Reviewerly/exHarmony huggingface dataset:
Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions

Word embeddings capture semantic meaning of individual words.How to bridge word-level linguistic knowledge with sentence-level language representation is an open problem.This paper examines whether sentence-level representations can be achieved by building a custom sentence database focusing on one aspect of a sentence's meaning.Our three separate semantic aspects are whether the sentence: (1) communicates a causal relationship, (2) indicates that two things are correlated with each other, and (3) expresses information or knowledge.The three classifiers provide epistemic information about a sentence's content.
Generation of synthetic images of infants for training and comparison of analysis algorithms

This article is devoted to generating synthetic datasets with computer models of infants with normal and abnormal general movement activity. Our solution is a software for creating data sets similar to real ones for use in video analysis algorithms. One of the important issues in video content analysis is comparison of different algorithms. Image and video dataset are availabel for many tasks like object classification, pose recognition, etc. This allows comparing different algorithms on the same data by various metrics like accuracy or time of work. However, there is no free, public, and large enough dataset for babies at the given instant. The reason for this is both in principle that videos of babies are less common, and because videos of babies are harder to look at from a legal point of view.
Proposal for a destructive controlled phase gate using linear optics

Abstract Knill, Laflamme, and Milburn showed that linear optics techniques could be used to implement a nonlinear sign gate. They also showed that two of their nonlinear sign gates could be combined to implement a controlled-phase gate, which has a number of practical applications. Here we describe an alternative implementation of a controlled-phase gate for a single-rail target qubit that only requires the use of a single nonlinear sign gate. This gives a much higher average probability of success when the required ancilla photons are generated using heralding techniques. This implementation of a controlled-phase gate destroys the control qubit, which is acceptable in a number of applications where the control qubit would have been destroyed in any event, such as in a postselection process.
Role of Social Media in Socioeconomic Development: Case of Facebook

To study the role of Information and Communication Technology (ICT) on countries’ socioeconomic development, the paper investigates the case of Facebook penetration on improving their standing as measured via GNI per capita PPP (Gross National Income per capita based on purchasing power parity). We use four macro factors categories (political, economic, demographic, and technological) in addition to Facebook penetration per capita in order to measure the potential influence of various factors on the socioeconomic level of countries. While the analyses of ICT effect on development has been the focus of many papers in the past, the specific analysis of social media is scarce. Compared to previous studies investigating social media role, we use a large dataset covering all classes of countries and examine holistically many types of determinants using different models. In addition, we distinguish our paper using the economic classification of countries according to the World Bank. Our study indicates that Facebook penetration has a significant positive role on the socioeconomic level of countries, but such role varies depending on the countries’ classification level. Besides, there is a decreasing marginal effect showing the importance for policy makers to assess the complex dynamic behind the characteristic of each country.
Multitask Learning in Hyper-Heuristic Domain with Dynamic Production Scheduling

This chapter attempts to solve multiple related dynamic scheduling tasks simultaneously, and adapts traditional multitask learning into the hyper-heuristic domain with genetic programming for this purpose. This chapter verifies the effectiveness of traditional multitask learning in genetic programming for dynamic scheduling and identifies a number of differences that need to be adapted. This chapter develops a multi-population multitask learning framework with genetic programming. In addition, this chapter proposes an effective origin-based offspring reservation strategy to keep the quality of individuals for one task and learn knowledge from other tasks. The learned scheduling heuristics show that the subpopulations do manage to learn from each other to solve tasks.
LANA: Latency Aware Network Acceleration

We introduce latency-aware network acceleration (LANA)-an approach that builds on neural architecture search technique to accelerate neural networks. LANA consists of two phases: in the first phase, it trains many alternative operations for every layer of a target network using layer-wise feature map distillation. In the second phase, it solves the combinatorial selection of efficient operations using a novel constrained integer linear optimization (ILP) approach. ILP brings unique properties as it (i) performs NAS within a few seconds to minutes, (ii) easily satisfies budget constraints, (iii) works on the layer-granularity, (iv) supports a huge search space $$O(10^{100})$$ , surpassing prior search approaches in efficacy and efficiency. In extensive experiments, we show that LANA yields efficient and accurate models constrained by a target latency budget, while being significantly faster than other techniques. We analyze three popular network architectures: EfficientNetV1, EfficientNetV2 and ResNeST, and achieve accuracy improvement (up to $$3.0\%$$ ) for all models when compressing larger models. LANA achieves significant speed-ups (up to 5 $$\times $$ ) with minor to no accuracy drop on GPU and CPU. Project page: https://bit.ly/3Oja2IF .
The authorship of disguised handwriting written with the unaccustomed hand: A preliminary study

