Reviewerly/exHarmony huggingface dataset:
Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Explaining Biases in Machine Learning

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicity.
Better Keep Cash in Your Boots - Hardware Wallets are the New Single Point of Failure

Hardware wallets are currently considered the most secure way to manage cryptocurrency keys and sign transactions. However, previous publications show that such tokens can be replaced or manipulated in a number of hard-to-detect ways pre- or post-delivery to the user and that implemented (remote) attestation and authenticity checks fail their purpose for multiple reasons.
Fast and Informative Model Selection Using Learning Curve Cross-Validation

Common cross-validation (CV) methods like k-fold cross-validation or Monte Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a large portion of the given data and testing it on the remaining data. These techniques have two major drawbacks. First, they can be unnecessarily slow on large datasets. Second, beyond an estimation of the final performance, they give almost no insights into the learning process of the validated algorithm. In this paper, we present a new approach for validation based on learning curves (LCCV). Instead of creating train-test splits with a large portion of training data, LCCV iteratively increases the number of instances used for training. In the context of model selection, it discards models that are unlikely to become competitive. In a series of experiments on 75 datasets, we could show that in over 90% of the cases using LCCV leads to the same performance as using 5/10-fold CV while substantially reducing the runtime (median runtime reductions of over 50%); the performance using LCCV never deviated from CV by more than 2.5%. We also compare it to a racing-based method and successive halving, a multi-armed bandit method. Additionally, it provides important insights, which for example allows assessing the benefits of acquiring more data.
Non-relational multi-level caching for mitigation of staleness &amp; stragglers in distributed deep learning

For efficient distributed deep neural network design, mitigation of stale gradients and stragglers is necessary. The stale gradient problem occurs during the distribution and parallelism of the deep neural networks on the multi-cluster/nodes. The proposed solution for stragglers is to use distributed non-relational database to update the intermediate results of weights and their respective nodes. The results from the database are given to the parameter server. If any delay in the parameter data due to straggling is detected, immediately the straggled data will be configured in another node as a server-less function. In this approach, each node is equipped with distributed in-memory cache and a non-relational database at the parameter server. The parameter server node is an intelligent node working based on a runtime threshold set and the error analysis for fixing the optimal value of 'K' for the K-SGD. The proposed solution for stale data is to efficiently utilize the multiple GPU with multiple levels of caching in the cloud for better performance and reduced response time. Response time is reduced by offloading and pushing data close to the nodes in multiple levels of distributed cache. Using GPU cache and Elastic Cache, data is updated to the individual nodes in optimal time intervals. Thus, an integrated solution for stragglers and staleness in both data-parallel and model parallel distributed deep learning networks.
BFR-SE: A Blockchain-Based Fair and Reliable Searchable Encryption Scheme for IoT with Fine-Grained Access Control in Cloud Environment

Due to capacity limitations, large amounts of data generated by IoT devices are often stored on cloud servers. These data are usually encrypted to prevent the disclosure, which significantly affects the availability of this data. Searchable encryption (SE) allows a party to store his data created by his IoT devices or mobile in encryption on the cloud server to protect his privacy while retaining his ability to search for data. However, the general SE techniques are all pay-then-use. The searchable encryption service providers (SESP) are considered curious but honest, making it unfair and unreliable. To address these problems, we combined ciphertext-policy attribute-based encryption, Bloom filter, and blockchain to propose a blockchain-based fair and reliable searchable encryption scheme (BFR-SE) in this paper. In BFR-SE, we constructed an attribute-based searchable encryption model that can provide fine-grained access control. The data owner stores the indices on SESP and stores some additional auxiliary information on the blockchain. After a data user initiates a request, SESP must return the correct and integral search results before the deadline. Otherwise, the data user can send an arbitration request, and the blockchain will make a ruling. The blockchain will only perform arbitrations based on auxiliary information when disputes arise, saving the computing resources on-chain. We analyzed the security and privacy of BFR-SE and simulated our scheme on the EOS blockchain, which proves that BFR-SE is feasible. Meanwhile, we provided a thorough analysis of storage and computing overhead, proving that BFR-SE is practical and has good performance.
TRANSACTIONS ON MACHINE LEARNING AND DATA MINING

