Reviewerly/exHarmony huggingface dataset:
Boson bunching is not maximized by indistinguishable particles

Boson bunching is amongst the most remarkable features of quantum physics. A celebrated example in optics is the Hong-Ou-Mandel effect, where the bunching of two photons arises from a destructive quantum interference between the trajectories where they both either cross a beam splitter or are reflected. This effect takes its roots in the indistinguishability of identical photons. Hence, it is generally admitted -- and experimentally verified -- that bunching vanishes as soon as photons can be distinguished, e.g., when they occupy distinct time bins or have different polarizations. Here we disproof this alleged straightforward link between indistinguishability and bunching by exploiting a recent finding in the theory of matrix permanents. We exhibit a family of optical circuits where the bunching of photons into two modes can be significantly boosted by making them partially distinguishable via an appropriate polarization pattern. This boosting effect is already visible in a 7-photon interferometric process, making the observation of this phenomenon within reach of current photonic technology. This unexpected behavior questions our understanding of multiparticle interference in the grey zone between indistinguishable bosons and classical particles.
Research on the Relationship between the Gathering of Scientific and Technological Talents and the High-Quality Development of Regional Economy—Taking Henan Province as an Example

Promoting China's economy from high-speed growth to high-quality development is an important judgment made by the 19th National Congress of the Communist Organization of China on China's economic development.Based on the panel data of 14 regions and cities in Henan Province from 2010 to 2017, this paper first calculates that the quality of economic growth in each province has spatial autocorrelation, and then uses a two-way fixed effect model to empirically test the quality of economic growth by gathering scientific and technological talents.The research results show that there is a significant positive correlation between scientific and technological talents gathering and the quality of economic growth in each city, And the level of regional economic development is the core factor affecting talent flow; Based on this, this paper puts forward relevant policy recommendations to promote the rational and orderly flow of scientific and technological talents and improve the quality of economic growth in various provinces.
BBSF: Blockchain Benchmarking Standardized Framework

In this paper, we propose the Blockchain Benchmark Standardized Format (BBSF), a framework for standardized, transparent, and fair benchmarks for blockchains. BBSF enables users and developers to compare blockchain platforms using metrics derived from realistic workloads. We outline the challenges in developing a blockchain benchmark with this degree of breadth and flexibility. We contrast the results using our approach with prior benchmark implementations and show why BBSF generates results that are more verifiable than prior published benchmarking data. We present an implementation of our framework, called Blockbench v3, which is a benchmarking system focusing on Web3 applications and workloads, primarily to be used by layer-1 blockchains. Blockbench v3 serves as a test case for our framework’s effectiveness as part of ongoing work in the characterization of blockchain performance.
Digital competency mapping dataset of pre-service teachers in Indonesia

This dataset used the Digital Competency Scale (DCS) to describe Indonesian pre-service teachers’ perceptions. The DCS instrument consisted of five constructs/dimensions, which are: 1) data and information literacy, 2) communication and collaboration, 3) digital content creation, 4) safety, and 5) problem-solving, with a total of 36 items using five-point agreement Likert scale. The data was gathered from 23 education and teacher training faculties at Muhammadiyah Universities in 14 provinces across Indonesia in the academic year 2021/2022. A total of 1400 students (18 to 23 years old) in their first to fifth years of study were recruited using the convenience sampling technique, where they participated in filling in the survey electronically using Google Form. The dataset was analysed with the Rasch model measurement approach using WINSTEPS version 5.2.3 software for data cleaning and validation, and reliability and validity testing of the instrument. This dataset analysis can help teacher-training institutions, or higher education policymakers design effective programmes to improve pre-service teachers' digital competencies. Furthermore, researchers can compare this dataset with more rigorous data from other countries.
Optimizing Parameterized Quantum Circuits With Free-Axis Single-Qubit Gates

Variational quantum algorithms, which utilize Parametrized Quantum Circuits (PQCs), are promising tools to achieve quantum advantage for optimization problems on near-term quantum devices.Their PQCs have been conventionally constructed from parametrized rotational angles of single-qubit gates around predetermined set of axes, and two-qubit entangling gates, such as CNOT gates.We propose a method to construct a PQC by continuous parametrization of both the angles and the axes of its singlequbit rotation gates.The method is based on the observation that when rotational angles are fixed, optimal axes of rotations can be computed by solving a system of linear equations whose coefficients can be determined from the PQC with small computational overhead.The method can be further simplified to select axes freely from continuous parameters with rotational angles fixed to π.We show the simplified free-axis selection method has better expressibility against other structural optimization methods when measured with Kullback-Leibler (KL) divergence.We also demonstrate PQCs with free-axis selection are more effective to search the ground states of Hamiltonians for condensed matter physics, quantum chemistry and combinatorial optimization.Because free-axis selection allows designing PQCs without specifying their single-qubit rotational axes, it may significantly improve the handiness of PQCs.
SHARP: A Short-Word Hierarchical Accelerator for Robust and Practical Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) is an emerging cryptographic technology that guarantees the privacy of sensitive user data by enabling direct computations on encrypted data. Despite the security benefits of this approach, FHE is associated with prohibitively high levels of computational and memory overhead, preventing its widespread use in real-world services. Numerous domain-specific hardware designs have been proposed to address this issue, but most of them use excessive amounts of chip area and power, leaving room for further improvements in terms of practicality.
Illicit COVID-19 products online: A mixed-method approach for identifying and preventing online health risks

