Reviewerly/exHarmony huggingface dataset:
Not by equations alone: Reasoning with extensible effects

Abstract The challenge of reasoning about programs with (multiple) effects such as mutation, jumps, or IO dates back to the inception of program semantics in the works of Strachey and Landin. Using monads to represent individual effects and the associated equational laws to reason about them proved exceptionally effective. Even then it is not always clear what laws are to be associated with a monad—for a good reason, as we show for non-determinism. Combining expressions using different effects brings challenges not just for monads, which do not compose, but also for equational reasoning: the interaction of effects may invalidate their individual laws, as well as induce emerging properties that are not apparent in the semantics of individual effects. Overall, the problems are judging the adequacy of a law; determining if or when a law continues to hold upon addition of new effects; and obtaining and easily verifying emergent laws. We present a solution relying on the framework of (algebraic, extensible) effects, which already proved itself for writing programs with multiple effects. Equipped with a fairly conventional denotational semantics, this framework turns useful, as we demonstrate, also for reasoning about and optimizing programs with multiple interacting effects. Unlike the conventional approach, equational laws are not imposed on programs/effect handlers, but induced from them: our starting point hence is a program (model), whose denotational semantics, besides being used directly, suggests and justifies equational laws and clarifies side conditions. The main technical result is the introduction of the notion of equivalence modulo handlers (“modulo observation”) or a particular combination of handlers—and proving it to be a congruence . It is hence usable for reasoning in any context, not just evaluation contexts—provided particular conditions are met. Concretely, we describe several realistic handlers for non-determinism and elucidate their laws (some of which hold in the presence of any other effect). We demonstrate appropriate equational laws of non-determinism in the presence of global state, which have been a challenge to state and prove before.
Face-Based Alumni Tracking on Social Media Using Deep Learning

Today's world is connected due to social media platforms. Our system can be used by universities to attract, interact and stay connected with the alumni by using various networks and engaging content. The system enables to retrieve an individual’s information from social media sites based on the facial image. A proper study of various face recognition algorithms was done and found that a convolutional neural network was most promising. Also, various web scraping tools and techniques were studied to retrieve the individual’s information. Our proposed system takes an input image of alumni and searches for alumni's name from the database (college database). The name and image are then used to search the active profile of the alumni from social media sites such as Facebook, Instagram and LinkedIn. Thus, the paper provides a model for uploading a photo and searching the person on a social media site by using face recognition and web scraping.
ATLaS

In order to meet the time-to-market constraint, High-level Synthesis (HLS) is being increasingly adopted by the semiconductor industry. HLS designs, which can be automatically translated into the Register Transfer Level (RTL), are typically written in SystemC at the Electronic System Level (ESL). Timing-based information leakage and its countermeasures, while well-known at RTL and below, have not been yet considered for HLS. The paper makes a contribution to this emerging research area by proposing ATLaS, a novel timing-based information leakage flows detection approach for SystemC HLS designs. The efficiency of our approach in identifying timing channels for SystemC HLS designs is demonstrated on two security-critical architectures which are shared interconnect and crypto core.
Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction

Current state-of-the-art joint entity and relation extraction framework is based on span-level entity classification and relation identification between pairs of entity mentions. However, while maintaining an efficient exhaustive search on spans, the importance of syntactic features is not taken into consideration. It will lead to a problem that the prediction of a relation between two entities is related based on corresponding entity types, but in fact they are not related in the sentence. In addition, although previous works have proven that extract local context is beneficial for the task, it still lacks in-depth learning of contextual features in local context. In this paper, we propose to incorporate syntax knowledge into multi-head self-attention by employing part of heads to focus on syntactic parents of each token from pruned dependency trees, and we use it to model the global context to fuse syntactic and semantic features. In addition, in order to get richer contextual features from the local context, we apply local focus mechanism on entity pairs and corresponding context. Based on applying the two strategies, we perform joint entity and relation extraction on span-level. Experimental results show that our model achieves significant improvements on both Conll04 and SciERC dataset compared to strong competitors.
DeepLTRS: A deep latent recommender system based on user ratings and reviews

