Research on the Basketball Goal Recognition Method Based on Improved MobileNet

Moving target detection is involved in many engineering projects, but it is difficult because of the strong time-varying speed and uncertain path. Goal recognition is the key technology of the basketball goal automatic test. Also, accurate and timely judgment of basketball goals has important practical value. Therefore, a basketball goal recognition method based on an improved lightweight deep learning network model (L-MobileNet) is proposed. First of all, the basket detection is carried out by the Hough circle transform algorithm. Then, in order to further improve the detection speed of basketball goals, based on the lightweight network MobileNet, an improved lightweight network (L-MobileNet) is proposed. First of all, for deeply separable convolution, channel compression and block convolution reduce the parameters and computational complexity of the module. At the same time, because block convolution will hinder the information exchange between characteristic channels, an improved channel shuffling method, IShuffle, is introduced. Then, combined with the residual structure to improve the generalization ability of the network, the RLDWS module is constructed. Finally, a more lightweight network L-MobileNet is constructed by using the RLDWS module. The experimental results show that the proposed method can effectively realize the judgment of basketball goals, and the judgment accuracy is improved by 8.35%. At the same time, the amount of parameters and computation is only 29.7% and 53.2% of the original, and it also has certain advantages compared with other lightweight networks.

When the Body Knows: Interoceptive Accuracy Enhances Physiological But Not Explicit Differentiation Between Liars and Truth-Tellers

Recent research suggests that people experience distinct physiological reactions to lies versus truths. It is unclear, however, if this experience is incorporated into greater truth-lie judgment accuracy. We hypothesized individuals with high interoceptive accuracy—those with greater access to bodily experiences and stronger physiological responses to emotional stimuli—might be particularly likely to accurately discriminate high-stakes, emotional lies and truths. Participants (n = 71) completed two study sessions: the first assessed their interoceptive accuracy with heartbeat detection measures and the second assessed their deception detection ability while measuring their physiological reactivity. Interoceptive accuracy was associated with a greater difference in vasoconstriction to liars (vs. truth-tellers), suggesting that interoception was positively associated with physiological sensitivity to deception. Interoceptive accuracy, however, was unrelated to deception detection accuracy. While better interoception provides enhanced physiological signals that could better discriminate lies from truths, it does not improve deception detection accuracy.

Why Not Interview?

Honesty-Humility is a valuable predictor in personnel selection; however, problems with self-report measures create a need for new tools to judge this trait. Therefore, this research examines the interview as an alternative for assessing Honesty-Humility and how to improve judgments of Honesty-Humility in the interview. Using trait activation theory, we examined the impact of interview question type on Honesty-Humility judgment accuracy. We hypothesized that general personality-tailored questions and probes would increase the accuracy of Honesty-Humility judgments. Nine hundred thirty-three Amazon Mechanical Turk workers watched and rated five interviews. Results found that general questions with probes and specific questions without probes led to the best Honesty-Humility judgments. These findings support the realistic accuracy model and provide implications for Honesty-Humility-based interviews.

Event-Related Potentials Elicited by Phonetic Errors Differentiate Children With Speech Sound Disorder and Typically Developing Peers

Purpose A growing body of research suggests that a deficit in speech perception abilities contributes to the development of speech sound disorder (SSD). However, little work has been done to characterize the neurophysiological processes indexing speech perception deficits in this population. The primary aim of this study was to compare the neural activity underlying speech perception in young children with SSD and with typical development (TD). Method Twenty-eight children ages 4;1–6;0 (years;months) participated in this study. Event-related potentials (ERPs) were recorded while children completed a speech perception task that included phonetic (speech sound) and lexical (meaning) matches and mismatches. Groups were compared on their judgment accuracy for matches and mismatches as well as the mean amplitude of the phonological mapping negativity (PMN) and N400 ERP components. Results Children with SSD demonstrated lower judgment accuracy across the phonetic and lexical conditions compared to peers with TD. The ERPs elicited by lexical matches and mismatches did not distinguish the groups. However, in the phonetic condition, the SSD group exhibited a more consistent left-lateralized PMN effect and a delayed N400 effect over frontal sites compared to the TD group. Conclusions These findings provide some of the first evidence of a delay in the neurophysiological processing of phonological information for young children with SSD compared to their peers with TD. This delay was not present for the processing of lexical information, indicating a unique difference between children with SSD and with TD related to speech perception of phonetic errors. Supplemental Material https://doi.org/10.23641/asha.16915579

A Study on the Application of LSTM to Judge Bike Accidents for Inflating Wearable Airbags

The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in “past information” through LSTM by regarding the driver motion as time-series data.

Do low-confidence individuals decrease group judgments’ accuracy? Investigations in terms of the wisdom of crowds framework

In group judgments in a binary choice task, the judgments of individuals with low confidence (i.e., they feel that the judgment was not correct) may be regarded as unreliable. Previous studies have shown that aggregating individuals’ diverse judgments can lead to high accuracy in group judgments, a phenomenon known as the wisdom of crowds. Therefore, if low-confidence individuals make diverse judgments between individuals and the mean of accuracy of their judgments is above the chance level (.50), it is likely that they will not always decrease the accuracy of group judgments. To investigate this issue, the present study conducted behavioral experiments using binary choice inferential tasks, and computer simulations of group judgments by manipulating group sizes and individuals’ confidence levels. Results revealed that (I) judgment patterns were highly similar between individuals regardless of their confidence levels; (II) the low-confidence group could make judgments as accurate as the high-confidence group, as the group size increased; and (III) even if there were low-confidence individuals in a group, they generally did not inhibit group judgment accuracy. The results suggest the usefulness of low-confidence individuals’ judgments in a group and provide practical implications for real-world group judgments.

Do low-confidence individuals decrease group judgments’ accuracy? Investigations in terms of the wisdom of crowds framework

In group judgments in a binary choice task, the judgments of individuals with low confidence (i.e., they feel that the judgment was not correct) may be regarded as unreliable. Previous studies have shown that aggregating individuals’ diverse judgments can lead to high accuracy in group judgments, a phenomenon known as the wisdom of crowds. Therefore, if low-confidence individuals make diverse judgments between individuals and the mean of accuracy of their judgments is above the chance level (.50), it is likely that they will not always decrease the accuracy of group judgments. To investigate this issue, the present study conducted behavioral experiments using binary choice inferential tasks, and computer simulations of group judgments by manipulating group sizes and individuals’ confidence levels. Results revealed that (I) judgment patterns were highly similar between individuals regardless of their confidence levels; (II) the low-confidence group could make judgments as accurate as the high-confidence group, as the group size increased; and (III) even if there were low-confidence individuals in a group, they generally did not inhibit group judgment accuracy. The results suggest the usefulness of low-confidence individuals’ judgments in a group and provide practical implications for real-world group judgments.

Implicit attitudes matter for social judgments of others’ preference, but do not make those judgments more or less accurate

Drawing from a large dataset of responses to implicit and explicit attitude measures and social judgments of others’ preferences (N = 97,176) across 95 distinct attitude domains, this Registered Report utilized a componential analysis of judgment accuracy to examine whether implicit attitudes affected the accuracy of social judgment. I found evidence that judgments of the population’s preferences were associated with the population’s true implicit (but not explicit) attitudes, and that individuals projected their implicit attitudes in addition to the projection of explicit attitudes when judging the population’s true preferences. However, I found no evidence that stronger or weaker implicit attitudes were uniquely associated with greater or less accuracy in judging the population’s true preferences. These results provide generalizable evidence that implicit attitudes matter greatly for social judgment accuracy in distinct and nuanced ways.

Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).