Reviewerly/exHarmony huggingface dataset:
Using micro-services and artificial intelligence to analyze images in criminal evidences

With the advancement of digital crimes, the field of digital forensic science grows more and more, and with this growth, the search for faster and more accurate solutions to aid the investigation process becomes a necessity. In the context of the Brazilian judicial system, during a criminal investigation, forensic specialists extract, decode, and analyze the evidence collected to allow the prosecutor to make legal demands for a prosecution. These specialists have a very short time to analyze to find criminal evidence and the process can take a long time. To solve this problem this paper proposes to use a micro-services-based application with artificial intelligence to process large amounts of images contained in criminal evidence using open-source software. The image classification module contains some pre-trained classifiers, considering the needs of forensic analysts of the Rio Grande do Norte District Attorney's Office (MPRN). The models were built to identify specific types of objects, for example, firearms, ammunition, Brazilian identity cards, text documents, cell phone screen captures, and nudity. The results obtained show that the system achieved good accuracy in most cases. This is extremely important in the context of this research, where false positives should be avoided in order to save analysts' working time. Moreover, the proposed architecture was able to speed up the image classification process using Apache Spark.
Learning Expected Emphatic Traces for Deep RL

Off-policy sampling and experience replay are key for improving sample efficiency and scaling model-free temporal difference learning methods. When combined with function approximation, such as neural networks, this combination is known as the deadly triad and is potentially unstable. Recently, it has been shown that stability and good performance at scale can be achieved by combining emphatic weightings and multi-step updates. This approach, however, is generally limited to sampling complete trajectories in order, to compute the required emphatic weighting. In this paper we investigate how to combine emphatic weightings with non-sequential, off-line data sampled from a replay buffer. We develop a multi-step emphatic weighting that can be combined with replay, and a time-reversed n-step TD learning algorithm to learn the required emphatic weighting. We show that these state weightings reduce variance compared with prior approaches, while providing convergence guarantees. We tested the approach at scale on Atari 2600 video games, and observed that the new X-ETD(n) agent improved over baseline agents, highlighting both the scalability and broad applicability of our approach.
RaScaNet: Learning Tiny Models by Raster-Scanning Images

Deploying deep convolutional neural networks on ultra-low power systems is challenging due to the extremely limited resources. Especially, the memory becomes a bottleneck as the systems put a hard limit on the size of on-chip memory. Because peak memory explosion in the lower layers is critical even in tiny models, the size of an input image should be reduced with sacrifice in accuracy. To overcome this drawback, we propose a novel Raster-Scanning Network, named RaScaNet, inspired by raster-scanning in image sensors. RaScaNet reads only a few rows of pixels at a time using a convolutional neural network and then sequentially learns the representation of the whole image using a recurrent neural network. The proposed method operates on an ultra-low power system without input size reduction; it requires 15.9–24.3× smaller peak memory and 5.3–12.9× smaller weight memory than the state-of-the-art tiny models. Moreover, RaScaNet fully exploits on-chip SRAM and cache memory of the system as the sum of the peak memory and the weight memory does not exceed 60 KB, improving the power efficiency of the system. In our experiments, we demonstrate the binary classification performance of RaScaNet on Visual Wake Words and Pascal VOC datasets.
Multi-criteria differential evolution

Evolutionary multitask optimization (EMTO) has shown great potential and attracted increasing attention for solving multitask optimization problem (MTOP). However, most existing EMTOs still treat the internal tasks just as different related problems, rather than the component problems of the entire MTOP, which is inefficient. Therefore, this paper proposes to treat the entire MTOP as a multi-criteria optimization problem (MCOP), so as to solve the MTOP more efficiently. To be specific, the fitness function of each task in the MTOP is treated as an evaluation criterion in the corresponding MCOP. During the evolutionary process, a round-robin multi-criteria strategy is adopted to better utilize the multiple criteria. This way, we can use different evaluation criteria in different generations or stages to guide the environmental selection and population evolution in ECs, so as to find out the optimal solutions for the criteria of different tasks. Based on the above, a multi-criteria differential evolution algorithm is developed for solving MTOPs. Experiments on widely-used MTOP benchmarks and comparisons with some state-of-the-art algorithms have verified the great effectiveness and efficiency of the proposed algorithm. Therefore, treating MTOPs as MCOPs is a potential and promising direction for solving MTOPs.
Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

