Reviewerly/exHarmony huggingface dataset:
A Deep Learning-Based Approach for Defect Detection and Removing on Archival Photos

Many archival photos are unique, existed only in a single copy. Some of them are damaged due to improper archiving (e.g. affected by direct sunlight, humidity, insects, etc.) or have physical damage resulting in the appearance of cracks, scratches on photographs, non-necessary signs, spots, dust, and so on. This paper proposed a system for detection and removing image defects based on machine learning. The method for detecting damage to an image consists of two main steps: the first step is to use morphological filtering as a pre-processing, the second step is to use the machine learning method, which is necessary to classify pixels that have received a massive response in the preprocessing phase. The second part of the proposed method is based on the use of the adversarial convolutional neural network for the reconstruction of damages detected at the previous stage. The effectiveness of the proposed method in comparison with traditional methods of defects detection and removal was confirmed experimentally.
Prioritizing trust in a globally distributed software engineering team to overcome complexity and make releases a non-event

We share the experience of a globally distributed software development organization in addressing the performance issues resulting from the increased complexity caused by rapidly changing market demands. We discuss the challenges caused by the complexity within the teams, which were characterized by the emergence of thinking in silos, reduced morale, and accountability for overall project results, and a reduction in trust. We outline our successful approach to restore performance, which centered on fixing the trust deficit. Over time, the team progressed and went on to surpass its previous high-performance levels. Eventually, the software release process became a non-event for the team.
Agent Environment Cycle Games

Partially Observable Stochastic Games (POSGs) are the most general and common model of games used in Multi-Agent Reinforcement Learning (MARL). We argue that the POSG model is conceptually ill suited to software MARL environments, and offer case studies from the literature where this mismatch has led to severely unexpected behavior. In response to this, we introduce the Agent Environment Cycle Games (AEC Games) model, which is more representative of software implementation. We then prove it's as an equivalent model to POSGs. The AEC games model is also uniquely useful in that it can elegantly represent both all forms of MARL environments, whereas for example POSGs cannot elegantly represent strictly turn based games like chess.
Ethnicity Classification Based On Facial Features Using Viola-Jones Algorithm

Biometric recognition system can use race classification to identify the human globally with a particular identity. This paper proposes Support Vector Machine and will compare the result with K-Nearest Neighbor for classification of people into two major races namely Indonesian western and eastern races. Firstly, the proposed classification method extracts the distinct primary facial feature and skin color model of the given face with Viola-Jones Algorithm to effectively classify the races. To increase the accuracy, the sample must not contain any background of other people skin, no movement and the pictures were taken from the mobile camera with no beauty filter.
Cycle Ynet: Semi-supervised Tracking of 3D Anatomical Landmarks

Real-time tracking of anatomical landmarks in 3D medical images is of great importance, ranging from live quantification to optimal visualization.Existing deep network models have shown promising performance but typically require a large amount of annotated data for training. However, obtaining accurate and consistent annotations on sequences of 3D medical images can be very challenging even for skilled clinicians. In this paper, we propose a semi-supervised spatial-temporal modeling framework for real-time anatomical landmark tracking in 3D transesophageal echocardiography (TEE) images, which requires annotations on only a small fraction of frames in a sequence. Specifically, a spatial discriminative feature encoder is first trained via deep Q-learning on static images across all patients. Then we introduce a Cycle Ynet framework that integrates the encoded spatial features and learns temporal landmark correspondence over a sequence using a generative model by enforcing both cycle-consistency and accurate prediction on a couple of annotated frames. We validate the proposed model using 738 TEE sequences with around 15,000 frames and demonstrate that by combining a discriminative feature extractor with a generative tracking model, we could achieve superior performance using a small number of annotated data compared to state-of-the-art methods.
Acoustic modelling as a strategy for composing site-specific music

This paper describes two site-specific musical compositions, focusing on how modelling was used in their respective composition processes. Primarily, the acoustics of the sites were modelled to aid in the preparation and composition of the pieces. From this we propose the general use of modelling as a way to work with the concept of site. But the idea of formulating a model is also applicable more widely in the work described and this is discussed with the two pieces as starting points.
Performance comparison of fixed and single axis tracker photovoltaic system in large scale solar power plants in Malaysia

