousands of mobile applications submitted to online application stores, the mobile application market has experienced significant growth. This growth is accompanied by an increase in malware presence. A possible solution would be to leverage those reports across all mobile ecosystems, creating a shared reputation system.
Mining discriminative itemsets in data streams using the tilted-time window model

A discriminative itemset is a frequent itemset in the target data stream with much higher frequency than that of the same itemset in the rest of the data streams in the dataset. The discriminative itemsets describe the distinguishing features between data streams. Mining discriminative itemsets in data streams is very important, where continuously arriving transactions can be inserted in fast speed and large volume. Compared with frequent itemset mining in single data stream, there are additional challenges in the discriminative itemset mining process as the Apriori property of subset is not applicable. We propose an efficient and high accurate method for mining discriminative itemsets in data streams using a tilted-time window model. The proposed single-pass H-DISSparse algorithm is designed particularly based on several well-defined characteristics aiming to improve the approximate frequencies of the itemsets in the tilted-time window model. The data structures are dynamically adjusted in offline time intervals to reflect the discriminative itemset frequencies in different time periods in unsynchronized data streams. Empirical analysis shows the efficient time and space complexity of the proposed method in the fast-growing big data streams.
Ownership structure, corruption, and capital investment: Evidence from firms in selected <scp>Sub‐Saharan</scp> African countries

Abstract In this study, we investigate the relationship between ownership structure, corruption, and capital investments in firms operating in a selected sample of Sub‐Saharan Africa (SSA) countries. Using a sample of an unbalanced panel of firms over different time periods that ranged from 2003 to 2016, and estimating with the fixed effects technique, we find that foreign ownership and bribery payments have positive and negative effects, respectively, on the capital investment of firms. Furthermore, the marginal effects analysis reveals that the effects of ownership structure and bribery payments differ significantly across our selected sample of countries and across different firm sizes. Policy implications were deduced from the findings.