Reviewerly/exHarmony huggingface dataset:
Inference over Graphs

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Mean-Square-Error Inference

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Realtime Authentication using 3D Password

The complex advancement of the computing world makes access to confidential information more vulnerable. The daily activities like banking, official back-office activities and etc. are performed completely online. The sustainable and successful practice for authentication is One Time Password. However, it needs end-user interaction and either internet or phone connection. In this regard, a new alternative scheme with less requirement of resources- 3D password is proposed that uses real-time features of end-users for accessing the confidential data/services. In this work, facial expression, eye-ball movement, and keyboard values are collated and normalize to form the 3D password and subsequently validated. The exhaustive experimental results exhibit around 80
Towards Quantifying the Privacy of Redacted Text

In this paper we propose use of a k-anonymity-like approach for evaluating the privacy of redacted text. Given a piece of redacted text we use a state of the art transformer-based deep learning network to reconstruct the original text. This generates multiple full texts that are consistent with the redacted text, i.e. which are grammatical, have the same non-redacted words etc., and represents each of these using an embedding vector that captures sentence similarity. In this way we can estimate the number, diversity and quality of full text consistent with the redacted text and so evaluate privacy.
A Smart Movie Suitability Rating System Based on Subtitle

With the enormous growth rate in the number of movies coming into our lives, it can be very challenging to decide whether a movie is suitable for a family or not. Almost every country has a Movie Rating System that determines movies’ suitability age. But these current movie rating systems require watching the full movie with a professional. In this paper, we developed a model which can determine the rating level of the movie by only using its subtitle without any professional interfere. To convert the text data to numbers, we use TF-IDF vectorizer, WIDF vectorizer and Glasgow Weighting Scheme. We utilized random forest, support vector machine, k-nearest neighbor and multinomial naive bayes to find the best combination that achieves the highest results. We achieved an accuracy of 85%. The result of our classification approach is promising and can be used by the movie rating committee for pre-evaluation.&#x0D; Cautionary Note: In some chapters of this paper may contain some words that many will find offensive or inappropriateness; however, this cannot be avoided owing to the nature of the work
BERT based Hierarchical Alternating Co-Attention Visual Question Answering using Bottom-Up Features

Answering a question from a given visual image is a very well-known vision language task where the machine is given a pair of an image and a related question and the task is to generate the natural language answer. Humans can easily relate image content with a given question and reason about how to generate an answer. But automation of this task is challenging as it involves many computer vision and NLP tasks. Most of the literature focus on a novel attention mechanism for joining image and question features ignoring the importance of improving the question feature extraction module. Transformers have changed the way spatial and temporal data is processed. This paper exploits the power of Bidirectional Encoder Representation from Transformer (BERT) as a powerful question feature extractor for the VQA model. A novel method of extracting question features by combining output features from four consecutive encoders of BERT has been proposed. This is from the fact that each encoder layer of the transformer attends to features from the word to a phrase and ultimately to a sentence-level representation. A novel BERT-based hierarchical alternating co-attention VQA using the Bottom-up features model has been proposed. Our model is evaluated on the publicly available benchmark dataset VQA v2.0 and experimental results prove that the model improves upon two baseline models by 9.37% and 0.74% respectively. DOI: https://doi.org/10.17762/ijisae.v10i3S.2427
Multinational corporations and domestic company’s development: Evidence from Nigeria

This research study looks at assessing, and reviewing how multinational corporations spurred up economic development, particularly in Nigeria to an extent. Multinational Corporations (MNCs) involves having operations in more than one Nation with its head office at their home country. They are subject to changes in international exchange rates, tariffs, duties, and restrictions on trade. Majority of the successful ones’ targets production points where labour is cheaper, with affordable transportation so as to deliver to their markets. The research used scholarly journals, articles, and textbooks to review the activities of MNCs in Nigeria’s economic development, as it relates to growth, technology transfers and policy issues. Based on the literature, the research discovered that multinational corporations have contributed to the Nigeria’s economic development, which varies. Meanwhile, the technology internalization as well as transfers is still a mirage. Although, some MNCs still engage in unethical business practices that soils their name, and their image of Nigeria. This research study recommends other things including that representatives of all stakeholders-employees, customers, society, government should be appointed as members of the Board of Directors of various corporations, to ensure direct representation, and as well as participation in the decision-making process. More so, polycentric model of staff selection should be imposed by government on these MNCs which will be stated under the terms of agreement with all the multinational corporations operating within Nigeria. This will improve skill acquisition as well as adequate transfer of technology.
Utilization of AI in 5G Edge Networks

