Reviewerly/exHarmony huggingface dataset:
“Invest in crypto!”: An analysis of investment scam advertisements found in Bitcointalk

This paper investigates the evolution of investment scam lures and scam-related keywords in the cryptocurrency online forum Bitcointalk over a period of 12 years. Our findings show a shift in scam-related keywords found within posts in the forum, where “Ponzi” was the most popular and most frequently mentioned in 2014 and 2018 and “HYIP” appeared more often in 2018 and 2021. We also identify that the financial principle is the tactic more likely to be used to lure people into investment scams from 2015 until 2017, coinciding with the period when “Ponzi” was the most commonly found keyword. This is followed by a transition to the authority and distraction principles from 2018 until 2022, which also coincides with the increase of popularity of “HYIP”.We collect more than 17.8M posts from 399k threads from the forum, from July 2010 until June 2022. Our longitudinal analysis shows the popularity transition between subforums and keywords across time. We design a categorisation criteria and annotate 4,218 posts from 2,630 threads based on it. We then use the annotated sample to train four machine learning statistical models. We use the best performing model to classify all 281k English-language threads into four categories: overt scams, potential scams, scam comments and not investment scam related. We analyze the frequency changes of scam-related threads across the 12 year period and observe that overt and potential scams peaked in 2015 and 2018 respectively. We see that potential scams also increased during the COVID-19 pandemic. We use heuristics to pinpoint the types of cryptocurrencies most frequently used within scam advertisements. Bitcoin is most commonly found in potential scams while Ethereum appears more often than other cryptocurrencies in overt scams. We use machine learning classifiers to identify the scam actor types behind the posts categorised as overt and potential scams. We also classify the type of lure used by scammers. Our results indicate that the time principle is not a tactic used as frequently as expected. Finally, we observe the influence of the pandemic in the strategies used to lure victims, reflected in higher than expected use of the kindness principle in 2021 and 2022.
The efficiency of fractional channels in the Heisenberg XYZ model

A fractional quantum state has created between two-qubit system by a Heisenberg XYZ model in the presence of the Dzyaloshinskii–Moriya (DM) interaction. The impact of the initial state settings and the interaction parameters on the amount of the quantum correlation is discussed. We used the concurrence and local quantum uncertainty to quantify these correlations. It is shown that, both quantifiers increase suddenly/gradually depending on the order of the fractional state, and whether the system is ferromagnetic or anti-ferromagnetic. The fractional parameter may increase/ stabilized the memory of the generated fractional state. Small values of the DM interaction can maximize the initial quantum correlation of a partial entangled state. The possibility of using these fractional time-dependent states as quantum channels to perform quantum teleportation has examined. It is shown that, preparing the system in anti-ferromagnetic regime improves the fidelity of the teleported state. The strength of the DM interaction and the fractional’s order has a clear effect on the fidelity’s behavior, where they may be used as control parameters to increase the efficiency of the fractional quantum channel in the context of quantum communication.
Red Panda Optimization Algorithm: An Effective Bio-Inspired Metaheuristic Algorithm for Solving Engineering Optimization Problems

This paper presents a new bio-inspired metaheuristic algorithm called Red Panda Optimization (RPO) that imitates the natural behaviors of red pandas in nature.The main design idea of RPO is derived from two characteristic natural behaviors of red pandas: (i) foraging strategy, and (ii) climbing trees to rest.The proposed RPO approach is mathematically modeled in two phases of exploration based on the simulation of red pandas' foraging strategy and exploitation based on the simulation of red pandas' movement in climbing trees.The main advantage of the proposed approach is that there is no control parameter in its mathematical modeling, and for this reason, it does not need a parameter adjustment process.The performance of RPO is evaluated on fifty-two standard benchmark functions including unimodal, highdimensional multimodal, and fixed-dimensional multimodal types as well as CEC 2017 test suite.The optimization results obtained by the proposed RPO approach are compared with the performance of twelve well-known metaheuristic algorithms.The simulation results show that RPO, by maintaining the balance between exploration and exploitation, is effective in solving optimization problems and its performance is superior over competitor algorithms.Based on the analysis of the optimization results, RPO has provided more successful performance compared to the competitor algorithms in 100% of unimodal functions, 100% of high-dimensional multimodal functions, 100% of fixed-dimensional multimodal functions, and 86.2% of CEC 2017 test suite benchmark functions.Also, the statistical analysis of the Wilcoxon rank sum test shows that the superiority of RPO in the competition with the compared algorithms is significant from a statistical point of view.In addition, the results of implementing RPO on four engineering design problems confirms the ability of the proposed approach to handle real-world optimization applications.
Student Perceptions of AI-Generated Avatars in Teaching Business Ethics: We Might not be Impressed

