Reviewerly/exHarmony huggingface dataset:
Securing Blockchain-Based Supply Chain Workflow against Internal and External Attacks

Blockchain is a revolutionary technology that is being used in many applications, including supply chain management. The primary goal of using a blockchain for supply chain management is to reduce the overall production cost while providing comprehensive security to the system. However, current blockchain-based supply-chain workflow(s) (BSW) are still susceptible to various cyber threats due to evolving business processes of different stakeholders involved in the process. In fact, current BSW protects the supply chain process based on the rules that have been implemented in the corresponding smart contracts. However, in practice, the requirements for the process keep evolving due to several organizational policies and directives of the involved stakeholders; therefore, current blockchain-based solutions fail to protect the supply chain process against attacks that exploit the process-related information that is not protected by smart contracts. Therefore, the goal of this work was to develop a methodology that enhances the protection of BSW against various internal (e.g., Stuxnet) and external (e.g., local data breach of a stakeholder) cyber threats through monitoring the stakeholder business process. Our methodology complements the blockchain-based solution because it protects the stakeholder’s local process against the attacks that exploit the process information that is not protected in the smart contracts. We implemented a prototype and demonstrated its application to a typical supply chain workflow example application by successfully detecting internal and external attacks to the application.
REVISITING THE ENVIRONMENTAL KUZNET CURVE IN AFRICA: THE INTERACTIVE ROLE OF FINANCIAL DEVELOPMENT IN SUSTAINABLE ENVIRONMENT

The aim of this study is to revisit the validity of EKC hypothesis in six leading African countries, with interaction effect of financial development and economic growth on the quality of environment over a period of 1970 to 2019.The research used the updated version of Driscoll and Kraay model by Hoechle, which resolved the consequences of heteroscedasticity and cross-sectional dependency.The empirical results of the study reveal that the EKC hypothesis is supported in these leading African economies.Carbon emissions increase due to foreign direct investment (FDI) and energy consumption.Financial development improves the environmental quality in these leading African economies.The interactive role of financial development and economic growth increases CO 2 emissions thereby degrading the quality of environment.The study recommends that environmental policies that minimize emissions should be enforced for the purpose of making the environment cleaner, FDI should be environmentally friendly and relevant incentives are required to redirect private credits towards green projects and renewable energy development.
In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle

Stochastic gradient descent (SGD) is the cornerstone of modern ML systems. Despite its computational efficiency, SGD requires random data access that is inherently inefficient when implemented in systems that rely on block-addressable secondary storage such as HDD and SSD, e.g., in-DB ML systems and TensorFlow/PyTorch over large files. To address this impedance mismatch, various data shuffling strategies have been proposed to balance the convergence rate of SGD (which favors randomness) and its I/O performance (which favors sequential access).
Identifying lexical change in negative word-of-mouth on social media

Abstract Negative word-of-mouth is a strong consumer and user response to dissatisfaction. Moral outrages can create an excessive collective aggressiveness against one single argument, one single word, or one action of a person resulting in hateful speech. In this work, we examine the change of vocabulary to explore the outbreak of online firestorms on Twitter. The sudden change of an emotional state can be captured in language. It reveals how people connect with each other to form outrage. We find that when users turn their outrage against somebody, the occurrence of self-referencing pronouns like ‘I’ and ‘me’ reduces significantly. Using data from Twitter, we derive such linguistic features together with features based on retweets and mention networks to use them as indicators for negative word-of-mouth dynamics in social media networks. Based on these features, we build three classification models that can predict the outbreak of a firestorm with high accuracy.
LNNet: Lightweight Nested Network for motion deblurring

Motion deblurring methods based on convolutional neural networks (CNN) have recently demonstrated their advantages over conventional methods. However, repetitions of scaling or slicing operations of these methods on the input images inevitably lead to spatial information loss. Meanwhile, some recent methods based on complex models inevitably bring a large model size and huge computing cost. It is still challenging to balance the deblurring performance and the cost. To this end, we propose a lightweight nested network (LNNet) for the motion-deblurring task. Our LNNet leverages several simple yet efficient sub-networks to process motion deblurring features at each stage. We design a nested connection, which is conducive to the model size reduction when connecting sub-networks so as to reuse deblurring information and facilitate deblurring information diversity. Meanwhile, we introduce the feature-fusion module to improve deblurring performance further. We perform extensive experiments on a workstation platform and an embedded mobile edge computing (MEC) platform to evaluate our LNNet as well as other existing methods. Extensive experimental results demonstrate that our LNNet achieves superior deblurring performance than state-of-the-art methods with a small model size within a short running time. Moreover, experimental results also show that our model is quite suitable for other embedded devices.
Traditional Chinese Medicine Word Representation Model Augmented with Semantic and Grammatical Information