&lt;span&gt;Malaysia is rapidly expanding the generation capacity of solar power through large scale solar (LSS) projects with the aim to achieve 20% renewable energy mix by 2025. This has motivated many solar industry players to explore the usage of solar PV with single axis tracker (SAT) system. However, many are still hesitant due to the lack of understanding on the comparative performance between fixed mounted solar PV with solar PV with SAT system. This paper aims to provide a comparative analysis on the performance of both systems. Simulation using PVSyst 6.83 was performed in five potential LSS sites spread across Peninsular Malaysia in Perlis, Kelantan, Pahang, Selangor and Johor with the same installed capacity of 10.32MWp. The energy yield and capacity factor for 21 years were simulated. On the average, it was found that SAT outperforms fixed mounted solar PV system by 15.08% based on their performance on their first year operation. &lt;/span&gt;
A New Beta Chaotic Map with DNA Encoding for Color Image Encryption

Images hold important information, especially in military and commercial surveillance as well as in industrial inspection and communication. Therefore, the protection of the image from abuse, unauthorized access, and damage became a significant demand. This paper introduces a new Beta chaotic map for encrypting and confusing the color image with Deoxyribonucleic Acid (DNA) sequence. First, the DNA addition operation is used for diffusing each component of the plain image. Then, a new Beta chaotic map is used for shuffling the DNA color image. In addition, two chaotic maps, namely the proposed new Beta and Sine chaotic maps, are used for key generation. Finally, the DNA XOR operation is applied between the generated key and shuffled DNA image to produce the cipher image. The experimental results prove that the proposed method surpassed the other methods in terms of Mean Square Error (MSE), Peak Signal-To-Noise Ratio (PSNR), entropy, and correlation coefficient.
A Study on Deep Learning in Neurodegenerative Diseases and Other Brain Disorders

Deep Learning (DL) is an emerging field that attracts researchers, especially in the field of engineering and medical sciences. DL gives us many solutions to date, that is why it is still an active field of interest and will for many years. In this paper, we provide a short introduction of deep learning architectures and the applications and the role of deep learning in the prediction of neurodegenerative diseases like Alzheimer’s, Parkinson’s, Huntington’s disease, mild cognitive impairment, and other dementia. We also discuss other brain disorders and how deep learning is essential, nowadays, in the medical sector for providing better, accurate, and fast treatment to the subject.
A Class of New Quadratic Vectorial Bent Functions

A class of quadratic vectorial bent functions having the form is investigated, where n; m; s1; s2 are positive integers and the coefficients a, b belong to the finite field F2n. Through some discussions on the permutation property of certain linearized polynomials over F2n, several classes of quadratic vectorial bent functions are presented for special cases of n, and it is also verified by computer that some vectorial bent functions proposed are extended affine inequivalent to all known quadratic vectorial bent functions.
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation

Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks. However, these methods usually ignore dependency label information, which conveys rich and useful linguistic knowledge for ED. In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected words through specific dependency types. Furthermore, to fully explore clues hidden in dependency edges, a node-aware edge update module is introduced, which refines the relation representations with contextual information. These two modules are complementary to each other and work in a mutual promotion way. We conduct experiments on the widely used ACE2005 dataset and the results show significant improvement over competitive baseline methods.
Speeding up Word Mover's Distance and Its Variants via Properties of Distances Between Embeddings.

The Word Mover's Distance (WMD) proposed by Kusner et al. is a distance between documents that takes advantage of semantic relations among words that are captured by their embeddings. This distance proved to be quite effective, obtaining state-of-art error rates for classification tasks, but is also impracticable for large collections/documents due to its computational complexity. For circumventing this problem, variants of WMD have been proposed. Among them, Relaxed Word Mover's Distance (RWMD) is one of the most successful due to its simplicity, effectiveness, and also because of its fast implementations. Relying on assumptions that are supported by empirical properties of the distances between embeddings, we propose an approach to speed up both WMD and RWMD. Experiments over 10 datasets suggest that our approach leads to a significant speed-up in document classification tasks while maintaining the same error rates.
Bring the past to the future: adapting stereoscope images for use in the Oculus Go

The purpose of this project was to explore methods for adapting images originally created for the analog stereoscope to use in contemporary virtual reality headsets. The Alabama Museum of the Health Sciences holds in its collections a set of medical images for the stereoscope published by Dr. S.I. Rainforth in 1910. We scanned 3 stereoscope cards from the collection at a resolution of 1200 dots per inch, then adapted the images for use in virtual reality using Adobe Photoshop and Unity. We successfully created a working application for the Oculus Go that displays the images stereoscopically in the headset. The current application allows only static display of the images. Our next steps in developing this project will be to add images from the collection to the virtual reality application, optimize parameters related to image display, and develop scripting that would allow users to dynamically select images from the collection. More information on this project is available on the Alabama Museum of the Health Sciences Virtual Exhibits website, and a short video demonstration is available on Vimeo.Virtual Projects are published on an annual basis in the Journal of the Medical Library Association (JMLA) following an annual call for virtual projects in MLAConnect and announcements to encourage submissions from all types of libraries. An advisory committee of recognized technology experts selects project entries based on their currency, innovation, and contribution to health sciences librarianship.
3D face creation via 2D images within blender virtual environment

