Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing Digital-7 Threat Assessment of Individuals in Digital Environments

One of the most challenging threats towards the security of the society is attacks from violent lone offenders, individuals that act alone or with minimal help from others without any economic gains or direct orders from organizations. Over the past few years, several terror attacks have been accompanied by manifestos published on social media platforms that outline ideology, motivation, and in some cases tactical choices. The trend in publishing manifestos and other communication on social media sites before committing an attack has increased the need for threat assessment in digital environments. Most existing methods for threat assessment are developed to be used in offline settings where information about an individual is accessible and cases where the individual is present and can answer questions. In this paper, we present seven indicators that can be used to assess the potential threat of violence based on digital communication only. The seven indicators are designed to be used when analyzing texts and can be seen as a complement to other risk assessment protocols.
GAMIT: A New Encoder-Decoder Framework with Graphical Space and Multi-grained Time for Traffic Predictions

Nowadays, many researchers study on characterizing complex and dynamic traffic environments by modeling the spatio-temporal dependencies in a road network for traffic predictions. However, existing works fail to investigate comprehensive spatio-temporal dependencies, because most of them ignore the spatial dependencies from the topological graph structure information in the road network, or only consider the temporal dependencies between fine-grained time slots whereas ignore those among coarse-grained time periods, in which a time period is composed of a number of time slots. To this end, we propose a new encoder-decoder framework called GAMIT with graphical space and multi-grained time by developing a spatiotemporal recurrent neural network (STRNN) for traffic predictions, where the graphical space consists of spatial networks which represent road networks. STRNN first devises a spatiotemporal convolution block to capture the fine-grained spatiotemporal dependencies between time slots in the road network. Then, STRNN uses the recurrent architecture to catch the coarsegrained spatio-temporal dependencies among time periods. The encoder finally applies STRNN to learn the multi-grained spatiotemporal dependencies which are fed into the decoder for computing traffic predictions based on STRNN as well. To evaluate the performance of GAMIT, we conduct extensive experiments on two real traffic flow datasets. Experimental results show that GAMIT outperforms the state-of-the-art traffic prediction models.
Predicting Liquidity Ratio of Mutual Funds via Ensemble Learning

We are entering a new era of AI, in which the core technology is machine learning. However, many machine learning models are opaque and not intuitive enough, making it difficult for users to understand how AI systems make decisions. In some scenarios, especially in the fields of finance, healthcare, automatic driving, users have a strong demand for the interpretability of the model. Although AI systems provide a lot of benefits, if the decision and behavior cannot be explained to users or regulators, the effectiveness and development of the systems will be restricted. To gain the trust of users, Explainable AI is necessary.Daily prediction of mutual fund holdings can be a very useful tool. If we can predict the daily holding positions of large mutual funds, we can gain insights into the sentiments of institutional investors which shed lights into the outlook of the market. How to get daily holding from the delayed disclosure information? We leverage on another source of key information - the price of a mutual fund is updated daily, often released a few hours after the market closes. Therefore, we can utilize the daily price fluctuation, combined with quarterly revealed holdings, to make daily predictions of mutual fund holdings. In this paper, we proposed an Ensemble Learning model to predict liquidity ratio of mutual funds. The model has strong interpretability, which is beneficial to users, developers and regulators and all parties involved. Compared with the real fund position data only disclosed once a quarter, our model effectuates timely and efficient high-frequency calculation. In the process of modeling, we creatively apply the framework of Ensemble Learning to portfolio decomposition for the first time. The Ensemble Learning model leverages the diversity of base learners to improve the overall prediction performance. Extensive empirical results on China A-Shares market show that our model can achieve superior accuracy, robustness, and generalization ability.
Long Short-term Memory Network Based Fatigue Detection with Sequential Mouth Feature

