Reviewerly/exHarmony huggingface dataset:
Forensic Accountant Data Analytical Skills Opportunity as Fraud Investigator in Industry 4.0 Era

In industry 4.0, the era as it is today, fraud has expanded from crimes that are visible and tangible such as theft, robbery, to subtle and hidden crimes such as financial statement manipulation which requires techniques such as data mining and financial data analysis to detect it. In the midst of the current development of the fraud investigator profession in Indonesia, we see that the parties who pursue it are mostly from a legal education background, and very rarely from an accountant background. This contrasts with the fact that in developed countries detective certifications such as the Certified Fraud Examiner (CFE) and Certified Fraud Auditor (CFrA) actually come from an accountant or auditor background. This is the basis research gap for making this paper. This paper is a descriptive qualitative research that discusses phenomenology. We collect facts about the fraud investigator profession and how an accountant or auditor can become a qualified investigator. We use primary data from observations and interviews with fraud investigator community members, while secondary data from international publications relate to our topic. The results of our study state that accountants or auditors have the advantage of being able to uncover subtle and covert cases of fraud such as manipulation of financial statements, but also have a weakness, namely the lack of knowledge of science related to the legal realm. In addition, the fraud investigator profession in Indonesia, does not yet have a legal license from the government body. In the future, it is hoped that it will be licensed with requirement from certification for someone who wants to become a fraud investigator.
Optimization of Bit Matrix Index for Temporal RDF

Temporal knowledge is crucial in many knowledge-based systems. A common approach for expressing temporal knowledge on the syntactical level is to add temporal triples as metadata for triples, called RDF reification. However, this will increase the volume and complexity of data, and leads to a decrease in the storage and retrieval performance for temporal RDF. In the field of RDF storage, the bit matrix is a simple yet highly efficient structure for indexing RDF data. In this paper, we provide an extension to bit matrix architecture (TBitStore) to support the indexing of temporal RDF. To begin with, TBitStore constructs an index over both Subject-Object key and temporal information. Then, it uses a time-bound matching mechanism to accelerate subquery execution. Moreover, it leverages a temporal statistics-based index to optimize the query plans. The experimental results show that TBitStore can reduce the storage space compared to the original bit matrix database for temporal RDF data, as well as improve the performance of querying temporal RDF.
Estimating Structural Disparities for Face Models

In machine learning, disparity metrics are often defined by measuring the difference in the performance or outcome of a model, across different sub-populations (groups) of datapoints. Thus, the inputs to disparity quantification consist of a model's predictions y, the ground-truth labels for the predictions y, and group labels g for the data points. Performance of the model for each group is calculated by comparing y and y for the datapoints within a specific group, and as a result, disparity of performance across the different groups can be calculated. In many real world scenarios however, group labels (g) may not be available at scale during training and validation time, or collecting them might not be feasible or desirable as they could often be sensitive information. As a result, evaluating disparity metrics across categorical groups would not be feasible. On the other hand, in many scenarios noisy groupings may be obtainable using some form of a proxy, which would allow measuring disparity metrics across sub-populations. Here we explore performing such analysis on computer vision models trained on human faces, and on tasks such as face attribute prediction and affect estimation. Our experiments indicate that embeddings resulting from an off-the-shelf face recognition model, could meaningfully serve as a proxy for such estimation.
Existence, Hypotheses and Categories in Knowledge Representation

Cognitive architectures employ different means for knowledge representation. In this work, we describe how the Cognitive Systems Toolkit (CST), a toolkit for the construction of cognitive architectures addresses the issue of knowledge representation, by introducing the notion of a computational idea, as being an abstract and generic building block for representing multiple different pieces of knowledge. We particularly address how computational ideas can be used to represent both facts that really happened at an environment and just hypothesis that are not to be considered as being a part of existence, explaining how these are instances of general categories. At the end, we provide different examples to illustrate the subtle differences that are possible to be represented using this knowledge representation scheme.
Application of DLT cybersecurity stack to TES applications for a scalable, cybersecure, and interoperable future

