Reviewerly/exHarmony huggingface dataset:
Dynamic Fusion Technology of Mobile Video and 3D GIS: The Example of Smartphone Video

Mobile videos contain a large amount of data, where the information interesting to the user can either be discrete or distributed. This paper introduces a method for fusing 3D geographic information systems (GIS) and video image textures. For the dynamic fusion of video in 3DGIS where the position and pose angle of the filming device change moment by moment, it integrates GIS 3D visualization, pose resolution and motion interpolation, and proposes a projection texture mapping method for constructing a dynamic depth camera to achieve dynamic fusion. In this paper, the accuracy and time efficiency of different systems of gradient descent and complementary filtering algorithms are analyzed mainly by quantitative analysis method, and the effect of dynamic fusion is analyzed by the playback delay and rendering frame rate of video on 3DGIS as indicators. The experimental results show that the gradient descent method under the Aerial Attitude Reference System (AHRS) is more suitable for the solution of smartphone attitude, and can control the root mean square error of attitude solution within 2°; the delay of video playback on 3DGIS is within 29 ms, and the rendering frame rate is 34.9 fps, which meets the requirements of the minimum resolution of human eyes.
LiDAR-only 3D object detection based on spatial context

LiDAR-based 3D Object detection is one of the popular topics in recent years, and it is widely used in the fields of autonomous driving and robot controlling. However, due to the scanning pattern of LiDAR, the point clouds of objects at far distance are sparse and more difficult to be detected. To solve this problem, we propose a two-stage network based on spatial context information, named SC-RCNN (Spatial Context RCNN), for object detection in 3D point cloud scenes. SC-RCNN first uses a backbone with sparse convolutions and submanifold sparse convolutions to extract the voxel features of point scenes and generate a series of candidate boxes. For the sparsity of far-distance point clouds, we design the local grid point pooling (LGP Pooling) to extract features and spatial context information around candidate regions for subsequent box refinement. In addition, we propose the pyramid candidate box augmentation (PCB Augmentation) to expand the candidate boxes with a multi-scale style, enriching the feature encoding. The experimental results show that SC-RCNN significantly outperforms previous methods on KITTI dataset and Waymo dataset, and is particularly robust to the sparsity of point clouds.
ASP-Based Declarative Reasoning in Data-Intensive Enterprise and IoT Applications

In the last few years, we have witnessed the spread of computing devices getting smaller and smaller (e.g., Smartphones, Smart Devices, Raspberry, etc.), and the production and availability of data getting bigger and bigger. This work presents DLV-EE, a framework based on Answer Set Programming (ASP) for performing declarative reasoning tasks over data-intensive, distributed applications. It relies on the DLV2 system and it features interoperability means for dealing with Big-Data over modern industry-level databases (relational and NoSQL). Furthermore, the work introduces DLV-IoT, an ASP system compatible with “mobile” technologies for enabling advanced reasoning capabilities on smart/IoT devices; eventually, DLV-EE and DLV-IoT via some real-world applications are illustrated as well.
RNNCTPs: A neural symbolic reasoning method using dynamic knowledge partitioning technology

Although traditional symbolic reasoning methods are highly interpretable, their application in knowledge graph link prediction is limited due to their low computational efficiency. In this paper, we propose a new neural symbolic reasoning method: RNNCTPs, which improves computational efficiency by re-filtering the knowledge selection of Conditional Theorem Provers (CTPs), and is less sensitive to the embedding size parameter. RNNCTPs is divided into relation selectors and predictors. The relation selectors are trained efficiently and interpretably, so that the whole model can dynamically generate knowledge for the inference of predictor. In all four datasets, this method shows competitive performance against traditional methods on the link prediction task, and can have higher applicability than CTPs on some datasets.
Improving virtual reality navigation tasks using a haptic vest and upper body tracking

Haptic feedback is an important component of immersive virtual reality (VR) applications that is often suggested to complement visual information through the sense of touch. This paper investigates the use of a haptic vest in navigation tasks. The haptic vest produces a repulsive vibrotactile feedback from nearby static virtual obstacles that augments the user spatial awareness. The tasks require the user to perform complex movements in a 3D cluttered virtual environment, like avoiding obstacles while walking backwards and pulling a virtual object. The experimental setup consists of a room-scale environment. Our approach is the first study where a haptic vest is tracked in real time using a motion capture device so that proximity-based haptic feedback can be conveyed according to the actual movement of the upper body of the user. User study experiments have been conducted with and without haptic feedback in virtual environments involving both normal and limited visibility conditions. A quantitative evaluation was carried out by measuring task completion time and error (collision) rate. Multiple haptic rendering techniques have also been tested. Results show that under limited visibility conditions proximity-based haptic feedback generated by a wearable haptic vest can significantly reduce the number of collisions with obstacles in the virtual environment.
Comparing Germanic, Romance and Slavic: Relationships among linguistic distances