Sometimes, a writer may try to disguise their normal handwriting to hide their identity. One of the most effective and less frequent modes of disguise is to write with the un-accustomed hand. In this type of disguise, due to a sudden loss of pen control, pictorial appearance of handwriting changes to such an extent that the writer assumes success and gets convinced that the disguised handwriting they have produced cannot be attributed to them by comparison with their normal writing style. In the present study, handwriting samples of several persons, both with the normal hand and the unaccustomed hand, were obtained and an attempt has been made to find out and identify those features that survive the hazards of disguise and could be helpful to accurately determine the authorship of such disguised handwriting in comparison with one’s normal handwriting samples. A detailed study of letter-forms, the connecting strokes between letters, and t-crossing written by several persons has been carried out. It has been found that, despite pictorial dissimilarities, there are certain unconscious features that are rarely disguised which could either form the basis for their association with a particular writer or indicate its probability for further investigation. The analysis, comparison, and evaluation of features unconsciously left by the writer, including the parameters selected for the present study, can be carried out by a trained forensic document examiner; and they can give whatever opinion is possible and justifiable in facts and circumstances of a case.
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space

Most existing theoretical analysis of reinforcement learning (RL) is limited to the tabular setting or linear models due to the difficulty in dealing with function approximation in high dimensional space with an uncertain environment.This work offers a fresh perspective into this challenge by analyzing RL in a general reproducing kernel Hilbert space (RKHS).We consider a family of Markov decision processes M of which the reward functions lie in the unit ball of an RKHS and transition probabilities lie in a given arbitrary set.We define a quantity called perturbational complexity by distribution mismatch ∆ M (ǫ) to characterize the complexity of the admissible state-action distribution space in response to a perturbation in the RKHS with scale ǫ.We show that ∆ M (ǫ) gives both the lower bound of the error of all possible algorithms and the upper bound of two specific algorithms (fitted reward and fitted Q-iteration) for the RL problem.Hence, the decay of ∆ M (ǫ) with respect to ǫ measures the difficulty of the RL problem on M. We further provide some concrete examples and discuss whether ∆ M (ǫ) decays fast or not in these examples.As a byproduct, we show that when the reward functions lie in a high dimensional RKHS, even if the transition probability is known and the action space is finite, it is still possible for RL problems to suffer from the curse of dimensionality.
When Should You Defend Your Classifier?

Adversarial machine learning, i.e., increasing the robustness of machine learning algorithms against so-called adversarial examples, is now an established field. Yet, newly proposed methods are evaluated and compared under unrealistic scenarios where costs for adversary and defender are not considered and either all samples or no samples are adversarially perturbed. We scrutinize these assumptions and propose the advanced adversarial classification game, which incorporates all relevant parameters of an adversary and a defender. Especially, we take into account economic factors on both sides and the fact that all so far proposed countermeasures against adversarial examples reduce accuracy on benign samples. Analyzing the scenario in detail, where both players have two pure strategies, we identify all best responses and conclude that in practical settings, the most influential factor might be the maximum amount of adversarial examples.
Reusable data is the new oil

New oil data is 21st century jargon. This movement has not been widely echoed in Indonesia. Although some initiatives should be recognized and appreciated, the status of the availability of reusable data in most countries, especially in Indonesia is still low. Most of the data published in Indonesian open access journals are in the form of pdf files that cannot be reused. We advise editors of Indonesian scientific journals to consider adopting FAIR data sharing by encouraging authors to share their data as additional files in a machine readables format, e.g. csv or xls. This effort will also contribute to the principles of transparency and sustainable development in Indonesia's research ecosystem.
Circa: Stochastic ReLUs for Private Deep Learning

The simultaneous rise of machine learning as a service and concerns over user privacy have increasingly motivated the need for private inference (PI). While recent work demonstrates PI is possible using cryptographic primitives, the computational overheads render it impractical. The community is largely unprepared to address these overheads, as the source of slowdown in PI stems from the ReLU operator whereas optimizations for plaintext inference focus on optimizing FLOPs. In this paper we re-think the ReLU computation and propose optimizations for PI tailored to properties of neural networks. Specifically, we reformulate ReLU as an approximate sign test and introduce a novel truncation method for the sign test that significantly reduces the cost per ReLU. These optimizations result in a specific type of stochastic ReLU. The key observation is that the stochastic fault behavior is well suited for the fault-tolerant properties of neural network inference. Thus, we provide significant savings without impacting accuracy. We collectively call the optimizations Circa and demonstrate improvements of up to 4.7x storage and 3x runtime over baseline implementations; we further show that Circa can be used on top of recent PI optimizations to obtain 1.8x additional speedup.
Clicking Matters: Towards Interactive Human Parsing