Aims The COVID-19 pandemic triggered a demand for vaccines, cures, and the need of related documentation for travel, work and other purposes. Our project aimed to identify the illicit availability of such products across the Dark Web Markets (DWMs). Methods A retrospective search for COVID-19 related products was carried out across 118 DWMs since the start of the pandemic (March 2020-October 2021). Data on vendors as well as advertised goods such as asking price, marketplace, listed date were collected and further validated through additional searches on the open web to verify the information relating to specific marketplaces. Both quantitative and qualitative methods were used for data analysis. Results Forty-two listings of unlicenced COVID-19 cures and vaccination certificates were identified across 8 marketplaces sold by 25 vendors with significant variation in prices. The listings were found to be geographically specific and followed the progression of the pandemic in terms of availability. Correlations between vendor portfolios of COVID-19 products and variety of goods of other illicit nature such as illegal weaponry, medication/drugs of abuse also emerged from our analysis. Conclusion This study is one of the first attempts to identify the availability of unlicenced COVID-19 products on DWMs. The easy accessibility to vaccines, fake test certificates and hypothetical/illegal cures poses serious health risks to (potential) buyers due to the uncontrolled nature of such products. It also exposes buyers to an unwanted contact with vendors selling a variety of other dangerous illicit goods. Further monitoring and regulatory responses should be implemented to protect the health and safety of citizens especially at times of global crisis.
Subjects and Subject-Subject Relations in Book Culture: Historical and Theoretical Aspects

The analysis of subject-subject relations, the identification and characterization of which this article is devoted to, is of great importance for the further development of theoretical and methodological aspects of book culture, its complete and in-depth study in various historical periods, identification of trends, scale, drivers and conditions for its development, disclosure and strengthening of its communicative nature. The article aims to formulate the main theoretical aspects of the manifestations of subjectivity and subject-subject relations in book culture with an emphasis on the historical and regional context. There is presented book culture as an extensive system of interacting subjects of book publishing, book distribution and reading, which have books as the main object. In the context of book culture, the article, for the first time, gives working definitions of the terms “subject” and “subject-subject relations”. The author names the universal properties of subjects, which are activity, dedication, initiative, responsibility, ability and readiness to transform the world and oneself. There are specified the subjects, the most significant and widespread subject-subject relations that are generated in book culture and form its fundamental ties, including those that were characteristic of the Russian Empire. The article presents the most active subjects of the book culture of Siberia and the Far East in the pre-revolutionary period, both individual and collective. The author indicates the dependence of tasks, pace, directions, scale, results of the development of book culture on its subjects.
A Weakly Supervised Deep Learning Model and Human–Machine Fusion for Accurate Grading of Renal Cell Carcinoma from Histopathology Slides

(1) Background: The Fuhrman grading (FG) system is widely used in the management of clear cell renal cell carcinoma (ccRCC). However, it is affected by observer variability and irreproducibility in clinical practice. We aimed to use a deep learning multi-class model called SSL-CLAM to assist in diagnosing the FG status of ccRCC patients using digitized whole slide images (WSIs). (2) Methods: We recruited 504 eligible ccRCC patients from The Cancer Genome Atlas (TCGA) cohort and obtained 708 hematoxylin and eosin-stained WSIs for the development and internal validation of the SSL-CLAM model. Additionally, we obtained 445 WSIs from 188 ccRCC eligible patients in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort as an independent external validation set. A human-machine fusion approach was used to validate the added value of the SSL-CLAM model for pathologists. (3) Results: The SSL-CLAM model successfully diagnosed the five FG statuses (Grade-0, 1, 2, 3, and 4) of ccRCC, and achieved AUCs of 0.917 and 0.887 on the internal and external validation sets, respectively, outperforming a junior pathologist. For the normal/tumor classification (Grade-0, Grade-1/2/3/4) task, the SSL-CLAM model yielded AUCs close to 1 on both the internal and external validation sets. The SSL-CLAM model achieved a better performance for the two-tiered FG (Grade-0, Grade-1/2, and Grade-3/4) task, with AUCs of 0.936 and 0.915 on the internal and external validation sets, respectively. The human-machine diagnostic performance was superior to that of the SSL-CLAM model, showing promising prospects. In addition, the high-attention regions of the SSL-CLAM model showed that with an increasing FG status, the cell nuclei in the tumor region become larger, with irregular contours and increased cellular pleomorphism. (4) Conclusions: Our findings support the feasibility of using deep learning and human-machine fusion methods for FG classification on WSIs from ccRCC patients, which may assist pathologists in making diagnostic decisions.
A Joint Entity and Relation Extraction Model based on Efficient Sampling and Explicit Interaction

