Reviewerly/exHarmony huggingface dataset:
Deep Learning Approach: YOLOv5-based Custom Object Detection

Object detection is of significant importance in the field of computer vision, since it has extensive applications across many sectors. The emergence of YOLO (You Only Look Once) has brought about substantial changes in this domain with the introduction of real-time object identification with exceptional accuracy. The YOLOv5 architecture is highly sought after because of its increased flexibility and computational efficiency. This research provides an in-depth analysis of implementing YOLOv5 for object identification. This research delves deeply into the architectural improvements and design ideas that set YOLOv5 apart from its predecessors to illuminate its unique benefits. This research examines the training process and the efficiency of transfer learning techniques, among other things. The detection skills of YOLOv5 may be greatly improved by including these features. This study suggests the use of YOLOv5, a state-of-the-art object identification framework, as a crucial tool in the field of computer vision for accurate object recognition. The results of the proposed framework demonstrate higher performance in terms of mAP (60.9%) when evaluated with an IoU criterion of 0.5 and when compared to current methodologies in terms of reliability, computing flexibility, and mean average precision. These advantages make it applicable in many real-world circumstances.
Tracking Everything Everywhere All at Once

We present a new test-time optimization method for estimating dense and long-range motion from a video sequence. Prior optical flow or particle video tracking algorithms typically operate within limited temporal windows, struggling to track through occlusions and maintain global consistency of estimated motion trajectories. We propose a complete and globally consistent motion representation, dubbed OmniMotion, that allows for accurate, full-length motion estimation of every pixel in a video. OmniMotion represents a video using a quasi-3D canonical volume and performs pixel-wise tracking via bijections between local and canonical space. This representation allows us to ensure global consistency, track through occlusions, and model any combination of camera and object motion. Extensive evaluations on the TAP-Vid benchmark and real-world footage show that our approach outperforms prior state-of-the-art methods by a large margin both quantitatively and qualitatively. See our project page for more results: omnimotion.github.io.
Unleashing Text-to-Image Diffusion Models for Visual Perception

Diffusion models (DMs) have become the new trend of generative models and have demonstrated a powerful ability of conditional synthesis. Among those, text-to-image diffusion models pre-trained on large-scale image-text pairs are highly controllable by customizable prompts. Unlike the unconditional generative models that focus on low-level attributes and details, text-to-image diffusion models contain more high-level knowledge thanks to the vision-language pre-training. In this paper, we propose VPD (Visual Perception with pre-trained Diffusion models), a new framework that exploits the semantic information of a pre-trained text-to-image diffusion model in visual perception tasks. Instead of using the pre-trained denoising autoencoder in a diffusion-based pipeline, we simply use it as a backbone and aim to study how to take full advantage of the learned knowledge. Specifically, we prompt the denoising decoder with proper textual inputs and refine the text features with an adapter, leading to a better alignment to the pre-trained stage and making the visual contents interact with the text prompts. We also propose to utilize the cross-attention maps between the visual features and the text features to provide explicit guidance. Compared with other pre-training methods, we show that vision-language pre-trained diffusion models can be faster adapted to downstream visual perception tasks using the proposed VPD. Extensive experiments on semantic segmentation, referring image segmentation, and depth estimation demonstrate the effectiveness of our method. Notably, VPD attains 0.254 RMSE on NYUv2 depth estimation and 73.3% oIoU on RefCOCO-val referring image segmentation, establishing new records on these two benchmarks. Code is available at https://github.com/wl-zhao/VPD.
Solar Tilt Measurement of Array for Building Application and Error Analysis

The optimum angle show some variations when compared with values reported in the literature. The amount of solar radiation incident on a tilted module surface is the component of the incident solar radiation which is perpendicular to the module surface. The array's tilt is the angle in degrees from horizontal. A flat roof has 0 degree tilt and a vertical wall mount has a 90 degrees tilt angle. Whether you are installing solar panel on a flat roof or a pitched roof, the output of the solar PV system would be increased by optimizing the tilt angle. This paper gives the information about the theoretical aspects of choosing a tilt angle for the solar flat-plate collectors used at different locations in India. So the calculations are based upon the measured values of monthly mean daily global and diffuse solar radiation on a horizontal surface. It is shown that nearly optimal energy can be collected if the angle of tilt is varied seasonally, four times a year. Annual optimum tilt angle is found to be approximately equal to latitude of the location. This study determined that PV module oriented towards the South gives the greatest value of electrical energy for the angle of 30 0 .
Comparative study of two models to estimate solar radiation on an inclined surface

