Reviewerly/exHarmony huggingface dataset:
TinyM <sup>2</sup> Net-V2: A Compact Low Power Software Hardware Architecture for <u>M</u> ulti <u>m</u> odal Deep Neural Networks

With the evaluation of Artificial Intelligence (AI), there has been a resurgence of interest in how to use AI algorithms on low-power embedded systems to broaden potential use cases of the Internet of Things (IoT). To mimic multimodal human perception, multimodal deep neural networks (M-DNN) have recently become very popular with the classification task due to their impressive performance for computer vision and audio processing tasks. This paper presents TinyM 2 Net-V2 - a compact low-power software hardware architecture for m ulti m odal deep neural networks for resource-constrained tiny devices. In order to compress the models to implement on tiny devices, cyclicly sparsification and hybrid quantization (4-bits weights and 8-bits activations) methods are used. Although model compression techniques are an active research area, we are the first to demonstrate their efficacy for multimodal deep neural networks, using cyclicly sparsification and hybrid quantization of weights/activations. TinyM 2 Net-V2 shows that even a tiny multimodal deep neural network model can improve the classification accuracy more than that of any unimodal counterparts. Parameterized M-DNN model architecture was designed to be evaluated in two different case-studies: vehicle detection from multimodal images and audios and COVID-19 detection from multimodal audio recordings. The most compressed TinyM 2 Net-V2 achieves 92.5% COVID-19 detection accuracy (6.8% improvement from the unimodal full precision model) and 90.6% vehicle classification accuracy (7.7% improvement from the unimodal full precision model). A parameterized and flexible FPGA hardware accelerator was designed as well for TinyM 2 Net-V2 models. To the best of our knowledge, this is the first work accelerating multimodal deep neural network models on low power Artix-7 FPGA hardware. We achieved energy efficiency of 9.04 GOP/s/W and 15.38 GOP/s/W for case-study 1 and case-study 2 respectively which is comparable to the state-of-the-art results. Finally, we compared our tiny FPGA hardware implementation results with off-the-shelf resource-constrained devices and showed our implementation is faster and consumed less power compared to the off-the-shelf resource-constrained devices.
An Overview of Artificial Intelligence Ethics: Issues and Solution for Challenges in Different Fields

Artificial Intelligence (AI) ethics are the values and principles that govern the creation and application of AI. As AI technology develops quickly, there is rising worry about the possible ethical ramifications of its application, including concerns about privacy, bias, accountability, transparency, safety, and the effect on society as a whole. Making sure AI systems are created and used in a way that respects human rights and values is one of the main concerns of AI ethics. For instance, there can be worries about the use of AI in surveillance or the possibility that these technologies will legitimise already-existing social prejudices and discrimination. Making sure AI systems are accountable and transparent is a key aspect of AI ethics. It can be challenging to comprehend how AI systems make judgements and who is accountable for those decisions as they get more complicated and autonomous. Transparency in AI research and decision-making, as well as systems for accountability and remedies when things go wrong, are becoming increasingly important. Additionally, it's important to guarantee the security and safety of AI systems. Concern over the possibility of cyberattacks and other types of harmful use is growing as AI systems become more linked and incorporated into our daily lives. Finally, it's important to make sure that AI is created and applied in a way that benefits all humanity. This entails tackling problems like employment loss, economic inequality, and the possibility that AI will be applied in ways that are detrimental to society. There is an increasing need for cooperation between business, government, academia, and civil society to address these and other ethical issues. This involves creating moral standards, norms, and best practises as well as the systems necessary to guarantee responsibility and compliance.
An RSU-crossed dependent task offloading scheme for vehicular edge computing based on deep reinforcement learning

Various interdependent and computationally intensive on-vehicle tasks have posed great pressure on the computing power of vehicles. Vehicular edge computing (VEC) is considered to be a promising paradigm to solve this problem. However, due to the high mobility, vehicles will pass through multiple road-side units (RSUs) during task computing. How to coordinate the offloading decision of RSUs is a challenge. In this study, we propose a dependent task offloading scheme by considering vehicle mobility, service availability, and task priority. Meanwhile, to coordinate the offloading decisions among the RSUs, a Markov decision process (MDP) is carefully designed, in which the action of each RSU is divided into three steps to decide whether, where, and how each task is offloaded separately. Then, an advanced DDPG-based deep reinforcement learning (DRL) algorithm is adopted to solve this problem. Simulation results show that the proposed scheme has better performance in reducing task processing latency and consumption.
On Unsupervised Uncertainty-Driven Speech Pseudo-Label Filtering and Model Calibration