Languages differ along multiple dimensions (lexis, phonology, morphology, syntax). Related languages descend from a common ancestor language but have diverged over time. This paper asks whether languages diverge equally along all dimensions, and, to the extent that they do not, which dimension reflects the traditional language family tree best. We computed measures of (i) lexical distance (ii) phonetic distance, and (iii) syntactic distance. The measures were computed on all words and sentences extracted from a corpus of translations of four relatively short English texts into another four Germanic languages (Danish, Dutch, German, Swedish), five Romance languages (French, Italian, Portuguese, Romanian, Spanish) and six Slavic languages (Bulgarian, Croatian, Czech, Polish, Slovakian, Slovenian). We examined the correlation structure of the distances for all pairs of Germanic (10), Romance (10) and Slavic (15) languages (i.e., within-family comparisons only). The results indicate that the linguistic dimensions are generally correlated (weakly but significantly), and that the correlations are stronger for pairs within families than when all 35 pairs are examined together. Cladistic family trees correlate best with the lexical distance (0.851 < r < 0.887). This confirms that the genealogical language trees are predominantly based on lexical rather than phonetic or syntactic considerations.
Shortcuts to adiabaticity in a fast controlled-phase gate in superconducting quantum circuits

Based on renewed interest in the shortcut-to-adiabaticity techniques in quantum control, we propose a reverse-engineering approach to modulate the longitudinal coupling between a pair of two-level systems with a quantized single-mode resonator. This allows us to suppress the unwanted transitions in the time-evolution operator such that the system dynamics resemble a controlled-phase gate acting in the qubit subspace at the nanosecond scale. The reduced gating time mitigates the detrimental effect produced by the loss mechanisms in all aspects. Moreover, we present a possible experimental implementation based on superconducting quantum circuits. Our work further demonstrates the versatility of the reverse-engineering method to enhance quantum protocols based on circuit quantum electrodynamic architecture.
Facial feedback effect on the sense of body ownership during the rubber hand illusion

The sense of body ownership, a feeling that one's body belongs to the self, is an essential aspect of self-consciousness. Studies have focused on emotions and bodily states that could influence multisensory integration for the sense of body ownership. Based on the Facial Feedback Hypothesis, the purpose of this study was to examine whether displaying specific facial expressions affects the rubber hand illusion. We hypothesized that the expression of a smiling face changes the emotional experience and facilitates the formation of a sense of body ownership. In the experiment, participants (n = 30) were asked to hold a wooden chopstick in their mouths to simulate smile, neutral, and disgusted facial expressions during the induction of the rubber hand illusion. The results did not support the hypothesis, showing that proprioceptive drift (an index of illusory experience) was enhanced when the subjects displayed a disgusted facial expression, while the subjective reports of the illusion were not affected. These results, together with the previous studies regarding the effect of positive emotions, suggest that bodily affective information, regardless of its valence, facilitates multisensory integration and could influence the conscious representation of the bodily self.
Remote 3D Displacement Sensing for Large Structures with Stereo Digital Image Correlation

The work performance of stereo digital image correlation (stereo-DIC) technologies, especially the operating accuracy and reliability in field applications, is not fully understood. In this study, the key technologies of the field remote 3D displacement sensing of civil structures based on stereo-DIC have been proposed. An image correlation algorithm is incorporated in improving the matching accuracy of control points. An adaptive stereo-DIC extrinsic parameter calibration method is developed by fusing epipolar-geometry-based and homography-based methods. Furthermore, a reliable reference frame that does not require artificial markers is established based on Euclidean transformation, which facilitates in-plane and out-of-plane displacement monitoring for civil structures. Moreover, a camera motion correction is introduced by considering background points according to the camera motion model. With an experiment, the feasibility and accuracy of the proposed system are validated. Moreover, the system is applied to sense the dynamic operating displacement of a 2 MW wind turbine’s blades. The results show the potential capability of the proposed stereo-DIC system in remote capturing the full-field 3D dynamic responses and health status of large-scale structures.
Feature Fusion and Metric Learning Network for Zero-Shot Sketch-Based Image Retrieval

