Reviewerly/exHarmony huggingface dataset:
Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes

Current scene graph datasets suffer from strong long-tail distributions of their predicate classes. Due to a very low number of some predicate classes in the test sets, no reliable metrics can be retrieved for the rarest classes. We construct a new panoptic scene graph dataset and a set of metrics that are designed as a benchmark for the predictive performance especially on rare predicate classes. To construct the new dataset, we propose a model-assisted annotation pipeline that efficiently finds rare predicate classes that are hidden in a large set of images like needles in a haystack. Contrary to prior scene graph datasets, Haystack contains explicit negative annotations, i.e. annotations that a given relation does not have a certain predicate class. Negative annotations are helpful especially in the field of scene graph generation and open up a whole new set of possibilities to improve current scene graph generation models. Haystack is 100% compatible with existing panoptic scene graph datasets and can easily be integrated with existing evaluation pipelines. Our dataset and code can be found here: https://lorjul.github.io/haystack/. It includes annotation files and simple to use scripts and utilities, to help with integrating our dataset in existing work.
PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference

Field-programmable gate arrays (FPGAs) are widely used to implement deep learning inference. Standard deep neural network inference involves the computation of interleaved linear maps and nonlinear activation functions. Prior work for ultra-low latency implementations has hardcoded the combination of linear maps and nonlinear activations inside FPGA lookup tables (LUTs). Our work is motivated by the idea that the LUTs in an FPGA can be used to implement a much greater variety of functions than this. In this paper, we propose a novel approach to training neural networks for FPGA deployment using multivariate polynomials as the basic building block. Our method takes advantage of the flexibility offered by the soft logic, hiding the polynomial evaluation inside the LUTs with minimal overhead. We show that by using polynomial building blocks, we can achieve the same accuracy using considerably fewer layers of soft logic than by using linear functions, leading to significant latency and area improvements. We demonstrate the effectiveness of this approach in three tasks: network intrusion detection, jet identification at the CERN Large Hadron Collider, and handwritten digit recognition using the MNIST dataset.
Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette

This paper introduces a novel graph-based filter method for automatic feature selection (abbreviated as GB-AFS) for multi-class classification tasks. The method determines the minimum combination of features required to sustain prediction performance while maintaining complementary discriminating abilities between different classes. It does not require any user-defined parameters such as the number of features to select. The methodology employs the Jeffries-Matusita (JM) distance in conjunction with t-distributed Stochastic Neighbor Embedding (t-SNE) to generate a low-dimensional space reflecting how effectively each feature can differentiate between each pair of classes. The minimum number of features is selected using our newly developed Mean Simplified Silhouette (abbreviated as MSS) index, designed to evaluate the clustering results for the feature selection task. Experimental results on public data sets demonstrate the superior performance of the proposed GB-AFS over other filter-based techniques and automatic feature selection approaches. Moreover, the proposed algorithm maintained the accuracy achieved when utilizing all features, while using only $7\%$ to $30\%$ of the features. Consequently, this resulted in a reduction of the time needed for classifications, from $15\%$ to $70\%$.
nanoT5: A PyTorch Framework for Pre-training and Fine-tuning T5-style Models with Limited Resources

State-of-the-art language models like T5 have revolutionized the NLP landscape, but their computational demands hinder a large portion of the research community. To address this challenge, we present nanoT5, a specially-optimized PyTorch framework for efficient pre-training and fine-tuning of T5 models. Drawing on insights from optimizer differences and prioritizing efficiency, nanoT5 allows a T5-Base model to be pre-trained on a single GPU in just 16 hours, without any loss in performance. With the introduction of this open-source framework, we hope to widen the accessibility to language modelling research and cater to the community's demand for more user-friendly T5 (Encoder-Decoder) implementations. We make our contributions, including configurations, codebase, pre-training insights, and pre-trained models, available to the public.
Expert systems! Who knows?