We introduce a deep latent recommender system named deepLTRS in order to provide users with high quality recommendations based on observed user ratings and texts of product reviews. The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information, thereby enhancing the predictive ability of the model. Our approach adopts a variational auto-encoder (VAE) architecture as a deep generative latent model for an ordinal matrix encoding ratings and a document-term matrix encoding the reviews. Taking into account both matrices as model inputs, deepLTRS uses a neural network to capture the relationship between latent factors and latent topics. Moreover, a user-majoring encoder and a product-majoring encoder are constructed to jointly capture user and product preferences. Due to the specificity of the model structure, an original row-column alternated mini-batch optimization algorithm is proposed to deal with user-product dependencies and computational burden. Numerical experiments on simulated and real-world data sets demonstrate that deepLTRS outperforms the state-of-the-art, in particular in context of extreme data sparsity.
GlassViz: Visualizing Automatically-Extracted Entry Points for Exploring Scientific Corpora in Problem-Driven Visualization Research

In this paper, we report the development of a model and a proof-of-concept visual text analytics (VTA) tool to enhance document discovery in a problem-driven visualization research (PDVR) context. The proposed model captures the cognitive model followed by domain and visualization experts by analyzing the interdisciplinary communication channel as represented by keywords found in two disjoint collections of research papers. High distributional intercollection similarities are employed to build informative keyword associations that serve as entry points to drive the exploration of a large document corpus. Our approach is demonstrated in the context of research on visualization for the digital humanities.
Learning twofold heterogeneous multi-task by sharing similar convolution kernel pairs

Heterogeneous multi-task learning (HMTL) is an important topic in multi-task learning (MTL). Most existing HMTL methods usually solve either scenario where all tasks reside in the same input (feature) space yet unnecessarily the consistent output (label) space or scenario where their input (feature) spaces are heterogeneous while the output (label) space is consistent. However, to the best of our knowledge, there is limited study on twofold heterogeneous MTL (THMTL) scenario where the input and the output spaces are both inconsistent or heterogeneous. In order to handle this complicated scenario, in this paper, we design a simple and effective multi-task adaptive learning (MTAL) network to learn multiple tasks in such THMTL setting. Specifically, we explore and utilize the inherent relationship between tasks for knowledge sharing from similar convolution kernels in individual layers of the MTAL network. To realize the sharing, we weightedly aggregate any pair of convolutional kernels with their similarity greater than some threshold ρ. Consequently, our model effectively performs cross-task learning while suppresses the intra-redundancy of the entire network. Finally, we conduct end-to-end training. Our experimental results demonstrate the significant competitiveness of our method in comparison with the current state-of-the-art methods.
Remote sensing image description based on word embedding and end-to-end deep learning

This study proposes an end-to-end image description generation model based on word embedding technology to realise the classification and identification of Populus euphratica and Tamarix in complex remote sensing images by providing descriptions in precise and concise natural sentences. First, category ambiguity over large-scale regions in remote sensing images is addressed by introducing the co-occurrence matrix and global vectors for word representation to generate the word vector features of the object to be identified. Second, a new multi-level end-to-end model is employed to further describe the content of remote sensing images and to better advance the description tasks for P. euphratica and Tamarix in remote sensing images. Experimental results reveal that the natural language sentences generated using this method can better describe P. euphratica and Tamarix in remote sensing images compared with conventional deep learning methods.
Style Transfer for Keypoint Matching Under Adverse Conditions

In this work, we address the difficulty of matching local features between images captured at distant points in time resulting in a global appearance change. Inspired by recent neural style transfer techniques, we propose to use an image transformation network to translate night images into day-like appearance, with the objective of better matching performance. We extend traditional style transfer, that optimize for content and style, with a keypoint matching loss function. The joint optimization of these losses allows our model to generate images that can significantly improve the performance of local feature matching, in a self-supervised way. As a result, our approach is flexible and does not require paired training data, which is difficult to obtain in practice. We show how our method can be used as an extension to a state-of-the-art differentiable feature extractor to improve its performance in challenging scenarios. This is demonstrated in our evaluation on day-night image matching and visual localization tasks with night-rain image queries.
On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models