Machine-learning solutions for pattern classification problems are nowadays widely deployed in society and industry. However, the lack of transparency and accountability of most accurate models often hinders their safe use. Thus, there is a clear need for developing explainable artificial intelligence mechanisms. There exist model-agnostic methods that summarize feature contributions, but their interpretability is limited to predictions made by black-box models. An open challenge is to develop models that have intrinsic interpretability and produce their own explanations, even for classes of models that are traditionally considered black boxes like (recurrent) neural networks. In this article, we propose a long-term cognitive network (LTCN) for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. For supporting the interpretability without affecting the performance, the model incorporates more flexibility through a quasi-nonlinear reasoning rule that allows controlling nonlinearity. Besides, we propose a recurrence-aware decision model that evades the issues posed by the unique fixed point while introducing a deterministic learning algorithm to compute the tunable parameters. The simulations show that our interpretable model obtains competitive results when compared to state-of-the-art white and black-box models.
Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

In the recent years, the face recognition task has attracted the attention of researchers due to its efficiency in several domains such as surveillance and access control. Unfortunately, there are multiple challenges that decrease the performance of face recognition. Partial occlusion is the most challenging one since it often causes a great lack of information. The main purpose of this paper is to prove that facial reconstruction improves the results of facial recognition compared to de-occlusion and full-face recognition in the presence of occlusion. Our objective is to achieve occluded-face recognition, de-occluded-face recognition, and reconstructed-face recognition. Regarding face reconstruction, we introduce two different methods based on Laplacian pyramid blending and CycleGANs. In order to validate our work, we perform two different feature extraction techniques: hand-crafted features and learned features exploiting the final layers of a pre-trained deep architecture model. The experimental results on the EURECOM Kinect Face Dataset (EKFD) and the IST-EURECOM Light Field Face Database (IST-EURECOM LFFD) show that the proposed face reconstruction approach, compared with the face de-occlusion and occluded-face recognition ones, clearly improves the face recognition task. Our method boosts the classification performance in comparison with the state-of-the-art methods, achieving 94.66% on EKFD and 72.35% on IST-EURECOM LFFD.
Real quadratic fields admitting universal lattices of rank 7

In this paper, we prove that if d is sufficiently large square-free positive rational integer, then there is no positive definite universal quadratic OF-lattice of rank 7 where F=Q(d).
Authorship Identification of Electronic Texts

Electronic text stylometry is concerned with analyzing the writing styles of input electronic texts to extract information about their authors. For example, such extracted data could be the authors’ identity or other aspects, such as their gender and age group. This survey paper presents the following contributions: 1) A description of all stylometry problems in probability terms, under a unified notation. 2) A survey of data representation (or feature extraction) methods. 3) A comprehensive evaluation of 23, 760 feature extraction methods followed by a thorough discussion of the results. This extensive evaluation is critical since the known data representation methods are often not evaluated under the same unified testbed.
A Structure-Aware Storage Optimization for Out-of-Core Concurrent Graph Processing

With the huge demand for graph analytics in many real-world applications, massive iterative graph processing jobs are concurrently performed on the same graphs and suffer from significant high data access cost. To lower the data access cost toward high performance, several out-of-core concurrent graph processing solutions are recently designed to handle concurrent jobs by enabling these jobs to share the accesses of the same graph data. However, the set of active vertices in each partition are usually different for various concurrent jobs and also evolve with time, where some high-degree ones (or called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hub-vertices</i> ) of these active vertices require more iterations to converge due to the power-law property of real-world graphs. In consequence, existing solutions still suffer from much unnecessary I/O traffic, because they have to entirely load each partition into the memory for concurrent jobs even if most vertices in this partition are inactive and may be shared by a few jobs. In this paper, we propose an efficient structure-aware storage system, called GraphSO, for higher throughput of the execution of concurrent graph processing jobs. It can be integrated into existing out-of-core graph processing systems to promote the execution efficiency of concurrent jobs with lower I/O overhead. The key design of GraphSO is a fine-grained storage management scheme. Specifically, it logically divides the partitions of existing graph processing systems into a series of small same-sized chunks. At runtime, these small chunks with active vertices are judiciously loaded by GraphSO to construct new logical partitions (i.e., each logical partition is a subset of active chunks) for existing graph processing systems to handle, where the most-frequently-used chunks are preferentially loaded to construct the logical partitions and the other ones are delayed to wait to be required by more jobs. In this way, it can effectively spare the cost of loading the graph data associated with the inactive vertices with low repartitioning overhead and can also enable the loaded graph data to be fully shared by concurrent jobs. Moreover, GraphSO also designs a buffering strategy to efficiently cache the most-frequently-used chunks in the main memory to further minimize the I/O traffic by avoiding repeated load of them. Experimental results show that GraphSO improves the throughput of GridGraph, GraphChi, X-Stream, DynamicShards, LUMOS, Graphene, and Wonderland by 1.4-3.5 times, 2.1-4.3 times, 1.9-4.1 times, 1.9-2.9 times, 1.5-3.1 times, 1.3-1.5 times, and 1.3-2.7 times after integrating with them, respectively.
CAS-AIR-3D Face: A Low-Quality, Multi-Modal and Multi-Pose 3D Face Database