As society advances toward the information era, the telecommunications network, a major aspect of this transformation, is undergoing rapid and profound change.Innovative methods, such as Artificial Intelligence (AI), are necessary to accomplish these transitions.In my proposal, the artificial intelligence is not designed to help the system that hosts it, but rather the terminal node that benefits from it by processing its incoming data stream before it arrives.I constructed an experimental Edge Computing system with a tightly integrated AI solution in order to attain this.The System comprises a traffic generator, an Ericsson UPF application, an artificial intelligence (AI) component, and software serving as the Edge Application.The produced traffic was routed through the UPF before being processed by the AI component and forwarded to the application.
Real-time Hand Gesture Pattern Recognition and Device Control through Inbuilt Webcam

Home automation technique is used to design and implement a remotely controlled, energy efficient and highly scalable Smart Home with basic features that safeguard the resident's comfort and security.Controlling electric home appliances and gadgets with the help of switches is difficult for old and disabled people.The basic problems faced by disabled people in day-to-day life in their homes is to turn ON or OFF the daily used equipment like lights, fans, and difficulty in analyzing switches are observed many times.In this paper, we attempt to propose a real-time hand gesturebased recognition system using a simple webcam and a microcontroller that can automate the control of electrical home appliances using simple hand gestures without using multiple sensors or any kind of special equipment.The proof of concept is demonstrated by controlling a set of different colored electric bulbs that represent the appliances or switches, which are further connected to an Arduino Uno Microcontroller, which in turn is connected to the personal computer where the gesture recognition is implemented with the help of real-time optimized computer vision library.
Crowdsourcing ratings for single lexical items

In this study, we investigate theoretical and practical issues connected to differentiating between core and peripheral vocabulary at different levels of linguistic proficiency using statistical approaches combined with crowdsourcing. We also investigate whether crowdsourcing second language learners’ rankings can be used for assigning levels to unseen vocabulary. The study is performed on Swedish single-word items. The four hypotheses we examine are: (1) there is core vocabulary for each proficiency level, but this is only true until CEFR level B2 (upper-intermediate); (2) core vocabulary shows more systematicity in its behavior and usage, whereas peripheral items have more idiosyncratic behavior; (3) given that we have truly core items (aka anchor items) for each level, we can place any new unseen item in relation to the identified core items by using a series of comparative judgment tasks, this way assigning a “target” level for a previously unseen item; and (4) non-experts will perform on par with experts in a comparative judgment setting. The hypotheses have been largely confirmed: In relation to (1) and (2), our results show that there seems to be some systematicity in core vocabulary for early to mid-levels (A1-B1) while we find less systematicity for higher levels (B2-C1). In relation to (3), we suggest crowdsourcing word rankings using comparative judgment with known anchor words as a method to assign a “target” level to unseen words. With regard to (4), we confirm the previous findings that non-experts, in our case language learners, can be effectively used for the linguistic annotation tasks in a comparative judgment setting.
Learning languages from parallel corpora

This work describes a blueprint for an application that generates language learning exercises from parallel corpora. Word alignment and parallel structures allow for the automatic assessment of sentence pairs in the source and target languages, while users of the application continuously improve the quality of the data with their interactions, thus crowdsourcing parallel language learning material. Through triangulation, their assessment can be transferred to language pairs other than the original ones if multiparallel corpora are used as a source. Several challenges need to be addressed for such an application to work, and we will discuss three of them here. First, the question of how adequate learning material can be identified in corpora has received some attention in the last decade, and we will detail what the structure of parallel corpora implies for that selection. Secondly, we will consider which type of exercises can be generated automatically from parallel corpora such that they foster learning and keep learners motivated. And thirdly, we will highlight the potential of employing users, that is both teachers and learners, as crowdsourcers to help improve the material.
Kinetic model of biogas production from food waste