Transactive Energy Systems (TES) are expected to improve upon existing grid operations and capabilities by enabling the integration of traditional grid resources with distributed energy resources (DER). Distributed Ledger Technology or DLT (e.g., blockchain) presents itself as a viable instrument to support decentralized, autonomous, and tamper-evident applications, which can be leveraged within TES’s ecosystem. DLTs can provide pertinent security controls including access controls, data immutability, and traceability in addition to other well-known advantages such as decentralization and scalability. This work demonstrates the DLT cybersecurity stack and its applicability to TES-based use-cases/applications. The seven-layer DLT cybersecurity stack is a DLT-agnostic framework that can quickly be used to classify and group the individual needs of an application into the different processing and cybersecurity layers offered by a DLT using a common taxonomy and an architectural mapping framework. This enables application engineers to demystify and strengthen the overall security aspects of their systems while maintaining an open perspective towards features and drawbacks that may hinder their performance in real-world scenarios. The paper leverages the work performed by the IEEE P2418.5 Blockchain for Energy Standards working group.
Conclusion and Way Forward

This chapter presents the conclusion from the research conducted and the way forward for SMEs in marketing effectiveness and accountability. This conclusion seeks to address the inferences from the study of SMEs and is not intended to be summative in discourse. Insights were presented on knowledge in marketing, marketing accountability programme, marketing efficiency, b2b model of repeat business for growth, hampering implementation, and management support. Taken together, these are the areas where inferences were drawn from the study conducted. The way forward for marketing effectiveness and accountability in SMEs will be shaped by marketing agility and the digital technological landscape. Suffice it to say that digital technologies have levelled the playing field, thereby allowing SMEs to mount competition in the markets of large firms through the digital link of digital marketing and data analytics. SMEs must get ahead with the technology if they are to capitalize on the merits of being smaller.
Covid Contact Tracing App

The new eruption of COVID-19 has overpowered the world, convinced lockdowns and pushed general clinical consideration systems. Covid is known to be a significantly overpowering disease, and spoiled individuals at first don't show signs, while some stay asymptomatic. The new eruption of COVID-19 has overpowered the world, convinced lockdowns and focused on general clinical consideration structures. Covid is known to be significantly compelling contamination, and corrupted individuals at first don't show signs, while some stay asymptomatic. In like manner, various assemblies have shown uncommon interest in cell contact following applications that help with robotizing the inconvenient task of following all new contacts of late perceived polluted individuals. We give an overview of several suggested methods following results in the increase, many of which have been distributed nationally, and discuss the concerns clients have expressed about their use.
Revealing Doubtful Data in 200k Images via Re-annotation Workshop by Researcher Community

In this paper, we conducted the ImageNet Reannotation workshop with researchers who use ImageNet to find doubtful data in ImageNet. Recent great growth of deep learning is supported by large scale datasets collected by cloud working such as ImageNet, but it seems to have not so few doubtful data for given tasks. We assume that the professionals can efficiently and accurately find doubtful data while they know what kind of data would be better for learning classification tasks. Moreover, we adopted a group working scheme so that it could be more efficient and accurate. This paper shows the re-annotation result that clarifies category and reason of doubtfulness in the large scale dataset constructed by cloud workers.
Automatic VHDL Description for Mechatronics System by Using Evolutionary Computation

Evolutionary computation to automatically generate control circuits to support the design of controllers for mechatronic systems is introduced. As control targets, we take an inverted pendulum, the most basic mechatronic system, and an air-conditioning system, a somewhat more complex system. For each Field Programmable Logic Array (FPGA), which constitutes the controller of each mechatronic system, VHDL (Very high speed integrated circuit Hardware Description Language), which describes the control circuits inside the FPGA, is automatically generated using evolutionary computation. First, basic information about FPGAs and VHDL is given. Next, how to code in VHDL when applying evolutionary computation will be explained in detail. Also, how to set up the objective function will be discussed. Specific applications will be detailed for inverted pendulums and air conditioning systems.
Detecting Extended Incidents in Urban Road Networks for Organic Traffic Control Using Density-Based Clustering of Traffic Flows

The control of urban traffic signals typically works on the basis of predefined plans or as a centralised planning system. At least in research work, a locally organised, self-adaptive approach has been established as a more robust, scalable and efficient alternative. In all three cases, the best case scenario is that the system reacts to observed current situations - but no incidents such as accidents, construction work or road blockages of varying duration and extents are detected and considered as a basis for control decisions. In this article, we present an approach for cluster-based detection of such disturbances without the need to extend the existing infrastructure. Based on our previous approach, additional urban road networks are evaluated, all comprised of intersections equipped with programmable traffic signals. An additional incident type, where not all lanes of a road are blocked, is assessed. The underlying traffic flow data is generated in simulations of varying traffic volumes.
Measuring Inconsistency in Some Logics with Tense Operators