The International Journal Transactions on Machine Learning and Data is a periodical appearing twice a year. The journal focuses on novel theoretical work for particular topics in Data Mining and applications on Data Mining. The topics are but not limited to Data Mining:MarketingMedicineE-Commerce (Mining Logfiles)BiotechnologyQuality ManagementMultimedia Data (Image, Video, Text, Signals)Web-MiningIntrusion Detection in NetworksCase-Based ReasoningClusteringClassification & PredictionAssociation RulesTelecommunicationDesign of ExperimentStrategy of ExperimentationCapability IndicesDeviation and Novelty DetectionControl ChartsDesign of ExperimentsCapability IndicesDesirabilitiesinductive learning including decision tree and rule induction learningconceptional learning and clusteringcase-based reasoning and learningsimilarity measures and learning of similarityassociation rulesvisualization and data miningvideo miningmining structural representations such as log files, text documents and HTML documentsstatistical learning and neural net based learningclassification and interpretation of images, text, videoorganisational learning and evolutional learningprobabilistic information retrievalmining gene data bases and biological data basesmining images, temporal-spatial data, images from remote sensingmining text documentsknowledge extraction from text, video, signals and imagesClassification and Model EstimationDecision TreesCase-Based Reasoning and Associative MemoryRule Induction and GrammarsNeural MethodsNonlinear Function Learning and Neural Net Based LearningSupport Vector MachinesBayesian Models and MethodsSubspace MethodsStatistical and Conceptual Clustering Methods: BasicsApplications of ClusteringFeature Grouping, Discretization, Selection and TransformationFeature LearningLearning/adaption of recognition and perceptionLearning of internal representations and modelsLearning of appropriate behaviourLearning of action patternsLearning in Image Pre-Processing and SegmentationStatistical and Evolutionary LearningRetrieval MethodsContent-Based Image RetrievalApplications in MedicineTime Series and Sequential Pattern MiningMining Financial or Stockmarket DataFrequent Pattern MiningMining Images in Computer VisionMining Images and TextureMining Motion from SequenceReal-Time Event Learning and DetectionSpeech AnalysisAspects of Data MiningText MiningSymbolic Learning and Neural Networks in Document ProcessingDeviation and Novelty DetectionLearning and adaptive controlLearning robotsLearning in process automationLearning for Handwriting RecognitionNetwork Analysis and Intrusion DetectionAutoamtic Semantic Annotation of Media ContentLearning of Semantic Inferencing RulesLearning of OntologiesLearning of Visual OntologiesHigh-Content Analysis of Microscopic Images in Medicine, Biotechnology and Chemistry
An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well-known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms.
A qualitative investigation of the digital literacy practices of doctoral students

Academic libraries are currently part of a landscape where there is a rapid growth of digital technologies and electronic resources and they have responded to this by developing their research services. Some of the most specialised and complex research in higher education is conducted by doctoral students and the effective use of digital tools and skills is often crucial to their research workflow and success. The need for digital literacy has been further emphasised during the global pandemic of 2020-21 which has required the maximisation of online working and digital skills to ensure the continuation of education, services and research productivity. This paper presents the findings of a qualitative research study in a UK university exploring factors influencing differences in the digital literacy skills of doctoral students. The literature included has been updated as digital skills and technologies are a constantly changing area of research.
ICTree

Many algorithms for virtual tree generation exist, but the visual realism of the 3D models is unknown. This problem is usually addressed by performing limited user studies or by a side-by-side visual comparison. We introduce an automated system for realism assessment of the tree model based on their perception. We conducted a user study in which 4,000 participants compared over one million pairs of images to collect subjective perceptual scores of a large dataset of virtual trees. The scores were used to train two neural-network-based predictors. A view independent ICTreeF uses the tree model's geometric features that are easy to extract from any model. The second is ICTreeI that estimates the perceived visual realism of a tree from its image. Moreover, to provide an insight into the problem, we deduce intrinsic attributes and evaluate which features make trees look like real trees. In particular, we show that branching angles, length of branches, and widths are critical for perceived realism. We also provide three datasets: carefully curated 3D tree geometries and tree skeletons with their perceptual scores, multiple views of the tree geometries with their scores, and a large dataset of images with scores suitable for training deep neural networks.
Hand gesture recognition algorithm combining hand-type adaptive algorithm and effective-area ratio for efficient edge computing