Biogas is one of the renewable energies to minimize the use of fossil fuels. Biogas can be generated from food wastes through anaerobic digestion. The study aimed to determine the kinetic model of biogas production from food waste using linear, exponential and Gaussian equations. The result showed that the simulation of the exponential equation is the ideal model to be applied to a kinetic model of biogas production from food waste with R2 &gt; 0.9.
Limit Shape of the Generalized Inverse Gaussian-Poisson Distribution

The generalized inverse Gaussian-Poisson (GIGP) distribution proposed by Sichel in the 1970s has proved to be a flexible fitting tool for diverse frequency data, collectively described using the item production model. In this paper, we identify the limit shape (specified as an incomplete gamma function) of the properly scaled diagrammatic representations of random samples from the GIGP distribution (known as Young diagrams). We also show that fluctuations are asymptotically normal and, moreover, the corresponding empirical random process is approximated via a rescaled Brownian motion in inverted time, with the inhomogeneous time scale determined by the limit shape. Here, the limit is taken as the number of production sources is growing to infinity, coupled with an intrinsic parameter regime ensuring that the mean number of items per source is large. More precisely, for convergence to the limit shape to be valid, this combined growth should be fast enough. In the opposite regime referred to as "chaotic", the empirical random process is approximated by means of an inhomogeneous Poisson process in inverted time. These results are illustrated using both computer simulations and some classic data sets in informetrics.
Algoritma Differential Evolution untuk Estimasi Parameter Sumber Anomali Self-Potential

Self-Potential (SP) anomaly is naturally occurring potential differences due to electrochemical, electro-kinetic, and thermoelectric sources in the subsurface. The Source of SP anomaly can be modeled as a simple-geometry body, e.g: spheres, cylinders, and inclined sheets. The model parameter of SP anomaly is generally estimated using local optimization such as gradient-search-based methods. However, these methods have some drawbacks. Therefore, this problem needs to address using global optimization, namely Differential Evolution (DE) algorithm. DE is one of the metaheuristic algorithms adopting biological evolution in the optimization process. In this work, the DE algorithm is implemented to estimate the parameters of SP anomaly sources. There are two stages in this work, e.g: synthetic test and field data inversion. In the synthetic test, DE is built and implemented in synthetic data generated from a cylinder body contaminated by noise. This test shows that DE can estimate the parameters of the cylinder body (SP anomaly source) well. In the field data inversion, DE is implemented to estimate the SP Surda anomaly which has been studied by other methods. The results of DE estimation are comparable to the previous studies, and able to provide uncertainty information. DE algorithm can be implemented to characterize the source of SP anomaly for futher study.
Disconnected from Reality: Do the core concepts of the metaverse exclude disabled individuals?

Commercially-driven metaverse development has been driven by philosophical and science fiction concepts. Through translating these concepts into products, the developers may have inadvertently excluded individuals with disabilities from this new expanded reality. This ideologically-driven development is presented in this paper through a brief background of what we see as the most influential of these concepts, and explain how these might affect disabled individuals wishing to engage with said products. It is our hope that these ideas prompt conversation on future inclusivity access from the concept stage of future metaverse development.
Attention-likelihood relationship in transformers

We analyze how large language models (LLMs) represent out-of-context words, investigating their reliance on the given context to capture their semantics. Our likelihood-guided text perturbations reveal a correlation between token likelihood and attention values in transformer-based language models. Extensive experiments reveal that unexpected tokens cause the model to attend less to the information coming from themselves to compute their representations, particularly at higher layers. These findings have valuable implications for assessing the robustness of LLMs in real-world scenarios. Fully reproducible codebase at https://github.com/Flegyas/AttentionLikelihood.
Neuro-symbolic Commonsense Social Reasoning