This paper starts the systematic study of inconsistency measures for propositional logics enriched with operators involving time. We use Prior's operators for tense logic: H, G, P, and F; however, we apply different semantics to them. We define two logics. The first one, ATPL, allows formulas with the application of any of the four operators any number of times to propositional logic formulas. The semantics is given in terms of TPL structures. We then show how to measure the inconsistency of a set of ATPL formulas using appropriately modified versions of standard inconsistency measures for propositional logic. Additionally, these new measures are checked for the satisfaction of various properties. We also investigate a new concept of inconsistency that cannot be defined for propositional logic. Then, ATPL is extended to CTPL by applying the propositional connectives to ATPL formulas and measuring inconsistency is extended to sets of CTPL formulas.
Ship Track Prediction Model Based on Improved ELM

Track prediction has always been a hot topic in the field of navigation, and plays an important role in ensuring the safety of ship navigation and improving the efficiency of marine traffic control. In this paper, a two-hidden-layer extreme learning machine trajectory prediction model is proposed, which can greatly shorten the prediction time while improving the prediction accuracy. Firstly, the DBSCAN clustering algorithm is used to cluster ship trajectories, and then the two-layer ELM trajectory prediction model is trained for different clusters. Finally, the experimental results are compared with the prediction results of the traditional ELM model and the widely used LSTM and GRU models in the past, which verifies the advantages of the tow-layer ELM model in terms of accuracy and speed of track prediction.
Assessment of Static and Dynamic Image Presentation for User Cognition and Understanding

The internet supports users who want to locate information with minimal search while remaining engaged. Using a graphical approach for data presentation supports both information and engagement, but it is unknown if static or dynamic graphical display improves cognitive function. Both displays provide information to the viewer, but they are different in functionality and implementation. Static images are still images represented as a PNG, JPEG or PDF, without hidden layers and interactivity. In contrast, dynamic or Scalable Vector Graphics (SVG) images have more flexibility, and different approaches can support interaction with the image. Layers can be hidden which can be revealed by the viewer. Dynamic images hold data that tell the same story but from different viewpoints. In this research, pandemic data including case rate, vaccination rate, and mortality rate data for different states is used. The scope of the data remains the same, with the values varying based on the geographic region or state. The research investigates the effectiveness of interactive visuals to improve cognitive function. Two images, one static and one dynamic, were sequentially presented to viewers, followed by a series of questions after each image to test the user’s cognition. The analyzed responses to the questions conclude whether the dynamic image improved cognition when compared to the static image. The research showed that users preferred dynamic images by a factor of 2 to 1, with the users preferring the interactivity of the image. Further research will fully determine changes in user cognition and understanding.
A Progressive Domain Adaptation for Object Detection via Coarse-grained Foreground Guidance

Domain adaptive object detection has always been a problem widely concerned by academia and industry. The variability of application scenes and the unknown target labels in the target domain make it challenging for well-trained detectors to generalize well by supervised learning. Due to the lack of instance-level information, previous approaches often tend to achieve image-level and pixel-level alignment using the model's own knowledge, but this is of finite help in separating the object to be detected from the image background, and it is difficult for the model itself to learn the foreground characteristics of the target domain. In this paper, we propose a method for domain-adaptive detection based on coarse-grained foreground guidance. Our approach uses an unsupervised foreground extraction algorithm to estimate the coarse locations of objects in the video, which can provide knowledge for instance features and progressively improve the cross-domain detection ability of the model. Experiments on the cross-domain object detection dataset confirm the effectiveness of our approach. We have also conducted experiments in real outdoor traffic scenarios, and the results indicate that our method can operate in real application scenarios.
DP-Opt: Identify High Differential Privacy Violation by Optimization

Differential privacy has become a golden standard for designing privacy-preserving randomized algorithms. However, such algorithms are subtle to design, as many of them are found to have incorrect privacy claim. To help identify this problem, one approach is designing disprovers to search for counterexamples that demonstrate high violation of claimed privacy level. In this paper, we present DP-Opt(mizer), a disprover that tries to search for counterexamples whose lower bounds on differential privacy exceed the claimed level of privacy guaranteed by the algorithm. We leverage the insights of counterexample construction proposed by the latest work, meanwhile resolve their limitations. We transform the search task into an improved optimization objective which takes into account the empirical error, then solve it with various off-the-shelf optimizers. An evaluation on a variety of both correct and incorrect algorithms illustrates that DP-Opt almost always produces stronger guarantees than the latest work up to a factor of 9.42, with runtime reduced by an average of $$19.2\%$$ .
REPLY TO MARÍA DE PONTE

