Reviewerly/exHarmony huggingface dataset:
Energy-Efficient Deep Neural Network Optimization via Pooling-Based Input Masking

Deep Neural Networks (DNNs) are increasingly deployed in battery-powered and resource-constrained devices. However, the most accurate DNNs usually require millions of parameters and operations, making them computation-heavy and energy-expensive, so it is an important topic to develop energy efficient DNN models. In this paper, we present an efficient DNN training framework under energy constraint to improve the energy efficiency of DNN inference. The key idea of this research is inspired by the observation that the input data of DNNs is usually inherently sparse and such sparsity can be exploited by sparse tensor DNN accelerators to eliminate ineffectual data access and compute. Therefore, we can enhance the inference accuracy within the energy budget by strategically controlling the sparsity of the input data. We build an energy consumption model for the sparse tensor DNN accelerator to quantify the inference energy consumption from the perspective of data access and data processing. In particular, we define a metric (named sporadic degree) to characterise the influence of the number of sporadic values in the sparse input on the energy consumption of data access for the sparse tensor DNN accelerator. Based on the proposed quantitative energy consumption model, we present an efficient pooling-based input mask training algorithm to optimize the energy efficiency of DNN inference by enhancing the input sparsity and reducing the number of sporadic values in the masked input. Experiments show that compared with the state-of-the-art methods, our proposed method can achieve higher inference accuracy with lower energy consumption and storage requirement owing to higher sparsity and lower sporadic degree of the masked input.
Target Tracking Algorithm Based on Multi-feature Adaptive Fusion in Complex Scenes

Traditional correlation filtering algorithms limit the tracking performance in complex scenes because they cannot fully utilize the features of the target. To solve this problem, a multi-feature adaptive fusion target tracking algorithm is proposed in this paper. First, the HOG feature, CN feature and Gray feature of the target are extracted respectively, and the CN feature and Gray feature are linearly splined into color feature by cat function to improve the discriminability of feature representation in the tracking process. Then, the maximum response values of HOG feature and color feature are calculated respectively, and the response values are normalized. Finally, the fusion weights of the features are determined according to the normalization results, and the adaptive fusion of the features is realized in the response layer. In order to ensure the feasibility of feature fusion, APCE value was introduced in this paper to compare the indicators before and after fusion, and experiments were conducted on OTB2015. The results show that the algorithm presented in this paper has significantly improved the success rate and accuracy, and has good robustness to deal with complex environments.
Dual Self-Paced SMOTE for Imbalanced Data

Imbalanced classification has always been a challenging issue. The minority class usually has degraded recognition rate. The key factors are sample scarcity of the minority class and intrinsic complex distribution characteristics in imbalanced data. SMOTE and its extensions are commonly used data-level methods. These SMOTE-related methods oversample all or unsafe minority class examples, without considering sample difference or only considering sample difference in spatial distribution. In this paper, we propose a dual self-paced SMOTE (DSP-SMOTE) method, which considers temporal-spatial distribution of samples. DSP-SMOTE adopts a two-way self-paced mechanism at different stages of sampling. The majority class is undersampled according to easy example first principle, whereas the minority class is oversampled following difficult sample first principle. In DSP-SMOTE, a hybrid (including static and dynamic) difficulty is introduced to estimate easy or difficult examples. Furthermore, the trade-off between static and dynamic difficulties is adjusted adaptively according to sample distribution and training status. Experimental results demonstrate that DSP-SMOTE outperforms previous SMOTE-related methods significantly in terms of AUC and sensitivity.
Applying Custom Patterns in Semantic Equality Analysis

This paper develops a novel approach to using code change patterns in static analysis of semantic equivalence of large-scale software. In particular, we propose a way to define custom code change patterns, describing changes that do change the semantics but in a safe way, and a graph-based algorithm to efficiently detect occurrences of such patterns between two versions of software. The proposed method allows one to reduce the number of false positive results generated by static code-pattern-based analysis of semantic equivalence by specifying which patterns of changes should be considered semantically equivalent. Our experiments with the Linux kernel show that it is possible to eliminate a substantial number of detected differences with just a small number of patterns, while maintaining a very high scalability of the overall analysis. Furthermore, the proposed concept allows for a possible future combination with automatic inference of patterns, which promises significant improvements in the area of static analysis of semantic equivalence.
3D CAD design of KUKA robot arm &amp; integration into AR environment to educational purposes