Joint entity and relation extraction (RE) construct a framework for unifying entity recognition and relationship extraction, and the approach can exploit the dependencies between the two tasks to improve the performance of the task. However, the existing tasks still have the following two problems. First, when the model extracts entity information, the boundary is blurred. Secondly, there are mostly implicit interactions between modules, that is, the interactive information is hidden inside the model, and the implicit interactions are often insufficient in the degree of interaction and lack of interpretability. To this end, this study proposes a joint entity and relation extraction model (ESEI) based on E fficient S ampling and E xplicit I nteraction. We innovatively divide negative samples into sentences based on whether they overlap with positive samples, which improves the model’s ability to extract entity word boundary information by controlling the sampling ratio. In order to increase the explicit interaction ability between the models, we introduce a heterogeneous graph neural network (GNN) into the model, which will serve as a bridge linking the entity recognition module and the relation extraction module, and enhance the interaction between the modules through information transfer. Our method substantially improves the model’s discriminative power on entity extraction tasks and enhances the interaction between relation extraction tasks and entity extraction tasks. Experiments show that the method is effective, we validate our method on four datasets, and for joint entity and relation extraction, our model improves the F1 score on multiple datasets.
Fusing In-storage and Near-storage Acceleration of Convolutional Neural Networks

Video analytics has a wide range of applications and has attracted much interest over the years. While it can be both computationally and energy-intensive, video analytics can greatly benefit from in/near memory compute. The practice of moving compute closer to memory has continued to show improvements to performance and energy consumption and is seeing increasing adoption. Recent advancements in solid state drives (SSDs) have incorporated near memory Field Programmable Gate Arrays (FPGAs) with shared access to the drive’s storage cells. These near memory FPGAs are capable of running operations required by video analytic pipelines such as object detection and template matching. These operations are typically executed using Convolutional Neural Networks (CNNs). A CNN is composed of multiple individually processed layers that perform various image processing tasks. Due to lack of resources, a layer may be partitioned into more manageable sub-layers. These sub-layers are then processed sequentially, however, some sub-layers can be processed simultaneously. Moreover, the storage cells within FPGA equipped SSDs are capable of being augmented with in-storage compute to accelerate CNN workloads and exploit the intra-parallelism within a CNN layer. To this end, we present our work, which leverages heterogeneous architectures to create an in/near-storage acceleration solution for video analytics. We designed a NAND flash accelerator and an FPGA accelerator, then mapped and evaluated several CNN benchmarks. We show how to utilize FPGAs, local DRAMs, and in-memory SSD compute to accelerate CNN workloads. Our work also demonstrates how to remove unnecessary memory transfers to save latency and energy.
Algorithms for Efficiently Computing Structural Anonymity in Complex Networks

This article proposes methods for efficiently computing the anonymity of entities in networks. We do so by partitioning nodes into equivalence classes where a node is k -anonymous if it is equivalent to k -1 other nodes. This assessment of anonymity is crucial when one wants to share data and must ensure the anonymity of entities represented is compliant with privacy laws. Additionally, in such an assessment, it is necessary to account for a realistic amount of information in the hands of a possible attacker that attempts to de-anonymize entities in the network. However, measures introduced in earlier work often assume a fixed amount of attacker knowledge. Therefore, in this work, we use a new parameterized measure for anonymity called d - k -anonymity. This measure can be used to model the scenario where an attacker has perfect knowledge of a node’s surroundings up to a given distance d . This poses nontrivial computational challenges, as naive approaches would employ large numbers of possibly computationally expensive graph isomorphism checks. This article proposes novel algorithms that severely reduce this computational burden. In particular, we present an iterative approach, assisted by techniques for preprocessing nodes that are trivially automorphic and heuristics that exploit graph invariants. We evaluate our algorithms on three well-known graph models and a wide range of empirical network datasets. Results show that our approaches significantly speed up the computation by multiple orders of magnitude, which allows one to compute d - k -anonymity for a range of meaningful values of d on large empirical networks with tens of thousands of nodes and over a million edges.
TyBox: an automatic design and code-generation toolbox for TinyML incremental on-device learning

