Reviewerly/exHarmony huggingface dataset:
Vision based 3D Object Detection using Deep Learning: Methods with Challenges and Applications towards Future Directions

For autonomous intelligent systems, 3D object detection can act as a basis for decision making by providing information such as object's size, position and direction to perceive information about surrounding environment.Successful application using robust 3D object detection can hugely impact robotic industry, augmented and virtual reality sectors in the context of Fourth Industrial Revolution (IR4.0).Recently, deep learning has become potential approach for 3D object detection to learn powerful semantic object features for various tasks, i.e., depth map construction, segmentation and classification.As a result, exponential development in the growth of potential methods is observed in recent years.Although, good number of potential efforts have been made to address 3D object detection, a depth and critical review from different viewpoints is still lacking.As a result, comparison among various methods remains challenging which is important to select method for particular application.Based on strong heterogeneity in previous methods, this research aims to alleviate, analyze and systematize related existing research based on challenges and methodologies from different viewpoints to guide future development and evaluation by bridging the gaps using various sensors, i.e., cameras, LiDAR and Pseudo-LiDAR.At first, this research illustrates critical analysis on existing sophisticated methods by identifying six significant key areas based on current scenarios, challenges, and significant problems to be addressed for solution.Next, this research presents strict comprehensive analysis for validating 3D object detection methods based on eight authoritative 3D detection benchmark datasets depending on the size of the datasets and eight validation matrices.Finally, valuable insights of existing challenges are presented for future directions.Overall extensive review proposed in this research can contribute significantly to embark further investigation in multimodal 3D object detection.
FPGA-Based Hardware Modeling on Pigeon-Inspired Optimization Algorithm

By learning behavioral characteristics and biological phenomena in nature, such as birds, ants, and fireflies, intelligent optimization algorithms (IOA) is proposed. IOA shows feasibility in solving complex optimization problems in reality. Pigeon-inspired optimization (PIO) algorithm, which belongs to intelligent optimization algorithms, is proposed by the pigeons homing navigation behavior inspired. PIO is superior to other algorithms in dealing with many optimization problems. However, the performance of PIO processing large-scale complex optimization problems is poor and the execution time is long. Population-based optimization algorithms (such as PIO) can be optimized by parallel processing, which enables PIO to be implemented in hardware for improving execution times. This paper proposes a hardware modeling method of PIO based on FPGA. The method focuses on the parallelism of multi-individuals and multi-dimensions in pigeon population. For further acceleration, this work uses parallel bubble sort algorithm and multiply-and-accumulator (MAC) pipeline design. The simulation result shows that the implementation of PIO based on FPGA can effectively improve the computing capability of PIO and deal with complex practical problems.
Comparative Assessment of PID, Fuzzy Logic and ANFIS Controllers in an Automatic Voltage Regulator of A Power System

A comparative study and performance analysis of three different controllers - namely proportional-integral-derivative (PID), PD-like fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) - utilized to control the output voltage of an automatic voltage regulator (AVR) of a power system are carried out. The obtained results show that the PID controller is capable of rejecting simultaneous disturbance signals effectively with zero steady-state error (SSE). However, it is not robust to unexpected parameter changes of the system. On the other hand, the fuzzy logic controller shows the ability to resist changes in the system parameters. Nonetheless, it exhibits both an increase of 12.5% in the SSE and fluctuations in disturbance rejection test. On the contrary, the ANFIS controller shows: i) superior performance and ii) robustness to disturbance signals and changes in the system parameters compared to the other two controllers. For these reasons, we believe that utilization of an ANFIS controller will not only promote safety, but also reliability of the AVR in power systems.
Experts’ Evaluation of a Proposed Taxonomy for Immersive Learning Systems

Immersive learning systems (ILSs) allow the recreation of an idealized world in virtual environments, providing experiences that can help learners in learning skills realistically. These environments are typically supported by immersive technologies to improve immersion and provide real-time feedback. However, designing an ILS is a strenuous process due to its wide selection of technologies, design practices, and pedagogical interventions. In this paper, we evaluate the unified taxonomy of ILS by conducting a qualitative analysis with 42 experts from various backgrounds. The evaluation covers the ILS strengths, ILS weaknesses, reusable ILS components, and additional ILS components.
Proposal for Thin Shell Gripper Fabricated by Demolding-Free Casting for Handling and Packing Multiple Cucumbers Simultaneously