Zero-shot sketch-based image retrieval (ZS-SBIR) is an important computer vision problem. The image category in the test phase is a new category that was not visible in the training stage. Because sketches are extremely abstract, the commonly used backbone networks (such as VGG-16 and ResNet-50) cannot handle both sketches and photos. Semantic similarities between the same features in photos and sketches are difficult to reflect in deep models without textual assistance. To solve this problem, we propose a novel and effective feature embedding model called Attention Map Feature Fusion (AMFF). The AMFF model combines the excellent feature extraction capability of the ResNet-50 network with the excellent representation ability of the attention network. By processing the residuals of the ResNet-50 network, the attention map is finally obtained without introducing external semantic knowledge. Most previous approaches treat the ZS-SBIR problem as a classification problem, which ignores the huge domain gap between sketches and photos. This paper proposes an effective method to optimize the entire network, called domain-aware triplets (DAT). Domain feature discrimination and semantic feature embedding can be learned through DAT. In this paper, we also use the classification loss function to stabilize the training process to avoid getting trapped in a local optimum. Compared with the state-of-the-art methods, our method shows a superior performance. For example, on the Tu-berlin dataset, we achieved 61.2 + 1.2% Prec200. On the Sketchy_c100 dataset, we achieved 62.3 + 3.3% mAPall and 75.5 + 1.5% Prec100.
Decomposing the deep: finding class-specific filters in deep CNNs

Interpretability of Deep Neural Networks has become a major area of exploration. Although these networks have achieved state-of-the-art results in many tasks, it is extremely difficult to interpret and explain their decisions. In this work, we analyze the final and penultimate layers of Deep Convolutional Networks for image classification with respect to $$\ell _1$$ norm and develop an algorithm for identifying subsets of features that contribute most toward the network’s decision for each class. We also develop a novel decomposed softmax to efficiently re-train the network such that the class-specific decomposition is preserved. We provide a comparison with other methods for identifying class-specific filters and show, using Pairwise Mutual Information Score, that our technique provides better decomposition. The resulting decomposed final layer provides a low-dimensional embedding (decreased by around a factor of 10) per class, which is far more interpretable. Such a low-dimensional class-specific embedding makes diagnosing issues with misclassifications of a certain class in the data easier as fewer weights contribute to the decision for the data points of that class. It also enables the network toward easier diagnostics and pruning as an entire part of the final and pre-final layer can be excluded to remove predictions for data points belonging to a particular label. The resulting layer also achieves a modest computational cost gain as compared to the final layer of the full network. Our algorithm is unsupervised in nature and can be applied to any CNN.
Effects of Dipole-Dipole Interaction and Time-Dependent Coupling on the Evolution of Entanglement and Quantum Coherence for Superconducting Qubits in a Nonlinear Field System

We examine the temporal comportment of formation entanglement and quantum coherence in a quantum system made up of two superconducting charge qubits (SC-Qs), in the case of two different classes of nonlinear field. The results discussed the impact role of time-dependent coupling (T-DC) and dipole-dipole interaction (D-DI) on the temporal comportment of quantum coherence and entanglement in the ordinary and nonlinear field. In addition, we show that the main parameters of the quantum model affect the entanglement of formation and the coherence of the system in a similar way.
The voice as a material clue: a new forensic Algerian Corpus

Dialects have received bigger interest in recent years as they are increasingly used on the web and social media. Because Algerian Arabic dialects suffer from a lack of appropriate speech corpora for speech recognition, a rich dialect corpus is needed to approach Algerian Accent recognition. The latter remains a key feature in the field of Forensic Voice Comparison (FVC) systems. This paper presents a new large-scale forensic Algerian speech corpus called Sawt El-Djazaïr. An important criterion in dealing with forensic corpora is the presence of session variability. For this purpose, we collected celebrity recordings in various regions of Algeria, from different social networks, in various scenarios, and at different times. In addition, we also recorded 87 participants using cellular calls and voice over IP (VoIP) applications including Viber, WhatsApp, and Google Meet. The corpus of approximately 50 hours covers various speech topics and is spoken in twelve Algerian sub-dialects. The design guidelines of the proposed corpus are described along with the grouping of dialects across different geographical locations. Sawt El-Djazaïr is available to the research community upon request.
Simulated Phishing Training Exercises versus Gamified Phishing Education Games