Abstract In our postdigital world, unseen algorithms and artificial intelligence (AI) underpin most business and educational technologies and systems. Also, the use of educational data to better understand and support teaching and learning is growing in higher education. Other AI technologies such as synthetic media and AI-generated avatars are increasingly used to present video-based content in business and society but are less common in educational content and lectures, as their effectiveness and impact on learning are still being researched and debated. In this study, an AI-generated avatar was implemented in the redesign of business ethics material in a postgraduate course to present videos and online activities and to prompt critical reflection and discussion of the social and ethical implications of algorithms. Using a qualitative research design, we then explored students’ perceptions of teaching and learning with AI-generated avatars. The students interviewed felt AI avatars were suitable, sometimes even preferred, for lecture delivery, with some enhancements. This study contributes insights into the use of AI-generated avatars in education by examining their potential benefits and challenges and generating three key pedagogical principles to consider. Future directions for educational design and research are discussed, particularly the pressing need to engage students creatively and critically with the social and ethical implications of AI avatars.
An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing

Abstract Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users’ jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits.
Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM

Abstract Gait and motion stability analysis in gait dysfunction problems is a very interesting research area. Usually, patients who undergo vestibular deafferentation are affected by changes in their dynamic balance. Therefore, it is important both patients and physicians are able to monitor the progress of the so-called vestibular compensation to observe the rehabilitation process objectively. Currently, the quantification of their progress is highly dependent on the physician’s opinion. In this article, we designed a novel methodology to classify the gait disorders associated with unilateral vestibular deafferentation in patients undergoing vestibular schwannoma surgery (model of complete vestibular loss associated with imbalance due to vestibular nerve section and eventual labyrinthectomy). We present a dual-head attentional transformer-LSTM (DHAT-LSTM) to evaluate the problem of rehabilitation from gait dysfunction, which is observed by a Kinect. A system consisting of a key-point-RCNN detector is used to compute body landmark measures and evaluate gait dysfunction based on a DHAT-LSTM network. This structure is used to quantitatively assess gait classification by tracking skeletal features based on the temporal variation of feature sequences. The proposed deep network analyses the features of the patient’s movement. These extracted high-level representations are then fed to the final evaluation of gait dysfunction. The result analytically demonstrates its effectiveness in classification evaluation when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. An accuracy greater than 81% was achieved for given sets of individuals using velocity-based, angle-based, and position features for both the whole body and the symmetric features of the body.
Contact-conditioned hand-held object reconstruction from single-view images

Reconstructing the shape of hand-held objects from single-view color images is a long-standing problem in computer vision and computer graphics. The task is complicated by the ill-posed nature of single-view reconstruction, as well as potential occlusions due to both the hand and the object. Previous works mostly handled the problem by utilizing known object templates as priors to reduce the complexity. In contrast, our paper proposes a novel approach without knowing the object templates beforehand but by exploiting prior knowledge of contacts in hand-object interactions to train an attention-based network that can perform precise hand-held object reconstructions with only a single forward pass in inference. The network we propose encodes visual features together with contact features using a multi-head attention module as a way to condition the training of a neural field representation. This neural field representation outputs a Signed Distance Field representing the reconstructed object and extensive experiments on three well-known datasets demonstrate that our method achieves superior reconstruction results even under severe occlusion compared to the state-of-the-art techniques.
Blockchain – A Deployment Mechanism for IoT Based Security