Our work is focused on the study of the solar radiation received on three inclined surface: 32°, 60° and 90° facing south. So, we proposed two theoretical models: Brichambaut and Liu Jordan models. Brichambaut model takes into account two input parameters (local time, day number), Liu &amp; Jordan takes into account the measures of the solar irradiance (diffuse and global radiations received on horizontal plane). We tested a day 10-02-2011. We noted that the Brichambaut model gives best results, the maximum relative errors are equal to: -0.43 %, -0.60 %, 0.76 % respectively for the angles 32°, 60°, 90°. While for the Liu &amp; Jordan model we obtained the following errors: 1.55 %, 1.56 %, and 3.92 %.
Privacy implications of room climate data

Smart heating applications promise to increase energy efficiency and comfort by collecting and processing room climate data. While it has been suspected that the sensed data may leak crucial personal information about the occupants, this belief has up until now not been supported by evidence. In th is work, we investigate privacy risks arising from the collection of room climate measurements. We assume that an attacker has access to the most basic measurements only: temperature and relative humidity. We train machine learning classifiers to predict the presence and number of room occupants and to discriminate between different types of activities. On data that was collected at three different locations, we show that occupancy can be detected from data measured by a single sensor with up to 93.5% accuracy. One can even distinguish between the cases that no, one, or two persons are present with up to 66.4% accuracy. Moreover, the four actions reading, working on a PC, standing, and walking, can be discriminated with up to 56.8% accuracy, which is likewise clearly better than guessing (25%). Constraining the set of actions allows to achieve even higher prediction rates. For example, we discriminate standing and walking occupants with 96.3% accuracy. In addition, we show that the accuracy can be increased in most cases if an attacker has access to measurements from two different sensors located in the same room. Our results provide evidence that even the leakage of such ‘inconspicuous’ data as temperature and relative humidity can seriously violate privacy.
Teaching Proficiency through Reading and Storytelling (TPRS) as a Technique to Foster Students’ Speaking Skill

Teaching Proficiency through Reading and Storytelling (TPRS) is quite essential to improve EFL learners’ speaking ability. It can be done successfully by means of the basic concept of TPRS known as comprehensible input in second language acquisition (SLA). This paper presents a study on learners’ speaking ability through TPRS making use of three important steps; Showing, Telling, and Reading. This is a quantitative study using quasi-experimental as the two intact groups are used; experimental and control group. The data are obtained from pre-test, post-test and questionnaires viewed from both students’ and teachers’ perspective. The data from pre-test and post-test are analyzed by using independent sample t-test. The experimental and control are ascertained to be homogenous in term of English performance from the pre-test analysis. The post-test are carried out from both groups after the treatment and the the result of the test are compared in order to prove if the null hypothesis is rejected indicating that there is significant difference performance between the two groups. The result of the study is expected to be beneficial for English teachers, EFL learners, and further researchers.
Single-Shot Secure Quantum Network Coding for General Multiple Unicast Network With Free One-Way Public Communication

It is natural in a quantum network system that multiple users intend to send their quantum message to their respective receivers, which is called a multiple unicast quantum network. We propose a canonical method to derive a secure quantum network code over a multiple unicast quantum network from a secure classical network code. Our code correctly transmits quantum states when there is no attack. It also guarantees the secrecy of the transmitted quantum state even with the existence of an attack when the attack satisfies a certain natural condition. In our security proof, the eavesdropper is allowed to modify wiretapped information dependently on the previously wiretapped messages. Our protocol guarantees the secrecy by utilizing one-way classical information transmission (public communication) in the same direction as the quantum network although the verification of quantum information transmission requires two-way classical communication. In the protocol, some nodes may share secret randomness as resources in advance. Our secure network code can be applied to several networks including the butterfly network.
CAT: a context aware teller for supporting tourist experiences

The aim of this paper is the introduction of a methodology for the dynamic creation of an adaptive generator of the stories related to a tourist context. The proposed approach selects the most suitable contents for the users and builds a context aware teller that can support them during the exploration of the context making it more appealing and immersive. The tourist can use the system by a hybrid app. The dynamic context aware telling engine grabs the contents from a knowledge base that contains data coming both from the knowledge base and from the web. The user profile is updated thanks to information obtained during the visit and from the social networks. A case study and some experimental results are presented and discussed.
Pedestrian detection method based on self-learning

Vision-based pedestrian detection technology is widely used in security surveillance, such as transportation, prisons, and important places. Security monitoring requires pedestrian detection not only to be accurate and reliable, but also to meet the needs of real-time monitoring, and the false alarm rate is low. The existing pedestrian learning method based on deep learning has high reliability, but the timeliness cannot meet the monitoring requirements. Traditional pedestrian detection methods are time-sensitive, but false alarm rates tend to be higher. This paper proposes a self-learning pedestrian detection method, combined with motion detection, HOG feature extraction, and SVM classification for preliminary pedestrian detection. The false alarms caused during the pedestrian detection process are manually confirmed, and then the system automatically detects the false alarm image at a specific time. Self-learning, retraining the classifier, so as to continuously reduce the pedestrian detection false alarm rate and improve the pedestrian detection performance index through self-learning during the system operation, and the timeliness can meet the needs of video surveillance. Experiments show that the false alarm rate of this method is low, timeliness is high, and the recognition rate is also high.
Fast and Provably Effective Multi-view Classification with Landmark-Based SVM