Social norms underlie all human social interactions, yet formalizing and reasoning with them remains a major challenge for AI systems. We present a novel system for taking social rules of thumb (ROTs) in natural language from the Social Chemistry 101 dataset and converting them to first-order logic where reasoning is performed using a neuro-symbolic theorem prover. We accomplish this in several steps. First, ROTs are converted into Abstract Meaning Representation (AMR), which is a graphical representation of the concepts in a sentence, and align the AMR with RoBERTa embeddings. We then generate alternate simplified versions of the AMR via a novel algorithm, recombining and merging embeddings for added robustness against different wordings of text, and incorrect AMR parses. The AMR is then converted into first-order logic, and is queried with a neuro-symbolic theorem prover. The goal of this paper is to develop and evaluate a neuro-symbolic method which performs explicit reasoning about social situations in a logical form.
SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference

The combination of Neural Architecture Search (NAS) and quantization has proven successful in automatically designing low-FLOPs INT8 quantized neural networks (QNN). However, directly applying NAS to design accurate QNN models that achieve low latency on real-world devices leads to inferior performance. In this work, we find that the poor INT8 latency is due to the quantization-unfriendly issue: the operator and configuration (e.g., channel width) choices in prior art search spaces lead to diverse quantization efficiency and can slow down the INT8 inference speed. To address this challenge, we propose SpaceEvo, an automatic method for designing a dedicated, quantization-friendly search space for each target hardware. The key idea of SpaceEvo is to automatically search hardware-preferred operators and configurations to construct the search space, guided by a metric called Q-T score to quantify how quantization-friendly a candidate search space is. We further train a quantized-for-all supernet over our discovered search space, enabling the searched models to be directly deployed without extra retraining or quantization. Our discovered models establish new SOTA INT8 quantized accuracy under various latency constraints, achieving up to 10.1% accuracy improvement on ImageNet than prior art CNNs under the same latency. Extensive experiments on diverse edge devices demonstrate that SpaceEvo consistently outperforms existing manually-designed search spaces with up to 2.5x faster speed while achieving the same accuracy.
Efficient and Secure Federated Learning for Financial Applications

The conventional machine learning (ML) and deep learning approaches need to share customers' sensitive information with an external credit bureau to generate a prediction model that opens the door to privacy leakage. This leakage risk makes financial companies face an enormous challenge in their cooperation. Federated learning is a machine learning setting that can protect data privacy, but the high communication cost is often the bottleneck of the federated systems, especially for large neural networks. Limiting the number and size of communications is necessary for the practical training of large neural structures. Gradient sparsification has received increasing attention as a method to reduce communication cost, which only updates significant gradients and accumulates insignificant gradients locally. However, the secure aggregation framework cannot directly use gradient sparsification. This article proposes two sparsification methods to reduce communication cost in federated learning. One is a time-varying hierarchical sparsification method for model parameter update, which solves the problem of maintaining model accuracy after high ratio sparsity. It can significantly reduce the cost of a single communication. The other is to apply the sparsification method to the secure aggregation framework. We sparse the encryption mask matrix to reduce the cost of communication while protecting privacy. Experiments show that under different Non-IID experiment settings, our method can reduce the upload communication cost to about 2.9% to 18.9% of the conventional federated learning algorithm when the sparse rate is 0.01.
VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation

We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently estimates bi-directional optical flows for multiple frames that are available in videos by sufficiently exploiting temporal cues. We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner. The information of the frame triplet is iteratively fused onto the center frame. To extend TROF for handling more frames, we further propose a MOtion Propagation (MOP) module that bridges multiple TROFs and propagates motion features between adjacent TROFs. With the iterative flow estimation refinement, the information fused in individual TROFs can be propagated into the whole sequence via MOP. By effectively exploiting video information, VideoFlow presents extraordinary performance, ranking 1st on all public benchmarks. On the Sintel benchmark, VideoFlow achieves 1.649 and 0.991 average end-point-error (AEPE) on the final and clean passes, a 15.1% and 7.6% error reduction from the best-published results (1.943 and 1.073 from FlowFormer++). On the KITTI-2015 benchmark, VideoFlow achieves an F1-all error of 3.65%, a 19.2% error reduction from the best-published result (4.52% from FlowFormer++). Code is released at \url{https://github.com/XiaoyuShi97/VideoFlow}.