Most existing gesture recognition algorithms have low recognition rates under rotation, translation, and scaling of hand images as well as different hand types. We propose a new hand gesture recognition algorithm that combines the hand-type adaptive algorithm and effective-area ratio based on feature matching. Samples are divided into several groups according to the subjects’ palm shapes and the algorithm is trained using self-collected data. The user’s hand type is paired with one of the sample libraries by the hand-type adaptive algorithm. To further improve the accuracy, the effective-area ratio of the gesture is calculated based on the minimum bounding rectangle, and the preliminary gesture is recognized by the effective-area ratio feature method. The results of experiments demonstrate that the proposed algorithm could accurately recognize gestures in real time and exhibits good adaptability to different hand types. The overall recognition rate is over 94%. The recognition rate still exceeds 93% when hand gesture images are rotated, translated, or scaled.
Uncovering Cybercrimes in Social Media through Natural Language Processing

Among the myriad of applications of natural language processing (NLP), assisting law enforcement agencies (LEA) in detecting and preventing cybercrimes is one of the most recent and promising ones. The promotion of violence or hate by digital means is considered a cybercrime as it leverages the cyberspace to support illegal activities in the real world. The paper at hand proposes a solution that uses neural network (NN) based NLP to monitor suspicious activities in social networks allowing us to identify and prevent related cybercrimes. An LEA can find similar posts grouped in clusters, then determine their level of polarity, and identify a subset of user accounts that promote violent activities to be reviewed extensively as part of an effort to prevent crimes and specifically hostile social manipulation (HSM). Different experiments were also conducted to prove the feasibility of the proposal.
Type-level programming with match types

Type-level programming is becoming more and more popular in the realm of functional programming. However, the combination of type-level programming and subtyping remains largely unexplored in practical programming languages. This paper presents match types , a type-level equivalent of pattern matching. Match types integrate seamlessly into programming languages with subtyping and, despite their simplicity, offer significant additional expressiveness. We formalize the feature of match types in a calculus based on System F sub and prove its soundness. We practically evaluate our system by implementing match types in the Scala 3 reference compiler, thus making type-level programming readily available to a broad audience of programmers.
Pengaruh Motivasi Keluarga Terhadap Kemampuan Kognitif Anak Usia 5-6 Tahun: Studi Masa Pandemi

During the pandemic, children aged 5-6 years need time to learn from home so that cognitive abilities during the pandemic experience obstacles. Family motivation has an important role in empowering children's cognitive abilities. The purpose of this study was to determine the effect of family motivation on the cognitive abilities of children aged 5-6 years during the pandemic. The research method used is quantitative. The research subjects found 60 children consisting and 60 parents of: grades B1, B2, B3, B4 at the Putera Bhineka Institute. Data collection techniques using observation, questionnaires, and documentation. The validity and reliability test showed that all the item instruments used were valid and reliable. Data analysis using SPSS 22.0 with simple linear regression test based on hypothesis testing. The results showed that there was no significant effect of motivation on children's cognitive abilities during the pandemic aged 5-6 years. The results of calculations with simple linear regression analysis and hypothesis testing can be said that tcount is 1.311 and a significance value is 0.195. The significant value is 0.195 &gt; 0.05 or seen in the tcount of 1.311 &lt; ttable 2.001. These results can be influenced by environmental factors in the community and the school environment. The findings of this study contribute that during the pandemic, children's cognitive abilities are not influenced by family motivation, but rather by environmental and other factors.
Backup Resource Allocation of Virtual Machines for Probabilistic Protection under Capacity Uncertainty