The use of virtual spaces is also part of Industry 4.0 technology. This is primarily due to the industry's requirement for increased productivity. When used, augmented and virtual reality technologies can have a direct and indirect positive impact on production. The main purpose of this paper is to make a robot more accessible, interactive, and safe with the help of Augmented Reality. An industrial robot unit will be designed as a 3D CAD model, which will then be integrated into a simulation environment where the original physical machine's parameters are entered for the virtual model, thereby simulating the operation of the machine unit. Following that, the 3D model is placed to create its own marker, which contains its documentation and enables virtual interaction. Although technological developments are bringing down the price of robots, the average individual or student still finds them to be expensive. In our approach, we assert that the use of augmented reality (AR) makes it simpler to visualize and simulate industrial situations. Additionally, it is feasible to enhance process programming, maintenance, training, and monitoring. Numerous applications can be built using this project.
A UAV Object Detection Algorithm Based on Improved YOLOX

In aerial photography, UAV will cause small objects and mutual occlusion between objects, bringing enormous challenges to the current one-stage object detection algorithm. In order to solve the above problems, this paper proposes a YOLOX-TBP one-stage object detection algorithm. Based on YOLOX-l, the algorithm adds a prediction head to detect objects of different scales to improve the detection effect of small objects. At the same time, the partial convolution of the backbone network is replaced by Transformer to improve the global information of the model, and a weighted bi-directional feature pyramid network (BiFPN) is introduced to improve the multi-scale feature fusion effect through the method of self-learning weights. The final experiments show that the MAP of the YOLOX-TBP model tested on the VisDrone-DET 2021 dataset reaches 40.27%, which is 0.84% higher than the previous SOTA method (DBNet).
A Study on Game-Based Chinese Vocabulary Learning Tool Powered by Knowledge Graph

Chinese vocabulary learning is important to Chinese second language learners. This research focuses on the design of an online Chinese vocabulary learning tool, especially on game-based interaction, and uses knowledge graph technology as the core driver to enhance interactivity, fun, personalization, and intelligence of this learning tool. It is verified that the tool developed in such a way can realize the essential functions of learning tools, that is, to help learners learn effectively.
Topic Modeling for Short Texts Via Dual View Collaborate optimization

Short text topic modeling attracts many researchers’ attention with the emergence of online social media platforms, such as news websites, Twitter and Facebook. Existing topic models for short texts mainly focus on relieving the sparse problem to enhance the accuracy performance of topic modeling. However, most previous topic modeling approaches introduce external corpus word embeddings to enrich the global semantic information in the topic modeling process, ignoring the local association information of the target corpus. And the global semantic information provided by word embedding may not be entirely suitable for the target corpus. In most cases, the noise will be introduced to interfere with the reasoning of the topic. This paper proposes a novel topic model for short text called the Dual View Biterm Topic Model (DV-BTM). Specifically, DV-BTM constructs two views while learning local information from the target corpus and global information to auxiliarily infer about the topic. The semantic similarity view provides global information obtained by introducing pre-trained word embeddings on an external corpus. The Wordnet view is constructed based on the target corpus itself, mainly providing local information about the corpus. Finally, through the collaborative optimization of the dual views, the consistency of the extracted topics is improved. The DV-BTM experiments on two real-world short text datasets demonstrate that DV-BTM has the best performance among the comparison methods in topic coherence and text classification aspects.
DDMA: Discrepancy-Driven Multi-agent Reinforcement Learning

Multi-agent reinforcement learning algorithms depend on quantities of interactions with the environment and other agents to derive an approximately optimal policy. However, these algorithms may struggle in the complex interactive relationships between agents and tend to explore the whole observation space aimlessly, which results in high learning complexity. Motivated by the occasional and local interactions between multiple agents in most real-world scenarios, in this paper, we propose a general framework named Discrepancy-Driven Multi-Agent reinforcement learning (DDMA) to help overcome this limitation. In this framework, we first parse the semantic components of each agent’s observation and introduce a proliferative network to directly initialize the multi-agent policy with the corresponding single-agent optimal policy, which bypasses the misalignment of observation spaces in different scenarios. Then we model the occasional interactions among agents based on the discrepancy between these two policies, and conduct more focused exploration on these areas where agents interact frequently. With the direct initialization and the focused multi-agent policy learning, our framework can help accelerate the learning process and promote the asymptotic performance significantly. Experimental results on a toy example and several classic benchmarks demonstrate that our framework can obtain superior performance compared to baseline methods.
Two Stages of Algebraic Matrix Decomposition in Optimal Image Steganographic Approach