Pseudo-label (PL) filtering forms a crucial part of Self-Training (ST) methods for unsupervised domain adaptation. Dropout-based Uncertainty-driven Self-Training (DUST) proceeds by first training a teacher model on source domain labeled data. Then, the teacher model is used to provide PLs for the unlabeled target domain data. Finally, we train a student on augmented labeled and pseudo-labeled data. The process is iterative, where the student becomes the teacher for the next DUST iteration. A crucial step that precedes the student model training in each DUST iteration is filtering out noisy PLs that could lead the student model astray. In DUST, we proposed a simple, effective, and theoretically sound PL filtering strategy based on the teacher model’s uncertainty about its predictions on unlabeled speech utterances. We estimate the model’s uncertainty by computing disagreement amongst multiple samples drawn from the teacher model during inference by injecting noise via dropout. In this work, we show that DUST’s PL filtering, as initially used, fail under severe source and target domain mismatch. We suggest several approaches to eliminate or alleviate this issue. Further, we bring insights from the research in neural network model calibration to DUST and show that a well-calibrated model correlates strongly with a positive outcome of the DUST PL filtering step. We demonstrate effectiveness of our methods on target domain GigaSpeech YouTube dataset.
A model of image retrieval based on KD-Tree Random Forest

Purpose The problem of image retrieval and image description exists in various fields. In this paper, a model of content-based image retrieval and image content extraction based on the KD-Tree structure was proposed. Design/methodology/approach A Random Forest structure was built to classify the objects on each image on the basis of the balanced multibranch KD-Tree structure. From that purpose, a KD-Tree structure was generated by the Random Forest to retrieve a set of similar images for an input image. A KD-Tree structure is applied to determine a relationship word at leaves to extract the relationship between objects on an input image. An input image content is described based on class names and relationships between objects. Findings A model of image retrieval and image content extraction was proposed based on the proposed theoretical basis; simultaneously, the experiment was built on multi-object image datasets including Microsoft COCO and Flickr with an average image retrieval precision of 0.9028 and 0.9163, respectively. The experimental results were compared with those of other works on the same image dataset to demonstrate the effectiveness of the proposed method. Originality/value A balanced multibranch KD-Tree structure was built to apply to relationship classification on the basis of the original KD-Tree structure. Then, KD-Tree Random Forest was built to improve the classifier performance and retrieve a set of similar images for an input image. Concurrently, the image content was described in the process of combining class names and relationships between objects.
Robust online learning based on siamese network for ship tracking

Abstract The complex and changeable inland river scenes resulting out of frequent occlusions of ships in the available tracking methods are not accurate enough to estimate the motion state of the target ship leading to object tracking drift or even loss. In view of this, an attempt is made to propose a robust online learning ship tracking algorithm based on the Siamese network and the region proposal network. Firstly, the algorithm combines the off-line Siamese network classification score and the online classifier score for discriminative learning, and establishes an occlusion determination mechanism according to the classification the fusion score. When the target is in the occlusion state, the target template is not updated, and the global search mechanism is activated to relocate the target, thereby avoiding object tracking drift. Secondly, an efficient adaptive online update strategy, UpdateNet, is introduced to improve the template degradation in the tracking process. Finally, on comparing the state-of-the-art tracking algorithms on the inland river ship datasets, the experimental results of the proposed algorithm show strong robustness in occlusion scenarios with an accuracy and success rate of 56.8% and 57.2% respectively. Supportive source codes for this research are publicly available at https://github.com/Libra-jing/SiamOL .
PANet: An End-to-end Network Based on Relative Motion for Online Multi-object Tracking

The popular tracking-by-detection paradigm of multi-object tracking (MOT) takes detections of each frame as the input and associates detections from one frame to another. Existing association methods based on the relative motion have attracted attention, because they restrain the effect of noisy detections and improve the performance of MOT. However, these methods depend only on the immediately previous frame, which may easily lead to inaccurate matches and even large accumulated errors. Furthermore, multiple objects involved in occlusions are not fully exploited in these existing methods, which leads to the aggravation of inaccurate matches. Motivated by these issues, we design the pivot to represent each object and propose a novel pivot association network (PANet) for the MOT task. Specifically, pivots are learned from spatial semantic and historical contextual clues, which alleviates the dependency on the immediately previous frame. Our online tracker PANet employs pivots and a lightweight associator to localize tracklets of objects, which can inhibit noise detections and improve the accuracy of tracklet prediction by learning the correlation responses between pivots and spatial search areas. Extensive experiments conducted on two-dimensional MOT15, MOT16, MOT17, and MOT20 demonstrate the effectiveness of the proposed method against numerous state-of-the-art MOT trackers.
Restoration of Time-Varying Graph Signals using Deep Algorithm Unrolling