A number of aspects of knowledge representation in expert systems are discussed. The aim is mainly to introduce to them, and as regards expert system technology the discussion is quite introductory, the perspective being more that of simple practice in the fieldthan state-of-the-art applications. The main subject is how knowledge is embodied in expert systems, in particular with respect to explicitness and articulation. It is suggested that expert systems may embody knowledge in ways more similar to human knowledgerepresentation than do conventional computer systems. Important reservations concerning this claim are also made.
Facial Based Gender Classification for Real Time Applications

Appearance and facial features play an important role in gender recognition through images. For gender classification, multiple techniques were presented to acquire better results in which preprocessing part is one of the major and very important for gender classification as it removes noise, enhances, images, and eliminates any unnatural colors from an image.&#x0D; Another major aspect is the efficient feature extraction method. If features extracted accurately then the result of classification will improve. Over the past few years, gender classification techniques work perfectly for a controlled environment. However, challenges occurred for real-time applications due to low resolution, off-angle poses, faces with occlusion, and various expressions. The main focus of this study is to overcome existing challenges and propose a method that can be implemented in real-time applications. This research work proposed a novel method in which CNN has been used for classification of gender for real-time application. To assess the performance of proposed method experiments were conducted on static images and video data sets. The proposed research work achieved 98% of accuracy during the experiments.
Explaining grokking through circuit efficiency

One of the most surprising puzzles in neural network generalisation is grokking: a network with perfect training accuracy but poor generalisation will, upon further training, transition to perfect generalisation. We propose that grokking occurs when the task admits a generalising solution and a memorising solution, where the generalising solution is slower to learn but more efficient, producing larger logits with the same parameter norm. We hypothesise that memorising circuits become more inefficient with larger training datasets while generalising circuits do not, suggesting there is a critical dataset size at which memorisation and generalisation are equally efficient. We make and confirm four novel predictions about grokking, providing significant evidence in favour of our explanation. Most strikingly, we demonstrate two novel and surprising behaviours: ungrokking, in which a network regresses from perfect to low test accuracy, and semi-grokking, in which a network shows delayed generalisation to partial rather than perfect test accuracy.
The Economic Measurement of Cyber Incidents

In recent decades, Information and Communication Technologies (ICT) have significantly evolved, further establishing the information society. However, ICT systems are subject to security incidents, and most malicious attacks have cascading effects. Decision-makers need to understand the potential financial effects of incidents if they wish to clearly perceive the potential risks and thus make an appropriate allocation of resources to ICT security.Our research attempts to develop a comprehensive toolset for the analysis of cybersecurity incidents. The toolset is based on conventional methodologies of cash-flow evaluation and balance of payments. We discuss several use cases of real-world examples with incidents affecting essential service providers and manufacturers. The case studies involve incidents affecting energy service providers, banks, water utilities, aircraft manufacturers, car manufacturers, IT software providers, air, rail, and water transport companies, the pharmacy, and the health sector. Analysis of the incidents involves our framework being applied at three levels: organisational, governmental, and international.
Advanced set containment deep learned Rabin certificateless signcryption for secured transmission with big data in cloud

Summary Cloud computing is distributed type of technology to offers distant services by the internet to manage, access, and store data on their server. The cloud server manages and distributes large amounts of data. During large amounts of complex structured and unstructured data transactions, in order to protect the data and cloud resources from harmful activities by using the main task of the security system. Therefore, a novel intrusion detection system is required in cloud networks to detect malicious activity and improve secure data transmission. In this paper, a novel technique called advanced set containment deep learned Rabin certificateless signcryption (ADRES) technique is employed to secure data transmission for achieving better confidentiality, and integrity, in cloud computing. The ADRES technique includes three major phases namely Registration and key generation, signcryption, and unsigncryption. At the first phase, the user registers details to the cloud server using secure transmission in big data analytics. The cloud server generates the private and public keys for each registered user. In the second phase, the signcryption process is determined by cloud user that encrypts and generates the signature by using the Rabin cryptosystem. Then encrypted data is stored in a cloud server with minimum space complexity. Finally, the unsigncryption is performed by using signature verification and decryption. First, the signature verification is performed by applying an Advanced set containment Deep belief neural network for identifying the authorized or unauthorized user (i.e., attacker). The authorized user decrypts data using the Rabin cryptosystem to achieve the original data. Thus, secure data transmission is obtained through improved data confidentiality and integrity. Simulation of the proposed ADRES technique is performed with respect to data confidentiality level, data integrity rate, and computation time, space complexity with several numbers of data. Experimental results reported ADRES obtains the improved data transmission security with better confidentiality rate by 7%, integrity by 7%, and lesser time 16% and space complexity by 18% than the conventional methods.
Computing SHAP Efficiently Using Model Structure Information