Soft grippers, including the conventional parallel shell gripper, are fabricated using traditional rubber casting process. In this research, we propose a demolding-free casting approach that does not require demolding silicon rubber from the mold. The rigid shell component serves as not only the mold for casting but also a part of the shell gripper. By using the proposed fabrication approach, shell gripper with thin structure can be realized, which is very essential for grasping objects in a clustered scenario. Experiments on grasping and packing multiple cucumbers simultaneously were conducted to evaluate the performance of the fabricated shell gripper.
An Image Classification Method Based on Self-attention ConvNeXt

In the past one or two years, Vision Transformers (ViTs) have almost replaced ConvNets as the best performing image classification model, but VIT still has certain limitations. It is used in general computer vision such as semantic segmentation and object detection. Task-wise applications are facing difficulties. However, hierarchical Transformers reintroduced ConvNet, which makes Transformers as general vision backbones is actually feasible, and shows excellent performance on various vision tasks. But the effectiveness of this approach is largely due to the inherent advantages of the Transformer, rather than the inductive bias inherent in convolution. The recently released ConvNexts network consists entirely of ConvNet modules and competes with Transformer in accuracy and scalability, achieving the highest accuracy on ImageNet and surpassing Swin Transformers in COCO detection and ADE20K segmentation, while retaining the basic properties of ConvNets, simplicity and efficiency. We use the ConvNeXt network structure and add an attention module to it. After experiments, the performance in the ISIC2018 dataset and Flowers image classification is better than the performance of using ConvNeXt alone.
Chinese Text Classification Using BERT and Flat-Lattice Transformer

Recently, large scale pre-trained language models such as BERT and models with lattice structure that consisting of character-level and word-level information have achieved state-of-the-art performance in most downstream natural language processing (NLP) tasks, including named entity recognition (NER), English text classification and sentiment analysis. For Chinese text classification, the existing methods have also tried such kinds of models. However, they cannot obtain the desired results since these pre-trained models are based on characters, which cannot be applied for Chinese language that is based on words. To address this problem, in this paper, we propose BFLAT which a simple but efficient model for Chinese text classification. Specifically, BFLAT utilizes BERT and word2vec to learn character-level and word-level vector representations, and then adopts the flat-lattice transformer to integrate both of the two-level vector representations. Experimental results on two datasets demonstrate that our proposed method outperforms the baseline methods over 1.38–21.82% and 3.42–20.7% in terms of relative F1-measure on two Chinese text classification benchmarks, respectively.
Correlations in Noisy Measurements

Although a measurement on the computational basis is devised in quantum computing, e.g., cloud-based quantum computing services, a quantum measurement in a realistic scenario is noisy. In particular, a measurement readout error in the era of noisy-intermediate-scale-quantum technologies is significant. In this work, we consider measurements on multiple qubits in a realistic scenario and present a method of analyzing noise in the measurement. Correlations existing in a noisy measurement are of particular interest so that local and crosstalk errors are introduced: the existence of correlations in a measurement in cloud-based quantum computing services is verified. We then present quantification of local and crosstalk errors and apply it to a noisy measurement in a cloud-based quantum computing service. Our results that analyze measurement errors can be used to mitigate these errors.
A survey on trends on mobile app development and applications

The widespread use of smart-phones and the increasing demand for mobile users to have similar desktop functionality and performance have resulted in better and faster mobile innovation technology. We present, in this survey, an overview of the body of the literature that deals with the latest emerging mobile technologies and examines its impact on the current mobile app ecosystem. To the best of our knowledge, this is the first work that combines the most significant mobile advancements while showing the futuristic potentials and challenges faced by the mobile app industry. Therefore, the paper defines the criteria for selecting these latest mobile developments since it cannot incorporate all of them due to the magnitude of the subject.
An Efficient Lattice-Based Encrypted Search Scheme with Forward Security

Public-key encryption with keywords search (PEKS) can realize the retrieval of ciphertext data, which is a vital cryptographic primitive in the field of cloud storage. However, in practical applications, the secret keys of users are often exposed due to careless store or computer attacks, which causes privacy disclosure. Furthermore, considering the attack from quantum computers, this paper designs a lattice-based PEKS scheme with forward security which can reduce the damage from key exposure. Different from previous schemes, this scheme combines the binary tree structure and lattice basis extension algorithm to achieve the one-way key evolution mechanism which allows data users to update their secret keys periodically. Thus, the security of the scheme can be still guaranteed when the secret keys are exposed. Moreover, with the update of secret keys, data users’ public keys in our construction are fixed, which helps to save storage cost further, while public key in other existing scheme changes along with the update. Theoretic analysis shows that our scheme is more efficient when compared with the previous scheme.
Deep Learning Assisted Key Recovery Attack for Round-Reduced Simeck32/64