Implementation of IoT domain invites tremendous attacking opportunities which demands end to – end security mechanism. Applications related to the domain of IoT varies from critical applications to normal business-oriented applications like Intelligent Transportation Systems, Smart Grid, Video Surveillance, Banking, Logistics, Insurance etc. A special support in terms of security must be required for critical applications as well as normal business applications. Large amount of security mechanisms implemented time to time. Blockchain technology proves to be useful in providing security to several applications based on IoT by following defense – in – depth or castle approach. Blockchain can be defined as a database for storing processed data in a sequential manner. Such data has been shared among participating users. Information can be stored on a public ledger which can’t be updated. Every device in the corresponding network must retain the same ledger. Through the medium of this paper, we try to highlight importance of Blockchain Mechanism in IoT environment and also point out that the concept of Blockchain mechanism is only concerned with security.
Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders

Visual motion segmentation (VMS) is an important and key part of many intelligent crowd systems. It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes, which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades. Trajectory clustering has become one of the most popular methods in VMS. However, complex data, such as a large number of samples and parameters, makes it difficult for trajectory clustering to work well with accurate motion segmentation results. This study introduces a spatial-angular stacked sparse autoencoder model (SA-SSAE) with <i>l2</i>-regularization and softmax, a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster. The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets (a.k.a ‘trajectories’). We adopt <i>l2</i>-regularization and sparsity regularization, which can learn sparse representations of features, to guarantee the sparsity of the autoencoders. We employ the softmax layer to map the data points into accurate cluster representations. One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly. This framework helps cluster the motion patterns effectively with higher accuracy. We put forward a new dataset with its manual ground truth, including 21 crowd videos. Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies. The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.
Interoperability of Heterogeneous Systems of Systems: Review of Challenges, Emerging Requirements and Options

Interoperability is one of the critical challenges in the construction and management of distributed and collaborative systems. Hence, a deep understanding of the fundamental barriers to interoperability and of the key requirements that systems must meet to be interoperable is essential. In this direction, in the first part of this research, we conducted a questionnaire survey of stakeholders and practitioners of distributed and collaborative systems. As a result, we identified eight essential interoperability requirements and corresponding challenges. Then, in the second part of our study, we performed a critical literature survey of the building blocks of interoperability to understand the ability of current conceptual approaches---and related technologies---to address the identified requirements. The results of our research can significantly impact the software engineering of interoperable systems by introducing their fundamental requirements and the best practices to address them.
2.5D cascaded context-based network for liver and tumor segmentation from CT images

&lt;abstract&gt; &lt;p&gt;The existing 2D/3D strategies still have limitations in human liver and tumor segmentation efficiency. Therefore, this paper proposes a 2.5D network combing cascaded context module (CCM) and Ladder Atrous Spatial Pyramid Pooling (L-ASPP), named CCLNet, for automatic liver and tumor segmentation from CT. First, we utilize the 2.5D mode to improve the training efficiency; Second, we employ the ResNet-34 as the encoder to enhance the segmentation accuracy. Third, the L-ASPP module is used to enlarge the receptive field. Finally, the CCM captures more local and global feature information. We experimented on the LiTS17 and 3DIRCADb datasets. Experimental results prove that the method skillfully balances accuracy and cost, thus having good prospects in liver and liver segmentation in clinical assistance.&lt;/p&gt; &lt;/abstract&gt;
Cycle index polynomials and generalized quantum separability tests

The mixedness of one share of a pure bipartite state determines whether the overall state is a separable, unentangled state. Here we consider quantum computational tests of mixedness, and we derive an exact expression of the acceptance probability of such tests as the number of copies of the state becomes larger. We prove that the analytical form of this expression is given by the cycle index polynomial of the symmetric group $S_k$, which is itself related to the Bell polynomials. After doing so, we derive a family of quantum separability tests, each of which is generated by a finite group; for all such algorithms, we show that the acceptance probability is determined by the cycle index polynomial of the group. Finally, we produce and analyze explicit circuit constructions for these tests, showing that the tests corresponding to the symmetric and cyclic groups can be executed with $O(k^2)$ and $O(k\log(k))$ controlled-SWAP gates, respectively, where $k$ is the number of copies of the state being tested.
DirectVoxGO++: Grid-based fast object reconstruction using radiance fields