Surgical risk increases significantly when patients present with comorbid conditions. This has resulted in the creation of numerous risk stratification tools with the objective of formulating associated surgical risk to assist both surgeons and patients in decision-making. The Surgical Outcome Risk Tool (SORT) is one of the tools developed to predict mortality risk throughout the entire perioperative period for major elective in-patient surgeries in the UK. In this study, we enhance the original SORT prediction model (UK SORT) by addressing the class imbalance within the dataset. Our proposed method investigates the application of diversity-based selection on top of common re-sampling techniques to enhance the classifier's capability in detecting minority (‘mortality’) events. Diversity amongst training datasets is an essential factor in ensuring re-sampled data keeps an accurate depiction of the minority/majority class region, thereby solving the generalization problem of mainstream sampling approaches. We incorporate the use of the Solow-Polasky measure as a drop-in functionality to evaluate diversity, with the addition of greedy algorithms to identify and discard subsets that share the most similarity. Additionally, through empirical experiments, we prove that the performance of the classifier trained over diversity-based dataset outperforms the original classifier over ten external datasets. Our diversity-based re-sampling method elevates the performance of the UK SORT algorithm by 1.4%.
How FAIR are plant sciences in the twenty-first century? The pressing need for reproducibility in plant ecology and evolution

The need for open, reproducible science is of growing concern in the twenty-first century, with multiple initiatives like the widely supported FAIR principles advocating for data to be Findable, Accessible, Interoperable and Reusable. Plant ecological and evolutionary studies are not exempt from the need to ensure that the data upon which their findings are based are accessible and allow for replication in accordance with the FAIR principles. However, it is common that the collection and curation of herbarium specimens, a foundational aspect of studies involving plants, is neglected by authors. Without publicly available specimens, huge numbers of studies that rely on the field identification of plants are fundamentally not reproducible. We argue that the collection and public availability of herbarium specimens is not only good botanical practice but is also fundamental in ensuring that plant ecological and evolutionary studies are replicable, and thus scientifically sound. Data repositories that adhere to the FAIR principles must make sure that the original data are traceable to and re-examinable at their empirical source. In order to secure replicability, and adherence to the FAIR principles, substantial changes need to be brought about to restore the practice of collecting and curating specimens, to educate students of their importance, and to properly fund the herbaria which house them.
Multidirectional Gabor Filter-Based Approach for Pectoral Muscle Boundary Detection

Accurate pectoral muscle boundary (PMB) detection is a crucial stage in any computer-aided detection (CAD) system, used for automatic detection of various possible signs of malignancy in a mammographic image. The presence of high-density glandular tissues overlapping PMB, pectoral muscle of small size and low density, presence of pectoralis minors, etc., poses a significant challenge for researchers working on automatic PMB detection problem. In mammograms with pectoral muscle superimposed by high-density glandular tissues, especially near the lower portion of the pectoral muscle, PMB forms fuzzy textural edges with surrounding mammary tissues regions. The performance of any intensity-based approach in detecting such fuzzy textural edges is poor. Here, we present a multidirectional Gabor filter (MDGF)-based approach for PMB detection. A set of three high-frequency bandpass Gabor filters is designed, which covers all the possible orientations of PMB present in a preprocessed mediolateral oblique (MLO) view mammogram. These filters are used to extract high- and mid-frequency range edge information corresponding to strong as well as weak fuzzy textural edges of PMB. The information obtained from magnitude and phase response along with a boundary search and merge algorithm (designed to connect broken PMB edges) is used to detect complete PMB with high accuracy. With PMB detection accuracy of 95.28% for MIAS, 96.50% for CBIS-DDSM, and 96.95% for INbreast database mammograms, the proposed method outperforms many state-of-the-art methods, thus show its suitability for a CAD system.
A novel Kernel Clustering with Quasi-Monte Carlo Random Feature Map

Conventional kernel clustering approaches are helpful in extracting the essential non-linear data structure, but the high computational complexity makes them unavailability to large data sets. To overcome this limitation, many attentions have been paid on Random Fourier feature (RFF) map based kernel clustering algorithms. However, as the RFF map cannot achieve sufficiently precise approximation to the kernel, the clustering performance of RFF map based kernel clustering algorithms are not superior enough. On the basis of the above issues, this paper puts forward a novel Quasi-Monte Carlo feature (QMCF) map based kernel fuzzy clustering method, which is named QMCF-FCM. In this method, low-rank random features are yielded and fuzzy c-means is executed by leveraging Quasi-Monte Carlo sequences in this feature space. Experiments on a synthetic data set show that the QMCF map achieves more accurate approximation to the kernel than the RFF map and the proposed method obtains better clustering results than RFF map based kernel clustering method.
T-SNE with High Order Truncation Fractional Gradient Descent Method