In CRYPTO’2019, Gohr firstly introduced deep learning into differential cryptanalysis. He successfully found 5/6/7/8-round neural differential distinguishers of Speck32/64 and mounted key recovery attacks against 11/12-round Speck32/64 with a variant of Bayesian optimization. In this paper, we make some improvements to Gohr’s framework and apply it to Simeck32/64. We also present some parameter tuning experience for running deep learning assisted key recovery attacks. As proof, we obtain 8/9/10-round neural differential distinguishers for Simeck32/64 and successfully recover the penultimate round and last round subkeys for 13/14/15-round Simeck32/64 with low data complexity and time complexity.
Prioritizing Corners in OoD Detectors via Symbolic String Manipulation

For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training dataset. This paper studies the problem of systematically testing OoD monitors to avoid cases where an input data point is tested as in-distribution by the monitor, but the DNN produces spurious output predictions. We consider the definition of “in-distribution” characterized in the feature space by a union of hyperrectangles learned from the training dataset. Thus the testing is reduced to finding corners in hyperrectangles distant from the available training data in the feature space. Concretely, we encode the abstract location of every data point as a finite-length binary string, and the union of all binary strings is stored compactly using binary decision diagrams (BDDs). We demonstrate how to use BDDs to symbolically extract corners distant from all data points within the training set. Apart from test case generation, we explain how to use the proposed corners to fine-tune the DNN to ensure that it does not predict overly confidently. The result is evaluated over examples such as number and traffic sign recognition.
Role of Information Technology in improving the work of External Auditor, A Study of Jordan

The purpose of this study was to investigate the IT usage rate among accountants in Jordan and the factors that affect the IT usage rate. The proposed model examines the impact of IT skills, IT education, and corporate categories on her use of IT. The researcher used a survey questionnaire and collected data from 96 external people representing companies operating in Jordan. All the responses from the people are analyzed in the SPSS software. A partial least squares (PLS) structural equation modelling approach was used to analyze the data and test the proposed model. As a result, it was found that IT skills and IT training significantly contributed to her IT utilization by external stakeholders. The originality of this white paper lies in the way it contributes to understanding the combined effects of IT knowledge and training to understand how to use IT. In addition, we develop our current understanding of the mitigating effects of Big 4 and non-Big four firm categories on the relationship between IT training and the external auditor's use of IT.
The effectiveness of teaching technology in the era of the COVID 19 pandemic through virtual classroom learning activities using telegram media

The COVID-19 pandemic has changed various aspects of human life today, especially in education. It requires all elements of education to adapt and continue learning in the following semester. The application of technology in education improves the ability of teachers to deliver material to students, makes it easier for students to understand the material presented and makes time more effective and efficient. Technology has a significant role in the field of education. The purpose of this study is to provide choices about the effectiveness of learning using telegram, learning technology media devices during the COVID-19 pandemic. This study uses a qualitative method, international articles, and similar sources related to learning solutions during a pandemic using telegram media. Online learning using telegram media is an effective alternative to activate classes even though schools have been closed considering that time and place are at risk during the current pandemic. However, it is essential to evaluate this learning technique according to local conditions considering that the distribution of facilities and the ability of parents to provide different online learning facilities to students in Indonesia is still minimal.
Classification of LPI Radar Signals Using Multilayer Perceptron (MLP) Neural Networks

LPI digital receivers play an important role in Electronics Warfare. The signals are generated for LPI radar using frequency and phase modulation techniques within the pulse. It is difficult to know the modulation parameters for ESM receivers under low signal to noise ratio conditions. Advanced signal processing algorithms are applied to extract the various modulation parameters. It is not sufficient to counter attack just by knowing the parameters. Besides modulation parameters, it is important to know the type of modulation technique. In this paper, a multilayer perceptron neural network is developed to classify the type of modulation technique under various noise conditions. The results are compared with the existing techniques.
Pengembangan Lembar Kerja Siswa (LKS) Berbasis Model Pembelajaran Think Pair Share (TPS) dengan Media Mind Map pada Materi Sistem Pencernaan Makanan Kelas VIII SMP Negeri 5 Kodi

Teaching materials are important to support the learning process. One type of teaching material is worksheets. The development of LKS with the Think Pair share learning model was carried out because it was carried out to meet the needs of students regarding interesting, fun, and innovative teaching materials. The purpose of this study was to determine the results of developing LKS Based on the TPS Learning Model with Mind Map media on Food Digestive System Material for Class VIII SMP Negeri 5 Kodi which met the valid criteria. The research method used in this study is a research and development method with a 4D model. The research procedure consists of the define (defining), design (design), develop (development) stages. The types of data used in this research are qualitative and quantitative data. Based on the results of the study, the validation of the mind map-based TPS learning model by material experts was 83.3% so that it met the criteria of good/decent and media experts was 98.8% so that it met the criteria of very good/very feasible.
Universally Composable $$\varSigma $$-protocols in the Global Random-Oracle Model