We introduce a fast and theoretically founded method for learning landmark-based SVMs in a multi-view classification setting which leverages the complementary information of the different views and linearly scales with the size of the dataset. The proposed method – called MVL-SVM – applies a non-linear projection to the dataset through multi-view similarity estimates w.r.t. a small set of randomly selected landmarks, before learning a linear SVM in this latent space joining all the views. Using the uniform stability framework, we prove that our algorithm is robust to slight changes in the training set leading to a generalization bound depending on the number of views and landmarks. We also show that our method can be easily adapted to a missing-view scenario by only reconstructing the similarities to the landmarks. Empirical results, both in complete and missing view settings, highlight the superior performances of our method, in terms of accuracy and execution time, w.r.t. state of the art techniques. Code related to this paper is available at: https://github.com/vzantedeschi/multiviewLSVM .
Quantum Computing with Octonions

There are two schools of "measurement-only quantum computation". The first ([11]) using prepared entanglement (cluster states) and the second ([4]) using collections of anyons, which according to how they were produced, also have an entanglement pattern. We abstract the common principle behind both approaches and find the notion of a graph or even continuous family of equiangular projections. This notion is the leading character in the paper. The largest continuous family, in a sense made precise in Corollary 4.2, is associated with the octonions and this example leads to a universal computational scheme. Adiabatic quantum computation also fits into this rubric as a limiting case: nearby projections are nearly equiangular, so as a gapped ground state space is slowly varied the corrections to unitarity are small.
MODIFIED MULTI-LEVEL STEGANOGRAPHY TO ENHANCE DATA SECURITY

Data-hiding using steganography algorithm becomes an important technique to prevent unauthorized users to have access to a secret data. In this paper, steganography algorithm has been constructed to hide a secret data in a gray and a color images, this algorithm is named deep hiding/extraction algorithm (DHEA) to modify multi-level steganography (MLS). The suggested hiding algorithm is based on modified least significant bit (MDLSB) to scatter data in a cover-image and it utilizes a number of levels; where each level perform hiding data on a gray image except the last level that applies a color image to keep secret data. Furthermore, proper randomization approach with two layers is implemented; the first layer uses random pixels selection for hiding a secret data at each level, while the second layer implements at the last level to move randomly from segment to the others. In addition, the proposed hiding algorithm implements an effective lossless image compression using DEFLATE algorithm to make it possible to hide data into a next level. Dynamic encryption algorithm based on Advanced Encryption Standard (AES) is applied at each level by changing cipher keys (Ck) from level to the next, this approach has been applied to increase the security and working against attackers. Soft computing using a meta-heuristic approach based on artificial bee colony (ABC) algorithm has been introduced to achieve smoothing on pixels of stego-image, this approach is effective to reduce the noise caused by a hidden large amount of data and to increase a stego-image quality on the last level. The experimental result demonstrates the effectiveness of the proposed algorithm with bee colony DHA-ABC to show high-performing to hide a large amount of data up to four bits per pixel (bpp) with high security in terms of hard extraction of a secret message and noise reduction of the stego-image. Moreover, using deep hiding with unlimited levels is promising to confuse attackers and to compress a deep sequence of images into one image.
Fast snow removal algorithm based on the maximum value of the degree of polarization and angle of polarization

Images of outdoor scenes usually have low contrast and degraded visibility in snowy weather. Snowflakes, deemed as particles in the atmosphere, cause light scattering and absorption, blurring the images. Polarization-based scenarios are proven effective in restoring the images. However, current methods either inaccurately estimate atmospheric airlight at infinity or have low efficiency. To enhance the accuracy and efficiency for increasing visibility and correcting the color shift, we propose a simple method based on polarimetric images. This method uses the maximum value of the degree of polarization and angle of polarization by global analysis of the Stokes vector to accurately estimate atmospheric airlight at infinity and the transmission map. Experimental results confirm that this method can significantly improve the image quality without complex model implementation or any other processing and has high computational efficiency. Moreover, the comparison results demonstrate that this method can recover more details and depth information.
A closed sets based learning classifier for implicit authentication in web browsing