In this work, we focus on Interactive Human Parsing (IHP), which aims to segment a human image into multiple human body parts with guidance from users’ interactions. This new task inherits the class-aware property of human parsing, which cannot be well solved by traditional interactive image segmentation approaches that are generally class-agnostic. To tackle this new task, we first exploit user clicks to identify different human parts in the given image. These clicks are subsequently transformed into semantic-aware localization maps, which are concatenated with the RGB image to form the input of the segmentation network and generate the initial parsing result. To enable the network to better perceive user's purpose during the correction process, we investigate several principal ways for the refinement, and reveal that random-sampling-based click augmentation is the best way for promoting the correction effectiveness. Furthermore, we also propose a semantic-perceiving loss (SP-loss) to augment the training, which can effectively exploit the semantic relationships of clicks for better optimization. To the best knowledge, this work is the first attempt to tackle the human parsing task under the interactive setting. Our IHP solution achieves 85% mIoU on the benchmark LIP, 80% mIoU on PASCAL-Person-Part and CIHP, 75% mIoU on Helen with only 1.95, 3.02, 2.84 and 1.09 clicks per class respectively. These results demonstrate that we can simply acquire high-quality human parsing masks with only a few human effort. We hope this work can motivate more researchers to develop data-efficient solutions to IHP in the future.
Crowdsourced Test Report Prioritization Based on Text Classification

In crowdsourced testing, crowd workers from different places help developers conduct testing and submit test reports for the observed abnormal behaviors. Developers manually inspect each test report and make an initial decision for the potential bug. However, due to the poor quality, test reports are handled extremely slowly. Meanwhile, due to the limitation of resources, some test reports are not handled at all. Therefore, some researchers attempt to resolve the problem of test report prioritization and have proposed many methods. However, these methods do not consider the impact of duplicate test reports. In this paper, we focus on the problem of test report prioritization and present a new method named DivClass by combining a diversity strategy and a classification strategy. First, we leverage Natural Language Processing (NLP) techniques to preprocess crowdsourced test reports. Then, we build a similarity matrix by introducing an asymmetric similarity computation strategy. Finally, we combine the diversity strategy and the classification strategy to determine the inspection order of test reports. To validate the effectiveness of DivClass, experiments are conducted on five crowdsourced test report datasets. Experimental results show that DivClass achieves 0.8887 in terms of APFD (Average Percentage of Fault Detected) and improves the state-of-the-art technique DivRisk by 14.12% on average. The asymmetric similarity computation strategy can improve DivClass by 4.82% in terms of APFD on average. In addition, empirical results show that DivClass can greatly reduce the number of inspected test reports.
LolliRAM: A Cross-Layer Design to Exploit Data Locality in Oblivious RAM

Oblivious RAM (ORAM) conceals memory access pattern by translating a single read/write operation into the accesses to a set of randomized locations. The obliviousness is achieved by adding redundancy to the memory system, which comes at the expense of increased performance overhead. In memory systems, locality has always been a critical factor, as accessing the data with temporal or spatial locality would result in a performance gain. Although the two design considerations of obliviousness and locality may seem contradictory at first glance, combining them in a unified design can potentially hide long memory access latency in ORAM without sacrificing provable data security.This paper presents LolliRAM, a cross-layer design to exploit data locality in Oblivious RAM. LolliRAM optimizes ORAM system through two different layers: (i) the data structure in ORAM, and (ii) the fast and secure cache in ORAM controller. The reuse of redundant memory accesses at the layer of data structure in ORAM can effectively reduce memory footprints. Both temporal and spatial locality can be exploited through the elastic grouping of blocks and the optimization at the layer of ORAM controller. We conduct a set of experiments using realistic workloads that are generated from standard benchmarks. Experimental results show that LolliRAM can reduce access latency by 71.57% on average (up to 81.50%) with negligible space overhead in comparison with representative schemes.
Information and Digital Competence as a Key Demand of Modern Ukrainian Education