Fatigue driving detection helps reduce the occurrence of traffic accidents. Currently, the fatigue detection methods still have following challenges: 1) In the visual fatigue detection system, face detection is the foundation of fatigue detection and it is always susceptible to changes of illumination and facial expression, which lowers the accuracy of face detection. 2) It is so difficult to distinguish some behaviors of a driver such as singing and yawning in a single frame that making false fatigue detection in the visual fatigue detection system. To overcome the problems mentioned above, a fatigue detection method based on sequential mouth feature with long short-term memory network is proposed. The proposed method mainly consists of two parts: locating of facial landmark based on face and mouth detection, and yawning detection based on residual network (Resnet) and long short-term memory (LSTM). The face detection method is based on Histogram of Oriented Gradient (HOG) feature and Support Vector Machine (SVM) classifier, which is robust to the changes of illumination and slightly facial expression. The mouth detection is based on cascaded regression tree, which extracts the mouth region from the detected face. The Resnet and LSTM based yawning detection extracts spatial-temporal features from the mouth regions between frames and distinguishes the behaviors about yawning to identify fatigue of a driver with sequential mouth feature. The proposed method achieves accuracy of 94.9% on the YawDD dataset, which outperforms the compared methods. Experimental results demonstrate that the proposed method satisfies the performance of fatigue detection.
Contrast-resolution Evaluation of Fourier Based High Frame Rate Imaging

Controlled experiments were designed to evaluate the high frame rate (HFR) imaging method and compare it with the conventional D&S method on sidelobe, spatial resolution, contrast resolution, and detectability. A point object or modified ATS512 contrast-detail tissue-mimicking phantom was placed in a water tank, and a modified Acuson V2 array transducer was used. RF echoes were obtained with a homemade HFR imaging system. Data were constructed with both the Fourier-based method (with 1, 11, 19, and 91 transmissions) and the D&S method of 91 transmissions. The Fourier-based method provides a continuous compromise between image frame rate and image quality. At the same frame rate (91 transmissions), the Fourier-based method has a higher resolution and lower sidelobe than the D&S method, even at its transmission focal distance.
Hydrothermal Synthesis and Improved CH₃OH-Sensing Performance of ZnO Nanorods With Adsorbed Au NPs

The high-density and well-aligned zinc oxide (ZnO) nanostructures with adsorbed gold (Au) nanoparticles (NPs) grown on indium-tin-oxide (ITO) glass substrates were used as a gas-sensing material. The Au NPs-adsorbed nanorod (NR) arrays were successfully grown on the substrates via the simple hydrothermal method at various Au NP sputter deposition times (0 and 30 s). The crystal characteristics, surface morphologies, and elemental distributions were studied by using the X-ray diffraction (XRD) spectrometry, the field-emission scanning electron microscopy (FE-SEM), and the high-resolution transmission electron microscopy (HR-TEM) with an energy dispersive X-ray (EDX) elemental mapping analysis, respectively. The NR structures were vertical to the substrate surface with hexagonal wurtzite structure and single-crystalline performance. All sensors were prepared in a self-made chamber, indicating that the response of the sample toward methanol (CH3OH) vapor at different working temperatures was enhanced by Au adsorption. Additionally, the sensor showed a remarkably enhanced response (62.65%) to 1000 ppm CH3OH at the optimal operating temperature of 150 °C. Simultaneously, the sensor evidently displayed stable and repeatable behavior, and selectivity toward the CH3OH vapor with no substantial response to isopropyl alcohol, acetone, and ethanol vapors. The designed sensor in this investigation may detect harmful gas in the Internet of Thing applications.
Recipe for Creating a Highly Accurate Wake Word Engine