Faced with both identity theft and the theft of means of authentication, users of digital services are starting to look rather suspiciously at online systems. To increase access security it is necessary to introduce some new factor of implicit authentication such as user behavior analysis. A behavior is made up of a series of observable actions and taken as a whole, the most frequent of these actions amount to habit. The challenge is to detect identity theft as quickly as possible and, reciprocally, to validate a legitimate identity for as long as possible. To take up this challenge, we introduce in this paper a closed set-based learning classifier. This classifier is inspired by classification in concept lattices from positive and negative examples and several works on emerging patterns. We also rely on the tf-idf parameter used in the context of information retrieval. We propose three heuristics named Htf−idfc, Hsupc and HsupMinc to select closed patterns for each class to be described. To compute performance of our models we have followed an experimental protocol described in a previous study which had the same purpose. Then, we compared the results from our own dataset of web navigation connection logs of 3,000 users over a six-month period with the heuristic Hsup introduced in this study. Moreover, to strengthen our analysis, we have designed and set up one model based on the naive Bayes classifier to be used as a reference statistical tool.
Effects of Simulator Sickness and Emotional Responses When Inter-pupillary Distance Misalignment Occurs

The aim of this study was to empirically investigate the effects of inter-pupillary distance (IPD) misalignment on simulator sickness and emotional responses. Twenty participants were recruited from an advertisement. The experiment has five conditions to change the differences between IPD and head-mounted display (HMD) optical systems. As results, simulator sickness significantly decreased by aligning the HMD optical systems based on the IPD. Extending previous studies, the results demonstrated the different patterns in emotional arousal and valence. For future study, we are conducting a second experiment using eye-tracker to investigate whether anomalies occur in pupil movements. This study might help to inform the important of IPD misalignment problem by measuring visual discomfort quantitatively.
CasNet: a cascade coarse-to-fine network for semantic segmentation

Semantic segmentation is a fundamental topic in computer vision. Since it is required to make dense predictions for an entire image, a network can hardly achieve good performance on various kinds of scenes. In this paper, we propose a cascade coarse-to-fine network called CasNet, which focuses on regions that are difficult to make pixel-level labels. The CasNet comprises three branches. The first branch is designed to produce coarse predictions for easy-to-label pixel regions. The second one learns to distinguish the relatively difficult-to-label pixels from the entire image. Finally, the last branch generates final predictions by combining both the coarse and the fine prediction results through a weighting coefficient that is estimated by the second branch. Three branches focus on their own objectives and collaboratively learn to predict from coarse-to-fine predictions. To evaluate the performance of the proposed network, we conduct experiments on two public datasets: SIFT Flow and Stanford Background. We show that these three branches can be trained in an end-to-end manner, and the experimental results demonstrate that the proposed CasNet outperforms existing state-of-the-art models, and it achieves prediction accuracy of 91.6% and 89.7% on SIFT Flow and Standford Background, respectively.
Improving Coordination in Multi-Agent Deep Reinforcement Learning through Memory-driven Communication.

Deep reinforcement learning algorithms have recently been used to trainmultiple interacting agents in a centralised manner whilst keeping theirexecution decentralised. When the agents can only acquire partial observationsand are faced with a task requiring coordination and synchronisation skills,inter-agent communication plays an essential role. In this work, we propose aframework for multi-agent training using deep deterministic policy gradientsthat enables the concurrent, end-to-end learning of an explicit communicationprotocol through a memory device. During training, the agents learn to performread and write operations enabling them to infer a shared representation of theworld. We empirically demonstrate that concurrent learning of the communicationdevice and individual policies can improve inter-agent coordination andperformance, and illustrate how different communication patterns can emerge fordifferent tasks.
Analytical Structures of Takagi-Sugeno Fuzzy Two-Input Two-Output Proportional-Integral/Proportional-Derivative Controllers With Multiple Fuzzy Sets

In this paper, an attempt is made to generalize the analytical structures of Takagi-Sugeno (TS) fuzzy two-input two-output (TITO) proportional-integral (PI)/proportional-derivative (PD) controllers using multiple input fuzzy sets. Two models of fuzzy TITO PI/PD controllers are proposed based on two distinct control strategies. The inputs are fuzzified by multiple fuzzy sets with trapezoidal/triangular membership functions. The generalized rule base consists of nine control rules imbibing the complete control strategy and is closer in spirit to the original TS rule base. Algebraic product (AP) triangular norm, bounded sum (BS) triangular conorm, and center of gravity (CoG) defuzzifier are applied to derive the models. The models of the fuzzy TITO PI/PD controllers with multiple input fuzzy sets are (nonlinear) variable gain/structure controllers. Also, each output of the fuzzy controller is the sum of two nonlinear PI/PD controllers with variable gains. The gain variation and properties of the proposed controllers are studied. Two examples of nonlinear dynamic processes are considered to demonstrate the applicability of the proposed controllers.