María de Ponte's contribution to the special issue on The Indexical Point of View.
A Survey on Multilevel Thresholding-Based Image Segmentation Techniques

Multilevel thresholding is one of the most widely used techniques for image segmentation. A thresholding technique for image segmentation is mainly categorized into two types such as bi-level and multilevel thresholding. A single threshold value is used in bi-level thresholding for image classification such as—foreground object and background object. Bi-level thresholding gives unsatisfactory segmentation results in case of complex image; hence, the idea of multilevel thresholding has been preferred over bi-level thresholding method. In multilevel thresholding, selection of threshold values mostly gives inaccurate values, and it is a time-consuming process. Hence, automatic multilevel thresholding techniques are used as an objective functions to choose optimal threshold values but faces high computational complexity problems. Meta-heuristic algorithms play an important role to reduce the computational complexity of multilevel thresholding. In this paper, we have surveyed various objective functions used in automatic multilevel thresholding and performed a comparative study about the performances of some recent meta-heuristic algorithms, which are widely used in multilevel thresholding. Also, discussed different datasets and metrics used to evaluate multilevel thresholding techniques. In addition, some applications of image segmentation are also discussed.
Towards Accurate Network Quantization with Equivalent Smooth Regularizer

Neural network quantization techniques have been a prevailing way to reduce the inference time and storage cost of full-precision models for mobile devices. However, they still suffer from accuracy degradation due to inappropriate gradients in the optimization phase, especially for low-bit precision network and low-level vision tasks. To alleviate this issue, this paper defines a family of equivalent smooth regularizers for neural network quantization, named as SQR, which represents the equivalent of actual quantization error. Based on the definition, we propose a novel QSin regularizer as an instance to evaluate the performance of SQR, and also build up an algorithm to train the network for integer weight and activation. Extensive experimental results on classification and SR tasks reveal that the proposed method achieves higher accuracy than other prominent quantization approaches. Especially for SR task, our method alleviates the plaid artifacts effectively for quantized networks in terms of visual quality.
Perception and Cognition Matters: A new light on sentiment analysis task

In this paper, we introduce a new psychology-related sentiment category: cognition-sentiment and perception-sentiment. The motivation comes from the investigation of real-world sentiment datasets where we find that sentiment datasets often get an unequal benefits from same external knowledge. We suggest that the difference can be viewed as a new category basis for sentiment data, with the emphasis on a deeper grasping of sentiment feature from an implicit perspective. To test our idea, we first construct a Chinese sentiment dataset based on our proposed categories. Then we set up three experiments to observe the category performance and verify whether our category is suitable for sentiment analysis. Experimental results demonstrate that our proposed category is feasible for the sentiment analysis. Besides, we further try to exploit how can we use the proposed category to help the sentiment task. Specifically, we suggest a new task: perception-sentiment and cognition-sentiment detection (PCSD). Then we set up a joint learning experimental scene utilize PCSD as an auxiliary task to verify the effect of using the category information. Experiments demonstrate the usefulness of our category. Compare with the current SA, our findings widen previous sentiment analysis studies that ignored the implicit feature and sentiment-related psychology knowledge.
A High-Speed Codec Architecture for Lagrange Coded Computing

The Lagrange Coded Computing (LCC), proposed recently by Yu et at., is regarded as a promising solution for most distributed learning algorithms thanks to its good tradeoff between resiliency, security, and privacy over cloud servers. As a kind of coded computing, LCC also costs extra computations in a local computer for encoding and decoding, which contains many complex operations, such as the continued product operations and divisions. In this paper, we present an efficient high-speed LCC codec architecture based on the linear regression algorithm for the first time. By analyzing the formulas and evaluating the hardware resource, we select a set of optimal parameters and remove most of the complex operations by storing the precomputed coefficients. Besides, the proposed architecture is inherently scalable and can be fully utilized and reused for encoding and decoding. The experimental results on an FPGA show that a significant speedup is achieved compared with the prior art.