In this paper, we propose a restoration method of time-varying graph signals, i.e., signals on a graph whose signal values change over time, using deep algorithm unrolling. Deep algorithm unrolling is a method that learns parameters in an iterative optimization algorithm with deep learning techniques. It is expected to improve convergence speed and accuracy while the iterative steps are still interpretable. In the proposed method, the minimization problem is formulated so that the time-varying graph signal is smooth both in time and spatial domains. The internal parameters, i.e., time domain FIR filters and regularization parameters, are learned from training data. Experimental results using synthetic data and real sea surface temperature data show that the proposed method improves signal reconstruction accuracy compared to several existing time-varying graph signal re- construction methods.
Pseudo Binocular Stereo Vision Camera Realignment using NCC

Stereo vision systems are used in various applications, such as industrial automation. This study used a fuzzy logic-based design to control the vergence of the binocular stereo-vision system in order to mimic the eyes in producing a singular image without the usage of image processing methods like rectification. Results of the study show that images perceived by the system can attain a similarity value averaging above 0.8 despite its low-quality equipment. The study recommends trying other object detection techniques to test for consistency of the system.
The Ustc System for Adress-m Challenge

This paper describes our submission to the ICASSP 2023 Signal Processing Grand Challenge (SPGC), which focuses on multilingual Alzheimer’s disease (AD) recognition through spontaneous speech. Our approaches include using a variety of acoustic features and silence-related information for AD detection and mini-mental state examination (MMSE) score prediction, and fine-tuning wav2vec2.0 models on speech in various frequency bands for AD detection. Our overall results on the test data outperform the baseline provided by the organizers, achieving 73.9% accuracy in AD detection by fine-tuning our bilingual wav2vec2.0 pre-trained model on the 0-1000Hz frequency band speech, and 4.610 RMSE (r = 0.565) in MMSE prediction through the fusion of eGeMAPS and silence features.
Application Of Artificial Intelligence In Indonesian Debate Education Game

Over time, educational games that exist is increasing. It is very unfortunate that there are some educational games that are too focused on the educational aspect so that they forget their identity as games that aim to relieve fatigue. This research was conducted to avoid this problem. To provide an interesting experience from the game being played, the artificial intelligence system is implemented, the first one is Bezier Curve Algorithm. An algorithm that forms a curve trajectory that will become a cross motion of obstacles. Finite State Machine for the Non-Player Character (NPC) behavior. The last one is Fuzzy Mamdani algorithm, it is an algorithm that will be used in calculating the score in the game. By applying all the algorithm to some aspects of the game, there will be an interesting experience in every game play, because one gameplay will not be similar to the previous one. This game was developed on the Android platform. The test was conducted on 33 high school/vocational high school students with the age range (15-20 years). The results obtained, 97% of respondents agree that the game is interesting for them and 90.9% feel interested in Indonesian debate after playing the game.
Social Media as a Determinant of Students’ Dropout Rates in Secondary Schools in Kenya

Education is the natural right of every human being and is an integral part of every human life. The purpose of the study was to investigate the influence of social media on secondary school students’ dropout in public day secondary schools in Kenya. The article is an extract from a study done in public day secondary schools in Kitui central sub-county, Kitui County, Kenya. The study was guided by Bertalanffy’s Systems Theory. The study adopted descriptive survey research design. The study population was 25 schools and a sample size of 384 comprising of 22 principals, 22 form three class teachers and 340 form three students. Questionnaires for principals, class teachers and students were used to collect both quantitative and qualitative data. Data analysis was done using Statistical Package for Social Sciences (SPSS) version 26. The corelation analysis results on principals’ responses showed a strong correlation (r=0.675. p=0.006) between social media and students’ dropout rate. A strong correlation (r =0.612, p =0.022) between social media and students’ dropout rates was also shown when the class teachers’ responses were analysed. However, corelation analysis of the students’ responses showed a weak correlation (r=0.214, p=0.041). The researcher therefore concluded that students drop out of school is influenced by social media. The study thus recommended that the community, teachers, and parents should educate students on good use of social media so as not to misuse it.
DYNAMIC PROGRAMMING FOR CURVED REFLECTION SYMMETRY DETECTION IN SEGMENTED IMAGES