Achieving good accuracy in Deep Learning (DL) models for Wake Word Engines (WWE) strongly depends on using the right type of dataset in training. Important WWE dataset dimensions are the number of wake words, the ratio of wake words to non-wake words, the composition of non-wake words and the augmentation techniques applied to the training dataset. In this paper, we make two crucial and first-of-a-kind contributions in improving the WWE performance: (a) Extensively studying and proving the effectiveness of three potent audio augmentation techniques that are previously not studied or less-studied for WWE and (b) Providing clear insight into the effect of wake-word to non-wake word ratio and the composition of the latter, specifically the impact of using non-wake words uttered by the speakers who also utter the wake words (vs) using non-wake words that are mined from elsewhere,We prove the effectiveness of our recipe of: (a) Data augmentation with Ambient Sound addition, Reverberation, Echo and Speed along with SpecAugment and (b) A non-wake word composition of Common Voice plus Freesound audio slices at a wake word to non-wake word ratio of 1:15 and 1:20 respectively. We show that the WWE model trained with our recipe for ‘Computer' exhibits a False Reject Rate (FRR) at 1 False Alarm (FA) per hour of 0%, 5.6%, 0.4%, and 0.7% under the four test environments trialed, whereas, the standard technique trained model exhibits an FRR of 1.1%, 18.3%, 3.7%, and 3.7%. Similar results are observed for wake word ‘Crystal'.
A higher-order filter approach to implement grid current based active damping in active front end converters

Active damping (AD) based on grid current feedback is a desirable option to deal with the LCL filter resonance issues, however, practical implementations can be problematical due to noise amplifications associated with the derivatives in the active damping term. To mitigate the noise amplification problem, this paper introduces a fourth-order filter for the practical implementation of grid current feedback AD in active front end (AFE) converter applications. In addition, to ensure stability and damp the resonant frequency, an active damping implementation approach using the proposed filter along with the control design is included in this paper. The proposed method is verified by way of extensive simulation. Additionally, specific practical problems as grid inductance uncertainty and digital delay effects on proposed control are investigated.
Modeling of radial uniformity at a wafer interface in a 2f-CCP for SiO2 etching

Two-frequency capacitively coupled plasmas (2f-CCP) are commonly used as a powerful tool for etching of metallic and dielectric materials. Functional separation between two sources, by which independent control of high-density plasma production and high-energy ion injection onto a wafer surface can be realized, will be strongly required for a precise control of the plasma etcher. When increasing the size of the wafer and aiming at high productivity, radial uniformity in the characteristics of SiO2 etching will become a more essential issue to be addressed. The predicted radial uniformity at the wafer interface and the functional separation have been examined numerically by using VicAddress [in Advances in Low Temperature RF plasmas, edited by T. Makabe (Elsevier, Amsterdam, 2002)] in a 2f-CCP etcher in CF4(5%)∕Ar at 50 mTorr. This etcher was driven at very high frequency (100 MHz) for the production of high-density plasma and at low frequency (1 MHz) for the bias source. The plasma structure and ion veloc.
Fast Footstep Planning with Aborting A

Footstep planning is the dominating approach when it comes to controlling the walk of a humanoid robot, even though a footstep plan is expensive to compute. The most prominent proposals typically spend up to a few seconds of computation time and output a sequence of up to 30 steps all the way to the goal. This way, footstep planning is applicable only in static environments where nothing changes after a plan has been computed. Since uncontrolled environments present challenges such as unforeseen motion of other objects and unexpected disturbances to balance, fast replanning of a footstep plan while the robot is in motion is highly desirable. We present a new way of fast footstep planning - Aborting A* - which is able to guarantee a replanning rate of 50 Hz by aborting an A* search before completion. We make aborting possible by using a novel, obstacle-aware heuristic function that lays out rotate-translate-rotate motions along the shortest path to the goal, enabling us to stop the planning progress prematurely with a target-oriented solution at any time during the search, even after only a few nodes have been expanded. We show in our experiments that despite the bounded computation time, our planner computes good results and does not get stuck in local minima.
Incentivizing Platform–User Interactions for Crowdsensing