With the evolution of social networks, the necessitate of providing information security methods becomes increasingly significant. Therefore, these methods should be studied and analyzed for presenting more secure and appropriate techniques. Since the images represent the most common and widely utilized visual formats, an optimized approach of image steganography based on algebraic matrix decomposition has been proposed in this paper. In particular, matrix decomposition methods represent a significant tool for increasing the security of the whole blent of digital steganographic approaches. In this paper, two stages of the Hessenberg matrix decomposition are implemented on the original image using modulo operation to show the impact of the matrix decomposition in the steganographic process. Additionally, a Chameleon Swarm optimization (CSO) algorithm is utilized to appoint the optimal positions in the embedding stage. The obtained results have been evaluated to show the difference between the utilization of decomposition or not.
A Case Study of Big Data Technology on Chinese Phonology

Chinese, as a language, has developed for thousands of years. Not only the shape and meaning of the characters have changed, but also the pronunciation has changed a lot. In order to deeply reveal the evolution law of Chinese language and pronunciation, and to realize the innovation and breakthrough of Chinese phonology research methods, we use big data technology to compare and analyze the materials of ancient Chinese pronunciation “Pingshui Rhyme” with that of modern Chinese pronunciation “Chinese New Rhyme”. The results of experiments are basically consistent with the results of traditional research methods. And the big data technology is efficient and accurate, which is conducive to discovering deep rules in the study of Chinese language and phonetics.
Optimizing Keyword Search Over Federated RDF Systems

The issue of keyword search in federated resource description framework(RDF) systems is revisited in this paper. Numerous independent SPARQL endpoints that solely offer SPARQL query interfaces make up a federated RDF system. Others find it challenging to remotely build up the global inverted indices over the full RDF network by downloading the dataset at various SPARQL endpoints. This poses a challenge for existing keyword search approaches. In this paper, we propose a keyword search approach for federated RDF systems without building up global inverted indices on the entire RDF graph. We build an offline schema graph for the federated RDF system. During query evaluation, the full-text search interfaces provided by existing SPARQL endpoints were used to map keywords to vertices in the schema graph. Subsequently, the schema graph was explored to construct SPARQL queries from the keywords. The constructed queries were evaluated over the underlying federated RDF systems. Some cost models were proposed to measure keyword mapping and query construction. On the other hand, to further improve the efficiency, a multiple query optimization strategy was designed to rewrite the queries and share the common computation during evaluation. Extensive experiments on real RDF datasets show that the proposed techniques are effective.
Influences EL Nino Southern Oscillation (ENSO) on rainfall in East Kalimantan-Indonesia

The El Nino Southern Oscillation (ENSO) is a global hydro-climate phenomenon that affects temperature [1] rainfall [2], discharge, and evaporation. The aims of the study are to examine influences ENSO on rainfall in East Kalimantan, Indonesia. This research uses Southern Oscillation Index (SOI) as ENSO parameters. The data set starts from 1986 to 2012 and consist of 24 rainfall stations. The correlation analysis uses Pearson Product-Moment Correlation. Correlation was calculated by dividing it into 4 seasons: January-February-March (JFM), April-May-June (AMJ), July- August-September (JAS), and October-November-December (OND). Testing were conducted from lag-0 to lag-3. The highest correlations were identified in seasons JAS and OND in lag 0, almost all of the stations indicated positive correlations with r greater than 0.3. These showed that the high correlation between ENSO and rainfall occurred in July to December during lag 0. In lag 1, several stations still have a positive correlation. Some stations maintained a correlation coefficient almost identical to the lag 0. The correlation was lower in lag 2 and lag 3. These lags generally have a negative correlation with r -0.3 to 0 (weak correlation). From the results, we could conclude that ENSO events have a direct impact on rainfall in East Kalimantan, Indonesia. The highest influence occurred in JAS and OND with moderate correlation strength. Rainfall was decreasing in El Nino event and increasing in La Nina event. The result also indicate that ENSO still have impact in three, six, and nine months lag time on rainfall. However, it had just a weak effect.
Transform-Domain Federated Learning for Edge-Enabled IoT Intelligence

Federated learning (FL) deployed in the edge network environment is a promising approach for combining the separated training results based on the isolated local data sensed by various Internet of Things (IoT) devices. However, the limited computing resources for the training of various application models in each edge server and the communication burden among the edge server and numerous IoT devices greatly impact the realization of IoT intelligence. In this article, we propose transform-domain FL schemes based on discrete cosine transform (DCT-FA) and discrete wavelet transform (DWT-FA) to achieve better training efficiency and reduce the communication burden for IoT devices. Furthermore, when the amount of training data is limited, we propose to combine time-domain features and frequency-domain features in FL (CDCT-FA) that turns out to achieve much higher test accuracy. From the experimental results, the transform-domain FL schemes are shown to be promising, given the different constraints and requirements of various IoT intelligence applications.
Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials

In this paper, we present a method for automated persona-driven video game tutorial level generation. Tutorial levels are scenarios in which the player can explore and discover different rules and game mechanics. Procedural personas can guide generators to create content which encourages or discourages certain playstyle behaviors. In this system, we use procedural personas to calculate the behavioral characteristics of levels which are evolved using the quality-diversity algorithm known as Constrained MAP-Elites. An evolved map’s quality is determined by its simplicity: the simpler it is, the better it is. Within this work, we show that the generated maps can strongly encourage or discourage different persona-like behaviors and range from simple solutions to complex puzzle-levels, making them perfect candidates for a tutorial generative system.
Essay Test Based E-Testing Using Cosine Similarity Vector Space Model

The covid-19 pandemic has been pushing the development of online learning systems in Indonesia. In online learning, computer-based essay tests and assessments have an essential role. Essay test systems are designed to mimic the concept of essay tests without being computer-based. The answer from the lecturer is compared to the response from the student. The TF-IDF (Term Frequency -Inverse Document Frequency) cosine similarity is used. It is one of the methods of information re-gathering systems. The process in this model consists of two types: 1) creating a corpus/ inverted file, and the second is cosine similarity (CS) for calculating the similarity of the user's answers with the lecturer's. Creating a corpus/inverted file involves several stages like data collection, parsing sentences into terms, stoplist, weighting with IDF, and term weighting using TF-IDF. The cosine similarity process consists of parsing users' answers, weighting users' answers using TF-IDF, and finding cosine similarity values of users' answers with lecturers' answers using the vector space model. The highest cosine similarity value is taken to give the user's answer points. Testing the Essay Test system produces excellent grades. The tests were done Mean Squared Error (MSE) values resulted in an average MSE value of 3.28 from three students.
SimFormer: Real-to-Sim Transfer with Recurrent Restoration

Real-to-Sim transfer is a popular research topic in robotics. Utilizing a simulated environment, the development processes can achieve lower costs and make the testing process easier. In addition, after the Real-to-Sim transfer, the simulated environment can lower the texture effect and light effect, which can be further applied to other computer vision tasks, such as robot grasping. Differing from artistic style transfer, Real-to-Sim transfer has higher accuracy requirements for content preservation. In this paper, we utilize the transformer to solve the Real-to-Sim transfer. We creatively design the restoration stage to preserve the content information. We also propose the restoration loss function. After these improvements, our architecture can achieve better performance on light removal, content preservation, and feature embedding.
Image Dehazing Algorithm Based on Region Segmentation and Block Optimization

In image dehazing, due to the influence of sky area and local light spot, dehazing images often have problems with brightness and light spot. Therefore, accordinng to the prior theory of dark channel, an image dehazing algorithm based on region segmentation and block optimization is proposed. Firstly, the quadrant tree cycle method is used to find the region with the highest local brightness in the whole image, and the atmospheric light value is estimated through this region. Secondly, combined with the image information, the image is divided into a bright region, transition region and dark region, and the transmittance of each region is compensated and controlled. Finally, the dehazing image is obtained by using the atmospheric scattering model, and the brightness of the image scene is improved by singular value decomposition. The experimental results show that the restored image is bright and natural, with obvious details and high color fidelity. The algorithm effectively solves the defect that the dark channel prior algorithm fails in the sky area and highlight area, and can restore a more natural sky highlight scene.
On the Order of Operators in the Continuous State Transition Algorithm

In order to investigate the influence of the order of operators in the continuous state transition algorithm on its optimization performance, this paper comprehensively considers six kinds of operator orders and four different dimensions of the decision space and carries out experiments on benchmark functions. By comparing the optimality, the robustness and the convergence speed of the experimental results, it is shown that the order of operators has no obvious effect on the optimization performance of the continuous state transition algorithm in the same dimension space. Besides, it also has good adaptability to the dimension of decision space. It means that the state transition algorithm has good robustness to the order of the operators and the potential to deal with high dimensional optimization problems. This work is not only of great significance for improving the theoretical basis of the state transition algorithm, but also provides some insight for the further evolution of the state transition algorithm.