This paper presents robust optimization models for minimizing the required backup capacity while providing probabilistic protection against multiple simultaneous failures of physical machines under uncertain virtual machine capacities in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the dedicated backup physical machines, which are determined in advance. We consider two uncertainties: failure event and virtual machine capacity. By adopting a robust optimization technique, we formulate six mixed integer linear programming problems. Numerical results show that for a small size problem, our presented models are applicable to the case that virtual machine capacities are uncertain, and by using these models, we can obtain the optimal solution of the allocation of virtual machines under the uncertainty. A simulated annealing heuristic is presented to solve large size problems. By using this heuristic, an approximate solution is obtained for a large size problem.
Cost-Effective Incremental Deep Model: Matching Model Capacity With the Least Sampling

Most existing approaches often utilize the pre-fixed structure and large number of labeled data for training complex deep models, which are difficult to implement on incremental scenarios. As a matter of fact, real-world data is always in stream form. Thereby, there exits two challenges for building incremental deep models: a) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Capacity Scalability</i> . The entire training data is not available before learning the task. It is a challenge to make the deep model structure scale with streaming data for flexible model evolution and faster convergence. b) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Capacity Sustainability</i> . The distribution streaming data usually changes in nature (concept drift), thus it is necessary to update the model while preserving previous knowledge for overcoming the catastrophic forgetting. To this end, we develop an incremental deep model (IDM), which expands the network structure according to streaming data and slows down forgetting with the adaptive fisher regularization. However, IDM ignores another significant challenge with streaming data: c) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Capacity Demand</i> . Training a deep model always needs a large amount of labeled data, whereas it is almost impossible to label all unlabeled instances in real time. The core problem is to select a small number of the most discriminative instances to label while keeping the predictive accuracy of the model. Thereby, we focus on the online semi-supervised learning scenario with abrupt changes in data distribution, and further improve IDM to a cost-effective incremental deep model (CE-IDM), which can adaptively select the most discriminative newly coming instances for query to reduce the manual labeling costs. Specifically, CE-IDM adopts a novel extensible deep network structure by using an extra attention model for hidden layers. Based on the adaptive attention weights, CE-IDM develops a novel instance selection criterion by jointly estimating unlabeled instances’ representative and informative degree to satisfy the capacity demand. With the newly labeled instances, CE-IDM can quickly update the model with adaptive depth from streaming data and enable capacity scalability. Also, we address capacity sustainability by exploiting the attention based fisher information matrix, which can slow down the forgetting in consequence. Finally, CE-IDM can deal with the three capacity challenges methioned above in a unified framework. We conduct extensive experiments on real-world data and show that CE-IDM outperforms the state-of-the-art methods with a substantial margin.
Application of Virtual Reality Based on Computer Vision in Sports Posture Correction

In recent years, with the rise of virtual and sports, people are often injured due to irregular movements during exercise. Based on this, this article takes the basic concept of computer virtual reality as the starting point and analyzes the adoption of computer virtual reality in sports correction. The choice of corrective exercises depends on the daily wrong exercises. If there is an error in any operation, please select the corresponding operation mode for the error operation for corrective training. If there are multiple errors at the same time, we compare and select the error operation that needs to be resolved first and the error operation that needs to be resolved later. In our daily life, the squat action pattern of Tai Chi Half-squat stance ball is closely related to us, and its action pattern can fully reflect the core stability of the subject. In this study, 42 students were selected as the students of a college sports college in Dalian. There are 7 people in each group, divided into 6 groups. The first three groups used exercise correction to assist the traditional teaching method (experimental group). It was found that the performance error rate of the first three groups reached 65%, and the last three groups used the assisted virtual reality teaching method, and the error rate was only 4%. Therefore, we adopted the teaching of virtual reality can reduce the error rate of the movement posture. We trained them for half a year and assessed and scored them once a month. We collect this data and analyze it to reach a conclusion. The experimental results prove that the experimental group has a significant difference before and after the squat test ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ), and postexperiment there is a remarkable disparity among the experimental group and the comparison group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). Therefore, squatting correction training is effective in improving the wrong squat. It is very effective for problems such as posture movement mode and joint limitation. Therefore, it is a very meaningful thing to explore the exercise correction training based on the virtual reality of computer vision.