Abstract. This study proposes a method for detecting curved reflection symmetry in binary and grayscale images. The crucial step is to construct a curvilinear symmetry axis generating a nonlinear transformation of the image coordinates that projects the curve on the Y axis and makes the image maximally symmetric about this axis in terms of the Jaccard index. We proposed analytical estimations for the symmetry axis curvature to make the transform bijective. We applied dynamic programming to construct the curvilinear symmetry axis. The axis points are generated one by one with a local direction change at each point. To improve the computational efficiency of the method for images of a given size, we construct a graph of possible transitions in advance. To estimate the symmetry in grayscale images, we proposed two analogs to the Jaccard index. The experiments with image libraries demonstrated that the method correctly handles images containing a single object on a homogeneous background.
Heartbleed Revisited: Is it Just a Buffer Over-Read?

In this work, we analyze in detail the weaknesses underlying the Heartbleed vulnerability, CVE-2014-0160, and show why and how highly sensitive information could be exposed via buffer over-read.
Optimized Parameter Tuning in a Recurrent Learning Process for Shoplifting Activity Classification

Abstract From recent past, shoplifting has become a serious concern for business in both small/big shops and stores. It customarily involves the buyer concealing store items inside clothes/bags and then leaving the store without payment. Unfortunately, no cost-effective solution is available to overcome this problem. We, therefore intend to build an expert monitoring system to automatically recognize shoplifting events in megastores/shops by recognizing object-stealing actions of humans. The method proposed utilizes a deep convolutional-based InceptionV3 architecture to mine the prominent features from video clips. These features are used to custom Long Short Term Memory (LSTM) network to discriminate human stealing actions in video sequences. Optimizing recurrent learning classifier using different modeling parameters such as sequence length and batch size is a genuine contribution of this work. The experiments demonstrate that the system proposed has achieved an accuracy of 89.36% on the synthesized dataset, which comparatively outperforms other existing methods.
Towards Understanding the Impacts of Textual Dissimilarity on Duplicate Bug Report Detection

About 40% of software bug reports are duplicates of one another, which pose a major overhead during software maintenance. Traditional techniques often focus on detecting duplicate bug reports that are textually similar. However, in bug tracking systems, many duplicate bug reports might not be textually similar, for which the traditional techniques might fall short. In this paper, we conduct a large-scale empirical study to better understand the impacts of textual dissimilarity on the detection of duplicate bug reports. First, we collect a total of 92,854 bug reports from three open-source systems and construct two datasets containing textually similar and textually dissimilar duplicate bug reports. Then we determine the performance of three existing techniques in detecting duplicate bug reports and show that their performance is significantly poor for textually dissimilar duplicate reports. Second, we analyze the two groups of bug reports using a combination of descriptive analysis, word embedding visualization, and manual analysis. We found that textually dissimilar duplicate bug reports often miss important components (e.g., expected behaviors and steps to reproduce), which could lead to their textual differences and poor performance by the existing techniques. Finally, we apply domain-specific embedding to duplicate bug report detection problems, which shows mixed results. All these findings above warrant further investigation and more effective solutions for detecting textually dissimilar duplicate bug reports.
Semantic Segmentation for Autonomous Driving

Recently, autonomous vehicles (namely self-driving cars) are becoming increasingly common in developed urban areas. It is of utmost importance for real-time systems such as robots and automatic vehicles (AVs) to understand visual data, make inferences and predict events in the near future. The ability to perceive RGB values (and other visual data such as thermal, LiDAR), and segment each pixel into objects is called semantic segmentation. It is the first step toward any sort of automated machinery. Some existing models use deep learning methods for 3D object detection in RGB images but are not completely efficient when they are fused with thermal imagery as well. In this paper, we summarize many of these architectures starting from those that are applicable to general segmentation and then those that are specifically designed for autonomous vehicles. We also cover open challenges and questions for further research.
Lattice Problems beyond Polynomial Time

We study the complexity of lattice problems in a world where algorithms, reductions, and protocols can run in superpolynomial time. Specifically, we revisit four foundational results in this context—two protocols and two worst-case to average-case reductions. We show how to improve the approximation factor in each result by a factor of roughly √n/logn when running the protocol or reduction in 2є n time instead of polynomial time, and we show a novel protocol with no polynomial-time analog. Our results are as follows.