For effective crowdsensing, it is essential to incentivize the interactions of participants and platforms. Existing approaches do not tailor users’ bidding to their preferences, i.e., personalized bidding (PB). To meet this need, we design an incentive mechanism, called Picasso, that achieves not only the expressiveness and description efficiency of PB for users, but also minimal social cost, computational efficiency, and strategy proof for platform owners. This design is, however, challenging due to the intrinsic conflicting goals of the platform owner and users. To handle these conflicts, Picasso represents bids in a novel 3-D expression space by orchestrating three logical operations to balance among expressiveness, computational complexity, and description efficiency. Moreover, we equivalently decompose and recombine the complex task dependencies of bids originated from the expressiveness of PB, thus achieving a constant-factor approximation of optimal task allocation with strategy proof in polynomial time. These properties of Picasso are proven theoretically. In addition to a detailed simulation study, our trace-driven evaluations show that, compared to existing approaches, Picasso can enable each user to bid $9.7\times $ more tasks, on average, and decrease the description length by 74%, thus encouraging more users’ participation. Picasso also reduces the platform owner’s payment by more than 61%, hence yielding a win–win solution for incentivizing platform–user interactions.
Decomposing the Black-White Wealth Gap in the United States, 1989-2013∗

This paper analyzes the evolution of wealth for Black and White individuals in the United States over the period of 1989 to 2013. Using the Survey of Consumer Finances, I nd that the stock of wealth owned by Black individuals grew slower than that of Whites. This increased the ratio of White to Black wealth per capita from 4.8 to 6.6 over the period. I decompose wealth accumulation to show that higher income and, in particular, higher savings rates for Whites all contributed to this increase in the Black-White wealth gap. Furthermore, I show that Black wealth su ered greater losses than White wealth during and after the Great Recession. Housing prices played a key role in this housing comprised a larger fraction of Black wealth, and home prices fell more than any other asset class during the recession. Finally, I show that the increase in the Black-White wealth gap is driven by the growth in the wealth of rich White individuals at the top of the distribution. Excluding the top quartile the White wealth distribution, the dynamics of Black and White wealth were almost identical over this period. JEL Classi cation: D31, J15
Trustworthy Conversational Agent Design for African Americans with Chronic Conditions during COVID-19

This paper discusses preliminary findings on how to design chatbots that can increase African Americans’ trust in health information, particularly those who have experienced chronic conditions during the COVID-19 pandemic. COVID-19 has disproportionately affected the African American community in terms of severity and mortality, and scholars point towards the long-held medical mistrust among this population as a possible reason. Recent studies on the impact of conversational agents (CAs) on increasing trust in health information suggest that CAs can be effective. Through interviews and design studies with ten participants, we present four findings on how to design trustworthy CAs for our target population.
Activation to Promote Cancer Cell Survival

Cyclin-dependent kinase 4 (CDK4) is well-known for its role in regulating the cell cycle, however, its role in cancer metabolism, especially mTOR signaling, is undefined. In this study, we established a connection between CDK4 and lysosomes, an emerging metabolic organelle crucial for mTORC1 activation.On the onehand, CDK4phosphorylated the tumor suppressor folliculin (FLCN), regulating mTORC1 recruitment to the lysosomal surface in response to amino acids. On the other hand, CDK4 directly regulated lysosomal function and was essential for lysosomal degradation, ultimately regulating mTORC1 activity. Pharmacologic inhibition or genetic inactivation of CDK4, other than retaining FLCN at the lysosomal surface, led to the accumulation of undigested material inside lysosomes,which impaired the autophagicflux and induced cancer cell senescence in vitro and in xenograft models. Importantly, the use of CDK4 inhibitors in therapy is known to cause senescence but not cell death. To overcome this phenomenon and based on our findings, we increased the autophagic flux in cancer cells by using an AMPK activator in combinationwith a CDK4 inhibitor. The cotreatment induced autophagy (AMPK activation) and impaired lysosomal function (CDK4 inhibition), resulting in cell death and tumor regression. Altogether, we uncovered a previously unknown role for CDK4 in lysosomal biology and propose a novel therapeutic strategy to target cancer cells. Significance: These findings uncover a novel function of CDK4 in lysosomal biology, which promotes cancer progression by activating mTORC1; targeting this function offers a new therapeutic strategy for cancer treatment.
Kaposi’s Sarcoma–Associated Herpesvirus Viral IFN Regulatory Factor 1 Inhibits Transforming Growth Factor–B Signaling