Text vectorization is the basic work of natural language processing tasks. High-quality vector representation with rich feature information can guarantee the quality of entity recognition and other downstream tasks in the field of traditional Chinese medicine (TCM). The existing word representation models mainly include the shallow models with relatively independent word vectors and the deep pre-training models with strong contextual correlation. Shallow models have simple structures but insufficient extraction of semantic and syntactic information, and deep pre-training models have strong feature extraction ability, but the models have complex structures and large parameter scales. In order to construct a lightweight word representation model with rich contextual semantic information, this paper enhances the shallow word representation model with weak contextual relevance at three levels: the part-of-speech (POS) of the predicted target words, the word order of the text, and the synonymy, antonymy and analogy semantics. In this study, we conducted several experiments in both intrinsic similarity analysis and extrinsic quantitative comparison. The results show that the proposed model achieves state-of-the-art performance compared to the baseline models. In the entity recognition task, the F1 value improved by 4.66% compared to the traditional continuous bag-of-words model (CBOW). The model is a lightweight word representation model, which reduces the training time by 51% compared to the pre-training language model BERT and reduces 89% in terms of memory usage.
A Survey on Recommender Systems Challenges and Solutions

A recommender system is a set of tools for information retrieval. It improves access and proactively recommends items and services that match users' tastes by considering their explicit and implicit preferences and behaviors. Recommender systems have become very popular in the e-commerce field. Today, the internet is flooded with diverse information that makes it very difficult for the end-users to reach out for what they need. Recommender systems provide tailored views to users who are constantly adapted to the users' changing tastes. Although many recommendation techniques have been developed in multiple domains, recommender systems still face problems and challenges that hinder their precision. This paper provides a comprehensive summary of the key challenges and problems when developing recommender systems and summarizes the latest research achievements and directions to resolve them. In addition to that, we go beyond this by presenting the evaluation techniques used to judge the performance of recommender systems.
Bacterial Evolutionary Algorithm-Trained Interpolative Fuzzy System for Mobile Robot Navigation

This paper describes the process of building a transport logic that enables a mobile robot to travel fast enough to reach a desired destination in time, but safe enough to prevent damage. This transport logic is based on fuzzy logic inference using fuzzy rule interpolation, which allows for accurate inferences even when using a smaller rule base. The construction of the fuzzy rule base can be conducted experimentally, but there are also solutions for automatic construction. One of them is the bacterial evolutionary algorithm, which is used in this application. This algorithm is based on the theory of bacterial evolution and is very well-suited to solving optimization problems. Successful transport is also facilitated by proper path planning, and for this purpose, the so-called neuro-activity-based path planning has been used. This path-planning algorithm is combined with interpolative fuzzy logic-based speed control of the mobile robot. By applying the described methods, an intelligent transport logic can be constructed. These methods are tested in a simulated environment and several results are investigated.
An Enhanced Visual Object Tracking Approach based on Combined Features of Neural Networks, Wavelet Transforms, and Histogram of Oriented Gradients

In this paper, a new Visual Object Tracking (VOT) approach is proposed to overcome the main problem the existing approaches encounter, i.e. the significant appearance changes which are mainly caused by heavy occlusion and illumination variation. The proposed approach is based on a combination of Deep Convolutional Neural Networks (DCNNs), Histogram of Oriented Gradient (HOG) features, and discrete wavelet packet transforms. The problem of illumination variation is solved by incorporating the coefficients of the image discrete wavelet packet transform instead of the image template to handle the case of images with high saturation in the input of the used CNN, whereas the inverse discrete wavelet packet transforms are used at the output for extracting the CNN features. By combining four learned correlation filters with the convolutional features, the target location is deduced using multichannel correlation maps at the CNN output. On the other side, the maximum value of the resulting maps from the correlation filters with convolutional features produced by the previously obtained HOG feature of the image template are calculated and are used as an updating parameter of the correlation filters extracted from CNN and from HOG. The major aim is to ensure long-term memory of the target appearance so that the target item may be recovered if tracking fails. In order to increase the performance of HOG, the coefficients of the discrete packet wavelet transform are employed instead of the image template. The obtained results demonstrate the superiority of the proposed approach.
Relational compilation for performance-critical applications: extensible proof-producing translation of functional models into low-level code

There are typically two ways to compile and run a purely functional program verified using an interactive theorem prover (ITP): automatically extracting it to a similar language (typically an unverified process, like Coq to OCaml) or manually proving it equivalent to a lower-level reimplementation (like a C program). Traditionally, only the latter produced both excellent performance and end-to-end proofs.
Homogeneous Batch Memory Deduplication Using Clustering of Virtual Machines