Benefiting from deep learning with large scale face databases, 2D face recognition has made significant progress in recent years. However, it still highly depends on lighting conditions and human poses, and suffers from face spoofing problem. In contrast, 3D face recognition reveals a new path that can overcome the previous limitations of 2D face recognition. One of the most important problems for 3D face recognition is to construct a suitable database, which can be exploited to train different 3D face recognition algorithms. In this work, we propose a new database, CAS-AIR-3D Face, for low-quality 3D face recognition. It includes 24713 videos from 3093 individuals, which is captured by Intel RealSense SR305. The database contains three modalities: color, depth and near infrared, and is rich in pose, expression, occlusion and distance variations. To the best of our konwledge, CAS-AIR-3D Face is the largest low-quality 3D face database in terms of the number of individuals and the sample variations. Moreover, we preprocess the data via a sophisticated face alignment method, and Point Cloud Spherical Cropping Method (SCM) is leveraged to remove the background noise in the depth images. Finally, an evaluation protocol is designed for fair comparison, and extensive experiments are conducted with different backbone networks to provide different baselines on this database.
Aligning digital literacy and student academic success: Lessons learned from COVID-19 pandemic

In this paper the alignment of computer and digital literacy as well as student academic success were examined. Lack of adequate functional digital literacy training and the unreadiness of higher education institutions for the impact of random shocks such as COVID-19 pandemic has gravely affected teaching and learning. The purpose of this paper is to make the case for preparedness of students to meet the needs of technology jobs by mandating and aligning digital literacy and student success. As COVID-19 Pandemic was spreading through the communities of the United States, institutions of higher education transitioned to fully online teaching and learning. Prior to the pandemic, fully online education was secondary to face-to-face format. Only about 20% of classes were fully online while 80% were face-to-face. Digital literacy was given a token treatment as a percentage of the entire curriculum and relegated to only the departments of computer information systems and computer sciences. Faculty, students, staff, families, and communities were not trained for the intensity of fully online education as the only format. Many of them never heard of most of the digital literacy tools. Both faculty and students were forced to learn the use of computers and digital literacy tools to survive the spring 2020 semester. Low-income families were left to the operational schedules of local libraries, many of whom did not have internet presence. The institutions provided limited training for faculty and students to meet the urgency of the time. Faculty and students were forced to purchase expensive tools and hardware to withstand the intensive demand of teaching and learning. Many students were overwhelmed by the pressure of the new way of learning; some dropped out of school while many performed very poorly in their examinations which negatively impacted their overall grade point averages. One year later in spring 2021, the student success outcomes have barely changed. At the same time, technology is advancing despite the raging COVID pandemic. Millions of technology-enabled jobs remain unfilled while millions of university graduates are unemployed. There continues to be a mismatch between current job requirements in the industries and graduating students’ skills. This paper discusses the indispensable value of building computer and digital literacy training into all undergraduate curriculums. We argue for mandated computer and digital literacy exit skills assessment test for all graduating students irrespective of their discipline. We also make the case for increased institutional investment in faculty training in computer and digital literacy readiness. There are a number of remedies suggested to address the speed of advancement in technology, faculty and student functional mastery of basic computer and digital literacy skills. We conclude that all institutions must be proactive rather than reactive to systemic shocks by preparing students for academic success and technological readiness for today’s job markets.
Analysis of Human Behavior by Mining Textual Data: Current Research Topics and Analytical Techniques

The goal of this study was to conduct a literature review of current approaches and techniques for identifying, understanding, and predicting human behaviors through mining a variety of sources of textual data with a focus on enabling classification of psychological behaviors regarding emotion, cognition, and social empathy. This review was performed using keyword searches in ISI Web of Science, Engineering Village Compendex, ProQuest Dissertations, and Google Scholar. Our findings show that, despite recent advancements in predicting human behaviors based on unstructured textual data, significant developments in data analytics systems for identification, determination of interrelationships, and prediction of human cognitive, emotional and social behaviors remain lacking.
Attribute Value Generation from Product Title using Language Models