&lt;p&gt;&lt;span&gt;Animation and virtual reality movie-making technologies are still witnessing significant progress to this day. Building and stimulating virtual characters inside these applications is a goal. Build a 3D face via using some special tools inside the virtual world is the most important part of identifying a 3D animation. Keen Tools Face Builder add-on for Blender. Interested in creating a 3D face of a famous figure, artist or the general public by adopting several 2D images added to the virtual blinder software environment. The main problem facing these tools is that they deal with high-resolution and sharpness pictures because some images that contain blurring, the result is to build a 3D face model that contains design distortions and non- clearly. in this proposed paper, build a data set for 2D pictures of a specific character (actor), at a resolution of 1920 x 1080 pixels. These images were caught by the camera, different in sharpness and blurring (four types of blurry). Using the “Laplacian Filter algorithm” and OpenCV library with Python language, to isolate blurry from sharpness 2D images. Sharpness images used to build a 3D face model that gave real and similar results to the character in the pictures. &lt;/span&gt;&lt;/p&gt;
Social impact of sport loads as a motivator for the practice of sports activities in the educational environment of a higher education institution

Abstract. The present study shows that sport loads for student environment determine not only the possibility of successful educational or scientific activities, but also successful social activity. The paper’s authors consider sports’ social meaning in higher education institutions as a function of implementing applied activities with correlation with successful professional medium. The relevance of the paper is in that doing sports in education institution is perceived not only as a part of the pedagogical load of topics from the general professional cycle, but also as a means of students’ socialization. The subject under study is students’ predisposition to implement the postulates of independent exercising. As a novelty, the issues of the development of motivation for students to do sports were studied. The main thesis of the research is to form a general idea on possibilities and motivation, as well as to carry out a complex evaluation of students’ readiness to do sports independently, without professors’ participation. A promising input is the testing of organizational-pedagogic conditions, and the integration of physical education and independent sports.
Query Caching Technique Over Cloud-Based MapReduce System: A Survey

The global data volume has been increasing at a high rate in recent years due to social media, smartphones, and other handheld devices. The constant challenge is how to access this information efficiently. MapReduce has emerged as a global tool for processing huge data (unstructured data like text, images, and videos) quickly. It offers distributed processing of data with features like flexibility, scalability, load balancing, fault tolerance, etc. Despite these advantages, this framework produces a large amount of intermediate data or values which get discarded after the task is finished. This intermediate data can be made available by caching. This paper presents the caching technique already being utilized by researchers in MapReduce queries and uncovers the less explored areas.
GAIT based Behavioral Authentication using Hybrid Swarm based Feed Forward Neural Network

Authentication of appropriate users for accessing the liable gadgets exists as one among the prime theme in security models.Illegal access of gadgets such as smart phones, laptops comes with an uninvited consequences, such as data theft, privacy breakage and a lot more.Straight forward approaches like pattern based security, password and pin based security are quite expensive in terms of memory where the user has to keep remembering the passwords and in case of any security issue risen then the password has to be changed and once again keep remembering the recent one.To avoid these issues, in this paper an effective GAIT based model is proposed with the hybridization of Artificial Neural Network model namely Feedforward Neural Network Model with Swarm based algorithm namely Krill Herd optimization algorithm (KH).The task of KH is to optimize the weight factor of FNN which leads to the convergence of optimal solution at the end of the run.The proposed model is examined with 6 different performance measures and compared with four different existing classification model.The performance analysis shows the significance of proposed model when compared with the existing algorithms.
Temporal Context Aggregation for Video Retrieval with Contrastive Learning