Virtualization is the backbone of cloud computing, which is a developing and widely used paradigm.By finding and merging identical memory pages, memory deduplication improves memory efficiency in virtualized systems.Kernel Same Page Merging (KSM) is a Linux service for memory pages sharing in virtualized environments.Memory deduplication is vulnerable to a memory disclosure attack, which uses covert channel establishment to reveal the contents of other colocated virtual machines.To avoid a memory disclosure attack, sharing of identical pages within a single user's virtual machine is permitted, but sharing of contents between different users is forbidden.In our proposed approach, virtual machines with similar operating systems of active domains in a node are recognised and organised into a homogenous batch, with memory deduplication performed inside that batch, to improve the memory pages sharing efficiency.When compared to memory deduplication applied to the entire host, implementation details demonstrate a significant increase in the number of pages shared when memory deduplication applied batch-wise and CPU (Central processing unit) consumption also increased.
Elaboration of science teachers’ technology-based lesson practices in terms of contextual factors influencing TPACK

Background One of the important elements of teachers’ technology integration is Technological Pedagogical Content Knowledge (TPACK). Though there are studies on TPACK, limited information exists about its context element. This gap causes context-isolated studies for TPACK-related research.Purpose The aim of this research is to elaborate on science teachers’ technology-based lesson practices in terms of contextual factors influencing TPACK.Sample The study participants consisted of 12 science teachers and 7 educational technologists by using snowball sampling.Design and method The case study design method was used in this study within the qualitative research paradigm. The data was collected with semi-structured interviews by following the protocol for strategic performance problems. The interviews were examined employing content analysis to reveal the themes.Results and conclusions The findings of the study indicated that TPACK is not only a stand-alone knowledge and skills, but also it is context-oriented. The multifaceted factors represent components of a multi-layered structure of the context. These factors can be perceived by the teachers at different levels according to school types. Furthermore, sub-themes of the contextual factors showed complex interrelationships. This study contributes to the literature by revealing the sub-themes of the context and in-depth examination of contextual factors suggested for future studies.
Optimum Design of Stair-Climbing Robots Using Taguchi Method

Environmental issues like pollution are major threats to human health.Many systems are developed to reduce pollution.In this paper, an optimal mobile robot design to reduce pollution in Green supply chain management system.Green supply chain management involves as similating environmentally and economically feasible solutions into the supply chain life-cycle.Smartness, advanced technologies, and advanced networks are becoming pillars of a sustainable supply chain management system.At the same time, there is much change happening in the logistics industry.They are moving towards a new logistics model.In the new model, robotic logistics has a vital role.The reasons for this change are the rapid growth of the e-commerce business and the shortage of workers.The advantages of using robotic logistics are reduction in human errors, faster delivery speed, better customer satisfaction, more safety for workers, and high workforce adaptability.A robot with rocker-bogie suspension is a six-wheeled mobile platform that has a distinctive potential to keep all wheels on the ground continuously.It has been designed to traverse rough and uneven terrain by distributing the load over its wheels equally.However, there is a limitation to achieving high-speed mobility against vertical barriers.In this research, an optimal design of product delivery wheeled robots for a sustainable supply chain system is proposed to ensure higher adaptability and maximum stability during climbing staircases.The design parameters of the proposed robot are optimized using Taguchi Method.The aim is to get a smooth trajectory of the robot's center-of-mass.The proposed approach realizes a robot with much-improved stability which can climb over heights more than the size of the wheel (i.e., 3 times the radius of wheels).The results reveal that the modified rocker-bogie system not only increases the stair-climbing capability but also thwarts instability due to overturning of a wheel of the robot.
Towards lifelong object recognition: A dataset and benchmark

Lifelong learning algorithms aim to enable robots to handle open-set and detrimental conditions, and yet there is a lack of adequate datasets with diverse factors for benchmarking. In this work, we constructed and released a lifelong learning robotic vision dataset, OpenLORIS-Object. This dataset was collected by RGB-D camera capturing dynamic environment in daily life scenarios with diverse factors, including illumination, occlusion, object pixel size and clutter, of quantified difficulty levels. To the best of our knowledge, this is an unique real-world dataset for robotic vision with independent and quantifiable environmental factors, which are currently unaccounted for in other lifelong learning datasets such as CORe50 and NICO. We tested 9 state-of-the-art algorithms with 4 evaluation metrics over the dataset in Domain Incremental Learning, Task Incremental Learning, and Class Incremental Learning scenarios. The results demonstrate that these existing algorithms are insufficient to handle lifelong learning task in dynamic environments. Our dataset and benchmarks are now publicly available at this website.2
Toward Semi-Supervised Graphical Object Detection in Document Images