Identifying the value of product attribute is essential for many e-commerce functions such as product search and product recommendations.Therefore, identifying attribute values from unstructured product descriptions is a critical undertaking for any e-commerce retailer.What makes this problem challenging is the diversity of product types and their attributes and values.Existing methods have typically employed multiple types of machine learning models, each of which handles specific product types or attribute classes.This has limited their scalability and generalization for large scale real world e-commerce applications.Previous approaches for this task have formulated the attribute value extraction as a Named Entity Recognition (NER) task or a Question Answering (QA) task.In this paper we have presented a generative approach to the attribute value extraction problem using language models.We leverage the largescale pretraining of the GPT-2 and the T5 textto-text transformer to create fine-tuned models that can effectively perform this task.We show that a single general model is very effective for this task over a broad set of product attribute values with the open world assumption.Our approach achieves state-of-the-art performance for different attribute classes, which has previously required a diverse set of models.
ITNLP at SemEval-2021 Task 11: Boosting BERT with Sampling and Adversarial Training for Knowledge Extraction

This paper describes the winning system in the End-to-end Pipeline phase for the NLP-ContributionGraph task.The system is composed of three BERT-based models and the three models are used to extract sentences, phrases and triples respectively.Experiments show that sampling and adversarial training can greatly boost the system.In End-to-end Pipeline phase, our system got an average F1 of 0.4703, significantly higher than the secondplaced system which got an average F1 of 0.3828.
Detecting Hashtag Hijacking for Hashtag Activism

Social media has changed the way we engage in social activities.On Twitter, users can participate in social movements using hashtags such as #MeToo; this is known as hashtag activism.However, while these hashtags can help reshape social norms, they can also be used maliciously by spammers or troll communities for other purposes, such as signal boosting unrelated content, making a dent in a movement, or sharing hate speech.We present a Tweet-level hashtag hijacking detection framework focusing on hashtag activism.Our weakly-supervised framework uses bootstrapping to update itself as new Tweets are posted.Our experiments show that the system adapts to new topics in a social movement, as well as new hijacking strategies, maintaining strong performance over time.
Creating Welsh Language Word Embeddings

Word embeddings are representations of words in a vector space that models semantic relationships between words by means of distance and direction. In this study, we adapted two existing methods, word2vec and fastText, to automatically learn Welsh word embeddings taking into account syntactic and morphological idiosyncrasies of this language. These methods exploit the principles of distributional semantics and, therefore, require a large corpus to be trained on. However, Welsh is a minoritised language, hence significantly less Welsh language data are publicly available in comparison to English. Consequently, assembling a sufficiently large text corpus is not a straightforward endeavour. Nonetheless, we compiled a corpus of 92,963,671 words from 11 sources, which represents the largest corpus of Welsh. The relative complexity of Welsh punctuation made the tokenisation of this corpus relatively challenging as punctuation could not be used for boundary detection. We considered several tokenisation methods including one designed specifically for Welsh. To account for rich inflection, we used a method for learning word embeddings that is based on subwords and, therefore, can more effectively relate different surface forms during the training phase. We conducted both qualitative and quantitative evaluation of the resulting word embeddings, which outperformed previously described word embeddings in Welsh as part of larger study including 157 languages. Our study was the first to focus specifically on Welsh word embeddings.
Usability of Blockchain Technology in Higher Education: A systematic review identifying the current issues in the education system

Abstract Blockchain facilitates the creation of a shared document of a digital transaction in a distributed way where no third-party regulates any data or related activity. Earlier blockchain was used to transmit money, but today it has a huge scope of utilization in various domains like banking, health care, the Internet of Things (IoT), and many more. Inside the educational domain, blockchain offers many circumstances for disperse administration of records in an interoperable way in higher educational institutions. The primary purpose of the study is to address the current issues relevant to educational institutions and to identify the appropriate blockchain characteristics that can solve these issues. To recognize and extract significant knowledge from the selected research studies, a systematic literature review approach has been followed by us. Our research study represents the current problems in three forms, i.e., financial, digital, and physical. The consequences of the study show that the main problems faced by education institutions are the risk of manipulation, the tough of authenticating, and difficulty in interchanging records among institutions. This research analyzes the blockchain characteristics such as fragmentation, traceability, and consensus system, focusing on a few problems relevant in this domain.