T-distributed stochastic neighbor embedding (T-SNE) algorithm with high order truncation is a kind of dimensionality reduction method, which belongs to nonlinear dimensionality reduction and is a popular learning method. In the T-SNE algorithm with high-order truncation, the T-distribution is used to replace the Gaussian distribution when calculating the probability between sample points in the low-dimensional space, and the objective function is solved by Kullback-Leibler divergence and high-order truncation. T-SNE is developed from SNE, and its long tail effect effectively solves the crowding problem of SNE. However, there is no unique optimal solution for T-SNE, and there is no estimated part. The number of iterations and the convergence of the objective function are the reasons that affect the visualization effect of T-SNE. In terms of the convergence of the objective function, it is found that the extreme value of fractional order is not equal to the true extreme value of the objective function, which will make the fractional step method impractical. In order to enhance the global convergence, the new fractional gradient descent method of high-order truncation is used to iterate to obtain the optimal solution of the objective function and optimize the visualization effect of T-SNE.
RESTful Web Service Implementation on Unklab Information System Using JSON Web Token (JWT)

Student data that are public and stored on Universitas Klabat currently be stored in a local database and can only be accessed by the database administrator. A RESTful web service acts as a medium that provides access to the data for those who have permission, improving the data's accessibility. Any user eligible by the web service can use the Universitas Klabat Information System data to develop a mobile or another web application without making another similar database, thus eliminating the redundant process of making a duplicate database. A JSON Web Token is also used in this web service to ensure that the person accessing the web service is authorized to access the specific data. The agile method is used to develop this application. In conclusion, the web service can create, read, update, delete operation regarding the student database and provide it to the user that is authorized with a valid JSON Web Token.
Transforming One-Stop E-Services in Iraq: Focusing on perception of Blockchain Technology in Digital Identity System

This study addressed obstructs related to operational e-government that leads to low satisfaction of citizens and low quality of e-services as well. This study demonstrated two models to provide a practical implementation in the transformation of reaching an effective public e-service by adopting Blockchain technology and utilizing the ID system few studies shed light on the success factors of Blockchain and how the public sectors would hit the peak of efficiency and effectiveness. The noteworthy objectives of this study are: To increase the trust and transparency, reduction of the cost of transactions and its processes efforts. The empirical results derive that the digital ID system and the Blockchain technology are significant mediations and play an imperative mediating role in the successful implementation of the Blockchain technology in One-Stop Service and enhancing the quality of e-service more than the traditional ICT. Another finding shows the highest risk to adopt the Blockchain in public service without applying an efficient digital identity system and management documents system to provide authorization of credentials. Interestingly it shows the vital role of authentication and identification domains at the digital ID system stage while the significant role of applying the authorization domain at the One-stop service stage.
A RD-T Network for Hand Gesture Recognition Based on Millimeter-Wave Sensor

Gesture recognition has become an important research in human-machine interface. Currently, most of gesture recognition schemes are based on vision sensor, which is sensitive to application environment. In contrast, millimeter wave sensor demonstrates better reliability to different environments. However, they normally deliver a less dense image and challenge high quality recognition. To address this issue, we propose a RD-T network-based gesture recognition method leveraging 4-dimension sensing capability of millimeter wave radar. By categorizing sensed images into Range-Time-Map (RTM), Doppler-Time-Map (DTM) and Range-Doppler-Map (RDM), we can perform high fidelity feature extraction and classification. In turn, an RD-T network has been designed to realize the branch network structure of the scheme. Experiments show that the achieved accuracy of RD-T network is 2% ~ 3% higher than a single parameter network, suggest the validity of the proposed high-dimensional feature fusion scheme.
Sigma-Lognormal Modeling of Speech

Human movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are changed. However, the inverse approach, in which the muscular response parameters and the subject's age are derived from real continuous speech, is not possible with such models. Instead, in the handwriting field, the kinematic theory of rapid human movements and its associated Sigma-lognormal model have been applied successfully to obtain the muscular response parameters. This work presents a speech kinematics based model that can be used to study, analyze, and reconstruct complex speech kinematics in a simplified manner. A method based on the kinematic theory of rapid human movements and its associated Sigma lognormal model are applied to describe and to parameterize the asymptotic impulse response of the neuromuscular networks involved in speech as a response to a neuromotor command. The method used to carry out transformations from formants to a movement observation is also presented. Experiments carried out with the (English) VTR TIMIT database and the (German) Saarbrucken Voice Database, including people of different ages, with and without laryngeal pathologies, corroborate the link between the extracted parameters and aging, on the one hand, and the proportion between the first and second formants required in applying the kinematic theory of rapid human movements, on the other. The results should drive innovative developments in the modeling and understanding of speech kinematics.