Phishing, a type of social engineering attack, continues to be a problem, and anti-phishing approaches must be implemented to stymie these attacks. With the rise of new educational methods that go beyond lecture-based, the question is whether there is a modern approach to education. One method that has existed for years is that of simulated phishing training, but it is thought that there needs to be a more engaging approach to this area of education. Gamification in phishing education is a more recent but not extremely well studied approach, which leads to the question – Which is better for phishing education of users, simulated phishing exercises or gamified phishing training? In this report, five research articles in each category are described and summarized, and then an analysis is conducted within the groups and between the two groups. The analysis is focused on quantitative metrics that appear in the selected research articles; there is a lesser focus on the qualitative analysis conducted in the reviewed reports. From this comparative analysis, gamified phishing training appears to be a more suitable method of educating users, as it reduces susceptibility and engages students at a higher level of learning.
Advantages of using Containerization Approach for Advanced Version Control System

DevOps is largely recognized as the one of the most influential development technique for a business, thus many companies have adopted it. The advantages of DevOps are numerous since it successfully enables both the development and operations departments to collaborate in order to reach the organization's objective goal more quickly and efficiently. Continuous Integration and Continuous Development are two major components or procedures in DevOps culture. The success of DevOps in a company is determined by how well workflow operations, such as automation and scaling, are implemented in the production environment. In this work, various advantages of using containerization approach for an advanced version control system are discussed with the help of few experimentations. Various DevOps systems which have been proposed for version manipulate migration are explored. Experimentation has been done with respect to success/error, average duration and API calls breakdown.
Deep Learning Methods for Query Auto Completion

Query Auto Completion (QAC) aims to help users reach their search intent faster and is a gateway to search for users. Everyday, billions of keystrokes across hundreds of languages are served by Bing Autosuggest in less than 100 ms. The expected suggestions could differ depending on user demography, previous search queries and current trends. In general, the suggestions in the AutoSuggest block are expected to be relevant, personalized, fresh, diverse and need to be guarded against being defective, hateful, adult or offensive in any way. In this tutorial, we will first discuss about various critical components in QAC systems. Further, we will discuss details about traditional machine learning and deep learning architectures proposed for four main components: ranking in QAC, personalization, spell corrections and natural language generation for QAC.
A Novel Model to gain Internet Anonymity using Elastic Cloud Computing

Anonymity systems are widely used nowadays to protect user identity, but there are various threats currently in the anonymity network, such as virtual private networks, onion routing, and proxy servers. This paper looked at the different anonymity networks that are already out there and proposed a new model to stay anonymous on the internet by using open source tools and methods. This eliminates the current threats. It works by creating a virtual instance on the cloud server and configuring it using open source technologies such as OpenVPN. This model uses elastic cloud computing technology running over existing technologies such as virtual private networks and onion routing. The framework is a new way to stay anonymous on the internet. It is made up of only open source technologies.
Do the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?

A number of learned sparse and dense retrieval approaches have recently been proposed and proven effective in tasks such as passage retrieval and document retrieval. In this paper we analyze with a replicability study if the lessons learned generalize to the retrieval of responses for dialogues, an important task for the increasingly popular field of conversational search. Unlike passage and document retrieval where documents are usually longer than queries, in response ranking for dialogues the queries (dialogue contexts) are often longer than the documents (responses). Additionally, dialogues have a particular structure, i.e. multiple utterances by different users. With these differences in mind, we here evaluate how generalizable the following major findings from previous works are: (F1) query expansion outperforms a no-expansion baseline; (F2) document expansion outperforms a no-expansion baseline; (F3) zero-shot dense retrieval underperforms sparse baselines; (F4) dense retrieval outperforms sparse baselines; (F5) hard negative sampling is better than random sampling for training dense models. Our experiments ( https://github.com/Guzpenha/transformer_rankers/tree/full_rank_retrieval_dialogues .)—based on three different information-seeking dialogue datasets—reveal that four out of five findings (F2–F5) generalize to our domain.
Blooms Taxonomy based Gradation of the Question Paper

The intention behind this paper is to grade the professional course (Engineering) question papers based on Blooms Taxonomy standards. Current situation badly need this analysis model which will enhance the level of setting the question paper among the faculty fraternity. The gradation of question paper is nothing but checking the quality, effectiveness and complexity of the questions in the question paper. It always focuses on the type of questions included which fulfills the requirements of the Blooms Taxonomy standards or not. The input parameters used in order to do so include, the number of high level questions on par with blooms taxonomy, the extent of coverage in terms of modules, the number of questions with ambiguity and no of questions attempted by most of the students. The system is trained in such a way that it can able to assess the grade of the question paper for a given input parameters. The type of the questions set should not be simple or nor complex. Any average student can able to attempt the question paper without much difficulty. The main focus is to select the questions by covering the entire syllabus. There should not be any questions selected from out of syllabus. The rating analysis will be held by counting cum grouping operation.