Incremental on-device learning is one of the most relevant and interesting challenges in the field of Tiny Machine Learning (TinyML). Indeed, differently from traditional TinyML solutions where the training is tipically carried out on the Cloud and inference-only occurs on the tiny devices (e.g., embedded systems or Internet-of-Things units), incremental on-device TinyML allows both the inference and the training of TinyML models directly on tiny devices. This ability paves the way for TinyML-enabled intelligent devices that can learn directly on-the-field and adapt to evolving environments, different working conditions or specific users. The TinyML literature in this field is very limited with very few solutions focusing only on the incremental fine-tuning of machine learning models, while a general solution encompassing algorithms and code-generation for incremental on-device TinyML is still perceived as missing. The aim of this paper is to introduce, for the first time in the literature, a toolbox, called TyBox , for the automatic design and code-generation of incremental on-device TinyML classification models. In more detail, starting from a ”static” TinyML model, TyBox is able to (i) automatically design the ”incremental” on-device version of the TinyML model that has been suitably designed to take into account the technological constraint on the RAM memory of the target tiny device and (ii) autonomously provide the C++ codes and libraries to support the inference and learning of the incremental on-device TinyML model directly on the tiny devices. TyBox has been extensively compared with a state-of-the-art incremental learning solution for TinyML and tested on an off-the-shelf tiny device (i.e., the Arduino Nano 33 BLE) in three relevant TinyML application tasks and scenarios, i.e., binary image classification, multi-class image classification and Ultra-Wide-Band human activity recognition. In addition, TyBox is released to the scientific community as a public repository.
Articulating (Uncertain) AI Futures of Artistic Practice: A Speculative Design and Manifesto Sprint Approach

AI is rapidly becoming enmeshed in our professional and private lives. The ubiquity of such technologies raises a host of ethical questions, value clashes, and unforeseen consequences that must be confronted. Developments such as Ai-Da and DALL-E 2 are exciting in that they present robust new capabilities in AI and creativity. However, the futures such technologies unlock are also unpredictable. Given the speed with which such technologies are emerging and becoming adopted, the need to engage target audiences to weigh in on possible AI futures is critical. Our pilot project, Artistic Process Futures and AI, seeks to explore the role and potential implications of AI technologies with artists. In this paper, we show how participatory speculative design processes might be channeled into a public statement, or manifesto, regarding possible and preferable AI futures for supporting the artistic process, and how our workshop exposed uncertainty at the core of such deliberation.
IMMERSIVE GAZE SHARING FOR ENHANCING COMMUNICATION IN DESIGN EDUCATION: AN INITIAL USER STUDY IN THE CONTEXT OF ARCHITECTURAL DESIGN CRITIQUES

Abstract Gaze sharing is an emerging technology which can enhance human communication and collaboration. As such, it is expected to play an important role in future educational practices. To date, its application was not explored in the context of design education. Additionally, it was mainly implemented in non-immersive environments, which are limited in their potential for engaging learners. Acknowledging the growing interest in immersive learning, as well as the promise made by gaze sharing technology, we strive to develop an immersive gaze sharing environment, to support design education. As a first step, we have implemented and tested an immersive gaze sharing system for supporting design learners. The system was then tested by focusing on a scenario of architectural design, with the aim of collecting valuable user feedback regarding its usability and potential. Our initial user study informs developers of such systems regarding potential issues that may be encountered during deployment in a real-world setting and proposes concrete ways to address these. These insights can help to pave the way for integrating gaze sharing system into design education practices in the near future.
User Needs for Explanations of Recommendations: In-depth Analyses of the Role of Item Domain and Personal Characteristics

Explanations can be provided with different goals, such as clarifying how the system works, how well the recommended item meets the user’s preferences, and how an explanation helps the user select an item faster. Although extensive research has been conducted in this research line, not much attention is paid to investigating user needs for explanations. To the best of our knowledge, no studies provide related insights, especially from the perspectives of item domain and personal characteristics. Up to now, it is not completely clear if user needs for explanations change across different item domains and vary according to user characteristics. To analyze these aspects, we developed three web-based prototype recommender systems for low-, average-, and high-involvement item domains and conducted a user study with 553 participants from different countries. Related results show that, in high-involvement item domains, users tend to have a look at explanations when they are not satisfied with the recommended items. An opposite tendency was found in low- and average-involvement item domains. Statistically, there is insufficient evidence to suggest correlations between users’ needs for explanations and item domains or between users’ needs and personal characteristics. However, the descriptive statistics show that users’ need for explanations varies across different item domains. In this study, we also found the best explanation approaches to be used in a specific recommendation domain.