Nowadays, digitalization in education is a digital transformation that is not a trend but a requirement of the times. Thus, the main task of teaching is the introduction of digital technologies, which provide education improvement, accessibility and efficiency, training the young generation for life in the digital society. Digital technologies are changed, in turn, it requires students and teachers to improve their knowledge, skills and to focus on the development. Information and digital competence in the education process helps teachers to teach the content effectively using the latest means that contribute to the achievement of the best results. With the development of key competencies, as an information and digital one, there is an urgent need to form it in the process of learning. The article is devoted to the problem of analyzing the meaning of the term “information and digital competence” in the conditions of modern Ukrainian education. The purpose of the article is to explore the term “information and digital competence” in scientific and pedagogic literature. To achieve the purpose the general theoretical scientific methods are used such as analysis, synthesis and systematization of researchers’ works as for terms: “competence”, “information competence”, “digital competence”, “information and digital competence”; logical-systemic, problem-targeted, comparative analyses as for theoretical basics of components of information and digital competence. There are some research results. The detailed analysis of terms that are related to the definition “information and digital competence” is made, and the stages of their development are considered. The particular attention is paid to the main competencies for learning. Based on the analysis of scientific and pedagogical literature, we can conclude that the information and digital competence of specialists in various fields is an urgent issue in pedagogical science.
Research Information Management in the United States : Part 1—Findings and Recommendations. Part 2—Case Studies

The Research Information Management in the United States two-part report series provides a first-of-its-kind documentation of RIM practices at US research universities that presents a thorough examination of RIM practices, goals, stakeholders, and system components. Research information management (RIM) is a rapidly growing area of investment in US research universities. While RIM practices are mature in Europe and other locales in support of nationalized reporting requirements, RIM practices at US research universities have taken a different—and characteristically decentralized—course. A complex environment characterized by multiple use cases, stakeholders, and systems has resulted. This report provides a landscape overview of the state of research information management in the United States, makes sense of the complexity, and offers recommendations targeted at University leaders and other institutional decision makers. Part 1—Findings and Recommendations This report provides much-needed context for institutional leaders to examine their own local practices by proposing: - A summary of six discrete RIM use cases - A RIM system framework - Recommendations for RIM stakeholders Part 2—Case Studies  This companion report offers an in-depth narrative of the RIM practices at five US research institutions:- Penn State University- Texas A&M University - Virginia Tech- UCLA- University of Miami
Foraminiferal Biostratigraphy of the Uppermost Cretaceous Period, Duhok Area, Kurdistan Region, North of Iraq

A relatively complete Late Maastrichtian succession of the upper part of the Shiranish Formation (Upper Cretaceous) in the Duhok area is investigated for the planktic foraminiferal assemblages. This studied interval consists mainly of bluish shale, marl, and thin beds of hard marly limestone all with grey-blue color. The upper contact of the Shiranish Formation is conformable with the overlies Danian Aaliji Formation. Based on the recorded planktic foraminifera and their ranges, the studied succession is subdivided into three main biozones of the latest Maastrichtian age, these zones are Pseudoguembelina hariaensis (CF3) Interval Zone, Pseudoguembelina palpebra (CF2) Interval Zone, and Plummerita hantkeninoides (CF1) Total Range Zone. These zones show continuous and complete Upper Cretaceous sediments, which are preserved in the Bade section of the Duhok area
Pretraining Representations for Data-Efficient Reinforcement Learning

Data efficiency is a key challenge for deep reinforcement learning. We address this problem by using unlabeled data to pretrain an encoder which is then finetuned on a small amount of task-specific data. To encourage learning representations which capture diverse aspects of the underlying MDP, we employ a combination of latent dynamics modelling and unsupervised goal-conditioned RL. When limited to 100k steps of interaction on Atari games (equivalent to two hours of human experience), our approach significantly surpasses prior work combining offline representation pretraining with task-specific finetuning, and compares favourably with other pretraining methods that require orders of magnitude more data. Our approach shows particular promise when combined with larger models as well as more diverse, task-aligned observational data -- approaching human-level performance and data-efficiency on Atari in our best setting. We provide code associated with this work at this https URL.
ATTEMPTING A DEFINITION OF CYBER CRIME

Cybercrime has no universal acceptable definition. Worse still is the fact that statutes have failed to define or give a concrete definition of the concept. While this leaves academics and the general tech-world with far reaching implications, it was argued that some scholars have attempted defining the concept based on their respective frame of reference. The paper looked at some of the already existing definitions and argued that though there is no universal acceptable definition, hardly will anyone dispute the fact that cyber-crime is a computer related crime perpetrated in the cyberspace.