Kaposi’s sarcoma–associated herpesvirus, also called human herpesvirus 8, has been implicated in the pathogenesis of Kaposi’s sarcoma, body cavity–based primary effusion lymphoma, and some forms of multicentric Castleman’s disease. The Kaposi’s sarcoma–associated herpesvirus open reading frame K9 encodes viral IFN regulatory factor 1 (vIRF1), which functions as a repressor of IFN-mediated signal transduction. vIRF1 expression in NIH 3T3 cells leads to transformation and consequently induces malignant fibrosarcoma in nude mice, suggesting that vIRF1 is a strong oncoprotein. Here, we show that vIRF1 inhibited transforming growth factor–B (TGF-B) signaling via its targeting of Smad proteins. vIRF1 suppressed TGF-B–mediated transcription and growth arrest. vIRF1 directly interacted with both Smad3 and Smad4, resulting in inhibition of their transactivation activity. Studies using vIRF1 deletion mutants showed that the central region of vIRF1 was required for vIRF1 association with Smad3 and Smad4 and that this region was also important for inhibition of TGF-B signaling. In addition, we found that vIRF1 interfered with Smad3-Smad4 complex formation and inhibited Smad3/Smad4 complexes from binding to DNA. These results indicate that vIRF1 inhibits TGF-B signaling via interaction with Smads. In addition, the data indicate the TGF-B pathway is an important target for viral oncoproteins. (Cancer Res 2005; 65(5): 1738-47)
Fully Homomorphic Encryption II

In the prior lecture, we were ﬁrst introduced to the concept of fully homomorphic encryption (FHE). In particular, we covered the motivation for the problem, its formal deﬁnition, and how FHE schemes can leverage learning with errors (LWE) in their construction. In this lecture, we continued our discussion of FHE, diving more deeply into the details of how to construct a leveled-FHE using LWE and why this construction is correct. The key technical diﬃculty focused on in this lecture is ensuring that the leveled-FHE construction is multiplicatively homomorphic, which requires signiﬁcantly more care than its additive analog. Indeed, the intuition behind the construction only becomes apparent in hindsight, after understanding how it behaves under multiplication. The lecture culminated with a discussion of how leveled-FHE, which allows only a bounded number of homomorphic operations, can be combined with an additional security assumption to produce FHE with an unbounded number of homomorphic operations [1]. This result, called bootstrapping
Prosody training of people with Down syndrome using an educational video game

The speech of people with Down syndrome presents multi-ple disorders affecting the different components of language (syntax, semantics, phonology and pragmatics). In particular, prosody is also affected, conditioning their personal development and their social integration. Due to these difﬁculties, prosody training is fundamental in their speech therapy. One aim of this work is the deﬁnition of a video game focused on training some language skills, speciﬁcally those ones related with prosody. Other aim is the creation of an analysis system to evaluate the prosodic quality of users’ utterances. The results show that the video game is useful to keep the attention of the players with Down syndrome during all game session. In addition, some statistically signiﬁcant differences are found between people with Down syndrome and people without intellectual disabilities in the frequency, energy, temporal and spectral domain. The accuracy of identifying a recording as produced by a person with Down syndrome or by a person without intellectual disabilities is up to 95%. Finally, an accuracy of 79.3% is achieved in the task of predicting the prosodic expert evaluation using an automatic classiﬁer trained with the acoustic features extracted from the recordings of people with Down syndrome.
A metallic intermediate band high efficiency solar cell

The theory of a novel type of solar cell is presented that contains a base made of a semiconductor with a metal‐like intermediate band (IB). The behavior of the IB and of the entire cell is studied taking into account photon recycling, that is, the emission of luminescent photons that may be subsequently absorbed. The results show that, in ideal conditions, this device is more efficient than the best two‐junction series‐connected tandem cells. This advantage might be maintained for less ideal cases. Although the material needed to achieve this device is yet unknown, some hints are given in the paper concerning its synthesis. Copyright © 2001 John Wiley & Sons, Ltd.