In recent years, the computer vision and computer graphics communities have seen the emergence of new classes of models for representing objects based on Neural Radiance Fields (NeRFs). These techniques use ideas based on traditional methods from computer graphics, for example, radiance fields, and combine them with implicit neural representations and neural image-based rendering (IBR). However, one significant drawback of this class of techniques was that they were too slow, taking in the range of hours in high-end GPUs. Due to these limitations, new techniques have been created for the fast reconstruction of scenes, such as Direct Voxel Grid Optimization (DirectVoxGO). Alongside this limitation, initially, NeRFs had limited compositing and modeling capabilities. For example, a simple task such as separating the background from the foreground could not be modeled with NeRFs until the emergence of new techniques such as NeRF++. Our method extends DirectVoxGO to allow the handling of unbounded scenes inspired by some ideas from NeRF++ and also adapts it to incorporate elements from a neural hashing approach employed by other works. Our technique improved photorealism compared with DirectVoxGO and Plenoxels on a subset of the Light Field Dataset on average in at least 2%, 8%, and 8% for Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) metrics, respectively, while also being an order of magnitude faster than NeRF++. Also, we demonstrate that, for the evaluated scenes, our technique has comparable training time and memory consumption than previous works. Code is available in https://github.com/danperazzo/dvgoplusplus.
Designing Secure and Privacy-Preserving Information Systems for Industry Benchmarking

Benchmarking is an essential tool for industrial organizations to identify potentials that allows them to improve their competitive position through operational and strategic means. However, the handling of sensitive information, in terms of (i) internal company data and (ii) the underlying algorithm to compute the benchmark, demands strict (technical) confidentiality guarantees—an aspect that existing approaches fail to address adequately. Still, advances in private computing provide us with building blocks to reliably secure even complex computations and their inputs, as present in industry benchmarks. In this paper, we thus compare two promising and fundamentally different concepts (hardware- and software-based) to realize privacy-preserving benchmarks. Thereby, we provide detailed insights into the concept-specific benefits. Our evaluation of two real-world use cases from different industries underlines that realizing and deploying secure information systems for industry benchmarking is possible with today's building blocks from private computing.
Efficient intent classification and entity recognition for university administrative services employing deep learning models

The design and implementation of a domain specific conversational agent requires efficient Natural Language Understanding (NLU). The task is harder when multiple languages have to be supported, and training datasets can be beneficial. This work focuses on the development of an intelligent system, an automated multilingual customer service conversational agent (chatbot) for university students, which supports both Greek and English and combines Intent Classification or Intent Extraction (IE) and Named Entity Recognition (NER) to understand the content (i.e. type of actions conveyed and respective entities) of users' messages. We focus on the development of the fundamental tasks required by a conversational agent to provide customer services in the education industry and manage requests with instant responses and increased customer satisfaction. Instead of handling IE and NER separately, as it is common in the related work, we develop a joint model that combines Bidirectional Long Short-Term Memory (BiLSTM) and Conditional Random Fields (CRF) layers and generates outputs both for IE and NER. We introduce a novel, open access dataset for customer services in education industry, the UniWay dataset, that has been used for training and evaluating our model, comprises students' questions in English and Greek about essential information related to their studies. A comparative evaluation of the proposed model versus state-of-the-art standalone and joint model solutions in UniWay and xSID datasets, results in improvement of the performance for the IE task up to 1.4% and it is on par with the state-of-the-art for the NER task. These results justify the intuition that closed domains can benefit from less sophisticated architectures, but less costly in terms of computational and memory resources, that jointly resolve multiple NLU tasks.