SHAP (SHapley Additive exPlanations) has become a popular method to attribute the prediction of a machine learning model on an input to its features. One main challenge of SHAP is the computation time. An exact computation of Shapley values requires exponential time complexity. Therefore, many approximation methods are proposed in the literature. In this paper, we propose methods that can compute SHAP exactly in polynomial time or even faster for SHAP definitions that satisfy our additivity and dummy assumptions (eg, kernal SHAP and baseline SHAP). We develop different strategies for models with different levels of model structure information: known functional decomposition, known order of model (defined as highest order of interaction in the model), or unknown order. For the first case, we demonstrate an additive property and a way to compute SHAP from the lower-order functional components. For the second case, we derive formulas that can compute SHAP in polynomial time. Both methods yield exact SHAP results. Finally, if even the order of model is unknown, we propose an iterative way to approximate Shapley values. The three methods we propose are computationally efficient when the order of model is not high which is typically the case in practice. We compare with sampling approach proposed in Castor & Gomez (2008) using simulation studies to demonstrate the efficacy of our proposed methods.
A Survey on What Developers Think About Testing

Software is infamous for its poor quality and frequent occurrence of bugs. While there is no doubt that thorough testing is an appropriate answer to ensure sufficient quality, the poor state of software generally suggests that developers may not always engage as thoroughly with testing as they should. This observation aligns with the prevailing belief that developers simply do not like writing tests. In order to determine the truth of this belief, we conducted a comprehensive survey with 21 questions aimed at (1) assessing developers' current engagement with testing and (2) identifying factors influencing their inclination toward testing; that is, whether they would actually like to test more but are inhibited by their work environment, or whether they would really prefer to test even less if given the choice. Drawing on 284 responses from professional software developers, we uncover reasons that positively and negatively impact developers' motivation to test. Notably, reasons for motivation to write more tests encompass not only a general pursuit of software quality but also personal satisfaction. However, developers nevertheless perceive testing as mundane and tend to prioritize other tasks. One approach emerging from the responses to mitigate these negative factors is by providing better recognition for developers' testing efforts.
LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models

Prompt engineering is a powerful tool used to enhance the performance of pre-trained models on downstream tasks. For example, providing the prompt "Let's think step by step" improved GPT-3's reasoning accuracy to 63% on MutiArith while prompting "a photo of" filled with a class name enables CLIP to achieve $80$\% zero-shot accuracy on ImageNet. While previous research has explored prompt learning for the visual modality, analyzing what constitutes a good visual prompt specifically for image recognition is limited. In addition, existing visual prompt tuning methods' generalization ability is worse than text-only prompting tuning. This paper explores our key insight: synthetic text images are good visual prompts for vision-language models! To achieve that, we propose our LoGoPrompt, which reformulates the classification objective to the visual prompt selection and addresses the chicken-and-egg challenge of first adding synthetic text images as class-wise visual prompts or predicting the class first. Without any trainable visual prompt parameters, experimental results on 16 datasets demonstrate that our method consistently outperforms state-of-the-art methods in few-shot learning, base-to-new generalization, and domain generalization.
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling

In addition to conveying the linguistic content from source speech to converted speech, maintaining the speaking style of source speech also plays an important role in the voice conversion (VC) task, which is essential in many scenarios with highly expressive source speech, such as dubbing and data augmentation. Previous work generally took explicit prosodic features or fixed-length style embedding extracted from source speech to model the speaking style of source speech, which is insufficient to achieve comprehensive style modeling and target speaker timbre preservation. Inspired by the style's multi-scale nature of human speech, a multi-scale style modeling method for the VC task, referred to as MSM-VC, is proposed in this paper. MSM-VC models the speaking style of source speech from different levels. To effectively convey the speaking style and meanwhile prevent timbre leakage from source speech to converted speech, each level's style is modeled by specific representation. Specifically, prosodic features, pre-trained ASR model's bottleneck features, and features extracted by a model trained with a self-supervised strategy are adopted to model the frame, local, and global-level styles, respectively. Besides, to balance the performance of source style modeling and target speaker timbre preservation, an explicit constraint module consisting of a pre-trained speech emotion recognition model and a speaker classifier is introduced to MSM-VC. This explicit constraint module also makes it possible to simulate the style transfer inference process during the training to improve the disentanglement ability and alleviate the mismatch between training and inference. Experiments performed on the highly expressive speech corpus demonstrate that MSM-VC is superior to the state-of-the-art VC methods for modeling source speech style while maintaining good speech quality and speaker similarity.
ADDRESSING TRUTH VALUES IN WRITTEN CORPORA – A B&amp;H CASE STUDY