The graphical page object detection classifies and localizes objects such as Tables and Figures in a document. As deep learning techniques for object detection become increasingly successful, many supervised deep neural network-based methods have been introduced to recognize graphical objects in documents. However, these models necessitate a substantial amount of labeled data for the training process. This paper presents an end-to-end semi-supervised framework for graphical object detection in scanned document images to address this limitation. Our method is based on a recently proposed Soft Teacher mechanism that examines the effects of small percentage-labeled data on the classification and localization of graphical objects. On both the PubLayNet and the IIIT-AR-13K datasets, the proposed approach outperforms the supervised models by a significant margin in all labeling ratios (1%, 5%, and 10%). Furthermore, the 10% PubLayNet Soft Teacher model improves the average precision of Table, Figure, and List by +5.4,+1.2, and +3.2 points, respectively, with a similar total mAP as the Faster-RCNN baseline. Moreover, our model trained on 10% of IIIT-AR-13K labeled data beats the previous fully supervised method +4.5 points.
The Effect of Bit Depth on High-Temperature Digital Image Correlation Measurements

Digital Image Correlation (DIC) is a camera-based method of measuring displacement and strain. High-temperature DIC is challenging due to light emitted from the sample which can saturate the image. This effect can be mitigated using optical bandpass filters, but the maximum sample temperature range of DIC remains dependent on the camera and camera settings. Among camera settings, bit depth, also referred to as color depth or number of bits, has received insufficient attention in high-temperature DIC literature. In this work, the effect of bit depth on DIC measurements is investigated both analytically and experimentally. It is shown that if image noise is sufficiently low, then increasing bit depth reduces DIC random error. A new metric, the effective number of bits, is presented to determine the appropriate number of bits for DIC images. Using increased bit depth, reduced exposure time, and low-noise images, the maximum sample temperature for DIC measurements was shown to increase without negatively impacting random error.
Hierarchical graph attention network with pseudo-metapath for skeleton-based action recognition

Skeleton-based action recognition has gained significant attention in computer vision. Most state-of-the-art (SOTA) approaches view the skeleton as a homogeneous graph. Unlike those approaches, this paper shows that methods in the heterogeneous graph manner can also achieve competitive performance. In this paper, a logical heterogeneous skeleton graph is built under the assumption of the heterogeneity of joints and bones at different positions, and the action recognition task is formulated as message aggregation and prediction on this heterogeneous graph. Specifically, a novel semantic concept named pseudo-metapath is introduced to represent dependencies between joints, based on which a hierarchical graph attention network with the joint-level attention and the semantic-level attention modules is proposed to capture richer skeleton features. The joint-level attention module intends to get the local difference among the joints within each pseudo-metapath, while the semantic-level attention module is capable of learning the global importance of different pseudo-metapaths. Extensive experiments on the NTU-RGB + D 60, NTU-RGB + D 120 and the SYSU datasets, validate that our model can attain comparable performance to the SOTA methods with 15x fewer input frames, 26.3x less FLOPs and 2.8x less parameters.
The Use of Deep Learning Model for Effect Analysis of Conventional Friction Power Confinement

Nonlinear friction could affect the high-precision motion system, resulting in poor tracking accuracy in the end. This is due to the fact that the Lugre friction model's parameter identification process comprises both static and dynamic parameter identification. The convolutional neural network (CNN) model is used in this study to create the friction identification system. We suggest a hybrid methodology that combines the CNN method and the classic least-squares technique. The convolutional layer (CONV), which is defined by a convolutional kernel, analyzes and extracts features from an input image. In terms of accuracy and convergence, the results reveal that the upgraded CNN friction model outperforms the original CNN friction model. You may successfully reduce the influence of friction on your system while improving its performance by applying the feedforward correction.
Introducing an Evaluation Method for Taxonomies

Background: Taxonomies are crucial for the development of a research field, as they play a major role in structuring a complex body of knowledge and help to classify processes, approaches, and solutions. While there is an increasing interest in taxonomies in the software engineering (SE) research field, we observe that SE taxonomies are rarely evaluated. Aim: To raise awareness and provide operational guidance on how to evaluate a taxonomy, this paper presents a three step evaluation method evaluating its structure, applicability, and purpose. Method: To show the feasibility and applicability of our approach, we provide a running example and additionally illustrate our approach to a practical case study in SE research. Results and Conclusion: Our method with operational guidance enables SE researchers to systematically evaluate and improve the quality of their taxonomies and support reviewers to systematically assess a taxonomy's quality.