Numerous cryptographic applications require efficient non-interactive zero-knowledge proofs of knowledge (NIZKPoK) as a building block. Typically they rely on the Fiat-Shamir heuristic to do so, as security in the random-oracle model is considered good enough in practice. However, there is a troubling disconnect between the stand-alone security of such a protocol and its security as part of a larger, more complex system where several protocols may be running at the same time. Provable security in the general universal composition model (GUC model) of Canetti et al. is the best guarantee that nothing will go wrong when a system is part of a larger whole, even when all parties share a common random oracle. In this paper, we prove the minimal necessary properties of generally universally composable (GUC) NIZKPoK in any global random-oracle model, and show how to achieve efficient and GUC NIZKPoK in both the restricted programmable and restricted observable (non-programmable) global random-oracle models.
Research of Federated Learning Application Methods and Social Responsibility

Federated learning is a multi-party distributed machine learning system that allows each participant to complete the training task without their data out of the locality. The real meaning of the realization of data availability is invisible to protect personal privacy and data security. Although some institutions need to obtain their data's benefit by sharing their own data sets, the potential trust risks lead to the data not being better utilized cooperatively. In addition,regulatory requirements on data computing and sharing are different in various countries. These contribute to enormous challenges in the actual application of federated learning. In this research, we make a comprehensive review of the application patterns of federated learning in different fields. Then, we illustrate the social responsibilities of federated learning from four dimensions, including compliance application, system security mechanism, the trust mechanism, and ethical security. Finally, based on the current characteristics and regulatory requirements of federated learning, we discuss the future research directions for federated learning.
Truncated Differential Properties of the Diagonal Set of Inputs for 5-Round AES

In the last couple of years, a new wave of results appeared, proposing and exploiting new properties of round-reduced AES. In this paper we survey and combine some of these results (namely, the multiple-of-n property and the mixture differential cryptanalysis) in a systematic way in order to answer more general questions regarding the probability distribution of encrypted diagonal sets. This allows to analyze this special set of inputs, and report on new properties regarding the probability distribution of the number of different pairs of corresponding ciphertexts are equal in certain anti-diagonal(s) after 5 rounds. An immediate corollary of the multiple-of-8 property is that the variance of such a distribution can be shown to be higher than for a random permutation. Surprisingly, also the mean of the distribution is significantly different from random, something which cannot be explained by the multiple-of-8 property. We propose a theoretical explanation of this, by assuming an APN-like assumption on the S-Box which closely resembles the AES-Sbox. By combining the multiple-of-8 property, the mixture differential approach, and the results just mentioned about the mean and the variance, we are finally able to formulate the probability distribution of the diagonal set after 5-round AES as a sum of independent binomial distributions.
Research on multimedia and interactive teaching model of college English

How to improve the quality of teaching effectively and effectively is a current research hotspot. This paper constructs a 'multi-interactive' English teaching model from the aspects of teaching content and teaching strategies to improve English teaching. The proposed method extracts and retrieves information from multiple teaching resources, optimises and controls the allocation of resources under load balancing conditions, and builds a data mining model technology for college English teaching resources in an information environment. The proposed method can maximise the enthusiasm and creativity of teachers and students, and can develop multimedia network resources, create a 'multiple interactive' teaching environment, and create a learning platform for students in universities.
A Survey on Integration of Blockchain and IoT (BIoT): Open Issues, Challenges &amp; Solution

Blockchain and IoT are becoming the buzzwords in the tech world. Blockchain in itself is a very vast topic with various applications and uses but it has its fair share of challenges. Since IoT consists of a vast number of devices, from batterypowered devices with less computational resources to powerful machines, connected over a network it also has hurdles with various barriers. But this paper provides an in-depth review of the current situation of integration of Blockchain and the Internet of Things(IoT) and the Security Issues or Challenges that arise. It starts by discussing the ins and outs of blockchain technology and IoT and their applications. It compares the challenges that arise from using different public blockchain technologies like scalability, interoperability, computational resource constraints, storage, etc. This review also analyzes the current solutions that are present to mitigate the above-mentioned challenges in the integration of Blockchain and IoT. This paper also shines some light on the applications of BIoT and how it is changing and influencing the security challenges in current IoT devices. A detailed and in-depth analysis of this integration of blockchain and IoT was missing so this paper tries to summarize the challenges and solutions.