This paper approaches the notion of truth( fulness) within the written media discourse, namely the three B&amp;H daily newspapers. Aimed at researching computeraccessible online articles published in the chosen three time frames, the paper focuses on narratives containing the keyphrase “Alan Šeranić” and the representation of the notions of values of trust and confidence found in extracts which contain both the chosen keyphrase and examples of news values. The results show that the attitude towards the researched keyphrase changes both in accordance with the time frames of its use and in accordance with the geographical area of the publication source.
Habù e habiù nei volgari veneti antichi: riflessioni su un caso di sovrabbondanza

This paper aims at analysing a case of “polymorphism” in Ancient Venetan dialects, that is the free alternation of (h)abù and (h)abiù in the perfect participle of ‘to have’ attested in XIII-XIV texts of Venice, Padua and Verona. I will treat this alternation as an instance of “overabundance” in order to show that it and similar cases largely attested in Venetan varieties do not represent “canonical” irregularity; on the contrary, they are coherent with diachronic trends and well attested comparatively.
Deep Learning Aided SRF Cavity Optimization for Quantum Computing

learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.
A large-scale exploratory study of android sports apps in the google play store

Prior studies on mobile app analysis often analyze apps across different categories or focus on a small set of apps within a category. These studies either provide general insights for an entire app store which consists of millions of apps, or provide specific insights for a small set of apps. However, a single app category can often contain tens of thousands to hundreds of thousands of apps. For example, according to AppBrain, there are 46,625 apps in the “Sports” category of Google Play apps. Analyzing such a targeted category of apps can provide more specific insights than analyzing apps across categories while still benefiting many app developers interested in the category. This work aims to study a large number of apps from a single category (i.e., the sports category). Our work can provide two folds contributions: 1) identifying insights that are specific to tens of thousands of sports apps, and 2) providing empirical evidence on the benefits of analyzing apps in a specific category. We perform an empirical study on over two thousand sports apps in the Google Play Store. We study the characteristics of these apps (e.g., their targeted sports types and main functionalities) through manual analysis, the topics in the user review through topic modeling, as well as the aspects that contribute to the negative opinions of users through analysis of user ratings and sentiment. We identified sports apps that cover 16 sports types (e.g., Football, Cricket, Baseball) and 15 main functionalities (e.g., Betting, Betting Tips, Training, Tracking). We also extracted 14 topics from the user reviews, among which three are specific to sports apps (accuracy of prediction, up-to-dateness, and precision of tools). Finally, we observed that users are mainly complaining about the advertisements and quality (e.g., bugs, content quality, streaming quality) of sports apps. It is concluded that analyzing a targeted category of apps (e.g., sports apps) can provide more specific insights than analyzing apps across different categories while still being relevant for a large number (e.g., tens of thousands) of apps. Besides, as a rapid-growing and competitive market, sports apps provide rich opportunities for future research, for example, to study the integration of data science or machine learning techniques in software applications or to study the factors that influence the competitiveness of the apps.
On the Nature of Discrete Speech Representations in Multilingual Self-supervised Models

Self-supervision has emerged as an effective paradigm for learning representations of spoken language from raw audio without explicit labels or transcriptions. Self-supervised speech models, such as wav2vec 2.0 (Baevski et al., 2020) and HuBERT (Hsu et al., 2021), have shown significant promise in improving the performance across different speech processing tasks. One of the main advantages of self-supervised speech models is that they can be pre-trained on a large sample of languages (Conneau et al., 2020; Babu et al.,2022), which facilitates cross-lingual transfer for low-resource languages (San et al., 2021). State-of-the-art self-supervised speech models include a quantization module that transforms the continuous acoustic input into a sequence of discrete units. One of the key questions in this area is whether the discrete representations learned via self-supervision are language-specific or language-universal. In other words, we ask: do the discrete units learned by a multilingual speech model represent the same speech sounds across languages or do they differ based on the specific language being spoken? From the practical perspective, this question has important implications for the development of speech models that can generalize across languages, particularly for low-resource languages. Furthermore, examining the level of linguistic abstraction in speech models that lack symbolic supervision is also relevant to the field of human language acquisition (Dupoux, 2018).