The current research focus on Content-Based Video Retrieval requires higher-level video representation describing the long-range semantic dependencies of relevant incidents, events, etc. However, existing methods commonly process the frames of a video as individual images or short clips, making the modeling of long-range semantic dependencies difficult. In this paper, we propose TCA (Temporal Context Aggregation for Video Retrieval), a video representation learning framework that incorporates long-range temporal information between frame-level features using the self-attention mechanism. To train it on video retrieval datasets, we propose a supervised contrastive learning method that performs automatic hard negative mining and utilizes the memory bank mechanism to increase the capacity of negative samples. Extensive experiments are conducted on multiple video retrieval tasks, such as CC_WEB_VIDEO, FIVR-200K, and EVVE. The proposed method shows a significant performance advantage (~17% mAP on FIVR-200K) over state-of-the-art methods with video-level features, and deliver competitive results with 22x faster inference time comparing with frame-level features.
Cross-Project Defect Prediction via Semi-Supervised Discriminative Feature Learning

Cross-project defect prediction (CPDP) is a feasible solution to build an accurate prediction model without enough historical data. Although existing methods for CPDP that use only labeled data to build the prediction model achieve great results, there are much room left to further improve on prediction performance. In this paper we propose a Semi-Supervised Discriminative Feature Learning (SSDFL) approach for CPDP. SSDFL first transfers knowledge of source and target data into the common space by using a fully-connected neural network to mine potential similarities of source and target data. Next, we reduce the differences of both marginal distributions and conditional distributions between mapped source and target data. We also introduce the discriminative feature learning to make full use of label information, which is that the instances from the same class are close to each other and the instances from different classes are distant from each other. Extensive experiments are conducted on 10 projects from AEEEM and NASA datasets, and the experimental results indicate that our approach obtains better prediction performance than baselines.
Impact of Prestigious Indicators on Sustainable Growth of Small and Medium-Sized Enterprises in Pakistan

This study aims to examine the prestigious indicators influencing the sustainable growth of Small and Medium Enterprises (SME's) in Pakistan. This research is comprised of primary data. The evidence for this analysis was collected through a standardized questionnaire. The study population structure is extracted from Pakistan's Punjab province. Sample selection is performed employing a random sample technique. The set of questionnaires were 200 circulated but 25 inadequate questionnaires were excluded. The research employed descriptive statistics and the process of regression to evaluate significant indicators concerning Pakistan's SMEs. The study findings demonstrate that the most imperative indicators associated with the sustainable growth of Pakistan's SME's are government funding, access to credit, and technology awareness turn out to have been vital indicators. Furthermore, results revealed that Pakistan SME's are not risk-focused. The research findings may enable business owners to identify essential indicators that compete with a considerable function in their enterprise's growth. The study indicates authorities should consider the results when implementing regulation that can give a sense which would be practicable for the sustainable growth of Pakistan's SMEs.
Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks

The large adoption of the self-attention (i.e. transformer model) and BERT-like training principles has recently resulted in a number of high performing models on a large panoply of vision-and-language problems (such as Visual Question Answering (VQA), image retrieval, etc.). In this paper we claim that these State-Of-The-Art (SOTA) approaches perform reasonably well in structuring information inside a single modality but, despite their impressive performances , they tend to struggle to identify fine-grained inter-modality relationships. Indeed, such relations are frequently assumed to be implicitly learned during training from application-specific losses, mostly cross-entropy for classification. While most recent works provide inductive bias for inter-modality relationships via cross attention modules, in this work, we demonstrate (1) that the latter assumption does not hold, i.e. modality alignment does not necessarily emerge automatically, and (2) that adding weak supervision for alignment between visual objects and words improves the quality of the learned models on tasks requiring reasoning. In particular , we integrate an object-word alignment loss into SOTA vision-language reasoning models and evaluate it on two tasks VQA and Language-driven Comparison of Images. We show that the proposed fine-grained inter-modality supervision significantly improves performance on both tasks. In particular, this new learning signal allows obtaining SOTA-level performances on GQA dataset (VQA task) with pre-trained models without finetuning on the task, and a new SOTA on NLVR2 dataset (Language-driven Comparison of Images). Finally, we also illustrate the impact of the contribution on the models reasoning by visualizing attention distributions.
SAR image denoising method based on sparse representation

The coherent nature of radar illumination causes the speckle effect, which gives the synthetic aperture radar (SAR) image its noisy appearance. The probability distribution of speckle noise is multiplicative rather than additive, which makes the interpretation and processing of SAR imagery more difficult. A novel SAR image denoising method is proposed. First the multiplicative noise is transformed into additive-like noise by logarithmic transformation. After that, a novel object function is proposed which combines a pre-trained dictionary model to deal with the image. Finally, exponential transform is employed to recover the image. Experimental results show that the proposed method can effectively remove the noise of SAR images, and indicate good performance compared with other state-of-the-art methods.